CN111507810A - Flight service method and system based on cluster analysis - Google Patents
Flight service method and system based on cluster analysis Download PDFInfo
- Publication number
- CN111507810A CN111507810A CN202010462065.3A CN202010462065A CN111507810A CN 111507810 A CN111507810 A CN 111507810A CN 202010462065 A CN202010462065 A CN 202010462065A CN 111507810 A CN111507810 A CN 111507810A
- Authority
- CN
- China
- Prior art keywords
- flight
- departure
- user
- users
- target user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007621 cluster analysis Methods 0.000 title claims abstract description 29
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000010006 flight Effects 0.000 claims description 21
- 239000006185 dispersion Substances 0.000 claims description 13
- 239000000284 extract Substances 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/02—Reservations, e.g. for tickets, services or events
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
- G06Q30/0635—Processing of requisition or of purchase orders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Economics (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Human Resources & Organizations (AREA)
- Data Mining & Analysis (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Development Economics (AREA)
- General Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Computing Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a flight service method and system based on cluster analysis, and relates to the field of aviation informatization management. The method comprises the following steps: a user side generates a ticket buying order; the service end pulls the target user into a flight social group corresponding to the flight according to the flight information in the ticket purchase order, and sends request information to all users in the group through the flight social group; a user side requests to acquire travel demand information of a target user; the service side judges whether preset conditions are met or not according to the travel demand information of all users in the flight social group, and if yes, clustering is carried out based on first preset conditions; if not, clustering is carried out based on a second preset condition; and after the clustering analysis is finished, configuring the departure time and the departure vehicle for the target user according to the clustering result. The invention reduces the probability of the user mistakenly taking the airplane, and can prevent the condition that the user mistakenly takes the airplane due to the fact that the user cannot be taken by the automobile and can be taken by the public transport to transfer the airplane by matching the automobile to receive the user at the departure time, thereby being convenient for the user to go out.
Description
Technical Field
The invention relates to the field of aviation informatization management, in particular to a flight service method and system based on cluster analysis.
Background
With the rapid development of passenger transport and aviation and the improvement of living standard of people, the airplane gradually becomes an ideal transportation tool when many people go out.
However, due to factors such as airplane noise and flight safety, airport construction in each city is usually far away from the city center, and traffic jam in the city is increasingly serious with modern construction of the city and the rapid increase of the number of private cars. The user often goes to the airport in the departure journey, the departure time is too early or too late due to inaccurate subjective judgment of the user, a large amount of time is wasted due to waiting for the taxi, the departure time is too late, the phenomenon that the taxi is easy to be taken without the taxi or the passenger receiving time is long is easily caused by temporary taxi taking, if a public transport is selected, the user is inconvenient to go out, the time is prolonged, the taxi is easy to be taken by mistake, troubles are caused for the user to go out, and the trip cost of the user is increased.
Disclosure of Invention
The invention aims to solve the technical problem that the departure time judgment is inaccurate due to the fact that the time required by a user to go to an airport is difficult to accurately estimate after the user orders a flight at present, and provides a flight service method and system based on cluster analysis.
The technical scheme for solving the technical problems is as follows:
a flight service method based on cluster analysis comprises the following steps:
the method comprises the steps that a user side obtains flights ordered by a target user, generates a ticket buying order and sends the ticket buying order to a server side;
the server receives the ticket purchase order, pulls the target user into a flight social group corresponding to the flight according to flight information in the ticket purchase order, and sends request information to all users in the group through the flight social group;
the user side receives the request information through the flight social group, requests to acquire travel demand information of the target user, and sends the travel demand information to the server side;
after receiving the travel demand information, the server judges whether preset conditions are met according to the travel demand information of all users in the flight social group, and if so, all users in the flight social group are clustered based on first preset conditions; if not, clustering all users in the flight social group based on a second preset condition; after the clustering analysis is finished, configuring departure time and departure vehicles for the target user according to a clustering result, and sending the departure time and the departure vehicles to the user side;
and the user side receives and displays the departure time and the departure vehicle.
Another technical solution of the present invention for solving the above technical problems is as follows:
a cluster analysis based flight service system comprising: user side and server side, wherein:
the user side is used for acquiring flights ordered by a target user, generating a ticket purchasing order and sending the ticket purchasing order to the server side;
the server is used for receiving the ticket purchase order, pulling the target user into a flight social group corresponding to the flight according to flight information in the ticket purchase order, and sending request information to all users in the group through the flight social group;
the user side is further used for receiving the request information through the flight social group, requesting to acquire travel demand information of the target user, and sending the travel demand information to the server side;
the server is further used for judging whether preset conditions are met or not according to the travel demand information of all users in the flight social group after the travel demand information is received, and if yes, clustering all users in the flight social group based on first preset conditions; if not, clustering all users in the flight social group based on a second preset condition; after the clustering analysis is finished, configuring departure time and departure vehicles for the target user according to a clustering result, and sending the departure time and the departure vehicles to the user side;
the user side is further used for receiving and displaying the departure time and the departure vehicle.
The invention has the beneficial effects that: according to the invention, the users who order flights are pulled into the flight social group to obtain the travel demand information of the users, and the conditions used for clustering are judged according to the travel conditions of all the users, so that an accurate clustering result can be obtained, and then the departure time and the departure vehicle are configured according to the clustering result, so that the trouble of estimating the departure time by the users can be avoided.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a schematic flow chart of a flight service method based on cluster analysis according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of cluster analysis provided in another embodiment of the flight service method based on cluster analysis according to the present invention;
FIG. 3 is a structural framework diagram provided by an embodiment of the cluster analysis-based flight service system of the present invention;
fig. 4 is a schematic diagram of network topology connection provided by an embodiment of the flight service system based on cluster analysis according to the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth to illustrate, but are not to be construed to limit the scope of the invention.
As shown in fig. 4, an exemplary network topology connection diagram of a flight service system based on cluster analysis is provided, a service end 10 of the flight service method provided by the present invention may be connected to a plurality of user ends 20 through the internet, and an exemplary communication link establishment method is provided below.
The user terminal 20 firstly sends an internet access request to the internet access gateway 30, the internet access gateway 30 sends the access request to the internet service gateway 40, and the internet service gateway 40 authenticates the access request. And when the authentication is successful, sending a message of successful authentication to the internet access gateway 30, and establishing a network communication transmission channel with the internet access gateway 30. The internet access gateway 30 sends the successful authentication message to the user terminal 20, and after receiving the successful authentication message, the user terminal 20 establishes a communication link with the server terminal 10 for data transmission.
Some possible embodiments of the present invention are explained below based on the above network structure.
As shown in fig. 1, a schematic flow chart provided by an embodiment of a flight service method based on cluster analysis according to the present invention is applicable to an automatic delivery service and an information push service after a user orders a flight, and includes:
s1, the user side acquires the flight ordered by the target user, generates a ticket order and sends the ticket order to the server side;
s2, the server receives the order of the ticket, pulls the target user into a flight social group corresponding to the flight according to the flight information in the order of the ticket, and sends request information to all users in the group through the flight social group;
the flight information comprises flight numbers, takeoff time, starting waypoints, arrival waypoints and the like;
s3, the user side receives the request information through the flight social group, requests to acquire the travel demand information of the target user, and sends the travel demand information to the server side;
s4, after receiving the travel demand information, the server judges whether preset conditions are met according to the travel demand information of all users in the flight social group, and if so, all users in the flight social group are clustered based on the first preset conditions; if not, clustering all users in the flight social group based on a second preset condition; after the clustering analysis is finished, configuring departure time and departure vehicles for the target user according to a clustering result, and sending the departure time and the departure vehicles to the user side;
and S5, the user side receives and displays the departure time and the departure vehicle.
It should be noted that, when a user joins in the flight social group of the flight corresponding to the purchased ticket, the flight social group may implement instant communication between all registered users, and the flight social group may be a multi-user communication group of instant chat software such as a wechat group or a QQ group.
The request information may be a request for obtaining a departure address of the user on the same day of departure time, a destination address of the user arriving at a city, travel demands and the like. The travel demand information returned by the user may include: the demand on the vehicle model, the starting address, whether to receive the carpool, the trip purpose and the like.
It should be noted that, because different users have different travel demands, the condition specifically used for the cluster analysis may be determined according to the travel demands of the users, and the first preset condition and the second preset condition may be set according to actual demands.
For example, the preset conditions are set to: the distance value between the departure address of the user exceeding the preset proportion in the flight social group and the cell A is smaller than the preset distance value, namely, the departure address of the user exceeding the preset proportion in the flight social group is closer to the cell A, if the departure address meets the preset conditions according to the departure required information judgment of all users in the flight social group, clustering is carried out based on a first preset condition, and the first preset condition is set as: the specific implementation of clustering based on the first preset condition for the starting address may be: and clustering all users in the flight social group by taking the starting address as a center in a cell or a street area. If the departure address does not meet the preset condition according to the departure required information of all users in the flight social group, clustering is carried out based on a second preset condition, wherein the second preset condition is as follows: if the flight social group corresponds to the departure time of the flight, the specific implementation of clustering based on the second preset condition may be: and clustering users in the flight social group corresponding to the flights with the departure time difference smaller than the preset difference (namely the flights with the departure times closer to each other) by taking the cell or the street area as a center.
For another example, assuming that different users have requirements for traveling and taking vehicle types, such as personal preferences, consumption tolerance or baggage carrying capacity, etc., the preset condition may be set such that the number of users in the flight social group required for the vehicle type a reaches a preset ratio. And if the number of users meeting the type A vehicle requirement in the flight social group meets the preset condition according to the starting requirement information judgment of all users in the flight social group, clustering based on a first preset condition. The first preset condition is set as: the specific implementation manner of clustering the vehicle type based on the first preset condition may be: and clustering all users in the flight social group by taking the required vehicle type as the center. And if the number of users required by the type A vehicle in the flight social group does not meet the preset condition according to the starting requirement information judgment of all users in the flight social group, clustering based on a second preset condition. The second preset condition is set as: the specific implementation of carrying the baggage amount and clustering based on the second preset condition may be: and clustering all users in the flight social group by taking the luggage carrying amount of the users as a center.
According to the embodiment of the invention, the departure addresses of the users in the flight group are clustered, so that the users can reasonably configure vehicles or carpools for traveling, the number of vehicles used during independent traveling can be reduced, the environment is protected, and the traveling cost of the users is reduced, or if the departure addresses of the users in the same flight group are too dispersed, the vehicles are configured for the users, so that the waste of traffic resources is easily caused, and for further improving the utilization rate of the traffic resources, clustering is performed based on a second preset condition, for example, the users included in the flight social group corresponding to flights with relatively close departure times are clustered by the departure addresses of the users, and the departure vehicles and the time are configured for the users according to the clustering result, so that the traveling cost of the users is further reduced. Or, the vehicle type requirements taken by the user during traveling are clustered to meet the requirements of different users, or the user is clustered according to the amount of the luggage carried by the user, so that the user can travel conveniently.
As shown in fig. 2, an exemplary cluster analysis diagram is provided, and the cluster analysis is used to find the dependencies between users, so as to remove or merge the users with close dependencies, where each point is a user, and the point enclosed by each closed circle is a cluster, and by characterizing features of different user groups, it is able to fully study user behaviors, thereby providing reasonable departure time and departure vehicles for users in different clusters.
It should be noted that the departure time can be determined by obtaining historical travel time from the departure address to the airport, and the time from the departure address to the airport on the day of flight departure is estimated according to the historical travel time, and specifically, the historical travel time query can be realized by calling the existing map APP through the plug-in.
According to the method, the users who order flights are pulled into the flight social group to obtain the travel demand information of the users, which conditions are used for clustering is judged according to the travel conditions of all the users, so that accurate clustering results can be obtained, the departure time and the departure vehicle are configured according to the clustering results, the trouble that the users estimate the departure time can be avoided, due to the adoption of the clustering algorithm, the obtained departure time is more accurate, the condition that the departure is too early or too late can not occur, the condition that the users cannot make a car and the users miss the car can be prevented, the probability of the users miss the car is reduced, the vehicles can be matched for a plurality of users in advance through the clustering results, the travel cost of the users can be reduced, and the users can conveniently travel.
Optionally, in some possible implementation manners, after receiving the travel demand information, the server determines whether preset conditions are met according to the travel demand information of all users in the flight social group, and if so, clusters all the users in the flight social group based on the first preset conditions; if not, clustering all users in the flight social group based on a second preset condition, specifically comprising:
after the server receives the travel demand information, extracting the starting address of each user from the travel demand information of all users in the flight social group, judging whether the dispersion of all the starting addresses is greater than the preset dispersion, and if so, clustering all the users in the n flight social groups by using the starting addresses; if not, clustering all users in the flight social group where the target user is located by using the starting address;
the n flight social groups comprise a flight social group where the target user is located, and the difference values of the takeoff time of flights corresponding to the rest n-1 flight social groups except the flight social group where the target user is located and the takeoff time of flights corresponding to the flight social group where the target user is located are smaller than the preset difference value.
It should be understood that for the convenience of comparison, the difference may be an absolute value, and the smaller the difference is, the closer the departure time of the flights corresponding to the two flight social groups is, and the preset difference may be set according to actual requirements.
For example, assume that there are A, B and C flights, the corresponding flight social groups are respectively a flight a group, a flight B group and a flight C group, assume that the flight a group is the flight social group where the target user is located, the departure time of flight a is 9 o ' clock, the departure time of flight B is 10 o ' clock, the departure time of flight C is 12 o ' clock, and assume that the preset difference is 2 hours, then it is found through comparison that the difference between the departure times of flight B and flight a is 1 hour, which is smaller than the preset difference by 2 hours, and the difference between the departure times of flight C and flight a is 3 hours, which is greater than the preset difference by 2 hours, then it can be considered that the n flight social groups include the flight a group and the flight B group.
It should be understood that the dispersion is used to indicate the degree of dispersion between the departure addresses, the larger the value, the more dispersed the departure addresses, the smaller the value, the more concentrated the departure addresses, the preset dispersion may be set according to actual requirements, and a manner that can be implemented to determine the dispersion between the departure addresses is given below.
Optionally, the coordinates of each departure address may be obtained through a preset plug-in, and the coordinates may be longitude and latitude, or may also be relative coordinates marked on the map APP, and then the mean square error of the coordinates is calculated, and the mean square error is used as the dispersion. The larger the mean square deviation is, the larger the difference between the coordinates is, the more scattered the user's departure address is, the smaller the mean square deviation is, the smaller the difference between the coordinates is, the more concentrated the user's departure address is. When the departure addresses of the users are concentrated, the departure addresses of all the users in the flight social group where the target user is located can be clustered, so that vehicle configuration or car sharing is facilitated, and when the departure addresses of the users are dispersed, the departure addresses of all the users in the flight social group corresponding to flights with relatively close departure time can be clustered, so that the waste of traffic resources caused by the fact that the departure addresses are too dispersed is solved, the utilization rate of the traffic resources is further improved, and the travel cost of the users is reduced.
Optionally, in some possible implementation manners, configuring a departure time and a departure vehicle for the target user according to the clustering result specifically includes:
the server determines a departure route and a departure vehicle for a target user according to the clustering result, determines estimated time according to the departure route, determines extra time according to the operation state of a departure airport of the flight, determines total departure time according to the estimated time and the extra time, and determines departure time according to the total departure time and the departure time of the flight.
For example, after performing cluster analysis on target users, it is found that the target users belong to a certain cluster, which is characterized by living in an F area, preferring to reach an airport in advance, and preferring vehicles of a vehicle type a, etc., then a route that reaches the airport in advance by n hours may be generated for users near the F area according to these characteristics of the users, the vehicles may be configured as vehicles a, and the users may travel in a manner of reasonably configuring the vehicles or sharing the vehicles, so that personalized vehicles and route configurations of the users may be generated, and user requirements are fully satisfied.
It should be noted that when the operation state of the takeoff airport is busy, such as spring transportation, national holidays, etc., due to a large number of people going out, traffic congestion and slow duty, the time of going out may not be enough in advance for 2 hours, and when the operation state of the takeoff airport is idle, the number of people going out is small, the resource occupation is small, the time of going out in advance can be correspondingly shortened, which is reflected as extra time, the specific corresponding relationship can be set according to the actual requirement, for example, in the spring transportation period, the extra time can be set to 2 hours, and in other times, the extra time can be set to 1 hour.
Alternatively, the departure time T may be calculated according to the following formula:
T＝t0-t
wherein, t0For the departure time of the flight, t is the total departure time, and t can be calculated according to the following formula:
t＝t1+t2
wherein, t1For estimation, t2For additional use.
By determining the departure time according to the busy degree of the departure airport of the flight, the total departure time can be dynamically adjusted by reasonably adjusting the parameter of the extra time, and the departure time can be more reasonably determined.
Optionally, in some possible implementations, the method further includes:
the server compares the starting route of the target user with the starting routes of other users in a cluster where the target user is located, and when any two starting routes are overlapped and the overlapping distance exceeds a preset distance, the two overlapped starting routes are combined, and a combination result is sent to the user side;
and the user side displays the merging result.
It should be understood that the preset distance can be set according to actual requirements, and a combining method which can be realized is given below.
For example, the sites or road names passed by the departure route may be compared, and taking the sites as an example, it is assumed that the sites passed by the departure route of the first user are: a1, A2, B1, B2, C1, C2 and C3, wherein the starting routes of the second user sequentially pass through the following sites: e1, E2, E3, B1, B2, C1, C2, and C3, where a1 is the departure address of the first user, E1 is the departure address of the second user, and C3 is an airport, then comparison shows that the departure route of the first user and the departure route of the second user have common sites B1, B2, C1, C2, and C3, and if the overlapping distance of these overlapping routes exceeds a preset distance, these two departure routes may be merged, for example, the first user may be joined, and then the second user may be joined, and then the merged departure routes are a1, a2, E1, E2, E3, B1, B2, C1, C2, and C3; for another example, if a user B is connected first and then a user a is connected, the combined departure route is E1, E2, E3, a1, a2, B1, B2, C1, C2, and C3.
It should be understood that the determination may also be made by road name, and the method is the same as the station and is not described again.
And after the routes are combined, re-determining the departure time according to the combined departure route.
Through merging the starting routes with most of overlapping, the number of vehicles used during traveling can be reduced, the environment is more protected, simultaneously, the traveling cost of a user can be reduced, the passenger routes are optimized, the sequence and the routes of the vehicle receiving users are reasonably arranged, and the time consumption can be reduced.
Optionally, in some possible implementations, the method further includes:
the service end judges the travel purpose of the target user according to the flight information in the ticket purchase order and the travel demand information of the target user, generates corresponding recommendation information according to the travel purpose of the target user and sends the recommendation information to the user end;
and displaying the recommendation information by the user side.
It should be noted that the travel purpose may be a business trip, a tour, or a visiting relation, and the corresponding recommendation information is also different, for example, if the travel purpose of the user is a tour, the recommendation information may be what the destination specialty should buy, how to buy, where to buy, why to buy, establish a specialty rank, and recommend to the user, so as to provide a better and more convenient service for the user's trip.
The travel purpose of the target user is judged through the flight information and the travel demand information of the target user, so that the relevant information can be accurately recommended for the user, the recommendation success rate is improved, and the recommended information is more in line with the demand of the user.
It is to be understood that in some possible embodiments, combinations of any or all of the above embodiments may be included.
As shown in fig. 3, a structural framework diagram provided for an embodiment of the flight service system based on cluster analysis according to the present invention is applicable to an automatic delivery service and an information push service after a user orders a flight, and includes: a user terminal 20 and a server terminal 10, wherein:
the user terminal 20 is configured to obtain a flight ordered by the target user, generate a ticket order, and send the ticket order to the service terminal 10;
the server 10 is configured to receive a ticket purchase order, pull a target user into a flight social group corresponding to a flight according to flight information in the ticket purchase order, and send request information to all users in the group through the flight social group;
the user terminal 20 is further configured to receive request information through the flight social group, request to acquire travel demand information of the target user, and send the travel demand information to the server terminal 10;
the server 10 is further configured to, after receiving the travel demand information, judge whether a preset condition is met according to the travel demand information of all users in the flight social group, and if yes, cluster all users in the flight social group based on a first preset condition; if not, clustering all users in the flight social group based on a second preset condition; after the clustering analysis is completed, configuring departure time and departure vehicles for the target user according to the clustering result, and sending the departure time and the departure vehicles to the user side 20;
the user terminal 20 is also used for receiving and displaying the departure time and the departure vehicle.
According to the method, the users who order flights are pulled into the flight social group to obtain the travel demand information of the users, which conditions are used for clustering is judged according to the travel conditions of all the users, so that accurate clustering results can be obtained, the departure time and the departure vehicle are configured according to the clustering results, the trouble that the users estimate the departure time can be avoided, due to the adoption of the clustering algorithm, the obtained departure time is more accurate, the condition that the departure is too early or too late can not occur, the condition that the users cannot make a car and the users miss the car can be prevented, the probability of the users miss the car is reduced, the vehicles can be matched for a plurality of users in advance through the clustering results, the travel cost of the users can be reduced, and the users can conveniently travel.
Optionally, in some possible implementation manners, the server 10 is specifically configured to, after receiving the travel demand information, extract a departure address of each user from the travel demand information of all users in the flight social group, determine whether the dispersion of all the departure addresses is greater than a preset dispersion, and if so, cluster all the users in the n flight social groups by using the departure addresses; if not, clustering all users in the flight social group where the target user is located by using the starting address;
the n flight social groups comprise a flight social group where the target user is located, and the difference values of the takeoff time of flights corresponding to the rest n-1 flight social groups except the flight social group where the target user is located and the takeoff time of flights corresponding to the flight social group where the target user is located are smaller than the preset difference value.
Optionally, in some possible implementation manners, the server 10 is specifically configured to determine a departure route and a departure vehicle for the target user according to the clustering result, determine estimated time according to the departure route, determine additional time according to an operation state of a departure airport of the flight, determine total departure time according to the estimated time and the additional time, and determine departure time according to the total departure time and the departure time of the flight.
Optionally, in some possible implementation manners, the server 10 is further configured to compare the departure route of the target user with departure routes of other users in a cluster where the target user is located, and when any two departure routes overlap and the overlap distance exceeds the preset distance, merge the two overlapping departure routes, and send a merging result to the user side 20;
the user terminal 20 is also used for displaying the merging result.
Optionally, in some possible implementation manners, the server 10 is further configured to determine a travel purpose of the target user according to the flight information in the ticket purchase order and the travel demand information of the target user, generate corresponding recommendation information according to the travel purpose of the target user, and send the recommendation information to the user terminal 20;
the user terminal 20 is also used for displaying recommendation information.
It is to be understood that in some possible embodiments, combinations of any or all of the above embodiments may be included.
The above embodiments correspond to product embodiments of previous method embodiments, and therefore, descriptions and corresponding technical effects related to various optional implementation manners of product embodiments may refer to the previous method embodiments, and are not repeated herein.
The reader should understand that in the description of this specification, reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described method embodiments are merely illustrative, and for example, the division of steps into only one logical functional division may be implemented in practice in another way, for example, multiple steps may be combined or integrated into another step, or some features may be omitted, or not implemented.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A flight service method based on cluster analysis is characterized by comprising the following steps:
the method comprises the steps that a user side obtains flights ordered by a target user, generates a ticket buying order and sends the ticket buying order to a server side;
the server receives the ticket purchase order, pulls the target user into a flight social group corresponding to the flight according to flight information in the ticket purchase order, and sends request information to all users in the group through the flight social group;
the user side receives the request information through the flight social group, requests to acquire travel demand information of the target user, and sends the travel demand information to the server side;
after receiving the travel demand information, the server judges whether preset conditions are met according to the travel demand information of all users in the flight social group, and if so, all users in the flight social group are clustered based on first preset conditions; if not, clustering all users in the flight social group based on a second preset condition; after the clustering analysis is finished, configuring departure time and departure vehicles for the target user according to a clustering result, and sending the departure time and the departure vehicles to the user side;
and the user side receives and displays the departure time and the departure vehicle.
2. The flight service method based on cluster analysis according to claim 1, wherein after receiving the travel demand information, the service end judges whether preset conditions are met according to the travel demand information of all users in the flight social group, and if so, clusters all users in the flight social group based on first preset conditions; if not, clustering all users in the flight social group based on a second preset condition, specifically comprising:
after receiving the travel demand information, the server extracts the starting address of each user from the travel demand information of all users in the flight social group, judges whether the dispersion of all the starting addresses is greater than a preset dispersion, and if yes, clusters all the users in the n flight social groups by using the starting addresses; if not, clustering all users in the flight social group where the target user is located by using the starting address;
the n flight social groups comprise a flight social group where the target user is located, and the difference values of the takeoff time of flights corresponding to the rest n-1 flight social groups except the flight social group where the target user is located and the takeoff time of flights corresponding to the flight social group where the target user is located are smaller than a preset difference value.
3. The cluster analysis-based flight service method according to claim 1 or 2, wherein configuring departure time and departure vehicle for the target user according to the clustering result specifically comprises:
the server determines a departure route and a departure vehicle for the target user according to the clustering result, determines estimated time according to the departure route, determines extra time according to the operation state of a departure airport of the flight, determines total departure time according to the estimated time and the extra time, and determines departure time according to the total departure time and the departure time of the flight.
4. The cluster analysis-based flight service method according to claim 3, further comprising:
the server compares the starting route of the target user with the starting routes of other users in the cluster where the target user is located, and when any two starting routes are overlapped and the overlapping distance exceeds a preset distance, the two overlapped starting routes are combined, and a combination result is sent to the user side;
and the user side displays the merging result.
5. The cluster analysis-based flight service method according to claim 1, further comprising:
the server side judges the travel purpose of the target user according to flight information in the ticket purchase order and travel demand information of the target user, generates corresponding recommendation information according to the travel purpose of the target user and sends the recommendation information to the user side;
and the user side displays the recommendation information.
6. A cluster analysis-based flight service system, comprising: user side and server side, wherein:
the user side is used for acquiring flights ordered by a target user, generating a ticket purchasing order and sending the ticket purchasing order to the server side;
the server is used for receiving the ticket purchase order, pulling the target user into a flight social group corresponding to the flight according to flight information in the ticket purchase order, and sending request information to all users in the group through the flight social group;
the user side is further used for receiving the request information through the flight social group, requesting to acquire travel demand information of the target user, and sending the travel demand information to the server side;
the server is further used for judging whether preset conditions are met or not according to the travel demand information of all users in the flight social group after the travel demand information is received, and if yes, clustering all users in the flight social group based on first preset conditions; if not, clustering all users in the flight social group based on a second preset condition; after the clustering analysis is finished, configuring departure time and departure vehicles for the target user according to a clustering result, and sending the departure time and the departure vehicles to the user side;
the user side is further used for receiving and displaying the departure time and the departure vehicle.
7. The flight service system based on cluster analysis according to claim 6, wherein the server is specifically configured to, after receiving the travel demand information, extract a departure address of each user from the travel demand information of all users in the flight social group, determine whether the dispersion of all departure addresses is greater than a preset dispersion, and if so, cluster all users in the n flight social groups with the departure addresses; if not, clustering all users in the flight social group where the target user is located by using the starting address;
the n flight social groups comprise a flight social group where the target user is located, and the difference values of the takeoff time of flights corresponding to the rest n-1 flight social groups except the flight social group where the target user is located and the takeoff time of flights corresponding to the flight social group where the target user is located are smaller than a preset difference value.
8. The flight service system based on cluster analysis as claimed in claim 6 or 7, wherein the service end is specifically configured to determine a departure route and a departure vehicle for the target user according to a clustering result, determine an estimated time according to the departure route, determine an extra time according to an operation state of a departure airport of the flight, determine a total departure time according to the estimated time and the extra time, and determine a departure time according to the total departure time and a departure time of the flight.
9. The cluster analysis-based flight service system of claim 8, wherein the service end is further configured to compare the departure route of the target user with departure routes of other users in a cluster where the target user is located, merge two overlapped departure routes when any two departure routes overlap and the overlap distance exceeds a preset distance, and send a merging result to the user end;
the user side is also used for displaying the merging result.
10. The flight service system based on cluster analysis according to claim 6, wherein the service end is further configured to determine a travel purpose of the target user according to flight information in the ticket purchase order and travel demand information of the target user, generate corresponding recommendation information according to the travel purpose of the target user, and send the recommendation information to the user end;
the user side is also used for displaying the recommendation information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010462065.3A CN111507810A (en) | 2020-05-27 | 2020-05-27 | Flight service method and system based on cluster analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010462065.3A CN111507810A (en) | 2020-05-27 | 2020-05-27 | Flight service method and system based on cluster analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111507810A true CN111507810A (en) | 2020-08-07 |
Family
ID=71870283
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010462065.3A Pending CN111507810A (en) | 2020-05-27 | 2020-05-27 | Flight service method and system based on cluster analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111507810A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112307342A (en) * | 2020-11-02 | 2021-02-02 | 沈阳民航东北凯亚有限公司 | Flight recommendation method and device |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014134940A1 (en) * | 2013-03-05 | 2014-09-12 | Tencent Technology (Shenzhen) Company Limited | Method and server of group recommendation |
| CA2934021A1 (en) * | 2015-07-30 | 2017-01-30 | Sci Limited | Evaluation and training for online vehicle request and response messaging |
| CN106557829A (en) * | 2015-09-30 | 2017-04-05 | 百度在线网络技术（北京）有限公司 | Method and apparatus with demand and transport power mismatch region are obtained in car business |
| CN107122461A (en) * | 2017-04-27 | 2017-09-01 | 东软集团股份有限公司 | One kind trip method of trajectory clustering, device and equipment |
| CN109325185A (en) * | 2018-07-16 | 2019-02-12 | 北京三快在线科技有限公司 | A kind of determination is got on the bus method, apparatus, equipment and storage medium a little |
| CN109711438A (en) * | 2018-12-10 | 2019-05-03 | 中国联合网络通信集团有限公司 | Bus traffic route acquisition methods, device and equipment |
| CN109831314A (en) * | 2019-04-10 | 2019-05-31 | 上海掌门科技有限公司 | Method and apparatus for establishing user group |
| CN110326311A (en) * | 2017-09-25 | 2019-10-11 | 北京嘀嘀无限科技发展有限公司 | It is a kind of for providing the system and method for transportation service |
| CN110689362A (en) * | 2018-07-04 | 2020-01-14 | 北京嘀嘀无限科技发展有限公司 | Region clustering method and system |
-
2020
- 2020-05-27 CN CN202010462065.3A patent/CN111507810A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014134940A1 (en) * | 2013-03-05 | 2014-09-12 | Tencent Technology (Shenzhen) Company Limited | Method and server of group recommendation |
| CA2934021A1 (en) * | 2015-07-30 | 2017-01-30 | Sci Limited | Evaluation and training for online vehicle request and response messaging |
| CN106557829A (en) * | 2015-09-30 | 2017-04-05 | 百度在线网络技术（北京）有限公司 | Method and apparatus with demand and transport power mismatch region are obtained in car business |
| CN107122461A (en) * | 2017-04-27 | 2017-09-01 | 东软集团股份有限公司 | One kind trip method of trajectory clustering, device and equipment |
| CN110326311A (en) * | 2017-09-25 | 2019-10-11 | 北京嘀嘀无限科技发展有限公司 | It is a kind of for providing the system and method for transportation service |
| CN110689362A (en) * | 2018-07-04 | 2020-01-14 | 北京嘀嘀无限科技发展有限公司 | Region clustering method and system |
| CN109325185A (en) * | 2018-07-16 | 2019-02-12 | 北京三快在线科技有限公司 | A kind of determination is got on the bus method, apparatus, equipment and storage medium a little |
| CN109711438A (en) * | 2018-12-10 | 2019-05-03 | 中国联合网络通信集团有限公司 | Bus traffic route acquisition methods, device and equipment |
| CN109831314A (en) * | 2019-04-10 | 2019-05-31 | 上海掌门科技有限公司 | Method and apparatus for establishing user group |
Non-Patent Citations (2)
| Title |
|---|
| 董纲;唐加福;孔媛;张军;: "免费接送机场服务的最小化成本模型及算法" * |
| 邵增珍;王洪国;刘弘;孟春华;张子鹏;: "车辆合乘匹配问题中服务需求分派算法研究" * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112307342A (en) * | 2020-11-02 | 2021-02-02 | 沈阳民航东北凯亚有限公司 | Flight recommendation method and device |
| CN112307342B (en) * | 2020-11-02 | 2023-10-10 | 沈阳民航东北凯亚有限公司 | Flight recommendation method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10410519B2 (en) | Public transportation navigator | |
| US20180018572A1 (en) | Method, apparatus, device, and system for predicting future travel volumes of geographic regions based on historical transportation network data | |
| KR101994631B1 (en) | System and method for managing supply of service | |
| d'Orey et al. | Empirical evaluation of a dynamic and distributed taxi-sharing system | |
| CN107767322B (en) | Carpooling method and device | |
| US20170169366A1 (en) | Systems and Methods for Adjusting Ride-Sharing Schedules and Routes | |
| CN107464413B (en) | Real-time single processing method and server | |
| WO2016008391A1 (en) | Method and system for booking taxi for third party in online taxi hiring system | |
| US20160195404A1 (en) | Peer to peer car sharing social-graph configurator | |
| CN105279955A (en) | Carpooling method and device | |
| TW201341759A (en) | Integration of contextual and historical data into route determination | |
| JP6976520B2 (en) | Management system, management method, and management program | |
| CN105991704B (en) | A kind of bus or train route information interacting method, equipment and system | |
| KR102390269B1 (en) | System and method for shuttle service management and shuttle service route and service derivation | |
| CN113393137B (en) | Scheduling sharing system based on Internet of vehicles | |
| CN106846789A (en) | One kind customization public transit system and customization public bus network formulating method | |
| CN112262418A (en) | Vehicle management system and vehicle management method | |
| CN109711588A (en) | Pick booking method, system, storage medium and the electronic equipment of machine service | |
| JP2002024659A (en) | Taxi dispatch reserving system | |
| CN111507810A (en) | Flight service method and system based on cluster analysis | |
| CN110111600B (en) | Parking lot recommendation method based on VANETs | |
| CN113269339B (en) | Method and system for automatically creating and distributing network appointment tasks | |
| WO2013044319A1 (en) | Method and system for coordinating usage of vehicles in a transport network | |
| Zhu et al. | Traffic efficiency improvement and passengers comfort in ridesharing systems in VANETs | |
| CN112258270A (en) | Method and device for generating carpooling travel |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200807 |