CN111126745A - New-route-opening income prediction method and system - Google Patents
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Abstract
The invention relates to the technical field of civil aviation transportation informatization systems, in particular to a method and a system for predicting the income of a new air route. The method comprises the following steps: s1, acquiring route data of a new route; s2, acquiring historical data of the existing flight of the newly opened flight line; s3, calculating the historical total income of the flight class; s4, predicting the total income; s5, acquiring the total seat number of the quasi-flight class; and S6, calculating a predicted value of the new air route income. According to the method, the total income of left and right flights in the new airline is predicted through the flight data of the new airline, the income of a single seat is calculated through the total income, and then the income of the single seat is multiplied by the number of seats of the single class of the planned flights in the airline, so that the income prediction value of the new airline can be obtained. The method and the device can obtain quantitative income prediction based on flight big data, can accurately predict the income of the new air route, and provide effective support for strategies of the new air route of an airline company or an airport.
Description
Technical Field
The invention relates to the technical field of civil aviation transportation informatization systems, in particular to a method and a system for predicting the income of a new air route.
Background
Airlines are often faced with the decision whether or not an airline should be newly launched. However, because of the new flight path, generally less support data is available, so that such decision is usually made through qualitative analysis, the accuracy is not high, and the profitability obtained by prediction is not large.
Disclosure of Invention
The invention provides a method for predicting the income of a new air traffic route, which solves the technical problem that the income of the new air traffic route cannot be scientifically predicted in the prior art.
In a first aspect, the present invention provides a method for predicting revenue of a new route, the method comprising the steps of:
s1, acquiring air route data of a new air route, wherein the air route data comprises air route types, single-class seat numbers and air points included in the air route;
s2, obtaining the historical data of the routes of the flying flights, which are the same as or similar to the new routes, from a database of the flying flights, wherein the historical data of the routes comprises the income of each shift of the flying flights which take off;
s3, grouping the historical data of the flight routes according to specific time units, and calculating the historical total income of the flight route in each specific time unit; the specific time units include years, quarters, and months;
s4, training a total income prediction model according to the historical total income of each specific time unit, and predicting the total income of the next specific time unit; the starting point of the next specific time unit is the first takeoff day of the scheduled flight in the new airline;
s5, acquiring the total seat number of the flight to be flown in the next specific time unit; the flight planning class comprises a first flight planning class and a second flight planning class, the first flight planning class comprises flight planning classes in a new airline, and the second flight planning class comprises flight planning classes which are the same as or similar to the new airline;
s6, calculating the income of a single seat according to the total income and the total seat number; and the product of the income of the single seat and the number of seats in the single class of the first planned flight is the predicted new air route income.
Further, the types of the routes comprise a straight flight route, a through-parking route and a throwing flight route; the straight flight route comprises a waypoint as a starting point and an end point; the navigation points of the stopping route and the throwing route are a starting point, a stopping point and an end point.
Further, the airline history data includes first airline history data and second airline history data; the first airline history data comprises airline data for the same on-flight as the new airline; the second airline history data comprises airline data for an on-flight that approximates the new airline;
the second flight plan comprises a first sub flight plan or a second sub flight plan; the first sub-flight plan comprises the same flight plan as the new airline; the second sub-flight plan comprises a flight plan that approximates the new airline.
Further, the step S2 further includes the following steps:
s21, comparing the new flight path with an on-flight path database, and judging whether on-flight path data identical to the new flight path exist or not;
s22, if the same in-flight data as the new air route exists, directly acquiring the in-flight data as historical data of the air route;
and if the on-flight data which is the same as the new flight path does not exist, acquiring the on-flight data which is similar to the new flight path and is used as historical data of the flight path.
Further, if the new flight path is a direct flight path, the step S22 further includes the following steps:
s2211, determining approximate waypoints of a starting point and an end point in the new flight path respectively;
s2212, sequencing the approximate waypoints of the starting point in sequence from high to low according to the approximate values, and sequencing the approximate waypoints of the terminal point in sequence from high to low according to the approximate values;
s2213, combining approximate waypoints of the starting point with the highest approximate value and the ordered approximate waypoints of the end point in sequence to form an approximate navigation section;
s2214, after each group of approximate flight sections, comparing the approximate flight sections with the existing in-flight data base, judging whether the approximate flight sections have in-flight data, and if the approximate flight sections have in-flight data, reading the in-flight data; and averaging the flight distance data in all the approximate flight sections to obtain the second flight path historical data.
Further, if the new route is a flyaway route or a stop route, the step S22 further includes the following steps:
s2221, respectively determining an approximate waypoint of a starting point, an approximate waypoint of a stopping point and an approximate waypoint of a terminal point;
s2222, sequencing the approximate waypoints of the starting point in sequence from high to low according to the approximate values, sequencing the approximate waypoints of the stopping point in sequence from high to low according to the approximate values, and sequencing the approximate waypoints of the terminal point in sequence from high to low according to the approximate values;
s2223, starting from the approximate waypoint of the starting point with the highest approximate value, sequentially combining the approximate waypoints with the ordered stopping points to form a first approximate waypoint section; combining the approximate waypoints of the starting point with the highest approximate value and the ordered approximate waypoints of the end point into a second approximate waypoint section in sequence; combining the approximate waypoints of the stop points with the highest approximate values with the approximate waypoints of the ordered terminal points in sequence to form a third approximate waypoint;
s2224, after each first approximate flight segment is combined, comparing the first approximate flight segment with an existing flight class database, judging whether flight class data exist in the first approximate flight segment, if the flight class data exist in the first approximate flight segment, reading the flight class data, and after the combination is finished, averaging all flight class data in the first approximate flight segment to obtain first sub-flight line historical data of the first approximate flight segment;
after the combination of the first approximate flight sections is stopped, combining the second approximate flight sections according to the same method to find second sub-flight path historical data of the second approximate flight sections; after the combination of the second approximate flight sections is stopped, combining third approximate flight sections according to the same method, and finding third sub-flight path historical data of the third approximate flight sections;
s2225, adding the historical data of the first sub-airline, the historical data of the second sub-airline and the historical data of the third sub-airline to obtain the historical data of the second airline.
In a second aspect, the present invention provides a new airline revenue prediction system, the system comprising:
the first receiving module is used for acquiring the air route data of a new air route, wherein the air route data comprises an air route type, a single-class seat number and an air point included in the air route;
a first calculation module, configured to obtain, from a database of in-flight flights, route history data of in-flight flights that are the same as or similar to the new route, where the route history data includes a benefit of each shift of the in-flight flights that have taken off;
the second calculation module is used for grouping the historical flight route data according to specific time units and calculating the historical total income of the flight class in each specific time unit; the specific time units include years, quarters, and months;
the third calculation module is used for training a total income prediction model according to the historical total income of each specific time unit and predicting the total income of the next specific time unit; the starting point of the next specific time unit is the first takeoff day of the scheduled flight in the new airline;
the second receiving module is used for acquiring the total seat number of the scheduled flight in the next specific time unit; the flight planning class comprises a first flight planning class and a second flight planning class, the first flight planning class comprises flight planning classes in a new airline, and the second flight planning class comprises flight planning classes which are the same as or similar to the new airline;
the fourth calculation module is used for calculating the income of a single seat according to the total income and the total seat number; and the product of the income of the single seat and the number of seats in the single class of the first planned flight is the predicted new air route income.
Further, the first calculation module includes:
the comparison unit is used for comparing the new flight path with an on-flight path database and judging whether on-flight path data same as the new flight path exist or not;
the calculation unit is used for directly acquiring the in-flight data as historical data of the air route if the in-flight data identical to the air route of the new air route exists; and if the on-flight data which is the same as the new flight path does not exist, acquiring the on-flight data which is similar to the new flight path and is used as historical data of the flight path.
Further, the calculation unit includes:
the first calculating subunit is used for respectively determining approximate waypoints of a starting point and an end point in the new flight path;
the first comparison subunit is used for sequencing the approximate waypoints of the starting point in sequence according to the approximate values from high to low and sequencing the approximate waypoints of the terminal point in sequence according to the approximate values from high to low;
the second calculation subunit is used for sequentially combining the approximate waypoints of the starting point with the highest approximate value and the approximate waypoints of the ordered end point into an approximate flight segment;
the second comparison subunit is used for comparing the approximate flight segment with the existing in-flight class database after each approximate flight segment is combined, judging whether the approximate flight segment has in-flight class data or not, and reading the in-flight class data if the approximate flight segment has in-flight class data; and averaging the flight distance data in all the approximate flight sections to obtain the second flight path historical data.
Further, the calculation unit further includes:
the third calculation subunit is used for respectively determining an approximate waypoint of the starting point, an approximate waypoint of the stopping point and an approximate waypoint of the end point;
the third comparison subunit is used for sequencing the approximate waypoints of the starting point in sequence according to the approximate values from high to low, sequencing the approximate waypoints passing through the stopping point in sequence according to the approximate values from high to low, and sequencing the approximate waypoints of the terminal point in sequence according to the approximate values from high to low;
the fourth calculating subunit is used for sequentially combining the approximate waypoints of the starting point with the highest approximate value and the ordered approximate waypoints of the stopping points into a first approximate waypoint; combining the approximate waypoints of the starting point with the highest approximate value and the ordered approximate waypoints of the end point into a second approximate waypoint section in sequence; combining the approximate waypoints of the stop points with the highest approximate values with the approximate waypoints of the ordered terminal points in sequence to form a third approximate waypoint;
the fourth comparison subunit is used for comparing the first approximate flight segment with the existing flight class database after each first approximate flight segment is combined, judging whether the first approximate flight segment has flight class data or not, reading the flight class data if the first approximate flight segment has the flight class data, and averaging all the flight class data in the first approximate flight segment after the combination is finished to obtain the first sub-flight line historical data of the first approximate flight segment; after the combination of the first approximate flight sections is stopped, combining the second approximate flight sections according to the same method to find second sub-flight path historical data of the second approximate flight sections; after the combination of the second approximate flight sections is stopped, combining third approximate flight sections according to the same method, and finding third sub-flight path historical data of the third approximate flight sections;
and the fifth calculation subunit is used for adding the first sub-airline historical data, the second sub-airline historical data and the third sub-airline historical data to obtain second airline historical data.
The invention has the following beneficial effects:
the method comprises the steps of forecasting the total income of flights in a new air route through the in-flight data of the new air route, calculating the income of a single seat through the total income, and multiplying the income of the single seat by the number of seats of a single class of the planned flight in the air route to obtain the income forecasting value of the new air route. The method and the device can obtain quantitative income prediction based on flight big data, can accurately predict the income of the new air route, and provide effective support for strategies of the new air route of an airline company or an airport.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for predicting revenue of a new route according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating the specific steps of obtaining flight route history data of an in-flight route that is the same as or similar to the new flight route from an in-flight database according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating specific steps of obtaining historical data of a second airline when the new airline is a direct flight airline according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart illustrating specific steps of acquiring historical data of a second airline when the new airline is a parked airline or a flying airline in the embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a revenue forecasting system for a new route according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a revenue forecasting system for a new route according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a terminal supporting the present invention in the embodiment of the present invention.
Detailed Description
Exemplary embodiments will be described in detail herein. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
Example 1
As shown in fig. 1, the method for predicting the revenue of a new route according to the embodiment includes the following steps:
s1, acquiring air route data of a new air route, wherein the air route data comprises air route types, single-class seat numbers and air points included in the air route; the types of the air routes comprise a direct flight air route, a through parking air route and a throwing flight air route; the straight flight route comprises a waypoint as a starting point and an end point; the navigation points of the stopping route and the throwing route are a starting point, a stopping point and an end point.
S2, obtaining the historical data of the routes of the flying flights, which are the same as or similar to the new routes, from a database of the flying flights, wherein the historical data of the routes comprises the income of each shift of the flying flights which take off; the airline historical data comprises first airline historical data and second airline historical data; the first airline history data comprises airline data for the same on-flight as the new airline; the second airline history data comprises airline data for an on-flight that approximates the new airline;
s3, grouping the historical data of the flight routes according to specific time units, and calculating the historical total income of the flight route in each specific time unit; the specific time units include years, quarters, and months;
s4, training a total income prediction model according to the historical total income of each specific time unit, and predicting the total income of the next specific time unit; the starting point of the next specific time unit is the first takeoff day of the scheduled flight in the new airline; the total revenue prediction model may be a time series model;
s5, acquiring the total seat number of the flight to be flown in the next specific time unit; the flight planning class comprises a first flight planning class and a second flight planning class, the first flight planning class comprises flight planning classes in a new airline, and the second flight planning class comprises flight planning classes which are the same as or similar to the new airline; the second flight plan comprises a first sub flight plan or a second sub flight plan; the first sub-flight plan comprises the same flight plan as the new airline; the second sub-flight plan comprises a flight plan that approximates the new airline.
S6, calculating the income of a single seat according to the total income and the total seat number; and the product of the income of the single seat and the number of seats in the single class of the first planned flight is the predicted new air route income.
As shown in fig. 2, the step S2 further includes the following steps:
s21, comparing the new flight path with an on-flight path database, and judging whether on-flight path data identical to the new flight path exist or not;
s22, if the same in-flight data as the new air route exists, directly acquiring the in-flight data as historical data of the air route;
and if the on-flight data which is the same as the new flight path does not exist, acquiring the on-flight data which is similar to the new flight path and is used as historical data of the flight path.
As shown in fig. 3, if the new flight path is a direct flight path, the step S22 further includes the following steps:
s2211, determining approximate waypoints of a starting point and an end point in the new flight path respectively;
determining an approximate waypoint according to factors such as distance factor/type/grade/throughput/subsidy policy of an airport and the like; for example, when the distance factor is considered, the approximate waypoint using the waypoint within the preset distance around the starting point as the center may be found, or it is determined that there is no second waypoint in the city corresponding to the starting point, for example, a shang sun airport, and if there is a second airport in the city corresponding to the waypoint as the approximate waypoint. Factors such as the type/grade/throughput/subsidy policy of the airport can be considered, and the approximate value of the approximate waypoint can be calculated through weighted summation; or, firstly, the starting point is taken as the circle center, a preset distance value is taken as the radius to draw a circle, all waypoints except the starting point in the circle are calculated by setting the weight value through factors such as the type/grade/throughput/subsidy policy of the airport and the like.
S2212, sequencing the approximate waypoints of the starting point in sequence from high to low according to the approximate values, and sequencing the approximate waypoints of the terminal point in sequence from high to low according to the approximate values;
s2213, combining approximate waypoints of the starting point with the highest approximate value and the ordered approximate waypoints of the end point in sequence to form an approximate navigation section;
s2214, after each group of approximate flight sections, comparing the approximate flight sections with the existing in-flight data base, judging whether the approximate flight sections have in-flight data, and if the approximate flight sections have in-flight data, reading the in-flight data; and averaging the flight distance data in all the approximate flight sections to obtain the second flight path historical data.
If the number of the approximate waypoints of the starting point is 4, the approximate values are sequentially ranked from high to low and are recorded as D1E1F1G1, and if the number of the approximate waypoints of the ending point is 5, the approximate values are sequentially ranked from high to low and are recorded as D1E1F1G1h 1. Starting from D1, sequentially combining with D1e1f1g1 to obtain the following 20 approximate legs: D1-D1, D1-E1, D1-F1, D1-G1, D1-h1, E1-D1, E1-E1, E1-F1, E1-G1, E1-h1, F1-D1, F1-E1, F1-F1, F1-G1, F1-h1, G1-D1, G1-E1, G1-F1, G1-h1, judging whether the combined approximate flight range data exists in the flight range data or not, if the four flight range data exist in the D1-D1, E1-E1, F1-F1 and G1-G1, obtaining four flight range data in a second flight range unit, and obtaining the four flight range data. The specific time unit can be selected according to actual conditions, and the past one-month in-flight data, the quarterly in-flight data and even the annual in-flight data can be selected.
Example 2
As shown in fig. 1-2, the method for predicting the revenue of a new route according to the present embodiment includes the following steps:
s1, acquiring air route data of a new air route, wherein the air route data comprises air route types, single-class seat numbers and air points included in the air route; the types of the air routes comprise a direct flight air route, a through parking air route and a throwing flight air route; the straight flight route comprises a waypoint as a starting point and an end point; the navigation points of the stopping route and the throwing route are a starting point, a stopping point and an end point.
S2, obtaining the historical data of the routes of the flying flights, which are the same as or similar to the new routes, from a database of the flying flights, wherein the historical data of the routes comprises the income of each shift of the flying flights which take off; the airline historical data comprises first airline historical data and second airline historical data; the first airline history data comprises airline data for the same on-flight as the new airline; the second airline history data comprises airline data for an on-flight that approximates the new airline;
s3, grouping the historical data of the flight routes according to specific time units, and calculating the historical total income of the flight route in each specific time unit; the specific time units include years, quarters, and months;
s4, training a total income prediction model according to the historical total income of each specific time unit, and predicting the total income of the next specific time unit; the starting point of the next specific time unit is the first takeoff day of the scheduled flight in the new airline;
s5, acquiring the total seat number of the flight to be flown in the next specific time unit; the flight planning class comprises a first flight planning class and a second flight planning class, the first flight planning class comprises flight planning classes in a new airline, and the second flight planning class comprises flight planning classes which are the same as or similar to the new airline; the second flight plan comprises a first sub flight plan or a second sub flight plan; the first sub-flight plan comprises the same flight plan as the new airline; the second sub-flight plan comprises a flight plan that approximates the new airline.
S6, calculating the income of a single seat according to the total income and the total seat number; and the product of the income of the single seat and the number of seats in the single class of the first planned flight is the predicted new air route income.
The step S2 further includes the following steps:
s21, comparing the new flight path with an on-flight path database, and judging whether on-flight path data identical to the new flight path exist or not;
s22, if the same in-flight data as the new air route exists, directly acquiring the in-flight data as historical data of the air route;
and if the on-flight data which is the same as the new flight path does not exist, acquiring the on-flight data which is similar to the new flight path and is used as historical data of the flight path.
As shown in fig. 4, if the new flight path is a flyaway flight path or a stop flight path, the step S22 further includes the following steps:
s2221, respectively determining an approximate waypoint of a starting point, an approximate waypoint of a stopping point and an approximate waypoint of a terminal point;
determining an approximate waypoint according to factors such as distance factor/type/grade/throughput/subsidy policy of an airport and the like; for example, when the distance factor is considered, the approximate waypoint using the waypoint within the preset distance around the starting point as the center may be found, or it is determined that there is no second waypoint in the city corresponding to the starting point, for example, a shang sun airport, and if there is a second airport in the city corresponding to the waypoint as the approximate waypoint. Factors such as the type/grade/throughput/subsidy policy of the airport can be considered, and the approximate value of the approximate waypoint can be calculated through weighted summation; or, firstly, the starting point is taken as the circle center, a preset distance value is taken as the radius to draw a circle, all waypoints except the starting point in the circle are calculated by setting the weight value through factors such as the type/grade/throughput/subsidy policy of the airport and the like.
S2222, sequencing the approximate waypoints of the starting point in sequence from high to low according to the approximate values, sequencing the approximate waypoints of the stopping point in sequence from high to low according to the approximate values, and sequencing the approximate waypoints of the terminal point in sequence from high to low according to the approximate values;
s2223, starting from the approximate waypoint of the starting point with the highest approximate value, sequentially combining the approximate waypoints with the ordered stopping points to form a first approximate waypoint section; combining the approximate waypoints of the starting point with the highest approximate value and the ordered approximate waypoints of the end point into a second approximate waypoint section in sequence; combining the approximate waypoints of the stop points with the highest approximate values with the approximate waypoints of the ordered terminal points in sequence to form a third approximate waypoint;
s2224, after each first approximate flight segment is combined, comparing the first approximate flight segment with an existing flight class database, judging whether flight class data exist in the first approximate flight segment, if the flight class data exist in the first approximate flight segment, reading the flight class data, and after the combination is finished, averaging all flight class data in the first approximate flight segment to obtain first sub-flight line historical data of the first approximate flight segment;
after the combination of the first approximate flight sections is stopped, combining the second approximate flight sections according to the same method to find second sub-flight path historical data of the second approximate flight sections; after the combination of the second approximate flight sections is stopped, combining third approximate flight sections according to the same method, and finding third sub-flight path historical data of the third approximate flight sections;
s2225, adding the historical data of the first sub-airline, the historical data of the second sub-airline and the historical data of the third sub-airline to obtain the historical data of the second airline.
If the number of the approximate waypoints at the starting point is 4, the approximate values are sequentially ordered from high to low and are recorded as D2E2F2G2, and if the number of the approximate waypoints at the stopping point is 5, the approximate values are sequentially ordered from high to low and are recorded as D2E2F2G2h 2. Starting from D2, sequentially combining with D2e2f2g2 to obtain the following 20 approximate legs: D2-D2, D2-E2, D2-F2, D2-G2, D2-h2, E2-D2, E2-E2, E2-F2, E2-G2, E2-h2, F2-D2, F2-E2, F2-F2, F2-G2, F2-h2, G2-D2, G2-E2, G2-F2, G2-h2, judging whether the combined approximate flight range data exists in the flight range data or not, if the four flight range data exist in the D2-D2, E2-E2, F2-F2 and G2-G2, obtaining the four flight range data in the first flight range unit, and obtaining the four flight range data.
The second sub-route historical data of the second route segment and the third sub-route historical data of the third route segment can be obtained in sequence through the method. And adding the income predicted values of the first flight segment, the second flight segment and the third flight segment to obtain second flight path historical data.
Example 3
As shown in fig. 5, a new route-opening profit prediction system disclosed in this embodiment includes:
the first receiving module 111 is used for acquiring the route data of a new route, wherein the route data comprises a route type, a single-class seat number and a waypoint included in the route;
a first calculation module 112, configured to obtain, from the database of in-flight flights, route history data of in-flight flights that are the same as or similar to the new routes, where the route history data includes revenue for each shift of in-flight flights that have taken off;
the second calculating module 113 is configured to group the route historical data according to specific time units, and calculate a historical total yield of the flight class in each specific time unit; the specific time units include years, quarters, and months;
a third calculating module 114, configured to train a total profit prediction model according to the historical total profit of each specific time unit, and predict the total profit of the next specific time unit; the starting point of the next specific time unit is the first takeoff day of the scheduled flight in the new airline;
a second receiving module 115, configured to obtain a total number of seats of the planned flight in the next specific time unit; the flight planning class comprises a first flight planning class and a second flight planning class, the first flight planning class comprises flight planning classes in a new airline, and the second flight planning class comprises flight planning classes which are the same as or similar to the new airline;
a fourth calculating module 116, configured to calculate a profit for a single seat according to the total profit and the total seat number; and the product of the income of the single seat and the number of seats in the single class of the first planned flight is the predicted new air route income.
Further, the first calculation module includes:
a comparing unit 121, configured to compare the new flight path with an on-flight-shift database, and determine whether there is on-flight-shift data that is the same as the new flight path;
a calculating unit 122, configured to directly obtain the in-flight data, which is the same as the route of the new route, if the in-flight data exists, and serve as route history data; and if the on-flight data which is the same as the new flight path does not exist, acquiring the on-flight data which is similar to the new flight path and is used as historical data of the flight path.
Further, the calculation unit includes:
a first calculating subunit 131, configured to determine approximate waypoints of a starting point and an ending point in the new departure route, respectively;
the first comparing subunit 132 is configured to sequence the approximate waypoints of the start point in sequence according to the approximate values from high to low, and sequence the approximate waypoints of the end point in sequence according to the approximate values from high to low;
a second calculating subunit 133, configured to combine, in sequence, the approximate waypoint of the starting point with the highest approximate value and the approximate waypoints of the sorted end points into an approximate leg;
a second comparing subunit 134, configured to compare the approximate flight segment with an existing in-flight class database after each group of one approximate flight segment, determine whether the approximate flight segment has in-flight class data, and if it is determined that the approximate flight segment has in-flight class data, read the in-flight class data; and averaging the flight distance data in all the approximate flight sections to obtain the second flight path historical data.
Example 4
As shown in fig. 6, a new route-opening profit prediction system disclosed in the present embodiment includes:
the first receiving module 111 is used for acquiring the route data of a new route, wherein the route data comprises a route type, a single-class seat number and a waypoint included in the route;
a first calculation module 112, configured to obtain, from the database of in-flight flights, route history data of in-flight flights that are the same as or similar to the new routes, where the route history data includes revenue for each shift of in-flight flights that have taken off;
the second calculating module 113 is configured to group the route historical data according to specific time units, and calculate a historical total yield of the flight class in each specific time unit; the specific time units include years, quarters, and months;
a third calculating module 114, configured to train a total profit prediction model according to the historical total profit of each specific time unit, and predict the total profit of the next specific time unit; the starting point of the next specific time unit is the first takeoff day of the scheduled flight in the new airline;
a second receiving module 115, configured to obtain a total number of seats of the planned flight in the next specific time unit; the flight planning class comprises a first flight planning class and a second flight planning class, the first flight planning class comprises flight planning classes in a new airline, and the second flight planning class comprises flight planning classes which are the same as or similar to the new airline;
a fourth calculating module 116, configured to calculate a profit for a single seat according to the total profit and the total seat number; and the product of the income of the single seat and the number of seats in the single class of the first planned flight is the predicted new air route income.
Further, the first calculation module includes:
a comparing unit 121, configured to compare the new flight path with an on-flight-shift database, and determine whether there is on-flight-shift data that is the same as the new flight path;
a calculating unit 122, configured to directly obtain the in-flight data, which is the same as the route of the new route, if the in-flight data exists, and serve as route history data; and if the on-flight data which is the same as the new flight path does not exist, acquiring the on-flight data which is similar to the new flight path and is used as historical data of the flight path.
Further, the calculation unit includes:
a third calculation subunit 135 for determining an approximate waypoint for the start point, an approximate waypoint for the stop point, and an approximate waypoint for the end point, respectively;
the third comparing subunit 136 is configured to sequence the approximate waypoints of the starting point in sequence according to the approximate values from high to low, sequence the approximate waypoints of the stopping point in sequence according to the approximate values from high to low, and sequence the approximate waypoints of the ending point in sequence according to the approximate values from high to low;
the fourth calculating subunit 137, configured to sequentially combine the approximate waypoints from the start point with the highest approximate value and the ordered approximate waypoints from the stop point to form a first approximate waypoint segment; combining the approximate waypoints of the starting point with the highest approximate value and the ordered approximate waypoints of the end point into a second approximate waypoint section in sequence; combining the approximate waypoints of the stop points with the highest approximate values with the approximate waypoints of the ordered terminal points in sequence to form a third approximate waypoint;
a fourth comparing subunit 138, configured to compare, after each first approximate flight segment is combined, the first approximate flight segment with an existing flight class database, determine whether the first approximate flight segment has flight class data, if the first approximate flight segment has flight class data, read the flight class data, and after the combination is completed, average all flight class data in the first approximate flight segment to obtain first sub-flight line historical data of the first approximate flight segment; after the combination of the first approximate flight sections is stopped, combining the second approximate flight sections according to the same method to find second sub-flight path historical data of the second approximate flight sections; after the combination of the second approximate flight sections is stopped, combining third approximate flight sections according to the same method, and finding third sub-flight path historical data of the third approximate flight sections;
and a fifth calculating subunit 139, configured to add the first sub-airline history data, the second sub-airline history data, and the third sub-airline history data to obtain second airline history data.
A terminal supporting the present system may include a central processor 1, a receiving module 2, a display module 3, and a memory 4, as shown in fig. 7. Those skilled in the art will appreciate that the present terminal may include more or fewer components than illustrated, or may combine certain components, or a different arrangement of components. Wherein:
the memory 4 may be used to store software programs and modules, and the processor 1 executes various functional applications and data processing by operating the software programs and modules stored in the memory 4. The memory 4 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as a page content display function), and the like; the storage data area may store data (such as page content data) created according to use of the system, and the like. Accordingly, the memory 4 may also include a memory controller to provide the processor 1 and the receiving module 2 access to the memory 4.
The receiving module 2 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
The display module 3 may be used to display information entered by or provided to the user as well as various graphical user interfaces of the system, which may be made up of graphics, text, icons, and any combination thereof.
The central processor 1 is a control center of the air route searching system disclosed by the invention, and executes various functions of the system and processes data by operating or executing software programs and/or modules stored in the memory 4 and calling data stored in the memory 4. When the receiving module 2 detects the selection or input operation of the user, the selection or input operation is transmitted to the central processing unit 1 to determine the type of the selection or input, and then the central processing unit 1 provides corresponding visual output on the display module 3 according to the type of the selection event.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagram illustrations of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or side component of the flow diagrams and/or side component diagrams, and combinations of flows and/or side components in the flow diagrams and/or side component diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for predicting revenue for a new route, the method comprising the steps of:
s1, acquiring air route data of a new air route, wherein the air route data comprises air route types, single-class seat numbers and air points included in the air route;
s2, obtaining the historical data of the routes of the flying flights, which are the same as or similar to the new routes, from a database of the flying flights, wherein the historical data of the routes comprises the income of each shift of the flying flights which take off;
s3, grouping the historical data of the flight routes according to specific time units, and calculating the historical total income of the flight route in each specific time unit; the specific time units include years, quarters, and months;
s4, training a total income prediction model according to the historical total income of each specific time unit, and predicting the total income of the next specific time unit; the starting point of the next specific time unit is the first takeoff day of the scheduled flight in the new airline;
s5, acquiring the total seat number of the flight to be flown in the next specific time unit; the flight planning class comprises a first flight planning class and a second flight planning class, the first flight planning class comprises flight planning classes in a new airline, and the second flight planning class comprises flight planning classes which are the same as or similar to the new airline;
s6, calculating the income of a single seat according to the total income and the total seat number; and the product of the income of the single seat and the number of seats in the single class of the first planned flight is the predicted new air route income.
2. The new line development profit prediction method according to claim 1, characterized by: the types of the air routes comprise a direct flight air route, a through parking air route and a throwing flight air route; the straight flight route comprises a waypoint as a starting point and an end point; the navigation points of the stopping route and the throwing route are a starting point, a stopping point and an end point.
3. The new line development profit prediction method according to claim 1, characterized by: the airline historical data comprises first airline historical data and second airline historical data; the first airline history data comprises airline data for the same on-flight as the new airline; the second airline history data comprises airline data for an on-flight that approximates the new airline;
the second flight plan comprises a first sub flight plan or a second sub flight plan; the first sub-flight plan comprises the same flight plan as the new airline; the second sub-flight plan comprises a flight plan that approximates the new airline.
4. The new line heading revenue prediction method of claim 3, wherein: the step S2 includes the steps of:
s21, comparing the new flight path with an on-flight path database, and judging whether on-flight path data identical to the new flight path exist or not;
s22, if the same in-flight data as the new flight line exists, directly acquiring the in-flight data as historical data of the flight line;
and if the on-flight data which is the same as the new flight path does not exist, acquiring the on-flight data which is similar to the new flight path and is used as historical data of the flight path.
5. The new line heading revenue prediction method of claim 4, wherein: if the new flight path is a straight flight path, the step S22 further includes the following steps:
s2211, determining approximate waypoints of a starting point and an end point in the new flight path respectively;
s2212, sequencing the approximate waypoints of the starting point in sequence from high to low according to the approximate values, and sequencing the approximate waypoints of the terminal point in sequence from high to low according to the approximate values;
s2213, combining approximate waypoints of the starting point with the highest approximate value and the ordered approximate waypoints of the end point in sequence to form an approximate navigation section;
s2214, after each group of approximate flight sections, comparing the approximate flight sections with the existing in-flight data base, judging whether the approximate flight sections have in-flight data, and if the approximate flight sections have in-flight data, reading the in-flight data; and averaging the flight distance data in all the approximate flight sections to obtain the second flight path historical data.
6. The new line heading revenue prediction method of claim 4, wherein: if the new route is a flyaway route or a stop-and-go route, the step S22 further includes the following steps:
s2221, respectively determining an approximate waypoint of a starting point, an approximate waypoint of a stopping point and an approximate waypoint of a terminal point;
s2222, sequencing the approximate waypoints of the starting point in sequence from high to low according to the approximate values, sequencing the approximate waypoints of the stopping point in sequence from high to low according to the approximate values, and sequencing the approximate waypoints of the terminal point in sequence from high to low according to the approximate values;
s2223, starting from the approximate waypoint of the starting point with the highest approximate value, sequentially combining the approximate waypoints with the ordered stopping points to form a first approximate waypoint section; combining the approximate waypoints of the starting point with the highest approximate value and the ordered approximate waypoints of the end point into a second approximate waypoint section in sequence; combining the approximate waypoints of the stop points with the highest approximate values with the approximate waypoints of the ordered terminal points in sequence to form a third approximate waypoint;
s2224, after each first approximate flight segment is combined, comparing the first approximate flight segment with an existing flight class database, judging whether flight class data exist in the first approximate flight segment, if the flight class data exist in the first approximate flight segment, reading the flight class data, and after the combination is finished, averaging all flight class data in the first approximate flight segment to obtain first sub-flight line historical data of the first approximate flight segment; after the combination of the first approximate flight sections is stopped, combining the second approximate flight sections according to the same method to find second sub-flight path historical data of the second approximate flight sections; after the combination of the second approximate flight sections is stopped, combining third approximate flight sections according to the same method, and finding third sub-flight path historical data of the third approximate flight sections;
s2225, adding the historical data of the first sub-airline, the historical data of the second sub-airline and the historical data of the third sub-airline to obtain the historical data of the second airline.
7. A new airline revenue prediction system, the system comprising:
the first receiving module is used for acquiring the air route data of a new air route, wherein the air route data comprises an air route type, a single-class seat number and an air point included in the air route;
a first calculation module, configured to obtain, from a database of in-flight flights, route history data of in-flight flights that are the same as or similar to the new route, where the route history data includes a benefit of each shift of the in-flight flights that have taken off;
the second calculation module is used for grouping the historical flight route data according to specific time units and calculating the historical total income of the flight class in each specific time unit; the specific time units include years, quarters, and months;
the third calculation module is used for training a total income prediction model according to the historical total income of each specific time unit and predicting the total income of the next specific time unit; the starting point of the next specific time unit is the first takeoff day of the scheduled flight in the new airline;
the second receiving module is used for acquiring the total seat number of the scheduled flight in the next specific time unit; the flight planning class comprises a first flight planning class and a second flight planning class, the first flight planning class comprises flight planning classes in a new airline, and the second flight planning class comprises flight planning classes which are the same as or similar to the new airline;
the fourth calculation module is used for calculating the income of a single seat according to the total income and the total seat number; and the product of the income of the single seat and the number of seats in the single class of the first planned flight is the predicted new air route income.
8. The open course revenue prediction system of claim 7, wherein the first calculation module includes:
the comparison unit is used for comparing the new flight path with an on-flight path database and judging whether on-flight path data same as the new flight path exist or not;
the calculation unit is used for directly acquiring the in-flight data as historical data of the air route if the in-flight data identical to the air route of the new air route exists; and if the on-flight data which is the same as the new flight path does not exist, acquiring the on-flight data which is similar to the new flight path and is used as historical data of the flight path.
9. The open course revenue prediction system of claim 8, wherein the calculation unit includes:
the first calculating subunit is used for respectively determining approximate waypoints of a starting point and an end point in the new flight path;
the first comparison subunit is used for sequencing the approximate waypoints of the starting point in sequence according to the approximate values from high to low and sequencing the approximate waypoints of the terminal point in sequence according to the approximate values from high to low;
the second calculation subunit is used for sequentially combining the approximate waypoints of the starting point with the highest approximate value and the approximate waypoints of the ordered end point into an approximate flight segment;
the second comparison subunit is used for comparing the approximate flight segment with the existing in-flight class database after each approximate flight segment is combined, judging whether the approximate flight segment has in-flight class data or not, and reading the in-flight class data if the approximate flight segment has in-flight class data; and averaging the flight distance data in all the approximate flight sections to obtain the second flight path historical data.
10. The open course revenue prediction system of claim 8, wherein the calculation unit includes:
the third calculation subunit is used for respectively determining an approximate waypoint of the starting point, an approximate waypoint of the stopping point and an approximate waypoint of the end point;
the third comparison subunit is used for sequencing the approximate waypoints of the starting point in sequence according to the approximate values from high to low, sequencing the approximate waypoints passing through the stopping point in sequence according to the approximate values from high to low, and sequencing the approximate waypoints of the terminal point in sequence according to the approximate values from high to low;
the fourth calculating subunit is used for sequentially combining the approximate waypoints of the starting point with the highest approximate value and the ordered approximate waypoints of the stopping points into a first approximate waypoint; combining the approximate waypoints of the starting point with the highest approximate value and the ordered approximate waypoints of the end point into a second approximate waypoint section in sequence; combining the approximate waypoints of the stop points with the highest approximate values with the approximate waypoints of the ordered terminal points in sequence to form a third approximate waypoint;
the fourth comparison subunit is used for comparing the first approximate flight segment with the existing flight class database after each first approximate flight segment is combined, judging whether the first approximate flight segment has flight class data or not, reading the flight class data if the first approximate flight segment has the flight class data, and averaging all the flight class data in the first approximate flight segment after the combination is finished to obtain the first sub-flight line historical data of the first approximate flight segment; after the combination of the first approximate flight sections is stopped, combining the second approximate flight sections according to the same method to find second sub-flight path historical data of the second approximate flight sections; after the combination of the second approximate flight sections is stopped, combining third approximate flight sections according to the same method, and finding third sub-flight path historical data of the third approximate flight sections;
and the fifth calculation subunit is used for adding the first sub-airline historical data, the second sub-airline historical data and the third sub-airline historical data to obtain second airline historical data.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111680833A (en) * | 2020-05-28 | 2020-09-18 | 悠桦林信息科技(上海)有限公司 | Automatic scheduling method for flight plan |
| CN116362788A (en) * | 2023-03-27 | 2023-06-30 | 中国南方航空股份有限公司 | Method, device, equipment and storage medium for predicting opening of new airlines |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111680833A (en) * | 2020-05-28 | 2020-09-18 | 悠桦林信息科技(上海)有限公司 | Automatic scheduling method for flight plan |
| CN116362788A (en) * | 2023-03-27 | 2023-06-30 | 中国南方航空股份有限公司 | Method, device, equipment and storage medium for predicting opening of new airlines |
| CN116362788B (en) * | 2023-03-27 | 2024-01-30 | 中国南方航空股份有限公司 | Method, device, equipment and storage medium for predicting opening of new airlines |
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