CN111784047A - Seasonal factor calculation method and related device - Google Patents
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Abstract
The invention discloses a season factor calculating method and a related device, and the inventor researches and discovers that in the prior art, the input data of a revenue management system is the market demand value of an departed flight, the output data is the market demand value of an undiscovered flight, but due to different seasons, the market demand values of the flights have great difference, if the input data of the revenue management system is not processed by season removal factors or the output data of the revenue management system is not processed by backfill season factors, the precision of the output data of the revenue management system is poorer, so the method provided by the application obtains the season factors under natural circumference dimensions according to the market demand values of a plurality of collection points of preset flights through processing such as classification, calculation and the like, and lays a foundation for the revenue management system to calculate the season removal factors of the input data and the backfill season factors of the output data, the method is favorable for improving the prediction precision of the yield management system on the market demand value of the non-departure flight.
Description
Technical Field
The present application relates to computer applications, and more particularly, to a method and related apparatus for calculating seasonal factors.
Background
With the increasing demand of passenger transportation and freight transportation, air transportation is an important means for meeting the demand of transportation.
In the course of Civil Aviation, most airlines use revenue management systems to automatically manage the inventory of non-departing flights. Specifically, the revenue management system automatically manages the inventory of the non-departing flights by using flight planning, inventory, departure and freight rate data based on a prediction and optimization model so as to realize the maximum utilization of the inventory of the non-departing flights and avoid the waste of the transportation capacity of the non-departing flights.
However, in the practical application process, the prediction accuracy of the market demand value of the non-departure flight by the yield management system is found to be poor.
Disclosure of Invention
In order to solve the technical problems, the application provides a seasonal factor calculation method and a related device, so as to achieve the purpose of calculating and obtaining seasonal factors under natural week dimensionality according to market demand values of a plurality of collection points of preset flights, lay a foundation for a revenue management system to calculate seasonal factors for input data removal and seasonal factors for output data backfill, and be beneficial to improving the prediction precision of the revenue management system on the market demand values of flights which are not departed from a port.
In order to achieve the technical purpose, the embodiment of the application provides the following technical scheme:
a method of calculating a seasonal factor, comprising:
acquiring market demand values of a plurality of acquisition points of a preset flight, wherein the plurality of acquisition points comprise a first type of acquisition points and a second type of acquisition points, the first type of acquisition points are preset data acquisition points of flights leaving the airport, the second type of acquisition points are preset data acquisition points of flights not leaving the airport, the preset data acquisition points of flights leaving the airport comprise fixed data acquisition points, floating data acquisition points and final data acquisition points, and the preset data acquisition points of flights not leaving the airport comprise floating data acquisition points and fixed data acquisition points;
classifying the market demand values of a plurality of acquisition points of the preset flight to obtain market demand value data taking natural weeks as cycles;
calculating to obtain a first average value according to the market demand values of all final data acquisition points of the departing flights, wherein the first average value comprises the average value of the market demand values of the final data acquisition points of the departing flights;
calculating to obtain a second average value according to the market demand values of all final data acquisition points of the departed flights in the preset natural week, wherein the second average value comprises the average value of the market demand values of all final data acquisition points of the departed flights in the preset natural week;
and calculating the ratio of the second average value to the first average value to obtain the seasonal factor of the preset nature week of the preset flight.
A system for calculating a seasonal factor, comprising:
the system comprises a data acquisition module, a data acquisition module and a data processing module, wherein the data acquisition module is used for acquiring market demand values of a plurality of acquisition points of a preset flight, the plurality of acquisition points comprise a first type of acquisition points and a second type of acquisition points, the first type of acquisition points are preset data acquisition points of flights which leave, the second type of acquisition points are preset data acquisition points of flights which do not leave, the preset data acquisition points of flights which leave comprise fixed data acquisition points, floating data acquisition points and final data acquisition points, and the preset data acquisition points of flights which do not leave comprise floating data acquisition points and fixed data acquisition points;
the data classification module is used for classifying the market demand values of the collection points of the preset flights to obtain market demand value data taking natural weeks as cycles;
the first calculation module is used for calculating and obtaining a first average value according to the market demand values of all final data acquisition points of the departed flights, wherein the first average value comprises the average value of the market demand values of the final data acquisition points of the departed flights;
the second calculation module is used for calculating and obtaining a second average value according to the market demand values of all final data acquisition points of the departed flights in the preset natural week, wherein the second average value comprises the average value of the market demand values of all final data acquisition points of the departed flights in the preset natural week;
and the third calculating module is used for calculating the ratio of the second average value to the first average value so as to obtain the seasonal factor of the preset natural week of the preset flight.
A system for calculating a seasonal factor, comprising: a memory and a processor;
the memory is used for storing program codes, and the processor is used for calling the program codes and executing the steps of the seasonal factor calculation method.
A storage medium having stored thereon program code which, when executed, implements the steps of the seasonal factor calculation method of any one of the above.
From the above technical solutions, the inventor has found that, in the prior art, input data of a revenue management system is market demand values of flights that have left an airport, output data of the revenue management system is market demand values of flights that have not left an airport, but the market demand values of the flights have great difference in different seasons or different time periods, and if the input data of the revenue management system is not processed by a season removal factor or the output data of the revenue management system is not processed by a backfill season factor, the precision of the output data of the revenue management system (i.e., the market demand values of the flights) is poor, so the method for calculating the season factors provided by the embodiment of the present application obtains the season factors in natural dimensions by classification, calculation and other processing according to the market demand values of a plurality of collection points of preset flights, the method lays a foundation for the revenue management system to calculate the season factor of the input data and the backfill season factor of the output data, and is favorable for improving the prediction precision of the revenue management system for the market demand value of the flights which are not departed.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for calculating a seasonal factor according to an embodiment of the present application;
FIG. 2 is a flow chart illustrating a method for calculating a seasonal factor according to another embodiment of the present application;
FIG. 3 is a flow chart illustrating a method for calculating a seasonal factor according to another embodiment of the present application;
FIG. 4 is a flow chart illustrating a method for calculating a seasonal factor according to yet another embodiment of the present application;
FIG. 5 is a block diagram of a seasonal factor computing system according to an embodiment of the present application;
FIG. 6 is a block diagram of a seasonal factor computing system according to another embodiment of the present application;
fig. 7 is a schematic structural diagram of a seasonal factor calculation system according to another embodiment of the present application.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure 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 disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
The term "include" and variations thereof as used herein are 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 disclosure 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 disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The embodiment of the application provides a method for calculating a seasonal factor, as shown in fig. 1, including:
s101: acquiring market demand values of a plurality of acquisition points of a preset flight, wherein the plurality of acquisition points comprise a first type of acquisition points and a second type of acquisition points, the first type of acquisition points are preset data acquisition points of flights leaving the airport, the second type of acquisition points are preset data acquisition points of flights not leaving the airport, the preset data acquisition points of flights leaving the airport comprise fixed data acquisition points, floating data acquisition points and final data acquisition points, and the preset data acquisition points of flights not leaving the airport comprise floating data acquisition points and fixed data acquisition points;
in step S101, the preset flight refers to a certain flight of a designated airline (or a target airline), and the market demand values of the collection points of the preset flight can be obtained by querying in the flight control system through the identification information of the preset flight, which includes but is not limited to the flight number of the preset flight.
In the embodiment of the present application, "departure" refers to leaving the airport, and "departed flight" refers to a flight that has already left the airport and performed a flight task, and the departure date is the date when the preset flight leaves the airport or the date when the preset flight is expected to leave the airport. Accordingly, "not departing" refers to a flight that has not left the airport, and "not departing flight" refers to a flight that has not left the airport.
The market demand values of the plurality of acquisition points of the preset flight are locally stored, that is, the flight control system comprises the total or incremental flight data and information of the preset flight, and the market demand values of the plurality of acquisition points of the preset flight can be obtained from the flight data and the information.
Step S101 may be set to be executed every predetermined time, that is, every predetermined time, for example, 24 hours, 12 hours, and the like, to acquire the market demand values of the plurality of collection points of the preset flight from the flight control system.
In the embodiment of the present application, the collection points (Data collection points, which may also be referred to as Data collection points) are determined by the number of days from the departure date of the flight, for example, the collection points 7 days from the departure date may be referred to as DCP20, the collection points 24 days from the departure date of the flight may be referred to as DCP11, the collection points 18 days from the departure date of the flight may be referred to as DCP12, and the like.
The first type of acquisition point information and the second type of acquisition point information are divided into departing flights and non-departing flights. Typically, the plurality of acquisition points comprises a plurality of acquisition points of a first type and a plurality of acquisition points of a second type.
The first type of acquisition points comprise at least one fixed data acquisition point of an departed flight and at least one floating data acquisition point of an departed flight;
the second type of acquisition points comprise fixed data acquisition points of at least one non-departing flight and floating data acquisition points of at least one non-departing flight;
the first type of acquisition point information comprises a corresponding relation between a fixed data acquisition point of at least one departing flight and the number of departure days of the flight and a corresponding relation between a floating data acquisition point of at least one departing flight and the number of departure days of the flight;
the second type of collection point information comprises a corresponding relation between a fixed data collection point of at least one non-departing flight and the number of departure days of the flight and a corresponding relation between a floating data collection point of at least one non-departing flight and the number of departure days of the flight.
In this embodiment, the Fixed data collection point (Fixed-DCP) is determined by the number of days the collection point is away from the airport, for example: and if the collection date of the designated flight of the designated airline company is 7 days before the departure date and corresponds to the DCP20, the DCP20 is the fixed data collection point. The Floating-data collection point (Floating-DCP), which may also be referred to as a Floating-data collection point, may have the same or different number of days between the collection date and the departure date than the collection date and the departure date of the fixed-data collection point. For example: the departure date of the designated airline flight is 20 days from the data collection date, wherein the collection date corresponding to the collection point DCP11 is 24 days from the departure days, and the collection date corresponding to the collection point DCP12 is 18 days from the departure days, so that the collection point is a floating data collection point and can be recorded as floatingcp 11.5.
The Final acquisition point (Final-DCP) refers to a data acquisition point of the current day of the departure date of the flight, and the data of the Final acquisition point corresponds to Final flight sales data and predicted data, and comprises the following steps: market demand values and seat reservation values, etc.
The number of fixed data collection points related to the departed flights contained in the first type of collection points is generally multiple, and the fixed data collection points are distributed in a manner that the density is higher when the fixed data collection points are closer to the departure date and the density is lower when the fixed data collection points are farther from the departure date, for example, the number of the fixed data collection points of the departed flights contained in the first type of collection points is 24, 12 of the fixed data collection points are distributed within 14 days from the departure date, and the other 12 fixed data collection points are distributed above 14 days from the departure date.
Similarly, the number of floating data acquisition points of the departed flights contained in the first type of acquisition points is generally multiple and is distributed in the same or similar way as the fixed data acquisition points in the first type of acquisition points.
Accordingly, the number of the fixed data acquisition points and the floating data acquisition points of the non-departure flights included in the second type of acquisition points is generally a plurality, and the distribution mode of the fixed data acquisition points in the second type of acquisition points is the same as or similar to that of the fixed data acquisition points in the first type of acquisition points.
S102: classifying the market demand values of a plurality of acquisition points of the preset flight to obtain market demand value data taking natural weeks as cycles;
s103: calculating to obtain a first average value according to the market demand values of all final data acquisition points of the departing flights, wherein the first average value comprises the average value of the market demand values of the final data acquisition points of the departing flights;
the market demand values of all final data collection points of the departed flights refer to the market demand values collected by all final data collection points of the departed flights of the preset flights in the history, for example, the flight number of the preset flight is CH, the flight with the flight number of CH flies twice in the last month, the two flights are the departed flights of the flight with the flight number of CH, and the market demand value of the departure day of the two flights is the market demand value of the final data collection points of the two departed flights.
S104: calculating to obtain a second average value according to the market demand values of all final data acquisition points of the departed flights in the preset natural week, wherein the second average value comprises the average value of the market demand values of all final data acquisition points of the departed flights in the preset natural week;
the preset natural week is a natural week of a natural month in a year, and may be, for example, the first week or the second week of february or the third week or the fourth week of october, which is not limited in the present application.
S105: and calculating the ratio of the second average value to the first average value to obtain the seasonal factor of the preset nature week of the preset flight.
Assuming that the average value of the market demand values of all the final data acquisition points of all the departing flights of the third week of april of the preset flight is 88.6, and the average value of the market demand values of all the final data acquisition points of all the departing flights of the preset flight is 90, the seasonal factor of the third week of april of the preset flight is 88.6/90-0.984.
In this embodiment, the seasonal factor calculation method obtains seasonal factors under natural week dimensions through classification, calculation and other processing according to market demand values of a plurality of collection points of preset flights, lays a foundation for a revenue management system to calculate seasonal factors for removing seasons of input data and backfilling the seasonal factors of output data, and is beneficial to improving the prediction accuracy of the revenue management system on the market demand values of flights not leaving a port.
It is to be noted 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 disclosure. 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.
On the basis of the above embodiment, in another embodiment of the present application, as shown in fig. 2, the method for calculating the seasonal factor further includes:
s106: calculating the season-removing factor market demand value of the fixed data acquisition point of the departing flight according to the season factor and the market demand value of the fixed data acquisition point of the departing flight;
s107: calculating the season-removing factor market demand value of the floating data acquisition point of the departing flight according to the season factor and the market demand value of the floating data acquisition point of the departing flight;
s108: calculating the backfill seasonal factor market demand value of the fixed data acquisition point of the non-departure flight according to the seasonal factor and the market demand value of the fixed data acquisition point of the non-departure flight;
s109: and calculating the backfill seasonal factor market demand value of the floating data acquisition point of the non-departure flight according to the seasonal factor and the market demand value of the floating data acquisition point of the non-departure flight.
In this embodiment, a method for calculating a season factor for removing market demand values of various collection points of an departed flight according to the season factor and a method for calculating a backfill season factor for market demand values of various collection points of an departed flight are provided.
Specifically, in step S106, the ratio of the market demand value of the fixed collection point of the departing flight to the season factor is used as the season-factor-removed market demand value of the fixed data collection point of the departing flight, and the ratio of the market demand value of the fixed collection point of the departing flight to the season factor corresponding to the fixed collection point of the departing flight is used as the season-factor-removed market demand value of the fixed data collection point of the departing flight, the season factor corresponding to the fixed acquisition point of the departing flight means that the season factor is the same as the natural week of the fixed acquisition point of the departing flight, namely, the natural week of the fixed collection point of the departed flight is assumed to be the third week of april, the seasonal factor corresponding to the fixed acquisition point for the departed flight on the third week of april is the seasonal factor of the third week of april.
Correspondingly, in step S107, the ratio of the market demand value of the floating data collection point of the departing flight to the season factor is used as the season factor-removed market demand value of the floating data collection point of the departing flight, and the ratio of the market demand value of the floating collection point of the departing flight to the season factor corresponding to the floating collection point of the departing flight is used as the season factor-removed market demand value of the floating data collection point of the departing flight.
In step S108, the product of the seasonal factor and the fixed data collection point of the non-departing flight is used as a backfill seasonal factor market demand value of the fixed data collection point of the non-departing flight, and the product of the fixed data collection point of the non-departing flight and the seasonal factor corresponding to the fixed data collection point of the non-departing flight is used as a backfill seasonal factor market demand value of the fixed data collection point of the non-departing flight.
In step S109, the product of the season factor and the floating data collection point of the non-departing flight is used as the backfill season factor market demand value of the floating data collection point of the non-departing flight, and the product of the floating data collection point of the non-departing flight and the season factor corresponding to the floating data collection point of the non-departing flight is used as the backfill season factor market demand value of the floating data collection point of the non-departing flight.
On the basis of the foregoing embodiment, in an embodiment of the present application, as shown in fig. 3, the classifying the market demand values of the collection points of the preset flights to obtain the market demand value data in the cycle of the natural week includes:
s1021: classifying the market demand values of the plurality of collection points of the preset flight according to a natural month to obtain market demand value data taking the natural month as a period;
s1022: and classifying the market demand value data taking the natural month as the period according to the natural week to obtain the market demand value data taking the natural week as the period.
On the basis of the above embodiment, in a further embodiment of the present application, as shown in fig. 4, the obtaining the market demand values of the plurality of collection points of the preset flight includes:
s1011: obtaining market demand values of a plurality of acquisition points of the preset flight in a preset time period;
the preset time period includes a time from three years before the system date to within one year after the system date.
The names of messages or information exchanged between devices in the embodiments disclosed herein 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 disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
Computer program code for carrying out operations for the present disclosure may be written in any combination of 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).
The following describes a system for calculating a seasonal factor provided by an embodiment of the present application, and the following system for calculating a seasonal factor may be referred to in correspondence with the above method for calculating a seasonal factor.
Accordingly, an embodiment of the present application provides a seasonal factor calculation system, as shown in fig. 5, including:
the data acquisition module 10 is configured to acquire market demand values of a plurality of acquisition points of a preset flight, where the plurality of acquisition points include a first type of acquisition point and a second type of acquisition point, the first type of acquisition point is a preset data acquisition point of an departed flight, the second type of acquisition point is a preset data acquisition point of an departed flight, the preset data acquisition point of the departed flight includes a fixed data acquisition point, a floating data acquisition point and a final data acquisition point, and the preset data acquisition point of the departed flight includes a floating data acquisition point and a fixed data acquisition point;
the data classification module 20 is configured to classify the market demand values of the plurality of collection points of the preset flight to obtain market demand value data with a natural week as a period;
the first calculating module 30 is configured to calculate and obtain a first average value according to the market demand values of all final data acquisition points of the departing flights, where the first average value includes an average value of the market demand values of the final data acquisition points of the departing flights;
the second calculating module 40 is configured to calculate and obtain a second average value according to the market demand values of all final data acquisition points of an outbound flight in the preset natural week, where the second average value includes an average value of the market demand values of all final data acquisition points of an outbound flight in the preset natural week;
and a third calculating module 50, configured to calculate a ratio of the second average value to the first average value to obtain a seasonal factor of a preset nature week of the preset flight.
The modules described in the embodiments of the present disclosure may be implemented by software or hardware. The name of the module does not constitute a limitation to the module itself in some cases, for example, the data classifying module 20 may be further described as "a module for classifying market demand values of a plurality of collection points of the preset flight to obtain market demand value data in a cycle of a natural week".
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.
Optionally, as shown in fig. 6, the system for calculating the seasonal factor further includes:
the first application module 60 is configured to calculate a season factor removing market demand value of the fixed data collection point of the departing flight according to the season factor and the market demand value of the fixed data collection point of the departing flight;
a second application module 70, configured to calculate a season-removed factor market demand value of the floating data collection point of the departing flight according to the season factor and the market demand value of the floating data collection point of the departing flight;
a third application module 80, configured to calculate a backfill seasonal factor market demand value of the fixed data collection point of the non-departing flight according to the seasonal factor and the market demand value of the fixed data collection point of the non-departing flight;
and a fourth application module 90, configured to calculate a backfill seasonal factor market demand value of the floating data collection point of the non-departing flight according to the seasonal factor and the market demand value of the floating data collection point of the non-departing flight.
Optionally, the first application module is specifically configured to use a ratio of the market demand value of the fixed data collection point of the departing flight to the seasonal factor as a season factor going market demand value of the fixed data collection point of the departing flight;
the second application module is specifically configured to use a ratio of the market demand value of the floating data collection point of the departing flight to the seasonal factor as a season factor going market demand value of the floating data collection point of the departing flight;
the third application module is specifically configured to take a product of the seasonal factor and the fixed data collection point of the non-departing flight as a backfill seasonal factor market demand value of the fixed data collection point of the non-departing flight;
the fourth application module is specifically configured to take a product of the seasonal factor and the floating data collection point of the non-departing flight as a backfill seasonal factor market demand value of the floating data collection point of the non-departing flight.
Optionally, the data classifying module includes:
the first classification unit is used for classifying the market demand values of the collection points of the preset flights according to natural months to obtain market demand value data taking the natural months as a period;
and the second classification unit is used for classifying the market demand value data taking the natural month as the period according to the natural week so as to obtain the market demand value data taking the natural week as the period.
Optionally, the data obtaining module is specifically configured to obtain market demand values of a plurality of collection points of the preset flight within a preset time period;
the preset time period includes a time from three years before the system date to within one year after the system date.
Accordingly, the embodiment of the present application further provides a seasonal factor computing system, as shown in fig. 7, including a memory 100 and a processor 200;
the memory 100 is configured to store program code, and the processor 200 is configured to call the program code, and the program code is configured to:
acquiring market demand values of a plurality of acquisition points of a preset flight, wherein the plurality of acquisition points comprise a first type of acquisition points and a second type of acquisition points, the first type of acquisition points are preset data acquisition points of flights leaving the airport, the second type of acquisition points are preset data acquisition points of flights not leaving the airport, the preset data acquisition points of flights leaving the airport comprise fixed data acquisition points, floating data acquisition points and final data acquisition points, and the preset data acquisition points of flights not leaving the airport comprise floating data acquisition points and fixed data acquisition points;
classifying the market demand values of a plurality of acquisition points of the preset flight to obtain market demand value data taking natural weeks as cycles;
calculating to obtain a first average value according to the market demand values of all final data acquisition points of the departing flights, wherein the first average value comprises the average value of the market demand values of the final data acquisition points of the departing flights;
calculating to obtain a second average value according to the market demand values of all final data acquisition points of the departed flights in the preset natural week, wherein the second average value comprises the average value of the market demand values of all final data acquisition points of the departed flights in the preset natural week;
and calculating the ratio of the second average value to the first average value to obtain the seasonal factor of the preset nature week of the preset flight.
The refinement function and the extension function of the program code may be as described above.
Accordingly, the present application further provides a storage medium, where the storage medium stores program code, and the program code implements the steps of the method for calculating a seasonal factor according to any one of the above embodiments when executed.
In the context of this disclosure, a storage 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 storage medium may be a machine-readable signal medium or a machine-readable storage medium. A storage 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 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 storage media described above in this disclosure can be computer readable signal media or computer readable storage media or any combination of the two. A 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 storage media 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 disclosure, a 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 contrast, in the present disclosure, a storage medium may include a propagated data signal with computer-readable program code embodied therein, either 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 storage 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 storage medium may be included in the electronic device; or may exist separately without being assembled into the electronic device.
Example 1 provides a method of calculating a seasonal factor, according to one or more embodiments disclosed herein, including:
acquiring market demand values of a plurality of acquisition points of a preset flight, wherein the plurality of acquisition points comprise a first type of acquisition points and a second type of acquisition points, the first type of acquisition points are preset data acquisition points of flights leaving the airport, the second type of acquisition points are preset data acquisition points of flights not leaving the airport, the preset data acquisition points of flights leaving the airport comprise fixed data acquisition points, floating data acquisition points and final data acquisition points, and the preset data acquisition points of flights not leaving the airport comprise floating data acquisition points and fixed data acquisition points;
classifying the market demand values of a plurality of acquisition points of the preset flight to obtain market demand value data taking natural weeks as cycles;
calculating to obtain a first average value according to the market demand values of all final data acquisition points of the departing flights, wherein the first average value comprises the average value of the market demand values of the final data acquisition points of the departing flights;
calculating to obtain a second average value according to the market demand values of all final data acquisition points of the departed flights in the preset natural week, wherein the second average value comprises the average value of the market demand values of all final data acquisition points of the departed flights in the preset natural week;
and calculating the ratio of the second average value to the first average value to obtain the seasonal factor of the preset nature week of the preset flight.
In one or more embodiments according to the present disclosure, example 2 provides the method of calculating a seasonal factor of example 1, further including:
calculating the season-removing factor market demand value of the fixed data acquisition point of the departing flight according to the season factor and the market demand value of the fixed data acquisition point of the departing flight;
calculating the season-removing factor market demand value of the floating data acquisition point of the departing flight according to the season factor and the market demand value of the floating data acquisition point of the departing flight;
calculating the backfill seasonal factor market demand value of the fixed data acquisition point of the non-departure flight according to the seasonal factor and the market demand value of the fixed data acquisition point of the non-departure flight;
and calculating the backfill seasonal factor market demand value of the floating data acquisition point of the non-departure flight according to the seasonal factor and the market demand value of the floating data acquisition point of the non-departure flight.
In accordance with one or more embodiments disclosed herein, example 3 provides the method of calculating a seasonal factor of example 1, the calculating a market demand value for the seasonal factor for the fixed data acquisition site of the departed flight from the seasonal factor and the market demand value for the fixed data acquisition site of the departed flight comprising:
taking the ratio of the market demand value of the fixed data acquisition point of the departing flight to the seasonal factor as the season factor-going market demand value of the fixed data acquisition point of the departing flight;
the calculating the season factor going market demand value of the floating data collection point of the departing flight according to the season factor and the market demand value of the floating data collection point of the departing flight comprises:
taking the ratio of the market demand value of the floating data acquisition point of the departing flight to the seasonal factor as the season factor-going market demand value of the floating data acquisition point of the departing flight;
the calculating the backfill seasonal factor market demand value of the fixed data acquisition point of the non-departing flight according to the seasonal factor and the market demand value of the fixed data acquisition point of the non-departing flight comprises:
taking the product of the seasonal factor and the fixed data acquisition point of the non-departure flight as a backfill seasonal factor market demand value of the fixed data acquisition point of the non-departure flight;
the calculating the backfill seasonal factor market demand value of the floating data acquisition point of the non-departing flight according to the seasonal factor and the market demand value of the floating data acquisition point of the non-departing flight comprises:
and taking the product of the seasonal factor and the floating data acquisition point of the non-departure flight as the backfill seasonal factor market demand value of the floating data acquisition point of the non-departure flight.
In accordance with one or more embodiments disclosed herein, example 4 provides the seasonal factor calculation method of example 1, wherein the classifying market demand values of a plurality of collection points of the preset flights to obtain market demand value data in a cycle of natural weeks includes:
classifying the market demand values of the plurality of collection points of the preset flight according to a natural month to obtain market demand value data taking the natural month as a period;
and classifying the market demand value data taking the natural month as the period according to the natural week to obtain the market demand value data taking the natural week as the period.
In accordance with one or more embodiments disclosed herein, example 5 provides the seasonal factor calculation method of example 1, the obtaining market demand values for a plurality of collection points for a preset flight includes:
obtaining market demand values of a plurality of acquisition points of the preset flight in a preset time period;
the preset time period includes a time from three years before the system date to within one year after the system date.
Example 6 provides, in accordance with one or more embodiments disclosed herein, a computing system of seasonal factors, comprising:
the system comprises a data acquisition module, a data acquisition module and a data processing module, wherein the data acquisition module is used for acquiring market demand values of a plurality of acquisition points of a preset flight, the plurality of acquisition points comprise a first type of acquisition points and a second type of acquisition points, the first type of acquisition points are preset data acquisition points of flights which leave, the second type of acquisition points are preset data acquisition points of flights which do not leave, the preset data acquisition points of flights which leave comprise fixed data acquisition points, floating data acquisition points and final data acquisition points, and the preset data acquisition points of flights which do not leave comprise floating data acquisition points and fixed data acquisition points;
the data classification module is used for classifying the market demand values of the collection points of the preset flights to obtain market demand value data taking natural weeks as cycles;
the first calculation module is used for calculating and obtaining a first average value according to the market demand values of all final data acquisition points of the departed flights, wherein the first average value comprises the average value of the market demand values of the final data acquisition points of the departed flights;
the second calculation module is used for calculating and obtaining a second average value according to the market demand values of all final data acquisition points of the departed flights in the preset natural week, wherein the second average value comprises the average value of the market demand values of all final data acquisition points of the departed flights in the preset natural week;
and the third calculating module is used for calculating the ratio of the second average value to the first average value so as to obtain the seasonal factor of the preset natural week of the preset flight.
Example 7 provides the seasonal factor computing system of example 1, in accordance with one or more embodiments disclosed herein, further comprising:
the first application module is used for calculating the season-going factor market demand value of the fixed data acquisition point of the departing flight according to the season factor and the market demand value of the fixed data acquisition point of the departing flight;
the second application module is used for calculating the season-going factor market demand value of the floating data acquisition point of the departing flight according to the season factor and the market demand value of the floating data acquisition point of the departing flight;
the third application module is used for calculating the backfill seasonal factor market demand value of the fixed data acquisition point of the non-departing flight according to the seasonal factor and the market demand value of the fixed data acquisition point of the non-departing flight;
and the fourth application module is used for calculating the backfill season factor market demand value of the floating data acquisition point of the non-departing flight according to the season factor and the market demand value of the floating data acquisition point of the non-departing flight.
In accordance with one or more embodiments disclosed herein, example 8 provides the computing system of seasonal factors of example 1, the first application module being further configured to determine a ratio of the market demand value of the fixed data collection site for the departed flight to the seasonal factor as a go-to-seasonal factor market demand value of the fixed data collection site for the departed flight;
the second application module is specifically configured to use a ratio of the market demand value of the floating data collection point of the departing flight to the seasonal factor as a season factor going market demand value of the floating data collection point of the departing flight;
the third application module is specifically configured to take a product of the seasonal factor and the fixed data collection point of the non-departing flight as a backfill seasonal factor market demand value of the fixed data collection point of the non-departing flight;
the fourth application module is specifically configured to take a product of the seasonal factor and the floating data collection point of the non-departing flight as a backfill seasonal factor market demand value of the floating data collection point of the non-departing flight.
Example 9 provides the seasonal factor computing system of example 1, in accordance with one or more embodiments disclosed herein, the data classification module comprising:
the first classification unit is used for classifying the market demand values of the collection points of the preset flights according to natural months to obtain market demand value data taking the natural months as a period;
and the second classification unit is used for classifying the market demand value data taking the natural month as the period according to the natural week so as to obtain the market demand value data taking the natural week as the period.
In accordance with one or more embodiments disclosed herein, example 10 provides the seasonal factor computing system of example 1, the data acquisition module is specifically configured to acquire market demand values for a plurality of acquisition points for the preset flight over a preset time period;
the preset time period includes a time from three years before the system date to within one year after the system date.
In accordance with one or more embodiments disclosed herein, example 11 provides a seasonal factor computing system, comprising: a memory and a processor;
the memory is used for storing program codes, and the processor is used for calling the program codes, and the program codes are used for executing the steps of the seasonal factor calculation method of any one of the above embodiments.
Example 11 provides, in accordance with one or more embodiments disclosed herein, a storage medium having program code stored thereon, the program code, when executed, implementing the steps of the method for calculating a seasonal factor as described in any one of the above embodiments.
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 disclosure. 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 foregoing description is only exemplary of the preferred embodiments of the disclosure 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 embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (12)
1. A method for calculating a seasonal factor, comprising:
acquiring market demand values of a plurality of acquisition points of a preset flight, wherein the plurality of acquisition points comprise a first type of acquisition points and a second type of acquisition points, the first type of acquisition points are preset data acquisition points of flights leaving the airport, the second type of acquisition points are preset data acquisition points of flights not leaving the airport, the preset data acquisition points of flights leaving the airport comprise fixed data acquisition points, floating data acquisition points and final data acquisition points, and the preset data acquisition points of flights not leaving the airport comprise floating data acquisition points and fixed data acquisition points;
classifying the market demand values of a plurality of acquisition points of the preset flight to obtain market demand value data taking natural weeks as cycles;
calculating to obtain a first average value according to the market demand values of all final data acquisition points of the departing flights, wherein the first average value comprises the average value of the market demand values of the final data acquisition points of the departing flights;
calculating to obtain a second average value according to the market demand values of all final data acquisition points of the departed flights in the preset natural week, wherein the second average value comprises the average value of the market demand values of all final data acquisition points of the departed flights in the preset natural week;
and calculating the ratio of the second average value to the first average value to obtain the seasonal factor of the preset nature week of the preset flight.
2. The method of claim 1, further comprising:
calculating the season-removing factor market demand value of the fixed data acquisition point of the departing flight according to the season factor and the market demand value of the fixed data acquisition point of the departing flight;
calculating the season-removing factor market demand value of the floating data acquisition point of the departing flight according to the season factor and the market demand value of the floating data acquisition point of the departing flight;
calculating the backfill seasonal factor market demand value of the fixed data acquisition point of the non-departure flight according to the seasonal factor and the market demand value of the fixed data acquisition point of the non-departure flight;
and calculating the backfill seasonal factor market demand value of the floating data acquisition point of the non-departure flight according to the seasonal factor and the market demand value of the floating data acquisition point of the non-departure flight.
3. The method of claim 2, wherein calculating the season factor-to-season factor market demand value for the fixed data acquisition site for the departed flight based on the season factor and the market demand value for the fixed data acquisition site for the departed flight comprises:
taking the ratio of the market demand value of the fixed data acquisition point of the departing flight to the seasonal factor as the season factor-going market demand value of the fixed data acquisition point of the departing flight;
the calculating the season factor going market demand value of the floating data collection point of the departing flight according to the season factor and the market demand value of the floating data collection point of the departing flight comprises:
taking the ratio of the market demand value of the floating data acquisition point of the departing flight to the seasonal factor as the season factor-going market demand value of the floating data acquisition point of the departing flight;
the calculating the backfill seasonal factor market demand value of the fixed data acquisition point of the non-departing flight according to the seasonal factor and the market demand value of the fixed data acquisition point of the non-departing flight comprises:
taking the product of the seasonal factor and the fixed data acquisition point of the non-departure flight as a backfill seasonal factor market demand value of the fixed data acquisition point of the non-departure flight;
the calculating the backfill seasonal factor market demand value of the floating data acquisition point of the non-departing flight according to the seasonal factor and the market demand value of the floating data acquisition point of the non-departing flight comprises:
and taking the product of the seasonal factor and the floating data acquisition point of the non-departure flight as the backfill seasonal factor market demand value of the floating data acquisition point of the non-departure flight.
4. The method of claim 1, wherein categorizing the market demand values for the plurality of collection points for the preset flights to obtain market demand value data in natural weeks comprises:
classifying the market demand values of the plurality of collection points of the preset flight according to a natural month to obtain market demand value data taking the natural month as a period;
and classifying the market demand value data taking the natural month as the period according to the natural week to obtain the market demand value data taking the natural week as the period.
5. The method of claim 1, wherein obtaining market demand values for a plurality of collection points for a preset flight comprises:
obtaining market demand values of a plurality of acquisition points of the preset flight in a preset time period;
the preset time period includes a time from three years before the system date to within one year after the system date.
6. A system for calculating a seasonal factor, comprising:
the system comprises a data acquisition module, a data acquisition module and a data processing module, wherein the data acquisition module is used for acquiring market demand values of a plurality of acquisition points of a preset flight, the plurality of acquisition points comprise a first type of acquisition points and a second type of acquisition points, the first type of acquisition points are preset data acquisition points of flights which leave, the second type of acquisition points are preset data acquisition points of flights which do not leave, the preset data acquisition points of flights which leave comprise fixed data acquisition points, floating data acquisition points and final data acquisition points, and the preset data acquisition points of flights which do not leave comprise floating data acquisition points and fixed data acquisition points;
the data classification module is used for classifying the market demand values of the collection points of the preset flights to obtain market demand value data taking natural weeks as cycles;
the first calculation module is used for calculating and obtaining a first average value according to the market demand values of all final data acquisition points of the departed flights, wherein the first average value comprises the average value of the market demand values of the final data acquisition points of the departed flights;
the second calculation module is used for calculating and obtaining a second average value according to the market demand values of all final data acquisition points of the departed flights in the preset natural week, wherein the second average value comprises the average value of the market demand values of all final data acquisition points of the departed flights in the preset natural week;
and the third calculating module is used for calculating the ratio of the second average value to the first average value so as to obtain the seasonal factor of the preset natural week of the preset flight.
7. The system of claim 6, further comprising:
the first application module is used for calculating the season-going factor market demand value of the fixed data acquisition point of the departing flight according to the season factor and the market demand value of the fixed data acquisition point of the departing flight;
the second application module is used for calculating the season-going factor market demand value of the floating data acquisition point of the departing flight according to the season factor and the market demand value of the floating data acquisition point of the departing flight;
the third application module is used for calculating the backfill seasonal factor market demand value of the fixed data acquisition point of the non-departing flight according to the seasonal factor and the market demand value of the fixed data acquisition point of the non-departing flight;
and the fourth application module is used for calculating the backfill season factor market demand value of the floating data acquisition point of the non-departing flight according to the season factor and the market demand value of the floating data acquisition point of the non-departing flight.
8. The system of claim 7, wherein the first application module is specifically configured to use a ratio of the market demand value of the fixed data collection point of the departed flight to the seasonal factor as the de-seasonal factor market demand value of the fixed data collection point of the departed flight;
the second application module is specifically configured to use a ratio of the market demand value of the floating data collection point of the departing flight to the seasonal factor as a season factor going market demand value of the floating data collection point of the departing flight;
the third application module is specifically configured to take a product of the seasonal factor and the fixed data collection point of the non-departing flight as a backfill seasonal factor market demand value of the fixed data collection point of the non-departing flight;
the fourth application module is specifically configured to take a product of the seasonal factor and the floating data collection point of the non-departing flight as a backfill seasonal factor market demand value of the floating data collection point of the non-departing flight.
9. The system of claim 6, wherein the data classification module comprises:
the first classification unit is used for classifying the market demand values of the collection points of the preset flights according to natural months to obtain market demand value data taking the natural months as a period;
and the second classification unit is used for classifying the market demand value data taking the natural month as the period according to the natural week so as to obtain the market demand value data taking the natural week as the period.
10. The system of claim 6, wherein the data acquisition module is specifically configured to acquire market demand values of the preset flights at a plurality of acquisition points within a preset time period;
the preset time period includes a time from three years before the system date to within one year after the system date.
11. A system for calculating a seasonal factor, comprising: a memory and a processor;
the memory is used for storing program codes, and the processor is used for calling the program codes and executing the steps of the seasonal factor calculation method according to any one of claims 1 to 5.
12. A storage medium characterized in that the storage medium has stored thereon a program code which, when executed, realizes each step of the seasonal factor calculation method according to any one of claims 1 to 5.
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