CN112308346A - Flight booking value processing method and system based on exponential smoothing model - Google Patents
Flight booking value processing method and system based on exponential smoothing model Download PDFInfo
- Publication number
- CN112308346A CN112308346A CN202011379712.0A CN202011379712A CN112308346A CN 112308346 A CN112308346 A CN 112308346A CN 202011379712 A CN202011379712 A CN 202011379712A CN 112308346 A CN112308346 A CN 112308346A
- Authority
- CN
- China
- Prior art keywords
- value
- booking
- flight
- historical sample
- exponential smoothing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000009499 grossing Methods 0.000 title claims abstract description 110
- 238000003672 processing method Methods 0.000 title claims abstract description 16
- 230000010006 flight Effects 0.000 claims abstract description 105
- 238000000034 method Methods 0.000 claims description 36
- 238000012545 processing Methods 0.000 claims description 14
- 238000004891 communication Methods 0.000 claims description 6
- 238000013480 data collection Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 6
- 238000004590 computer program Methods 0.000 description 5
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 description 2
- 102100030988 Angiotensin-converting enzyme Human genes 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 108010083141 dipeptidyl carboxypeptidase Proteins 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 101000873780 Homo sapiens m7GpppN-mRNA hydrolase Proteins 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 102100035860 m7GpppN-mRNA hydrolase Human genes 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/02—Reservations, e.g. for tickets, services or events
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a flight booking value processing method and system based on an exponential smoothing model, which are used for determining a data acquisition point and departure time of a target flight; determining historical sample flights with the departure time of the target flight and the departure time of the target flight in the same week dimension; acquiring a first booking value of a designated cabin of a target flight corresponding to the data acquisition point; acquiring a second booking value of the designated cabin of the historical sample flight corresponding to the data acquisition point, and acquiring a third booking value of the designated cabin of the historical sample flight when the historical sample flight leaves; determining whether the historical sample flight meets the use requirement; if the first booking value is met and is more than or equal to 1, predicting the departure booking prediction value of the designated cabin of the target flight by using an exponential smoothing model, and calculating the standard deviation of the departure booking prediction value. And according to the calculated predicted value and standard deviation of the departure booking seat, the navigation department is subjected to income management and market trend prediction, so that the income of the navigation department is ensured.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a flight booking value processing method and system based on an exponential smoothing model.
Background
In the operation process of an airline company, in order to guarantee income, each seat of each segment of each flight needs to be sold to different types of passengers at different prices at proper time. When the seat selling price of the flight is formulated, the seat booking value of the flight when the flight leaves the port needs to be predicted, the market demand condition is determined by the seat booking value, and then the seat selling price of the flight is formulated according to the market demand condition.
That is, in order to guarantee the revenue of the airline company, the booking value of the flight when leaving the port needs to be predicted, and how to predict the booking value of the flight when leaving the port is a problem to be solved.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a flight booking value processing method and system based on an exponential smoothing model, so as to predict a booking value of a flight when the flight leaves.
In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
the first aspect of the embodiment of the invention discloses a flight booking value processing method based on an exponential smoothing model, which comprises the following steps:
determining a data acquisition point and departure time of a target flight, wherein the data acquisition point indicates the number of days of flight distance from departure;
determining historical sample flights with the departure time of the target flight and the departure time of the target flight in the same week dimension;
acquiring a first booking value of a designated cabin of the target flight corresponding to the data acquisition point;
acquiring a second booking value of the appointed cabin of the historical sample flight corresponding to the data acquisition point, and acquiring a third booking value of the appointed cabin of the historical sample flight when the historical sample flight leaves;
determining whether the historical sample flights meet usage requirements based on the second seat value, the third seat value, and the number of historical sample flights;
if the historical sample flight meets the use requirement and the first booking value is more than or equal to 1, predicting the departure booking prediction value of the designated cabin of the target flight based on the first booking value, the second booking value, the third booking value and the number of the historical sample flights in combination with a preset exponential smoothing model;
calculating a standard deviation of the departure reservation forecast based on the second reservation value, the third reservation value, and the number of historical sample flights.
Preferably, the predicting the predicted departure seat of the designated cabin of the target flight based on the first seat booking value, the second seat booking value, the third seat booking value and the number of the historical sample flights by combining a preset exponential smoothing model includes:
determining a constant of a preset exponential smoothing model based on the second seat value, the third seat value and the number of the historical sample flights;
and predicting to obtain the predicted departure booking value of the designated cabin of the target flight by combining the exponential smoothing model according to the constant of the exponential smoothing model, the first booking value and the model coefficient of the exponential smoothing model.
Preferably, the determining whether the historical sample flight meets the usage requirement based on the second seat value, the third seat value and the number of the historical sample flights includes:
determining whether the second booking value, the third booking value and the number of the historical sample flights meet all preset conditions, and if so, determining that the historical sample flights meet the use requirements;
the preset conditions are as follows:
the second booking value and the third booking value are both greater than or equal to 1;
the number of historical sample flights is greater than or equal to 1.
Preferably, the determining constants of a preset exponential smoothing model based on the second seat value, the third seat value and the number of historical sample flights includes:
utilizing the second and third booking values for each of the historical sample flights and based on the number of historical sample flights
Determining a Constant of a preset exponential smoothing model, Bd being the third booking value of each historical sample flight, Bn being the second booking value of each historical sample flight, Obse being the number of the historical sample flights, ConstespIs an exponential smoothing constant, ConstespGreater than 0 and less than 1;
correspondingly, the predicting the departure reservation predicted value of the designated space of the target flight according to the constant of the exponential smoothing model, the first reservation value and the model coefficient of the exponential smoothing model and by combining the exponential smoothing model comprises:
and according to the Constant of the exponential smoothing model, the first order value and the model Coefficient of the exponential smoothing model, combining the exponential smoothing model, and calculating the predicted departure order value Demand of the specified cabin of the target flight through Demand + Constant + coeffient Bf, wherein Bf is the first order value and coeffient is the model Coefficient.
Preferably, the calculating a standard deviation of the departure reservation forecast based on the second reservation value, the third reservation value and the number of historical sample flights comprises:
passing through based on the second order value, the third order value, and the number of historical sample flights
Calculating the standard deviation StdError of the predicted departure reservation value.
The second aspect of the embodiment of the present invention discloses a flight booking value processing system based on an exponential smoothing model, wherein the system includes:
the system comprises a first determining unit, a second determining unit and a third determining unit, wherein the first determining unit is used for determining a data acquisition point and departure time of a target flight, and the data acquisition point indicates the number of days of the flight from departure;
a second determining unit, configured to determine a historical sample flight whose departure time is the same week dimension as the departure time of the target flight;
the first acquisition unit is used for acquiring a first booking value of a designated cabin of the target flight corresponding to the data acquisition point;
a second obtaining unit, configured to obtain a second booking value of the designated cabin of the historical sample flight corresponding to the data acquisition point, and obtain a third booking value of the designated cabin of the historical sample flight when the historical sample flight leaves;
a third determining unit, configured to determine whether the historical sample flight meets a usage requirement based on the second booking value, the third booking value, and the number of historical sample flights;
the prediction unit is used for predicting the departure booking prediction value of the designated cabin of the target flight based on the first booking value, the second booking value, the third booking value and the number of the historical sample flights and combined with a preset exponential smoothing model if the historical sample flights meet the use requirements and the first booking value is more than or equal to 1;
and the calculating unit is used for calculating the standard deviation of the departure reservation predicted value based on the second reservation value, the third reservation value and the number of the historical sample flights.
Preferably, the prediction unit includes:
a determining module, configured to determine a constant of a preset exponential smoothing model based on the second booking value, the third booking value, and the number of flights of the historical sample;
and the prediction module is used for predicting to obtain the predicted departure booking value of the designated cabin of the target flight according to the constant of the exponential smoothing model, the first booking value and the model coefficient of the exponential smoothing model and by combining the exponential smoothing model.
Preferably, the third determining unit is specifically configured to: determining whether the second booking value, the third booking value and the number of the historical sample flights meet all preset conditions, and if so, determining that the historical sample flights meet the use requirements;
the preset conditions are as follows:
the second booking value and the third booking value are both greater than or equal to 1;
the number of historical sample flights is greater than or equal to 1.
A third aspect of an embodiment of the present invention discloses an electronic device, including: the system comprises a processor and a memory, wherein the processor and the memory are connected through a communication bus; the processor is used for calling and executing the program stored in the memory; the memory is used for storing a program, and the program is used for implementing the flight order value processing method based on the exponential smoothing model as disclosed in the first aspect of the embodiment of the invention.
A fourth aspect of the embodiments of the present invention discloses a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and the computer-executable instructions are used to execute the method for processing a flight order value based on an exponential smoothing model disclosed in the first aspect of the embodiments of the present invention.
Based on the flight booking value processing method and system based on the exponential smoothing model provided by the embodiment of the invention, the data acquisition point and the departure time of the target flight are determined; determining historical sample flights with the departure time of the target flight and the departure time of the target flight in the same week dimension; acquiring a first booking value of a designated cabin of a target flight corresponding to the data acquisition point; acquiring a second booking value of the designated cabin of the historical sample flight corresponding to the data acquisition point, and acquiring a third booking value of the designated cabin of the historical sample flight when the historical sample flight leaves; determining whether the historical sample flights meet the use requirements or not based on the second booking value, the third booking value and the number of the historical sample flights; if the historical sample flight meets the use requirement and the first booking value is more than or equal to 1, predicting the departure booking prediction value of the designated cabin of the target flight based on the first booking value, the second booking value, the third booking value and the number of the historical sample flights in combination with a preset exponential smoothing model; and calculating the standard deviation of the departure reservation predicted value based on the second reservation value, the third reservation value and the number of the historical sample flights. And according to the calculated predicted value and standard deviation of the departure booking seat, the navigation department is subjected to income management and market trend prediction, so that the income of the navigation department is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a flight booking value processing method based on an exponential smoothing model according to an embodiment of the present invention;
fig. 2 is a flowchart of determining a predicted departure reservation value according to an embodiment of the present invention;
FIG. 3 is a block diagram of a flight booking value processing system based on an exponential smoothing model according to an embodiment of the present invention;
FIG. 4 is another block diagram of a flight booking value processing system based on an exponential smoothing model according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
As can be seen from the background art, in order to guarantee the revenue of the airline company, the airline company needs to predict the booking value when the flight leaves the airport, and can use the predicted booking value to determine the market demand and then set up the corresponding selling price of the seat.
Therefore, the embodiment of the invention provides a flight booking value processing method and system based on an exponential smoothing model, which are used for determining a data acquisition point and departure time of a target flight and determining historical sample flights of which the departure time is the same week dimension as the departure time of the target flight. The method comprises the steps of obtaining a first booking value of a designated cabin of a target flight corresponding to a data acquisition point, obtaining a second booking value of a designated cabin of a historical sample flight corresponding to the data acquisition point, and obtaining a third booking value of the designated cabin of the historical sample flight when the historical sample flight leaves. And determining whether the historical sample flights meet the use requirements based on the second order value, the third order value and the number of the historical sample flights. If the historical sample flight meets the use requirement and the first booking value is larger than or equal to 1, predicting the departure booking prediction value of the designated cabin of the target flight based on the first booking value, the second booking value, the third booking value and the number of the historical sample flights by combining a preset exponential smoothing model, and calculating the standard deviation of the departure booking prediction value. And according to the predicted value and the standard deviation of the departure booking seat obtained by calculation, the navigation department is subjected to income management and market trend prediction so as to ensure the income of the navigation department.
Referring to fig. 1, a flowchart of a flight booking value processing method based on an exponential smoothing model according to an embodiment of the present invention is shown, where the flight booking value processing method includes:
step S101: and determining the data acquisition point and departure time of the target flight.
It should be noted that each Data Collection Point (DCP) corresponds to a Data collection time, and each Data collection point indicates the number of departure days of a flight, that is, different Data collection points indicate the number of departure days of a flight, such as: DCP1 corresponds to a number of hong kong days (days away from hong kong) of 120 days, DCP2 corresponds to a number of hong kong days of 900 days, and DCP24 corresponds to a number of hong kong days of 0 days (in this case, DCP24 is also referred to as DCP-final).
It is understood that, in the process of predicting the departure reservation prediction value of the target flight at the departure time, the corresponding data needs to be utilized, and therefore, before performing step S101, flight data information corresponding to the target airline (the airline corresponding to the target flight) needs to be acquired.
It should be noted that the flight control system of the target airline company includes the flight data information of the index smoothing or increment of the driver (airline company), so that the flight data information corresponding to the driver can be acquired from the flight control system of the target airline company at predetermined time intervals (for example, at 24-hour intervals).
It should be further noted that, from the obtained flight data information, inventory data of a designated flight of the target airline company is obtained, where the inventory data includes inventory data of flights that have departed from the airport and inventory data of flights that have not departed from the airport, and the inventory data includes details of the flight space, the booking value of each space, the flight number, the origin airport, the arrival airport, the departure date (i.e., departure time) of the flight, and a week dimension corresponding to the departure date of the flight, and the week dimension indicates that the departure date of the flight is a day of the week.
It should be further noted that, in order to ensure the accuracy of the predicted departure reservation value of the designated space of the predicted target flight, it is necessary to ensure the richness of the obtained inventory data, so that when obtaining inventory data, a sufficiently rich amount of inventory data is obtained as much as possible, such as: taking the system date of the designated flight of the target airline company as a reference date, acquiring inventory data of departure flights three years (which can be calibrated) past the reference date, and acquiring inventory data of non-departure flights one year (which can be calibrated) in the future of the reference date.
In the process of implementing step S101, a data collection point of the target flight is determined, and the departure time of the target flight is determined.
Step S102: historical sample flights are determined whose departure time is the same week dimension as the departure time of the target flight.
According to the above, the inventory data of the departed flights and the non-departed flights (including the target flights) is obtained, and at least the week dimension corresponding to the departure date and the departure date is included in the inventory data.
In the process of implementing step S102, from the departing flights, flights with the same week dimension of the departing time as that of the target flight are determined, and the determined flights are used as historical sample flights.
Such as: and if the departure time of the target flight is 12 months and 1 day in 2020, and 12 months and 1 day in 2020 is Tuesday, determining the flight with the departure time of Tuesday from the departure flights as the historical sample flight.
Step S103: and acquiring a first booking value of the appointed cabin of the target flight corresponding to the data acquisition point.
It will be understood that a data collection point corresponds to a data collection time, and thus for a flight, the data collection point may obtain a corresponding order value from the inventory data of the flight, such as: the reservation value of the flight corresponding to DCP1 (120 days from port) is obtained.
In the process of implementing step S103 specifically, a first booking value of a designated slot of the target flight corresponding to the data collection point (DCPn) is obtained from the obtained inventory data of the target flight.
Step S104: and acquiring a second booking value of the appointed cabin of the historical sample flight corresponding to the data acquisition point, and acquiring a third booking value of the appointed cabin of the historical sample flight when the historical sample flight leaves.
In the process of implementing step S104, a second booking value of the designated slots of the historical sample flight corresponding to the data collection point (DCPn) is obtained from the inventory data of the acquired historical sample flight (departing flight), and a third booking value (i.e., departing booking value) of the designated slots of the historical sample flight when the historical sample flight departs from the airport is obtained.
Step S105: and determining whether the historical sample flights meet the use requirements based on the second order value, the third order value and the number of the historical sample flights.
Before predicting the departure reservation predicted value of the target flight by using the data corresponding to the historical sample flight, whether the data corresponding to the historical sample flight meets the use requirement needs to be determined.
Specifically, in the process of step S105, a plurality of preset conditions are preset, it is determined whether the second booking value, the third booking value, and the number of historical sample flights satisfy all the preset conditions, and if all the preset conditions are satisfied, it is determined that the historical sample flights satisfy the use requirements.
The preset conditions are as follows:
the second booking value and the third booking value are both more than or equal to 1;
the number of historical sample flights is greater than or equal to 1.
That is, for each historical sample flight, there is a booking value for the historical sample flight corresponding to the data acquisition point (i.e., the second booking value is greater than or equal to 1) and a departure booking value (i.e., the third booking value is greater than or equal to 1). And the number of the historical sample flights is more than or equal to 1.
And when the historical sample flight meets the conditions, predicting the departure reservation predicted value of the target flight by using the data corresponding to the historical sample flight. And when the historical sample flight does not meet the condition, the corresponding historical sample flight needs to be obtained again until the obtained historical sample flight meets the condition, and the following steps are continuously executed.
Step S106: if the historical sample flight meets the use requirement and the first booking value is larger than or equal to 1, predicting the departure booking prediction value of the designated cabin of the target flight based on the first booking value, the second booking value, the third booking value and the number of the historical sample flights by combining a preset exponential smoothing model.
It should be noted that a corresponding exponential smoothing model is preset.
It is understood that before the index smoothing model is used to determine the predicted departure reservation value, the target flight must have a reservation value corresponding to the data acquisition point, i.e. the first reservation value of the target flight is greater than or equal to 1.
In the process of implementing step S106 specifically, if the historical sample flight meets the use requirement and the first booking value is greater than or equal to 1, the second booking value and the third booking value of the historical sample flight are used as sample data, and the departure booking prediction value of the designated slot of the target flight is predicted by combining the first booking value of the target flight, the number of the historical sample flights and the exponential smoothing model, that is, the booking value of the designated slot of the target flight when the target flight leaves is predicted.
Step S107: and calculating the standard deviation of the departure reservation predicted value based on the second reservation value, the third reservation value and the number of the historical sample flights.
In the specific implementation process of step S107, based on the second booking value, the third booking value and the number of historical sample flights, a standard deviation stdreror of the departure booking prediction value is calculated by formula (1), and the standard deviation is used as an important basis in revenue management of the airline company.
In formula (1), Bd is the third booking value of each historical sample flight, Bn is the second booking value of each historical sample flight, Obse is the number of historical sample flights, Constant is a Constant of the exponential smoothing model, Coefficient is a model Coefficient of the exponential smoothing model, and Coefficient can set a corresponding value, for example, Coefficient ═ 1.
The specific content of the Constant of the exponential smoothing model is as in equation (2).
In the formula (2), ConstespIs an exponential smoothing constant, ConstespGreater than 0 and less than 1.
It is understood that the above standard deviation of the predicted departure reservation value is calculated by using the relevant parameters of the exponential smoothing model, i.e. the calculated standard deviation corresponds to the exponential smoothing model. Similarly, when determining the predicted departure seat value by using other types of models, the corresponding standard deviation can also be calculated by using the relevant parameters of the other types of models, that is, each type of model can calculate the standard deviation corresponding to the type of model.
In practical application, an optimal model can be selected from a plurality of models in the following modes: and selecting the optimal model from the multiple models by using a minimum standard deviation method according to the standard deviations corresponding to the multiple models.
When the exponential smoothing model is applied, the actual effect of predicting the departure reservation predicted value of the designated cabin of the target flight by the exponential smoothing model can be determined by using a T test method.
In the embodiment of the invention, a data acquisition point and a departure time of a target flight are determined, and a historical sample flight with the departure time being in the same week dimension as the departure time of the target flight is determined. The method comprises the steps of obtaining a first booking value of a designated cabin of a target flight corresponding to a data acquisition point, obtaining a second booking value of a designated cabin of a historical sample flight corresponding to the data acquisition point, and obtaining a third booking value of the designated cabin of the historical sample flight when the historical sample flight leaves. And determining whether the historical sample flights meet the use requirements based on the second order value, the third order value and the number of the historical sample flights. If the historical sample flight meets the use requirement and the first booking value is larger than or equal to 1, predicting the departure booking prediction value of the designated cabin of the target flight based on the first booking value, the second booking value, the third booking value and the number of the historical sample flights by combining a preset exponential smoothing model, and calculating the standard deviation of the departure booking prediction value. And according to the calculated departure reservation predicted value and standard deviation, carrying out revenue management and market trend prediction on the navigation department, improving the accuracy of the predicted reservation value and ensuring the revenue of the navigation department.
In the above embodiment of the present invention, referring to fig. 2, the process of determining the predicted departure reservation value of the designated slot of the target flight in step S106 in fig. 1 shows a flowchart for determining the predicted departure reservation value according to an embodiment of the present invention, which includes the following steps:
step S201: and determining a constant of a preset exponential smoothing model based on the second order value, the third order value and the number of the historical sample flights.
In the process of implementing step S201 specifically, the Constant of the exponential smoothing model is determined by using the above formula (2) based on the second order value, the third order value and the number of historical sample flights.
Step S202: and predicting to obtain the predicted departure booking value of the designated cabin of the target flight by combining the exponential smoothing model according to the constant of the exponential smoothing model, the first booking value and the model coefficient of the exponential smoothing model.
In the process of specifically implementing the step S202, the predicted departure seat value Demand of the designated cabin of the target flight is calculated by using the constant of the exponential smoothing model, the first seat booking value and the model coefficient of the exponential smoothing model and combining the exponential smoothing model through the formula (3).
Demand＝Constant+Coefficient*Bf (3)
In the formula (3), Bf is a first order value, Coefficient is a model Coefficient of the exponential smoothing model, Coefficient can set a corresponding value, for example, Coefficient is 1, Constant is a Constant of the exponential smoothing model, and specific content of Constant is referred to the above formula (2).
In the embodiment of the invention, the constant of the exponential smoothing model is determined by utilizing the second booking value, the third booking value and the number of the historical sample flights, the departure booking prediction value of the designated cabin of the target flight is predicted and obtained by combining the exponential smoothing model according to the constant of the exponential smoothing model, the first booking value and the model coefficient of the exponential smoothing model, and the navigation department utilizes the departure booking prediction value to carry out revenue management, so that the accuracy of the predicted booking value is improved and the revenue of the navigation department is ensured.
Corresponding to the above method for processing a flight order value based on an exponential smoothing model provided by the embodiment of the present invention, referring to fig. 3, an embodiment of the present invention further provides a structural block diagram of a system for processing a flight order value based on an exponential smoothing model, where the system includes: a first determination unit 301, a second determination unit 302, a first acquisition unit 303, a second acquisition unit 304, a third determination unit 305, a prediction unit 306, and a calculation unit 307;
a first determining unit 301, configured to determine a data acquisition point and a departure time of a target flight, where the data acquisition point indicates a number of days that the flight is away from the departure.
A second determining unit 302, configured to determine historical sample flights whose departure time is the same week dimension as the departure time of the target flight.
The first obtaining unit 303 is configured to obtain a first booking value of a designated slot of a target flight corresponding to a data acquisition point.
The second obtaining unit 304 is configured to obtain a second booking value of the designated slot of the historical sample flight corresponding to the data collection point, and obtain a third booking value of the designated slot of the historical sample flight when the historical sample flight leaves.
A third determining unit 305, configured to determine whether the historical sample flight meets the usage requirement based on the second order value, the third order value and the number of historical sample flights.
In a specific implementation, the third determining unit 305 is specifically configured to: determining whether the second booking value, the third booking value and the number of the historical sample flights meet all preset conditions, and if so, determining that the historical sample flights meet the use requirements;
the preset conditions are as follows:
the second seat booking value and the third seat booking value are both more than or equal to 1;
the number of historical sample flights is greater than or equal to 1.
The prediction unit 306 is configured to predict the departure reservation prediction value of the designated cabin of the target flight based on the first reservation value, the second reservation value, the third reservation value and the number of the historical sample flights and in combination with a preset exponential smoothing model if the historical sample flights meet the use requirement and the first reservation value is greater than or equal to 1.
And the calculating unit 307 is configured to calculate a standard deviation of the departure reservation prediction value based on the second reservation value, the third reservation value and the number of the historical sample flights.
In a specific implementation, the calculating unit 307 is specifically configured to: based on the second order value, the third order value, and the number of historical sample flights, a standard deviation stdreror of the departure order prediction value is calculated by equation (1) above.
In the embodiment of the invention, a data acquisition point and a departure time of a target flight are determined, and a historical sample flight with the departure time being in the same week dimension as the departure time of the target flight is determined. The method comprises the steps of obtaining a first booking value of a designated cabin of a target flight corresponding to a data acquisition point, obtaining a second booking value of a designated cabin of a historical sample flight corresponding to the data acquisition point, and obtaining a third booking value of the designated cabin of the historical sample flight when the historical sample flight leaves. And determining whether the historical sample flights meet the use requirements based on the second order value, the third order value and the number of the historical sample flights. If the historical sample flight meets the use requirement and the first booking value is larger than or equal to 1, predicting the departure booking prediction value of the designated cabin of the target flight based on the first booking value, the second booking value, the third booking value and the number of the historical sample flights by combining a preset exponential smoothing model, and calculating the standard deviation of the departure booking prediction value. And according to the calculated departure reservation predicted value and standard deviation, carrying out revenue management and market trend prediction on the navigation department, improving the accuracy of the predicted reservation value and ensuring the revenue of the navigation department.
Preferably, referring to fig. 4 in conjunction with fig. 3, another structural block diagram of a flight booking value processing system based on an exponential smoothing model according to an embodiment of the present invention is shown, and the prediction unit 306 includes a determination module 3061 and a prediction module 3062;
a determining module 3061, configured to determine a constant of the preset exponential smoothing model based on the second order value, the third order value and the number of historical sample flights.
In a specific implementation, the determination module 3061 is specifically configured to: the Constant of the exponential smoothing model is determined using equation (2) above based on the second and third booking values for each historical sample flight and based on the number of historical sample flights.
The prediction module 3062 is configured to predict the departure reservation predicted value of the designated cabin of the target flight according to the constant of the exponential smoothing model, the first reservation value and the model coefficient of the exponential smoothing model in combination with the exponential smoothing model.
In particular implementations, the prediction module 3062 is specifically configured to: and (4) calculating the departure reservation predicted value Demand of the designated space of the target flight according to the constant of the exponential smoothing model, the first reservation value and the model coefficient of the exponential smoothing model and by combining the exponential smoothing model through the formula (3).
In the embodiment of the invention, the constant of the exponential smoothing model is determined by utilizing the second booking value, the third booking value and the number of the historical sample flights, the departure booking prediction value of the designated cabin of the target flight is predicted and obtained by combining the exponential smoothing model according to the constant of the exponential smoothing model, the first booking value and the model coefficient of the exponential smoothing model, and the navigation department utilizes the departure booking prediction value to carry out revenue management, so that the accuracy of the predicted booking value is improved and the revenue of the navigation department is ensured.
An embodiment of the present invention further provides an electronic device, where the electronic device includes: the processor and the memory are connected through a communication bus; the processor is used for calling and executing the program stored in the memory; and the memory is used for storing a program which is used for realizing the flight booking value processing method based on the exponential smoothing model.
Referring now to FIG. 5, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, the electronic device may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 506 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure 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 by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 501.
Furthermore, an embodiment of the present invention further provides a computer-readable storage medium, in which computer-executable instructions are stored, and the computer-executable instructions are configured to execute a flight order value processing method based on an exponential smoothing model.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: determining a data acquisition point and a departure time of a target flight, and determining a historical sample flight with the departure time being the same week dimension as the departure time of the target flight. The method comprises the steps of obtaining a first booking value of a designated cabin of a target flight corresponding to a data acquisition point, obtaining a second booking value of a designated cabin of a historical sample flight corresponding to the data acquisition point, and obtaining a third booking value of the designated cabin of the historical sample flight when the historical sample flight leaves. And determining whether the historical sample flights meet the use requirements based on the second order value, the third order value and the number of the historical sample flights. If the historical sample flight meets the use requirement and the first booking value is larger than or equal to 1, predicting the departure booking prediction value of the designated cabin of the target flight based on the first booking value, the second booking value, the third booking value and the number of the historical sample flights by combining a preset exponential smoothing model, and calculating the standard deviation of the departure booking prediction value.
In the context of this disclosure, a machine-readable medium may be a 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.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A flight booking value processing method based on an exponential smoothing model is characterized by comprising the following steps:
determining a data acquisition point and departure time of a target flight, wherein the data acquisition point indicates the number of days of flight distance from departure;
determining historical sample flights with the departure time of the target flight and the departure time of the target flight in the same week dimension;
acquiring a first booking value of a designated cabin of the target flight corresponding to the data acquisition point;
acquiring a second booking value of the appointed cabin of the historical sample flight corresponding to the data acquisition point, and acquiring a third booking value of the appointed cabin of the historical sample flight when the historical sample flight leaves;
determining whether the historical sample flights meet usage requirements based on the second seat value, the third seat value, and the number of historical sample flights;
if the historical sample flight meets the use requirement and the first booking value is more than or equal to 1, predicting the departure booking prediction value of the designated cabin of the target flight based on the first booking value, the second booking value, the third booking value and the number of the historical sample flights in combination with a preset exponential smoothing model;
calculating a standard deviation of the departure reservation forecast based on the second reservation value, the third reservation value, and the number of historical sample flights.
2. The method of claim 1, wherein predicting the predicted departure seat value for the designated slot of the target flight based on the first seat value, the second seat value, the third seat value, and the number of historical sample flights in combination with a pre-set exponential smoothing model comprises:
determining a constant of a preset exponential smoothing model based on the second seat value, the third seat value and the number of the historical sample flights;
and predicting to obtain the predicted departure booking value of the designated cabin of the target flight by combining the exponential smoothing model according to the constant of the exponential smoothing model, the first booking value and the model coefficient of the exponential smoothing model.
3. The method of claim 1, wherein determining whether the historical sample flights meet usage requirements based on the second seat value, the third seat value, and the number of historical sample flights comprises:
determining whether the second booking value, the third booking value and the number of the historical sample flights meet all preset conditions, and if so, determining that the historical sample flights meet the use requirements;
the preset conditions are as follows:
the second booking value and the third booking value are both greater than or equal to 1;
the number of historical sample flights is greater than or equal to 1.
4. The method of claim 2, wherein determining constants of a preset exponential smoothing model based on the second order value, the third order value and the number of historical sample flights comprises:
the second and third booking values for flights based on each of the historical samplesBooking value, and utilizing based on the number of historical sample flights
Determining a Constant of a preset exponential smoothing model, Bd being the third booking value of each historical sample flight, Bn being the second booking value of each historical sample flight, Obse being the number of the historical sample flights, ConstespIs an exponential smoothing constant, ConstespGreater than 0 and less than 1;
correspondingly, the predicting the departure reservation predicted value of the designated space of the target flight according to the constant of the exponential smoothing model, the first reservation value and the model coefficient of the exponential smoothing model and by combining the exponential smoothing model comprises:
and according to the Constant of the exponential smoothing model, the first order value and the model Coefficient of the exponential smoothing model, combining the exponential smoothing model, and calculating the predicted departure order value Demand of the specified cabin of the target flight through Demand + Constant + coeffient Bf, wherein Bf is the first order value and coeffient is the model Coefficient.
5. The method of claim 4, wherein calculating the standard deviation of the departure reservation forecast based on the second reservation value, the third reservation value, and the number of historical sample flights comprises:
passing through based on the second order value, the third order value, and the number of historical sample flights
Calculating the standard deviation StdError of the predicted departure reservation value.
6. A system for processing flight booking values based on an exponential smoothing model, the system comprising:
the system comprises a first determining unit, a second determining unit and a third determining unit, wherein the first determining unit is used for determining a data acquisition point and departure time of a target flight, and the data acquisition point indicates the number of days of the flight from departure;
a second determining unit, configured to determine a historical sample flight whose departure time is the same week dimension as the departure time of the target flight;
the first acquisition unit is used for acquiring a first booking value of a designated cabin of the target flight corresponding to the data acquisition point;
a second obtaining unit, configured to obtain a second booking value of the designated cabin of the historical sample flight corresponding to the data acquisition point, and obtain a third booking value of the designated cabin of the historical sample flight when the historical sample flight leaves;
a third determining unit, configured to determine whether the historical sample flight meets a usage requirement based on the second booking value, the third booking value, and the number of historical sample flights;
the prediction unit is used for predicting the departure booking prediction value of the designated cabin of the target flight based on the first booking value, the second booking value, the third booking value and the number of the historical sample flights and combined with a preset exponential smoothing model if the historical sample flights meet the use requirements and the first booking value is more than or equal to 1;
and the calculating unit is used for calculating the standard deviation of the departure reservation predicted value based on the second reservation value, the third reservation value and the number of the historical sample flights.
7. The system of claim 6, wherein the prediction unit comprises:
a determining module, configured to determine a constant of a preset exponential smoothing model based on the second booking value, the third booking value, and the number of flights of the historical sample;
and the prediction module is used for predicting to obtain the predicted departure booking value of the designated cabin of the target flight according to the constant of the exponential smoothing model, the first booking value and the model coefficient of the exponential smoothing model and by combining the exponential smoothing model.
8. The system according to claim 6, wherein the third determination unit is specifically configured to: determining whether the second booking value, the third booking value and the number of the historical sample flights meet all preset conditions, and if so, determining that the historical sample flights meet the use requirements;
the preset conditions are as follows:
the second booking value and the third booking value are both greater than or equal to 1;
the number of historical sample flights is greater than or equal to 1.
9. An electronic device, comprising: the system comprises a processor and a memory, wherein the processor and the memory are connected through a communication bus; the processor is used for calling and executing the program stored in the memory; the memory is used for storing a program for implementing the flight booking value processing method based on the exponential smoothing model according to any one of claims 1 to 5.
10. A computer-readable storage medium having stored thereon computer-executable instructions for performing the method of any one of claims 1-5 for processing flight booking values based on an exponential smoothing model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011379712.0A CN112308346A (en) | 2020-11-30 | 2020-11-30 | Flight booking value processing method and system based on exponential smoothing model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011379712.0A CN112308346A (en) | 2020-11-30 | 2020-11-30 | Flight booking value processing method and system based on exponential smoothing model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112308346A true CN112308346A (en) | 2021-02-02 |
Family
ID=74487422
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011379712.0A Pending CN112308346A (en) | 2020-11-30 | 2020-11-30 | Flight booking value processing method and system based on exponential smoothing model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112308346A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114298356A (en) * | 2021-12-27 | 2022-04-08 | 中国民航信息网络股份有限公司 | Flight booking number prediction method and device |
| CN114330908A (en) * | 2021-12-31 | 2022-04-12 | 中国民航信息网络股份有限公司 | Seat booking demand prediction method and device and revenue management system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130132128A1 (en) * | 2011-11-17 | 2013-05-23 | Us Airways, Inc. | Overbooking, forecasting and optimization methods and systems |
| CN108846493A (en) * | 2018-06-22 | 2018-11-20 | 山东大学 | A kind of air ticket booking number prediction technique based on improved incremental model |
| CN110377447A (en) * | 2019-07-17 | 2019-10-25 | 腾讯科技（深圳）有限公司 | A kind of abnormal deviation data examination method, device and server |
-
2020
- 2020-11-30 CN CN202011379712.0A patent/CN112308346A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130132128A1 (en) * | 2011-11-17 | 2013-05-23 | Us Airways, Inc. | Overbooking, forecasting and optimization methods and systems |
| CN108846493A (en) * | 2018-06-22 | 2018-11-20 | 山东大学 | A kind of air ticket booking number prediction technique based on improved incremental model |
| CN110377447A (en) * | 2019-07-17 | 2019-10-25 | 腾讯科技（深圳）有限公司 | A kind of abnormal deviation data examination method, device and server |
Non-Patent Citations (1)
| Title |
|---|
| 孙晓东: "邮轮收益管理 需求预测与收益优化", 经济与管理科学, 15 July 2011 (2011-07-15), pages 49 - 57 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114298356A (en) * | 2021-12-27 | 2022-04-08 | 中国民航信息网络股份有限公司 | Flight booking number prediction method and device |
| CN114330908A (en) * | 2021-12-31 | 2022-04-12 | 中国民航信息网络股份有限公司 | Seat booking demand prediction method and device and revenue management system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110400024B (en) | Order prediction method, device, equipment and computer readable storage medium | |
| CN112308346A (en) | Flight booking value processing method and system based on exponential smoothing model | |
| CN112396243B (en) | Processing method and system for flight booking value based on addition model | |
| CN112396244A (en) | Flight booking value processing method and system based on zero booking model | |
| CN110969390A (en) | Method, apparatus, device and medium for partitioning | |
| CN110765596B (en) | Modeling method and device for auditing process simulation model and electronic equipment | |
| CN110766302A (en) | Sampling amount configuration and sampling error estimation method and device and electronic equipment | |
| CN111859172B (en) | Information pushing method, device, electronic equipment and computer readable storage medium | |
| CN112396248A (en) | Method and system for processing flight booking value based on mean value model | |
| CN112418468A (en) | Flight booking value processing method and system based on multiplication model | |
| CN113159453B (en) | Resource data prediction method, device, equipment and storage medium | |
| CN112396245B (en) | Processing method and system for flight booking value based on linear model | |
| CN112308347A (en) | Flight booking value processing method and system based on exponential model | |
| CN112308343A (en) | Flight booking value processing method and system based on full-scale model | |
| CN111047342B (en) | Method and device for determining delivery target, electronic equipment and readable medium | |
| CN113837814B (en) | Method and device for predicting quantity of released resources, readable medium and electronic equipment | |
| CN113312399B (en) | Method and system for processing fare searching | |
| CN110781583B (en) | Audit mode optimization method and device and electronic equipment | |
| CN112416989A (en) | Management method and device of Internet performance broker platform and electronic equipment | |
| CN114298359A (en) | Method and device for predicting number of scheduled flight reservations, storage medium and electronic equipment | |
| CN111612198B (en) | Method and device for predicting success rate of spelling and electronic equipment | |
| JP6307105B2 (en) | Gas consumption forecasting system and forecasting method | |
| CN117670409A (en) | Flight seat reservation curve fitting method and related device | |
| CN111177524A (en) | Method and device for determining value of display information, electronic equipment and readable medium | |
| CN111178929A (en) | Value adjusting method and device for display information, electronic equipment and readable medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |