CN112651671A - Flight space adjusting method and related equipment - Google Patents
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Abstract
The application discloses a flight cabin space adjusting method and related equipment, which can improve the accuracy of cabin adjustment and increase the applicability of a model. The method comprises the following steps: acquiring a flight slot control instruction mirror image, a flight inquiry mirror image and a flight inventory state mirror image of an on-sale flight; processing the flight inquiry mirror image and the flight inventory state mirror image to obtain processed target data; inputting the target data into a cascade model to obtain the type of a flight cabin control instruction; selecting a target secondary model according to the type of the flight slot control instruction, wherein the target secondary model is contained in a plurality of secondary models, and the plurality of secondary models are in an association relationship with the cascade model; determining the adjustment information corresponding to the on-sale flight according to the target secondary model; and adjusting the space of the on-sale flight according to the adjustment information corresponding to the on-sale flight.
Description
Technical Field
The application relates to the field of aviation, in particular to a flight space adjusting method and related equipment.
Background
In the civil aviation industry, airlines mainly provide aviation travel services for passengers. Airline companies make and release flight plans, which are released one year ahead until the flight takes off, and passengers can book tickets and book seats at any time. The needs of passengers are various, and airlines provide differentiated services for passengers by dividing slots (generally expressed by english letters, such as F, Y) on the same flight, wherein different slots correspond to different fare classes. Except that the first class cabin, the business class cabin or the economy class cabin have substantial differences in physical structure, the physical seats corresponding to different cabin space levels are often slightly different. The same seat, in turn, can be sold at both high-price (high discount) and low-price (low discount) bays. Typically, an airline determines the number of slots that can be sold (or limits the number of sales) in a flight plan, but the number and status of the slots are dynamically adjusted.
To maximize revenue, early in the flight open booking, the marketable status and quantity of low discount slots is often limited, which is to reserve seats for passengers with higher price willingness to pay. Generally, an airline administrator is configured to perform inventory management, and continuously adjust the marketable status and number of different slots of a flight, i.e., make a flight.
For the domestic civil aviation market, the revenue level of a flight is directly related to the experience of the airline administrator. The flight line manager with rich experience can comprehensively consider various factors such as flight sales progress and sales conditions of competitors in the market, and timely adjust the cabin. For the airlines introducing the revenue management system, although the system gives the cabin space limited sales number according to algorithm models such as prediction and optimization, whether the system is adopted or not is finally determined by a flight line manager, and the revenue management system is high in construction cost, complex in technology and not suitable for small and medium-sized airlines.
Disclosure of Invention
In view of this, the present application provides a flight slot adjusting method and related equipment, which can reduce the construction cost and is widely applicable to various types of airlines.
A first aspect of the embodiments of the present application provides a flight slot adjusting method, including:
acquiring a flight slot control instruction mirror image, a flight inquiry mirror image and a flight inventory state mirror image of an on-sale flight;
processing the flight inquiry mirror image and the flight inventory state mirror image to obtain processed target data;
inputting the target data into a cascade model to obtain the type of a flight cabin control instruction;
selecting a target secondary model according to the type of the flight slot control instruction, wherein the target secondary model is contained in a plurality of secondary models, and the plurality of secondary models are in an association relationship with the cascade model;
determining the adjustment information corresponding to the on-sale flight according to the target secondary model;
and adjusting the space of the on-sale flight according to the adjustment information corresponding to the on-sale flight.
A second aspect of the embodiments of the present application provides a flight space adjusting device, including:
the acquisition unit is used for acquiring a flight slot control instruction mirror image, a flight inquiry mirror image and a flight inventory state mirror image of an on-sale flight;
the processing unit is used for processing the flight inquiry mirror image and the flight inventory state mirror image to obtain processed target data;
the determining unit is used for inputting the target data into a cascade model to obtain the type of the flight space control instruction;
the selecting unit is used for selecting a target secondary model according to the type of the flight slot control instruction, the target secondary model is contained in a plurality of secondary models, and the plurality of secondary models and the cascade model have an association relation;
the determining unit is further configured to determine adjustment information corresponding to the on-sale flight according to the target secondary model;
and the adjusting unit is used for adjusting the space of the flight on sale according to the adjusting information corresponding to the flight on sale.
A third aspect of the present application provides a computer device comprising at least one processor and a memory connected to each other, wherein the memory is used for storing program code, and the program code is loaded and executed by the processor to implement the steps of the flight space adjusting method according to the first aspect.
A fourth aspect of embodiments of the present application provides a machine-readable medium, which includes instructions that, when executed on a machine, cause the machine to perform the steps of the flight space adjustment method according to the first aspect.
In summary, it can be seen that, in the embodiment provided by the application, the flight slot adjusting device may process the query mirror image of the flight on sale and the inventory status of the flight through the pre-trained cascade model and the plurality of secondary models to obtain slot adjusting information of the flight on sale, and then perform adjustment based on the adjusting information, so as to simulate an excellent airline manager to perform slot adjustment, thereby achieving slot adjustment, and compared with the existing method, the method can reduce construction cost, reduce technical complexity, and is widely applicable to various types of airlines.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of a flight space adjusting method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a cascade model and a plurality of secondary models provided in an embodiment of the present application;
fig. 3 is a schematic view of a virtual structure of a flight space adjusting device according to an embodiment of the present application;
FIG. 4 is a schematic illustration of a machine-readable medium provided by an embodiment of the present application;
fig. 5 is a schematic diagram of a hardware structure of a service provided in the embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are for illustration purposes only and are not intended to limit the scope of the present application.
The terms "include" and variations thereof as used herein are inclusive and open-ended, i.e., "including but not limited to. The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present application are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this application are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that reference to "one or more" unless the context clearly dictates otherwise.
Some of the terms referred to in this application will first be explained:
a cabin position: generally, the English letters such as F, Y are used to represent different discount rates, and represent that different services are provided;
cabin adjustment: the process that the airline manager regulates and controls the marketable state and the number of each level of cabin of an on-sale flight;
IM, RO and AV are all operation instruction sets in the Chinese civil aviation booking system, wherein IM is a flight cabin control instruction set, RO is a flight stock state query instruction set, and AV is a flight seat sale state instruction set; wherein, the IM instruction includes IM: N-PR, IM: L-LS and IM: I-NL instruction, IM: the N-PR instruction is used to set or remove the permanent application identification, IM: the L-LS instruction is used to modify the flight slot limit sales number, IM: the I-NL command modifies the flight slot limit sales portfolio. In addition, the IM instruction may also include other instructions, such as IM: P-PC (modified PCF Table number), IM: C-CS (setting or removing mix level usage flag) and IM: S-CC (type of modifying seat layout, etc.), and is not particularly limited.
Referring to table 1, table 1 shows IM types and various types of commands and descriptions thereof:
TABLE 1
The flight space adjusting method provided by the present application is explained from the perspective of a flight space adjusting device, which may be a server or a service unit in the server, and is not particularly limited.
Referring to fig. 1, fig. 1 is a schematic flow chart of a flight space adjusting method provided in the present application, including:
101 obtains a flight slot control instruction image, a flight query image and a flight inventory status image of the selling flight.
In this embodiment, when the slot of an on-sale flight needs to be adjusted, the flight slot adjusting device may obtain a flight slot control instruction (IM instruction) mirror flight inquiry (AV instruction) mirror and a flight inventory status (RO instruction) mirror of the on-sale flight, where the obtaining manner is not particularly limited.
102. And processing the flight inquiry mirror image and the flight inventory state mirror image to obtain processed data.
In this embodiment, after obtaining the flight query image and the flight inventory state image of the on-sale flight, the flight slot adjusting device may process the flight query image and the flight inventory state image to obtain processed data; the following explains a specific process of the treatment: the IM instruction image, the AV instruction image and the RO instruction image may be parsed first, then extracting analyzed information such as an operation airline manager code, an operation date, time, a navigation department code, a flight number, a departure city code, a destination city code, an IM instruction type, a configuration before-effect state of IM instruction modification, a configuration after-effect state of IM instruction modification, a flight inquiry state (including bookable conditions of all slots of flights provided by other airlines) after the IM instruction takes effect, inventory states of all slots of the flights (including whether the flights are domestic flights, the number of remaining days of distance takeoff, the flights, the number of stop times, stop city codes, slot lists, slot layout numbers, reserved numbers of all slots, a predetermined number of all slot groups, state identification of all slots, limited sales numbers of all slots and the like) after the IM instruction takes effect; then, the analyzed information is integrated and preprocessed, the data integration and preprocessing comprises operation time sequencing, null value and special value replacement, non-airline manager operation record elimination, frequent operation record combination, record splitting into multiple lines according to the cabin space, summary level attribute calculation, AV state calculation of the competitive airline flight of the same airline department (including whether the same cabin space of the competitive airline flight is allowed to receive reservation or not, the competitive airline flight can receive the reserved lowest cabin space, and the like), flight cabin space adjustment operation time period calculation, and the like, finally, the data after data integration and preprocessing is processed again to obtain target data, the data after data integration and preprocessing is processed again, and the data after data integration and preprocessing comprises the processing of applying characteristic engineering to screen high-correlation characteristics, eliminating collinearity characteristics, characteristic expansion, data transformation, discount rate conversion, data normalization, label attribute mapping, and the like, and finally obtaining target data, wherein the discount rate conversion refers to converting the cabin space codes into cabin space discount rates according to a cabin space price table (namely a published freight rate table).
103. And inputting the target data into the cascade model to obtain the type of the flight cabin control instruction.
In this embodiment, the flight slot adjusting device may input the target data into a pre-trained cascade model to obtain the type of the flight slot control command, and refer to the description of the flight slot control command, the type of the flight slot control command may be PR, LS, NL or Null, where Null identifies no action.
104. And selecting a target secondary model according to the type of the flight slot control instruction.
In this embodiment, after obtaining the type of the flight slot control command, a target secondary model may be selected according to the type of the flight slot control command, where the target secondary model is included in a plurality of secondary models, and the plurality of secondary models have an association relationship with the cascade model. Suitable instruction types, model types, algorithm types, outputs of the models, and data used for training for the cascade model and the plurality of secondary models are described below in connection with table 2:
TABLE 2
As shown in table 2, the type of the flight slot control command is PR, LS, NL or Null, where Null identifies no action, and if the type of the flight slot control command is PR, the slot to be operated is further determined (model 1.1, regression model, output value slot price discount rate, which can be converted into slot code through slot price table (published freight rate table)) and the state to be set (model 1.2, classification model, output value ON, OFF); if the type of the flight slot control instruction is LS, further determining the slot needing to be operated (a model 2.1, a regression model, an output value slot price discount rate which can be converted into a slot code through a slot price table (a published freight price table)), the state needing to be set (a model 2.2, a classification model, the output value is ON and OFF), and the limited sales number LSV (a model 2.3, a regression model) needing to be set; if the type of the flight slot control command is NL, outputting a limit sales number LSV (model 3.1, regression model) according to the slot; if Null, no action is output. Thus, a target secondary model corresponding to the type can be selected from the plurality of secondary models according to the type of the flight slot control instruction.
It should be noted that, in order to deduce the slot code, model 1.1 uses a regression model instead of a classification model, which has the following advantages: 1. the space code and the discount rate have corresponding relation and can be converted with each other; 2. by adopting the return model, the relationship of high and low prices among the cabins is included, and the prediction accuracy can be improved. For example: the discount rate corresponding to the space codes Y/B/M/H/K/L is 100/95/90/85/80/75, it can be seen that the space codes contain sequential relationship, for example, M cabin (90 is folded), the adjacent spaces are B (95 folded) and H (85 folded), the sequential relationship cannot be expressed by taking the space codes as a prediction target, but the space discount rate is used as the prediction target and a regression model is applied, so that the error can be limited to be near the real space discount rate, and the space codes are converted into the adjacent spaces of the real space codes, and the adjacent spaces are also located, so that the prediction accuracy is improved.
105. And determining the adjustment information corresponding to the on-sale flight according to the target secondary model.
In this embodiment, after the flight slot adjusting apparatus selects the target secondary model, it may determine the adjustment information of several flights according to the target secondary model, for example, if the output of the cascade model is PR, it may determine that the target secondary model is the model 1.1 and the model 1.2 in table 2, and then execute the model 1.1 and the model 1.2 to determine the slot code and the status.
106. And adjusting the space of the on-sale flight according to the adjustment information corresponding to the on-sale flight.
In this embodiment, the flight slot adjusting device may adjust the slot of the flight on sale according to the adjustment information corresponding to the flight on sale, for example, the flight slot limit sale combination may be modified according to the adjustment data.
In summary, it can be seen that, in the embodiment provided by the application, the flight slot adjusting device may process the query mirror image of the flight on sale and the inventory status of the flight through the pre-trained cascade model and the plurality of secondary models to obtain slot adjusting information of the flight on sale, and then perform adjustment based on the adjusting information, so as to simulate an excellent airline manager to perform slot adjustment, thereby achieving slot adjustment, and compared with the existing method, the method can reduce construction cost, reduce technical complexity, and is widely applicable to various types of airlines.
The training procedure for the cascade model and the plurality of secondary models is described below.
Referring to fig. 2, fig. 2 is a schematic diagram of a training process of a cascade model and a plurality of secondary models according to an embodiment of the present application, including:
201. and acquiring a cabin control instruction mirror image, a flight query mirror image and a flight inventory state mirror image of the target flight within a preset time length.
In this embodiment, the flight slot adjusting device may first collect the flight slot control instruction (IM instruction) mirror image, the flight inquiry (AV instruction) mirror image, and the flight inventory status (RO instruction) mirror image at regular time according to the flight list for the excellent airline administrator, which is determined in advance, and store the flight slot control instruction (IM instruction) mirror image, the flight inquiry (AV instruction) mirror image, and the flight inventory status (RO instruction) mirror image in the local file. The IM command includes operations such as modifying the sales limit number of a certain bay, determining whether or not a predetermined modification is accepted for a certain bay, setting or canceling the sales limit combination of bays (after the bays participate in the combination, the high bay can be sold with the seat of the low bay), and the like. The IM instruction is triggered by an airline administrator firstly, an upstream system triggers and calls an AV instruction and an RO instruction after receiving and executing the IM instruction, and then stores a timestamp, a state mirror image before and after modification of a configuration item operated by the IM instruction, and an AV mirror image and an RO mirror image after execution of the IM instruction in a database. Therefore, the IM instruction mirror image, the AV instruction mirror image and the RO instruction mirror image collected here are identified by timestamps, and the three correspond to each other one by one, and include the state of configuration information changed before and after the dispatching of the flight control manager, and the state of marketable space and inventory after the dispatching of the flight control manager. After accumulating data for a period of time (preset duration), the IM instruction mirror, AV instruction mirror, and RO instruction mirror data of a target flight within the preset duration, which is a flight in charge of a predetermined excellent airline manager, may be acquired from the stored local file.
202. Analyzing the flight space control instruction mirror image, the flight inquiry mirror image and the flight inventory state mirror image to obtain state information before and after modification of the configuration item of the flight space control instruction operation, first flight inquiry information and first flight inventory state information.
In this embodiment, the flight slot adjusting device may analyze the obtained flight slot control instruction mirror, flight query mirror, and flight inventory state mirror to obtain state information before and after modification of the configuration item of the flight slot control instruction operation, the first flight query information, and the first flight inventory state information, and store the information in the local file. It is understood that the IM instruction image, AV instruction image, RO instruction image data are usually stored in plain text format such as XML or JSON, and compression is often performed in the database to reduce the storage space. Therefore, at this time, the flight slot adjusting device can decompress and decode the collected data of the IM instruction mirror image, AV instruction mirror image and RO instruction mirror image, and then extract the administrator code of the operation airline, the operation date, the time, the driver code, the flight number, the departure city code, the destination city code, the IM instruction type, the state before configuration validation of the IM instruction modification, the state after configuration validation of the IM instruction modification, the flight inquiry state after validation of the IM instruction (including the bookable situation of each slot of the flight provided by other flight drivers of the airline), the stock state of each slot of the flight after validation of the IM instruction (including whether the flight is a domestic flight, the number of remaining days, the flight line, the number of times of passing-out, the code of the passing-out city, the slot list, the number of slot layouts, the reserved number of each slot, the predetermined number of each slot group, the status identifier of each slot, the position, The number of sales restricted for each bay, etc.).
203. And calculating second flight query information and second flight inventory state information before executing the flight space control instruction according to the state information before and after modification of the configuration item operated by the flight space control instruction, the first flight query information and the first flight inventory state information based on a preset service rule.
In this embodiment, the flight slot adjusting device may calculate, based on the preset service rule, the second flight query information and the second flight inventory status information before the execution of the flight slot control instruction through the configuration item, the first flight query information, and the first flight inventory status information operated by the flight slot control instruction, that is, calculate, through the analyzed data, the flight query (AV instruction) and the flight inventory status (RO instruction) information before the execution of the flight slot control instruction (IM instruction), and store the flight query (AV instruction) and the flight inventory status (RO instruction) information in the local file. Specifically, the flight slot adjusting device has the states before and after modification of the configuration item operated by the flight slot control command (IM command), the flight inquiry (AV command) after execution of the IM command, and the flight inventory state (RO command) after the IM command through the known information, so that the limit sales number of each slot before execution of the IM command can be reversely deduced according to the configuration item modified by the IM command, and the AV information and the RO information can be calculated by combining the information such as the reserved number. For example, the AV information corresponds to the slot limit sales number and the slot status flag of the RO information, and when the slot status flag includes a P flag, the AV is Q; AV is S when the number of the cabin limit sales is # and is A when 0; the airline ticket is sold out, and AV is L.
204. And performing data integration and preprocessing on the state information, the first flight query information, the first flight inventory state information, the second flight query information and the second flight inventory state information before and after modification of the configuration item operated by the flight slot control instruction to obtain preprocessed data.
In this embodiment, the flight slot adjusting device may perform data integration and preprocessing on the status information before and after modification of the configuration item operated by the flight slot control instruction (IM instruction), the flight inquiry (AV instruction) before and after execution of the IM instruction, and the flight inventory status (RO instruction) information before and after execution of the IM instruction, to obtain preprocessed data; the data integration and pretreatment comprises operations of sequencing according to operation time, replacing null values and special numerical values, excluding non-airline manager operation records, merging frequent operation records, splitting the records into multiple lines according to the space, calculating summary level attributes, calculating AV states of competitive airline flights of the same airline (including whether the same space of the competitive airline flights is allowed to receive reservation or not, the competitive airline flights can receive the reserved lowest space and the like), calculating flight space adjustment operation time periods and the like.
205. And carrying out secondary processing on the preprocessed data to obtain training data.
In this embodiment, after obtaining flight query (AV command) and flight inventory status (RO command) information before and after execution of a flight slot control command (IM command) after processing, the flight slot adjusting device may perform secondary processing on the data, where the secondary processing includes processing such as applying feature engineering to screen high correlation features, excluding collinearity features, feature expansion, data transformation, discount rate conversion, data normalization, and tag attribute mapping, and finally obtains training data of the model. It will be appreciated that after the training data is obtained, the training data may be stored to a local file.
206. And carrying out model training based on the training data to obtain a cascade model and a plurality of secondary models.
In this embodiment, after obtaining the training data, the flight space adjusting device may divide the training data into a training set and a test set according to time (certainly, the training set may also be divided in other manners, and is not limited specifically), then perform model training through the training set, and test the model through the test set, so as to obtain the cascade model and the plurality of secondary models. It will be appreciated that after the cascade model and the plurality of secondary models are obtained, the cascade model and the plurality of secondary models may be stored.
It should be noted that, after the flight space control device obtains the cascade model and the plurality of secondary models through training, the flight space control device may determine the first evaluation index according to the cascade model and the test set, and determine whether the first evaluation index meets expectations, and if not, adjust the model parameters of the cascade model and perform model training again until expectations are reached. The same is true for multiple secondary models, and thus a cascade model and multiple secondary models can be obtained that are fully expected. It is to be understood that the present application provides two evaluation metrics for evaluating models, wherein a regression model is evaluated by Root Mean Square Error (RMSE), a classification model is evaluated by area under the ROC curve (AUC), wherein ROC is a receiver operating characteristic curve, as model 0, model 1.2, model 2.2 in table 2 can be evaluated by AUC, and model 1.1, model 2.1, model 2.3, and model 3.1 in table 2 can be evaluated by RMSE.
It should be noted that, the model evaluation is performed only by using the RMSE and the ROC as evaluation indexes, and in practical applications, other model evaluation indexes such as accuracy, precision, recall, confusion matrix, and the like may also be included, which is not limited specifically.
In summary, in the embodiment provided by the application, when the flight space adjusting device trains the cascade model and the plurality of secondary models, the cascade model and the plurality of secondary models obtained by training are personalized models according to specific flights by modeling the historical operation behavior of excellent airline managers by using a big data and machine learning method and independent of fixed rules, so that the applicability is strong, more accurate operation instructions and states can be output, and cabin adjustment can be performed more accurately.
It is to be understood that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The names of messages or information exchanged between a plurality of devices in the embodiments of the present application are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Although the operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous.
It should be understood that the various steps recited in the method embodiments of the present application may be performed in a different order and/or in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present application is not limited in this respect.
Additionally, the present application may also be written with computer program code for performing the operations of the present application in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, Smalltalk, C + +, 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 computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
As shown in fig. 3, for a schematic structural diagram of an embodiment of a flight space adjusting device provided in the present application, a flight space adjusting device 300 includes:
an obtaining unit 301, configured to obtain a flight slot control instruction mirror image, a flight query mirror image, and a flight inventory status mirror image of an on-sale flight;
the processing unit 302 is configured to process the flight inquiry mirror image and the flight inventory status mirror image to obtain processed target data;
a determining unit 303, configured to input the target data into a cascade model to obtain a type of a flight slot control instruction;
a selecting unit 304, configured to select a target secondary model according to the type of the flight slot control instruction, where the target secondary model is included in a plurality of secondary models, and the plurality of secondary models have an association relationship with the cascade model;
the determining unit 303 is further configured to determine, according to the target secondary model, adjustment information corresponding to the flight on sale;
an adjusting unit 305, configured to adjust the slot of the flight on sale according to the adjustment information corresponding to the flight on sale.
In a possible implementation manner, the apparatus further includes:
a model training unit 306, the model training unit 306 configured to:
acquiring a cabin control instruction mirror image, a flight query mirror image and a flight inventory state mirror image of a target flight in a preset time length, wherein the target flight is a flight for which a predetermined excellent airline manager is responsible;
analyzing the flight space control instruction mirror image, the flight inquiry mirror image and the flight inventory state mirror image to obtain state information, first flight inquiry information and first flight inventory state information before and after modification of a configuration item of the flight space control instruction operation;
based on a preset service rule, calculating second flight query information and second flight inventory state information before the execution of the flight space control instruction through the state information before and after the modification of the configuration item operated by the flight space control instruction, the first flight query information and the first flight inventory state information;
performing data integration and preprocessing on the state information before and after modification of the configuration item operated by the flight space control instruction, the first flight query information, the first flight inventory state information, the second flight query information and the second flight inventory state information to obtain preprocessed data;
performing secondary processing on the preprocessed data to obtain training data;
and carrying out model training based on the training data to obtain the cascade model and the plurality of secondary models.
In a possible implementation manner, the apparatus further includes:
a storage unit 307, the storage unit 307 being configured to:
storing the flight space control instruction mirror image, the flight inquiry mirror image and the flight inventory state mirror image of the target flight which are acquired at regular time into a local file in an XML or JSON plain text format;
storing the state information before and after modification of the configuration item operated by the flight slot control instruction, the first flight query information and the first flight inventory state information into a local file;
storing the second flight query information and the second flight inventory status information to a local file;
storing the preprocessed data and the training data to a local file;
storing the cascade model and the plurality of secondary models to a local file.
In a possible implementation manner, the model training unit 306 is further configured to:
determining a first evaluation index corresponding to the cascade model according to the cascade model;
when the first evaluation index is not in accordance with expectation, adjusting the model parameters of the cascade model and carrying out model training again;
determining a second evaluation indicator of the plurality of secondary models from the plurality of secondary models;
and when the second evaluation index is not in accordance with the expectation, adjusting the model parameters of the plurality of secondary models and carrying out model training again.
In a possible implementation, the data integration and preprocessing includes at least one of the following operations:
sorting according to operation time, replacing null values and special numerical values, excluding non-airline manager operation records, merging frequent operation records, dividing the records into a plurality of lines according to the space, calculating summary level attributes, calculating flight inquiry states of competitive airline department flights of the same airline and calculating flight space adjustment operation time intervals, wherein the flight inquiry states of the competitive airline department flights of the same airline comprise whether the same space of the competitive airline department flights is allowed to receive reservation and the lowest space of the competitive airline department flights can receive reservation;
the secondary treatment comprises at least one of the following treatments:
the method comprises the steps of screening high correlation characteristics by applying characteristic engineering, excluding co-linear characteristics, expanding characteristics, transforming data, converting discount rate, normalizing data and mapping label attributes.
In summary, it can be seen that, in the embodiment provided by the application, the flight slot adjusting device may process the query mirror image of the flight on sale and the inventory status of the flight through the pre-trained cascade model and the plurality of secondary models to obtain slot adjusting information of the flight on sale, and then perform adjustment based on the adjusting information, so as to simulate an excellent airline manager to perform slot adjustment, thereby achieving slot adjustment, and compared with the existing method, the method can reduce construction cost, reduce technical complexity, and is widely applicable to various types of airlines.
It should be noted that the units described in the embodiments of the present application may be implemented by software, and may also be implemented by hardware. Here, the name of the unit does not constitute a limitation of the unit itself in some cases, and for example, the acquisition unit may also be described as "a unit that acquires credential information of a target user".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
Referring to fig. 4, fig. 4 is a schematic diagram of an embodiment of a machine-readable medium according to the present disclosure.
As shown in fig. 4, the present embodiment provides a machine-readable medium 400, on which a computer program 411 is stored, and when the computer program 411 is executed by a processor, the steps of the time-series abnormal data detection method described above in fig. 1 are implemented.
In the context of this application, a machine-readable 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 machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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.
It should be noted that the machine-readable medium described above in this application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. A computer 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 of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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. In the present application, a computer 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. In this application, however, a computer readable signal medium may include a propagated data signal with computer 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 computer readable signal medium may also be any computer readable medium that is not a computer 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 computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
Referring to fig. 5, fig. 5 is a schematic diagram of a hardware structure of a server according to an embodiment of the present disclosure, where the server 500 may have a relatively large difference due to different configurations or performances, and may include one or more Central Processing Units (CPUs) 522 (e.g., one or more processors) and a memory 532, and one or more storage media 530 (e.g., one or more mass storage devices) storing applications 542 or data 544. Memory 532 and storage media 530 may be, among other things, transient storage or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a series of instruction operations for the server. Still further, the central processor 522 may be configured to communicate with the storage medium 530, and execute a series of instruction operations in the storage medium 530 on the server 500.
The server 500 may also include one or more power supplies 526, one or more wired or wireless network interfaces 550, one or more input-output interfaces 558, and/or one or more operating systems 541, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and so forth.
The steps performed by the flight slot adjusting apparatus in the above embodiment may be based on the server structure shown in fig. 5.
It should be further noted that, according to the embodiment of the present application, the process of the time-series abnormal data detection method described in the flowchart in fig. 1 above may be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated in the flow chart diagram of fig. 2 described above.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
While several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the disclosure. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (10)
1. A flight space adjusting method is characterized by comprising the following steps:
acquiring a flight slot control instruction mirror image, a flight inquiry mirror image and a flight inventory state mirror image of an on-sale flight;
processing the flight inquiry mirror image and the flight inventory state mirror image to obtain processed target data;
inputting the target data into a cascade model to obtain the type of a flight cabin control instruction;
selecting a target secondary model according to the type of the flight slot control instruction, wherein the target secondary model is contained in a plurality of secondary models, and the plurality of secondary models are in an association relationship with the cascade model;
determining the adjustment information corresponding to the on-sale flight according to the target secondary model;
and adjusting the space of the on-sale flight according to the adjustment information corresponding to the on-sale flight.
2. The method of claim 1, further comprising:
acquiring a cabin control instruction mirror image, a flight query mirror image and a flight inventory state mirror image of a target flight in a preset time length, wherein the target flight is a flight for which a predetermined excellent airline manager is responsible;
analyzing the flight space control instruction mirror image, the flight inquiry mirror image and the flight inventory state mirror image to obtain state information, first flight inquiry information and first flight inventory state information before and after modification of a configuration item of the flight space control instruction operation;
based on a preset service rule, calculating second flight query information and second flight inventory state information before the execution of the flight space control instruction through the state information before and after the modification of the configuration item operated by the flight space control instruction, the first flight query information and the first flight inventory state information;
performing data integration and preprocessing on the state information before and after modification of the configuration item operated by the flight space control instruction, the first flight query information, the first flight inventory state information, the second flight query information and the second flight inventory state information to obtain preprocessed data;
performing secondary processing on the preprocessed data to obtain training data;
and carrying out model training based on the training data to obtain the cascade model and the plurality of secondary models.
3. The method of claim 2, wherein the method comprises:
storing the flight space control instruction mirror image, the flight inquiry mirror image and the flight inventory state mirror image of the target flight which are acquired at regular time into a local file in an XML or JSON plain text format;
storing the state information before and after modification of the configuration item operated by the flight slot control instruction, the first flight query information and the first flight inventory state information into a local file;
storing the second flight query information and the second flight inventory status information to a local file;
storing the preprocessed data and the training data to a local file;
storing the cascade model and the plurality of secondary models to a local file.
4. The method of claim 2, further comprising:
determining a first evaluation index corresponding to the cascade model according to the cascade model;
when the first evaluation index is not in accordance with expectation, adjusting the model parameters of the cascade model and carrying out model training again;
determining a second evaluation indicator of the plurality of secondary models from the plurality of secondary models;
and when the second evaluation index is not in accordance with the expectation, adjusting the model parameters of the plurality of secondary models and carrying out model training again.
5. The method of claim 2, wherein the data integration and pre-processing comprises at least one of:
sorting according to operation time, replacing null values and special numerical values, excluding non-airline manager operation records, merging frequent operation records, dividing the records into a plurality of lines according to the space, calculating summary level attributes, calculating flight inquiry states of competitive airline department flights of the same airline and calculating flight space adjustment operation time intervals, wherein the flight inquiry states of the competitive airline department flights of the same airline comprise whether the same space of the competitive airline department flights is allowed to receive reservation and the lowest space of the competitive airline department flights can receive reservation;
the secondary treatment comprises at least one of the following treatments:
the method comprises the steps of screening high correlation characteristics by applying characteristic engineering, excluding co-linear characteristics, expanding characteristics, transforming data, converting discount rate, normalizing data and mapping label attributes.
6. A flight slot adjustment device, comprising:
the acquisition unit is used for acquiring a flight slot control instruction mirror image, a flight inquiry mirror image and a flight inventory state mirror image of an on-sale flight;
the processing unit is used for processing the flight inquiry mirror image and the flight inventory state mirror image to obtain processed target data;
the determining unit is used for inputting the target data into a cascade model to obtain the type of the flight space control instruction;
the selecting unit is used for selecting a target secondary model according to the type of the flight slot control instruction, the target secondary model is contained in a plurality of secondary models, and the plurality of secondary models and the cascade model have an association relation;
the determining unit is further configured to determine adjustment information corresponding to the on-sale flight according to the target secondary model;
and the adjusting unit is used for adjusting the space of the flight on sale according to the adjusting information corresponding to the flight on sale.
7. The apparatus of claim 6, further comprising:
a model training unit to:
acquiring a cabin control instruction mirror image, a flight query mirror image and a flight inventory state mirror image of a target flight in a preset time length, wherein the target flight is a flight for which a predetermined excellent airline manager is responsible;
analyzing the flight space control instruction mirror image, the flight inquiry mirror image and the flight inventory state mirror image to obtain state information, first flight inquiry information and first flight inventory state information before and after modification of a configuration item of the flight space control instruction operation;
based on a preset service rule, calculating second flight query information and second flight inventory state information before the execution of the flight space control instruction through the state information before and after the modification of the configuration item operated by the flight space control instruction, the first flight query information and the first flight inventory state information;
performing data integration and preprocessing on the state information before and after modification of the configuration item operated by the flight space control instruction, the first flight query information, the first flight inventory state information, the second flight query information and the second flight inventory state information to obtain preprocessed data;
performing secondary processing on the preprocessed data to obtain training data;
and carrying out model training based on the training data to obtain the cascade model and the plurality of secondary models.
8. The apparatus of claim 7, wherein the model training unit is further configured to:
determining a first evaluation index corresponding to the cascade model according to the cascade model;
when the first evaluation index is not in accordance with expectation, adjusting the model parameters of the cascade model and carrying out model training again;
determining a second evaluation indicator of the plurality of secondary models from the plurality of secondary models;
and when the second evaluation index is not in accordance with the expectation, adjusting the model parameters of the plurality of secondary models and carrying out model training again.
9. The apparatus of claim 7, wherein the data integration and pre-processing comprises at least one of:
sorting according to operation time, replacing null values and special numerical values, excluding non-airline manager operation records, merging frequent operation records, dividing the records into a plurality of lines according to the space, calculating summary level attributes, calculating flight inquiry states of competitive airline department flights of the same airline and calculating flight space adjustment operation time intervals, wherein the flight inquiry states of the competitive airline department flights of the same airline comprise whether the same space of the competitive airline department flights is allowed to receive reservation and the lowest space of the competitive airline department flights can receive reservation;
the secondary treatment comprises at least one of the following treatments:
the method comprises the steps of screening high correlation characteristics by applying characteristic engineering, excluding co-linear characteristics, expanding characteristics, transforming data, converting discount rate, normalizing data and mapping label attributes.
10. A machine-readable medium, comprising:
instructions which, when run on a machine, cause the machine to carry out the steps of the flight deck adjustment method of any one of claims 1 to 5 above.
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