WO2022252850A1

WO2022252850A1 - Seasonal classification method and apparatus for flights, and machine-readable medium

Info

Publication number: WO2022252850A1
Application number: PCT/CN2022/087286
Authority: WO
Inventors: 张毅; 周榕; 梁巍; 陈思
Original assignee: 中国民航信息网络股份有限公司
Priority date: 2021-05-31
Filing date: 2022-04-18
Publication date: 2022-12-08
Also published as: CN113297336A; CN113297336B

Abstract

The present application provides a seasonal classification method and apparatus for flights, and a machine-readable medium, which can improve the accuracy of seasonal classification and avoid the bias phenomenon caused by the artificial seasonal classification of flights. The method comprises: obtaining first flight data of a first flight to be subjected to seasonal classification in a target airline; obtaining second flight data of each historical flight in a historical flight set corresponding to the first flight; determining N data pools for seasonal classification, N being an integer greater than or equal to 2; calculating first income data of each historical flight according to the second flight data; determining center data of each data pool in the N data pools according to the first income data; calculating second income data of the first flight according to the first flight data; and determining seasonal classification of the first flight according to the second income data and the center data of each data pool in the N data pools.

Description

Flight Seasonal Classification Method, Apparatus, and Machine-Readable Medium

This application claims the priority of the Chinese patent application with the application number 202110604313.8 and the title of the invention "Seasonal Classification Method, Device and Machine-Readable Medium for Flights" submitted to the China Patent Office on May 31, 2021, the entire content of which Incorporated in this application by reference.

technical field

The present application relates to the field of aviation, in particular to a method, device and machine-readable medium for seasonal classification of flights.

Background technique

Due to the cyclical influence of climate conditions, emergencies, industrial and agricultural production and life, residents' holidays and other customs, as well as national economic development and other factors, the passenger and cargo volume of the civil aviation transportation industry fluctuates seasonally. in the field of air transport. By classifying the departing historical flights into low and peak seasons, it is also called seasonal classification.

At present, the seasonal classification of aviation flights is mainly carried out quantitatively or qualitatively. However, artificially judging the seasonal classification of aviation flights will inevitably lead to biased results, which will lead to the accuracy of seasonal classification of aviation flights. not tall.

Contents of the invention

The application provides a method, device and machine-readable medium for seasonal classification of flights, which can improve the accuracy of seasonal classification and avoid bias caused by artificial seasonal classification of flights.

The first aspect of the embodiment of the present application provides a seasonal classification method for flights, including:

Obtain the first flight data of the first flight to be classified seasonally in the target airline company;

Obtaining the second flight data of each historical flight in the historical flight set corresponding to the first flight;

Determine N data pools for seasonal classification, wherein said N is an integer greater than or equal to 2;

calculating the first revenue data of each historical flight according to the second flight data;

determining the central data of each of the N data pools according to the first income data;

calculating second revenue data for the first flight based on the first flight data;

The seasonal classification of the first flight is determined according to the second revenue data and the central data of each of the N data pools.

The seasonal classification corresponding to the second data pool is determined as the seasonal classification of the first flight.

The second aspect of the embodiment of the present application provides a flight seasonal classification device, including:

The first acquisition unit is used to acquire the first flight data of the first flight to be classified seasonally in the target airline company;

The second obtaining unit is configured to obtain the second flight data of each historical flight in the historical flight set corresponding to the first flight;

The first determination unit is used to determine N data pools for seasonal classification, wherein the N is an integer greater than or equal to 2;

a first calculation unit, configured to calculate the first revenue data of each historical flight according to the second flight data;

A second determining unit, configured to determine the central data of each of the N data pools according to the first income data;

a second calculation unit, configured to calculate second revenue data of the first flight according to the first flight data;

The third determination unit is configured to determine the seasonal classification of the first flight according to the second income data and the central data of each data pool in the N data pools.

In a possible design, the second determining unit is specifically configured to:

Determining the target revenue data as the central data of the first data pool, the target revenue data being the revenue data corresponding to the second flight, the second flight being any flight in the set of historical flights, and the first data pool Any one of the N data pools;

Calculate the first distance between the third income data and the target income data, the third income data is the income data corresponding to any flight in the flight subset, and the flight subset is the historical flight set except a collection of flights other than said second flight;

Divide the fourth revenue data into the first data pool, the fourth revenue data is the revenue data corresponding to the third flight, and the third flight is the third flight in the subset of flights that is related to the target revenue data. - the flight corresponding to the nearest revenue data;

Calculate central data of the first data pool after division.

In a possible design, the calculation of the first distance between the third income data and the target income data by the second determination unit includes:

Calculate and calculate the first distance between the third income data and the target income data according to the following formula:

D(i,j)=W(t)×|X _it -Y _jt |;

Wherein, D(i, j) is the first distance between the target income data i and the third income data j, and the third income data j is the income data corresponding to any flight in the flight subset , W(t) is the weight of the third income data, X _it is the market demand value of the target income data, and Y _jt is the market demand value corresponding to the third income data.

In a possible design, the calculation of the center data of the target data pool after division by the second determining unit includes:

Calculate the center data of the target data pool after division by the following formula:

Wherein, New_mean(t) is the central data of the target data pool after dividing the fourth income data into the target data pool, and Old_mean(t) is dividing the fourth income input into the target data pool The central data of the aforementioned target data pool, m is the number of central data in the target data pool before the fourth income input is divided into the target data pool, and X(t) is the number of central data in the target data pool The market demand value corresponding to any income data.

In a possible design, the first calculation unit is specifically configured to:

The first revenue data of each historical flight is calculated by the following formula:

Wherein, Revenue Dcp(x) is the first revenue data of the xth flight in each of the historical flights, i represents the cabin, k is the total number of cabins, and BKG (i) is the reservation of the i-th cabin, Fare(i) is the fare of the ith cabin.

In a possible design, the third determining unit is specifically configured to:

calculating a second distance between the second income data and each of the N central data, wherein the N central data correspond to the N data pools;

Dividing the second income data into a second data pool, the second data pool being the data pool corresponding to the central data with the smallest second distance between the N central data and the second income data ;

The third aspect of the present application provides a computer device, including: a memory, a processor, and a bus system; wherein, the memory is used to store programs, and the bus system is used to connect the memory and the processor so that the memory and the processor communicate; The device is used to execute the program in the memory, and execute the seasonal classification method of the flight according to the above-mentioned first aspect according to the instruction in the program code.

The fourth aspect of the embodiment of the present application provides a machine-readable medium, which includes instructions, and when it is run on a machine, causes the machine to execute the method for seasonally classifying flights described in the first aspect above.

In summary, it can be seen that in the embodiment provided by this application, when the flight seasonal classification device determines the seasonal classification of the first flight, it can obtain the historical flight data corresponding to the first flight, and then the historical flight data The flight data is clustered to obtain the central data of N clusters, and the seasonal classification of the first flight is determined according to the central data of the N clusters and the income data of the first flight. Compared with the existing artificial For the seasonal classification of flights, the accuracy of seasonal classification can be improved, and the bias caused by artificial seasonal classification of flights can be avoided.

Description of drawings

The above and other features, advantages and aspects of the various embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.

Fig. 1 is a schematic flow chart of the seasonal classification method of flights provided by the embodiment of the present application;

Fig. 2 is the virtual structural schematic diagram of the flight season classification device that the embodiment of the present application provides;

FIG. 3 is a schematic structural diagram of a machine-readable medium provided by an embodiment of the present application;

FIG. 4 is a schematic diagram of a hardware structure of a server provided by an embodiment of the present application.

Detailed ways

Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the drawings, it should be understood that the application may be embodied in various forms and should not be construed as limited to the embodiments set forth herein; A more thorough and complete understanding of the application. It should be understood that the drawings and embodiments of the present application are for exemplary purposes only, and are not intended to limit the protection scope of the present application.

The term "comprising" and its variants used in this application are open-ended, ie "including but not limited to". The term "based on" is "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one further embodiment"; the term "some embodiments" means "at least some embodiments." Relevant definitions of other terms will be given in the description below.

It should be noted that concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the sequence of functions performed by these devices, modules or units or interdependence.

It should be noted that the modifications of "one" and "multiple" mentioned in this application are illustrative and not restrictive. Those skilled in the art should understand that unless the context clearly indicates otherwise, it should be understood as "one or more" multiple".

By classifying the departing historical flights into low and peak seasons, the sample data after classification can better reflect the real historical market conditions and market trends in each time period, and the calculation for predicting output results is more accurate. For example, for the National Day For market forecasting of flights with long holidays, it is necessary to use the flight data of departing flights that are also National Day or long holidays of the same quality as a sample. At present, airlines divide aviation flights quantitatively or qualitatively into off-peak seasons, but through Artificial division of low and peak seasons will inevitably lead to biased results, leading to inaccurate seasonal divisions.

In view of this, a method for seasonal classification of flights provided in the embodiment of the present application uses cluster analysis to perform cluster analysis on the historical data of the same period as the flights to be classified seasonally, and obtains N clusters class, and then classify the flights to be seasonally classified according to N clusters. Compared with the existing artificial seasonal return, it is more accurate and avoids biased classification results.

Clustering is to divide the sample space into multiple subspaces according to a certain similarity criterion, so that the sample points in each subspace are similar and similar, and the differences between sample points in different subspaces are far and wide. The process is an unsupervised learning The process can realize the blind classification of the sample space. Clustering is widely used in statistics, machine learning, pattern recognition, data analysis and other fields. At present, there are nearly a hundred kinds of clustering algorithms applied in many fields, and the processing objects range from general databases to ultra-large-scale databases, from low-dimensional data spaces to high-dimensional data spaces, and from digital attribute data to multi-attribute data.

The following is an illustration of the seasonal classification method of the flight provided by the application from the perspective of the flight seasonal classification device. Please refer to FIG. 1. FIG. 1 is a schematic flow chart of the seasonal classification method of the flight provided by the embodiment of the application. include:

101. Obtain the first flight data of the first flight to be classified seasonally in the target airline company.

In this embodiment, the device for seasonal flight classification acquires the first flight data of the first flight to be classified seasonally in the target airline company. The first flight includes at least one flight segment. The segment of the voyage, for example, the first flight is the flight corresponding to Beijing-Shanghai-San Francisco, there are three possible passenger voyages: Beijing-Shanghai, Shanghai-San Francisco and Beijing-San Francisco, that is, the first flight includes Beijing-Shanghai Flight segment, Shanghai-San Francisco flight segment and Beijing-San Francisco flight segment, a total of 3 flight segments. The first flight data is the data collection points (Data collection points, DCP) data of the designated airline (that is, the target airline company), including at least one of the following: flight number, departure airport, arrival airport, flight segment departure date Reservation value and corresponding transportation value of each class of cabin and time, flight segment.

It can be understood that the first flight data may also include the departure date of the flight, the week corresponding to the departure date, the specific departure time, reservation value data and market demand value. Here, the flight data of the flight is used for description, and of course, the flight segment in the flight can also be used as an example for description, which is not specifically limited. The market demand value refers to the demand that passengers have the ability to purchase and have actual purchase needs. Actual orders may or may not be generated. In the revenue management system, the limit algorithm calculation model is used to output the market demand value, and the market demand value is obtained. It is widely used in judging market trends and dividing market seasons, and is an important input value applied to the optimization module of the core algorithm subsystem of the revenue management system. The revenue management system is used by airlines to use flight plans, inventory, departure and freight data. An automated management system that automatically manages the inventory of non-departure flights. The method of obtaining the market demand value of the flight is not specifically limited here. For example, obtain the flight information of the specified flight of the target airline company, and obtain inventory data based on the flight information. The inventory data specifically includes the inventory information of departing flights and non-departing flights Inventory information, where the inventory data of departed flights refers to the flight inventory data of departing flights in the past three years based on the current date of the specified flight, and the inventory data of non-departing flights refers to the future of the designated flight based on the current date One year's flight inventory data; judge the sales status of the specified flight class based on the inventory data. According to the data collection point corresponding to the target airline, the flight information of the designated airline, the inventory data of the designated flight, etc., to identify the sales status of the designated cabin of the designated flight. The sales status includes locked cabin, open cabin, etc., and finally based on the preset algorithm Process the sales status to obtain the market demand value of the specified flight.

It should be noted that when the number of available seats in a designated cabin is less than or equal to zero, and the available status is open or closed, it means that the designated cabin is in a non-saleable state, that is, the cabin is locked; the number of available seats in a designated cabin is greater than zero, and the available If the status is closed, it means that the specified cabin is not available for sale; it is locked; if the number of available seats in the specified cabin is greater than zero, the available status is open, indicating that the specified cabin is available for sale; that is, the cabin is opened;

The following describes how to process the sales status based on the preset algorithm to obtain the demand value of the specified flight duration:

If the sales status of the data collection point DCP(n+1) is open, and the sales status of the data collection point DCP(n) is open:

If the data collection point (n) has more seats than the data collection point (n+1), use the following formula to calculate the market demand value of the data collection point DCP (n+1):

Market demand value DCP(n+1)=market demand value DCP(n)+reservation value increase change value DCP(n), wherein, reservation increase change value=actual reservation value DCP(n+1)-actual reservation value Block value DCP(n).

If the data collection point (n) has less reservations than the data collection point (n+1), the market demand value of the data collection point DCP(n+1) is calculated by the following formula:

Market demand value DCP(n+1)=market demand value DCP(n)+reservation reduction fluctuation value DCP(n), wherein, reservation reduction fluctuation value=(actual reservation value DCP(n+1)×market demand value (n))/actual reservation value DCP(n)-market demand value (n).

If the sales status of the data collection point DCP(n+1) is locked, and the sales status of the data collection point DCP(n) is open:

When the number of reservations at the data collection point (n) is less than that of the data collection point (n+1), it is calculated by the following formula

Market demand value DCP(n+1)=market demand value DCP(n)+reservation reduction fluctuation value DCP(n), wherein, reservation reduction fluctuation value=(actual reservation value DCP(n+1)×market demand value (n))/actual reservation value DCP(n)-market demand value (n). It can be understood that the above calculation of the market demand value is an iterative process, that is, the market demand value of DCP(1) is equal to the actual reservation value, and the market demand value of DCP+=1 is iteratively calculated.

It should be noted that the DCP data is determined by the number of days away from the port at the current moment, and there is a one-to-one correspondence with the number of days away from the port. The following examples illustrate:

For example, the data collection point can be set to 24 (Dcp ₁ , Dcp ₂ , ..., Dcp ₂₄ ), and the date from the port can be set to 365 days, wherein the data collection point corresponds to the number of days from the port, and the data collection point The data collection point Dcp ₁ corresponds to 365 days from the port date, and Dcp ₂₄ corresponds to 1 day from the port date. The corresponding relationship of is just an example and is not specifically limited.

It should also be noted that the data collection point can also include data such as cabin class, seat reservation, and ticket price, among which:

Seat is the general term of the same category of payment price, service content and settings;

The seat reservation is marked as BKG, and the reservation of the passenger's reserved seat and class of boarding or the weight and volume of luggage;

The fare is marked as Fare, which refers to the fee charged by passengers from point A to point B, or it can be said that it is a price that passengers need to pay, and the conditions attached to this price to stipulate the use of this price (refers to freight, The sum of rules and various restrictions), the above are the basic conditions for automatic fare calculation. The international freight rate mainly includes the following contents: city pair (also known as the market), rule number, freight level, footnote (optional), currency, amount, effective date, deadline, mileage, etc.

102. Obtain the second flight data of each historical flight in the historical flight set corresponding to the first flight.

In this embodiment, the device for seasonal flight classification can obtain the second flight data of each historical flight in the historical flight set corresponding to the first flight. Since the local database regularly stores the flight data of the target airline company, the first When the second flight data of each historical flight in the historical flight collection corresponding to the first flight only needs to be extracted from the local database. In addition, the set of historical flights corresponding to the first flight is the departing flight associated with the current date in the three years before the current date (of course, it can also be other durations, such as 4 days, not specifically limited), such as the first The flight is a certain flight on April 25, 2021, and April 25 is a Sunday, then the historical flight collection is a collection of flights corresponding to the target flight on all Sundays in the past 3 years.

103. Determine N data pools for seasonal classification.

In this embodiment, the device for seasonal classification of flights may determine N data pools for seasonal classification, where N is a positive integer greater than or equal to 2. It can be understood that, in this application, the number of N is set to 7, and the 7 data pools are divided into peak season 1 (denoted as peak), peak season 2 (denoted as peak1), off-season 1 (denoted as peak2), and average season 1 (denoted as peak2). Season 2 (denoted as offpeak2), off-season 1 (denoted as offpeak1), off-season 2 (denoted as offpeak) and the uncategorized data pool default. Do limited.

It should be noted that, through step 101, the first flight data of the first flight to be classified seasonally in the target airline company can be obtained, and through step 102, the data of each historical flight in the historical flight set corresponding to the first flight can be obtained For the second flight data, N data pools for seasonal classification can be determined through step 103. However, there is no restriction on the sequence of execution between these three steps. Step 101 can be executed first, or step 102 can be executed first, or Step 103 is executed first, or executed at the same time, which is not specifically limited.

104. Calculate the first revenue data of each historical flight according to the second flight data.

In this embodiment, after obtaining the second flight data of each historical flight in the historical flight collection, the device for seasonal flight classification can calculate the first income data of each historical flight according to the second flight data, specifically as follows The formula calculates the first revenue data for each historical flight:

It can be understood that, for the sake of simplicity of calculation, Dcp ₁₉ , Dcp ₂₀ , Dcp ₂₁ , Dcp ₂₂ , Dcp ₂₃ and Dcp ₂₄ can be used as the historical flights of the first flight, and of course other flights can also be used The data collection points are not limited.

105. Determine the central data of each data pool in the N data pools according to the first income data.

In this embodiment, after obtaining the first revenue data of each historical flight, the device for seasonal flight classification can determine the central data of each data pool in the N data pools according to the first revenue data of each historical flight.

In one embodiment, the flight seasonal classification device determines the central data of each of the N data pools according to the first revenue data including:

Determine the target revenue data as the central data of the first data pool, the target revenue data is the revenue data corresponding to the second flight, the second flight is any flight in the historical flight collection, and the first data pool is any of the N data pools One;

Calculate the first distance between the third income data and the target income data, the third income data is the income data corresponding to any flight in the flight subset, and the flight subset is the flight collection in the historical flight collection except the target flight ;

Divide the fourth income data into the first data pool, the fourth income data is the income data corresponding to the third flight, and the third flight is the flight corresponding to the first income data closest to the target income data in the flight subset ;

Calculate the central data of the divided first data pool.

In this embodiment, the flight season classification device may first determine the target revenue data as the central data of the first data pool, the target revenue data is the revenue data of the target flight, and the target flight is any flight in the historical flight collection , the first data pool is any one of the N data pools, that is, the revenue data corresponding to the N historical flights can be randomly selected here as the central data of the N data pools.

Then calculate the first distance between the third income data and the target income data, the third income data is the income data corresponding to any flight in the flight subset, and the flight subset is the flight collection except the target flight in the historical flight collection, Specifically, the first distance between the third income data and the central data of the N data pools can be calculated by the following formula:

D(i,j)=W(t)×|X _it -Y _jt |;

After calculating the first distance between the revenue data corresponding to any flight in the flight subset and the target revenue data, the device for seasonal flight classification can divide the fourth revenue data into the first data pool, and the fourth revenue data is The revenue data corresponding to the third flight, the third flight is the flight corresponding to the revenue data with the closest distance to the target revenue data in the flight subset, that is to say, each flight in the flight subset can be separately Divide into the data pool closest to each flight.

Finally, the flight season classification device can calculate the center data of the divided first data pool, specifically, the center data of the first data pool can be calculated by the following formula:

Among them, New_mean(t) is the central data of the target data pool after the fourth income data is divided into the target data pool, Old_mean(t) is the central data of the target data pool before the fourth income input is divided into the target data pool, and m is The amount of central data in the target data pool before the fourth income input is divided into the target data pool, and X(t) is the market demand value corresponding to any income data in the target data pool.

Therefore, the revenue data corresponding to each historical flight in the historical flight collection can be clustered and analyzed to divide the revenue data corresponding to each flight into N data pools, and the divided N data can be obtained pool and the central data of each of the N data pools.

That is to say, the flight seasonal classification device can randomly select N second income data from the second income data of each flight in the historical flight collection as the central data of N data pools, and then randomly select from the flight subset A flight, calculate the distance between the second income data corresponding to the randomly selected flight and the central data of N data pools, and divide the second income data corresponding to the randomly selected flight into the The second revenue data corresponding to the flight is the closest data pool, and calculate the central data of the data pool; then select another flight from the flight subset, and repeat the above process until all the flights in the historical flight collection correspond to Until the division of the second income data is completed, the divided N data pools are obtained, and then the central data of the N data pools can be calculated.

106. Calculate the second income data of the first flight according to the data of the first flight.

In this embodiment, the device for seasonal classification of flights can calculate the second income data of the first flight according to the data of the first flight, that is, calculate the income data of the first flight to be classified seasonally. Specifically, the following formula can be used Calculation:

Among them, Revenue Dcp(x) is the second income data, i represents the i-th cabin in the first flight, k is the total number of cabins in the first flight, BKG(i) is the reservation of the i-th cabin, Fare( i) is the fare of the i-th cabin.

107. Determine the seasonal classification of the first flight according to the second income data and the central data of each data pool.

In this embodiment, after calculating the second revenue data of the first flight, the device for seasonal classification of flights can determine the seasonal classification of the first flight according to the second revenue data and the central data of each data pool, specifically , you can first calculate the second distance between the second income data and each of the N center data, where the N center data correspond to the N data pools, specifically, the second income data and N can be calculated by the following formula The second distance of each center data in center data:

D(i,j)=W(t)×|X _it -Y _jt |;

Among them, D(i, j) is the first distance between the target income data i and the third income data j, the third income data j is the income data corresponding to any flight in the flight subset, W(t) is The weight of the third income data, X _it is the market demand value of the target income data, and Y _jt is the market demand value corresponding to the third income data.

Then divide the second income data into the second data pool, the second data pool is the data pool corresponding to the center data with the second smallest distance between the N center data and the second income data, that is, the second income The data is divided into the most similar clusters;

It should be understood that the flowcharts and block diagrams in the figures illustrate the architecture, functions and operations of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or portion of code that contains one or more logical functions for implementing specified executable instructions. It should also be noted that, 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 they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.

The names of messages or information exchanged between multiple devices in the embodiments of the present application are used for illustrative purposes only, and are not used to limit the scope of these messages or information.

Although operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or to be performed in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous.

It should be understood that the various steps described in the method implementations of the present application may be executed in different orders, and/or executed in parallel. Additionally, method embodiments may include additional steps and/or omit performing illustrated steps. The scope of the application is not limited in this respect.

In addition, the present application can also write computer program codes for performing the operations of the present application in one or more programming languages or combinations thereof, and the above-mentioned programming languages include but are not limited to object-oriented programming languages—such as Java, Smalltalk, C++, and also conventional procedural programming languages—such as "C" or similar programming languages. The program code 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 cases involving a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as through an Internet service provider). Internet connection).

The embodiment of the present application is described above from the perspective of the seasonal flight classification method, and the embodiment of the present application is described below from the perspective of the flight seasonal classification device.

Please refer to Fig. 2, Fig. 2 is the imaginary structural representation of the flight seasonal classification device that the embodiment of the present application provides, and this flight seasonal classification device 200 comprises:

The first obtaining unit 201 is used to obtain the first flight data of the first flight to be classified seasonally in the target airline company;

The second obtaining unit 202 is used to obtain the second flight data of each historical flight in the historical flight set corresponding to the first flight;

The first determination unit 203 is configured to determine N data pools for seasonal classification, wherein the N is an integer greater than or equal to 2;

The first calculation unit 204 is configured to calculate the first revenue data of each historical flight according to the second flight data;

The second determination unit 205 is configured to determine the central data of each data pool in the N data pools according to the first income data;

The second calculation unit 206 is configured to calculate the second income data of the first flight according to the first flight data;

The third determining unit 207 is configured to determine the seasonal classification of the first flight according to the second revenue data and the central data of each of the N data pools.

In a possible design, the second determining unit 205 is specifically configured to:

Calculate central data of the first data pool after division.

In a possible design, the calculation of the first distance between the third income data and the target income data by the second determination unit 205 includes:

D(i,j)=W(t)×|X _it -Y _jt |;

In a possible design, the calculation of the center data of the target data pool after division by the second determination unit 205 includes:

In a possible design, the first computing unit 204 is specifically configured to:

In a possible design, the third determining unit 207 is specifically configured to:

It should be noted that the units involved in the embodiments described in the present application may be implemented by means of software or by means of hardware. Wherein, the name of the unit does not constitute a limitation on the unit itself under certain circumstances, for example, the acquisition unit may also be described as "a unit that acquires the credential information of the target user".

The functions described herein above may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), System on Chips (SOCs), Complex Programmable Logical device (CPLD) and so on.

Please refer to FIG. 3 . FIG. 3 is a schematic diagram of an embodiment of a machine-readable medium provided in an embodiment of the present application.

As shown in FIG. 3 , this embodiment provides a machine-readable medium 300 on which a computer program 311 is stored, and when the computer program 311 is executed by a processor, the seasonal classification method for flights described in FIG. 1 is implemented. A step of.

It should be noted that, in the context of the present application, a machine-readable medium may be a tangible medium, which may contain or store a program for use by an instruction execution system, device, or device or in combination with an instruction execution system, device, or device. . A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer discs, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

It should be noted that the machine-readable medium mentioned above in this application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. A computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device . Program code embodied on a computer readable medium may be transmitted by any appropriate medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

The above-mentioned computer-readable medium may be included in the above-mentioned electronic device, or may exist independently without being incorporated into the electronic device.

Please refer to FIG. 4. FIG. 4 is a schematic diagram of the hardware structure of a server provided by the embodiment of the present application. The server 400 may have relatively large differences due to different configurations or performances, and may include one or more central processing units (central processing units, CPU) 422 (eg, one or more processors) and memory 432, and one or more storage media 430 (eg, one or more mass storage devices) for storing application programs 442 or data 444. Wherein, the memory 432 and the storage medium 430 may be temporary storage or persistent storage. The program stored in the storage medium 430 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the server. Furthermore, the central processing unit 422 may be configured to communicate with the storage medium 430 , and execute a series of instruction operations in the storage medium 430 on the server 400 .

The server 400 can also include one or more power supplies 426, one or more wired or wireless network interfaces 450, one or more input and output interfaces 458, and/or, one or more operating systems 441, such as Windows Server™, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

The steps performed by the device for classifying flight seasons in the above embodiments may be based on the server structure shown in FIG. 4 .

It should also be noted that, according to an embodiment of the present application, the process of the seasonal classification method for flights described in the schematic flowchart of FIG. 1 may be implemented as a computer software program. For example, the embodiments of the present application include a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, the computer program including a program for performing the method shown in the flowchart schematic diagram of FIG. 1 above code.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

While several specific implementation details are contained in the above discussion, these should not be construed as limitations on the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

The above description is only a preferred embodiment of the present application and an illustration of the applied technical principle. Those skilled in the art should understand that the scope of disclosure involved in this application is not limited to the technical solutions formed by the specific combination of the above technical features, but also covers the technical solutions made by the above technical features or Other technical solutions formed by any combination of equivalent features. For example, a technical solution formed by replacing the above-mentioned features with technical features with similar functions disclosed in (but not limited to) this application.

Claims

A seasonal classification method for flights, characterized in that it includes:

Obtain the first flight data of the first flight to be classified seasonally in the target airline company;

Obtaining the second flight data of each historical flight in the historical flight set corresponding to the first flight;

Determine N data pools for seasonal classification, wherein said N is an integer greater than or equal to 2;

calculating the first revenue data of each historical flight according to the second flight data;

determining the central data of each of the N data pools according to the first income data;

calculating second revenue data for the first flight based on the first flight data;

The seasonal classification of the first flight is determined according to the second revenue data and the central data of each of the N data pools.
The method according to claim 1, wherein said determining the central data of each data pool in said N data pools according to said first income data comprises:

Determining the target revenue data as the central data of the first data pool, the target revenue data being the revenue data corresponding to the second flight, the second flight being any flight in the set of historical flights, and the first data pool Any one of the N data pools;

Calculate the first distance between the third income data and the target income data, the third income data is the income data corresponding to any flight in the flight subset, and the flight subset is the historical flight set except a collection of flights other than said second flight;

Divide the fourth revenue data into the first data pool, the fourth revenue data is the revenue data corresponding to the third flight, and the third flight is the third flight in the subset of flights that is related to the target revenue data. - the flight corresponding to the nearest revenue data;

Calculate central data of the first data pool after division.
The method according to claim 2, wherein the calculating the first distance between the third income data and the target income data comprises:

Calculate and calculate the first distance between the third income data and the target income data according to the following formula:

D(i,j)=W(t)×|X it -Y jt |;

Wherein, D(i, j) is the first distance between the target income data i and the third income data j, and the third income data j is the income data corresponding to any flight in the flight subset , W(t) is the weight of the third income data, X it is the market demand value of the target income data, and Y jt is the market demand value corresponding to the third income data.
The method according to claim 2, wherein the calculation of the center data of the target data pool after division comprises:

Calculate the center data of the target data pool after division by the following formula:

Wherein, New_mean(t) is the central data of the target data pool after dividing the fourth income data into the target data pool, and Old_mean(t) is dividing the fourth income input into the target data pool The central data of the aforementioned target data pool, m is the number of central data in the target data pool before the fourth income input is divided into the target data pool, and X(t) is the number of central data in the target data pool The market demand value corresponding to any income data.
The method according to any one of claims 1 to 4, wherein the calculating the first revenue data of each historical flight according to the second flight data comprises:

The first revenue data of each historical flight is calculated by the following formula:

Wherein, Revenue Dcp(x) is the first revenue data of the xth flight in each of the historical flights, i represents the cabin, k is the total number of cabins, and BKG (i) is the reservation of the i-th cabin, Fare(i) is the fare of the ith cabin.
The method according to any one of claims 1 to 4, characterized in that the said first flight is determined according to said second income data and the central data of each data pool in said N data pools Seasonal classifications include:

calculating a second distance between the second income data and each of the N central data, wherein the N central data correspond to the N data pools;

Dividing the second income data into a second data pool, the second data pool being the data pool corresponding to the central data with the smallest second distance between the N central data and the second income data ;

The seasonal classification corresponding to the second data pool is determined as the seasonal classification of the first flight.
A flight season classification device is characterized in that it comprises:

The first acquisition unit is used to acquire the first flight data of the first flight to be classified seasonally in the target airline company;

The second obtaining unit is configured to obtain the second flight data of each historical flight in the historical flight set corresponding to the first flight;

The first determination unit is used to determine N data pools for seasonal classification, wherein the N is an integer greater than or equal to 2;

a first calculation unit, configured to calculate the first revenue data of each historical flight according to the second flight data;

A second determining unit, configured to determine the central data of each of the N data pools according to the first income data;

a second calculation unit, configured to calculate second revenue data of the first flight according to the first flight data;

A third determining unit, configured to determine the seasonal classification of the first flight according to the second revenue data and the central data of each of the N data pools.
The device according to claim 7, wherein the second determination unit is specifically configured to:

Determining the target revenue data as the central data of the first data pool, the target revenue data being the revenue data corresponding to the second flight, the second flight being any flight in the set of historical flights, and the first data pool Any one of the N data pools;

Calculate the first distance between the third income data and the target income data, the third income data is the income data corresponding to any flight in the flight subset, and the flight subset is the historical flight set except a collection of flights other than said second flight;

Divide the fourth revenue data into the first data pool, the fourth revenue data is the revenue data corresponding to the third flight, and the third flight is the third flight in the subset of flights that is related to the target revenue data. - the flight corresponding to the nearest revenue data;

Calculate central data of the first data pool after division.
A computer device, characterized in that it includes: a memory, a processor, and a bus system;

Wherein, the memory is used to store programs;

The bus system is used to connect the memory and the processor, so that the memory and the processor can communicate;

The processor is used to execute the program in the memory, and execute the seasonal classification method according to any one of claims 1 to 6 according to the instructions in the program code.
A machine-readable medium, which is characterized by comprising instructions, and when the instructions are run on the machine, the machine is made to execute the seasonal classification method according to any one of claims 1 to 6 above.