CN112711714B - Travel route recommendation method based on 5G and AR - Google Patents

Travel route recommendation method based on 5G and AR Download PDF

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CN112711714B
CN112711714B CN202110056723.3A CN202110056723A CN112711714B CN 112711714 B CN112711714 B CN 112711714B CN 202110056723 A CN202110056723 A CN 202110056723A CN 112711714 B CN112711714 B CN 112711714B
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肖剑
张伟
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Shanghai Jinguniverse Intelligent Technology Group Co ltd
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Abstract

The invention relates to a 5G and AR based travel route recommendation method, which specifically comprises the following steps: s1, entering a map interface to acquire user AR selection information; s2, judging whether the user AR selection information is in an AR mode, if so, turning to the step S3, and if not, stopping on a map interface; s3, acquiring a scene image shot by a user, identifying attributes corresponding to the scene image and entities in the scene according to a preset real scene database, displaying the attributes and introduction information of the entities, and guiding routes leading to the entities; s4, recording the selection operation of the user on the attributes and the entities in the current scene image to obtain a user behavior record, and displaying a plurality of related playing recommended routes according to the user behavior record; and S5, judging whether the request for exiting the AR mode is received, if so, exiting the AR mode and returning to the map interface, otherwise, staying in the AR mode. Compared with the prior art, the method has the advantages of improving the stability of AR travel recommendation, effectively improving the travel experience of the user and the like.

Description

Travel route recommendation method based on 5G and AR
Technical Field
The invention relates to the technical field of travel scenes, in particular to a travel route recommendation method based on 5G and AR.
Background
With the increasing standard of living, more and more people begin to like outdoor travel. However, at present, a series of problems such as insufficient infrastructure in a plurality of scenic spots, uneven business quality of guide and the like still exist. Can not bring good experience to the tourists in the aspects of eating, living, playing, swimming, purchasing, entertainment and the like. At present, the 5G era is accelerating, and the main economic bodies in the world accelerate the 5G business landing. Under the drive of policy support, technical progress and market demand, the 5G industry is rapidly developed, AR rises in 2015, and the industry is less hot due to the limitations of computing capacity and resolution of hardware equipment, dizziness and unclear image quality of products. The 5G high-speed network transmission capability can improve the image quality, improve the calculation speed and improve the popularity of the AR fused into the travel.
Disclosure of Invention
The invention aims to provide a 5G and AR based travel route recommendation method for overcoming the defect of poor AR travel experience in the prior art.
The purpose of the invention can be realized by the following technical scheme:
a travel route recommendation method based on 5G and AR specifically comprises the following steps:
s1, entering a map interface to acquire user AR selection information;
s2, judging whether the user AR selection information is in an AR mode, if so, turning to the step S3, otherwise, staying in a map interface;
s3, acquiring a scene image shot by a user, identifying attributes corresponding to the scene image and entities in the scene according to a preset real scene database, and displaying introduction information of the attributes and the entities and a guide route leading to the entities;
s4, recording the selection operation of the user on attributes and entities in the current scene image to obtain a user behavior record, and displaying a plurality of related playing recommended routes according to the user behavior record;
and S5, judging whether the request for exiting the AR mode is received, if so, exiting the AR mode and returning to a map interface, otherwise, staying in the AR mode.
And the live-action database is set according to the acquired live-action data and the GIS map data.
The live-action database is provided with a navigation function and a recommendation function based on 5G + AR, the navigation function comprises all around viewing, zooming, advancing, explaining and path planning, and the recommendation function comprises scene recommendation and entity recommendation.
And the live-action database is provided with a knowledge graph based on scenes, attributes and entities.
Furthermore, a plurality of relation lines are arranged in the knowledge graph, and the relation lines comprise relation lines between scenes and attributes and relation lines between attributes and entities.
The types of scenes include sights, hotels, lodging, restaurants, and stores.
The types of attributes include history, ethnicity, architecture, religion, diet, and science and technology.
The play recommendation route comprises an intra-scene recommendation route or an inter-scene recommendation route.
Further, the calculation process of the recommended route in the scene is as follows:
s411, acquiring identification information of a single entity in an AR mode, and displaying attributes of the entity according to the identification information of the entity;
s412, judging whether the selection behavior information of the user on the attribute of the entity exists, if so, turning to a step S413, otherwise, turning to a step S414;
s413, acquiring the selective behavior information, screening out an entity with high attribute coincidence degree of the attribute corresponding to the selective behavior information, and turning to the step S415;
s414, searching the attribute with the lowest frequency in the attributes of the single entity, and screening out the entity with high attribute coincidence degree according to the residual attributes except the attribute with the lowest frequency;
and S415, generating and displaying recommended routes leading to the screened entities, judging whether the number of the attributes of the screened entities is only 1, and if so, backtracking and adding the attributes of the original entities.
Further, the calculation process of the recommended route between scenes is as follows:
s421, acquiring information and identification sequence information of the entity identified by the user, and generating an entity sequence;
s422, obtaining the staying time of the user in the entity, and calculating the initial interest score of each attribute according to the staying time and the length of the staying time;
s423, calculating to obtain an interest transfer directed graph according to the entity sequence, iteratively calculating a real-time interest score of the attribute according to the interest transfer directed graph and the initial interest score, judging whether an iteration termination condition is met, and if so, outputting the current real-time interest score as a final interest score;
and S424, displaying a scene with high interest degree coincidence degree according to the final interest score, and displaying a recommended route leading to the scene and having the minimum modification cost to the original route.
Further, the calculation formula of the initial interest score is specifically as follows:
Figure BDA0002900907060000031
the calculation formula of the real-time interest score is specifically as follows:
Figure BDA0002900907060000032
wherein, PR (b)i) Interest score for i-th attribute, biScore for the ith attribute, PR (b)j) Interest score for jth attribute, B is the set of attribute nodes connected to attribute i, LjThe out-degree number for the j attribute.
The iteration termination condition of the real-time interest score in the step S423 includes that the iteration number reaches an upper limit, or the real-time interest score does not change or fluctuates within a small range within a certain time.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the 5G + AR-based live-action database is constructed through live-action data and GIS map data, the scene knowledge map is constructed by using data information, and a scene knowledge system is obtained, so that the image quality definition in an AR mode is improved, the dizziness of users using the AR mode is reduced, and the stability of AR travel recommendation is improved.
2. According to the efficient characteristic of the 5G network, the user position is obtained in real time, the high-quality tour route is provided by combining the user interest point and the interest entity position, the user can have a purposeful and organized tour in the tour process, the tour in a flower type by horse is avoided, the unreasonable route is avoided, and the tour experience of the user is effectively improved.
3. The invention provides detailed background explanation for each scene, improves the quality and benefit of scenic spots, provides technical and intelligent energization for tourism related enterprises comprehensively, and improves the quality, efficiency and benefit of the whole tourism industry.
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FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a schematic flow chart of the present invention;
FIG. 3 is a schematic diagram of a live-action database according to the present invention;
FIG. 4 is a schematic diagram of a scene in an AR mode according to the present invention;
FIG. 5 is a schematic diagram of the structure of the scenes and attributes in the knowledge-graph of the present invention;
FIG. 6 is a schematic diagram of the structure of attributes and entities in a knowledge-graph according to the present invention;
FIG. 7 is a schematic flow chart of calculating a recommended route in a scenario according to the present invention;
FIG. 8 is a schematic diagram of the structure of the entity sequence of the present invention;
FIG. 9 is a structural diagram of an interest transfer directed graph according to the present invention;
FIG. 10 is a diagram illustrating the structure of the AR mode home page according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
As shown in fig. 2, a travel route recommendation method based on 5G and AR specifically includes the following steps:
s1, entering a map interface to acquire user AR selection information;
s2, judging whether the AR selection information of the user is in an AR mode, if so, turning to the step S3, otherwise, staying in a map interface;
s3, acquiring a scene image captured by a user, as shown in fig. 10, identifying attributes corresponding to the scene image and entities in the scene according to a preset live-action database, displaying introduction information of the attributes and the entities, and a guide route leading to the entities;
s4, recording the selection operation of the user on the attributes and the entities in the current scene image to obtain a user behavior record, and displaying a plurality of related playing recommended routes according to the user behavior record;
and S5, judging whether the request for exiting the AR mode is received, if so, exiting the AR mode and returning to the map interface, otherwise, staying in the AR mode.
As shown in fig. 1, the live-action database is located in the background, and through the data collected in the background, the recommendation system in the middle station calculates a travel recommended route, and finally the travel recommended route is displayed to the user by the applet or the public number in the front station.
As shown in fig. 3, the live-action database is set according to the acquired live-action data and GIS map data.
The live-action database is provided with a navigation function and a recommendation function based on 5G + AR, the navigation function comprises all around viewing, zooming, advancing, explaining and path planning, and the recommendation function comprises scene recommendation and entity recommendation.
As shown in fig. 4, the AR scene marks the position of the entity in the scene with the attribute according to the user selection result, and performs reasonable route guidance and related explanation for the user
And the live-action database is provided with a knowledge graph based on scenes, attributes and entities.
As shown in fig. 5 and 6, a plurality of relation lines are arranged in the knowledge graph, and the relation lines include a relation line ab between scenes and attributes, and a relation line bc between attributes and entities.
Types of scenes include attractions, hotels, lodging, restaurants, and stores.
Types of attributes include history, ethnicity, architecture, religion, diet, and science and technology.
The entity in the scene may be a product, an experience point, a statue, or an ancient building in the scene.
The playing recommendation routes comprise intra-scene recommendation routes or inter-scene recommendation routes, and the recommendation principle follows entity priority in the same scene, namely, the intra-scene recommendation routes are planned first, and then the inter-scene recommendation routes are planned.
As shown in fig. 7, the calculation process of the recommended route in the scene is as follows:
s411, acquiring identification information of a single entity in an AR mode, and displaying attributes of the entity according to the identification information of the entity;
s412, judging whether the selection behavior information of the user on the attribute of the entity exists, if so, turning to the step S413, otherwise, turning to the step S414;
s413, acquiring the selective behavior information, screening out an entity with high attribute coincidence degree of the attribute corresponding to the selective behavior information, and turning to the step S415;
s414, searching the attribute with the lowest frequency in the attributes of the single entity, and screening out the entity with high attribute coincidence degree according to the residual attributes except the attribute with the lowest frequency;
and S415, generating and displaying recommended routes leading to the screened entities, judging whether the number of the attributes of the screened entities is only 1, and if so, backtracking and adding the attributes of the original entities.
The calculation process of the recommended route between scenes is as follows:
s421, acquiring information and identification sequence information of the entity identified by the user, and generating an entity sequence as shown in FIG. 8;
s422, obtaining the stay time of the user in the entity, and calculating the initial interest score of each attribute according to the stay time and the length of the stay time;
s423, calculating according to the entity sequence to obtain an interest transfer directed graph as shown in fig. 9, iteratively calculating a real-time interest score of the attribute according to the interest transfer directed graph and the initial interest score, determining whether an iteration termination condition is met, and if so, outputting the current real-time interest score as a final interest score;
and S424, displaying a scene with high interest degree coincidence degree according to the final interest score, and displaying a recommended route leading to the scene and having the minimum modification cost to the original route.
The interest degree of the user in each attribute is determined by the stay time length of the user under the entity of the attribute, and the longer the stay time is, the more interest the user is in the attribute under the entity.
The initial interest score is calculated as follows:
Figure BDA0002900907060000061
the formula for calculating the real-time interest score is as follows:
Figure BDA0002900907060000062
wherein, PR (b)i) Interest score for i-th attribute, biScore for the ith attribute, PR (b)j) Interest score for jth attribute, B is the set of attribute nodes connected to attribute i, LjThe out-degree number for the j attribute.
The iteration termination condition of the real-time interest score in step S423 includes that the number of iterations reaches an upper limit, or the real-time interest score does not change or fluctuates within a small range within a certain time.
In addition, it should be noted that the specific embodiments described in the present specification may have different names, and the above descriptions in the present specification are only illustrations of the structures of the present invention. All equivalent or simple changes in the structure, characteristics and principles of the invention are included in the protection scope of the invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.

Claims (7)

1. A travel route recommendation method based on 5G and AR is characterized by comprising the following steps:
s1, entering a map interface to acquire user AR selection information;
s2, judging whether the user AR selection information is in an AR mode, if so, turning to the step S3, otherwise, staying in a map interface;
s3, acquiring a scene image shot by a user, identifying attributes corresponding to the scene image and entities in the scene according to a preset real scene database, and displaying introduction information of the attributes and the entities and a guide route leading to the entities;
s4, recording the selection operation of the user on attributes and entities in the current scene image to obtain a user behavior record, and displaying a plurality of related playing recommended routes according to the user behavior record;
s5, judging whether an AR mode exiting request is received, if so, exiting the AR mode and returning to a map interface, otherwise, staying in the AR mode;
the playing recommended route comprises an intra-scene recommended route or an inter-scene recommended route;
the calculation process of the recommended route in the scene is as follows:
s411, acquiring identification information of a single entity in an AR mode, and displaying attributes of the entity according to the identification information of the entity;
s412, judging whether the selection behavior information of the user on the attribute of the entity exists, if so, turning to the step S413, otherwise, turning to the step S414;
s413, acquiring the selective behavior information, screening out an entity with high attribute coincidence degree of the attribute corresponding to the selective behavior information, and turning to the step S415;
s414, searching the attribute with the lowest frequency in the attributes of the single entity, and screening out the entity with high attribute coincidence degree according to the residual attributes except the attribute with the lowest frequency;
s415, generating and displaying recommended routes leading to the screened entities, judging whether the number of the attributes of the screened entities is only 1, and if so, backtracking and adding the attributes of the original entities;
the calculation process of the recommended route between the scenes is as follows:
s421, acquiring information and identification sequence information of the entity identified by the user, and generating an entity sequence;
s422, obtaining the staying time of the user in the entity, and calculating the initial interest score of each attribute according to the staying time and the length of the staying time;
s423, calculating to obtain an interest transfer directed graph according to the entity sequence, iteratively calculating a real-time interest score of the attribute according to the interest transfer directed graph and the initial interest score, judging whether an iteration termination condition is met, and if so, outputting the current real-time interest score as a final interest score;
and S424, displaying a scene with high interest degree coincidence degree according to the final interest score, and displaying a recommended route leading to the scene and having the minimum modification cost to the original route.
2. The 5G and AR based travel route recommendation method according to claim 1, wherein the live action database is configured according to the acquired live action data and GIS map data.
3. The 5G and AR based travel route recommendation method as claimed in claim 1, wherein the live action database is provided with a knowledge graph based on scene, attribute and entity.
4. The 5G and AR based travel route recommendation method according to claim 3, wherein a plurality of relation lines are arranged in the knowledge graph, and the relation lines comprise relation lines between scenes and attributes, relation lines between attributes and entities.
5. The 5G and AR based travel route recommendation method of claim 1, wherein the types of scenes comprise attractions, hotels, lodging, restaurants and stores.
6. The 5G and AR based travel route recommendation method of claim 1, wherein the types of attributes include history, ethnicity, architecture, religion, diet and science.
7. The method as claimed in claim 1, wherein the initial interest score is calculated by the following formula:
Figure FDA0003563392480000021
the calculation formula of the real-time interest score is specifically as follows:
Figure FDA0003563392480000022
wherein, PR (b)i) Interest score for i-th attribute, biScore for the ith attribute, PR (b)j) Interest score for jth attribute, B is the set of attribute nodes connected to attribute i, LjThe out-degree number for the j attribute.
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