KR102042919B1 - A System of Providing Theme Travel AI Curation Based on AR through Customization Learning of Virtual Character - Google Patents

Abstract

The AR-based theme tour AI curation providing system through the custom learning of the virtual character according to the present invention, a travel information input unit for receiving a travel information including a travel destination and a travel destination from a user, and associated with the travel information from the user A travel information input module having a request input unit for receiving a travel plan request form written in sentences; A travel preference confirmation module including a travel destination information database in which travel data is stored and a travel preference survey unit for determining a travel preference of the user based on the travel plan request document and deriving a plurality of detailed travel destinations from the travel data; A character development module including a character generation unit for generating a virtual character and a character development unit for modifying and developing the appearance of the character according to the progress of deriving the detailed travel destination from the travel preference confirmation module; And a travel information providing module for providing the user with a travel map displaying each of the detailed travel destinations on a map and guide information about the detailed travel destinations based on AR (Augmented Reality) through the raised character. .

Description

A System of Providing Theme Travel AI Curation Based on AR through Customization Learning of Virtual Character}

The present invention relates to an AR-based theme travel AI curation providing system through customizing the virtual character, more specifically, to provide a journey by selecting a variety of travel destinations according to the traveler's preference according to the traveler's request, in real time The present invention relates to an AR-based theme travel AI curation providing system through customizing virtual characters that can be modified according to changing surrounding conditions or issues.

It is common for people who have decided on a destination they want to visit to collect the information they need to travel in a variety of ways and to plan accordingly.

However, despite the fact that they have collected and planned their own destinations in their own way, they often fail to travel without experiencing the features of the destinations. If you do, you will be fooled about the trips you can't easily leave, and you can lose money. This may be due to a situation in which there is no systematic service for realizing the current know-how and travel-related information to another person who wants to travel the same destination or the person who has already visited the same destination. As a result, people planning to travel do not have to ask for help, even though they need more specific practical help, so they have to bear the inefficient aspect of financially wasting time and wasting their energy. to be.

In particular, in the case of theme tours that travel based on certain criteria, most of the current theme tours are designed by tourist agencies or local governments with commercial goals. I had to come back after enjoying only travel, and general satisfaction was very low.

As a solution to solve this problem, in Korean Patent Registration No. 10-0769247 'Travel information providing method using a travel information system,' customizing learning of a travel information virtual character including a traveler terminal, an internet network and a travel information providing server. A travel information providing method using an AR-based theme travel AI curation providing system, comprising: (A1) a request signal to a web page for searching for travel-related information after a user of the traveler terminal joins the travel information providing server; If the send the server, the travel information providing server to display a map that can be traveled by the traveler previously stored in the geographic information DB of the storage unit through the output unit of the traveler terminal; (A2) receiving, by the travel information providing server, a selection signal of a travel destination to be traveled and a tourist destination around the travel destination from a traveler terminal; (A3) determining whether the user of the traveler terminal is a previously traveled tourist destination and inputting a travel schedule, a travel course, and traffic information to be used in the travel information providing server if the travel destination is not an existing travel destination. ; And (A4) the geographic information DB, the existing traveler course information DB, traffic information DB, weather information DB, accommodation information DB, price information DB, specific region information DB, and event information, which the travel information providing server is previously stored in a storage unit. Disclosed is a travel information providing method comprising the step of calculating a travel schedule, a travel course, a travel expense, and the like, which are overall travel-related information for reaching a travel destination to be traveled through a DB, and confirming them through an output unit of a traveler terminal. .

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However, since this method receives arbitrary travel information, there is a problem that it is still unknown whether the information is suitable for the traveler.

SUMMARY OF THE INVENTION The present invention has been made to overcome the problems of the above technology, and a main object of the present invention is to provide a travel destination recommendation system for recommending a user's preferred travel destinations and detailed travel destinations.

Another object of the present invention is to propose a system for providing information through a character helper using augmented reality when recommending and guiding a travel destination.

It is still another object of the present invention to provide a system that a user may always be interested in the process of acquiring travel information through a character customization (deformation / cultivation) function when providing travel information.

It is a further object of the present invention to provide a system that allows travel information to be re-planned by changing the order of visits according to the current location of the user and the current detailed destination.

In order to achieve the above object, the AR-based theme travel AI curation providing system through the custom learning of the virtual character according to the present invention, a travel information input unit for receiving a travel information including a travel destination and a destination from the user, and the user A travel information input module having a request input unit for receiving a travel plan request form written in sentences in relation to the travel information; A travel preference confirmation module including a travel destination information database storing travel destination data and a travel preference survey unit for determining a user's travel preference based on the travel plan request document and deriving a plurality of detailed travel destinations from the travel destination data; A character development module including a character generation unit for generating a virtual character and a character development unit for modifying and developing the appearance of the character according to the progress of deriving the detailed travel destination from the travel preference confirmation module; And a travel information providing module for providing the user with a travel map displaying each of the detailed travel destinations on a map and guide information about the detailed travel destinations based on AR (Augmented Reality) through the raised character. .

In addition, the travel preference research unit, a comparison derivation part for deriving valid terms through a clustering factor clustered according to the frequency and proximity of terms included in the travel planning request, and the detailed destination based on the valid terms It is characterized by having a detailed destination derivation part to deduce.

Further, the clustering factor is characterized in that it is calculated through the following equation (1).

Equation 1.

Where CF _ab is the clustering factor of term b for term a, Cont _{(a, b)} is the cumulative sum of term b in close proximity to term a, Cont _{(b, a)} is the cumulative sum of term a in close proximity to term b, Cont _{(a, all)} is the cumulative sum of all the terms that are close to term a, and Cont _{(b, all)} is the cumulative sum of all the terms that are close to term b.)

In addition, the travel preference research unit, the terms included in the travel planning request in a line, the interval grasping part for grasping the interval of each of the listed terms, and the clustering by further applying the interval weight according to the height of the interval And an extended derivation part for deriving extended valid terms through a clustering factor, and an extended destination derivation part for deriving the detailed destination based on a plurality of matched destination data by matching the extended valid term with the travel data. It features.

The AR-based theme tour AI curation providing system through customizing the virtual character according to the present invention,

1) Provide a travel recommendation system for recommending a user's preferred travel destinations and detailed travel destinations.

2) presents a system that provides information through the character helper using augmented reality when recommending and guiding the destination,

3) Presenting a system that the user can always be interested in the process of acquiring travel information through character customization (deformation / cultivation) function when providing travel information,

4) Furthermore, the present invention provides a system that allows the travel information to be re-planned by changing the visit order according to the current location of the user and the current detailed destination.

1 is a conceptual diagram illustrating a schematic configuration of an AR-based theme travel AI curation providing system through customizing the virtual character of the present invention.
Figure 2 is a block diagram showing the configuration for the AR-based theme tour AI curation providing system through the custom learning of the virtual character of the present invention.
3 is a flow chart illustrating the basic process flow of the present invention.
Figure 4 is an exemplary view showing an embodiment of a method for inputting a travel plan request form of the present invention.
5 is a flowchart illustrating a process flow for a text analysis method based on a travel plan request form according to the present invention.
Figure 6 is an exemplary view showing an embodiment of the text analysis process and the character meat growth surface according to the present invention.
Figure 7 is an exemplary view showing an embodiment of the guide scene after deriving the detailed travel destination in the present invention.
8 is a flow chart showing a process flow for the path comparison diagram generating method of the present invention.
9 is an exemplary view showing an embodiment of a guide scene to which the path comparison diagram of the present invention is applied.
10 is an exemplary view showing an embodiment of a guide scene to which the actual AR is applied.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. The accompanying drawings are not drawn to scale, and like reference numerals in each of the drawings refer to like elements.

1 is a conceptual diagram illustrating a schematic configuration of an AR-based theme tour AI curation providing system through custom learning of a virtual character of the present invention.

AR-based theme travel AI curation providing system (1) through the custom learning of the virtual character 20 of the present invention is a system (1) provided in the central server, parents or students who are going to travel with students or students preparing to travel A system 1 is provided for use by a user 2, a concept including a guardian such as a teacher, to plan a trip, but the user 2 is connected to a communication network to utilize the system 1. It is a configuration that can be used by connecting to the system (1) through the user (2) terminal (3), and furthermore, the system (1) can have a separate administrator or AI bots inside the system (1) This means that the administrator or bot can be configured to perform maintenance and data updates.

Figure 2 is a block diagram showing the configuration of the AR-based theme tour AI curation providing system through the custom learning of the virtual character of the present invention, Figure 3 is a flow chart showing the basic process flow of the present invention, Figure 4 is the present invention Is an exemplary view showing an embodiment of a method of inputting a travel plan request form.

This system 1 will be described in more detail with reference to FIGS. 2 to 4 as follows.

AR-based theme tour AI curation providing system (1) through the custom learning of the virtual character 20 of the present invention is at least one travel purpose such as relaxation, entertainment, history, experience from the user (2), Seoul, Composed of a plurality of words related to the travel information from the user (2) and the travel information input unit 110 for receiving a travel destination that wants to leave a relatively wide range of travel, such as a city or region, such as Busan, Tokyo, Paris, etc. A travel information input module 100 including a request form input unit for receiving a travel request form written in a sentence; Cebu destinations, which are subordinate to earlier destinations (for example, if the destination was Busan, Cebu destinations may include captains, Haeundae, Seomyeon, etc.) or more detailed buildings, monuments, stores, restaurants, etc. Determine a travel preference of the user 2 based on the travel information database 210 in which at least one travel data is stored and the travel plan request form 10 to determine a plurality of details from the travel data included in the travel information database 210. The travel preference confirmation module 200 may include a travel preference survey unit 220 for deriving a travel destination.

In other words, by analyzing the request received from the user 2, it is possible to determine which travel destination the user 2 is planning to travel for and what travel destination. It can be used as a basis for providing a travel-centered travel plan.

In particular, the travel destination information database 210 is an information database updated in real time or at a specific cycle. Preferably, the travel destination information database 210 can update the travel destination information while checking blogs, cafes, and posts located on the Internet through a web bot. For example, it may contain information on detailed destinations, specific buildings, monuments, specific stores, restaurants, etc. Examples of such travel information include, for example, Gyeongbokgung Palace as the ruins of Seoul. The location of Gyeongbokgung Palace is located at Sejong-ro, Jongno-gu, Seoul, so this address information, the appearance of Gyeongbokgung Palace, description of Gyeongbokgung Palace, and the way to Gyeongbokgung Palace. It may include transportation and information around Gyeongbokgung Palace. As another example, in the case of Jeollanam-do, various detailed travel destinations may be included, but information such as Yeosu, Jeolla-ja swimming, and Jinnam-gwan may be referred to as information corresponding to the information shown in the above-described travel plan request 10. .

Since the destination information is stored in the destination information database 210, when the user 2 inputs the travel plan request form 10, the destination information database 210 compares the destination information with the destination information included in the destination information database 210. This allows you to derive detailed destinations, which are more detailed plans.

Furthermore, the travel preference research unit 220 determines the travel preferences of the user 2 based on the travel plan request form 10 previously provided to the user 2, and detailed travel destinations based on the travel preferences in the selection data. Basically, the travel plan request form (10) prepared by the user (2) is used to identify the user's preferences through words or emotion words frequently found through text analysis, and the corresponding information is displayed in the travel information database. Extracting at 210 allows the user 2 to derive the detailed travel destination, which is a customized travel destination.

Each of the derived detailed travel destinations is displayed on a map (preferably, the map is best provided through an external GIS database or used with its own map database) to generate the travel map 30. And, the configuration of the travel information providing module 400 that can simultaneously provide the user 2 with the guide information for each detailed travel destination may be further included. In this case, the guide information does not necessarily need to be provided in a state where all the guides for the detailed travel destinations are stored in the travel destination information database 210, and as described above, the webbot may share the guide information when constructing the travel destination information database 210. Collected as a travel destination data in the travel destination information database 210 or may be implemented to the extent that only provides an external link through a web bot in real time.

At this time, the provision of the travel information is provided through the web environment of the PC used by the user 2 to access the system 1 or to the user 2 terminal 3 such as a smartphone possessed by the user 2. Of course, it may be provided by utilizing the stored application. In addition, since the travel map 30 and the guidance are somewhat fixed data, such information may be provided by a large map supplier such as Google or SK Networks to provide offline maps or encyclopedias in a storage medium. Of course, it can be provided as filed information that (2) can hold.

In addition, when deriving a detailed travel destination from the previous travel preference check module 200, AR may be considered as a method of providing the process more user-friendly. AR stands for Augmented Reality, a technology that has attracted public interest with the success of the recently popular game Pokemon Go. When augmented reality captures an image of a real object or surrounding environment through a photographing device such as a camera, the virtual character 20, a speech balloon, an image, and the like are synthesized in the terminal 3 displaying the image. It refers to a technology in which composites behave or position as if they were originally in the captured image.

5 is a flowchart illustrating a process flow for a text analysis method based on a travel plan request form of the present invention, and FIG. 6 is an exemplary view showing an embodiment of a text analysis process and a character meat growth surface according to the present invention. 7 is an exemplary view showing an embodiment of a guide scene after deriving a detailed travel destination in the present invention.

As can be seen in FIG. 2, in the present technology, the character generator 310 generates a virtual character 20 that displays the process so that the user 2 can easily determine the process when deriving a detailed destination, and the previous travel preference. The confirmation module 200 may further include a configuration of the character development module 300 including a character development unit 320 to grow the character 20 by modifying the appearance of the character 20 according to the progress of deriving the detailed destination. . Here, the character 20 may be easily used as a medium for guiding information through augmented reality, and famous IPs such as Pikachu may be utilized simply from abstract marks such as smiles or circles. Particularly, in the case of the nurturing part, since the step or configuration of deriving the detailed destination in the travel preference confirmation module 200 is composed of at least two or more, the appearance of the character 20 according to the flow of such configuration, that is, the progress level By raising or changing step by step can be applied that the character 20 is nurtured.

When the character 20 is prepared in this way, the travel information providing module 400 may synthesize the character 20 on the basis of the travel map 30 based on augmented reality to provide guidance through the character 20. For example, it has the same function of displaying the route 31 by floating the virtual travel map 30 like the navigation of a car, and at the same time displaying the travel map 30 on the travel map 30 according to the characteristics of the travel map 30. AR) by further synthesizing the character 20 is configured to provide additional guidance through the character 20.

Herein, a method of deriving a detailed travel destination through the previous travel preference research unit 220 will be described in more detail with reference to FIGS. 2 and 5 as follows.

First, the travel preference survey unit 220 compares the derivation part 221 for deriving valid terms through a clustering factor clustered according to the appearance and proximity of terms included in the travel plan request form 10, and the valid terminology for the destination data. It may be configured as a preferred destination derivation part to match the and to derive a plurality of matched destination data as a detailed destination.

The travel plan request form 10 prepared by the user 2 includes a plurality of sentences including a number of terms. Therefore, among these terms, the user 2 actually needs some kind of text analysis process to find out what travel information is of interest.

For this purpose, a text extracting part (not shown) may be additionally included. The text extracting part may separate texts included in the travel plan request form by word, but again, such as a noun having a specific meaning by itself. And an emotional text indicating emotion, a link text used for linking between texts, and the like. This is one of the processes that can be performed as a basic task in the general text analysis process, and depending on how small the term is divided in this text extraction part, the accuracy of the final destination to be derived may increase or decrease. to be.

However, the terminology of dividing the term is not complicated, but simply divides the term based on a single word, but this word may contain an investigation or affix. Of course, it can also have a configuration to make.

When the term can be determined by dividing the term in the travel plan request form 10 using this method, the comparison derivation part 221 which derives the effective term through the clustering factor clustered according to the frequency and proximity of the terms. It can be equipped with For example, if the word Jeju appears most repetitively in the travel plan request form 10 or the word Jeju appears repeatedly in a specific sentence of the travel plan request form 10 to determine the frequency of appearance, and in case of proximity. Although it may vary to some extent depending on how it is applied, for example, terms like Jeju Island or Jeju Island are highly likely to appear repeatedly. 'I want to go to the sea' and other terms are located between Jeju Island, that is, it is usually located at a certain distance. Therefore, the Jeju Island / Good Set and the Jeju Island / Want to Go Set have different proximity, so when these samples are gathered in various ways, comparing these proximity can actually determine how big the two words are. Can be. In the case of such proximity determination, various data mining engines that are currently used in operations such as word mining in the big data field are disclosed, and it can be said that they can be used, but simply compare the proximity and the frequency of occurrence respectively. It's an inefficient way to handle a lot of data.

Therefore, as a method for solving such a problem, the comparison derivation part 221 performs clustering of these terms through a clustering factor, which clusters two or more terms repeatedly and compares the appearance frequency and the proximity to request a travel plan. The importance of each of the terms in 10) is identified so that effective terms to be used in actual analysis can be derived. In other words, terms can be determined through the clustering factor.

This clustering factor is configured to extract only the terms that are expected to have meaning in advance when comparing the terms in terms of terms, and to utilize them when comparing the terms. That is, the frequency and proximity of each term can be used as a valid reference value. For example:

It is assumed that the contents of the travel plan request form 10 uploaded by the specific user 2 are as follows and the history and experience are designated as the purpose of the trip.

'I'm a sophomore in high school. For this summer vacation, I would like to take a look at Jeolla Jun Swim, the Adjunct Officer of Admiral Yi Sun-shin, seen in the history textbook for the second semester from x y to x z. I would also like to see the ruins of Admiral Yi Sun-shin near Jeollasa Swimming. '

In these sentences, terms such as 'Jeollasak swimming', 'General Yi Sun-sin' and 'I want to' can be seen in a set of trip plan requests 10 repeatedly. These terms are higher in frequency compared to other terms, and General Yi Sun-sin and Jeolla-na Swimming can be found closer than Jeolla-na Swimming / I want or Yi Sun-shin. Of course, there is no proximity of these three terms except for these three terms, so it can be seen that they are formed higher than 0. Therefore, if a valid term is derived through the clustering factor, it is this Jeolla left swimming, Yi Soon Shin, It can be determined that the term can be derived.

Application of such a clustering factor is shorter when the effective text extraction time is compared with other existing technologies, so that text can be extracted quickly, so that the length of the travel plan request form 10 is long or many users (2) In the case of sending a text message, the quick text extraction enables the analysis of the system 1 even if the user has the same hardware.

Herein, the clustering factor will be described in detail. The clustering factor may be described by Equation 1 below.

Equation 1.

Where CF _ab is the clustering factor of term b for term a, Cont _{(a, b)} is the cumulative sum of term b in proximity to term a, Cont _{(b, a)} is the cumulative sum of term a in proximity to term b, Cont _{(a, all)} means the cumulative sum of all the terms that are close to the term a, and Cont _{(b, all)} means the cumulative sum of all the terms that are close to the term b.

In other words, if you continue to use the previous travel planning request (10) to explain, the two terms 'General Yi Sun-sin' and 'Jeollasak swimming' are frequently used in close proximity, and the frequency of appearance is also high. Therefore, when performing clustering on this, it is likely that 'General Yi Sun-shin' and 'Jeolla-ja swimming' will be clustered into one group.

Furthermore, in terms of dates, terms representing dates and times such as 'x month', 'y day', and 'z day' are frequently used in close proximity. Therefore, when grouping such texts into a group, the sentence It can be said that faster clustering can be performed as compared to classification of all terms included therein.

When clustering is performed in this manner, the valid terms can be matched with the destination data included in the destination information database 210 again to derive a plurality of matched destination data as detailed destinations. It may be referred to as a detailed destination derivation part 222. That is, in the case of Admiral Yi Sun-sin and Jeollasa-swimming, detailed travel information such as Jinnamgwan, a building located in Jeollasa-swimming, Gungi-go, and Byeonsan Peninsula and Yeosu can be derived. Such information uses a kind of similarity comparison, but the similarity comparison can be applied by using a commercial engine or related analysis method widely used in big data analysis (especially text analysis) field, and thus detailed description thereof is omitted. Do it. Furthermore, by utilizing this method as a reverse, it can also be derived through a process of tracking a larger range of travel maps (30) called Jeollanam-do.

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8 is a flowchart illustrating a process flow for a method of generating a path comparison diagram according to the present invention, FIG. 9 is an exemplary view showing an embodiment of a guide scene to which a path comparison diagram according to the present invention is applied, and FIG. An exemplary view showing an embodiment of a guide scene.

Further, the travel information providing module 400 includes a map receiver 410 for receiving a map around the detailed travel destination from the GIS database, a map application unit 420 for displaying the location of the detailed travel destination on the received map, and the detailed travel destination. The route display unit 430 may be further configured to display the route 31 on the map based on at least one moving line among the roads and sidewalks to be connected to generate the travel map 30. This is to indicate the configuration of the route 31 to be sequentially moved to the corresponding travel destination by displaying the expected travel destination on the map when planning the trip. Here, the path display unit 430 may determine the road and walking information through the GIS database, and may specify the path 31 through various conditions such as the shortest distance or the path 31 going in the straightest direction.

In addition, the travel information providing module 400 may be provided with a configuration for identifying the real-time location of the user 2 to provide location-based information.

That is, the position detecting unit 440 for grasping the current position of the user 2 based on the GPS information measured by the user 2 terminal 3 held by the user 2 and the starting point 41 from the current position. Passing point designation unit 450 that designates a detailed travel destination that can be reached next from the starting point 41 through the path 31 by way of the waypoint 42 and the waypoint 42 can be reached next. An expansion point designation unit 460 for calculating a plurality of detailed travel destinations and designating the expansion point 43 as an expansion point 43, and a starting point 41 disposed in a state in which a starting point 41 is disposed at a central portion as a chart generated separately from a map. Comparing a route in which a plurality of waypoints 42 are arranged in a hierarchical structure based on the above, and a plurality of extension points 43 are further arranged in a hierarchical structure based on each waypoint 42. The comparison diagram generating unit 470 for generating the diagram 40 may further include a configuration.

This configuration is to provide a configuration for recommending other routes 31 based on the current location to the user 2 by grasping the current location of the user 2 through GPS, since the trip is not always made on time, In the middle of the trip, it is necessary to keep track of the detailed destination and adjust the schedule. In this case, it can be said that the path comparison diagram 40 which can look back on the route 31 of the detailed travel destinations based on the current location is available. The route comparison diagram 40 sets the current position as the starting point 41 and designates the detailed travel destinations that can be moved from the starting point 41 as the waypoint 42, from the starting point 41 to each waypoint 42. All information such as length of time to go and estimated time is listed in a hierarchical structure. In addition, since there may be enough room to go through one or more detailed travel destinations, the detailed travel destination that can be moved next to the reference point via the stopover point 42 is designated as the extension point 43, and the stopover point 42 In this case, the path 31 moving from the expansion base 43 to the extension base 43 is displayed in a hierarchical structure.

Therefore, the user 2 can check the detailed travel destinations and the moving lines which can go based on the current position at a glance, and thus the travel and movement can be much more analyzed than when the user 2 moves only by the feeling or experience of the user 2.

Here, the following method of further utilizing the path comparison diagram 40 may also be considered.

First, in order to configure the system 1, at least one of each destination is installed, and a destination complexity sensor for generating object detection information by detecting the number of objects including at least one of a vehicle and a traveler located in the destination. (4) may be further provided. This complexity sensor is a sensor that can determine the complexity by grasping the number of people located in a specific area that is recently utilized, of course, CCTV or infrared sensors can be used.

Furthermore, the travel information providing module 400 includes a complexity determination unit 480 for determining the complexity of each of the waypoint 42 and the expansion point 43 based on the object detection information, and the waypoint 42 and the expansion point. A comparison line generation unit 490 for deriving a plurality of comparison lines connecting 43 and a complexity index for designating the complexity difference between the transit point 42 and the expansion point 43 connected to the comparison line as the complexity of the comparison line. A portion 500 and a comparison line synthesis unit 510 for synthesizing the comparison lines with the path comparison diagram 40 may be provided.

In this case, the complexity may be derived through Equation 3 below.

Equation 3.

In Equation 3, X _D is the complexity of a specific comparison line, Y ₁ is the number of objects detected by one of the complexity sensors as a reference, and Y ₂ is detected by another distribution sensor forming a comparison line with Y ₁ . Number of objects, r is the weight of importance between 0 and 1, where the detailed travel destination including the distribution sensor is differentially applied depending on whether it is a route point 42 or an extension point 43, and L is the actual distance of the comparison line do.

In other words, the number of objects identified by the complexity detecting sensor may be, for example, the complexity of the corresponding transit point 42 or the expansion point 43. Here, the starting point 41 may be calculated by setting the complexity to 0. Furthermore, in the case of importance weighting, as a predetermined value, for example, the distribution of 100 people in a detailed destination that can accommodate 3,000 people and the distribution of 100 by a detailed destination that can accommodate 300 people can be enormous. I can understand. In addition, since only a smaller area than usual may be opened or a sudden problem may occur due to repair, etc., the importance weight may be specified between 1 and 0 in advance to correct such a difference.

Therefore, the complexity of each transit point 42, the expansion point 43, and the comparative moving line may be derived according to the measured distribution and the importance weight.

Furthermore, the travel information providing module 400 may further include a configuration of a complexity reflecting unit 520 that controls the length of the comparison line in proportion to the complexity, wherein the length of the comparison line is expressed by Equation 4 below. It can be calculated through

Equation 4.

Here, A _{(x, y)} is the length of the comparative copper wire, N _K is the reference length of the copper copper wire, K ₀ is the maximum complexity of the copper copper wire, K ₁ is the selected comparative copper wire among the total number of objects detected by the total complexity sensor the ratio of the complexity of the number of objects detected by the sensors included in, and K ₂ indicates the complexity of the selected comparative copper.

Accordingly, the comparison line may have a length that varies according to the complexity at a specific reference length, and in particular, the length of the comparison line does not merely consider the complexity of the comparison line, but also considers the complexity of all the comparison lines. More complex lengths can be applied. Therefore, the user 2 can, of course, make it possible to modify the appropriate copper line simply by comparing the lengths of the comparative copper lines with the naked eye by determining sufficient complexity.

As described so far, the configuration and operation of the AR-based theme tour AI curation providing system through the custom learning of the virtual character according to the present invention have been expressed in the above description and the drawings, but this is merely described as an example and the spirit of the present invention. This is not limited to the above description and drawings, and various changes and modifications are possible without departing from the spirit of the present invention.

1: system 2: user
3: terminal 4: travel complexity sensor
10: Travel Plan Request Form 20: Character
30: Travel Map 31: Routes
40: path comparison diagram 41: starting point
42: transit hub 43: expansion hub
100: travel information input module 110: travel information input unit
200: Travel confirmation number module 210: Travel information database
220: travel preference research unit 221: comparison derived parts
222: Cebu destination depart
300: character development module 310: character generator
320: character development unit 400: travel information providing module
410: map receiver 420: map application unit
430: route display unit 440: position grasping unit
450: Route point designation unit 460: Expansion point designation unit
470: comparison degree generation unit 480: complexity determination unit
490: comparison line generation unit 500: complexity designation unit
510: non-combination line synthesis unit 520: complexity reflecting unit

Claims (10)

As an AR-based theme travel AI curation providing system through custom learning of virtual characters,
A travel information input module including a travel information input unit configured to receive travel information including a travel destination and a travel destination from a user, and a request input unit configured to receive a travel plan request form written in sentences related to the travel information from the user;
A travel preference confirmation module including a travel destination information database in which travel data is stored and a travel preference survey unit for determining a travel preference of the user based on the travel plan request document and deriving a plurality of detailed travel destinations from the travel data;
A character development module including a character generation unit for generating a virtual character and a character development unit for modifying and developing the appearance of the character according to the progress of deriving the detailed travel destination from the travel preference confirmation module;
A travel information providing module for providing the user with a travel map displaying each of the detailed travel destinations on a map and guide information about the detailed travel destinations based on AR (Augmented Reality) through the raised character;
At least one for each detailed travel destination, a travel destination complexity detection sensor for generating the object detection information by detecting the number of objects including at least any one of the vehicle and the traveler located in the detailed travel destination;
The travel information providing module,
A map receiver configured to receive a map around the detailed travel destination from a GIS database;
A map application unit displaying the location of the detailed travel destination on the map;
A route display unit configured to generate a travel map by displaying a route on the map based on at least one moving line of a road and a sidewalk connecting the detailed travel destinations;
A location detecting unit for grasping the current location of the user based on the GPS information of the user terminal possessed by the user;
A transit point designation unit for setting the current position as a starting point and designating the detailed travel destination that can be reached next through the route from the starting point;
An expansion point designation unit configured to calculate a plurality of detailed travel destinations that can be reached next through the route from the waypoint and designate an extension point;
As a chart generated separately from the map, a plurality of waypoints are arranged in a hierarchical structure based on the starting point, and a route comparison in which the plurality of extension points are further arranged in a hierarchical structure based on the waypoints is compared. Comparative diagram generating unit for generating a diagram,
A complexity determination unit that determines the complexity of each of the route point and the expansion point based on the object detection information;
A comparison line generation unit for deriving a plurality of comparison lines connecting the transit point and the extension point;
A complexity designation unit for designating a difference in complexity between the transit point and the extension point connected to the comparison line as the complexity of the comparison line;
A comparative copper synthesizing unit for synthesizing the comparative copper to the path comparison diagram,
And a complexity reflecting unit configured to control the length of the comparison line in proportion to the complexity through the following equation.
Equation.

(Where A _{(x, y)} is the length of the comparison line, N _K is the reference length of the comparison line, K ₀ is the maximum complexity of the comparison line, K ₁ is the total number of objects detected by the total complexity sensor The ratio of the number of objects detected by the complexity sensor included in the movement line, K ₂ means the complexity of the selected comparison movement.) The method of claim 1,
The travel preference survey unit,
A comparative derivation part for deriving valid terms through a clustering factor clustered according to the frequency and proximity of terms included in the travel plan request form;
And a detailed destination derivation part for deriving the detailed destination based on the valid term. The method of claim 2,
The clustering factor is,
The AR-based theme travel AI curation providing system, characterized in that calculated through the following equation (1).
Equation 1.

Where CF _ab is the clustering factor of term b for term a, Cont _{(a, b)} is the cumulative sum of term b in close proximity to term a, Cont _{(b, a)} is the cumulative sum of term a in close proximity to term b, Cont _{(a, all)} is the cumulative sum of all the terms that are close to term a, and Cont _{(b, all)} is the cumulative sum of all the terms that are close to term b.) The method of claim 1,
The complexity designation unit,
An AR-based theme travel AI curation providing system, characterized in that for calculating the complexity through the following equation (3).
Equation 3.

(Where X _D is the complexity of a particular comparable copper line, Y ₁ is the number of objects identified by one of the complexity sensors used as a reference, and Y ₂ is determined by another distribution sensor forming a comparative copper line with Y ₁ The number of objects, r is an importance weight value between 0 and 1 to which the detailed travel destination including the distribution sensor is differentially applied depending on whether the transit point and the expansion point, L means the actual distance of the comparison line. ) delete delete delete delete delete delete

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