CN111382900B - Tourism prediction platform and method for realizing big data analysis - Google Patents

Tourism prediction platform and method for realizing big data analysis Download PDF

Info

Publication number
CN111382900B
CN111382900B CN202010078939.5A CN202010078939A CN111382900B CN 111382900 B CN111382900 B CN 111382900B CN 202010078939 A CN202010078939 A CN 202010078939A CN 111382900 B CN111382900 B CN 111382900B
Authority
CN
China
Prior art keywords
journey
user
travel
module
prediction
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.)
Active
Application number
CN202010078939.5A
Other languages
Chinese (zh)
Other versions
CN111382900A (en
Inventor
不公告发明人
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing Terminus Technology Co Ltd
Original Assignee
Chongqing Terminus Technology Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Chongqing Terminus Technology Co Ltd filed Critical Chongqing Terminus Technology Co Ltd
Priority to CN202010078939.5A priority Critical patent/CN111382900B/en
Publication of CN111382900A publication Critical patent/CN111382900A/en
Application granted granted Critical
Publication of CN111382900B publication Critical patent/CN111382900B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries
    • 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
    • G06Q50/14—Travel agencies

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Tourism & Hospitality (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Software Systems (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Quality & Reliability (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Navigation (AREA)

Abstract

The invention discloses a tourism prediction platform for realizing big data analysis and a method thereof, wherein the tourism prediction platform comprises the following steps: the system comprises an acquisition module, a stroke characteristic analysis module, a generation module, a prediction module and a display module; the acquisition module acquires a user journey; the travel characteristic analysis module is used for carrying out travel characteristic analysis on the user travel to generate a travel characteristic analysis result; the generation module generates a big data set aiming at the user journey according to the journey characteristic analysis result; the prediction module predicts according to the progress of the user journey based on the big data set and generates a prediction prompt; the display module displays the prediction prompt on the user trip. The system can accurately predict the relevant information of the tourism and the intelligent service, and improves the user experience.

Description

Tourism prediction platform for realizing big data analysis and method thereof
Technical Field
The invention relates to the technical field of travel prediction, in particular to a travel prediction platform and a travel prediction method for realizing big data analysis.
Background
At present, personal tours and family tours with higher flexibility gradually replace group tours, in order to adapt to the change, intelligent tourism based on information technologies such as mobile internet, positioning and the like is increasingly and deeply developed, and various services such as scenic spot navigation, automatic weather forecast, peripheral scenic spots and service push, reservation of lodging dining place tickets, scenic spot number and queuing time advance notice and the like can be realized.
However, in general, the depth of big data information collection and analysis by the current intelligent travel service is insufficient, so that the predictability of the provided travel-related information and the intelligent service needs to be enhanced. The travel forecasting service is caused by the following reasons that the travel forecasting service relates to a plurality of data sources and data types, the data volume is large, the time-varying property of the data is strong, for example, the travel forecasting service relates to a plurality of types and sources of weather, traffic, people flow, positions, service supply and demand of merchants, ticketing and the like, the change speed of each type of data is different, the effective duration difference is large, the standard is difficult to unify, and great difficulty is caused to deep mining analysis. The travel characteristics and service requirements of guests are diversified, resulting in the range of data involved in providing service to each particular guest being not readily determinable. Furthermore, various intelligent travel services lack a unified human-computer interface and interaction mode, tourists need to call a lot of apps, and various services and information lack integration.
Therefore, how to improve the predictability of the travel-related information and the intelligent service is a problem to be solved urgently by the technical personnel in the field.
Disclosure of Invention
In view of the above problems, the present invention aims to solve the problems that the data sources and data types of the current travel prediction service design are many, the data amount is large, the time-varying property of data is strong, the data range related to the service provided by each tourist is not easy to determine, and the prediction of the provided travel related information and the intelligent service is not strong due to the lack of the same human-computer interface and interaction mode, so that the travel related information and the intelligent service are accurately predicted, and the user experience is improved.
The embodiment of the invention provides a travel prediction platform for realizing big data analysis, which comprises: the system comprises an acquisition module, a stroke characteristic analysis module, a generation module, a prediction module and a display module;
the acquisition module is connected with the journey characteristic analysis module and is used for acquiring the user journey according to the starting point of the user journey and the end point of the user journey and sending the user journey to the journey characteristic analysis module;
the travel characteristic analysis module is connected with the acquisition module and the generation module and is used for performing travel characteristic analysis on the user travel to generate a travel characteristic analysis result and sending the travel characteristic analysis result to the generation module;
the generating module is connected with the travel characteristic analysis module and the prediction module and used for generating a big data set aiming at the user travel according to the travel characteristic analysis result and sending the big data set to the prediction module;
the prediction module is connected with the generation module and used for predicting according to the progress of the user journey based on the big data set, generating a prediction prompt and sending the prediction prompt to the display module;
the display module is connected with the acquisition module and the prediction module and is used for acquiring the starting point of the user journey and the end point of the user journey, sending the starting point of the user journey and the end point of the user journey to the acquisition module and displaying the prediction prompt on the user journey.
In one embodiment, the user trip comprises:
and taking the starting point selected by the user as the starting point of the user journey route, and generating the path of the user journey route according to the starting point and the end point.
In one embodiment, the user trip further comprises:
and taking the real-time position of the user as a starting point of the user journey route, and generating a route of the user journey route according to the starting point and the end point.
In one embodiment, the trip characteristic analysis module includes: the system comprises a spatial scale analysis unit, a departure place and destination attribute analysis unit, a journey path attribute analysis unit and a journey path attribute analysis unit;
the spatial scale analysis unit is connected with the acquisition module and is used for carrying out spatial scale analysis on the user journey according to the accumulated distance of the user journey or the spatial distances of the starting point and the ending point of the journey;
the departure place and destination attribute analysis unit is connected with the acquisition module and is used for analyzing the attributes of the departure place and the destination of the user journey;
the on-road attribute analysis unit is connected with the acquisition module and is used for judging the attribute of the on-road passing area of the user journey;
the travel route attribute analysis unit is connected with the acquisition module and is used for analyzing the road, traffic condition attribute, vehicle supply amount and route difficulty attribute of the user travel route.
In one embodiment, the generating module includes: a definition unit, a generation unit;
the definition unit is connected with the generation unit and used for defining travel modes corresponding to different travel characteristics and sending the travel modes to the generation unit;
the generating unit is connected with the journey feature analysis module, the defining unit and the prediction module and is used for determining the journey mode with the highest matching degree with the journey feature of the user, forming a mode according to a data set corresponding to the journey mode and generating a large data set aiming at the journey of the user.
In one embodiment, the predictive alert includes:
the prediction hint is displayed in the form of a descriptive label.
In view of the above, in a second aspect of the present application, there is provided a prediction method for a travel prediction platform implementing big data analysis, including:
the display module collects the starting point of the user journey and the end point of the user journey and sends the starting point of the user journey and the end point of the user journey to the acquisition module;
according to the starting point of the user journey and the end point of the user journey, the obtaining module obtains the user journey and sends the user journey to the journey characteristic analysis module;
the journey characteristic analysis module is used for carrying out journey characteristic analysis on the user journey to generate a journey characteristic analysis result, and the journey characteristic analysis result is sent to the generation module;
according to the travel characteristic analysis result, the generation module generates a large data set aiming at the user travel;
based on the big data set, the prediction module predicts according to the progress of the user journey, generates a prediction prompt and sends the prediction prompt to the display module;
the display module displays the prediction prompt on the user journey.
In one embodiment, the process characteristic analysis module performs process characteristic analysis on the user process to generate process characteristic analysis results, including:
according to the accumulated distance of the user journey or the spatial distance of the journey starting point and the journey ending point, a spatial scale analysis unit carries out spatial scale analysis on the user journey;
analyzing attributes of the departure place and the destination of the user journey through an attribute analysis unit of the departure place and the destination;
the journey attribute analysis unit judges the attribute of the user journey passing area;
the travel route attribute analysis unit analyzes the road, the traffic condition attribute, the vehicle supply amount and the route difficulty attribute of the user travel route.
In one embodiment, the generating module generates a large data set for the user trip according to the trip feature analysis result, including:
the method comprises the steps that stroke modes corresponding to different stroke characteristics are defined through a definition unit, and the stroke modes are sent to a generation unit;
and determining the travel mode with the highest matching degree with the travel characteristics of the user, and generating a big data set aiming at the user travel according to a data set composition mode corresponding to the travel mode by the generating unit.
In one embodiment, the generating unit generates a large data set for the user journey according to a data set composition mode corresponding to the journey mode, and includes:
and according to a data set composition mode corresponding to the journey mode, organizing data sources and data types, setting a data updating mode, and generating a large data set aiming at the user journey.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
according to the travel prediction platform and the method for realizing big data analysis, provided by the embodiment of the invention, the travel characteristic analysis module is used for carrying out multi-dimensional analysis on the user travel, so that a big data set matched with the user travel characteristic is formed, and the data recorded by the big data set and the updating mode are matched with the user travel characteristic, so that the problems of large data volume, multiple data sources and types and strong time-varying property of the data are solved, the pertinence and the time-varying property of travel prediction service are enhanced, the prediction of travel related information and intelligent service is improved, and the user experience is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of a travel prediction platform for implementing big data analysis according to an embodiment of the present invention;
FIG. 2 is a flowchart of a prediction method of a travel prediction platform for implementing big data analysis according to an embodiment of the present invention;
fig. 3 is a flowchart of step S203 according to an embodiment of the present invention;
fig. 4 is a flowchart of step S204 according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Referring to fig. 1, a travel prediction platform for implementing big data analysis according to an embodiment of the present invention includes: the system comprises an acquisition module 1, a travel characteristic analysis module 2, a generation module 3, a prediction module 4 and a display module 5;
the obtaining module 1 is connected with the journey feature analysis module 2, and is used for obtaining the user journey according to the starting point of the user journey and the end point of the user journey, and sending the user journey to the journey feature analysis module 2.
Specifically, the user journey includes: taking a starting point selected by a user as a starting point of the user journey route, and generating a path of the user journey route according to the starting point and the end point;
or, the real-time position of the user is used as the starting point of the user journey route, and the route of the user journey route is generated according to the starting point and the end point.
Further, when a plurality of selectable paths exist between the starting point and the end point, the user is prompted to select the selectable paths, or each selectable path can be used as an alternative journey; the user does not need to strictly advance according to the pre-input journey route in actual travel, and can find that the position information of the tourist deviates from the pre-input journey route according to the position information of the tourist collected in real time in the travel process, and update the set journey route or a plurality of alternative journey routes by taking the current real-time position of the tourist as a starting point and the target position input by the tourist as a terminal point.
The journey feature analysis module 2 is connected with the acquisition module 1 and the generation module 3, and is configured to perform journey feature analysis on a user journey, generate a journey feature analysis result, and send the journey feature analysis result to the generation module 3.
The generating module 3 is connected to the journey feature analyzing module 2 and the predicting module 4, and is configured to generate a big data set for the user journey according to the journey feature analyzing result, and send the big data set to the predicting module 4.
Specifically, the big data set includes a plurality of data sources and updates various types of data in real time, and the included data and the updating mode are matched with the user travel characteristics; the generation module 3 is connected with a large number of data sources related to tourism to obtain various types of data, for example, real-time weather data is obtained from a meteorological data source, traffic flow and/or people flow data of each monitoring point is obtained from a traffic data source, public traffic operation data such as public transport and subway are obtained from a public traffic operator data source, supply quantity and demand quantity data of services of merchants such as catering, shopping and accommodation are obtained from a tourism service data source, and ticketing data is obtained from a scenic spot operator, so that big data are formed.
The prediction module 4 is connected to the generation module 3, and is configured to predict a user's travel progress based on the big data set, generate a prediction prompt, and send the prediction prompt to the display module 5.
Specifically, on the basis of the large data set, prediction with different timeliness and different scales is performed according to the progress of the current journey of the user.
The display module 5 is connected with the acquisition module 1 and the prediction module 4, and is configured to collect a starting point of the user journey and an end point of the user journey, send the starting point of the user journey and the end point of the user journey to the acquisition module 1, and display the prediction prompt on the user journey.
Specifically, the prediction prompt includes: the prediction hint is displayed in the form of a descriptive label. For example, prediction labels such as "congestion", "bus convenience", and "queue need" are marked on the route, thereby giving a user a visual prompt.
In this embodiment, carry out the analysis of multidimension degree to user's journey through journey characteristic analysis module to form with user's journey characteristic assorted big data set, and the data that big data set was included and the mode of renewal and user's journey characteristic phase-match, it is big to have solved the data volume, and data source and type are many, the strong problem of the time variation nature of data, the pertinence and the time variation nature of tourism prediction service have been strengthened, the predictability to tourism relevant information and intelligent service has been improved, user experience has been improved.
In one embodiment, the travel characteristic analysis module 2 includes: a spatial scale analysis unit 6, a departure place and destination attribute analysis unit 7, a waypoint attribute analysis unit 8, a journey path attribute analysis unit 9;
the spatial scale analysis unit 6 is connected to the acquisition module 1, and configured to perform spatial scale analysis on the user journey according to the accumulated distance of the user journey or the spatial distances between the starting point and the ending point of the journey.
Specifically, whether the user journey belongs to a space large-scale moving journey or a space small-scale journey is judged according to the accumulated distance of the user journey or the space distances of the journey starting point and the journey ending point.
The origin and destination attribute analysis unit 7 is connected to the acquisition module 1, and is configured to analyze attributes of the origin and destination of the user journey.
Specifically, the attributes of the departure place and the destination of the user journey include: the origin/destination is a natural scenic spot (e.g. a mountain, a forest), a human scenic spot (e.g. a fairground, a town, a resort) or an urban area (e.g. a shopping mall, a shopping street, a food street).
The journey attribute analysis unit 8 is connected to the acquisition module 1, and is configured to determine the attribute of the area where the user travels along the journey.
Specifically, a spatial range within a certain distance from any point in the travel route is called a waypoint, and the attributes and the number of main geographic objects along the waypoint and the distance between the geographic objects and the travel route are determined.
The travel route attribute analysis unit 9 is connected to the acquisition module 1, and is configured to analyze roads, traffic conditions attributes, vehicle supply amounts (e.g., the number of subway bus stops, the stop density, and the number of stop numbers) and route difficulty attributes (e.g., mountain roads, ascending roads, descending roads, level roads, and trayed roads) of the user travel route.
In one embodiment, the generating module 3 includes: a definition unit 10 and a generation unit 11;
the defining unit 10 is connected to the generating unit 11, and is configured to define the travel patterns corresponding to different travel features, and send the travel patterns to the generating unit 11.
For example, the travel pattern of a downtown short haul corresponds to travel characteristics of "small spatial scale", "origin/destination attribute is urban area", "geographic targets are many along the way", "traffic and traffic flow are large", "vehicle supply amount is large"; the travel mode of the long-distance scenic spot corresponds to travel characteristics of 'large space scale', 'departure place/destination attribute as scenic spot', 'few geographic objects along the way', 'few traffic and people flow' and 'few vehicle supply'.
The generating unit 11 is connected to the journey feature analysis module 2, the defining unit 10 and the prediction module 4, and is configured to determine the journey pattern with the highest matching degree with the journey feature of the user, form a pattern according to a data set corresponding to the journey pattern, and generate a large data set for the user journey.
The working process of the travel forecasting platform is specifically described by the following embodiments:
example 1:
the user selects the place of departure as the home palace and the destination as the Wangfu well through the display module 5, and the acquisition module 1 acquires a path taking the home palace as a starting point and the Wangfu well as a terminal point;
the journey characteristic analysis module 2 performs characteristic analysis on a route which takes the old palace as a starting point and the Wangfu well as a terminal point to obtain that the journey belongs to a spatial small-scale moving journey, the Wangfu well belongs to an urban area, a traffic worker has 103 buses and 128 buses, and the route belongs to a flat road;
aiming at the travel characteristics, selecting a travel mode of the downtown of the short distance downtown, organizing traffic flow data, people flow data, public transportation operation data and business service data along the way according to the travel mode of the downtown of the short distance downtown, and setting a data updating mode to be high-frequency updating so as to form a large data set;
based on the large data set, according to the user journey schedule, the prediction module 4 carries out short-time-effect and small-space-scale traffic prediction to generate a prediction prompt;
the journey route from the old palace to the Wangfu well is displayed on the electronic map, and the 'crowded' route, the 'convenient traffic' route and the '2 minutes away from the next bus' route are marked on the journey route.
Example 2:
the user selects a route with the place of departure as Beijing and the destination as the Beijing river through the display module 5, and the acquisition module 1 acquires the route with the Beijing as a starting point and the Beijing as a terminal point;
the travel characteristic analysis module 2 performs forming characteristic analysis on a path taking Beijing as a starting point and a Beidaihe river as a terminal point to obtain that the travel belongs to a spatial large-scale moving travel, the Beidaihe river belongs to a natural scenic spot, vehicles comprise trains and buses, and the path belongs to a mountain road;
selecting a travel mode of a long-distance scenic spot according to the travel characteristics, organizing a data source, a data type and a data updating mode according to the travel mode of the long-distance scenic spot, and organizing weather data, traffic flow data, business service data and ticket data of a destination North-worn river of each city along the way, wherein the data updating mode is low-frequency updating, so that a large data set is formed;
based on the large data set, according to the user journey progress, the prediction module 4 carries out long-timeliness and large-scale space prediction to generate a prediction prompt;
the journey route from Beijing to the Beijing river is displayed on the electronic map, and the journey route is marked with ' smooth traffic ', and the distance from the Beijing river station is 12 minutes '.
Referring to fig. 2, a prediction method of a travel prediction platform for implementing big data analysis includes:
s201, a display module collects the starting point of the user journey and the end point of the user journey, and sends the starting point of the user journey and the end point of the user journey to an acquisition module.
S202, according to the starting point and the end point of the user journey, the obtaining module obtains the user journey and sends the user journey to the journey characteristic analysis module.
Specifically, the user journey includes: taking a starting point selected by a user as a starting point of the user journey route, and generating a path of the user journey route according to the starting point and the end point;
or, the real-time position of the user is used as the starting point of the user journey route, and the route of the user journey route is generated according to the starting point and the end point.
S203, the journey characteristic analysis module carries out journey characteristic analysis on the user journey to generate a journey characteristic analysis result, and the journey characteristic analysis result is sent to the generation module.
And S204, according to the travel characteristic analysis result, the generation module generates a large data set aiming at the user travel.
Specifically, the large data set includes a plurality of types of data from a plurality of data sources, and is updated in real time, and the included data and the updating mode are matched with the user journey characteristics.
S205, based on the big data set, the prediction module predicts according to the progress of the user journey, generates a prediction prompt and sends the prediction prompt to the display module.
Specifically, on the basis of the large data set, prediction with different timeliness and different scales is performed according to the progress of the current journey of the user.
And S206, the display module displays the prediction prompt on the user journey.
Specifically, the prediction prompt includes: the prediction hint is displayed in the form of a descriptive label. For example, prediction labels such as "congestion", "bus convenience", and "queue need" are marked on the route, thereby giving a user a visual prompt.
In one embodiment, as shown in fig. 3, step S203, performing a journey feature analysis on the user journey by the journey feature analysis module, and generating a journey feature analysis result includes:
s2031, according to the accumulated distance of the user journey or the space distance of the journey starting point and the journey ending point, the space scale analysis unit carries out space scale analysis on the user journey.
Specifically, whether the user journey is a spatial large-scale moving journey or a spatial small-scale journey is judged according to the accumulated distance of the user journey or the spatial distance between the starting point and the ending point of the journey.
S2032, analyzing the attributes of the departure place and the destination of the user trip by the departure place and destination attribute analyzing unit.
Specifically, the attributes of the departure place and the destination of the user journey include: the origin/destination is a natural scenic spot (e.g. a mountain, a forest), a human scenic spot (e.g. a fairground, a town, a resort) or an urban area (e.g. a shopping mall, a shopping street, a food street).
And S2033, the journey attribute analysis unit judges the attribute of the user journey passing area.
Specifically, a spatial range within a certain distance from any point in the travel route is referred to as a waypoint, and attributes and the number of main geographic objects along the waypoint and a distance between the geographic objects and the travel route are determined.
S2034, the route attribute analysis unit analyzes the road, traffic condition attribute, vehicle supply amount (e.g. number of subway and bus stops, stop density, number of stop times) and route difficulty attribute (e.g. mountain road, uphill road, downhill road, flat road, winding road, etc.) of the user' S travel route.
In one embodiment, as shown in fig. 4, in step S204, according to the result of the journey feature analysis, the generating module generates a large data set for the user journey, including:
s2041, defining travel modes corresponding to different travel characteristics through a defining unit, and sending the travel modes to a generating unit.
S2042, determining the travel mode with the highest travel feature matching degree with the user, and generating a big data set aiming at the user travel according to a data set composition mode corresponding to the travel mode by the generating unit.
Specifically, a data source and a data type are organized according to a data set composition mode corresponding to the journey mode, and a data updating mode is set, so that a large data set for the user journey is generated.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (5)

1. A travel prediction platform for implementing big data analysis, comprising: the system comprises an acquisition module, a stroke characteristic analysis module, a generation module, a prediction module and a display module;
the acquisition module is connected with the journey characteristic analysis module and is used for acquiring the user journey according to the starting point of the user journey and the end point of the user journey and sending the user journey to the journey characteristic analysis module;
the travel characteristic analysis module is connected with the acquisition module and the generation module and is used for performing travel characteristic analysis on the user travel to generate a travel characteristic analysis result and sending the travel characteristic analysis result to the generation module;
the generation module is connected with the journey feature analysis module and the prediction module and is connected with a data source related to the journey, and the generation module is used for obtaining various types of data, generating a big data set aiming at the user journey according to the journey feature analysis result and sending the big data set to the prediction module; the big data set records and updates various types of data from a plurality of data sources in real time, and the recorded data and the updating mode are matched with the travel characteristics;
the prediction module is connected with the generation module and used for predicting according to the progress of the user journey based on the big data set, generating a prediction prompt and sending the prediction prompt to the display module;
the display module is connected with the acquisition module and the prediction module, and is used for acquiring the starting point of the user journey and the end point of the user journey, sending the starting point of the user journey and the end point of the user journey to the acquisition module, and displaying the prediction prompt on the user journey;
the travel characteristic analysis module comprises: the system comprises a space scale analysis unit, a departure place and destination attribute analysis unit, a journey path attribute analysis unit and a journey path attribute analysis unit;
the spatial scale analysis unit is connected with the acquisition module and is used for carrying out spatial scale analysis on the user journey according to the accumulated distance of the user journey or the spatial distances of the starting point and the ending point of the journey;
the departure place and destination attribute analysis unit is connected with the acquisition module and is used for analyzing the attributes of the departure place and the destination of the user journey;
the journey attribute analysis unit is connected with the acquisition module and is used for judging the attribute of the user journey passing area;
the travel route attribute analysis unit is connected with the acquisition module and is used for analyzing the road, traffic condition attribute, vehicle supply amount and route difficulty attribute of the user travel route;
the generation module comprises: a definition unit, a generation unit;
the definition unit is connected with the generation unit and used for defining travel modes corresponding to different travel characteristics and sending the travel modes to the generation unit;
the generating unit is connected with the journey feature analysis module, the defining unit and the prediction module and is used for determining the journey mode with the highest matching degree with the journey feature of the user, forming a mode according to a data set corresponding to the journey mode and generating a large data set aiming at the journey of the user.
2. The travel prediction platform for implementing big data analysis of claim 1, wherein the user travel itinerary comprises:
and taking the starting point selected by the user as the starting point of the user journey route, and generating the path of the user journey route according to the starting point and the end point.
3. The travel prediction platform for implementing big data analysis of claim 2, wherein the user travel itinerary further comprises:
and taking the real-time position of the user as a starting point of the user journey route, and generating a route of the user journey route according to the starting point and the end point.
4. The travel prediction platform for implementing big data analysis of claim 1, wherein the prediction hint comprises:
the prediction hint is displayed in the form of a descriptive label.
5. A prediction method of a travel prediction platform for realizing big data analysis is characterized by comprising the following steps:
the display module collects a starting point of a user journey and an end point of the user journey and sends the starting point of the user journey and the end point of the user journey to the acquisition module;
according to the starting point and the end point of the user journey, the obtaining module obtains the user journey and sends the user journey to the journey characteristic analysis module;
the journey characteristic analysis module is used for carrying out journey characteristic analysis on the user journey to generate a journey characteristic analysis result, and the journey characteristic analysis result is sent to the generation module;
the generation module is connected with data sources related to travel and used for acquiring various types of data and generating a big data set aiming at the user travel according to the travel characteristic analysis result, the big data set collects and updates various types of data from a plurality of data sources in real time, and the collected data and the updating mode are matched with travel characteristics;
based on the big data set, the prediction module predicts according to the progress of the user journey, generates a prediction prompt and sends the prediction prompt to the display module;
the display module displays the prediction prompt on the user journey;
through the journey characteristic analysis module carries out journey characteristic analysis to user's journey, generates the journey characteristic analysis result, includes:
according to the accumulated distance of the user journey or the spatial distance of the journey starting point and the journey ending point, a spatial scale analysis unit carries out spatial scale analysis on the user journey;
analyzing attributes of the departure place and the destination of the user journey through an attribute analysis unit of the departure place and the destination;
the journey attribute analysis unit judges the attribute of the user journey passing area;
the travel route attribute analysis unit analyzes the road, traffic condition attribute, vehicle supply quantity and route difficulty attribute of the user travel route;
according to the travel feature analysis result, the generation module generates a big data set aiming at the user travel, and the big data set comprises:
the method comprises the steps that stroke modes corresponding to different stroke characteristics are defined through a definition unit, and the stroke modes are sent to a generation unit;
determining the travel mode with the highest matching degree with the travel characteristics of the user, and generating a big data set aiming at the user travel according to a data set composition mode corresponding to the travel mode by the generating unit;
the generating unit generates a large data set aiming at the user journey according to a data set composition mode corresponding to the journey mode, and the large data set comprises the following steps:
and according to a data set composition mode corresponding to the journey mode, organizing data sources and data types, setting a data updating mode, and generating a large data set aiming at the user journey.
CN202010078939.5A 2020-02-03 2020-02-03 Tourism prediction platform and method for realizing big data analysis Active CN111382900B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010078939.5A CN111382900B (en) 2020-02-03 2020-02-03 Tourism prediction platform and method for realizing big data analysis

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010078939.5A CN111382900B (en) 2020-02-03 2020-02-03 Tourism prediction platform and method for realizing big data analysis

Publications (2)

Publication Number Publication Date
CN111382900A CN111382900A (en) 2020-07-07
CN111382900B true CN111382900B (en) 2022-07-19

Family

ID=71217113

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010078939.5A Active CN111382900B (en) 2020-02-03 2020-02-03 Tourism prediction platform and method for realizing big data analysis

Country Status (1)

Country Link
CN (1) CN111382900B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114676323B (en) * 2022-03-24 2023-05-12 贵州优特云科技有限公司 User travel information recommendation system based on big data

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101078634A (en) * 2007-06-20 2007-11-28 江苏新科数字技术有限公司 Navigation method for along-path information prompt for navigation device
CN103810509A (en) * 2012-11-08 2014-05-21 无锡津天阳激光电子有限公司 Method and device of traveling Internet of Things
CN103955479A (en) * 2014-04-02 2014-07-30 北京百度网讯科技有限公司 Implementation method and device of electronic map
CN109636679A (en) * 2018-12-19 2019-04-16 航天物联网技术有限公司 A kind of interactive tour schedule planing method based on artificial intelligence
CN109995852A (en) * 2019-03-11 2019-07-09 南京邮电大学 A kind of intelligent tourism system based on mobile Internet of Things

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140114705A1 (en) * 2012-10-23 2014-04-24 Olset, Inc. Methods and systems for making travel arrangements
CN107230330A (en) * 2017-07-12 2017-10-03 东南大学 A kind of tourist communications management system based on big data
CN108932686A (en) * 2018-05-09 2018-12-04 哈尔滨商业大学 A kind of tourist famous-city tourist flow analysis method based on big data
CN110222277B (en) * 2019-05-06 2020-04-24 特斯联（北京）科技有限公司 Big data analysis-based travel information recommendation method and device

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101078634A (en) * 2007-06-20 2007-11-28 江苏新科数字技术有限公司 Navigation method for along-path information prompt for navigation device
CN103810509A (en) * 2012-11-08 2014-05-21 无锡津天阳激光电子有限公司 Method and device of traveling Internet of Things
CN103955479A (en) * 2014-04-02 2014-07-30 北京百度网讯科技有限公司 Implementation method and device of electronic map
CN109636679A (en) * 2018-12-19 2019-04-16 航天物联网技术有限公司 A kind of interactive tour schedule planing method based on artificial intelligence
CN109995852A (en) * 2019-03-11 2019-07-09 南京邮电大学 A kind of intelligent tourism system based on mobile Internet of Things

Also Published As

Publication number Publication date
CN111382900A (en) 2020-07-07

Similar Documents

Publication Publication Date Title
Park et al. An optimization approach for the placement of bicycle-sharing stations to reduce short car trips: An application to the city of Seoul
CA2496870C (en) Apparatus and method for providing traffic information
Nejadkoorki et al. An approach for modelling CO2 emissions from road traffic in urban areas
US20060129313A1 (en) System and method for driving directions based on non-map criteria
EP1918895A2 (en) Apparatus and method for providing traffic information
JP5615312B2 (en) Traffic jam prediction method and traffic jam prediction device
CN202444622U (en) Weather forecast and disaster pre-warning system for vehicular information services
KR20140128063A (en) Traffic prediction system
US20130151297A1 (en) Urban Computing of Route-Oriented Vehicles
CN110288205B (en) Traffic influence evaluation method and device
GB2497981A (en) Method of generating travel time data for output to a mobile device
CN107293142A (en) Transport information active push method and system based on LBS
CN111382900B (en) Tourism prediction platform and method for realizing big data analysis
Tafidis et al. Exploring crowdsourcing information to predict traffic-related impacts
JP3873148B2 (en) Measurement point data creation method, data processing method, data processing apparatus and data processing system, program, and information storage medium
Tian et al. Identifying residential and workplace locations from transit smart card data
KR102054985B1 (en) Monitoring method of path-based traffic, monitoring system, operating method for monitoring service of path-based traffic and operating system
JP2020091122A (en) Information processing device, terminal device, program, and gradient table creation method
JP2001243307A (en) Information providing method for advertisement or the like of rental car, server for information managing dealer, server for rental car dealer and recording medium
Moreira-Matias et al. An online learning framework for predicting the taxi stand's profitability
Gerike et al. Network level design for cycling
KR102466467B1 (en) Method and Apparatus for Generating and Using Traveling Path Data
Prayudyanto Sustainability index assessment of urban transport services in developing cities
Cavaliere Bolzano Traffic: an example of open ITS deployment for advanced traveller information services
Smith et al. Estimating intra-metropolitan journey-to-work CO2 emissions: a multi-modal network approach applied to the London Region 2001

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
GR01 Patent grant
GR01 Patent grant