CN112348291B - Travel information management method - Google Patents
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Abstract
The invention provides a travel information management method, which comprises the following steps: generating a user portrait and a scenery spot portrait; calculating preference scores of the user corresponding to each tourist attraction; pushing tourist attractions to the user according to the preference scores; generating a recommended tour route according to the departure time selected by the user, the current positioning information of the user, the position information corresponding to each candidate tour spot and the corresponding play time; predicting the current crowding degree of each candidate tourist attraction according to the historical crowding degree; and adjusting the recommended tour route according to the current crowdedness and arrival time of each candidate tour spot. According to the invention, individual differences and current actual conditions are more considered, the recommended tourist attractions and the planned tourist routes are more intelligent and humanized, and the actual requirements of users can be better met.
Description
Technical Field
The invention relates to the field of travel management, in particular to a travel information management method.
Background
The improvement of the physical life of people drives the improvement of the mental life. More and more people begin to pay attention to travel, and through experiencing different regional geomancy, the people can add power to the later more diligent work and life while increasing the insight. Nowadays, people can enjoy and know the landscape and the food in different places through various sharing software platforms (such as small red books, trembling and the like), and the special automatic 'good for others' characteristic of the software can more actively push related videos to people interested in the aspect. However, the accuracy of personalized push still has room for improvement.
In addition, as the viewing opportunities increase, people are undoubtedly driven to move to the desired in-person scenic spot experience. However, the expectation is always different from the reality, the sharing software is only shared on the best scenic spot, and the related information about the destination, such as how to go to, the playing route and the like, is unknown. Therefore, in view of the above situation, there is a need to provide a solution that allows people to have a planned tour of the sights of interest known from the software platform.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for managing the travel information can be used for accurately recommending favorite tourist attractions to the user in a targeted manner, and customizing a reasonable travel route according to the tourist attractions selected by the user.
In order to solve the technical problems, the invention adopts the technical scheme that:
the travel information management method comprises the following steps:
acquiring a historical tourism video which is in the preset first historical duration and is interested by a user;
classifying the historical tourism videos according to the content emphasis of the tourism videos, wherein the categories comprise folk custom, landscape and food;
generating a user portrait according to the weight of each category in the historical travel video;
obtaining scenic spot images of each tourist spot in a preset area range corresponding to the category according to the current positioning information of the user;
calculating preference scores of the user corresponding to the scenic spots according to the user portrait and the scenic spot portraits of the scenic spots;
and sequencing the tourist attractions according to the preference scores, and pushing the tourist attractions to the user according to the sequencing.
Further, receiving a tourist attraction selected from each tourist attraction by a user and a playing time corresponding to the tourist attraction, and determining the selected tourist attraction as a candidate tourist attraction;
generating a recommended tour route according to the departure time selected by the user, the current positioning information of the user, the position information corresponding to each candidate tour spot and the corresponding play time, wherein the recommended tour route comprises a traffic mode adopted for reaching each candidate tour spot and the reaching time of each candidate tour spot;
acquiring the congestion degree of each candidate tourist attraction counted every day in a preset second historical time length, wherein the second historical time length is more than one year;
predicting the current crowding degree of each candidate tourist attraction according to the crowding degree;
and adjusting the recommended tour route according to the current crowdedness and arrival time of each candidate tour spot.
Further, the obtaining of the historical travel video in which the user is interested within the preset first historical duration includes:
and obtaining historical tourism videos in which the user is interested within a preset first historical time length from the sharing platform.
Further, the acquiring of the historical travel video in which the user is interested within the preset first historical time includes:
the sharing platform obtains historical tourism videos in which a user is interested within a preset first historical time.
Furthermore, the user images are scores of the user corresponding to the categories respectively; the scenic spot portrait is a score of a scenic spot corresponding to the category respectively; the preference score is a preference score of a user corresponding to a tourist attraction, which is calculated according to the user portrait and the scenic spot portrait.
Further, the crowding degree is calculated according to the historical pedestrian volume and the preset fully loaded pedestrian volume.
Further, the adjusting the recommended travel route according to the today's congestion degree and arrival time of each candidate tourist attraction includes:
and performing self-adaptive adjustment on the candidate tourist attractions in the recommended tourist route according to the current daily crowdedness corresponding to the time period from the arrival time of each candidate tourist attraction to the preset time after the end of the playing time.
The invention has the beneficial effects that: constructing a user portrait capable of reflecting user preferences by performing multi-dimensional analysis on browsing records of a user; performing multi-dimensional analysis on each tourist spot to construct a scenic spot portrait capable of reflecting the characteristics of each tourist spot; according to the personalized user portrait and the fixed scenic spot portraits of each scenic spot, the preference scores of the user to different scenic spots are obtained, so that the scenic spots in which the user is interested can be accurately released and recommended according to the preference scores, the attention of the user to the recommended content is improved, and accurate marketing is realized. Furthermore, after the probability of tourism is improved through accurate recommendation, when the user actually travels, the user can directly select candidate tourist attractions and corresponding play time from the recommended tourist attractions, then feasible tourism routes are intelligently analyzed according to the departure time, the current location and the position information and the play time corresponding to each candidate tourist attraction, the tourism routes can be adjusted according to the current congestion degree predicted by each candidate tourist attraction, the scenic spot routes and the reasonable travel routes obtained by current popularity planning are comprehensively considered, and the travel satisfaction of the user is improved.
Drawings
Fig. 1 is a schematic flow chart of a travel information management method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating an example of a user image of a user A according to an embodiment of the present invention;
FIG. 3 is an exemplary diagram of scenic spot images of tourist attractions of a cave in a flood according to one embodiment of the present invention;
fig. 4 is a flowchart illustrating a travel information management method according to a second embodiment of the present invention.
Detailed Description
In order to explain the technical contents, the objects and the effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
The most key concept of the invention is as follows: the system can accurately release recommendations for tourist attractions in which the user is interested according to the preference scores, so that the attention of the user to the recommended content is improved; when the intelligent tour system is in actual tour, scenic spot paths and reasonable travel routes obtained by current popularity planning are comprehensively considered through intelligent analysis and acquisition, and travel satisfaction of users is improved.
Example one
Referring to fig. 1 to 3, the invention provides a method for managing travel information, which not only can accurately release the interesting tourist attractions to users, but also can customize and comprehensively consider the scenic spot paths and the reasonable travel route obtained by current popularity planning during actual travel, and is beneficial to improving the travel satisfaction of the users.
Referring to fig. 1, the method may include the following steps:
s1: and acquiring a tour video in which the user A is interested.
In this embodiment, the third-party platform may execute the subsequent steps after acquiring the travel video in which the user a is interested by the sharing platform; in another embodiment, the sharing platform may directly obtain the travel video in which the user a is interested and perform the subsequent steps.
The sharing platform refers to a platform for watching and sharing audio and video such as tremble, red book, fast hand, micro-vision, watermelon and the like. The interest may be determined by performing multidimensional analysis such as whether to set attention, viewing time, whether to view completely, whether to approve, whether to comment, whether to forward, and the like, for example, different weights may be set for each of the above cases, and the degree of interest may be determined according to the total weight.
S2: and classifying the travel videos according to the content emphasis of the obtained travel videos, wherein the categories comprise folk custom, landscape, food and the like. This step can be done by the system analysis automatically.
S3: and generating a user portrait of the user A according to the weights of the various categories corresponding to the historical travel videos in the preset first historical time.
The user representation is a visual representation of data associated with the user, i.e., user information tagging. In this embodiment, the user is referred to as a user and the scores of the categories are respectively corresponding to the user, that is, each travel video is divided into different categories according to the travel video tour record of the user, and then the evaluation data of the user corresponding to the different categories is counted according to the division result. The user portrait is an important link for personalized accurate recommendation.
The first historical time duration may be a preset time duration of the past 3 days, a week, a month, and the like. And the browsing time is used as a weighted item of the corresponding category and is used for enhancing the preference of the user to the specific category.
For example, referring to FIG. 2, the user portrait of user A is folk 0.3 points, landscape 0.5 points, and cate 0.2 points.
In addition, for another embodiment described in step S1, in the process that the user a watches the travel videos through the sharing platform, the category and the watching time corresponding to each travel video watched by the user a may be obtained in real time, and then the user portrait of the user a may be generated by performing statistics according to the categories and the watching times corresponding to all the travel videos watched by the user in the first historical duration.
S4: and acquiring the sight spot images of the categories corresponding to each tourist sight spot in the preset area range according to the current positioning information of the user A.
The preset area range, such as the conventional region ranges of north, south, domestic, Asia, Europe and the like, can even be a specific province and city; the area range may be set by the user or may be a default of the system.
The scenic spot portrait refers to a score of a scenic spot corresponding to the category, and in this embodiment, each scenic spot will count the evaluation data by comparing with the category of S2. For example, referring to fig. 3, the scenic spot images of the cave are: folk 1 point, landscape 6 point and cate 3 point.
For example, if the user A is located in a lake or interior area of Xiamen city and the user presets a national area range, this step will generate a corresponding sight spot image for all tourist sights nationwide.
S5: and calculating the preference scores of the users corresponding to the scenic spots according to the user portrait of the user A and the scenic spot portrait of each scenic spot.
The preference score is a preference score of a user corresponding to a tourist attraction, which is calculated according to the user portrait and the scenic spot portrait.
The step fuses the preference of a specific user with the fixed characteristics of the scenic spots, accurately predicts the preference degree of the user to each scenic spot, and realizes individuation of a large number of scenic spots. The calculation mode is reasonable, and the accuracy of the calculation result is high.
For example, as a specific example, the preference score of the user a corresponding to the cliff hole is: 0.3 × 1+0.5 × 6+0.2 × 3=3.9 points.
S6: and sequencing the tourist attractions according to the preference scores, and pushing the tourist attractions to the user according to the sequencing.
And sequencing the mass tourist attractions from high to low according to the preference scores of the corresponding users A, and then accurately recommending the users according to the sequencing.
The tourist attractions are pushed to the user, the tourist attractions can be recommended to the user in a text description mode, or directly related contents of the tourist attractions, such as at least one of delicious food, scenery and folk custom related audio and video of specific tourist attractions, can be pushed to the user, and the specific pushing mode is not limited.
Through this embodiment, realized recommending the tourist attraction of its interest to the user accuracy, not only helped the development of tourism industry, can guide the user to get into next step moreover, develop the tourism route recommendation action after to attract more users to use, improve user's viscidity.
Example two
After the user arrives at the interested tourist attractions according to the pushed tourist attractions, the user needs to face the problem of how to better plan the tourism of each tourist attraction that the user wants to go to, because the good planning can make full use of the time, the cost of the distance and the time is greatly saved, and the tourism quality is improved. Therefore, the embodiment is further expanded on the basis of the first embodiment, and the intelligent planning function of the tourist route to the ground is realized by adding the following steps in an overlapping manner.
Referring to fig. 4, after the step S6 of the first embodiment, the method of the present embodiment further includes:
s7: and receiving a tourist attraction selected by the user and the corresponding play time, and determining the selected tourist attraction as a candidate tourist attraction.
The method comprises the steps of selecting one tourist attraction and corresponding playing time (time spent on playing the tourist attraction plan) corresponding to one selection instruction of a user, supporting simultaneous reception of a plurality of selection instructions and selection of a plurality of candidate tourist attractions.
The selected tourist attractions are the tourist attractions recommended to the user in the S6, and compared with the time limit, the most probability of the recently recommended tourist attractions is the actual tourist attractions of the user.
S8: and generating a recommended tour route according to the departure time selected by the user, the current positioning information of the user, the position information corresponding to each candidate tour spot and the corresponding play time, wherein the recommended tour route comprises the traffic mode adopted for reaching each candidate tour spot and the reaching time of each candidate tour spot.
The user-defined content also includes the time of departure of the trip, thereby determining the expected arrival times of all of the subsequent candidate tourist attractions.
In the step, the departure time, the current location, the positions of the tourist attractions to be visited and the resident playing time are determined, so that the recommended tourist routes with reasonable time and routes can be generated through intelligent analysis. And the recommended tour route shows each candidate tourist attraction going forward in sequence, and the arrival time, the attraction switching and the transportation transfer of each candidate tourist attraction. The selection of the transfer tool comprehensively considers according to distance and real-time traffic road conditions, covers various traffic modes such as public transport, subway, bicycle and walking, and focuses on the continuity and convenience of transfer.
Preferably, the recommended travel route can be marked in a map, so that the user can more intuitively know the distance and the traffic;
preferably, the recommended travel route can be switched to a graphic display form or a list display form, the transfer route and the transfer vehicles are highlighted, and errors are avoided.
And then, optimizing and adjusting the recommended route by combining the current popularity of each candidate tourist attraction so as to improve the rationality of the recommended tourist route.
S9: and acquiring the congestion degree of each candidate tourist attraction counted every day in a preset second historical time length, wherein the second historical time length refers to a time range of at least one year in the past, and the congestion degree can be obtained by calculating the pedestrian volume/full pedestrian volume (the maximum number of people can be accommodated in the attraction).
S10: and predicting the current crowding degree of each candidate tourist attraction according to the crowding degree.
The daily congestion level means today's hourly congestion level.
For example, if the second historical duration is one year, the degree of congestion of each candidate tourist attraction today in the present year can be predicted according to the degree of congestion of each candidate tourist attraction in the present year. The more the historical congestion data of the same day is contained in the second historical time length, the more accurate the prediction result is.
S11: and adjusting the recommended tour route according to the current crowdedness and arrival time of each candidate tour spot.
The predicted arrival time of each candidate tourist attraction is determined in the recommended tourist route generated in step S8, and in this step, whether crowds are congested when arriving at the attraction is predicted according to the current congestion degree predicted by each candidate tourist attraction, if so, the tourist route is adaptively adjusted to stagger the congestion period, thereby optimizing the tourist experience. The adjustment mode is self-adaptive adjustment, namely, the distance and the traffic between the candidate tourist attractions and the congestion degree corresponding to the arrival time are automatically integrated to analyze and adjust the sequence between the candidate tourist attractions in the recommended tourist route so as to obtain a reasonable tourist route, namely, the congestion time periods of all the tourist attractions can be staggered, and the travel is relatively convenient.
In a preferred embodiment, before the line adjustment is carried out, the reason for the adjustment and the adjustment condition are informed to a user through prompt; after the prompt, whether the adjustment is performed or not can be confirmed according to the user instruction, the user is given the independent option, and the user experience is improved.
In a preferred embodiment, the step adaptively adjusts the candidate tourist attractions in the recommended tourist route according to the current daily crowdedness corresponding to the time period from the arrival time of each candidate tourist attraction to a preset time after the end of the play time. Taking the candidate tourist attraction a as an example, the predicted congestion degree of each time period from 2:00 to 4:00 (preset playing time of 2 hours) of the arrival time at the attraction a needs to be simultaneously determined. Optionally, it may be determined whether each time interval exceeds a preset threshold, if so, the position of the scenic spot a in the line is adjusted, and if the congestion degree of only one hour exceeds the threshold, it may be considered not to perform adjustment, or a user autonomously determines whether to perform adjustment by adopting a prompt manner. Of course, the route may be adaptively adjusted when the congestion degree in one period exceeds a threshold value.
For the embodiment, the crowdedness in the playing time period is also taken into consideration, so that the rationality of the planned travel route can be further optimized, and the planned travel route is humanized and practical.
In conclusion, the travel information management method provided by the invention can accurately release and recommend the tourist attractions in which the user is interested according to the user preference, thereby improving the attention of the user to the recommended content and realizing accurate marketing; and when travelling, can recommend reasonable, humanized and the strong tourism route of practicality for the user according to the traffic conditions and the scenic spot stream of people condition at present, convenience of customers goes on a journey, improves the tourism quality greatly. According to the invention, individual differences and current actual conditions are more considered, the recommended tourist attractions and the planned tourist routes are more intelligent and humanized, and the actual requirements of users can be better met.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (6)
1. The travel information management method is characterized by comprising the following steps:
acquiring a historical tourism video in which a user is interested within a preset first historical time;
classifying the historical tourism videos according to the content emphasis of the tourism videos, wherein the categories comprise folk custom, scenery and food;
generating a user portrait according to the weight of each category in the historical travel video;
obtaining scenic spot images of each tourist spot in a preset area range corresponding to the category according to the current positioning information of the user; the user image is a score of a user corresponding to the category respectively; the scenic spot portrait is a score of a scenic spot corresponding to the category respectively;
calculating preference scores of the user corresponding to the scenic spots according to the user portrait and the scenic spot portraits of the scenic spots;
sequencing the tourist attractions according to the preference scores, and pushing the tourist attractions to the user according to the sequencing;
further comprising:
receiving a tourist attraction selected from each tourist attraction by a user and a playing time corresponding to the tourist attraction, and determining the selected tourist attraction as a candidate tourist attraction;
generating a recommended tour route according to the departure time selected by the user, the current positioning information of the user, the position information corresponding to each candidate tour spot and the corresponding play time, wherein the recommended tour route comprises a traffic mode adopted for reaching each candidate tour spot and the reaching time of each candidate tour spot;
acquiring the congestion degree of each candidate tourist attraction counted every day in a preset second historical time length, wherein the second historical time length is more than one year;
predicting the current crowding degree of each candidate tourist attraction according to the crowding degree;
and adjusting the recommended tour route according to the current crowdedness and arrival time of each candidate tour spot.
2. The method for managing travel information according to claim 1, wherein said obtaining historical travel videos in which the user is interested within a preset first historical time period comprises:
and obtaining historical tourism videos in which the user is interested within a preset first historical time length from the sharing platform.
3. The method for managing travel information according to claim 1, wherein said obtaining historical travel videos in which the user is interested within a preset first historical time period comprises:
the sharing platform obtains historical tourism videos in which a user is interested within a preset first historical time.
4. The method of claim 1, wherein the preference score is a user preference score for a tourist attraction calculated based on the user representation and the attraction representation.
5. The method of managing travel information as claimed in claim 1, wherein the degree of congestion is calculated according to the historical flow rate of people and the preset flow rate of people in full load.
6. The method of managing travel information of claim 1, wherein said adjusting said recommended travel route according to today's congestion and arrival time of each candidate tourist attraction comprises:
and performing self-adaptive adjustment on the candidate tourist attractions in the recommended tourist route according to the current time crowdedness corresponding to the time period from the arrival time of each candidate tourist attraction to a preset time length after the playing time is finished.
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| CN117909588B (en) * | 2024-01-23 | 2024-06-28 | 扬州自在岛生态旅游投资发展有限公司 | Travel route recommendation system and method based on big data |
| CN118411201A (en) * | 2024-05-08 | 2024-07-30 | 湖北省城建设计院股份有限公司 | Urban tourist attraction-oriented tourism MAAS service platform |
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