Method, system and device for predicting reachable market demand value of flight and storage medium

Abstract

The invention belongs to the technical field of aviation income management, and provides a method, system and device for predicting a flight reachable market demand value and a storage medium, and the method comprises the steps: obtaining all flight information of a specified flight segment of a specified airline; based on the flight information, obtaining inventory data and historical market demand values of a certain future flight Dcp; on the basis of the inventory data, predicting a market demand value of the latest Dcp flight segment shipping space of a future flight; based on the historical market demand value, establishing a historical flight data pool from granularity to shipping space level for a certain future flight; based on the historical flight data pool, drawing a flight seat reservation curve for the flight segment shipping space of a certain future flight; and the reachable demand values of all the shipping spaces of a certain future flight are predicted. According to the invention, the reachable demand value of the leg (shipping space) during departure is calculated by using the income management system, so that market limitation factors are reflected, the function of the income management system is perfected, and the performance of the income management system and the income management working efficiency of an airline company are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of aviation revenue management, and in particular relates to a method, system, device and storage medium for predicting the achievable market demand value of a flight. Background Art

[0002] Revenue management is the management of prices and seats by airlines using scientific means such as forecasting and optimization, so that every seat on every segment of every flight can be sold to different types of passengers at different prices in a timely manner, thereby obtaining maximum revenue.

[0003] The Revenue Management System (Basic Edition) is a system that automatically manages the inventory of flights that have not departed based on forecasting and optimization models using flight schedules, inventory, departure and freight rate data. In the Revenue Management System, a certain forecast target flight that has not departed is determined as follows: when the actual number of bookings for a certain segment (cabin) of a future flight in the latest Dcp is less than the historical average number of bookings for the Dcp, or when a certain segment (cabin) of a future flight is locked in the latest Dcp, it indicates that the market demand at this time is affected by certain limiting factors (aircraft seat number, protection number, etc.).

[0004] The purpose of the technology and methods of the revenue management system (basic version) is to determine the achievable demand value for each leg (cabin) of a future flight Future Departure when it departs. The achievable market demand value plays an extremely important role in the revenue management business and the revenue management system. It is not only the main output of the prediction module in the revenue management system, but also the main input of the optimization module. It is not realistic to perform massive manual calculations. The revenue management system suffers from the lack of prediction theory and technical support. This is a long-standing problem in revenue management work and the construction of the revenue management system.

[0005] How to solve the problems and difficulties mentioned above and meet the market demand value forecasts at various granularities, from flight segments to cabin classes, is a domestic gap that has not yet been filled in the systematic operation of market demand value forecasting business. Summary of the invention

[0006] In order to solve the above problems, the present invention provides a method, system, device and storage medium for predicting the achievable market demand value of a flight.

[0007] A method for predicting the achievable market demand value of a flight according to the present invention comprises:

[0008] S1. Obtain all flight information of a specified flight segment of a specified airline; based on the flight information, obtain inventory data and historical market demand value of a future flight Dcp;

[0009] S2. Based on the inventory data, predict the market demand value of the latest Dcp of a future flight segment space;

[0010] S3. Based on the historical market demand value, establish a historical flight data pool with granularity down to cabin level for the future flight;

[0011] S4. Drawing a flight booking curve for the flight segment space of the future flight based on the historical flight data pool;

[0012] S5. Predicting the achievable demand values ​​of all seats of the future flight.

[0013] Furthermore, the acquisition of all flight information of a specified flight segment of a specified airline specifically includes:

[0014] Obtain the full information and incremental information of the specified flight segment of the specified airline, and organize and store them in the database; the incremental information is the latest flight data during the night maintenance time from 02:00 to 04:00 every night.

[0015] Further, the obtaining of inventory data of a future flight Dcp based on the flight information specifically includes:

[0016] Obtain the flight inventory data of the departing flights of the specified flight segment of the specified airline, based on the current date or the system date of the revenue management system for the past three years, as the departing flight inventory data;

[0017] Get the flight inventory data of the specified flight segment of the specified airline for the next year based on the current date or the system date of the revenue management system as the flight inventory data that has not departed.

[0018] Furthermore, the flight information also includes ASTD aviation standard data provided by the ICS flight control system, and the ASTD aviation standard data specifically includes:

[0019] Flight schedule data SCH: airline, flight number, origin, destination, flight departure date and time, flight arrival date and time;

[0020] Flight booking data INV: electronic ticket identification, cabin structure table, flight physical layout number, and maximum number of seats available for sale on the flight;

[0021] Flight operation data FLT: actual departure time of the flight.

[0022] Furthermore, the drawing of a flight booking curve for the flight segment space of the future flight based on the historical flight data pool specifically includes:

[0023] The flight booking curve includes the booking curve of historical flights that have departed, the booking curve of flights that have not departed, and the predicted booking curve of future flights;

[0024] The flight booking curve is composed of the number of bookings for each Dcp, and the number of bookings for each Dcp is the average of the number of bookings for all corresponding Dcps in the historical flight data pool;

[0025] If Class i There are N records in the historical flight data pool, then Class i The number of reservations for the jth Dcp point on the flight reservation curve is:

[0026]

[0027] Among them, Class i _DCP j _H is Class i The number of seats booked for the jth Dcp in the flight booking curve, Class i _BKD m For Class i The Class on the mth record in the historical flight data pool i Number of bookings.

[0028] Furthermore, the predicted achievable demand value of all seats of a future flight specifically includes:

[0029] S5-1. Compare the latest Dcp class of a future flight i The size of the number of bookings BKD and the number of bookings BKD corresponding to Dcp on the flight booking curve;

[0030] If the number of bookings BKD of the flight class Classi of the latest Dcp of a future flight is less than the number of bookings BKD of the corresponding Dcp on the flight booking curve, then go to step S5-4, otherwise go to step S5-6;

[0031] S5-2. Determine the class of the latest Dcp of a future flight i Whether it is in the locked cabin Posted state;

[0032] If the latest Dcp class of a future flight i If the state is not Posted, go to step S5-5, otherwise go to step S5-6;

[0033] S5-3. Calculate the class of the latest Dcp of a future flight i AchievableDemand;

[0034] If the booking value BKD of the latest Dcp of a future flight is greater than the historical average booking number and is not locked, the predicted flight segment will be booked at Dcp 23The market demand value of the point is taken as the achievable demand value, that is, AchievableDemand = Demand;

[0035] S5-4. Calculate the class of the latest Dcp of a future flight i The difference between the number of bookings BKD and the number of bookings corresponding to Dcp on the flight booking curve is calculated as follows:

[0036] Difference = i_DCP A _Future-i_DCP A _History

[0037] S5-5. Number of seats corresponding to the flight booking curve i_Dcp 23 _History, calculate the Class of a future flight segment i In DCP 23 The achievable demand value Achievable Demand is calculated as follows:

[0038] Achievable Demand = i_DCP 23 _History+Difference

[0039] Among them, i_DCP 23 _History is the corresponding DCP on the flight booking curve 23 Number of bookings;

[0040] S5-6. Determine all the classes of a future flight segment i Whether all the AchievableDemand values ​​are calculated;

[0041] S5-7. Output the achievable demand value Achievable Demand.

[0042] Furthermore, the method for determining whether the space is in the locked and posted state is as follows:

[0043] In the ICS system of China Aviation Information Network, the main judgment basis is:

[0044] If the maximum available value LSS of the flight booking INV inventory data of the space is a positive integer, and the space sales status IND is marked as EK\EAK\ELK\EALK, the space of the flight segment is not locked and posted, and the space is in a saleable state;

[0045] If the maximum available value LSS of the flight booking INV inventory data of the space is less than or equal to 0, or the space sales status IND is marked as EPK\EALP\EAPK\EALPK, the space of the flight segment is locked and posted, and the space of the flight segment is stopped from being sold;

[0046] In overseas ICS systems, the main basis for judgment is:

[0047] The number of BKD seats booked for the class of the flight segment reaches the class of that class i If the AU is greater than the maximum available value, the space for this flight segment is Posted and the space for this flight segment is no longer available for sale; otherwise, the space for this flight segment is not Posted and is available for sale.

[0048] The present invention also provides a system for predicting the achievable market demand value of a flight, the system comprising:

[0049] An acquisition module is used to acquire all flight information of a specified flight segment of a specified airline; based on the flight information, acquire inventory data and historical market demand value of a future flight Dcp;

[0050] A module for predicting market demand value, used to predict the market demand value of the flight segment space of the latest Dcp of a future flight based on the inventory data;

[0051] Constructing a historical flight data pool module, for establishing a historical flight data pool with a granularity down to the cabin level for the future flight based on the historical market demand value;

[0052] A flight booking curve drawing module is used to draw a flight booking curve for the flight segment space of the future flight based on the historical flight data pool;

[0053] The module for predicting the achievable demand value is used to predict the achievable demand value of all seats of the future flight.

[0054] The present invention also provides a device, including a processor, which is coupled to a memory; the processor is used to read and execute the computer program stored in the memory to implement the aforementioned method for predicting the market demand value that a flight can reach.

[0055] The present invention also provides a computer-readable storage medium storing a program or instruction. When the program or instruction is executed on a computer, the computer executes the aforementioned method for predicting the achievable market demand value of a flight.

[0056] Compared with the prior art, the present invention has the following advantages:

[0057] The present invention utilizes the revenue management system to reflect market constraints by calculating the achievable demand value of the flight segment (cabin space) at the time of departure, thereby improving the function of the revenue management system, the performance of the revenue management system and the efficiency of the revenue management work of the airline company. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 A schematic flow chart of a method for predicting the achievable market demand value of a flight according to the present invention;

[0060] Figure 2 A schematic diagram of the structure of a system for predicting the achievable market demand value of a flight according to the present invention;

[0061] Figure 3 It is a schematic diagram of the structure of the electronic device of the present invention;

[0062] Figure 4 A business flow chart of predicting the market demand value of a designated flight segment (cabin space) of a designated airline company according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] The following are some professional terms for explaining the present invention:

[0065] Revenue Management System (Basic Edition): A system that automatically manages the inventory of flights that have not departed based on forecasting and optimization models using flight schedules, inventory, departure and freight rate data.

[0066] Inventory: refers to the information that affects flight seat sales decisions, including seat availability (Availability), seat reservation value (SeatSold), number of available seats (Seat Open), and various control parameters (Inventory Parameter).

[0067] ICS (Inventory Control System): The airline reservation system mentioned in this article is also called inventory control system.

[0068] Data Collection Points (Dcp): set by airlines based on their own route quality, flight attributes and other considerations. The data collection code is determined by the number of days before departure and has a one-to-one correspondence with the number of days before departure. Usually, airlines set Dcp to 24, and the closer to the flight departure date, the larger the Dcp value. For example: the data collection point Dcp is called departure Dcp and the corresponding number of days before departure is 0 days.

[0069] Fixed-DCP: A data collection point set by an airline, which is determined by the number of days until departure. An airline can set up two sets of data collection points in the flight control system (or revenue management system), one for domestic flight routes and one for international flight routes.

[0070] Floating-DCP: A data collection point that is not a fixed data collection point. For example, a flight is scheduled to leave the port in 20 days, but DCP 11 The corresponding distance to the port is 24 days, Dcp 12 The corresponding distance to the port is 18 days, so this data collection point is a floating data collection point, which can be recorded as Dcp 11.5 .

[0071] Final-DCP: The data collection point on the day of flight departure is also called departure DCP;

[0072] Latest data collection point Last actual-DCP: the data collection point for the most recent data acquisition of flights that have not departed; the revenue management forecasting and optimization module uses the data of the flight segment-level inventory acquired based on the Last actual-DCP as the independent variable.

[0073] BKD: The seat, sleeping space, luggage or cargo space or weight reserved and pre-allocated to the passenger;

[0074] Class: The fare that passengers pay for the services and seats provided to them;

[0075] Achievable Demand: It can be understood as the achievable demand, which is the quantity of flight products that passengers can purchase at a certain price or service composition within a booking period and under certain price and service prerequisites.

[0076] Posted: The status or action of stopping the sale of a flight segment (cabin class);

[0077] Available seats: Authorized Booking Level foreign ICS logic, recorded as AU;

[0078] LSS (domestic ICS logic): the number of seats allowed to be sold in the cabin class;

[0079] Market demand value Demand: The quantity of flight products that passengers are willing and able to purchase at a certain price or service level within a booking period.

[0080] Booking Value Bkg: The space or weight of a seat, sleeping space, baggage or cargo reserved by a passenger and pre-allocated to the passenger.

[0081] Market demand value Demand: The quantity of flight products that passengers are willing and able to purchase at a certain price or service level within a booking period.

[0082] In one embodiment of the present invention, a method for predicting the market demand value of a flight is provided, such as Figure 1 and Figure 4 As shown, the method includes:

[0083] S1. Obtain all flight information of a specified flight segment of a specified airline according to the two-letter airline code; based on the flight information, obtain inventory data and historical market demand value of a future flight Dcp;

[0084] The acquisition of all flight information of a specified flight segment of a specified airline specifically includes:

[0085] Obtain the full information and incremental information of the specified flight segment of the specified airline, and organize and store them in the database; the incremental information is the latest flight data during the night maintenance time from 02:00 to 04:00 every night.

[0086] The obtaining of inventory data of a future flight Dcp based on the flight information specifically includes:

[0087] Obtain the flight inventory data of the departing flights of the specified flight segment of the specified airline, based on the current date or the system date of the revenue management system for the past three years, as the departing flight inventory data;

[0088] Get the flight inventory data of the specified flight segment of the specified airline for the next year based on the current date or the system date of the revenue management system as the flight inventory data that has not departed.

[0089] The flight information also includes ASTD aviation standard data provided by the ICS flight control system, including:

[0090] Flight schedule data SCH: airline, flight number, origin, destination, flight departure date and time, flight arrival date and time;

[0091] Flight booking data INV: electronic ticket identification, cabin structure table, flight physical layout number, maximum number of seats available for sale on the flight;

[0092] Flight operation data FLT: actual departure time of the flight.

[0093] S2. Based on the inventory data, predict the market demand value of the latest Dcp of a future flight segment space;

[0094] S3. Based on the market demand value, a historical flight data pool with a granularity down to the cabin level is established for the future flight.

[0095] S4. Drawing a flight booking curve for the flight segment space of the future flight based on the historical flight data pool;

[0096] The flight booking curve includes the historical flight booking curve that has departed, the booking curve of the flight that has not departed, and the future predicted booking curve;

[0097] The flight booking curve is composed of the number of bookings for each Dcp, and the number of bookings for each Dcp is the average of all corresponding Dcp bookings in the historical flight data pool;

[0098] If Class i There are N records in the historical flight data pool, then Class i The number of reservations for the jth Dcp point on the flight reservation curve is:

[0099]

[0100] Among them, Class i _DCP j _H is Class i The number of seats booked for the jth Dcp in the flight booking curve, Class i _BKD m For Class i The Class on the mth record in the historical flight data pool i Number of bookings.

[0101] S5. Predict all the classes of a future flight i The achievable demand value AchievableDemand includes:

[0102] S5-1. Compare the latest Dcp class of a future flight i The size of the number of bookings BKD and the number of bookings BKD corresponding to Dcp on the flight booking curve;

[0103] If the latest Dcp class of a future flight i If the number of bookings BKD of the flight is less than the number of bookings BKD of the flight corresponding to Dcp on the flight booking curve, then go to step S5-4, otherwise go to step S5-6;

[0104] For example: For a future flight, the current flight class is the highest class in economy class, Class Y, and the latest Dcp is Dcp 10 , Y cabin in Dcp 10 The number of seats booked is 40, that is, Y_Dcp 10 _Future=40, and the flight booking curve Dcp 10 The number of seats booked is 50, that is, Y_Dcp 10 _History=50, so go to step S5-4.

[0105] S5-2. Determine the class of the latest Dcp of a future flight i Whether it is in the locked cabin Posted state;

[0106] If the latest Dcp class of a future flight i If the state is not Posted, go to step S5-5, otherwise go to step S5-6;

[0107] Among them, the method for determining whether the space is locked and posted is:

[0108] In the ICS system of China Aviation Information Network, the main judgment basis is:

[0109] If the maximum available value LSS of the flight booking INV inventory data of the space is a positive integer, and the space sales status IND is marked as EK\EAK\ELK\EALK, the space of the flight segment is not locked and posted, and the space is in a saleable state;

[0110] If the maximum available value LSS of the flight booking INV inventory data of the space is less than or equal to 0, or the space sales status IND is marked as EPK\EALP\EAPK\EALPK, the space of the flight segment is locked and posted, and the space of the flight segment is stopped from sale.

[0111] In overseas ICS systems, the main basis for judgment is:

[0112] The number of BKD seats booked for the class of the flight segment reaches the class of that class i If the maximum available value of AU (Available Units) is reached, the space for this flight segment is Posted and the space for this flight segment is no longer available for sale; otherwise, the space for this flight segment is Posted and is available for sale.

[0113] For example: For a future flight, the current flight class is the highest class in economy class, Class Y, and the latest Dcp is Dcp 10 , Y cabin in Dcp 10 The number of seats booked is 40, that is, Y_Dcp 10 _Future=40, and the maximum available value LSS of cabin Y at this time is 45, that is, cabin Y is not locked, so enter step S5-5.

[0114] S5-3. Calculate the class of the latest Dcp of a future flight i AchievableDemand;

[0115] If the booking value BKD of the latest Dcp of a future flight is greater than the historical average booking number and is not locked, the predicted flight segment will be 23 The market demand value of the point is taken as the achievable demand value, that is, AchievableDemand = Demand;

[0116] For example: For a future flight, the current flight class is the highest class in economy class, Class Y, and the latest Dcp is Dcp 10 , Y cabin in Dcp 10 The number of seats booked is 40, and the flight booking curve is Dcp 10 The number of seats booked is 38, that is, Y_Dcp 10 _Future>Y_Dcp 10 _History;

[0117] The maximum available LSS value of cabin Y is 45, which is unlocked;

[0118] The predicted Dcp of cabin Y at departure 23 The market demand value Demand is 80;

[0119] Therefore, the Achievable Demand of cabin Y is equal to Dcp at departure time. 23 The market demand value Demand is 80.

[0120] S5-4. Calculate the class of the latest Dcp of a future flight i The difference between the number of bookings BKD and the number of bookings corresponding to Dcp on the flight booking curve is calculated as follows:

[0121] Difference = i_DCP A _Future-i_DCP A _History

[0122] For example: For a future flight, the current flight class is the highest class in economy class, Class Y, and the latest Dcp is Dcp 10 , Y cabin in Dcp 10 The number of seats booked is 40, and the flight booking curve is Dcp 10 The number of seats booked by BKD is 50, then

[0123] Difference=YDcp 10 Future-YDcp 10 History = -10.

[0124] S5-5. Number of seats corresponding to the flight booking curve i_Dcp 23 _History, calculate the Class of a future flight segment i In DCP 23 The achievable demand value Achievable Demand is calculated as follows:

[0125] Achievable Demand = i_DCP 23 _History+Difference

[0126] Among them, i_DCP 23 _History is the corresponding DCP on the flight booking curve 23 Number of bookings;

[0127] For example, for a future flight, the current flight class is the highest class in the economy class, Class Y. Class Y is Dcp on the booking curve. 23 The market demand value Demand is 80, and Difference is -10 calculated by step S5-4. Therefore, the Y cabin is Dcp at departure. 23 The point's AchievableDemand=80+(-10)=70.

[0128] S5-6. Determine all the classes of a future flight segment i Whether all the AchievableDemand values ​​are calculated;

[0129] S5-7. Output the achievable demand value Achievable Demand.

[0130] The embodiment of the present invention also provides a system for predicting the market demand value of a flight. Figure 2 As shown, the system comprises:

[0131] The acquisition module 201 is used to acquire all flight information of a specified flight segment of a specified airline; based on the flight information, acquire inventory data and historical market demand value of a future flight Dcp;

[0132] A module 202 for predicting market demand value, for predicting the market demand value of the latest Dcp of a future flight segment space based on the inventory data;

[0133] A historical flight data pool building module 203 is used to build a historical flight data pool with a granularity down to the cabin level for the future flight based on the historical market demand value;

[0134] A flight booking curve drawing module 204 is used to draw a flight booking curve for the flight segment space of the future flight based on the historical flight data pool;

[0135] The module 205 for predicting the achievable demand value is used to predict the achievable demand value of all seats of the future flight.

[0136] like Figure 3 As shown, an embodiment of the present invention further provides a device, including: a processor 301, the processor 301 is coupled to a memory 302, and the processor 301 is used to read and execute a computer program stored in the memory 302 to implement a method for predicting the market demand value of a flight as described in the above method embodiment.

[0137] An embodiment of the present invention also provides a computer-readable storage medium, which stores a program or instruction. When the program or instruction is executed on a computer, the computer executes a method for predicting the market demand value of a flight as described in the above method embodiment.

[0138] Obviously, those skilled in the art should understand that the modules or steps of the above-mentioned embodiments of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the embodiments of the present invention are not limited to any specific combination of hardware and software.

[0139] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting the market demand value that a flight can reach, characterized in that: The method comprises: S1. Obtain all flight information of a specified flight segment of a specified airline; based on the flight information, obtain inventory data and historical market demand value of a future flight Dcp; S2. Based on the inventory data, predict the market demand value of the latest Dcp of a future flight segment space; S3. Based on the historical market demand value, establish a historical flight data pool with granularity down to cabin level for the future flight; S4. Drawing a flight booking curve for the flight segment space of the future flight based on the historical flight data pool; S5. Predicting the achievable demand values ​​of all seats of the future flight.

2. The method according to claim 1, characterized in that The acquisition of all flight information of a specified flight segment of a specified airline specifically includes: Obtain the full information and incremental information of the specified flight segment of the specified airline, and organize and store them in the database; the incremental information is the latest flight data during the night maintenance time from 02:00 to 04:00 every night.

3. The method according to claim 1, characterized in that The obtaining of inventory data of a future flight Dcp based on the flight information specifically includes: Obtain the flight inventory data of the specified flight segment of the specified airline, and the flight inventory data of the departed flights in the past three years based on the current date or the system date of the revenue management system as the departed flight inventory data; Get the flight inventory data of the specified flight segment of the specified airline, based on the current date or the system date of the revenue management system for the next year, as the flight inventory data that has not departed.

4. The method according to claim 1, characterized in that The flight information also includes ASTD aviation standard data provided by the ICS flight control system. The ASTD aviation standard data specifically includes: Flight schedule data SCH: airline, flight number, origin, destination, flight departure date and time, flight arrival date and time; Flight booking data INV: electronic ticket identification, cabin structure table, flight physical layout number, and maximum number of seats available for sale on the flight; Flight operation data FLT: actual departure time of the flight.

5. The method according to claim 1, characterized in that Drawing a flight booking curve for the flight segment space of the future flight based on the historical flight data pool specifically includes: The flight booking curve includes the booking curve of historical flights that have departed, the booking curve of flights that have not departed, and the predicted booking curve of future flights; The flight booking curve is composed of the number of bookings for each Dcp, and the number of bookings for each Dcp is the average of the number of bookings for all corresponding Dcps in the historical flight data pool; If Class i There are N records in the historical flight data pool, then Class i The number of reservations for the jth Dcp point on the flight reservation curve is: Among them, Class i _DCP j _H is Class i The number of seats booked for the jth Dcp in the flight booking curve, Class i _BKD m For Class i The Class on the mth record in the historical flight data pool i Number of bookings.

6. The method according to claim 1, characterized in that The predicted achievable demand value of all seats of a future flight specifically includes: S5-1. Compare the class of the latest Dcp of a future flight i The size of the number of bookings BKD and the number of bookings BKD corresponding to Dcp on the flight booking curve; If the latest Dcp class of a future flight i If the number of bookings BKD of the flight is less than the number of bookings BKD of the flight corresponding to Dcp on the flight booking curve, then go to step S5-4, otherwise go to step S5-6; S5-2. Determine the class of the latest Dcp of a future flight i Whether it is in the locked cabin Posted state; If the latest Dcp class of a future flight i If the state is not Posted, go to step S5-5, otherwise go to step S5-6; S5-3. Calculate the class of the latest Dcp of a future flight i Achievable Demand; If the booking value BKD of the latest Dcp of a future flight is greater than the historical average booking number and is not locked, the predicted flight segment will be booked at Dcp 23 The market demand value of the point is taken as the achievable demand value, that is, AchievableDemand = Demand; S5-4. Calculate the class of the latest Dcp of a future flight i The difference between the number of bookings BKD and the number of bookings corresponding to Dcp on the flight booking curve is calculated as follows: Difference=i_DCP A _Future-i_DCP A _History S5-5. Number of seats corresponding to the flight booking curve i_Dcp 23 _History, calculate the Class of a future flight segment i In DCP 23 The achievable demand value Achievable Demand is calculated as follows: Achievable Demand=i_DCP 23 _History+Difference Among them, i_DCP 23 _History is the corresponding DCP on the flight booking curve 23 Number of bookings; S5-6. Determine all the classes of a future flight segment i Whether all the Achievable Demand values ​​are calculated; S5-7. Output the achievable demand value Achievable Demand.

7. The method according to claim 6, characterized in that The method to determine whether the space is in the locked and posted state is: In the ICS system of China Aviation Information Network, the main judgment basis is: If the maximum available value LSS of the flight booking INV inventory data of the space is a positive integer, and the space sales status IND is marked as EK\EAK\ELK\EALK, the space of the flight segment is not locked and posted, and the space is in a saleable state; If the maximum available value LSS of the flight booking INV inventory data of the space is less than or equal to 0, or the space sales status IND is marked as EPK\EALP\EAPK\EALPK, the space of the flight segment is locked and posted, and the space of the flight segment is stopped from being sold; In overseas ICS systems, the main basis for judgment is: The number of BKD seats booked for the class of the flight segment reaches the class of that class i If the AU is greater than the maximum available value, the space for this flight segment is Posted and the space for this flight segment is no longer available for sale; otherwise, the space for this flight segment is not Posted and is available for sale.

8. A system for predicting the market demand value of flights, characterized in that: The system comprises: An acquisition module is used to acquire all flight information of a specified flight segment of a specified airline; based on the flight information, acquire inventory data and historical market demand value of a future flight Dcp; A module for predicting market demand value, used to predict the market demand value of the flight segment space of the latest Dcp of a future flight based on the inventory data; Constructing a historical flight data pool module, for establishing a historical flight data pool with granularity down to cabin level for the future flight based on the historical market demand value; A flight booking curve drawing module is used to draw a flight booking curve for the flight segment space of the future flight based on the historical flight data pool; The module for predicting the achievable demand value is used to predict the achievable demand value of all seats of the future flight.

9. A device, characterized in that: comprising a processor coupled to a memory; The processor is used to read and execute the computer program stored in the memory to implement a method for predicting the market demand value of a flight as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A program or instruction is stored, and when the program or instruction is executed on a computer, the computer is caused to execute a method for predicting the market demand value of a flight as described in any one of claims 1-7.

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