Flight dynamic pricing method, system and device and medium

Abstract

The invention belongs to the technical field of freight rate search, and particularly relates to a flight dynamic pricing method and system, electronic equipment and a storage medium. The method comprises the following steps: adjusting and determining sales targets including a target passenger load factor, a target seat kilometer income and a lower bound of a minimum discount rate; the average discount rate and the real-time remaining seat number of each virtual shipping space are obtained, and according to the suggested discount rate of the lowest cabin, the lowest cabin which can be sold currently, the suggested sales seat number of the lowest cabin and the seat number which can be distributed by each shipping space are determined; and updating the lowest cabin suggested discount rate and the number of seats which can be distributed by each cabin space to a dynamic pricing result table. According to the invention, real-time data and historical experience can be combined to provide a more flexible and accurate pricing strategy for an airline company, thereby realizing a key business target set by the airline company, and finally improving the efficiency and effect of income management.

Description

Technical Field

[0001] The present invention belongs to the technical field of freight rate search, and in particular relates to a flight dynamic pricing method, system, electronic equipment and storage medium. Background Art

[0002] Revenue management is an important tool for airlines to improve economic efficiency. It originated in the US aviation industry in the 1970s. After nearly 30 years of development, it has become an important branch of management science and has been widely used. At present, the new trend of airline revenue management technology is dynamic pricing. The dynamic pricing strategy needs to consider the variability of market demand (such as seasonal changes, holidays, economic conditions, fuel price fluctuations and competitor behavior). These factors lead to a high degree of uncertainty and dynamism in market demand, requiring airlines to flexibly adjust dynamic pricing to adapt to these changes. Summary of the invention

[0003] To solve the above problems, the present invention provides a flight dynamic pricing method, system, electronic device and storage medium.

[0004] A flight dynamic pricing method of the present invention comprises:

[0005] Adjust and determine sales targets, including target passenger load factor, target seat-kilometer revenue and minimum discount rate lower limit;

[0006] Calculate the expected sales target based on the target passenger load factor, and calculate the expected minimum discount and the expected number of seats sold based on the target seat-kilometer revenue and the expected sales target;

[0007] Obtain the current lowest cabin discount rate of foreign airlines, the average discount rate of remaining seats on the flight, the lower limit of the lowest discount rate, the average expected discount rate of remaining seats on the flight, and the real-time data of the previous lowest cabin discount rate, and calculate the lowest cabin recommended discount rate based on the expected lowest discount;

[0008] Obtain the average discount rate and real-time remaining number of seats for each virtual cabin, and determine the lowest cabin currently available for sale, the recommended number of seats for sale for the lowest cabin, and the number of seats available for allocation in each cabin based on the recommended discount rate for the lowest cabin;

[0009] The lowest class recommended discount rate and the number of seats available for allocation in each class are updated to the dynamic pricing result table.

[0010] Furthermore, the adjusting and determining of sales targets includes:

[0011] Determine sales targets based on sales lead time, route nature and time factors.

[0012] Furthermore, the expected sales target calculated according to the target occupancy rate includes:

[0013] The number of economy class seats and the current number of bookings are obtained, and the expected sales target is obtained based on the target passenger load factor.

[0014] Further, the expected minimum discount and the expected number of seat sales are calculated according to the target seat-kilometer revenue and the expected sales target, including:

[0015] Obtain the target seat-kilometer revenue, current seat-kilometer revenue, number of economy class seats and flight distance to obtain the remaining expected revenue;

[0016] The expected minimum discount and the expected number of seats sold are obtained according to the remaining expected revenue and the expected sales target.

[0017] The present invention also provides a flight dynamic pricing system, the system comprising: a target adjustment module, a target prediction module, a data analysis module, a target output module and a pricing determination module;

[0018] The target adjustment module is used to adjust and determine sales targets, including target passenger load factor, target seat-kilometer revenue and minimum discount rate lower limit;

[0019] The target prediction module is used to calculate the expected sales target according to the target passenger load factor, and to calculate the expected minimum discount and the expected number of seats sold according to the target seat-kilometer revenue and the expected sales target;

[0020] The data analysis module is used to obtain the current lowest cabin discount rate of foreign airlines, the average discount rate of remaining seats on the flight, the lower limit of the lowest discount rate, the average expected discount rate of remaining seats on the flight, and the real-time data of the previous lowest cabin discount rate, and calculate the lowest cabin recommended discount rate based on the expected lowest discount;

[0021] The target output module is used to obtain the average discount rate of each virtual cabin and the real-time remaining number of seats, and determine the lowest cabin currently available for sale, the recommended number of seats for sale in the lowest cabin, and the number of seats available for allocation in each cabin according to the recommended discount rate of the lowest cabin;

[0022] The pricing determination module is used to update the lowest class recommended discount rate and the number of seats available for allocation in each class into the dynamic pricing result table.

[0023] Furthermore, the target adjustment module is also used to determine the sales target according to the sales lead time, route nature and time factor.

[0024] Furthermore, the target prediction module is also used to obtain the number of economy class seats and the current number of bookings, and obtain the expected sales target based on the target passenger load factor.

[0025] Furthermore, the target prediction module is also used to obtain the target seat-kilometer revenue, the current seat-kilometer revenue, the number of economy class seats and the flight distance to obtain the remaining expected revenue;

[0026] The target prediction module is further used to obtain the expected minimum discount and the expected number of seat sales based on the remaining expected revenue and the expected sales target.

[0027] The present invention also provides an electronic device, comprising:

[0028] processor;

[0029] and a memory storing computer executable instructions, wherein the computer executable instructions, when executed, cause the processor to perform the method described in any one of the above embodiments.

[0030] The present invention also provides a computer storage medium, wherein the computer storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method described in any one of the above embodiments is implemented.

[0031] Compared with the prior art, the present invention has the following advantages: it can combine real-time data and historical experience to provide airlines with more flexible and accurate pricing strategies, thereby achieving the key business goals set by the airlines and ultimately improving the efficiency and effectiveness of revenue management.

[0032] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] 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.

[0034] Figure 1 A schematic diagram showing the flow of the flight dynamic pricing method of the present invention is shown;

[0035] Figure 2 The structure diagram of the flight dynamic pricing system of the present invention is shown;

[0036] Figure 3 A schematic structural diagram of an electronic device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0037] 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.

[0038] The same reference numerals in the drawings represent the same or similar elements, components or parts, and thus the repeated description of the same or similar elements, components or parts may be omitted below. It should also be understood that although the first, second, third and other attributives representing the numbers may be used herein to describe various devices, elements, components or parts, these devices, elements, components or parts should not be limited by these attributives. In other words, these attributives are only used to distinguish one from another. For example, the first device may also be called the second device, but it does not deviate from the essential technical solution of the present invention. In addition, the terms "and / or" and "and / or" refer to all combinations including any one or more of the listed items.

[0039] In order to have a deeper understanding of the present invention, the relevant background and the data tables therein are explained as follows:

[0040] Table 1 Flight booking data table

[0041]

[0042] Table 2 Flight remaining seat value table

[0043]

[0044] Table 3 DCP division table

[0045]

[0046] Table 4 Time impact factor table

[0047]

[0048] Table 5 Flight long-term, medium-term and short-term settings table (time period division table)

[0049]

[0050] Table 6 Comparison table of lunar and solar calendars

[0051]

[0052] Table 7 Route nature

[0053]

[0054] Table 8 Segment sales characteristics

[0055]

[0056] Table 9 Average discount for remaining seats on flights

[0057]

[0058] Table 10 Dynamic pricing results

[0059]

[0060] The flowchart of the flight dynamic pricing method provided by the embodiment of the present invention specifically includes:

[0061] S100: Adjust and determine sales targets, including target passenger load factor, target seat-kilometer revenue and minimum discount rate lower limit.

[0062] S110: Obtain target passenger load factor, seat-kilometer revenue, and minimum discount rate lower bound.

[0063] S111: Flights are classified into long-term, medium-term and near-term according to the sales lead time, i.e. the difference in number of days between the departure date and the current date.

[0064] S112: Set route nature and time factor:

[0065] The nature of routes is differentiated according to the market share of a certain airline on that route, and is generally divided into four levels: routes with absolute competitive advantage, routes with competitive advantage, general competitive routes, and general competitive routes with competitive disadvantage. The specific classification standards can be formulated by the airline based on its own business characteristics.

[0066] When choosing a flight, airline customers will choose the most suitable departure time based on their personal needs and preferences. In order to better meet the needs of different customer groups, this method divides flights into different levels according to departure time, and uses this as a time factor in the subsequent calculation logic.

[0067] S113: Considering the flight route nature, time factor and sales target, the table objecttable is looked up to obtain the target passenger load factor ObjectLF, the target seat-kilometer revenue ObjectSKR and the minimum discount rate lower bound ObjectLB. The above target parameters are sales targets set by the airline.

[0068] S120: Adjust sales targets.

[0069] S121: Calculate the current passenger load factor, predicted passenger load factor, current seat-kilometer revenue, and predicted seat-kilometer revenue.

[0070] Current passenger load factor (currentlfct) = current number of bookings / number of economy class seats*100, mainly using flight booking table FlightStat data.

[0071] Current seat kilometer revenue (currentseatkilometersrev) = (current number of bookings × current average discount rate × Y class fare) ÷ (flight distance × (current number of bookings + remaining seats)), mainly using the flight booking table FlightStat data.

[0072] Predicted passenger load factor (fcstLF) = (current number of bookings + predicted number of each virtual cabin) ÷ total number of economy class seats, mainly using the flight remaining seat value table RemainSeatValue and table FlightStat data.

[0073] Predicted seat-kilometer revenue (fcstseatkilometersrev) = (current number of bookings × current average discount rate + ∑ (predicted number of each virtual class * average discount rate of each virtual class) × Y class fare) ÷ (flight distance × (current number of bookings + remaining number of seats)), mainly using the FlightStat and RemainSeatValue tables.

[0074] S122: After the calculation is completed, adjust the sales target.

[0075] S122-1: If the predicted passenger load factor fcstLF> the target passenger load factor ObjectLF, adjust the corresponding ObjectLF in the objecttable to the predicted passenger load factor fcstLF; otherwise, ObjectLF remains unchanged.

[0076] S122-2: If the predicted seat-kilometer revenue fcstseatkilometersrev>the target seat-kilometer revenue ObjectSKR, adjust the corresponding ObjectSKR in objecttable to the predicted seat-kilometer revenue fcstseatkilometersrev; otherwise, ObjectSKR remains unchanged.

[0077] S122-3: Look up the time division field in the Flightstat table to obtain the time factor of the flight. Further look up the time influence factor table pktime_factor to obtain the specific time influence factor factor. Continue to adjust the target passenger load factor ObjectLF = target passenger load factor ObjectLF × time influence factor factor.

[0078] Through the above calculations, we can get the target passenger load factor ObjectLF, the target seat-kilometer revenue ObjectSKR and the minimum discount rate lower limit ObjectLB, and the sales target is determined.

[0079] S200: Calculate an expected sales target based on the target passenger load factor, and calculate an expected minimum discount and an expected number of seats sold based on the target seat-kilometer revenue and the expected sales target.

[0080] S210: Calculate the number of seats that the current DCP still needs to sell (denoted as X1) based on the target passenger load factor. The specific calculation formula is as follows:

[0081] X1 = target load factor × number of economy class seats – current number of bookings.

[0082] S220: This method calculates the possible lowest discount LowDisc1 and the possible number of seats sold X2 based on the target seat-kilometer revenue.

[0083] S221: In order to achieve the seat-kilometer target ObjectSKR, it is necessary to calculate the expected revenue EPRev for the remaining sales period. The calculation expression is as follows:

[0084] EPRev = (target seat-kilometer revenue - current seat-kilometer revenue) × number of economy class seats × flight distance.

[0085] S222: Calculate LowDisc1 and X2 based on the remaining value field Remvalue in the RemainingSeatValue table.

[0086] S222-1: The Remvalue field is the expected discount rate for each remaining seat. The result after sorting them from large to small is recorded as {SE1, SE2, …, SE n}, and accumulate the value one by one.

[0087] S222-2: When satisfied When i∈[1,n], the smallest k value is taken as the X2 value, that is, And at this time LowDisc1=SE k .

[0088] S222-3: It is possible that after all seats are sold, EPRev cannot be reached. In this case, X2 = n, LowDisc1 = SE n .

[0089] S300: Obtain the current lowest cabin discount rate of foreign airlines, the average discount rate of remaining seats on the flight, the lower limit of the lowest discount rate, the average expected discount rate of remaining seats on the flight, and the real-time data of the previous lowest cabin discount rate, and calculate the lowest cabin recommended discount rate based on the expected lowest discount.

[0090] S310: Take the value of the above LowDisc1, recorded as S1.

[0091] S320: Get the current real-time data of the lowest cabin discount rate of foreign airlines (i.e. other airlines), recorded as S2. When the real-time data of "the current lowest cabin discount rate of foreign airlines" is greater than or equal to 3, S2 = data median; when the real-time data of "the current lowest cabin discount rate of foreign airlines" is equal to 2, S2 = data mean; when the real-time data of "the current lowest cabin discount rate of foreign airlines" is equal to 1, S2 = data itself; when "the current lowest cabin discount rate of foreign airlines" is empty, S2 = the current lowest cabin discount rate of this airline. Special note: If the real-time data of the current lowest cabin discount rate of foreign airlines cannot be obtained, this part of the function will be temporarily replaced by the average discount rate of foreign airlines' economy class.

[0092] S330: Get the average discount rate of the remaining period of the DCP in the last month corresponding to the flight remaining seat average discount table RemSAVGDis, recorded as S3. The number of data items and the method of obtaining values ​​are the same as those described in the above step S320.

[0093] S340: Take the value of the ObjectLB lower bound of the minimum discount rate calculated in S122-3, and record it as S4.

[0094] S350: Calculate the average expected discount rate of the remaining seats, denoted as S5.

[0095] S5 = expected average price per seat in the future / published freight rate.

[0096] S360: Get the current real-time data of the lowest cabin discount rate, recorded as S6.

[0097] S370: Let S = P1*S1+P2*S6+P3*S2. P1, P2, P3 are weighted coefficients, which can be adjusted according to market conditions. If one of the items Si = 0, its coefficient is evenly distributed to the other two items.

[0098] S380: Let AVGS = (S3 + S5) / 2. In particular, when S3 = 0, AVGS = S5; when S5 = 0, AVGS = S3.

[0099] S390: Minimum cabin recommended discount rate LD = max(S, AVGS, S4).

[0100] S400: Obtain the average discount rate and real-time remaining number of seats for each virtual cabin, and determine the lowest cabin currently available for sale, the number of seats recommended for sale for the lowest cabin, and the number of seats available for allocation in each cabin based on the recommended discount rate for the lowest cabin.

[0101] S410: Calculate the real-time remaining number of seats REMC. First, obtain the real-time sales data, that is, the number of seats sold from the time of data download at midnight to the current time.

[0102] REMC = number of remaining seats at the time of data download in the early morning - real-time sales data.

[0103] S420: Obtain the average discount rates AvgDiscVY, AvgDiscVQ, and AvgDiscVX of each virtual cabin, and arrange them from high to low, determine in which average discount rate interval the lowest cabin recommended discount rate LD is located, and then provide the lowest cabin recommended seat number LAU by querying the flight remaining seat value table RemainingSeatValue.

[0104] If LD≤AvgDiscVX, the lowest class that can be sold is VX, that is,

[0105] LAU=RemainingSeatValue.XAU;

[0106] If AvgDiscVX≤LD≤AvgDiscVQ, the lowest class that can be sold is VQ, that is,

[0107] LAU=RemainingSeatValue.QAU;

[0108] If LD ≥ AvgDiscVQ, the lowest class that can be sold is VY, that is,

[0109] LAU=RemainingSeatValue.YAU.

[0110] S430: Recalculate the number of seats YAU, QAU and XAU available for allocation in each virtual cabin according to the real-time remaining number of seats REMC in step S410. Let dif = YAU-REMC, then adjust QAU = QAU-dif and XAU = XAU-dif in turn. If the calculation result of YAU, QAU or XAU is less than 0, just set it to 0, indicating that the seats in this cabin cannot be sold. Then adjust the corresponding AU value in step S420 according to the new AU value, and modify the calculation result of LAU accordingly. At this point, the lowest cabin currently available for sale and its recommended number of seats for sale LAU, the recommended discount rate of the lowest cabin LD, and the number of seats YAU, QAU and XAU available for allocation in each cabin can be determined.

[0111] S500: The lowest class recommended discount rate and the number of seats available for allocation in each class are updated to the dynamic pricing result table.

[0112] S510: Update the calculated lowest recommended class discount rate LD and the number of seats available for allocation in each class YAU, QAU and XAU to the dynamic pricing result table Dyprice_result.

[0113] S520: The airline reads the data in the dynamic pricing result table to determine when and on which flight to reserve at least a certain number of seats for different passengers and the lowest price for the corresponding seats, thereby achieving the effect of dynamically setting prices for different groups of passengers based on multiple market demand factors.

[0114] The embodiment of the present invention also provides a flight dynamic pricing system, such as Figure 2 As shown, including:

[0115] The target adjustment module 11 is used to adjust and determine the sales target, including the target passenger load factor, the target seat-kilometer revenue and the minimum discount rate lower limit.

[0116] The target prediction module 12 is used to calculate the expected sales target according to the target passenger load factor, and to calculate the expected minimum discount and the expected number of seats sold according to the target seat-kilometer revenue and the expected sales target.

[0117] The data analysis module 13 is used to obtain the current lowest class discount rate of foreign airlines, the average discount rate of the remaining seats on the flight, the lower limit of the lowest discount rate, the average expected discount rate of the remaining seats on the flight, and the real-time data of the previous lowest class discount rate, and based on the expected lowest discount, calculate the lowest class recommended discount rate.

[0118] The target output module 14 is used to obtain the average discount rate of each virtual cabin and the real-time remaining number of seats, and determine the currently available lowest cabin, the recommended number of seats for sale in the lowest cabin, and the number of seats available for allocation in each cabin based on the recommended discount rate of the lowest cabin.

[0119] The pricing determination module 15 is used to update the lowest class recommended discount rate and the number of seats available for allocation in each class into the dynamic pricing result table.

[0120] The target adjustment module 11 is used to determine the sales target according to the sales lead time, route nature and time factor.

[0121] The target prediction module 12 is used to obtain the number of economy class seats and the current number of bookings, and obtain the expected sales target based on the target passenger load factor.

[0122] The target prediction module 12 is used to obtain the target seat-kilometer revenue, the current seat-kilometer revenue, the number of economy class seats and the flight distance to obtain the remaining expected revenue.

[0123] The target prediction module 12 is used to obtain the expected minimum discount and the expected number of seats sold according to the remaining expected income and the expected sales target.

[0124] like Figure 3 As shown, an embodiment of the present invention provides an electronic device, including a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140;

[0125] Memory 1130, used for storing computer programs;

[0126] The processor 1110 is configured to implement any of the above determination methods when executing a program stored in the memory 1130 .

[0127] In the electronic device provided by the embodiment of the present invention, the processor 1110 obtains the fluid flow rate of each branch in different switching states by executing the program stored in the memory 1130, and determines the initial volume flow rate of each branch; corrects the initial volume flow rate according to the pipeline parameters and fluid parameters of each branch when it is in working condition and standard condition, and obtains the standard condition volume flow rate of each branch; obtains multiple total standard condition volume flow rates according to the standard condition volume flow rates of each branch in different switching states, and determines the optimal switching state of each branch through the switching state of each branch when the total standard condition volume flow rate reaches a preset target.

[0128] The communication bus 1140 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 1140 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 figure, but it does not mean that there is only one bus or one type of bus.

[0129] The communication interface 1120 is used for communication between the above electronic device and other devices.

[0130] The memory 1130 may include a random access memory 1130 (RAM), or may include a non-volatile memory 1130 (non-volatile memory), such as at least one disk memory 1130. Optionally, the memory 1130 may also be at least one storage device located away from the processor 1110.

[0131] The above-mentioned processor 1110 can be a general-purpose processor 1110, including a central processing unit 1110 (CPU), a network processor 1110 (NP), etc.; it can also be a digital signal processor 1110 (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0132] An embodiment of the present invention provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors 1110 to implement the determination method of any of the above embodiments.

[0133] 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 embodiment of the present invention 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. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk SolidStateDisk (SSD)), etc.

[0134] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A flight dynamic pricing method, characterized in that: The method comprises: Adjust and determine sales targets, including target passenger load factor, target seat-kilometer revenue and minimum discount rate lower limit; Calculate the expected sales target based on the target passenger load factor, and calculate the expected minimum discount and the expected number of seats sold based on the target seat-kilometer revenue and the expected sales target; Obtain the current lowest cabin discount rate of foreign airlines, the average discount rate of remaining seats on the flight, the lower limit of the lowest discount rate, the average expected discount rate of remaining seats on the flight, and the real-time data of the previous lowest cabin discount rate, and calculate the lowest cabin recommended discount rate based on the expected lowest discount; Obtain the average discount rate and real-time remaining number of seats for each virtual cabin, and determine the lowest cabin currently available for sale, the recommended number of seats for sale for the lowest cabin, and the number of seats available for allocation in each cabin based on the recommended discount rate for the lowest cabin; The lowest class recommended discount rate and the number of seats available for allocation in each class are updated to the dynamic pricing result table.

2. The flight dynamic pricing method according to claim 1, characterized in that: The adjustment and determination of sales targets include: Determine sales targets based on sales lead time, route nature and time factors.

3. The flight dynamic pricing method according to claim 1, characterized in that: The expected sales target calculated according to the target passenger load factor includes: The number of economy class seats and the current number of bookings are obtained, and the expected sales target is obtained based on the target passenger load factor.

4. The flight dynamic pricing method according to claim 1, characterized in that: The expected minimum discount and the expected number of seats sold calculated according to the target seat-kilometer revenue and the expected sales target include: Obtain the target seat-kilometer revenue, current seat-kilometer revenue, number of economy class seats and flight distance to obtain the remaining expected revenue; The expected minimum discount and the expected number of seats sold are obtained according to the remaining expected revenue and the expected sales target.

5. A flight dynamic pricing system, characterized in that: The system includes: a target adjustment module, a target prediction module, a data analysis module, a target output module and a pricing determination module; The target adjustment module is used to adjust and determine sales targets, including target passenger load factor, target seat-kilometer revenue and minimum discount rate lower limit; The target prediction module is used to calculate the expected sales target according to the target passenger load factor, and to calculate the expected minimum discount and the expected number of seats sold according to the target seat-kilometer revenue and the expected sales target; The data analysis module is used to obtain the current lowest cabin discount rate of foreign airlines, the average discount rate of remaining seats on the flight, the lower limit of the lowest discount rate, the average expected discount rate of remaining seats on the flight, and the real-time data of the previous lowest cabin discount rate, and calculate the lowest cabin recommended discount rate based on the expected lowest discount; The target output module is used to obtain the average discount rate of each virtual cabin and the real-time remaining number of seats, and determine the lowest cabin currently available for sale, the recommended number of seats for sale in the lowest cabin, and the number of seats available for allocation in each cabin according to the recommended discount rate of the lowest cabin; The pricing determination module is used to update the lowest class recommended discount rate and the number of seats available for allocation in each class into the dynamic pricing result table.

6. The flight dynamic pricing system according to claim 5, characterized in that: include: The target adjustment module is also used to determine the sales target according to the sales lead time, route nature and time factor.

7. The flight dynamic pricing system according to claim 5, characterized in that: include: The target prediction module is also used to obtain the number of economy class seats and the current number of bookings, and to obtain the expected sales target based on the target passenger load factor.

8. The flight dynamic pricing system according to claim 5, characterized in that: include: The target prediction module is also used to obtain the target seat-kilometer revenue, the current seat-kilometer revenue, the number of economy class seats and the flight distance to obtain the remaining expected revenue; The target prediction module is further used to obtain the expected minimum discount and the expected number of seat sales based on the remaining expected revenue and the expected sales target.

9. An electronic device, characterized in that: include: processor; and a memory storing computer executable instructions which, when executed, cause the processor to perform the method according to any one of claims 1 to 4.

10. A computer storage medium, characterized in that: in, The computer storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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