CN111831710A - Fuzzy search method for air tickets - Google Patents
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Abstract
The invention discloses an air ticket fuzzy search method. The method comprises the following steps: establishing a set A consisting of all airports and establishing a navigation matrix O based on the A; respectively determining a specific departure airport belonging to a fuzzy departure place and a specific destination airport belonging to a fuzzy destination; calculating a score for an airline from any origin airport to any destination airport; sequencing all the scores in a sequence from high to low to obtain N routes arranged in front; determining specific dates contained in the fuzzy dates, and inquiring all flights and fares of the N routes on each date; and screening the flights based on the fare, and recommending the screened flights with N routes on each date to the user. According to the invention, by embodying the fuzzy search condition, flight information meeting the user requirement can be recommended to the user according to the fuzzy condition input by the user, and the problem that the accurate search cannot meet the user diversity and uncertain search requirements is solved.
Description
Technical Field
The invention belongs to the technical field of airplane/ticket inquiry, and particularly relates to an airplane ticket fuzzy search method.
Background
With the rapid development of national economy and the improvement of the living standard of people, business trip becomes the essential content of daily life of people, and the trip by airplane becomes one of the important modes of public trip. When buying an air ticket, a user generally adopts an accurate searching method, and needs to input a specific departure place city name, a specific destination city name and a specific travel date. However, in many cases, the user does not have a specific travel plan, and needs to determine a travel route according to information such as season, holidays, or national regions. In this case, the conventional method for searching for air tickets cannot meet the searching requirement of uncertainty of users.
Therefore, the invention provides a fuzzy query method, which fuzzifies the query conditions, wherein the starting point can be a country, a continent, a region (southeast Asia, Europe and the like), and the date can be various combinations of years, months, holidays, date intervals and the like.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an air ticket fuzzy search method.
In order to achieve the purpose, the invention adopts the following technical scheme:
an airplane ticket fuzzy search method comprises the following steps:
step 2, acquiring a fuzzy departure place and a fuzzy destination input by a user, and respectively determining a specific departure place airport belonging to the fuzzy departure place and a specific destination airport belonging to the fuzzy destination;
step 3, calculating the score s of the air route from the ith departure airport to the jth destination airportij：
sij＝(ri 2+rj 2+ri×rj/(1+exp(-Omn/pij)))×1000/Dij(1)
In the formula, pijIs the average low price of the route in units of elements, ri、rjRespectively scoring the ith departure airport and the jth destination airport, m and n respectively being the corresponding row number and column number of the ith departure airport and jth destination airport in the matrix O, DijThe distance between the ith departure airport and the jth destination airport is expressed in kilometers;
step 4, sequencing all scores according to the sequence from high to low to obtain N routes corresponding to the N scores arranged in the front;
step 5, acquiring fuzzy dates input by a user, determining specific dates contained in the fuzzy dates, and inquiring all flights and fares of the N routes on each date;
and 6, screening the flights based on the fare, and recommending the screened flights with N routes on each date to the user.
Further, each airport is scored as follows:
wherein r is the airport score, fiScore the ith scoring factor, kiIs fiM is the number of scoring factors.
Further, D is calculated as followsij：
Dij＝R×arccos(sin(Lati)×sin(Latj)+cos(Lati)×cos(Latj)×cos(Loni-Lonj)) (3)
In the formula, Loni、LatiLongitude and latitude, Lon, respectively, of the ith origin airportj、LatjLongitude and latitude, Lon, respectively, of the jth destination airporti、Lati、Lonj、LatjThe unit of (A) is radian, R is the radius of the earth and the unit is thousandAnd (4) rice.
Compared with the prior art, the invention has the following beneficial effects:
the method comprises the steps of establishing a set A consisting of all airports and establishing a navigation matrix O based on the set A, respectively determining a specific departure place airport belonging to a fuzzy departure place and a specific destination airport belonging to a fuzzy destination, calculating scores of routes from any departure place airport to any destination airport, sequencing all the scores according to a sequence from high to low to obtain N routes arranged in front, determining specific dates contained in fuzzy dates, inquiring all flights and fares of the N routes on each date, screening the flights based on the fares, recommending the flights of the N routes on each date after screening to a user, and realizing fuzzy search of air tickets. According to the invention, by embodying the fuzzy search condition, flight information meeting the user requirement can be recommended to the user according to the fuzzy condition input by the user, and the problem that the accurate search cannot meet the user diversity and uncertain search requirements is solved.
Drawings
Fig. 1 is a flowchart of an air ticket fuzzy search method according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The invention discloses an air ticket fuzzy search method, a flow chart of which is shown in figure 1, and the method comprises the following steps:
s101, establishing a set A consisting of all airports, and establishing a navigation matrix O, O based on AijThe navigation characteristic values of the ith airport and the jth airport in A are obtained, and if the navigation is carried out and the flight is straight, O is carried outij2; if navigation and transit are required, O ij1 is ═ 1; if not navigation, then Oij＝0；OiiWhen the number is 0, O is an I-order square matrix, and I is the total number of airports;
s102, acquiring a fuzzy departure place and a fuzzy destination input by a user, and respectively determining a specific departure place airport belonging to the fuzzy departure place and a specific destination airport belonging to the fuzzy destination;
s103, calculating the ithScoring of routes from origin airport to jth destination airportij：
sij＝(ri 2+rj 2+ri×rj/(1+exp(-Omn/pij)))×1000/Dij(1)
In the formula, pijIs the average low price of the route in units of elements, ri、rjRespectively scoring the ith departure airport and the jth destination airport, m and n respectively being the corresponding row number and column number of the ith departure airport and jth destination airport in the matrix O, DijThe distance between the ith departure airport and the jth destination airport is expressed in kilometers;
s104, sequencing all the scores in a sequence from high to low to obtain N routes corresponding to the N scores arranged in the front;
s105, acquiring fuzzy dates input by a user, determining specific dates contained in the fuzzy dates, and inquiring all flights and fares of the N routes on each date;
s106, screening the flights based on the fare, and recommending the screened flights with N routes on each date to the user.
In this embodiment, step S101 is mainly used to establish a navigation matrix O. First, a set a composed of all airports is established, and the set a of the present embodiment may only include all airports of one country, or may include all airports of countries in the world. A navigation matrix O is then established based on a. And the value of each element in the O is a navigation characteristic value between two airports corresponding to the row and the column. The navigation characteristic value of this embodiment has 3 different integer values, which are 2, 1, and 0, respectively. When navigation is carried out between two airports and the airport flies straight, the characteristic value is the highest and is 2; when navigation is performed and transit is performed, the characteristic value is 1. The number of the middle rotation is not more than two in general; when the navigation is not performed, the characteristic value is minimum and is 0. It is clear that O is a square matrix with the order of the total number of airports. Element O on diagonal of OiiIt does not matter, and it may be set to 0 or any other value without taking into account the following score calculation.
In this embodiment, step S102 is mainly used to convert the fuzzy departure place and the fuzzy purpose input by the user into a specific airport. For example, if the fuzzy origin is northeast, then all airports in the three provinces of Heilongjiang, Liaoning and Jilin belong to the origin airport. In practical application, airport three-character codes can be adopted to represent specific airport names for the convenience of inquiry; the fuzzy departure/destination is represented by a national two-character code, a city three-character code, etc. And establishing a data table associating the fuzzy departure place/destination with the specific airport, and conveniently inquiring the corresponding specific airport according to the input fuzzy departure place/destination code.
In this embodiment, step S103 is mainly used to calculate the score of the route from any departure airport to any destination airport. The formula of the score of the air route is shown in the formula (1), and the score value is mainly determined by the score of two airports, the characteristic value of the two airports, the distance between the two airports and the average low price of the air route. Airport scoring considerations include location dominance, regional economic dominance, industry risk, airport levels, passenger throughput, freight throughput, airline business revenue, and management levels, among others. The average low price of the flight is the average of the lowest prices of flights based on historical data. (1) The 1000 in the formula is a balance factor, and since the distance value between two airports is large, generally more than 1000 km, a balance factor is set in order to avoid making the score value too small.
In this embodiment, step S104 is mainly used to obtain N routes with the highest scores by ranking the routes. Due to the large number of routes, screening is required. This step is the first screening by scoring and sorting.
In this embodiment, step S105 is mainly used to determine the specific date included in the fuzzy date input by the user, and query the lowest price of the N routes on each date. For example, the fuzzy travel date input by the user is october, the specific dates included in the fuzzy travel date should be 10/1/2020-10/31/2020, and 31 specific dates in total are required to check all flights and fares of N routes every 31 days.
In this embodiment, step S106 is mainly used to further filter flights of N routes per day and recommend the flights to the user. Mainly screening according to the fare, for example, deleting flights in each route whose fare exceeds a set threshold; or each route only recommends the flights with the lowest fare; .
As an alternative, each airport is scored as follows:
wherein r is the airport score, fiScore the ith scoring factor, kiIs fiM is the number of scoring factors.
The embodiment provides a technical scheme for scoring the airport. The airport scoring formula is shown as formula (2). And (4) scoring each factor influencing the scoring of the airport respectively, and then weighting and summing the scores of each factor to obtain the score of the airport.
As an alternative embodiment, D is calculated as followsij：
Dij＝R×arccos(sin(Lati)×sin(Latj)+cos(Lati)×cos(Latj)×cos(Loni-Lonj)) (3)
In the formula, Loni、LatiLongitude and latitude, Lon, respectively, of the ith origin airportj、LatjLongitude and latitude, Lon, respectively, of the jth destination airporti、Lati、Lonj、LatjThe units of (A) and (B) are radians, and R is the radius of the earth and has the unit of kilometers.
The embodiment provides a technical scheme for calculating the distance between two airports. The formula (3) is a general formula for calculating the distance between two airports according to the longitude and latitude coordinates and the radius of the earth, and will not be further described here.
The above description is only for the purpose of illustrating a few embodiments of the present invention, and should not be taken as limiting the scope of the present invention, in which all equivalent changes, modifications, or equivalent scaling-up or down, etc. made in accordance with the spirit of the present invention should be considered as falling within the scope of the present invention.
Claims (3)
1. The airplane ticket fuzzy search method is characterized by comprising the following steps:
step 1, establishing a set A consisting of all airports, and establishing a navigation matrix O, O based on AijThe navigation characteristic values of the ith airport and the jth airport in A are obtained, and if the navigation is carried out and the flight is straight, O is carried outij2; if navigation and transit are required, Oij1 is ═ 1; if not navigation, then Oij＝0；OiiWhen the number is 0, O is an I-order square matrix, and I is the total number of airports;
step 2, acquiring a fuzzy departure place and a fuzzy destination input by a user, and respectively determining a specific departure place airport belonging to the fuzzy departure place and a specific destination airport belonging to the fuzzy destination;
step 3, calculating the score s of the air route from the ith departure airport to the jth destination airportij：
sij＝(ri 2+rj 2+ri×rj/(1+exp(-Omn/pij)))×1000/Dij(1)
In the formula, pijIs the average low price of the route in units of elements, ri、rjRespectively scoring the ith departure airport and the jth destination airport, m and n respectively being the corresponding row number and column number of the ith departure airport and jth destination airport in the matrix O, DijThe distance between the ith departure airport and the jth destination airport is expressed in kilometers;
step 4, sequencing all scores according to the sequence from high to low to obtain N routes corresponding to the N scores arranged in the front;
step 5, acquiring fuzzy dates input by a user, determining specific dates contained in the fuzzy dates, and inquiring all flights and fares of the N routes on each date;
and 6, screening the flights based on the fare, and recommending the screened flights with N routes on each date to the user.
2. The fuzzy search method of air tickets according to claim 1, characterized in that each airport is scored according to the following formula:
wherein r is the airport score, fiScore the ith scoring factor, kiIs fiM is the number of scoring factors.
3. The fuzzy search method of air tickets according to claim 1, wherein D is calculated as followsij：
Dij＝R×arccos(sin(Lati)×sin(Latj)+cos(Lati)×cos(Latj)×cos(Loni-Lonj))(3)
In the formula, Loni、LatiLongitude and latitude, Lon, respectively, of the ith origin airportj、LatjLongitude and latitude, Lon, respectively, of the jth destination airporti、Lati、Lonj、LatjThe units of (A) and (B) are radians, and R is the radius of the earth and has the unit of kilometers.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112507207A (en) * | 2020-10-29 | 2021-03-16 | 南京意博软件科技有限公司 | Travel recommendation method and device |
| CN113159888A (en) * | 2021-04-19 | 2021-07-23 | 海南太美航空股份有限公司 | Flight information recommendation method and system and electronic equipment |
| CN113900961A (en) * | 2021-12-08 | 2022-01-07 | 深圳市活力天汇科技股份有限公司 | Sample generation method, device, equipment and medium for automatic testing |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004030178A (en) * | 2002-06-25 | 2004-01-29 | Nec Corp | System, method, and program for selling airline ticket |
| CN101636757A (en) * | 2007-03-13 | 2010-01-27 | 费尔卡斯特股份有限公司 | Deal identification system |
| AU2010100090A4 (en) * | 2010-01-29 | 2010-03-04 | Brokepacker Pty Ltd | Electronic booking system |
| CN104169911A (en) * | 2012-04-26 | 2014-11-26 | 艾玛迪斯简易股份公司 | Categorizing and ranking travel-related search results |
| CN105630979A (en) * | 2015-12-25 | 2016-06-01 | 中国民航信息网络股份有限公司 | Flight query method, device and system |
| US20170061555A1 (en) * | 2015-08-24 | 2017-03-02 | Mastercard International Incorporated | Method and system for predicting lowest airline ticket fares |
| CN106682207A (en) * | 2016-12-30 | 2017-05-17 | 中国民航信息网络股份有限公司 | Method and device for finding airlines |
| CN106802944A (en) * | 2017-01-06 | 2017-06-06 | 中国东方航空股份有限公司 | A kind of flight searching system and method |
| CN107066610A (en) * | 2017-05-02 | 2017-08-18 | 中国联合网络通信集团有限公司 | A kind of price queries method and apparatus |
| KR101813475B1 (en) * | 2017-01-31 | 2017-12-29 | 주식회사 스켈터랩스 | Artificial intelligence-based travel and ticket recommendation system |
| CN107844611A (en) * | 2017-12-15 | 2018-03-27 | 携程旅游网络技术（上海）有限公司 | Multiple spot to multiple spot flight search method, system, equipment and storage medium |
| CN109783545A (en) * | 2019-01-24 | 2019-05-21 | 深圳市活力天汇科技股份有限公司 | A kind of air ticket real-time recommendation method |
| CN109934678A (en) * | 2019-03-11 | 2019-06-25 | 深圳市活力天汇科技股份有限公司 | A kind of flight scoring method based on user preference |
| CN110060129A (en) * | 2019-04-22 | 2019-07-26 | 深圳市活力天汇科技股份有限公司 | A kind of air ticket intelligent recommendation method |
| KR20190095573A (en) * | 2018-01-22 | 2019-08-16 | (주) 올윈웨어 | Method and apparatus for searching flight ticket |
-
2020
- 2020-07-17 CN CN202010695624.5A patent/CN111831710B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004030178A (en) * | 2002-06-25 | 2004-01-29 | Nec Corp | System, method, and program for selling airline ticket |
| CN101636757A (en) * | 2007-03-13 | 2010-01-27 | 费尔卡斯特股份有限公司 | Deal identification system |
| AU2010100090A4 (en) * | 2010-01-29 | 2010-03-04 | Brokepacker Pty Ltd | Electronic booking system |
| CN104169911A (en) * | 2012-04-26 | 2014-11-26 | 艾玛迪斯简易股份公司 | Categorizing and ranking travel-related search results |
| US20170061555A1 (en) * | 2015-08-24 | 2017-03-02 | Mastercard International Incorporated | Method and system for predicting lowest airline ticket fares |
| CN105630979A (en) * | 2015-12-25 | 2016-06-01 | 中国民航信息网络股份有限公司 | Flight query method, device and system |
| CN106682207A (en) * | 2016-12-30 | 2017-05-17 | 中国民航信息网络股份有限公司 | Method and device for finding airlines |
| CN106802944A (en) * | 2017-01-06 | 2017-06-06 | 中国东方航空股份有限公司 | A kind of flight searching system and method |
| KR101813475B1 (en) * | 2017-01-31 | 2017-12-29 | 주식회사 스켈터랩스 | Artificial intelligence-based travel and ticket recommendation system |
| CN107066610A (en) * | 2017-05-02 | 2017-08-18 | 中国联合网络通信集团有限公司 | A kind of price queries method and apparatus |
| CN107844611A (en) * | 2017-12-15 | 2018-03-27 | 携程旅游网络技术（上海）有限公司 | Multiple spot to multiple spot flight search method, system, equipment and storage medium |
| KR20190095573A (en) * | 2018-01-22 | 2019-08-16 | (주) 올윈웨어 | Method and apparatus for searching flight ticket |
| CN109783545A (en) * | 2019-01-24 | 2019-05-21 | 深圳市活力天汇科技股份有限公司 | A kind of air ticket real-time recommendation method |
| CN109934678A (en) * | 2019-03-11 | 2019-06-25 | 深圳市活力天汇科技股份有限公司 | A kind of flight scoring method based on user preference |
| CN110060129A (en) * | 2019-04-22 | 2019-07-26 | 深圳市活力天汇科技股份有限公司 | A kind of air ticket intelligent recommendation method |
Non-Patent Citations (3)
| Title |
|---|
| JIAN CAO 等: "PFS:A Personalized Flight Recommendation Service via Cross-Domain Triadic Factorization", 2018 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES(ICWS), pages 249 - 256 * |
| 易淑娟: "一种改进的企业差旅国内机票推荐算法", 广东通信技术, vol. 40, no. 03, pages 61 - 66 * |
| 陈梦曦等: "考虑等级的民航个性化航空路线推荐模型", 工业工程与管理, vol. 24, no. 03, pages 139 - 146 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112507207A (en) * | 2020-10-29 | 2021-03-16 | 南京意博软件科技有限公司 | Travel recommendation method and device |
| CN113159888A (en) * | 2021-04-19 | 2021-07-23 | 海南太美航空股份有限公司 | Flight information recommendation method and system and electronic equipment |
| CN113900961A (en) * | 2021-12-08 | 2022-01-07 | 深圳市活力天汇科技股份有限公司 | Sample generation method, device, equipment and medium for automatic testing |
| CN113900961B (en) * | 2021-12-08 | 2022-03-01 | 深圳市活力天汇科技股份有限公司 | Sample generation method, device, equipment and medium for automatic testing |
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