CN117670409A

CN117670409A - Flight seat reservation curve fitting method and related device

Info

Publication number: CN117670409A
Application number: CN202311633927.4A
Authority: CN
Inventors: 张毅; 赵耀帅; 张立功; 宋歌; 冯迪; 尹世豪; 修珊珊
Original assignee: China Travelsky Technology Co Ltd
Current assignee: China Travelsky Technology Co Ltd
Priority date: 2023-11-30
Filing date: 2023-11-30
Publication date: 2024-03-08

Abstract

The invention provides a flight reservation curve fitting method and a related device, which are used for responding to a flight reservation curve fitting instruction, acquiring flight information of a target flight, and extracting historical sample flight data corresponding to the target flight from a historical sample flight data pool so as to generate a historical flight reservation curve according to the historical sample flight data corresponding to the target flight. Calculating the difference between the number of departure data acquisition points and the number of latest data acquisition points on a future flight booking curve to obtain a future booking variable quantity, calculating the difference between the number of departure data acquisition points and the number of latest data acquisition points on a historical flight booking curve to obtain a historical booking variable quantity, and judging whether the historical booking trend is consistent with the future booking trend according to the future booking variable quantity and the historical booking variable quantity, so that the future flight booking curve is fitted by adopting different methods according to different conditions, and the accuracy of the future flight booking curve is improved.

Description

Flight seat reservation curve fitting method and related device

Technical Field

The invention relates to the technical field of data processing, in particular to a flight reservation curve fitting method and a related device.

Background

The revenue management is that the airlines manage the price and the seats by using scientific means such as prediction, optimization and the like, so that each seat of each leg of each flight is sold to different types of passengers at different prices in time, thereby obtaining the maximum revenue.

At present, qualitative analysis is generally carried out on historical sample flight data manually, then translation is carried out on the historical sample flight data once to a future flight on the departure date, and fitting is carried out on the seat reservation curves of the future flight and the historical flight, so that the seat reservation data of the future flight is predicted, but the accuracy of the curve fitting method is lower.

Disclosure of Invention

In view of the above, the invention provides a method and a related device for fitting a flight reservation curve, which improve the accuracy of future flight reservation curves.

In order to achieve the above purpose, the specific technical scheme provided by the invention is as follows:

in a first aspect, an embodiment of the present invention provides a method for fitting a flight reservation curve, including:

responding to a flight seat reservation curve fitting instruction, and acquiring flight information of a target flight, wherein the flight information of the target flight at least comprises a flight number, a departure date, the number of the latest data acquisition point and the seat reservation number of the latest data acquisition point;

According to the flight number and departure date of the target flight, extracting historical sample flight data corresponding to the target flight from a historical sample flight data pool;

generating a historical flight booking curve according to historical sample flight data corresponding to the target flight, wherein each data acquisition point on the historical flight booking curve corresponds to a booking number;

calculating the difference between the number of departure data acquisition points and the number of latest data acquisition points on a future flight reservation curve to obtain the future reservation variable quantity, wherein the future flight reservation curve only comprises the latest data acquisition points and the departure data acquisition points;

calculating the difference value between the number of departure data acquisition points and the number of latest data acquisition points on the historical flight reservation curve to obtain the historical reservation variable quantity, wherein the serial numbers of the historical flight reservation curve and the latest data acquisition points on the future flight reservation curve are the same;

judging whether the historical booking trend is consistent with the future booking trend according to the future booking variable quantity and the historical booking variable quantity;

if the historical booking trend is consistent with the future booking trend, calculating booking number increment ratio according to the future booking variable quantity and the historical booking variable quantity, and fitting the future flight booking curve according to the booking number increment ratio and the historical flight booking curve;

If the historical booking trend is inconsistent with the future booking trend, determining a local booking trend on the historical flight booking curve, and fitting the future flight booking curve according to the local booking trend.

In a second aspect, an embodiment of the present invention provides a flight reservation curve fitting apparatus, including:

the system comprises a flight information acquisition unit, a data acquisition unit and a data acquisition unit, wherein the flight information acquisition unit is used for responding to a flight seat reservation curve fitting instruction to acquire the flight information of a target flight, and the flight information of the target flight at least comprises a flight number, a departure date, the number of the latest data acquisition point and the seat reservation number of the latest data acquisition point;

the historical sample extraction unit is used for extracting historical sample flight data corresponding to the target flight from a historical sample flight data pool according to the flight number and departure date of the target flight;

the historical seat reservation curve generation unit is used for generating a historical flight seat reservation curve according to historical sample flight data corresponding to the target flight, wherein each data acquisition point on the historical flight seat reservation curve corresponds to one seat reservation number;

the first variation amount calculating unit is used for calculating the difference value between the number of the departure data acquisition points and the number of the latest data acquisition points on a future flight reservation curve to obtain the future reservation variation amount, and the future flight reservation curve only comprises the latest data acquisition points and the departure data acquisition points;

The second variation amount calculating unit is used for calculating the difference value between the number of departure data acquisition points and the number of latest data acquisition points on the historical flight reservation curve to obtain the historical reservation variation amount, and the number of the latest data acquisition points on the historical flight reservation curve is the same as that of the latest data acquisition points on the future flight reservation curve;

the booking trend judging unit is used for judging whether the historical booking trend is consistent with the future booking trend according to the future booking variable quantity and the historical booking variable quantity;

the first curve fitting unit is used for calculating an booking number increment ratio according to the future booking variable quantity and the historical booking variable quantity if the historical booking trend is consistent with the future booking trend, and fitting the future flight booking curve according to the booking number increment ratio and the historical flight booking curve;

and the second curve fitting unit is used for determining a local booking trend on the historical flight booking curve if the historical booking trend is inconsistent with the future booking trend, and fitting the future flight booking curve according to the local booking trend.

In a third aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory;

The memory is used for storing program codes and transmitting the program codes to the processor;

the processor is configured to perform a flight seat curve fitting method as described in any one of the implementations of the first aspect according to instructions in the program code.

In a fourth aspect, embodiments of the present invention provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of flight-seat curve fitting as described in any of the implementations of the first aspect.

Compared with the prior art, the invention has the following beneficial effects:

according to the flight seat reservation curve fitting method and the related device, flight information of a target flight is obtained in response to a flight seat reservation curve fitting instruction, historical sample flight data corresponding to the target flight is extracted from a historical sample flight data pool, and therefore a historical flight seat reservation curve is generated according to the historical sample flight data corresponding to the target flight. Calculating the difference between the number of departure data acquisition points and the number of latest data acquisition points on a future flight booking curve to obtain a future booking variable quantity, calculating the difference between the number of departure data acquisition points and the number of latest data acquisition points on a historical flight booking curve to obtain a historical booking variable quantity, and judging whether the historical booking trend is consistent with the future booking trend according to the future booking variable quantity and the historical booking variable quantity, so that the future flight booking curve is fitted by adopting different methods according to different conditions, and the accuracy of the future flight booking curve is improved.

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for fitting a curve of a seat reservation for a flight according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a device for fitting a curve of a seat reservation for a flight according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

The term "including" and variations thereof as used herein are intended to be open-ended, i.e., 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 additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.

It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

In order to more clearly clarify the technical solution of the present application, related concepts related to the present application are explained below.

Revenue management system: a system for automatically managing inventory of unopened flights based on a predictive and optimization model using flight plans, inventory, departure and freight rate data;

Inventory: english name Inventory refers to a series of information that affects the sales decisions of the seats on the flight, such as Availability of seats on the flight, seat reservation value (Seat solution), available Seat number (Seat Open), and various control parameters (Inventory Parameter). ICS is a short name of Inventory Control System, and an airline booking system referred to in the invention is also called an inventory control system, and English is simply called ICS (Inventory Control System);

data acquisition points: english is abbreviated as DCP (Data collection points). For each flight, each DCP point corresponds to an acquisition time, which corresponds to the number of days from the departure date of the flight one to one. The secondary fields of the partial data acquisition points are as follows:

fixed data acquisition Point Fixed-DCP: the data acquisition points set by the airlines are determined by the number of days of departure, and the airlines can set two sets of data acquisition points in total of domestic flight routes and international flight routes in a flight control system (or a profit management system);

floating data acquisition point Floating-DCP: data acquisition points of the category of non-stationary data acquisition points; for example: the designated flight distance harbor date is 20 days, but DCP ₁₁ The corresponding distance is 24 days, DCP ₁₂ The corresponding distance is 18 days, and the data acquisition point is a floating data acquisition point and can be recorded as DCP _11.5 。

Final data acquisition Point Final-DCP: data acquisition points on the same day as departure of flights;

the latest data acquisition point Lastactl-DCP is the data acquisition point of the last acquired data of the non-departure flight; the revenue management prediction and optimization module applies data based on Lastaactual-DCP acquired avionics level inventory as arguments.

Referring to fig. 1, the method for fitting a flight reservation curve disclosed in the present embodiment specifically includes the following steps:

s101: responding to a flight seat reservation curve fitting instruction, and acquiring flight information of a target flight;

the target flight is a non-departure flight, and the flight seat curve corresponding to the target flight represents the seat number corresponding to the future data acquisition point which is not acquired. The flight information of the target flight at least comprises a flight number, an departure date, the number of the latest data acquisition point and the number of the latest data acquisition point reservation.

Wherein, the flight number is the flight number of the airline company, and consists of two parts: the airline code and flight number, such as AA1234, represent the 1234-number flight of the AA airline. The data collection points Data Collection Points, abbreviated as Dcp, are set by the airlines according to the consideration factors such as the own route quality, the flight attribute and the like, and are data collection codes determined by the distance harbor days, and have a one-to-one correspondence with the distance harbor days. The number of the Dcp is set to 24 by an airline company, the number of the Dcp is 0-23, and the number value of the Dcp is larger when the number is closer to the departure date of the flight; for example: data acquisition Point Dcp ₂₃ The number of days indicating the date of the data collection at the time interval harbor is 0 days, namely the departure data collection point. The latest data acquisition point Lastactl-Dcp is the data acquisition point of the latest acquired data of the non-departure flight, and the number of the latest data acquisition point can be any one of 0 to 23.

The implementation manner of the flight seat reservation curve fitting instruction corresponding to the target flight can be various, and the following two alternative implementation manners are introduced:

for example, the user may input the flight information of the target flight in the visual interface, and then generate a flight seat curve fitting instruction corresponding to the target flight by clicking the submit button, for generating a future flight seat curve corresponding to the target flight.

For example, the flight seat curve fitting task corresponding to the target flight may be set as a timing task, and the task is executed periodically, such as once a day, once a week, etc., where the timing task is set, the flight information of the target flight needs to be set, and when the timing task is triggered, the flight seat curve fitting instruction corresponding to the target flight is automatically generated.

S102: according to the flight number and departure date of the target flight, extracting historical sample flight data corresponding to the target flight from a historical sample flight data pool;

The historical sample flight data pool is pre-constructed, and the historical sample flight data in the historical sample flight data pool comprises, but is not limited to, flight time data SCH, flight booking data INV and flight operation data FLT; it includes, but is not limited to, the following production data: airline, flight number, origin, destination, flight departure date and time, flight arrival date and time, electronic ticket sign, billboards, physical layout number of flights, maximum marketable seats of flights. It will be appreciated that the number of seats for each data acquisition point in each historical sample flight data for the target flight may be derived based on the data described above.

S103: generating a historical flight booking curve according to historical sample flight data corresponding to the target flight, wherein each data acquisition point on the historical flight booking curve corresponds to a booking number;

the historical flight booking curve is composed of each Dcp booking number, and the booking number of each Dcp is the average value of all corresponding Dcp booking numbers in the historical sample flight data pool.

In a Class of _i Representing the target flight object, the granularity may be fine to the bunk level. If Class _i N records in the historical sample flight data pool, then Class _i The reservation number of the jth Dcp point on the historical flight reservation curve is:

wherein, class _i _DCP _j H is Class _i The reservation number of the jth Dcp on the historical flight reservation curve, class _i BKm is a Class _i Class in record m in historical sample flight data pool _i Is a seat number of the seat number.

S104: calculating the difference between the reservation number of the departure data acquisition points and the reservation number of the latest data acquisition points on a future flight reservation curve to obtain the future reservation variable quantity;

the future flight reservation curve only comprises the latest data acquisition point and the departure data acquisition point, the reservation number of the latest data acquisition point is the latest data acquisition, the reservation number of the departure data acquisition point is predicted based on historical data, and the prediction method can be various, such as a machine learning model, and the like. It should be noted that, due to the specificity of the departure data acquisition points, the prediction of the number of seats of the departure data acquisition points is relatively accurate, but the number of seats of the data acquisition points between the latest data acquisition points and the departure data acquisition points is affected by a plurality of factors, and cannot be predicted accurately, and needs to be obtained according to a trend fitting curve.

Future seat order change ChangeF is Class _i Dcp on future flight reservation curve _LA Is the number of orders and Dcp of (d) ₂₃ Difference of seat number Dcp _LA To be the latest data acquisition point, dcp ₂₃ Is the departure data acquisition point.

ChangeF＝Class _i _DCP ₂₃ _F-Class _i _DCP _A _F

Wherein, class _i _Dcp ₂₃ F is the departure data acquisition point Dcp on future flight reservation curve ₂₃ Is of the order of Class _i _Dcp _LA F is the latest data acquisition point Dcp on future flight reservation curve _LA Is a seat number of the seat number.

S105: calculating the difference between the number of departure data acquisition point reservation and the number of latest data acquisition point reservation on the historical flight reservation curve to obtain a historical reservation variable quantity;

the change of the history seat is Class _i Dcp on historical flight reservation curve _LA Is the number of orders and Dcp of (d) ₂₃ Difference in number of seats.

ChangeH＝Class _i _Dcp ₂₃ _H-Class _i _Dcp _LA _H

Wherein, class _i _Dcp ₂₃ H is the departure data acquisition point Dcp on the historical flight booking curve ₂₃ Is of the order of Class _i _Dcp _LA H is the latest data acquisition point Dcp on the historical flight seat reservation curve _LA Is a seat number of the seat number. The historical flight seat order curve and the future flight orderThe number of the latest data acquisition points on the seat curve is the same.

S106: judging whether the historical booking trend is consistent with the future booking trend according to the future booking variable quantity and the historical booking variable quantity;

the booking trend can have three situations: 1. the latest data acquisition point and the departure data acquisition point have the same booking number, namely the booking number is unchanged; 2. the booking number of the latest data acquisition point is smaller than the booking number of the departure data acquisition point, namely the booking number is increased; 3. the booking number of the latest data acquisition point is larger than that of the departure data acquisition point, namely the booking number is reduced.

The future booking trend can be determined according to the future booking variable quantity, and the historical booking trend can be determined according to the historical booking variable quantity, so that whether the historical booking trend is consistent with the future booking trend or not can be judged.

S107: if the historical booking trend is consistent with the future booking trend, calculating booking number increment ratio according to the future booking variable quantity and the historical booking variable quantity, and fitting the future flight booking curve according to the booking number increment ratio and the historical flight booking curve;

if the historical booking trend is consistent with the future booking trend, fitting of the future flight booking curve can be assisted according to the historical booking trend.

S108: if the historical booking trend is inconsistent with the future booking trend, determining a local booking trend on the historical flight booking curve, and fitting the future flight booking curve according to the local booking trend.

If the historical booking trend is inconsistent with the future booking trend, the fitting of the future flight booking curve is inaccurate according to the historical booking trend, and in practical application, the local booking trend, such as the booking trend between two adjacent data acquisition points, is relatively stable and has better reference value, so if the historical booking trend is inconsistent with the future booking trend, the local booking trend on the historical flight booking curve is determined, and the future flight booking curve is fitted according to the local booking trend.

It should be further noted that, in the present embodiment, the future flight reservation curve of the target flight may be accurate to the flight leg (cabin) so as to meet the requirement of the follow-up accurate prediction.

It can be seen that, in the flight seat order curve fitting method disclosed in this embodiment, the flight information of the target flight is obtained in response to the flight seat order curve fitting instruction, and the historical sample flight data corresponding to the target flight is extracted from the historical sample flight data pool, so that the historical flight seat order curve is generated according to the historical sample flight data corresponding to the target flight. Calculating the difference between the number of departure data acquisition points and the number of latest data acquisition points on a future flight booking curve to obtain a future booking variable quantity, calculating the difference between the number of departure data acquisition points and the number of latest data acquisition points on a historical flight booking curve to obtain a historical booking variable quantity, and judging whether the historical booking trend is consistent with the future booking trend according to the future booking variable quantity and the historical booking variable quantity, so that the future flight booking curve is fitted by adopting different methods according to different conditions, and the accuracy of the future flight booking curve is improved.

Wherein, S106: according to the future booking variable quantity and the historical booking variable quantity, an alternative implementation way for judging whether the historical booking trend is consistent with the future booking trend comprises the following steps: it is determined whether changeh×changef is less than 0. It can be understood that if changeh×changef < 0, it means that one of ChangeH and ChangeF is necessarily greater than 0 and one is less than 0, i.e. one seat number is increased by one seat number is decreased, which indicates that the historical seat trend is inconsistent with the future seat trend; if changeh×changef > 0, it indicates that both ChangeH and ChangeF are greater than 0 or less than 0, i.e., both have an increased number of seats or a decreased number of seats, indicating that the historical seat reservation trend is consistent with the future seat reservation trend.

In addition, if changeh=0 indicates that the number of seats of the latest data acquisition point and the departure data acquisition point on the historical flight seat reservation curve is the same, the future flight seat reservation curve cannot be accurately fitted based on the overall seat reservation trend, and in this case, S108 needs to be executed: and determining a local booking trend on the historical flight booking curve, and fitting the future flight booking curve according to the local booking trend. If ChangeH is not equal to 0, then S106 is performed: and judging whether the historical booking trend is consistent with the future booking trend according to the future booking variable quantity and the historical booking variable quantity, so as to determine which future flight booking curve fitting method is executed according to whether the historical booking trend is consistent with the future booking trend.

Wherein, if the historical booking trend is consistent with the future booking trend, an alternative implementation of S107 includes the following steps:

a1: calculating Dcp on future flight reservation curve _LA Point to Dcp ₂₃ The delta number of booking points relative to Dcp on historical flight booking curve _LA Point to Dcp ₂₃ The number of seats ordered increment Ratio of the point is calculated as follows:

a2: determining the booking number of the known data acquisition points with the same number as the unknown data acquisition points on the historical flight booking curve as the reference booking number aiming at each unknown data acquisition point between the latest data acquisition point and the departure data acquisition point on the future flight booking curve;

A3: the product of the reference seat count and the seat count increment ratio is determined as the seat count of the unknown data acquisition point.

Ordering Dcp on a future flight _LA Point to Dcp ₂₃ Other unknown Dcp points between points are determined by collecting the Dcp points from unknown data as follows _LA+1 The points are as examples:

Class _i _Dcp _LA+1 _F＝Class _i _Dcp _LA+1 _H×Ratio；

wherein, class _i _Dcp _LA+1 F, class on reservation curve for future flights _i At Dcp _LA+1 Number of orders for a dot, class _i _Dcp _LA+1 H is Class _i Dcp on historical flight reservation curve _LA+1 Number of orders for a dot, class _i _Dcp _LA+1 H is already found at S103 as a known number.

The process is a cyclic process, ordering Dcp on future flights _LA Point to Dcp ₂₃ And each Dcp between the points is calculated, and the circulation is not ended until the number of seats of all unknown Dcp points on the future flight seat reservation curve is determined, so that the whole future flight seat reservation curve is obtained.

If the historical booking trend is inconsistent with the future booking trend, an alternative implementation of S108 comprises the steps of:

b1: determining a first linear equation corresponding to the latest data acquisition point and the departure data acquisition point on the historical flight reservation curve according to the number and the reservation number of the latest data acquisition point and the number and the reservation number of the departure data acquisition point on the historical flight reservation curve;

Determining Dcp above historical flight reservation curve _LA Corresponding P _1H Point and Dcp ₂₃ Corresponding P _2H And (5) a dot. P (P) _1H Is (LA, bkg) _1H )，P _2H The coordinates of the points were (23, bkg _2H ) Wherein LA and 23 represent the numbers of the Dcp points, bkg _1H Is Dcp _LA Is Bkg _2H Is Dcp ₂₃ Is a seat number of the seat number. Then by P _1H Point and P _2H The first linear equation for a point is:

b2: aiming at each known data acquisition point between the latest data acquisition point and the departure data acquisition point on the historical flight reservation curve, calculating the trend reservation number corresponding to the known data acquisition point according to a first linear equation;

with known data acquisition points Dcp _LA+1 Corresponding P on historical flight seat reservation curve _3H Point is exemplified by P _3H The coordinates of the points are (B, bkg _3H ) Wherein B is Dcp _LA+1 Number Bkg of (2) _3H Is the known data acquisition point Dcp _LA+1 Is a seat number of the seat number.

Calculating a known data acquisition point Dcp according to a first linear equation _LA+1 Corresponding trend seat number Bkg _4H ：

From coordinates (B, bkg) _4H ) Can determine the known data acquisition point Dcp _LA+1 P on a first straight line _4H And (5) a dot.

B3: calculating trend parameters of the known data acquisition points according to the actual booking numbers and the trend booking numbers of the known data acquisition points, wherein the trend parameters represent local trends of the booking numbers of the known data acquisition points in the historical flight booking curves;

It will be appreciated that P _4H The point may be equal to P _3H Dot overlap, P _4H The point may also be at P _3H Above the point, P _4H The point may also be at P _3H Below the point, i.e. the known data acquisition point Dcp _LA+1 Is a local trend of historical flight booking curves. Representing the known data acquisition point Dcp by a trend parameter Index _LA+1 Local trend of the booking numbers in the historical flight booking curve:

if Index is a positive number, P is represented _3H Point at P _4H Above the dot, if Index is negative, P is represented _3H Point at P _4H Below the point, if Index is 0, P is represented _3H Point at P _4H Overlapping of points.

B4: determining a second linear equation corresponding to the latest data acquisition point and the departure data acquisition point on the future flight reservation curve according to the number and the reservation number of the latest data acquisition point and the number and the reservation number of the departure data acquisition point on the future flight reservation curve;

in the same way as B1, by P _1F Point and P _2F The second linear equation corresponding to the point is:

wherein, dcp is arranged on future flight reservation curve _LA+1 Corresponding to P _1F Point, dcp ₂₃ Corresponding to P _2F And (5) a dot. P (P) _1F The coordinates of the points are (LA, bkg _1F )，P _2F The coordinates of the points were (23, bkg _2F ). LA and 23 represent the numbers of the Dcp points, bkg _1F Is Dcp _LA Is Bkg _2F Is Dcp ₂₃ Is a seat number of the seat number.

B5: substituting the number of the unknown data acquisition point between the latest data acquisition point and the departure data acquisition point on the future flight reservation curve into a second linear equation to obtain the initial reservation number of the unknown data acquisition point, wherein the number of the unknown data acquisition point is the same as the number of the known data acquisition point;

Unknown data acquisition point Dcp on future flight seat reservation curve _LA+1 Substituting the number B of the second straight line equation to obtain an unknown data acquisition point Dcp on the second straight line _LA+1 Corresponding P _4F Ordinate Bkg of point _4F I.e. unknown data acquisition point Dcp _LA+1 Is set up in advance of the initial subscription number of (a):

obtaining P _4F Coordinates (B, bkg) _4F ) It can be appreciated that since the second linear equation represents the trend of the number of seats ordered between the latest data acquisition point and the departure data acquisition point, the trend is relatively extensive, and the unknown data acquisition point Dcp _LA Initial seat number Bkg _4F Is inaccurate.

B6: correcting the seat number of the unknown data acquisition points by using the trend parameters of the known data acquisition points to obtain the seat number of the unknown data acquisition points until the seat number of all the unknown data acquisition points is determined, so as to obtain a future flight seat curve consisting of the latest data acquisition points, all the unknown data acquisition points and the departure data acquisition points.

Correcting the seat reservation number of the unknown data acquisition point by using the trend parameter of the known data acquisition point to obtain the unknown data acquisition point Dcp on the future flight seat reservation curve _LA+1 Corresponding P _3F Ordinate Bkg of point _3F ：

Bkg _3F ＝Bkg _4F +Bkg _4F ×Index

The data acquisition point Dcp on the future flight seat reservation curve is obtained _LA+1 Is a seat number of the seat number. If Index is a positive number, P is obtained _3F At P _4F If Index is negative, P is obtained _3F At P _4F If Index is 0, the resulting P _3F At P _4F Is a part of the overlapping of the two.

The flowcharts 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 disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). 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 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 which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

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

It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language 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 the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

Based on the above embodiment, the present embodiment correspondingly discloses a device for fitting a flight reservation curve, please refer to fig. 2, which includes:

a flight information obtaining unit 201, configured to obtain flight information of a target flight in response to a flight seat reservation curve fitting instruction, where the flight information of the target flight includes at least a flight number, a departure date, a number of a latest data acquisition point, and a seat reservation number of the latest data acquisition point;

a historical sample extraction unit 202, configured to extract historical sample flight data corresponding to the target flight from a historical sample flight data pool according to the flight number and departure date of the target flight;

a historical seat reservation curve generating unit 203, configured to generate a historical seat reservation curve according to historical sample flight data corresponding to the target flight, where each data acquisition point on the historical seat reservation curve corresponds to one seat reservation number;

a first variation calculating unit 204, configured to calculate a difference between a number of departure data acquisition points and a number of latest data acquisition points on a future flight reservation curve, to obtain a future reservation variation, where the future flight reservation curve only includes the latest data acquisition points and the departure data acquisition points;

A second variation calculating unit 205, configured to calculate a difference between the number of departure data acquisition points and the number of latest data acquisition points on the historical flight reservation curve to obtain a historical reservation variation, where the historical flight reservation curve is the same as the number of the latest data acquisition points on the future flight reservation curve;

a booking trend judging unit 206, configured to judge whether the historical booking trend is consistent with the future booking trend according to the future booking variable amount and the historical booking variable amount;

a first curve fitting unit 207, configured to calculate a booking number increment ratio according to the future booking variable amount and the historical booking variable amount if the historical booking trend is consistent with the future booking trend, and fit the future flight booking curve according to the booking number increment ratio and the historical flight booking curve;

the second curve fitting unit 208 is configured to determine a local booking tendency on the historical flight booking curve if the historical booking tendency is inconsistent with the future booking tendency, and fit the future flight booking curve according to the local booking tendency.

In some embodiments, the historical seat reservation curve generating unit 203 is specifically configured to calculate a seat reservation number average value corresponding to each data acquisition point in the historical sample flight data corresponding to the target flight; and generating the historical flight booking curve composed of all the data acquisition points by taking the data acquisition point numbers as an abscissa and taking the booking number average value corresponding to the data acquisition points as an ordinate.

In some embodiments, the apparatus further comprises:

a history variation judging unit for judging whether the history seat reservation variation is 0; triggering the second curve fitting unit 208 if the history seat order variation is 0; if the historical seat reservation change is not 0, the seat reservation trend determination unit 206 is triggered.

In some embodiments, the booking trend determining unit 206 is specifically configured to determine whether a product of the future booking variable amount and the historical booking variable amount is less than 0; if the historical booking trend is smaller than 0, determining that the historical booking trend is inconsistent with the future booking trend; and if the historical booking trend is not smaller than 0, determining that the historical booking trend is consistent with the future booking trend.

In some embodiments, the first curve fitting unit 207 is specifically configured to determine, for each unknown data acquisition point between the latest data acquisition point and the departure data acquisition point on the future flight reservation curve, a reservation number of known data acquisition points having the same number as the unknown data acquisition point on the historical flight reservation curve as a reference reservation number; and determining the product of the reference seat number and the seat number increment ratio as the seat number of the unknown data acquisition points until the seat number of all the unknown data acquisition points is determined, so as to obtain the future flight seat reservation curve formed by the latest data acquisition points, all the unknown data acquisition points and the departure data acquisition points.

In some embodiments, the second curve fitting unit 208 is specifically configured to:

determining a first linear equation corresponding to the latest data acquisition point and the departure data acquisition point on the historical flight reservation curve according to the number and the reservation number of the latest data acquisition point and the number and the reservation number of the departure data acquisition point on the historical flight reservation curve;

aiming at each known data acquisition point between the latest data acquisition point and the departure data acquisition point on the historical flight booking curve, calculating the trend booking number corresponding to the known data acquisition point according to the first linear equation;

calculating trend parameters of the known data acquisition points according to the actual booking numbers of the known data acquisition points and the trend booking numbers, wherein the trend parameters represent local trends of the booking numbers of the known data acquisition points in the historical flight booking curves;

determining a second linear equation corresponding to the latest data acquisition point and the departure data acquisition point on the future flight reservation curve according to the number and the reservation number of the latest data acquisition point and the number and the reservation number of the departure data acquisition point on the future flight reservation curve;

Substituting the serial numbers of the unknown data acquisition points between the latest data acquisition points and the departure data acquisition points on the future flight seat reservation curve into the second linear equation to obtain the initial seat reservation number of the unknown data acquisition points, wherein the serial numbers of the unknown data acquisition points are the same as the serial numbers of the known data acquisition points;

and correcting the seat number of the unknown data acquisition points by using the trend parameters of the known data acquisition points to obtain the seat number of the unknown data acquisition points until the seat number of all the unknown data acquisition points is determined, so as to obtain the future flight seat curve formed by the latest data acquisition points, all the unknown data acquisition points and the departure data acquisition points.

The embodiment discloses a flight seat curve fitting device, which responds to a flight seat curve fitting instruction to acquire flight information of a target flight, and extracts historical sample flight data corresponding to the target flight from a historical sample flight data pool, so that a historical flight seat curve is generated according to the historical sample flight data corresponding to the target flight. Calculating the difference between the number of departure data acquisition points and the number of latest data acquisition points on a future flight booking curve to obtain a future booking variable quantity, calculating the difference between the number of departure data acquisition points and the number of latest data acquisition points on a historical flight booking curve to obtain a historical booking variable quantity, and judging whether the historical booking trend is consistent with the future booking trend according to the future booking variable quantity and the historical booking variable quantity, so that the future flight booking curve is fitted by adopting different methods according to different conditions, and the accuracy of the future flight booking curve is improved.

The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The name of the unit does not in any way constitute a limitation of the unit itself, for example the first acquisition unit may also be described as "unit acquiring at least two internet protocol addresses".

The functions described above herein 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: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

Referring now to fig. 3, a schematic diagram of an electronic device 600 suitable for use in implementing embodiments of the present disclosure is shown. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 3 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.

As shown in fig. 3, the electronic device 600 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

In general, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 600 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: and responding to the flight seat reservation curve fitting instruction, acquiring the flight information of the target flight, and extracting the historical sample flight data corresponding to the target flight from the historical sample flight data pool, so as to generate a historical flight seat reservation curve according to the historical sample flight data corresponding to the target flight. Calculating the difference between the number of departure data acquisition points and the number of latest data acquisition points on a future flight booking curve to obtain a future booking variable quantity, calculating the difference between the number of departure data acquisition points and the number of latest data acquisition points on a historical flight booking curve to obtain a historical booking variable quantity, and judging whether the historical booking trend is consistent with the future booking trend according to the future booking variable quantity and the historical booking variable quantity, so that the future flight booking curve is fitted by adopting different methods according to different conditions, and the accuracy of the future flight booking curve is improved.

Alternatively, the computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: and responding to the flight seat reservation curve fitting instruction, acquiring the flight information of the target flight, and extracting the historical sample flight data corresponding to the target flight from the historical sample flight data pool, so as to generate a historical flight seat reservation curve according to the historical sample flight data corresponding to the target flight. Calculating the difference between the number of departure data acquisition points and the number of latest data acquisition points on a future flight booking curve to obtain a future booking variable quantity, calculating the difference between the number of departure data acquisition points and the number of latest data acquisition points on a historical flight booking curve to obtain a historical booking variable quantity, and judging whether the historical booking trend is consistent with the future booking trend according to the future booking variable quantity and the historical booking variable quantity, so that the future flight booking curve is fitted by adopting different methods according to different conditions, and the accuracy of the future flight booking curve is improved.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via communication means 609, or from storage means 608, or from ROM 602. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 601.

According to one or more embodiments of the present disclosure, there is provided a flight reservation curve fitting method [ example 1 ], comprising:

According to one or more embodiments of the present disclosure, there is provided a method of example 1 [ example 2 ], the generating a historical flight seat order curve from historical sample flight data corresponding to the target flight, including:

calculating the average value of seat booking numbers corresponding to each data acquisition point in the historical sample flight data corresponding to the target flight;

and generating the historical flight booking curve composed of all the data acquisition points by taking the data acquisition point numbers as an abscissa and taking the booking number average value corresponding to the data acquisition points as an ordinate.

According to one or more embodiments of the present disclosure, there is provided a method of example 1 [ example 3 ], further comprising: judging whether the historical seat reservation variable quantity is 0;

if the historical booking variable quantity is 0, executing the local booking trend on the historical flight booking curve, and fitting the future flight booking curve according to the local booking trend;

and if the historical booking variable quantity is not 0, executing the step of judging whether the historical booking trend is consistent with the future booking trend according to the future booking variable quantity and the historical booking variable quantity.

According to one or more embodiments of the present disclosure, there is provided a method of example 1 [ example 4 ], the determining whether a historical booking trend is consistent with a future booking trend according to the future booking variable and the historical booking variable, including:

judging whether the product of the future seat reservation variable quantity and the historical seat reservation variable quantity is smaller than 0;

if the historical booking trend is smaller than 0, determining that the historical booking trend is inconsistent with the future booking trend;

and if the historical booking trend is not smaller than 0, determining that the historical booking trend is consistent with the future booking trend.

According to one or more embodiments of the present disclosure, there is provided a method of example 1, the fitting the future flight reservation curve according to the reservation number increment ratio and the historical flight reservation curve comprising:

determining the booking number of known data acquisition points with the same number as the unknown data acquisition points on the historical flight booking curve as a reference booking number aiming at each unknown data acquisition point between the latest data acquisition point and the departure data acquisition point on the future flight booking curve;

and determining the product of the reference seat number and the seat number increment ratio as the seat number of the unknown data acquisition points until the seat number of all the unknown data acquisition points is determined, so as to obtain the future flight seat reservation curve formed by the latest data acquisition points, all the unknown data acquisition points and the departure data acquisition points.

According to one or more embodiments of the present disclosure, there is provided a method of example 1, the determining a local booking trend on the historical flight booking curve comprising:

and calculating trend parameters of the known data acquisition points according to the actual booking numbers of the known data acquisition points and the trend booking numbers, wherein the trend parameters represent local trends of the booking numbers of the known data acquisition points in the historical flight booking curves.

According to one or more embodiments of the present disclosure, there is provided a method of example 1, the fitting the future flight booking curve according to the local booking trend, comprising:

According to one or more embodiments of the present disclosure, there is provided [ example 8 ] a flight reservation curve fitting apparatus comprising:

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 example forms of implementing the claims.

While several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the disclosure. 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 foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).

Claims

1. A method of curve fitting a flight reservation comprising:

2. The method of claim 1, wherein generating a historical flight seat order curve from historical sample flight data corresponding to the target flight comprises:

3. The method according to claim 1, wherein the method further comprises:

judging whether the historical seat reservation variable quantity is 0;

4. The method of claim 1, wherein determining whether the historical booking trend is consistent with the future booking trend based on the future booking variable and the historical booking variable comprises:

5. The method of claim 1, wherein said fitting said future flight reservation curve based on said reservation number increment ratio and said historical flight reservation curve comprises:

6. The method of claim 1, wherein said determining a local booking trend on the historical flight booking curve comprises:

7. The method of claim 6, wherein said fitting the future flight booking curve based on the local booking trend comprises:

8. A flight reservation curve fitting device, comprising:

9. An electronic device comprising a processor and a memory;

the processor is configured to perform a flight seat curve fitting method as claimed in any one of claims 1 to 7 in accordance with instructions in the program code.

10. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, which computer program, when executed by a processor, implements a method of fitting a flight seat curve as claimed in any one of claims 1 to 7.