Flight seat controlled cabin opening and closing method and device and electronic equipment

Abstract

The invention provides a flight seat control cabin opening and closing method and device and electronic equipment. Comprising the following steps: sequentially obtaining an upward nested shipping space protection number at a current moment # imgabs0 # (# imgabs1 # is an initial moment) and a sales restriction number at the initial moment from a specified flight in an airline reservation stock system; determining an upward nested shipping space protection number predicted value at a # imgabs2 moment through an adaptive approximation algorithm, and nesting a sales restriction number at a current shipping space through a linear nesting algorithm; calculating the available shipping space sales limit number of each shipping space through a seat nesting principle, and transferring the available shipping space sales limit number to an airline reservation stock system; whether the available sales limit number of each shipping space is larger than zero or not is judged, if the available nested sales limit number of one shipping space is not larger than zero, the shipping space is closed, and the shipping space is closed sequentially from the lowest shipping space to the highest shipping space; in the iteration process, if the available shipping space sales limit number is changed from being not larger than 0 to being larger than 0, the shipping space is opened again; seat control is implemented through flight cabin opening and closing, and revenue maximization is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of airline cabin control, and in particular to a cabin opening and closing method, device and electronic equipment for flight seat control. Background Art

[0002] Airlines improve their revenue levels by developing reasonable seat allocation strategies, so seat control is the core of revenue management. Before implementing seat control, it is necessary to set up cabin products that meet market characteristics, determine reasonable fare combinations, and estimate the demand level of flights from different levels. The aircraft cabin layout is mainly divided into physical cabins such as first class, business class, and economy class. Airlines further subdivide physical cabins into multiple logical cabins with different fares, and sell ticket products with different fares for different segmented passenger markets. The principle of cabin control is to protect tickets (seats) in high-priced logical cabins from being occupied by passengers in low-priced logical cabins. The means adopted is to limit the number of seats sold in low-priced logical cabins. In a related technology, the EMSR (expected marginal seat revenue) algorithm is used to predict the number of seats sold in each logical cabin. An important premise of this method is that for price-sensitive passengers, the seat booking data of historical flight passengers in a certain period is used to statically predict the number of seats sold in the same period of each logical cabin. The static control of the cabin opening and closing strategy is based on the predicted sales situation in the future. This is the best method for predicting sales quantity in the industry so far. However, this method does not make full use of the latest sales data. However, the real-time adjustment of the seat sales quantity limit during an airline's ticket sales period is significantly different from the actual passenger demand. Passengers' access traffic to the ticketing system cannot bring about effective order conversion, and the software and hardware configuration resources of the existing ticketing system are also wasted. Summary of the invention

[0003] The embodiments of the present application provide a method, device and electronic device for controlling the opening and closing of flight seats, so as to dynamically adjust the booking limits of each cabin to meet market changes, and solve the problem of untimely and insensitive market response in related technologies.

[0004] In a first aspect, an embodiment of the present application provides a method for opening and closing a cabin for controlling flight seats, comprising: Step S1, obtain the latest historical static data of passengers traveling on the target flight in the airline reservation inventory system, count the mean and variance of each cabin of the target flight, and use the cabin level from high to low as the current cabin in accordance with the EMSRb seat allocation method and the cabin linear nesting structure to obtain the current cabin in The number of upward nested slots protected at the moment and the current slots at k The number of nested sales limits for the time slot; is the initial moment; Step S2, according to the current cabin The number of upward nested space protections at the moment, and the current space at The actual number of people in the nested cabin corresponding to the time is determined by the preset gain coefficient in the adaptive approximation algorithm. The predicted value of the upward nested space protection number at the moment, and the current space nesting sales limit number determined by the linear nesting algorithm; Step S3, obtaining the number of tickets sold for each cabin of the target flight through the airline's seat reservation inventory system, obtaining the available seat sales limit for each cabin through the cabin linear nested structure, and transferring it to the airline's seat reservation inventory system; Step S4, determining whether the number of available space sales restrictions for each space is greater than zero; if the number of available nested sales restrictions for the current space is not greater than zero, notifying the airline's booking inventory system to perform cabin closing processing on the current space and the following spaces, wherein the cabin closing includes adding a non-sale mark in the airline's booking inventory system for the current space and the following spaces; Step S5, during the iteration process, if the sales limit number of available cabin space changes from not greater than zero to greater than zero and the above cabin space is in an open state, then the ticket reservation system is notified to perform an open process for the current cabin space, and the open process includes adding a sellable mark for the current cabin space in the airline's reservation inventory system.

[0005] In a second aspect, an embodiment of the present application provides a device for opening and closing a cabin controlled by a flight seat, comprising: The initial value acquisition unit is used to obtain the latest historical static data of passengers traveling on the target flight from the airline's reservation inventory system, calculate the mean and variance of each cabin of the target flight, and use the cabin level from high to low as the current cabin in accordance with the EMSRb seat allocation method and the cabin linear nesting structure to obtain the current cabin in The number of upward nested slots protected at the moment and the current slots at k The number of nested sales limits for the time slot; is the initial moment; Prediction unit, used to predict the current space The number of upward nested space protections at the moment, and the current space at The actual number of people in the nested cabin corresponding to the time is determined by the preset gain coefficient in the adaptive approximation algorithm. The predicted value of the upward nested space protection number at the moment, and the current space nesting sales limit number determined by the linear nesting algorithm; A calculation unit, used to obtain the number of tickets sold for each class of the target flight through the airline's seat reservation inventory system, obtain the available seat sales limit number of each class through the linear nested structure of the seats, and transfer it to the airline's seat reservation inventory system; A cabin closing unit is used to determine whether the number of available cabin sales restrictions for each cabin is greater than zero. If the number of available nested sales restrictions for the current cabin is not greater than zero, the airline's booking inventory system is notified to perform cabin closing processing on the current cabin and the cabins below. The cabin closing processing includes adding a non-sale mark in the airline's booking inventory system for the cabins of the current cabin and the cabins below; The cabin opening unit is used to notify the airline's booking inventory system to perform cabin opening processing on the current cabin if the sales limit number of the available cabin changes from not greater than zero to greater than zero and the above cabin is in the cabin opening state during the iteration process. The cabin opening processing includes adding a sellable mark for the current cabin in the airline's booking inventory system.

[0006] In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor implements any of the above methods when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above is implemented. Compared with the prior art, this application has the following advantages: According to the embodiment of the present application, the current The number of upward nested cabins protected at the moment and the actual number of people in the upward nested cabins corresponding to the current cabin at the current moment are determined by the gain coefficient preset in the adaptive approximation algorithm. The predicted value of the number of upward nested cabin protection at the moment, and the nested sales limit number of the current cabin is determined by the linear nesting algorithm, and the nested sales limit number of the current cabin is calculated, and the adaptive approximation algorithm is used to approximate the optimal value of the seat sales quantity of each cabin, and the historical ticket sales data is added to the configuration basis of the future ticket sales quantity, and the airline's booking inventory system is notified to perform cabin closing processing on the current cabin and the following cabins, that is, to add a non-sale mark in the flight booking system for the current cabin and the following cabins; or to notify the airline's booking inventory system (ICS) to perform cabin opening processing on the current cabin, that is, to add a sellable mark in the flight booking system for the current cabin. In this way, the available seat limit of the airline's booking inventory system can be updated in real time with market changes, and the cabin opening and closing operations on the cabins according to the updated available seat sales limit number can quickly track market changes and fully mine and utilize the historical ticket sales data. On the other hand, the configuration of the available seat limit sales number of the airline's reservation inventory system refers to the historical ticket sales data, which can quickly track the changes in actual sales demand, thereby effectively implementing the accuracy of cabin control, and eliminating the difference between the effective supply of each cabin and the actual passenger demand within a certain period of time. Compared with other previous technical solutions based on other data, the cabin opening and closing strategy based on this application ensures that the supply of each cabin of the airline is effectively matched with the actual passenger demand within a certain period of time, avoiding the loss of revenue to the airline due to the imbalance between supply and demand. For example, the number of configured seats sold exceeds the actual number of ticket buyers, resulting in wasted seats and waste of software and hardware of the ticketing system. As a reference and basis for configuring the available seat limit sales number, the opening and closing of flight cabins can be better adjusted. Although it is impossible to predict the exact number of people in the rapidly changing market for each cabin at each time point, as a reference, the iterative optimization of this application is closer to market passenger demand from a long-term trend. The solution of this application can improve the sales rate of cabin space, and can also reduce the waste of software and hardware resources of the ticketing system caused by invalid system access, thereby effectively implementing cabin space control, so that the number of seats available for sale in each cabin matches the actual needs of passengers, reducing the waste of aircraft seats, maximizing the needs of passengers to take flights, and effectively increasing flight revenue. According to actual application tests, compared with other previous technical solutions, when comparing sales in a certain sales cycle, the number of seat sales obtained using this application is closer to the actual number of ticket buyers, effectively avoiding the waste of seats and loss of revenue caused by the mismatch between the number of configured seat sales and the actual number of ticket buyers.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments according to the present application and should not be regarded as limiting the scope of the present application.

[0010] Figure 1 is a flow chart showing a method for opening and closing a cabin of an airline seat control according to an embodiment of the present application; Figure 2 is an iterative result showing the number of seats sold according to an embodiment of the present application; Figure 3 1 is an iterative result showing the seat sales quantity based on the periodically updated gain factor according to an embodiment of the present application; FIG4 is a structural block diagram showing a cabin opening and closing device for controlling flight seats according to an embodiment of the present application; FIG. 5 is a block diagram showing an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0011] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the concept or scope of the present application. Therefore, the drawings and descriptions are considered to be exemplary in nature and not restrictive.

[0012] To facilitate understanding of the technical solutions of the embodiments of the present application, the following describes the related technologies of the embodiments of the present application. The following related technologies can be combined with the technical solutions of the embodiments of the present application as optional solutions, and they all belong to the protection scope of the embodiments of the present application.

[0013] An embodiment of the present application provides a method 100 for opening and closing a cabin for controlling flight seats, which adopts an adaptive iterative approximation algorithm. The method 100 for opening and closing a cabin for controlling flight seats can be executed on a computer device, especially on a computing device of an airline, or on a cloud through cloud computing. No matter which computer is used for execution, it can be shared in all airlines through uploading and sharing. Figure 1 The cabin opening and closing method 100 for controlling the flight seats of the present application is described. Figure 1 FIG. 1 is a flow chart showing a method for opening and closing a cabin of an airline seat control system according to an embodiment of the present invention. Figure 1 As shown, the cabin opening and closing method 100 for controlling flight seats includes the following steps S1 to S5. Steps S1 to S5 will be described below in conjunction with a specific embodiment.

[0014] First, proceed to step S1. In step S1, the latest historical static data of passengers traveling on the target flight is obtained from the airline's reservation inventory system, and the mean and variance of each cabin of the target flight are counted. According to the EMSRb seat allocation method and the cabin linear nesting structure, the cabin levels are ranked from high to low in turn as the current cabin, and the current cabin is obtained. The number of upward nested slots protected at the moment and the current slots at k The number of nested sales limits for the time slot; is the initial moment.

[0015] In one embodiment, the airline reservation inventory system (ICS: Inventory Control System), wherein the ICS is responsible for updating the availability of seats in real time. Once a passenger reserves a seat through the agent reservation system, the ICS will immediately receive the reservation information, lock the corresponding seat, and update the inventory to ensure the accuracy of subsequent sales.

[0016] In one embodiment, the current cabin can be a logical cabin subdivided in the physical cabin, and the physical cabin is a cabin with different seat space layout and meal service. The logical cabin is a cabin with the same seat space and meal, but it is distinguished by different restrictions. For example, when the passengers have different logical cabins, the policies they enjoy are different when they cancel or change tickets. The policies of each airline are different. For example, some airlines stipulate that tickets purchased at a 30% discount price cabin cannot be refunded or changed; tickets purchased at a 50% discount price cabin can be refunded 40% and can be changed; tickets purchased at a 20% discount price cabin can be refunded 60% and can be changed, and tickets purchased at a full price cabin can be refunded 90% and can be changed. For a further simple example, an airline can divide the cabin of an aircraft into physical cabins such as first class, business class, and economy class, and can further divide the economy class into 5 logical cabins. The seat layout and meal service of these 5 logical cabins are the same, but they have different prices, different ticket usage conditions, and different mileage points rewards. The five logical classes are sorted in descending order according to ticket prices: economy full price Y, basic discount U, preferred discount V, special discount W, and super special price T. The higher the ticket price, the higher the cabin class. Therefore, the five logical classes are sorted in descending order according to cabin class: Y class, U class, V class, W class, and T class.

[0017] The sales limit number of a class is also called the Booking Limit (BL) or class control, and the class protection number is also called the Protection Level (PL). The nested sales limit number means that when selling, a higher-class class can "borrow" seats from a lower-class class for sale. For example, the economy class of a flight has three logical classes, Y, B, and M, from high to low. The nested sales limit numbers of these three logical classes are 100, 75, and 45, respectively. The nested sales limit number is used to provide the seat sales system with the configuration of the number of tickets to be sold, so that the seat sales system can configure more seats for classes with higher class levels, thereby increasing the revenue from ticket sales. If the market is in short supply and the passengers are all business travelers, there will be 100 tickets sold in the Y cabin, and 0 tickets in the B and M cabins. If the market is in the off-season and the passengers are leisure travelers, there will be a situation of algorithm restriction, 45 tickets sold in the M cabin, 30 tickets sold in the B cabin, and 25 tickets sold in the Y cabin. At this time, the protection number of the Y cabin is 25, the protection number of the B cabin is 30, and the protection number of the M cabin is 45. The cabin control is that the Y cabin is always open and ticket sales are available. When the B cabin reaches the nesting protection number of 75 (30+45), it is closed, and tickets are not allowed to be sold in this cabin and the cabins below it. When the M cabin reaches the nesting protection number of 45, it is closed, and tickets are not allowed to be sold in this cabin. The ticket sales situation is embedded in the configuration basis of the cabin ticket sales quantity, so that the configuration of the airline ticketing system can be updated with market changes, and the historical ticket sales quantity data can be fully mined and utilized.

[0018] In one embodiment, the upward nested class refers to all classes above the class, the upward nested class protection number refers to the sum of all class protection numbers above the class, and the highest class sales limit number is the maximum number of seats on the aircraft; the current class is not the lowest class, and taking the above five logical classes as an example, the current class can be any one of the full-price economy class Y, basic discount U, preferred discount V, and preferential discount W. If the current class is the full-price economy class Y (not the lowest class), the upward nested class of the current class Y is the full-price economy class Y itself, if the current class is the basic discount U, the upward nested class of U is the combination of Y and U, similarly, the upward nested class of the preferred discount V is the combination of Y, U and V, and the upward nested class of the preferential discount W is the combination of Y, U, V and W.

[0019] In one embodiment, the class linear nesting structure includes allowing seats of higher class to occupy seats of lower class, but not allowing seats of lower class to occupy seats of higher class when a flight has multiple layers of clear class.

[0020] In one embodiment, when When the time is initial, the upward nested cabin protection number of the initial time is obtained, specifically including: obtaining the mean number of users of each cabin above the current cabin, the variance of the number of users and the ticket price and the ticket price of the next level cabin of the current cabin according to the historical data before the initial time; based on the mean number of passengers of the cabins nested upward at the current level, the variance of the number of passengers, the average revenue level and the ticket price of the cabins of the next level of the current level, the upward nested cabin protection number of the initial time is obtained.

[0021] The initial time indicates the initial time of a time period for predicting the number of seat sales. For example, if the time prediction is how to open and close the cabins of flights after June 1, 2023, the initial time is May 31, 2023. First, the mean number of passengers, the variance of the number of passengers, and the ticket price of each cabin can be obtained based on the historical data before the initial time. The cumulative number of passengers in cabin i, the cumulative variance of the number of passengers, and the mean ticket price can be calculated by the following formula: , , ;in, For the j The average number of users per cabin, Indicates j The variance of the number of users per cabin, express j The fare for each cabin class, i Indicates the current cabin class number from high to low. i =1,2,…,n-1, 1 represents the highest level, n represents the lowest level; jFigure 2 , j =1,2,…, i .

[0022] In one embodiment, based on the mean number of passengers of the upwardly nested cabins, the mean of the variance of the number of passengers, the mean of the ticket price, and the ticket price of the current next cabin, obtaining the number of upwardly nested cabins protected at the initial moment includes: calculating the number of upwardly nested cabins protected at the initial moment by the following formula: ,in, represents the number of protections for upward nested compartment i at the initial moment, represents the inverse function of the normal distribution function, Indicates the fare of the next class of cabin below the current cabin. Indicates the average fare of the cabins nested above the current cabin. It represents the cumulative average number of passengers in the nested cabins above the current cabin i, Indicates the cumulative variance of the number of passengers in the current nested cabin.

[0023] In one embodiment, the historical statistical data of a flight in a statistical period are shown in Table 1 below. The flight CA1415 is from Beijing Capital Airport to Chengdu Shuangliu Airport, with a departure time of 09:30. The selected aircraft has 355 seats. The airline further divides the economy class of the flight into 5 logical classes. The 5 logical classes are sorted in descending order of ticket price, for example, they can be economy full price Y, basic discount U, preferred discount V, special discount W, and super special price T. Among them, the class with a higher ticket price has a higher class level. Therefore, the 5 logical classes are sorted in descending order of class level as economy full price Y, basic discount U, preferred discount V, special discount W, and super special price T. In Table 1, the mean Indicates the number of passengers in the corresponding cabin, standard deviation Indicates the standard deviation of the number of passengers in the corresponding cabin, the ticket price Using relative fares, the full-fare economy Y class is set to a unit value of 1.

[0024]

[0025] The following will illustrate the calculation of the number of available seats for sale using the EMSRb algorithm for initialization and the flight seat control cabin opening and closing method 100 provided in the embodiment of the present application through the data in Table 1.

[0026] First, the seat sales quantity is initialized using the following EMSRb algorithm using the data in Table 1.

[0027] The average number of users in the nested cabins above the current cabin can be calculated by the following formula: , the cumulative variance of the number of users in the current cabin and nested cabins can be calculated by the following formula: , the cumulative fare of the nested cabins of the current cabin can be calculated by the following formula: , see Table 2, calculated as follows: For upward nesting U-bays:

[0028] For upward nesting V-pods:

[0029] For upward nested W compartments:

[0030] The upward nested space protection number at the initial moment can be calculated by the following formula : , see Table 2, calculated as follows:

[0031] The nested sales limit of the current cabin can be calculated by the cabin linear nesting structure formula: , , See Table 3, the calculation is as follows:

[0032] For the description of each parameter in these formulas, please refer to the description of the parameters in the above embodiment, and the calculated results are shown in Table 3 below.

[0033]

[0034] In one embodiment, it is planned to start selling seats for flight CA1415 on July 1, 2023. Based on the historical data of flight CA1415 as of May 31, 2023, the predicted nested sales limit number in Table 3 is obtained. This is the initial state setting value. The booking limit is set in the revenue system to open all seats in a saleable state. Assuming that the flight is fully booked and all passengers are price sensitive, they buy tickets for lower-class cabins first and then higher-class cabins. In this scenario, there is a high probability that separate cabin protection numbers will appear for each cabin. The seats allocated for T cabin are 50, for W cabin are 69, for V cabin are 82, for U cabin are 105, and for Y cabin are 49. The estimated total revenue is no less than R1=49 * 1+105 * 0.87+82 * 0.68+69* 0.45+50 * 0.25= 239.66.

[0035] Next, enter step S2. In step S2, according to the current cabin space obtained in step S1, The number of upward nested space protections at the moment, and the current space at The actual number of people in the upward nested cabin corresponding to the time is determined by the preset gain coefficient in the adaptive approximation algorithm. The predicted value of the upward nested space protection number at the moment, and the current space nesting sales limit number determined by the linear nesting algorithm; In one embodiment, after obtaining The number of upward nested cabin protections at the moment and the actual number of passengers in the corresponding upward nested cabin are determined by using the preset gain coefficient in the adaptive approximation algorithm. The predicted value of the upward nested space protection number at the time, including: Get the actual number of passengers in the nested cabin class, the actual number of passengers on flight CA1415, see Table 4

[0036] The current The number of upward nested space protection at the moment is the same as the current The error of the actual number of people in the corresponding nested cabin at the moment; ,in, Indicates the current Time and Space i The number of upward nested slot protections, Indicates the current The actual number of people in the corresponding upward nested cabin at the time, i Indicates the current cabin class number from high to low. i =1,2,…,n, 1 represents the highest level, n represents the lowest level. For example, when i=2, the current cabin is nested upwards as cabin 2 and cabin 1 combined. Indicates the actual number of users of the combined cabins corresponding to cabin 2 and cabin 1 at the previous moment, Indicates the number of nested slots protected by slot 2 and slot 1 at the previous moment. You can start the calculation from the highest slot 1, then calculate the second highest slot 2, and so on, until the calculation of the second lowest slot is completed.

[0037] The following formula is used to calculate the The predicted value of nested space protection number at the time:

[0038] Indicates the Upward nesting of time slots i The predicted value of the protection number, Indicates the current The time of getting the nested space upwards i Protection number, Indicates that The number of upward nested space protection at the moment The error of the actual number of people in the combined cabin at the time, The gain factor configured at that moment.

[0039] The application method is more sensitive to market changes and can better track recent market changes, overcoming the deficiency of statically determining the number of nested cabin protections through historical batch data, so that the number of seat sales can quickly follow the recent changes in the number of actual ticket purchases, and effectively avoiding the loss of revenue caused by a large gap between the number of configured seat sales and the actual number of ticket purchases within a certain time period.

[0040] In one embodiment, the Calculated by the following formula: ,in, express As the number of iterations increases, the gain coefficient gradually converges to a smaller value, so the calculation The algorithm for calculating the nested slot protection number at the moment gradually converges to a steady-state value and does not produce significant changes. In one example, the gain coefficient can be updated regularly. , as the number of iterations increases, the calculation The algorithm of nested space protection number at each moment gradually converges to another steady-state value, but the market changes frequently, such as when the off-season changes to the peak season, the seat control personnel can update the gain coefficient according to the periodicity presented by the market. For example, In the formula, k is set to 0 to update the gain coefficient .

[0041]

[0042] In one embodiment, the The predicted values ​​of the upward nested cabin protection number corresponding to all cabins except the lowest cabin at the time are shown in Table 5. The predicted sales limit number of each level of nested seats at the moment is used to provide the seat sales system with the configuration of the number of tickets sold, where the highest cabin sales limit number predicted value is the total number of seats on the aircraft. Specifically, it includes obtaining the total number of seats on the aircraft, calculating the total number of seats on the aircraft and The difference in the number of nested space protection at the time The predicted value of the upward nested sales limit number corresponding to the next level of space at the moment.

[0043]

[0044] According to the predicted nested sales limit number in Table 6, the separated class sales limit number of each class can be obtained. Since T class is a special class, it is usually used for frequent flyer redemption tickets, employee free tickets, and mileage redemption tickets of airlines, so it needs to be strictly controlled. In this example, the T class is a special ticket with a quota of 105. Assuming that the flight sales are full and passengers buy tickets for low-class classes first and then high-class classes, the seats of T class are allocated to 105, the seats of W class are allocated to 6, the seats of V class are allocated to 19, the seats of U class are allocated to 63, and the seats of Y class are allocated to 162. The total revenue R2=162*1+63*0.87+19*0.68+6*0.45+105*0.25=258.68.

[0045] Compared with the use of EMSR algorithm to predict the number of seat sales, the total revenue R2 obtained by using the cabin opening and closing method 100 of flight seat control increased by about 10%. The predicted number of seat sales is closer to the actual number of ticket buyers and better tracks market changes.

[0046] By using the cabin opening and closing method 100 for flight seat control, the seat sales quantity at a later time can be iteratively predicted as a basis for cabin opening and closing. Figure 2 Graph 1 shows the iterative result of the seat sales quota according to an embodiment of the present application. Figure 3 As shown, as the number of iterations increases, the predicted results of the method tend to be stable due to the decrease of the gain factor, so the gain factor can be updated periodically or when the market is switched between off-season and peak-season, so that the method keeps pace with the market. For example, the gain factor can be updated every 10 time periods. Figure 3 FIG. 1 is an iterative result of the seat sales quantity based on the periodic update gain factor according to an embodiment of the present application. Figure 4 As shown, since the gain factor is updated every 10 time periods, the number of seats sold also presents a periodic change characteristic.

[0047] Next, enter step S3. In step S3, obtain the number of tickets sold for each class of the target flight through the airline's reservation inventory system, obtain the available space sales limit for each class through the linear nested structure of the class, and transfer it to the airline's reservation inventory system; Among them, the nested sales limit number of the current cabin can be predicted by the following formula: in, Indicates the cabin class number from high to low, 1 represents the highest class, n-1 represents the second lowest class, C represents the total number of seats on the aircraft, express The number of nested sales limits for the highest cabin class at the time, express The number of nested sales limits at the moment, express Nested cabins at time i Protection number; when When it is greater than C, the cabin class i The following cabins are closed and the corresponding number of seats will not be allocated.

[0048] Get the number of passengers who have made advance reservations for the flight , the available sales limit quantity for opening and closing the cabin is calculated based on the nested sales limit quantity. , where i=1,2,...,n.

[0049] In Table 6, for the number of passengers who have booked seats in scenario 1, the available sales limit is calculated as follows: For T cabin: 105-35= 70, For W compartment: 111-35-1= 75, For V compartment: 130-35-1-0 = 94, For U compartment: 193-35-1-0-180 = - 23, For Y compartment: 355-35-1-0-180-71= 68, Then the booking limit for opening and closing cabins is: 68 for Y cabin, and 0 for U, V, W, and T cabins.

[0050] Similarly, for scenarios 2 to 6, the specific calculation results of the booking limit for opening and closing the cabin are shown in Table 6.

[0051]

[0052] The number of available cabin sales limits obtained based on the prediction of the above process can be used as a reference for configuring airlines to open and close cabins for flight seats. In practical applications, the impact of factors such as time, season, and holidays on the number of cabin sales limits can also be considered, and the impact of natural factors such as time, season, and holidays on the number of cabin sales limits can be analyzed. By analyzing the historical static data in the inventory system of the flight reservation system and the number of tickets sold in each cabin of the target flight, the fluctuation trend of historical data, peak and trough data, the average corresponding to different seasons, the impact of holidays on the monthly average data and other influencing factors can be obtained. The number of available cabin sales limits obtained by the above prediction process can be further corrected based on the influencing factors.

[0053] Next, enter step S4. In step S4, determine whether the available space sales limit number of each class is greater than zero. If the available nested sales limit number of the current class is not greater than zero, notify the airline's booking inventory system to perform cabin closing processing on the current class and the following classes. The cabin closing processing includes adding a non-sale mark to the airline's booking inventory system for the current class and the following classes. The cabin closing sequence is to close the lowest class until the highest class is sold out. In one embodiment, for scenario 1 in Table 7, since the available sales limit number for cabin U is not greater than 0, it means that the limit for cabin U and the following cabins V\W\T has been reached and ticket sales are restricted. The airline seat control personnel implement cabin closure for cabins U\V\W\T, but there are still 68 tickets for cabin Y available for purchase, see Table 8.

[0054]

[0055] Next, enter step S5. In step S5, during the iteration process, if the available space sales limit number changes from less than 0 to greater than 0 and the above space is in the open state, the airline reservation inventory system is notified to perform the open process for the current space, and the open process includes adding a sellable mark for the current space in the airline reservation inventory system. The highest predicted value of the space sales limit number is the total number of seats on the aircraft.

[0056] In one embodiment, in scenario 6 in Table 6, since the aircraft with 355 seats is used, and since the available booking limit of the highest cabin class Y is 0, cabin Y and all the following cabins are closed, see Table 9.

[0057]

[0058] In one embodiment, since the CA1415 flight is in short supply, the airline decides to use a larger aircraft with 399 seats. In this case, the sales limit of cabin Y of flight CA1415 is expanded to 399 when it is initially opened for sale. Similarly, cabins U, V, W and T are changed to 237, 174, 155 and 149 respectively, as shown in Table 10.

[0059]

[0060] In scenario 6, after a period of time, the available sales limit of cabin Y becomes 399 - (347 + 0 + 1 + 0 + 7) = 44, so the available sales numbers of cabins Y, U, V, W and T for opening and closing cabins are all 44. See Table 11.

[0061]

[0062] Table 11 The device corresponding to the method for opening and closing the cabin of a flight seat control described in the embodiment of the application can be deployed in the local server or cloud server corresponding to the airline seat booking inventory system ICS system. When the prediction device executes the method corresponding to this application, it first communicates and interacts with the airline seat booking inventory system to obtain the actual sales data at the historical moment. After executing the method, it obtains After the sales quantity used at the moment is calculated, the forecast result is sent to the airline's reservation inventory system for configuring the number of tickets sold for each class of cabin.

[0063] Corresponding to the application scenario and method of the method provided in the embodiment of the present application, an embodiment of the present application also provides a cabin opening and closing device for controlling flight seats, which is deployed in a computer device. Figure 4 The cabin opening and closing device for controlling the flight seats of the present application is described. FIG4 is a structural block diagram of the cabin opening and closing device for controlling the flight seats of an embodiment of the present application. Figure 5As shown, the cabin opening and closing device 400 for flight seat control may include: an initial value acquisition unit 401, a prediction unit 402, a calculation unit 403, a cabin closing unit 404 and a cabin opening unit 405.

[0064] The initial value acquisition unit 401 is used to obtain the latest historical static data of passengers traveling on the target flight from the airline's seat inventory system (ICS), calculate the mean and variance of each cabin of the target flight, and use the cabin level from high to low as the current cabin in accordance with the EMSRb seat allocation method and the cabin linear nesting structure to obtain the current cabin in the The number of upward nested slots protected at the moment and the current slots at k The number of nested sales limits for the time slot; is the initial moment.

[0065] The prediction unit 402 is used to determine the current cabin at the time of the nested cabin protection number and the actual number of people in the nested cabin corresponding to the current cabin at the time of the current cabin by using the preset gain coefficient in the adaptive approximation algorithm. The predicted value of the upward nested space protection number at the moment, and the current space nesting sales limit number determined by the linear nesting algorithm.

[0066] The calculation unit 403 is used to obtain the ticket quantity of each class of the target flight through the airline reservation inventory system, obtain the available class sales limit number of each class through the class linear nested structure, and transfer it to the airline reservation inventory system.

[0067] The cabin closing unit 404 is used to determine whether the available cabin sales limit number of each cabin is greater than zero. If the available nested sales limit number of the current cabin is not greater than zero, the airline booking inventory system is notified to perform cabin closing processing on the current cabin and the cabins below. The cabin closing processing includes adding a non-sale mark in the airline booking inventory system for the cabins of the current cabin and the cabins below.

[0068] The cabin opening unit 405 is used to notify the airline's booking inventory system to perform cabin opening processing on the current cabin if the available cabin sales limit number changes from not greater than 0 to greater than 0 and the above cabin is in an open state during the iteration process. The cabin opening processing includes adding a sellable mark for the current cabin in the airline's booking inventory system.

[0069] In one embodiment, the EMSRb seat allocation method, when at the initial moment, the cabin opening and closing device 400 of the flight seat control includes an initial moment nested cabin protection number acquisition unit, which is used to acquire the nested cabin protection number at the initial moment based on the mean number of passengers in all cabins, the variance of the number of passengers, and the fare. The calculation method includes calculating the upward nested cabin protection number at the initial moment by the following formula: ,in, represents the number of nested space protections at the initial moment, represents the inverse function of the normal distribution function, Indicates the fare of the next class of cabin below the current cabin. Indicates the weighted average fare of the classes nested above the current class. Indicates the cumulative number of users in the nested cabins above the current cabin. Indicates the cumulative difference in the number of users in the current cabin and the cabin nested above the current cabin; The mean number of passengers, variance of the number of passengers, and ticket prices for all classes can be calculated by the following formula: , , ;in, For the j The average number of users per cabin, Indicates j The variance of the number of users per cabin, express j The fare for each cabin class, i Indicates the current cabin class number from high to low. i =1,2,…,n-1, 1 represents the highest level, n represents the lowest level; j Indicates the next level of cabin space of the current cabin space. j =1,2,…, i .

[0070] In one embodiment, the prediction unit 402 is further configured to calculate the The predicted value of the upward nested space protection number at the time is calculated by the following formula: The number of upward nested space protection at the moment The error of the actual number of people in the corresponding cabin at the time, ,in, express Timetable nested space i The number of nested slot protections, express The actual number of people in the corresponding nested cabin at the time, i Indicates the current cabin class number from high to low.i =1,2,…,n-1, 1 represents the highest level, n represents the lowest level; The following formula is used to calculate the The predicted value of nested space protection number at the time: ; in, Indicates the Upward nesting of time slots i The predicted value of the protection number, Indicates the Upward nesting of time slots i Protection number, Indicates that The number of upward nested space protection at the moment The error of the actual number of people in the corresponding nested cabin at the time, The gain factor configured at that moment.

[0071] In one embodiment, the Calculated by the following formula: ,in, express The truth value of .

[0072] In one embodiment, the cabin closing unit 404 is further used to obtain the total number of seats on the aircraft, calculate the total number of seats on the aircraft and The difference in the number of nested space protection at the time The predicted value of the number of nested sales restrictions corresponding to the next level of space at the moment includes: ;in, Indicates the cabin class number from high to low, 1 represents the highest class, n-1 represents the second lowest class, C represents the total number of seats on the aircraft, express The number of nested sales limits for the highest cabin class at the time, express The number of nested sales limits at the moment, express Nested cabins at time i Protection number; when When it is greater than C, the cabin class i The following cabins are closed and the corresponding number of seats will not be allocated.

[0073] Get the number of passengers who have made advance reservations for the flight , the available sales limit quantity for opening and closing the cabin is calculated based on the nested sales limit quantity. ,in represents the sales limit number of cabin class i at time k+1, max( , ) represents the maximum value between the two, i Indicates the current cabin class number from high to low, where i=1,2,…,n.

[0074] The functions of each module in each device in the embodiments of the present application can be found in the corresponding description in the above method, and have corresponding beneficial effects, which will not be repeated here.

[0075] FIG5 is a block diagram of an electronic device according to an embodiment of the present application. Figure 5 As shown, the electronic device includes: a memory 501 and a processor 502. The memory 501 stores a computer program that can be run on the processor 502. When the processor 502 executes the computer program, the method in the above embodiment is implemented. The number of the memory 501 and the processor 502 can be one or more.

[0076] The electronic device also includes: The communication interface 503 is used to communicate with external devices and perform data exchange transmission.

[0077] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the memory 501, the processor 502 and the communication interface 503 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, ​ Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0078] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0079] It should be understood that the above processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor that supports the Advanced RISC Machines (ARM) architecture.

[0080] Further, optionally, the above-mentioned memory may include a read-only memory and a random access memory. The memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. Among them, the non-volatile memory may include a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may include a random access memory (RAM), which is used as an external cache. By way of exemplary but not limiting description, many forms of RAM are available. For example, static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct memory bus random access memory (DR RAM).

[0081] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0082] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0083] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0084] Any process or method described in the flow chart or otherwise described herein can be understood as a module, fragment or portion of a code representing one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0085] The logic and / or steps described in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0086] It should be understood that the various parts of the present application can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium, and when the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0087] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0088] The above is only an exemplary embodiment of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope recorded in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for opening and closing a cabin for controlling flight seats, wherein: The steps include: Step S1, obtain the latest historical static data of passengers traveling on the target flight in the airline reservation inventory system, count the mean and variance of each cabin of the target flight, and use the cabin level from high to low as the current cabin in accordance with the EMSRb seat allocation method and the cabin linear nesting structure to obtain the current cabin in The number of upward nested slots protected at the moment and the current slots at k The number of nested sales limits for the time slot; is the initial moment; Step S2, according to the current cabin The number of upward nested space protections at the moment, and the current space at The actual number of people in the nested cabin corresponding to the time is determined by the gain coefficient preset in the adaptive approximation algorithm. The predicted value of the upward nested space protection number at time +1, and the current space nesting sales limit number determined by the linear nesting algorithm; Step S3, obtaining the number of tickets sold for each cabin of the target flight through the airline's seat reservation inventory system, obtaining the available seat sales limit for each cabin through the cabin linear nested structure, and transferring it to the airline's seat reservation inventory system; Step S4, judging whether the number of available space sales restrictions for each class is greater than zero. If the number of available nested sales restrictions for the current class is not greater than zero, notifying the airline's booking inventory system to perform cabin closing processing for the current class and the following classes, wherein the cabin closing processing includes adding a non-sale mark in the airline's booking inventory system for the current class and the following classes; Step S5, during the iteration process, if the sales limit number of available cabin space changes from not greater than zero to greater than zero and the above cabin space is in an open state, then notify the airline's booking inventory system to perform an open process for the current cabin space, and the open process includes adding a sellable mark for the current cabin space in the airline's booking inventory system.

2. A method for opening and closing cabins for controlling flight seats according to claim 1, wherein: The method comprises: The upwardly nested cabins include all cabins of the current cabin level and above, the upwardly nested cabin protection number includes the sum of all cabin protection numbers of the current cabin level and above, and the maximum cabin sales limit number is the maximum number of seats on the aircraft.

3. A method for opening and closing cabins for controlling flight seats according to claim 2, wherein: The EMSRb seat allocation method, when in the initial moment, obtains the upward nested cabin protection number at the initial moment, including: Based on the mean number of passengers, the variance of the number of passengers, and the mean ticket price of all the cabins, the nested cabin protection number at the initial moment is obtained, and the calculation method includes calculating the upward nested cabin protection number at the initial moment by the following formula: = ,in, represents the number of nested space protections at the initial moment, represents the inverse function of the normal distribution function, Indicates the fare of the next class of cabin below the current cabin. Indicates the cumulative fare of the classes nested above the current class. Indicates the average number of users in the nested cabins above the current cabin is accumulated, Indicates the cumulative difference in the number of users in the current cabin and the cabin nested above the current cabin; The mean number of passengers, variance of the number of passengers, and mean ticket price of all cabins can be calculated by the following formula: , , ;in, For the j The average number of users per cabin, Indicates j The variance of the number of users per cabin, express j The average fare for each class, i Indicates the current cabin class number from high to low. i =1,2,…,n-1, 1 represents the highest level, n represents the lowest level; j Indicates the next level of cabin space of the current cabin space. j =1,2,…, i .

4. A method for opening and closing cabins for controlling flight seats according to claim 1, wherein: The linear nested structure of the cabin space includes: When a flight has multiple layers of clear classes of cabins, higher-class cabins are allowed to occupy seats in lower-class cabins, but lower-class cabins are not allowed to occupy seats in higher-class cabins.

5. A method for opening and closing cabins for controlling flight seats according to claim 1, wherein: The current cabin is determined by the preset gain coefficient in the adaptive approximation algorithm. The predicted value of the upward nested space protection number at the +1 moment includes: The following formula is used to calculate the The number of upward nested space protection at the moment The error of the actual number of people in the corresponding cabin at the time, = - ;in, express Timetable nested space i The number of nested slot protections, express The actual number of people in the corresponding nested cabins at the time, i Indicates the current cabin class number from high to low. i =1,2,…,n-1, 1 represents the highest level, n represents the lowest level; The following formula is used to calculate the The predicted value of nested space protection number at time +1: = ； in, Indicates the Nested cabins at time i The predicted value of the protection number, Indicates the Upward nesting of time slots i Protection number, Indicates that The number of upward nested space protection at the moment The error of the actual number of people in the corresponding nested cabin at the time, The gain factor configured at that moment.

6. A method for opening and closing cabins for controlling flight seats according to claim 5, wherein: Said Gain factor configured at all times Calculated by the following formula: = ,in, express The truth value of .

7. A method for opening and closing cabins for controlling flight seats according to claim 1, wherein: The step S2 comprises: Get the total number of seats C of the aircraft, and calculate the total number of seats and +1 time, and use the difference as The predicted value of the number of nested sales restrictions corresponding to the next level of space at time +1, including: = C, = C- , in, =1,2,…,n-1 represents the cabin class number from high to low, 1 represents the highest class, n-1 represents the second lowest class, C represents the total number of seats on the aircraft, express +1 The number of nested sales limits for the highest cabin class at the time, express +1 nested sales limit number at the moment, express +1 time nested upwards i Protection number; when When it is greater than C, Not greater than zero, cabin class i+1 Close the cabin.

8. A method for opening and closing cabins for controlling flight seats according to claim 1, wherein: The step S3 comprises: Get the number of tickets sold for each cabin of the target flight, and calculate the available sales limit number for opening and closing cabins according to the nested sales limit number: ,in represents the sales limit number of cabin class i at time k+1, max( , ) represents the maximum value between the two, i Indicates the current cabin class number from high to low, where i=1,2,…,n.

9. A cabin opening and closing device for controlling seats in an airline, comprising: The initial value acquisition unit is used to obtain the latest historical static data of passengers traveling on the target flight from the airline's reservation inventory system, calculate the mean and variance of each cabin of the target flight, and use the cabin level from high to low as the current cabin in accordance with the EMSRb seat allocation method and the cabin linear nesting structure to obtain the current cabin in The number of upward nested slots protected at the moment and the current slots at k The number of nested sales limits for the time slot; is the initial moment; Prediction unit, used to predict the current space The number of upward nested space protections at the moment, and the current space at The actual number of people in the nested cabin corresponding to the time is determined by the preset gain coefficient in the adaptive approximation algorithm. The predicted value of the upward nested space protection number at time +1, and the current space nesting sales limit number determined by the linear nesting algorithm; A calculation unit, used to obtain the number of tickets sold for each class of the target flight through the airline's seat reservation inventory system, obtain the available seat sales limit number of each class through the linear nested structure of the seats, and transfer it to the airline's seat reservation inventory system; A cabin closing unit is used to determine whether the number of available cabin sales restrictions for each cabin is greater than zero. If the number of available nested sales restrictions for the current cabin is not greater than zero, the airline's booking inventory system is notified to perform cabin closing processing on the current cabin and the cabins below. The cabin closing processing includes adding a non-sale mark in the airline's booking inventory system for the cabins of the current cabin and the cabins below; The cabin opening unit is used to notify the airline's booking inventory system to perform cabin opening processing on the current cabin if the sales limit number of the available cabin changes from not greater than zero to greater than zero and the above cabin is in the cabin opening state during the iteration process. The cabin opening processing includes adding a sellable mark for the current cabin in the airline's booking inventory system.

10. An electronic device comprising a memory, a processor and a computer program stored in the memory, wherein the processor implements the method according to any one of claims 1 to 8 when executing the computer program.

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