CN115809903A - Ticket buying method and device, electronic equipment and storage medium - Google Patents
Ticket buying method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of travel expense control, in particular to a ticket buying method, a ticket buying device, electronic equipment and a storage medium. The method comprises the following steps: acquiring the journey task information of the journey; estimating the label returning and changing probability of the journey based on the journey task information; based on the ticket change quitting probability, estimating the expected price of the air ticket under different ticket buying strategies; and selecting a ticket buying strategy with the lowest expected price of the air ticket to buy the ticket. By the arrangement, the ticket buying strategy with the lowest expected ticket price can be selected for ticket buying, and the cost for ticket buying in the journey task can be reduced as much as possible.
Description
Technical Field
The application relates to the technical field of travel expense control, in particular to a ticket buying method, a ticket buying device, electronic equipment and a storage medium.
Background
With the development of science and technology and logistics, the distance cost of journey tasks, especially the cost of air tickets, is more and more. There is a need to manage and optimize ticket booking flows to reduce expenses for ticket booking.
But the prior art lacks a method for managing the travel ticket buying strategy.
Disclosure of Invention
In view of the above, embodiments of the present application are directed to providing a ticket purchasing method, apparatus, electronic device and storage medium.
A first aspect of the present application provides a ticket purchasing method, including:
acquiring the journey task information of the journey;
estimating the label returning and changing probability of the journey based on the journey task information;
based on the probability of returning and changing the ticket, the expected price of the ticket under different ticket buying strategies is estimated;
and selecting a ticket buying strategy with the lowest expected price of the air ticket to buy the ticket.
In some embodiments, the journey task information comprises: travel flight information and travel personnel information;
the travel flight information includes: the method comprises the following steps of (1) obtaining information of flight of an air ticket, take-off date and time, discount level of cabin space, cabin and the like, weather information of flight time period and historical refund and change information of flight;
the trip personnel information includes: the information of the identity of the passenger, the information of the positions of the passengers and the units where the passengers are located.
In some embodiments, said estimating, based on said journey mission information, a sign-back probability for the journey comprises:
inputting the journey task information into a preset calculation model to obtain the label change quitting probability;
the calculation model is a pre-trained deep learning model and is used for calculating to obtain the label change quitting probability based on the journey task information.
In some embodiments, estimating the sign-back probability for the trip based on the trip task information comprises:
digitally processing the journey task information;
inputting the digitalized journey task information into a preset formula to obtain the label withdrawing and changing probability; the preset formula is constructed based on the functional relation between each item of data in the digitalized journey task information and the label refunding and changing probability.
In some embodiments, estimating the expected price of the flight ticket under different ticket buying strategies based on the false negative ticket probability comprises:
determining different ticket buying strategies: wherein each ticket purchase strategy corresponds to the purchase of a discounted airline ticket;
and aiming at each ticket buying strategy, calculating the expected price of the ticket for completing the journey after the discounted ticket is purchased under the probability of returning and changing the ticket.
In some embodiments, for each ticket-buying strategy, calculating an expected price of the ticket for completing the trip after purchasing the discounted ticket under the probability of a refund ticket-buying comprises:
calculating the sum of the refund and change charge and the price of the ticket for transfer if refund and change occur to obtain a first numerical value; wherein, if cancel this journey after taking place to move back to change sign, think if take place move back to change the price of signing and transferring the ticket for airline and be 0, promptly: only charge the refund commission;
calculating the product of the probability of label change and the first numerical value to obtain a second numerical value;
calculating the difference value of subtracting the label-changing-back probability from 1 to obtain a third numerical value; the third value is the probability that the original flight is taken without change.
Calculating the product of the price of the discount air ticket corresponding to the ticket buying strategy and the third numerical value to obtain a fourth numerical value;
and calculating the sum of the second numerical value and the fourth data to obtain the expected price of the air ticket.
In some embodiments, the price of the ticket for transfer if change occurs is the price of the ticket with the greatest discount strength for the flight if change occurs.
A second aspect of the present application provides a ticket purchasing apparatus, comprising:
the acquisition module is used for acquiring the journey task information of the journey;
the estimation module is used for estimating the label returning and changing probability of the journey based on the journey task information;
the estimation module is used for estimating the expected price of the air ticket under different ticket buying strategies based on the ticket change quitting probability;
and the ticket purchasing module is used for selecting a ticket purchasing strategy with the lowest expected price of the air ticket to purchase the ticket.
A third aspect of the present application provides an electronic device comprising:
a processor, and a memory for storing the processor executable program;
the processor is used for implementing the ticket purchasing method by running the program in the memory.
A fourth aspect of the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, causes the processor to perform a method of ticketing as described above.
The ticket buying method provided by the application comprises the steps of firstly obtaining the journey task information of the journey; estimating the label returning and changing probability of the journey based on the journey task information; based on the ticket change quitting probability, estimating the expected price of the air ticket under different ticket buying strategies; and selecting a ticket buying strategy with the lowest expected price of the air ticket to buy the ticket. By the arrangement, the ticket buying strategy with the lowest expected ticket price can be selected for ticket buying, and the cost for ticket buying in the journey task can be reduced as much as possible.
Drawings
The above and other objects, features and advantages of the present application will become more apparent by describing in more detail embodiments of the present application with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings, like reference numbers generally represent like parts or steps.
Fig. 1 is a schematic flow chart of a ticket purchasing method according to an embodiment of the present application.
Fig. 2 is a schematic flow chart of a ticket purchasing method according to another embodiment of the present application.
Fig. 3 is a schematic structural diagram of a ticket purchasing apparatus according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Summary of the application
With the development of science and technology and logistics, the distance cost of journey tasks, especially the cost of air tickets, is more and more. There is a need to manage and optimize the flow of ticket purchases to reduce the expense for ticket purchases.
The existing ticket buying strategies are mainly as follows: in a general enterprise or company, different travel personnel are specified to have different cabin space levels; namely, the passengers can ride first class cabins or economic cabins according to the job grades of the travelers. But the same economy class is also discounted by different classes and the like. The discounts correspond to different rates for refunding and refunding the change, which is actually a profit maximization sale strategy made by the airline company according to the probability of refunding and refunding the change of different users, that is, generally, the higher the discount strength is, the higher the fee for refunding and refunding the change is. Generally, a business trip (a passenger) often directly subscribes to a full-price ticket (e.g., a full-price ticket for an economy class, which does not enjoy discount offers) for the same class or the like. However, since the refund label is not generated in general, it is usually wasted to directly purchase the highest price of the economy class.
In order to solve the problems, the application provides a ticket buying method which combines the property of a unit where a passenger is located, the situation of label returning and changing of the passenger, seasons, flights and the like, comprehensively considers the possibility of label returning and changing, selects a strategy with the lowest expected ticket buying cost to buy tickets and further controls the total ticket buying cost.
Having described the general principles of the present application, various non-limiting embodiments of the present application will now be described with reference to the accompanying drawings.
Exemplary method
Fig. 1 is a schematic flow chart of a ticket purchasing method according to an embodiment of the present application. As shown in fig. 1, the method includes the following.
Step S110, obtaining the journey task information of the journey;
specifically, the mode of acquiring the journey information can be that the personnel who have the relevant functions collect and fill in the uploaded journey information, and the personnel who go out can upload the journey information by themselves.
Specifically, the journey task information includes: travel flight information and travel personnel information; wherein the travel flight information comprises: the method comprises the following steps of (1) obtaining information of flight of an air ticket, take-off date and time, discount level of cabin space, cabin and the like, weather information of flight time period and historical refund and change information of flight; the trip personnel information includes: information of the identity of the passenger, information of the job of the passenger and information of the unit where the passenger is located; all the information has influence on the probability of label change. For example, different units tend to have different probability of a false negative. The probability of refund is lower for some departments or industries needing to participate in or organize the large conference. Compared with the prior art, the probability of the logout and the change of the basic service departments such as sales and the like is higher. Generally, the lower the duty level of the crew, the lower the probability of a fallback change (e.g., the probability of a fallback change for a regular employee is lower than the probability of a fallback change for a high-level administration). In general, if the weather information of the flight time period is strong wind and heavy rain, the situation of flight cancellation may occur, and the probability of tag withdrawal and change is high. There are, of course, individual differences, such as a high probability that an individual will change their label. There is also a case where the probability of a flight being returned or changed is high. According to the scheme provided by the application, the acquired journey task information should be as comprehensive and detailed as possible, so that follow-up operation can be conveniently executed based on the journey task information, the ticket refunding and changing probability can be accurately estimated, and a proper ticket purchasing strategy can be selected for ticket purchasing in an auxiliary mode.
Step S120, estimating the label returning and changing probability of the journey based on the journey task information;
it should be noted that, after the relationship between the journey task information and the label returning and changing probability is grasped, the label returning and changing probability can be estimated through the journey task information based on the relationship. The specific estimation methods are not listed here.
Step S130, based on the probability of changing the ticket, estimating the expected price of the air ticket under different ticket buying strategies;
it should be noted that the scheme provided by the present application mainly guides ticket purchasing. Based on the method, various ticket purchasing strategies can be listed in an exhaustion method mode, and the expected prices of the tickets under the different ticket purchasing strategies are calculated respectively.
It should be emphasized that the expected price of a ticket is not the price of a single ticket, but the total cost of the ticket required to complete the journey;
and step S140, selecting a ticket buying strategy with the lowest expected price of the air ticket, and buying the ticket.
By the arrangement, the ticket buying strategy with the lowest expected ticket price can be selected for ticket buying, and the cost for ticket buying in the journey task can be reduced as much as possible.
Specifically, there are various specific implementation manners of the step S120 "estimating the sign-off/change probability of the journey based on the journey task information", and two specific implementation manners are listed here.
One implementation manner is as follows:
inputting the journey task information into a preset calculation model to obtain the label change quitting probability; the calculation model is a pre-trained deep learning model and is used for calculating to obtain the label change quitting probability based on the journey task information.
The arrangement can utilize the deep learning model to better predict the probability of label change. As long as the training of the deep learning model is reasonable, the estimated result is accurate. Specifically, historical data can be collected and processed to obtain training samples. And training a pre-built deep learning model through the training sample so that the deep learning model can correctly predict the label change quitting probability.
The other realization mode is as follows:
digitally processing the journey task information; inputting the digitalized journey task information into a preset formula to obtain the label withdrawing and changing probability; the preset formula is constructed based on the functional relation between each item of data in the digitalized journey task information and the label refunding and changing probability.
It should be noted that the verification result can be obtained through continuous verification and analysis, and is based on the mathematical relationship between each item of data in the journey task information and the label-off and change probability. This relationship may be complex and needs to be expressed as a function, which is not specifically shown here. The functional relation can be determined based on historical data, and the label reversing and changing probability of the tasks which are not traveled can be accurately estimated based on the functional relation.
Further step S130 "based on the label-withdrawal probability, estimate the expected price of the air ticket under different ticket-buying strategies" specifically implement the following:
determining different ticket buying strategies: wherein each ticket purchase strategy corresponds to the purchase of a discounted airline ticket; each particular booking strategy may further include selecting a flight for a second purchase in the event of a change back.
And calculating the expected price of the air ticket for the journey after the discounted air ticket is purchased under the probability of returning and changing tickets aiming at each ticket buying strategy.
Specifically, the manner of calculating the expected price of the air ticket under each purchase strategy is as follows:
firstly, calculating the sum of the refund and change charge and the price of the ticket for transfer if refund and change occur to obtain a first numerical value; if the ticket is returned and changed, the journey is cancelled, the price of the ticket is 0, and only the ticket returning charge is collected; calculating the product of the probability of label change and the first numerical value to obtain a second numerical value;
then, calculating a difference value obtained by subtracting the probability of returning and changing the tags from 1 (namely the probability of taking the original flight without returning and changing), and obtaining a third numerical value; calculating the product of the price of the discount air ticket corresponding to the ticket buying strategy and the third numerical value to obtain a fourth numerical value;
and finally, calculating the sum of the second numerical value and the fourth data to obtain the expected price of the air ticket.
It should be noted that, in the solution provided in the present application, the sequence between the above steps can be flexibly adjusted. The core of the scheme is to calculate the expected price of the air ticket under different ticket buying strategies.
Specifically, the expected price of the air ticket is the sum of the fourth value and the second value; the fourth numerical value is the product of the price of the discount air ticket corresponding to the ticket buying strategy and the third numerical value; the third value is "1 minus the difference of the false-change probability"; wherein the second value is the product of the probability of false-positive and the first value; the first value is the sum of the calculated refund ticket renewal fee and the price of the transfer ticket if refund ticket occurs.
In general, after a ticket is refunded and changed once, the ticket is usually not refunded and changed for the second time, and on the basis, the ticket with the highest discount strength and the most preferential best discount can be directly selected when the ticket is purchased for the second time after the ticket is refunded and changed once. Therefore, the price of the ticket which is signed and transferred if being changed is the price of the ticket with the largest discount strength of the flight if being changed.
The ticket purchasing method provided by the present application is further described below with reference to specific application scenarios.
Specifically, referring to fig. 2, the industrial and commercial method provided by the present application is mainly as follows.
Step S201, collecting the air ticket booking data of past reimbursement of a unit, and counting relevant attributes of the air ticket and the air passenger, wherein the relevant attributes comprise an air passenger ID, a passenger duty level, an air ticket flight, a take-off date and time, a cabin discount level and the like;
step S202, counting the probability of sign off and change of a specific passenger, a specific job, specific unit attributes and specific seasons (such as 7 and 8 months rainy seasons) according to the condition of the ticket reimbursed in the past of each unit;
and step S203, recommending or limiting the discount of the air ticket which can be purchased according to the flight, cabin and the like selected by the current booking person and the probability of returning and changing the ticket, so as to assist the booking person to purchase the ticket according to the ticket purchasing strategy with the lowest expected price of the air ticket.
For example, in the case of a minimum probability of refunding and changing the ticket, purchasing the lowest-price ticket (i.e. a high discount ticket, a ticket with the greatest discount strength); under the condition that the ticket is changed back with high probability, the high-price ticket is recommended to be purchased (namely, the ticket with low discount strength or the full-amount ticket) so as to achieve the optimal total ticket purchasing cost control in the unit statistical sense.
Further, if the travel is cancelled after the sign is returned and changed, the price of the ticket which is returned and changed for transfer is considered to be 0, so that the condition that only the ticket returning charge is charged under the condition that the travel is cancelled after the sign is returned and changed is represented;
it should be noted that, in practical applications, it is also necessary to estimate whether the flight will be cancelled directly when the sign of the departure/change occurs, for example, for some trips participating in some large conferences, the time of the trip is relatively tight, that is, after the sign of the departure/change occurs, the next flight cannot participate in the conference. In this case, if the user cancels the journey after the user has returned the ticket, the price of the ticket for transfer is 0, so as to represent that only the ticket refund fee is charged when the user cancels the journey after the user has returned the ticket.
Exemplary devices
The embodiment of the device can be used for executing the embodiment of the method. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Fig. 3 is a block diagram illustrating a ticket purchasing apparatus according to an embodiment of the present application. As shown in fig. 3, the apparatus includes:
an obtaining module 31, configured to obtain the journey task information of the journey;
the estimation module 32 is used for estimating the label returning and changing probability of the journey based on the journey task information;
the estimation module 33 is configured to estimate expected airfare prices under different airfare purchase strategies based on the label change quitting probability;
and the ticket buying module 34 is used for selecting a ticket buying strategy with the lowest expected price of the air ticket to buy the ticket.
In some embodiments, the journey task information comprises: travel flight information and travel personnel information;
the travel flight information includes: the method comprises the following steps of (1) obtaining information of flight of an air ticket, take-off date and time, discount level of cabin space, cabin and the like, weather information of flight time period and historical refund and change information of flight;
the trip personnel information includes: the information of the identity of the passenger, the information of the duty of the passenger and the information of the unit where the passenger is located.
In some embodiments, estimating the sign-back probability for the trip based on the trip task information comprises:
inputting the journey task information into a preset calculation model to obtain the label change quitting probability;
the calculation model is a pre-trained deep learning model and is used for calculating to obtain the label change quitting probability based on the journey task information.
In some embodiments, estimating the sign-back probability for the trip based on the trip task information comprises:
digitally processing the journey task information;
inputting the digitalized journey task information into a preset formula to obtain the label withdrawing and changing probability; the preset formula is constructed based on the functional relation between each item of data in the digitalized journey task information and the label refunding and changing probability.
In some embodiments, estimating the expected price of the flight ticket under different ticket buying strategies based on the false negative ticket probability comprises:
determining different ticket buying strategies: wherein each ticket purchase strategy corresponds to the purchase of a discounted airline ticket;
and aiming at each ticket buying strategy, calculating the expected price of the ticket for completing the journey after the discounted ticket is purchased under the probability of returning and changing the ticket.
In some embodiments, for each ticket-buying strategy, calculating an expected price of the ticket for completing the trip after purchasing the discounted ticket under the probability of a refund ticket-buying comprises:
calculating the sum of the refund and change charge and the price of the ticket for transfer if refund and change occur to obtain a first numerical value;
calculating the product of the probability of label change and the first numerical value to obtain a second numerical value;
calculating the difference value of subtracting the label-changing-back probability from 1 to obtain a third numerical value; wherein the third value represents the probability that the original flight is taken without changing;
calculating the product of the price of the discount air ticket corresponding to the ticket buying strategy and the third numerical value to obtain a fourth numerical value;
and calculating the sum of the second numerical value and the fourth data to obtain the expected price of the air ticket.
In some embodiments, the price of the ticket for transfer if change occurs is the price of the ticket with the greatest discount strength for the flight if change occurs; if the journey is cancelled after the sign is returned and changed, the price of the ticket which is returned and changed to be the ticket is 0, and only the ticket returning charge is collected.
Exemplary electronic device
Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 4. FIG. 4 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application.
As shown in fig. 4, electronic device 400 includes one or more processors 410 and memory 420.
The processor 410 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 400 to perform desired functions.
Memory 420 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by processor 410 to implement the ticket purchasing methods of the various embodiments of the present application described above and/or other desired functions. Various contents such as category correspondence may also be stored in the computer-readable storage medium.
In one example, the electronic device 400 may further include: an input device 430 and an output device 440, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The input device 430 may also include, for example, a keyboard, a mouse, an interface, and the like. The output device 440 may output various information including analysis results and the like to the outside. The output devices 440 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, among others.
Of course, for simplicity, only some of the components of the electronic device relevant to the present application are shown in fig. 4, and components such as buses, input/output interfaces, and the like are omitted. In addition, the electronic device may include any other suitable components, depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the method of generating tickets according to various embodiments of the present application described in the "exemplary methods" section of this specification above.
The computer program product may be written with program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform the steps in the method of purchasing tickets according to various embodiments of the present application described in the "exemplary methods" section above of this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (10)
1. A method of purchasing tickets, comprising:
acquiring the journey task information of the journey;
estimating the label returning and changing probability of the journey based on the journey task information;
based on the ticket change quitting probability, estimating the expected price of the air ticket under different ticket buying strategies;
and selecting a ticket buying strategy with the lowest expected price of the air ticket to buy the ticket.
2. The ticket buying method of claim 1, wherein said travel mission information comprises: travel flight information and travel personnel information;
the travel flight information includes: the method comprises the following steps of (1) obtaining information of flight of an air ticket, take-off date and time, discount level of cabin space, cabin and the like, weather information of flight time period and historical refund and change information of flight;
the trip personnel information includes: the information of the identity of the passenger, the information of the duty of the passenger and the information of the unit where the passenger is located.
3. The method of claim 1, wherein estimating the probability of a refund and a change for the trip based on the trip mission information comprises:
inputting the journey task information into a preset calculation model to obtain the label change quitting probability;
the calculation model is a pre-trained deep learning model and is used for calculating to obtain the label change quitting probability based on the journey task information.
4. The method of claim 1, wherein estimating the probability of a refund and a change for the trip based on the trip mission information comprises:
digitally processing the journey task information;
inputting the digitalized journey task information into a preset formula to obtain the label withdrawing and changing probability; the preset formula is constructed based on the functional relation between each item of data in the digitalized journey task information and the label refunding and changing probability.
5. The method for purchasing tickets according to claim 1, wherein the estimating the expected price of the tickets under different ticket purchasing strategies based on the false change probability comprises:
determining different ticket buying strategies: wherein each ticket purchase strategy corresponds to the purchase of a discounted airline ticket;
and aiming at each ticket buying strategy, calculating the expected price of the ticket for completing the journey after the discounted ticket is purchased under the probability of returning and changing the ticket.
6. The ticket buying method of claim 5, wherein calculating, for each ticket buying strategy, the expected price of the ticket for completing the trip after buying the discounted ticket at the refund probability comprises:
calculating the sum of the refund and change charge and the price of the ticket for transfer if refund and change occur to obtain a first numerical value;
calculating the product of the probability of label change and the first numerical value to obtain a second numerical value;
calculating the difference value of subtracting the label-changing-back probability from 1 to obtain a third numerical value; wherein the third value represents the probability that the original flight is taken without changing;
calculating the product of the price of the discount air ticket corresponding to the ticket buying strategy and the third numerical value to obtain a fourth numerical value;
and calculating the sum of the second numerical value and the fourth data to obtain the expected price of the air ticket.
7. The ticket buying method of claim 6, wherein said ticket price for said transfer if a change back occurs is a price for a ticket with a greatest discount strength for said flight if a change back occurs;
and if the ticket is returned and changed, canceling the journey, considering that the price of the ticket is 0, and only charging the ticket returning commission under the condition of canceling the journey after the ticket is returned and changed.
8. A ticket-purchasing device, comprising:
the acquisition module is used for acquiring the journey task information of the journey;
the estimation module is used for estimating the label returning and changing probability of the journey based on the journey task information;
the estimation module is used for estimating the expected price of the air ticket under different ticket buying strategies based on the ticket change quitting probability;
and the ticket purchasing module is used for selecting a ticket purchasing strategy with the lowest expected price of the air ticket to purchase the ticket.
9. An electronic device, comprising:
a processor, and a memory for storing the processor executable program;
the processor is configured to implement the ticket purchasing method according to any one of claims 1 to 7 by executing the program in the memory.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, causes the processor to carry out the ticket purchasing method according to any one of claims 1 to 7.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105976039A (en) * | 2016-05-06 | 2016-09-28 | 上海交通大学 | Hybrid purchase decision method based on air ticket price predication |
| US20170061555A1 (en) * | 2015-08-24 | 2017-03-02 | Mastercard International Incorporated | Method and system for predicting lowest airline ticket fares |
| CN109472399A (en) * | 2018-10-23 | 2019-03-15 | 上海交通大学 | Consider the air ticket purchase decision method and system of uncertainty in traffic |
| CN112199405A (en) * | 2020-09-30 | 2021-01-08 | 中国民航信息网络股份有限公司 | Method and device for searching international air ticket change price |
| CN113643076A (en) * | 2021-10-13 | 2021-11-12 | 中航信移动科技有限公司 | Air ticket price prediction method and device, computer equipment and storage medium |
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2023
- 2023-02-06 CN CN202310063539.0A patent/CN115809903A/en active Pending
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|---|---|---|---|---|
| US20170061555A1 (en) * | 2015-08-24 | 2017-03-02 | Mastercard International Incorporated | Method and system for predicting lowest airline ticket fares |
| CN105976039A (en) * | 2016-05-06 | 2016-09-28 | 上海交通大学 | Hybrid purchase decision method based on air ticket price predication |
| CN109472399A (en) * | 2018-10-23 | 2019-03-15 | 上海交通大学 | Consider the air ticket purchase decision method and system of uncertainty in traffic |
| CN112199405A (en) * | 2020-09-30 | 2021-01-08 | 中国民航信息网络股份有限公司 | Method and device for searching international air ticket change price |
| CN113643076A (en) * | 2021-10-13 | 2021-11-12 | 中航信移动科技有限公司 | Air ticket price prediction method and device, computer equipment and storage medium |
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