CN114511122A - Method, system, equipment and storage medium for dynamically adjusting air ticket booking - Google Patents
Method, system, equipment and storage medium for dynamically adjusting air ticket booking Download PDFInfo
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Abstract
The invention provides a method, a system, equipment and a storage medium for dynamically adjusting airline ticket booking, wherein the method comprises the following steps: extracting the characteristics of the air ticket data through a decision tree, and cutting subdivided nodes to obtain N characteristics which influence the orderable booking and serve as important characteristics, wherein N is a preset value; obtaining a non-selling entity based on historical data of the last time window based on an algorithm model; obtaining an upsell duration for each upsell entity based on the number of upsell times in a number of past continuous time windows; entering into the forbidden sale when the passing rate pass of the forbidden sale entity is smaller than the forbidden sale threshold; otherwise, no sale prohibition is entered; a unique selling-prohibited entity is obtained through the de-duplication merging algorithm. The invention can find the ticket management defect of the airline department in time and make dynamic production adjustment in time, thereby improving the flow experience of booking airline tickets for users.
Description
Technical Field
The present invention relates to the field of interactive operations, and in particular, to a method, system, device, and storage medium for dynamically adjusting airline reservation.
Background
The user orders the international air ticket, the process before the order mainly goes through five pages, an inquiry page for specifying inquiry conditions, a filling-in page for inputting passenger information, a value-added page for choosing additional products or services, a payment page for paying a fee and a completion page for confirming the details of the order. After the user specifies the journey type, departure destination, date, cabin and the like, the passenger type and other query parameters on the query page, the system initiates search to each quotation system for the user, merges the results and displays the results to the user. After browsing the quotation list, the user integrates a plurality of factors such as navigation paths, quotation, retreat and change, luggage, auxiliary products and the like, selects a cardio-meter navigation group and starts a preset flow. The background initiates some key checks for each page to help the user better confirm the trip. For example, when the user enters the filling page, the background initiates a bookable check (Bookability), which includes a Fare check (Fare Validation) and a slot check (Availability Validation). The freight rate verification is used for verifying whether the freight rate calculation, the tax item calculation and the like during engine quotation are correct, and the cabin space verification is used for verifying whether the cabin space acquisition during quotation is accurate. For example, in the value added page, the user initiates a booking operation in the background, initiates a request for reserving seats to the global Distribution system GDS (global Distribution system), and the GDS simultaneously initiates a request to the driver booking system crs (central Reservation system). In the process, the navigation department verifies various information such as passengers, travel and the like, wherein the most important information is verification on cabin position data of the GDS and cabin position consistency of the navigation department. If the key verification fails, the front-end interception prompt similar to 'price unavailable' and 'cabin space sold out' exists, the predetermined experience is not smooth, and the user is dissatisfied. The user often cannot effectively distinguish interception caused by external factors or Trip factors, and even mishaps of 'more expensive over points' and 'big data killing' are caused. It can be seen that a reliable quotation system is required to continuously output quotations with high quality and high stability under the dual constraints of cost and coverage.
Therefore, the invention provides a method, a system, equipment and a storage medium for dynamically adjusting airline reservation.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a method, a system, equipment and a storage medium for dynamically adjusting airline ticket booking, overcomes the difficulties in the prior art, can find ticket management defects of an airline department in time and make dynamic production adjustment in time, and improves the process experience of booking airline tickets for users.
The embodiment of the invention provides a method for dynamically adjusting airline ticket booking, which comprises the following steps:
s110, extracting the characteristics of the air ticket data through a decision tree, and pruning subdivided nodes to obtain N characteristics which influence the orderable positions as important characteristics, wherein N is a preset value;
s120, obtaining a sale prohibition entity based on the historical data of the previous time window based on an algorithm model;
s130, obtaining the sale prohibition duration for each sale prohibition entity based on the sale prohibition times in a plurality of past continuous time windows;
s140, when the passing rate pass of the sale forbidding entity is smaller than the sale forbidding threshold value, entering sale forbidding; otherwise, no sale prohibition is entered;
and S150, obtaining a unique selling prohibition entity through a de-duplication merging algorithm.
Preferably, in step S110, the important features include at least one of a type of journey, a city of departure, a country of departure, a space selling place, a reservation data warehouse, and a ticket outlet.
Preferably, the step S120 includes the steps of:
s121, learning historical data of a previous time window by an algorithm, constructing a model and applying the model to the current time window;
s122, selecting time windows t, wherein each time window is equal in length, and the length is r;
s123, selecting a stage f, namely, defining a stage or a booking stage, forming a data matrix and training a model based on N characteristic dimensions as data columns and data of the stage in a preset time range as data rows M;
and S124, executing each round of level i:
s125, in the ith round, selecting i characteristics
And the characteristics are used for aggregating N characteristic dimensions based on the data matrix, respectively calculating the total amount and the passing rate, and generating the sale forbidding entity consisting of (N-i) definite characteristics and i virtual characteristics.
Preferably, in the step S122, as the length r of the time window decreases, the accuracy of the model increases;
the step S124 includes: and adding an sale prohibition mark to the data object when the passing rate is lower than a set threshold value under the characteristic that the i is 0 round.
Preferably, the step S130 includes the following steps:
s131, obtaining the sale prohibition status of each sale prohibition entity in the past T consecutive time windows, and if the defined status st is 0, then not prohibiting sale; st is 1, sale is forbidden, and the value range of T is 1 to T;
s132, defining penalty factor
Calculating the number of times that the entity is marked as forbidden in the historical window period;
s133, defining reward factors
Indicating the proportion of the longest length of the historical window in which the entity is continuously marked as not-for-sale, wherein L indicates the maximum length of the historical time window which can be continuously marked from t-1 to st-0;
and S134, obtaining the sale prohibition time d ═ 1-pass × (1+ max { p-b,0 }).
Preferably, the step S140 includes the steps of:
s141, based on the fact that the model prediction situation intersects with the true situation in the confusion matrix, 4 situations of true negative, false positive and true positive are obtained, wherein the negative corresponding model does not execute sale forbidding, the positive corresponding model executes sale forbidding, the true corresponding model prediction result is consistent with the actual situation, and the false corresponding model prediction result is inconsistent with the actual situation;
s142, defining the improvement rate as the actual intercepted ratio entering the forbidden sale, namely TN/All;
s143, defining the false prohibition rate as the actual passing ratio but entering the forbidden sale, namely FN/All;
s144, traversing all sale prohibition thresholds according to a preset step diameter interval, and calculating to obtain a curve of improvement rate and prohibition error rate under the threshold;
s145, calculating improved marginal utility and forbidden marginal cost, and finding out a forbidden sale threshold with high improvement rate and low forbidden rate, wherein the forbidden sale threshold is considered as the most valuable forbidden sale threshold;
and S146, taking the sale prohibition threshold with large improvement rate and small forbidding rate as the finest granularity sale prohibition threshold, and attenuating the sale prohibition thresholds of the rest levels according to the sale prohibition threshold with large improvement rate and small forbidding rate/N uniform step diameter.
Preferably, in the step S150, when the high-Level sale prohibition message includes the low-Level sale prohibition message, the low-Level sale prohibition message is removed, and only the high-Level sale prohibition message is reserved.
The embodiment of the present invention further provides a system for dynamically adjusting airline reservation, which is used for implementing the above method for dynamically adjusting airline reservation, and the system for dynamically adjusting airline reservation includes:
the characteristic extraction module is used for extracting the characteristics of the air ticket data through the decision tree, and cutting subdivided nodes to obtain N characteristics which influence the orderable position as important characteristics, wherein N is a preset value;
the sale forbidding entity module is used for obtaining a sale forbidding entity based on historical data of the last time window and an algorithm model;
the sale prohibition duration module is used for obtaining sale prohibition duration for each sale prohibition entity based on the sale prohibition times in a plurality of past continuous time windows;
executing a sale forbidding module, and entering sale forbidding when the pass rate pass of the sale forbidding entity is less than a sale forbidding threshold; otherwise, no sale prohibition is entered;
and the de-duplication merging module obtains a unique selling prohibition entity through a de-duplication merging algorithm.
An embodiment of the present invention further provides a device for dynamically adjusting airline reservation, including:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the above-described method of dynamically adjusting airline reservations via execution of the executable instructions.
An embodiment of the present invention further provides a computer-readable storage medium for storing a program, which when executed, implements the steps of the above-mentioned method for dynamically adjusting airline reservation.
The invention aims to provide a method, a system, equipment and a storage medium for dynamically adjusting airline ticket booking, which can find ticket management defects of an airline department in time and make dynamic production adjustment in time, and improve the process experience of booking airline tickets for users.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method of dynamic adjustment of airline reservations of the present invention.
Fig. 2 is a schematic diagram of an incentive model in the implementation process of the method for dynamically adjusting airline reservations of the present invention.
Fig. 3 is a schematic diagram of a reservation model in an implementation process of the method for dynamically adjusting airline reservations of the present invention.
Fig. 4 is a schematic diagram of a compromise model in the implementation of the method for dynamic adjustment of airline reservations of the present invention.
FIG. 5 is a block diagram of a system for dynamic adjustment of airline reservations of the present invention.
Fig. 6 is a schematic structural diagram of the dynamic ticket booking adjustment apparatus of the present invention.
Fig. 7 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present application is provided by way of specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure herein. The present application is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present application. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Embodiments of the present application will be described in detail below with reference to the accompanying drawings so that those skilled in the art to which the present application pertains can easily carry out the present application. The present application may be embodied in many different forms and is not limited to the embodiments described herein.
Reference throughout this specification to "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," or the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. Furthermore, the particular features, structures, materials, or characteristics shown may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of different embodiments or examples presented in this application can be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the expressions of the present application, "plurality" means two or more unless specifically defined otherwise.
In order to clearly explain the present application, components that are not related to the description are omitted, and the same reference numerals are given to the same or similar components throughout the specification.
Throughout the specification, when a device is referred to as being "connected" to another device, this includes not only the case of being "directly connected" but also the case of being "indirectly connected" with another element interposed therebetween. In addition, when a device "includes" a certain constituent element, unless otherwise specified, it means that the other constituent element is not excluded, but may be included.
When a device is said to be "on" another device, this may be directly on the other device, but may also be accompanied by other devices in between. When a device is said to be "directly on" another device, there are no other devices in between.
Although the terms first, second, etc. may be used herein to describe various elements in some instances, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first interface and the second interface are represented. Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, items, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions, steps or operations are inherently mutually exclusive in some way.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" include plural forms as long as the words do not expressly indicate a contrary meaning. The term "comprises/comprising" when used in this specification is taken to specify the presence of stated features, regions, integers, steps, operations, elements, and/or components, but does not exclude the presence or addition of other features, regions, integers, steps, operations, elements, and/or components.
Although not defined differently, including technical and scientific terms used herein, all terms have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. Terms defined in commonly used dictionaries are to be additionally interpreted as having meanings consistent with those of related art documents and the contents of the present prompts, and must not be excessively interpreted as having ideal or very formulaic meanings unless defined.
Currently, offer regulation can be generally divided into rate regulation and bay regulation. Freight rate regulation and control mainly embody initial pricing differentiation in different condition intervals, such as different periods (off-season busy seasons, weekends and weekends in the week), different conditions for changing back and changing, and different prices under different luggage conditions, so that freight rate data are relatively static. Meanwhile, the cost for acquiring the freight rate data by the Trip is fixed, and the accuracy is not limited by the cost. The cabin position is used as data of a navigation driver profit Management (Yield Management or Yield Management) system for embodying, and the essence is that the regulation and control of a supply and demand relationship are relied on, and when the cabin position of a certain freight rate is predetermined, the navigation driver profit Management system can recalculate to determine whether the cabin position is continuously opened; if the user behavior can be regarded as a random process, the cabin space data is disturbed randomly by the user predetermined behavior all the time and changes continuously. Plus the user always tends to select low priced products among all the offers, which tend to be faster and faster due to large variations in demand. Until now, the iterative optimization of the system, whether the cabin or the reservation, is mainly limited by the unpredictability of external data. 700+ engines cover the whole aviation department, and the vast majority of all-service aviation departments and part of low-cost aviation are involved. The cabin data sources are all GDS or drivers, and the accuracy of the original data provided for the Trip may have random problems of the drivers, air lines, regions and the like in different degrees, and the random problems are expressed as local and sporadic data problems or business problems. For example, in the epidemic situation period, the navigation department cancels flights in a large area, each navigation department data is issued to different levels, and the price passing rate, the cabin passing rate and the booking passing rate are impacted to different degrees. The random and long-tail characteristics of the part of data make it necessary to construct a data-driven dynamic model to intelligently deal with a class of problems of predetermined interception. After a series of yield management related optimizations are on line in the cabin booking position, a data model of 'passing rate feedback' is operated based on big data of user predetermined behaviors, and the method becomes possible. It can be seen that the impact on user interception is basically an external cause, and the long tail is unpredictable in advance. The current method of each quotation engine is relatively mechanical, or after single interception occurs, the product is forbidden to sell for a fixed number of hours according to a series of attributes; or after the interception is carried out for a plurality of times, products with low quality are forbidden to be sold for a long time manually, and the products are forbidden to be reviewed and resoluted periodically. And the current sale prohibition occurs after the user encounters interception "already", and is lack of predictability.
FIG. 1 is a flow chart of a method of dynamic adjustment of airline reservations of the present invention. As shown in fig. 1, an embodiment of the present invention provides a method for dynamically adjusting airline reservations, including the following steps:
s110, extracting the characteristics of the air ticket data through a decision tree, and pruning subdivided nodes to obtain N characteristics which influence the orderable positions as important characteristics, wherein N is a preset value;
s120, obtaining a sale prohibition entity based on the historical data of the previous time window based on an algorithm model;
s130, obtaining the sale prohibition duration for each sale prohibition entity based on the sale prohibition times in a plurality of past continuous time windows;
s140, entering into forbidden sale when the passing rate pass of the forbidden sale entity is smaller than a forbidden sale threshold value; otherwise, no sale prohibition is entered;
and S150, obtaining a unique selling prohibition entity through a de-duplication merging algorithm.
In a preferred embodiment, in step S110, the important features include at least one of a type of journey, a city of departure, a country of departure, a space selling place, a reservation data warehouse, and a ticket gate.
In a preferred embodiment, step S120 includes the following steps:
s121, learning historical data of a previous time window by an algorithm, constructing a model and applying the model to the current time window;
s122, selecting time windows t, wherein each time window is equal in length, and the length is r;
s123, selecting a stage f, namely, defining a stage or a booking stage, forming a data matrix and training a model based on N characteristic dimensions as data columns and data of the stage in a preset time range as data rows M;
and S124, executing each round of level i:
s125, in the ith round, selecting i characteristics
Based on the data matrix, aggregating N characteristic dimensions, respectively calculating total amount and passing rate, and generating sale forbidding entity composed of (N-i) clear characteristics and i virtualized characteristics
In a preferred embodiment, in step S122, as the length r of the time window decreases, the accuracy of the model increases;
step S124 includes: the i-0 round is the finest granularity, and when the pass rate is lower than the set threshold under the characteristics of the round, the sale prohibition mark is added to the data object.
In a preferred embodiment, step S130 includes the following steps:
s131, obtaining the sale prohibition status of each sale prohibition entity in the past T consecutive time windows, and if the defined status st is 0, then not forbidding sale; if st is 1, the sale is forbidden, and the value range of T is 1 to T;
s132, defining penalty factor
Calculating the number of times that the entity is marked as forbidden in the historical window period;
s133, defining reward factors
Indicating the proportion of the longest length of the historical window in which the entity is continuously marked as not-for-sale, wherein L indicates the maximum length of the historical time window which can be continuously marked from t-1 to st-0;
and S134, obtaining the sale prohibition time d ═ 1-pass × (1+ max { p-b,0 }).
In a preferred embodiment, step S140 includes the following steps:
s141, based on the fact that the model prediction situation intersects with the true situation in the confusion matrix, 4 situations of true negative, false positive and true positive are obtained, wherein the negative corresponding model does not execute sale forbidding, the positive corresponding model executes sale forbidding, the true corresponding model prediction result is consistent with the actual situation, and the false corresponding model prediction result is inconsistent with the actual situation;
and S142, defining the improvement rate as the proportion actually intercepted and entering into sale prohibition, namely TN/All,
s143, defining the false forbidding rate as the ratio of actually passing but entering forbidden sale, namely FN/All,
and S144, traversing all the forbidden selling thresholds according to the preset step diameter interval, and calculating to obtain a curve of the improvement rate and the forbidden error rate under the threshold.
S145, calculating improved marginal utility and forbidden marginal cost, and finding out a forbidden sale threshold value with high improvement rate and low forbidden rate, wherein the forbidden sale threshold value is regarded as the most valuable forbidden sale threshold value.
And S146, taking the value as a finest granularity sale forbidding threshold value, and attenuating the sale forbidding threshold values of the rest levels according to the value/N uniform step diameter.
In a preferred embodiment, in step S150, when the high-Level sale forbidding message contains the low-Level sale forbidding message, the low-Level sale forbidding message is removed, and only the high-Level sale forbidding message is reserved.
The invention can actively intervene in advance when the preset quality is known not to be ideal through the established sale prohibition system. In addition, the statically matched sale prohibiting entity is the sale prohibiting duration of a fixed period, and also needs to be optimized into a dynamic mode, and the model is expected to have the capability of intelligent abstraction. The model system obtained by machine learning can forbid selling a series of entities with common diseases in advance, so that user interception does not occur any more, and the preset quality is directly improved. More importantly, after the quotation engine consumes the model, the next lowest quotation with better quality and better conversion can be output for the user to select, rather than simply subtracting from the result set.
In recent years, with the technology advancement, business expansion and globalization strategy, the quoted price resources of the Trip international air ticket are gradually enriched, the air line coverage is gradually expanded, and the competitive advantages are continuously expanded. At the same time, the smoothness of the user at each predetermined stage, as part of the system reliability, is also of paramount importance, requiring further improvements and optimizations. The dynamic model aims to create a highly smooth reservation system, which is helpful to promote conversion and increase revenue on one hand, and also fits the concept of high-quality service promised by Trip on the other hand.
The specific implementation process of the invention is as follows:
(1) and (4) preparing data.
User orderable data using the fill-in page and user order data for the value added page.
(2) And (4) selecting the characteristics.
And (3) using the CART decision tree as feature extraction, pruning nodes excessively subdivided, and obtaining N important features influencing the orderable positions. Further, the features extracted by the classification tree-based method also need to be deeply consistent with the understanding of the international airline ticket business. For example, the Trip Type (Trip Type), the departure City (Origin City), the departure Country (Origin Country) and the POC revenue management mode adopted by the european navigation are closely related; the POS income management mode adopted by the North American aviation department is highly related to a cabin sales space (POS), a booking GDS (booking GDS) and a Ticketing platform (Ticketing Agency). The information of city pairs and country pairs helps to capture randomly distributed airline problems.
(3) Algorithm design
(3.1) sale-prohibiting entity
The sale forbidding entity refers to a type of data object for sale forbidding, and the characteristics extracted from 2.2 are used as entity attributes.
The algorithm learns the historical data of the last time window, constructs a model and applies the model to the current time window.
Time t of a time window is selected, each time window is equal in length, and the length is r. The model can be refined by continuously reducing r, which is equivalent to differentiation. The value of r needs to take into account the trade-off between refinement and overfitting.
And selecting a stage phase f, and establishing a stage or a booking stage, wherein the stage or the booking stage is formed by forming a data matrix and training a model based on N characteristic dimensions as data columns and data of the stage in a certain time range as data rows M. The key indicators used here are the total (counts) and the pass rate (pass).
And (3) executing each round of level i:
round i, select i features, for
And the characteristics are aggregated by N characteristic dimensions based on the data matrix, the total amount and the passing rate are respectively calculated, and the sale forbidding entity consisting of (N-i) clear characteristics and i virtualized characteristics is generated. Where round i-0 is the finest granularity, entity without any ghosting features. If the pass rate is below the set threshold under the round characteristic, a sale prohibition flag is set.
(3.2) length of sale ban
And calculating the current sale prohibition time length linearly according to the passing rate formula. Based on data expression of a plurality of historical time windows, a reward and punishment mechanism is set for the sale prohibition duration, products with excellent recent expression are historically provided, and when the current product quality is slightly poor, the sale prohibition duration is set to be relatively short (reward); historically, products with poor long-term performance have more strict (punishment) time forbidding duration, and the time forbidding duration is more reasonable due to a reward and punishment mechanism.
For a non-selling entity,
the historical time window is made up of a series of successive time windows in the past, from near to far, T from 1 to T.
Defining State stIf the value is 0, the selling is not forbidden;
stwhen the product is 1, the product is forbidden to sell.
Defining penalty factors
Calculating the number of times that the entity is marked as forbidden in the historical window period; and representing the repeated interception punishment.
Defining a reward factor
Representing the proportion of the longest length of the historical window in which the entity is continuously marked as not being contraindicated. Where L denotes the historical time window starting from t ═ 1, st0 can be a continuous maximum length,for measuring the number of times the "continuous" quality is superior. And the ratio of the continuous better times to the size of the historical window is used as the rewarding force.
Calculating the duration of sale prohibition
Duration d＝(1-pass)*r*(1+max{p-b,0})
(3.3) sale prohibition threshold
Giving a sale forbidding threshold value, and entering sale forbidding if the passing rate of the sale forbidding entity is less than the sale forbidding threshold value; otherwise, no sale prohibition is entered. Based on the idea of Confusion Matrix (fusion Matrix), the True case and the model prediction case are crossed to obtain 4 possibilities of True Negative (TN, True Negative), False Negative (FN, False Negative), False Positive (FP, False Positive) and True Positive (TP, True Positive). And the yin and yang respectively correspond to the model without executing forbidden sale and executing forbidden sale, and the true and false respectively correspond to the model with consistent and inconsistent prediction results and actual conditions. Defining the improvement rate as the actually intercepted occupation ratio entering the forbidden sale, namely TN/All, which is reflected in the improvement of the passing rate in production after the forbidden sale model takes effect. Defining the false rejection rate as the percentage of actual passing but entering into forbidden sale, i.e. FN/All, can be regarded as the opportunity cost brought by forbidden sale. And traversing all possible sale forbidding thresholds according to a certain step diameter interval gap g, and calculating to obtain a curve of the improvement rate and the error forbidding rate under the threshold. And calculating improved marginal utility and forbidden marginal cost, and finding out a forbidden sale threshold with high improvement rate and low forbidden rate, wherein the forbidden sale threshold is considered as the most valuable forbidden sale threshold. The value is used as a finest granularity (Level 0) sale forbidding threshold value, and the sale forbidding threshold values of the rest levels are attenuated according to the value/N uniform step diameter.
(3.4) Dedualization
In practical implementation, the operation of Level0 is based on original data, and the data of Level1.. LEVELN should theoretically be based on the data of Level N-1, and for simplifying the calculation, all the data are based on Level 0. This allows for containment relationships between all of the entities that are not on sale, which relationships are redundant. The same record of the sale prohibition may be contributed to different sale prohibition entities. A de-merger algorithm is required that reveals distinct unique entities for sale prohibition. And when the high-Level sale forbidding message contains the low-Level sale forbidding message, only the high-Level sale forbidding message is reserved.
4 types of de-duplication merging algorithms are designed, and the essence is that a path leading to a level where a certain sale-prohibiting entity is located is found in a constructed multi-branch tree.
The first, the aggressive model (fig. 2), allows arbitrary levels of cross-layer merging, relatively free. In the graph, nodes represent sale forbidding entities and do at levels, connecting lines represent that two entities have inclusion relationship, and high Level entities comprise low levels. The second conservative model (fig. 3) does not allow any override merging, i.e. all reserved sale-forbidding entities satisfy each level at the level where the entity is located, and there exists a corresponding sale-forbidding entity, i.e. the depth of the subtree must be equal to level +1. The dashed nodes in the graph represent entities that fail to generate an upsell as compared to the aggressive model. The dotted node does not actually exist, and is only used to illustrate that the reason that L3 cannot be generated is the lack of support of L1, L2.
The third tradeoff model (fig. 4), does not allow any cross-layer merging, only allows merging down when there are neighboring layer entities present. Unlike the conservative model, the trade-off model preserves the sale-prohibited entities as long as it is satisfied that there is one path instead of all paths, such that the tree depth is less than or equal to Level +1. The lower the level of the sale forbidding entity is, the finer and more scattered the granularity is; the higher the rating, the greater the level of improvement in the contra-sales, and the need for extreme caution in the merger. The trade-off model combines, relatively speaking, an aggressive model and a conservative model, with an optimal balance.
In some scenes, the compromise model inevitably generates low-level sale prohibition entities, so that the generalization capability of the model is weakened; a composite version is then optimized on the compromise model. Selecting N, and applying a compromise model between the levels N +1.. N; an aggressive model is applied between levels 0-n. And (5) iteratively optimizing n to find an optimal composite model.
(4) Model evaluation
In addition to the service effect evaluation using the improvement rate and the forbidding rate defined in (3.3), the model is also evaluated using the Accuracy (Precision), Recall (Recall) and Precision (Accuracy) indexes of the machine learning classical general purpose.
(5) Model application
The model is used as a producer to access the message management center, and when a new sale prohibition message is generated, a writing interface provided by the message management center is called to write the latest message; the air ticket international engine is used as a consumer real-time monitoring message management center, and can immediately consume after a new message exists and output suboptimal solutions.
The scheme of the invention has the following effects after actual use:
production was described as 20191220 and 2020121050 weeks old Bookable (BK) and booking (RV) data, covering pre-epidemic (20200201 before) and post-epidemic.
And (5) iterating to obtain the optimal model and parameter combination.
Selecting characteristics: the value of the Trip Type, POS,
Shopping Engine，Booking GDS，
Validating Airline，Ticketing Agency，
Ori-Dest Country Pair,Ori-Dest City Pair；
selecting a model: compound model, n is 3;
parameter optimization: time t ═ 8h [05,13,21 ];
threshold BK is 35%, Threshold RV is 20%, and the step diameter intervals are uniformly linear.
The invention constructs and obtains a dynamic model with the sale forbidding entity and the sale forbidding duration generated by data. Before epidemic situation (20200201), the system can determine that the BK passing rate is maintained at 92%, the model improvement rate is 1.6%, and the false rejection rate is 0.3%; in the early epidemic stage (202002 plus 202008), the passing rate is maintained at 86-89%, the model improvement rate is 3.5-6%, and the error prohibition rate is 0.5%; in the late stage of epidemic situation, the passing rate is 90-92%, the improvement rate is 2-2.5%, and the error banning rate is 0.3-0.4%. The reservation RV throughput behaves similarly. The model can lift the curve of the passing rate of the downward sliding or downward concave hole, so that the passing rate of the ordable and binding position is stabilized at 92 percent, and the 'controllable' is achieved.
Further, it can be found that the lifting degree of the model is different for different raw passing rates. If the original pass rate is poor, the boost is large. If the international epidemic situation outbreak occurs in the middle and late 3 months, 70% of international flights are produced and are cancelled, 75% of original BK is generated, and the algorithm can be stably maintained to be near 89%. If the raw throughput is better, the lift is smaller, such as 1 month, the raw BK is 92-93%, and the model is only lifted to about 93.5%. In the face of unpredictable great reduction, the model can improve the system to a reliable level, and when the external data environment performance is better, the model is maintained at a stable level, and the performance is in line with expectations.
In addition, the model can help find clinically significant production problems and help to make production response in time. The system can help to find the revenue management logic behind the phenomena of 'inquirability and unsubscribability' and 'virtual cabin' of the navigation department or the problem of third-party data with inconsistent GDS cabin space and navigation department cabin space.
FIG. 5 is a block diagram of a system for dynamic adjustment of airline reservations of the present invention. As shown in fig. 5, the system 5 for dynamically adjusting airline reservations of the present invention includes:
the feature extraction module 51 is used for extracting features of the air ticket data through a decision tree, and cutting subdivided nodes to obtain N features influencing bookable and bookable positions as important features, wherein N is a preset value;
a sale forbidding entity module 52, which obtains a sale forbidding entity based on the historical data of the last time window and an algorithm model;
a sale prohibition duration module 53 for obtaining a sale prohibition duration for each sale prohibition entity based on the sale prohibition times in a plurality of past continuous time windows;
executing a sale forbidding module 54, and entering sale forbidding when the pass rate pass of the sale forbidding entity is less than the sale forbidding threshold; otherwise, no sale prohibition is entered;
the de-duplication merging module 55 obtains a unique selling prohibition entity through the de-duplication merging algorithm.
The air ticket booking dynamic adjusting system can timely find out ticket management defects of the airline department and timely make production dynamic adjustment, and improves the process experience of booking air tickets for users.
The above-mentioned embodiments are only preferred examples of the present invention, and are not intended to limit the present invention, and any equivalent substitutions, modifications and changes made within the principle of the present invention are within the protection scope of the present invention.
The embodiment of the invention also provides a device for dynamically adjusting the air ticket booking, which comprises a processor. A memory having stored therein executable instructions of the processor. Wherein the processor is configured to perform the steps of the method for dynamic adjustment of airline reservations via execution of executable instructions.
As shown above, the dynamic adjustment system for airline ticket booking of the embodiment of the present invention can find out ticket management defects of the airline department in time and make dynamic production adjustment in time, thereby improving the process experience of booking airline tickets for users.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" platform.
Fig. 6 is a schematic structural diagram of the dynamic ticket booking adjustment apparatus of the present invention. An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the electronic device 600 is in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting the different platform components (including the memory unit 620 and the processing unit 610), a display unit 640, etc.
Wherein the storage unit stores program code executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, processing unit 610 may perform the steps as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, to name a few.
The embodiment of the invention also provides a computer readable storage medium for storing a program, and the program realizes the steps of the method for dynamically adjusting the air ticket booking when being executed. In some possible embodiments, the aspects of the present invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of this specification, when the program product is run on the terminal device.
As shown above, the dynamic adjustment system for airline ticket booking of the embodiment of the present invention can find out ticket management defects of the airline department in time and make dynamic production adjustment in time, thereby improving the process experience of booking airline tickets for users.
Fig. 7 is a schematic structural diagram of a computer-readable storage medium of the present invention. Referring to fig. 7, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ 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 be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination 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.
A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written 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. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the present invention is directed to a method, a system, a device and a storage medium for dynamically adjusting airline ticket booking, which can timely find ticket management defects of an airline department and timely make dynamic production adjustment, thereby improving the process experience of booking airline tickets for users.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, numerous simple deductions or substitutions may be made without departing from the spirit of the invention, which shall be deemed to belong to the scope of the invention.
Claims (10)
1. A method for dynamically adjusting airline reservations, comprising the steps of:
s110, extracting the characteristics of the air ticket data through a decision tree, and pruning subdivided nodes to obtain N characteristics which influence the orderable positions as important characteristics, wherein N is a preset value;
s120, obtaining a sale prohibition entity based on the historical data of the previous time window based on an algorithm model;
s130, obtaining the sale prohibition duration for each sale prohibition entity based on the sale prohibition times in a plurality of past continuous time windows;
s140, entering into forbidden sale when the passing rate pass of the forbidden sale entity is smaller than a forbidden sale threshold value; otherwise, no sale prohibition is entered;
and S150, obtaining a unique selling forbidding entity through a de-duplication merging algorithm.
2. The method for dynamically adjusting airline reservation according to claim 1, wherein in the step S110, the important feature includes at least one of a type of travel, a city of departure, a country of departure, a sales space of a cabin, a reservation data warehouse, and a ticket desk.
3. The method for dynamically adjusting airline reservation according to claim 1, wherein the step S120 comprises the steps of:
s121, learning historical data of a previous time window by an algorithm, constructing a model, and applying the model to a current time window;
s122, selecting time windows t, wherein each time window is equal in length, and the length is r;
s123, selecting a stage f, namely, defining a stage or a booking stage, forming a data matrix and training a model based on N characteristic dimensions as data columns and data of the stage in a preset time range as data rows M;
and S124, executing each round of level i:
s125, in the ith round, selecting i characteristics
And the characteristics are aggregated by N characteristic dimensions based on the data matrix, the total amount and the passing rate are respectively calculated, and the sale forbidding entity consisting of (N-i) clear characteristics and i virtualized characteristics is generated.
4. The method for dynamically adjusting airline reservations according to claim 3, characterized in that, in step S122, as the length r of the time window decreases, the accuracy of the model increases;
the step S124 includes: and adding an sale prohibition mark to the data object when the passing rate is lower than a set threshold value under the characteristic that the i is 0 round.
5. The method for dynamically adjusting airline reservation according to claim 1, wherein the step S130 comprises the steps of:
s131, obtaining the sale prohibition status of each sale prohibition entity in the past T consecutive time windows, and if the defined status st is 0, then not prohibiting sale; st is 1, sale is forbidden, and the value range of T is 1 to T;
s132, defining penalty factor
Calculating the number of times that the entity is marked as forbidden in the historical window period;
s133, defining reward factors
Indicating the proportion of the longest length of the historical window in which the entity is continuously marked as not-for-sale, wherein L indicates the maximum length of the historical time window which can be continuously marked from t-1 to st-0;
and S134, obtaining the sale prohibition time d ═ 1-pass × (1+ max { p-b,0 }).
6. The method for dynamically adjusting airline reservation according to claim 4, wherein the step S140 comprises the steps of:
s141, based on the fact that the model prediction situation intersects with the true situation in the confusion matrix, 4 situations of true negative, false positive and true positive are obtained, wherein the negative corresponding model does not execute sale forbidding, the positive corresponding model executes sale forbidding, the true corresponding model prediction result is consistent with the actual situation, and the false corresponding model prediction result is inconsistent with the actual situation;
s142, defining the improvement rate as the actual intercepted ratio entering the forbidden sale, namely TN/All;
s143, defining the false prohibition rate as the actual passing ratio but entering the forbidden sale, namely FN/All;
s144, traversing all sale prohibition thresholds according to a preset step diameter interval, and calculating to obtain a curve of improvement rate and prohibition error rate under the threshold;
s145, calculating improved marginal utility and forbidden marginal cost, and finding out a forbidden sale threshold with high improvement rate and low forbidden rate, wherein the forbidden sale threshold is considered as the most valuable forbidden sale threshold;
and S146, taking the sale prohibition threshold with large improvement rate and small forbidding rate as the finest granularity sale prohibition threshold, and attenuating the sale prohibition thresholds of the rest levels according to the sale prohibition threshold with large improvement rate and small forbidding rate/N uniform step diameter.
7. The method for dynamically adjusting airline reservation according to claim 1, wherein in step S150, when the high-Level sale prohibition message includes a low-Level sale prohibition message, the low-Level sale prohibition message is removed, and only the high-Level sale prohibition message is retained.
8. A system for dynamically adjusting airline reservations, for implementing the method for dynamically adjusting airline reservations of claim 1, comprising:
the characteristic extraction module is used for extracting the characteristics of the air ticket data through the decision tree, and cutting subdivided nodes to obtain N characteristics which influence the orderable position as important characteristics, wherein N is a preset value;
the forbidden entity module is used for obtaining a forbidden entity based on historical data of the last time window and an algorithm model;
the sale prohibition duration module is used for obtaining sale prohibition duration for each sale prohibition entity based on the sale prohibition times in a plurality of past continuous time windows;
executing a sale forbidding module, and entering sale forbidding when the pass rate pass of the sale forbidding entity is less than a sale forbidding threshold; otherwise, no sale prohibition is entered;
and the de-duplication merging module obtains a unique selling prohibition entity through a de-duplication merging algorithm.
9. A device for dynamically adjusting airline reservations, comprising:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the method of dynamic adjustment of airline reservations of any of claims 1-7 via execution of the executable instructions.
10. A computer-readable storage medium storing a program which, when executed by a processor, performs the steps of the method for dynamic adjustment of airline reservations of any one of claims 1 to 7.
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