CN108717640B - Data processing method of travel information and electronic equipment - Google Patents
Data processing method of travel information and electronic equipment Download PDFInfo
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- 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
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0259—Targeted advertisements based on store location
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- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/12—Hotels or restaurants
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- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/14—Travel agencies
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The application provides a data processing method of travel information, which comprises the following steps: providing a travel route comprising a target city string and target scenic spots poi of each target city; obtaining head, middle and/or tail poi in each trip from the target city string; traversing nearby hotels of the head, middle and/or tail poi, judging whether hotels within a first distance threshold exist, and if so, creating a basic hotel set according to the hotels within the first distance threshold; if not, screening according to the urban hotel ranking list obtained in the real user travel database, and then creating a basic hotel set; acquiring poi coverage indexes of all hotels in the basic hotel set, namely the number of target scenic spots poi covered by all hotels within a second distance threshold range; and sorting according to the poi coverage index to obtain a recommended hotel set. By the data processing method provided by the application, the intelligent mode is closer to the actual traveling condition, and the data processing method is more reasonable and efficient.
Description
Technical Field
The application relates to the technical field of big data of computers, in particular to a processing method of travel route data and electronic equipment.
Background
Today, with rapid development of internet technology, internet services gradually penetrate into various aspects of social life, traffic, shopping, catering, travel and the like, netizens release own travel information on the internet to form travel champions, shortages or travel marks, other travelers compile personalized travel routes according to own favorites according to the information, the travel experience is more diversified, information asymmetry in the travel industry is broken, sharing economy represented by free behaviors is stimulated, and common travel agencies are gradually replaced with group travel.
The current computer aided travel planning technology is endless, and patent with application number of CN201310092521.X provides a travel planning assisting method and system, firstly, according to travel destination information submitted by Internet user, obtaining related data information of various travel resources corresponding to the travel destination from a server; analyzing the related data information of each travel resource, creating an operable page element, and displaying in a travel resource display area of the page; then, monitoring the operation executed by the user on the page element, and determining specific travel resources needing to be added to the travel itinerary; creating page elements corresponding to the specific travel resources and displaying the page elements in a travel schedule planning area; and finally, displaying the formulated travel plan information according to each page element contained in the travel plan formulation area of the page.
In the information such as hotels, scenic spots, restaurants and the like provided by the method, advertisement implantation is carried out according to manual setting or hotel star level, and even advertisement implantation is not close to the real demands of users, and the convenience of travel is not considered.
Disclosure of Invention
The application provides a data processing method and a data processing system for a travel route, which are used for solving the problem that the prior art cannot more intelligently and accurately create a reasonable and detailed travel route.
The application provides the following scheme: a data processing method of travel information, comprising:
providing a travel route comprising a target city string and target scenic spots poi of each target city;
obtaining head, middle and/or tail poi in each trip from the target city string;
traversing nearby hotels of the head, middle and/or tail poi, judging whether hotels within a first distance threshold exist, and if so, creating a basic hotel set according to the hotels within the first distance threshold; if not, screening according to the urban hotel ranking list obtained in the real user travel database, and then creating a basic hotel set;
acquiring poi coverage indexes of all hotels in the basic hotel set, namely the number of target scenic spots poi covered by all hotels within a second distance threshold range;
and sorting according to the poi coverage index to obtain a recommended hotel set.
Further comprises: and calculating at least one preferred recommended hotel from the recommended hotel set according to the position value, the popularity value and/or the cost value.
Screening and obtaining a basic hotel set according to a ranking list of urban hotels obtained from a real user travel database, wherein the basic hotel set comprises the following steps:
inquiring the city ID of the last city in the travel route;
acquiring all hotels conforming to the city ID and sorting according to star grades;
and screening the basic hotel set from high to low according to a star level according to a preset value.
The location values include hotel poi coverage factors and/or traffic factors.
The popularity value includes: internet platform popularity factors, number of occurrences in real user trips factors, and/or hotel order volume factors.
The cost performance value comprises: the same star rating price ratio factor and/or the same star rating score ratio factor.
The creation of the basic hotel set may also be preceded by screening according to a preset luxury.
And the step of obtaining the recommended hotel set further comprises the step of inquiring comprehensive description information of the recommended hotels from a real user travel itinerary database.
If the hot spot poi or the traffic poi does not belong to the target spot poi, the hotel poi coverage factor further comprises the number of covered hot spots poi or traffic poi.
The application also provides a data processing electronic device of the travel information, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
providing a travel route comprising a target city string and target scenic spots poi of each target city;
obtaining head, middle and/or tail poi in each trip from the target city string;
traversing hotels near all target scenic spots poi, judging whether hotels within a first distance threshold exist, and if so, creating a basic hotel set according to the hotels within the first distance threshold; if not, screening according to the urban hotel ranking list obtained in the real user travel database, and then creating a basic hotel set;
acquiring poi coverage indexes of all hotels in the basic hotel set, namely the number of target scenic spots poi covered by all hotels within a second distance threshold range;
and sorting according to the poi coverage index to obtain a recommended hotel set.
By the data processing method of the travel journey provided by the application, the travel journey data processing method is more intelligent, is close to the actual travel condition, and is more reasonable and efficient.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed 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 application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method according to a first embodiment of the present application.
FIG. 2 is a flow chart of a method of generating travel itineraries in accordance with an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which are derived by a person skilled in the art based on the embodiments of the application, fall within the scope of protection of the application.
In the embodiments of the present application, the following terms have at least such meanings:
travel information of real user: refers to the travel itinerary, log, purview (e.g., poor tour mall), experience, etc. that an actual traveler physically experiences after completing his own itinerary, published on an internet platform, including but not limited to a forum (e.g., poor tour forum), microblog, web page, or blog.
Travel journey: i.e., travel planning, the entire detailed schedule formulated for travel, including but not limited to, destination, travel time, traffic, attractions, dining, accommodation, shopping, entertainment, weather, and fee.
POI: POIs (POI) are abbreviations of "Point of Interest" and can be translated into "information points", each POI containing information on four aspects, name, category, latitude and longitude, nearby hotel shops, etc.
Hotels in embodiments of the application include, but are not limited to, hotels, restaurants, civilians, night markets, and the like, where dining and/or accommodation services are provided.
Example 1
As shown in fig. 1, the present embodiment provides a data processing method for travel information, which intelligently recommends information such as hotels or restaurants in a travel route according to operation and processing of real travel big data of tens of millions of users, and is closer to real demands of users, and meanwhile, the convenience of travel is considered.
The data processing method of travel information of the embodiment comprises the following steps:
step S1: a travel itinerary is provided that includes a string of target cities and target attractions poi for each target city.
The travel route can be through poor travel TM Travel assistant TM The computer system intelligently creates and optimizes, for example, firstly creates a playing path of a target country according to the preset target country and days of travel, calculates the playing days of the target country according to the national standard playing day table in the real user travel database (Traveler Big Data, TBD), then calculates the shortest playing path and playing days of the target cities of the target countries, and finally generates a scenic spot route of each target city, namely a target scenic spot poi.
Preferably, also according to poor game TM Statistical information in real user travel databaseThe stroke is optimized, so that the stroke is more reasonable. The necessary factors in the intelligent travel route are also the arrangement of dining and lodging, so that the hotel and dining information needs to be recommended for the user as much as possible, and the intelligent travel route is arranged reasonably according to the scenic spot path.
Step S2: head, middle and/or tail poi in each trip are obtained from the target city string. For example, obtaining that city strings of Day4 in travel are paris and valsalva by querying city IDs in travel, and obtaining all scenic spots poi of the valsalva, namely Gong Yuan of holy-lewy main church, king vegetable garden, palace of valsalva and Mary-An Tuowa inner t if the last city in the Day is the valsalva; from this, the head poi-san Louis main church, the middle poi-King vegetable garden, and the tail poi-Mary-An Tuowa Net Gong Yuan were obtained according to the travel poi sequence.
In other embodiments of the application, it is also possible to acquire only one or two combinations of the head poi, tail poi and middle poi in the daily course, preferably, the head, middle and tail poi simultaneously.
Step S3: traversing hotels near the head, middle and/or tail poi, judging whether hotels within a first distance threshold exist, and if so, creating a basic hotel set according to the hotels within the first distance threshold; if not, a basic hotel set is created after screening according to the urban hotel ranking list obtained in the real user travel database.
For example, hotels 1 km (first distance threshold) near the holy lewis main church are queried, the hotels are used as basic hotel sets, if no conditional hotels are met within the first distance threshold, a city hotel ranking list business_host/reservation_city_rank in a real user travel database is called, a hotel set in the city (meeting city ID) meeting the user set star is screened out, or hotels with star ranks at the front are selected according to a preset value, and then the basic hotel set is created. The urban hotel ranking list is a ranking list of all hotels meeting the urban ID according to star grades.
Step S4: and acquiring the poi coverage index of each hotel in the basic hotel set, namely the number of the target scenic spots poi covered by each hotel in a second distance threshold range. For example, the basic hotel set includes hotels A, B, C within 1 km around the head poi san diei main church, and the number of target scenic spots poi covered by the hotels A, B, C within 2 km radius is obtained, that is, how many poi in the current day trip are included in each of the three hotels within 2 km radius.
Step S5: and sorting according to the poi coverage index to obtain a recommended hotel set. That is, the ranking of hotels with the largest number of target attractions poi is the top, ordered by the number of target attractions poi included in the current trip.
Hotel A: covering the number of poi 5;
hotel B: covering the number of poi 3;
hotel C: covering the poi number 2.
In this way, hotels with a large number of points poi covering the whole scene are recommended according to hotels near the head, middle and/or tail poi in each journey, so that the hotels can be selected by a user, the hotels are closely combined with the personalized journey of the user, and the comprehensive distance is optimized and more reasonable.
In other embodiments of the present application, the method for processing data of travel information further includes step S6: and calculating at least one recommended hotel from the basic hotel set according to the position value, the popularity value or the cost value. According to the scheme, more intelligent recommended hotels can be provided, and users can increase corresponding priority recommended conditions only by increasing screening conditions: location value, popularity value, and/or cost value ratio.
The location value is used for evaluating hotel traffic convenience and specifically comprises a hotel poi coverage factor and/or a hotel traffic factor. The hotel poi coverage factor refers to the number of scenic spots poi within a threshold distance around the hotel, for example, 3 scenic spots poi within 1 km around the hotel in the hotel coverage travel route, that is, the san dieyi main church, the King vegetable garden and the Versailles palace, the poi coverage factor of the Versailles hotel is 3, and 1 scenic spot poi within 1 km around the hotel in the Versailles palace coverage travel route is 1, that is, the Versailles palace, the poi coverage factor is 1. The traffic factors refer to the traffic information score of the hotel poi, and the factors of the hotel approaching the traffic hub, the subway station, the main road and the like are all factors for increasing the traffic information score.
Preferably, the position value is calculated by the following formula:
position value = m hotel poi coverage factor + n hotel traffic factor, m, n being weights
The popularity value is used for evaluating the frequency of the real user focusing on or using a hotel, and specifically comprises the following steps: internet platform heat factor, and/or hotel order volume factor.
The internet platform popularity factor refers to the frequency of occurrence of a hotel in the contents of travel routes, attacks and the like which are published on the internet platform and experienced by the hotel in person, such as the frequency of occurrence of a Versailles hotel in a poor game bbs article, and the internet platform can also attack the travel in other travel forums, microblogs or blogs.
The number of times of occurrence factor in the real user journey refers to the number of times that a hotel occurs in the journey made by the real user, for example, the real user utilizes poor tour TM Travel assistant TM In the intelligently formulated journey of the computer system, the number of times the Versailles appear in the journey of the Versailles hotel can be selected, and the journey of the real user can be obtained by other automatic journey creation methods in the prior art.
The hotel order quantity factor refers to the order quantity of a hotel reserved by a real user through an internet platform including but not limited to a poor game network TM Travel network platforms such as a travel network, a hotel network and the like.
The three factors may be historical accumulated values or values over a specific period of time, such as by year, by quarter, by month, etc.
Preferably, the popularity value is calculated by the following formula:
man value = x internet platform heat factor + y number of occurrences factor + z hotel order quantity factor in real user trip, x, y, z are weights
The cost performance value is used for measuring the comprehensive cost performance of the hotel, and specifically comprises the following steps: a co-star price ratio factor (composite_price) and/or a co-star score ratio factor (composite_grade).
The same-star price ratio factor refers to how much cheaper a hotel is than the same-star hotel, and the same-star grade grading ratio factor refers to how much higher a hotel is than the same-star hotel.
According to the data processing method for the travel information, provided by the embodiment of the application, the recommended hotels in the travel are calculated from the information of the travel database of the real user in a combined way through the factors such as the position value, the popularity value and the cost performance ratio, the position value enables the recommended hotels to be more convenient in transportation and reach the most scenic spots, the popularity value enables the recommended hotels to be closer to the experience of the real user, the cost performance ratio enables the recommended hotels to be more reasonable, and in short, the intelligent recommended information of the food in the travel is closer to the real demands of the user, and meanwhile, the convenience of travel is taken into account.
Example two
In general, the customer travel-selected sink service is based on personal economic conditions, and the intelligent travel route creation needs to meet the needs of such differentiation, and compared with the above embodiment, the data processing method of travel information provided in this embodiment further includes a step of screening according to a preset luxury degree before the creation of the basic hotel set.
The luxury is to classify the hotels in a database according to the consumption level and write in classification labels, for example, the hotels are classified into economy type (no star-2 star), ordinary type (3 star-4 star) and luxury type (5 star and above) according to the star standard of accommodation type hotels, whether the hotels are obtained by a distance threshold or a city hotel ranking list, and a basic hotel set is created after screening through the preset luxury.
For example, if a user presets a luxury as "normal", the user inquires about hotels 1 km (distance threshold) around all target attractions of the valve event, screens out hotels with a luxury label of normal, and creates a basic hotel set. The luxury level may also be set to a luxury level according to average consumer prices of the hotel, with the hotel being classified directly according to the luxury level.
In another embodiment of the present application, after the calculating to obtain the recommended hotel, the calculating further includes querying comprehensive description information (business_hotel_description) of the recommended hotel from a real user travel itinerary database, so as to obtain hotel details, and adding the hotel details to the itinerary, including but not limited to location, traffic, food, environment, service, decoration, etc.
In another embodiment, if the hot spot poi or the traffic poi does not belong to the target spot poi, the poi coverage factor of the hotel further includes the number of covered hot spots poi or traffic poi.
Correspondingly, the embodiment of the application also provides a data processing electronic device for travel information, which comprises:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
providing a travel route comprising a target city string and target scenic spots poi of each target city;
obtaining head, middle and/or tail poi in each trip from the target city string;
traversing hotels near all target scenic spots poi, judging whether hotels within a first distance threshold exist, and if so, creating a basic hotel set according to the hotels within the first distance threshold; if not, screening according to the urban hotel ranking list obtained in the real user travel database, and then creating a basic hotel set;
acquiring poi coverage indexes of all hotels in the basic hotel set, namely the number of target scenic spots poi covered by all hotels within a second distance threshold range;
and sorting according to the poi coverage index to obtain a recommended hotel set.
The application also provides a travel route generation method, which is used for intelligently making a travel route for a user based on analysis and excavation of structured data such as user requirements, behavior preferences and the like by analyzing and excavating travel information (such as mass UGC content generated by 6000 ten thousand user interactions of a poor game network).
As shown in fig. 2, the method specifically includes:
step S1': analyzing the travel information of the real user to generate a real user travel database;
after the traveler finishes the travel journey, the journey can be with the group tour or free, the travel journey, the log and the attack of the traveler can be personally experienced by the traveler, which are published on the internet, such as posting in a poor tour forum, sharing the travel itinerary and experience of the traveler, and the like.
The travel information is different from general introduction such as travel guidance and the like, and is derived from the in-person experience of the traveler, and is more real, more specific and fresher. Of course, not limited to forums, but also includes microblogs, webpages, blogs, or other online information distribution and communication channels.
Specifically, the analyzing the travel information of the real user includes:
decomposing the travel information into metadata, the metadata comprising: POIs, traffic information, play time, door opening and closing time, and/or visas;
the travel information of the real user is collected, personalized information of thousands of travelers is various in form, different in content length, photo-containing, video-containing and text-containing, and metadata in the embodiment of the application extracts and decomposes the travel information of the real user, and the travel structured data.
And the metadata is input into the real user travel database according to a travel data structure.
In addition, the travel information of the real users on the internet is good and bad, repeated, wrong, outdated and inferior information is doped, and screening processing is necessary to improve the effectiveness of the data. Preferably, the analyzing the travel information of the real user further includes:
travel information with quality metrics above a set threshold is screened, and the influencing factors of the quality metrics include: content fullness, release time, and/or user liveness.
The quality is used for measuring the quality of the real user travel information and is obtained through calculation of a plurality of influencing factors.
The content fullness expresses the length of the space of the travel information, and the longer the space is, the more the content is plump, and the higher the authenticity and the sharing value of the content are; the release time represents release and update time of the travel information on the internet, and the more up-to-date travel information is reliable; the user liveness represents the login, release, editing or comment frequency of a real user on the online platform, and the higher the frequency is, the higher the liveness is, and the higher the quality of travel information is.
Step S2': and creating a recommended form and a recommended value of the travel information according to the real user travel database.
And ordering and assigning the scenic spot POIs, hotels, traffic, catering, lines and the like in the metadata by the recommendation values through recommendation rules.
The recommendation form includes, but is not limited to: country recommended play day table count_play_day, city line table play_count_route_mod, city recommended play day table city_play_day, sight recommended play time table poi_standard_tour_time, city sight table play_recommendable_trip, city traffic table play_recommendable_traffic, and the like.
And S3', generating a travel journey according to the recommended form and the recommended value.
Preferably, the method of the present application further comprises adding a category label to the generated travel itinerary, the category label including, but not limited to: food, nature scenery, historical humanity, shopping or parent-child, so that the user can select different types of strokes according to own preference.
In other embodiments of the present application, the method for processing travel data further includes, first obtaining an update data packet of the travel information of the real user, and then analyzing the update data packet. Thus, the latest and most effective travel information can be continuously added to the real user travel database, so that a better and more real travel journey can be obtained.
From the above description of embodiments, it will be apparent to those skilled in the art that the present application may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present application.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for a system or system embodiment, since it is substantially similar to a method embodiment, the description is relatively simple, with reference to the description of the method embodiment being made in part. The systems and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present application without undue burden.
The principles and embodiments of the present application have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present application and the core ideas thereof; also, it is within the scope of the present application to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the application.
Claims (5)
1. A method for processing travel information data, comprising:
providing a travel route comprising a target city string and target scenic spots poi of each target city;
obtaining head, middle and tail poi in each trip from the target city string;
traversing nearby hotels of the head, middle and tail poi, judging whether hotels within a first distance threshold exist, and if so, creating a basic hotel set according to the hotels within the first distance threshold; if not, screening according to the urban hotel ranking list obtained in the real user travel database, and then creating a basic hotel set;
acquiring poi coverage indexes of all hotels in the basic hotel set, namely the number of target scenic spots poi covered by all hotels within a second distance threshold range;
according to the poi coverage index ranking, a recommended hotel set is obtained, namely, the ranking of hotels with the largest number of target scenic spots poi is higher according to the ranking of the number of target scenic spots poi in the trip of the current day;
further comprises: calculating at least one preferred recommended hotel from the recommended hotel set according to a location value, wherein the location value comprises a hotel poi coverage factor and a hotel traffic factor;
the hotel poi coverage factor refers to the number of scenic spots poi in a distance threshold near a hotel, the hotel traffic factor refers to the traffic information score of the hotel, and the position value is calculated by the following formula:
location value = m hotel poi coverage factor + n hotel traffic factor, m, n being weights.
2. The method according to claim 1, wherein the basic hotel set is obtained by screening according to a ranking list of urban hotels obtained in a travel database of real users, and specifically comprises:
inquiring the city ID of the last city in the travel route;
acquiring all hotels conforming to the city ID and sorting according to star grades;
and screening the basic hotel set from high to low according to a star level according to a preset value.
3. The method of claim 1, further comprising, prior to creating the base hotel set, screening according to a predetermined luxury level.
4. The method of claim 1, further comprising querying a real user travel itinerary database for comprehensive description information of the recommended hotels after the obtaining of the recommended hotel set.
5. A data processing electronic device for travel information, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
a method of performing the travel information data processing method of any one of claims 1-4.
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| CN201810069005.8A CN108717640B (en) | 2018-01-24 | 2018-01-24 | Data processing method of travel information and electronic equipment |
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