Personalized tourism treasure hunting system and business model

Abstract

The invention relates to the technical field of tourism treasure hunting, and particularly discloses a personalized tourism treasure hunting system and a business mode, and the system comprises a view finding module, a 3D model construction module, a drama compiling module, a material making module, a treasure detecting plot triggering module, a data analysis module, a prop exchange module, and a business mode module. Personalized tourism treasure hunting experience is created through advanced technologies such as AR / VR, the immersion and interestingness of tourists are improved, and the tourists are encouraged to explore different areas of the scenic area. By combining local human stories, historical ledges and other contents, a brand-new tourism mode of learning while playing while walking is brought to tourists, local culture is popularized in modes of story drama, virtual props, character roles and the like, the tourists can learn local historical and cultural backgrounds in game interaction, and deeper memory is formed. The scenic spot can increase brand exposure through the system, and improves popularity and influence of tourism brands.

Description

Technical Field

[0001] The present invention belongs to the technical field of tourism treasure hunting, and in particular relates to a personalized tourism treasure hunting system and business model. Background Art

[0002] With the booming tourism industry, people's demands for travel experiences are becoming increasingly diverse. Traditional sightseeing tours are no longer able to meet modern tourists' pursuit of interactivity and fun. In this context, personalized tourism treasure hunt systems have emerged. By integrating modern technology with tourism resources, they aim to provide tourists with a more unique, enriching, and engaging travel experience.

[0003] However, existing tourism treasure hunt systems suffer from numerous shortcomings. First, when it comes to acquiring and displaying attraction information, many systems still rely on simple graphic and text descriptions, lacking vivid and intuitive three-dimensional presentations, making it difficult for visitors to fully experience the attractions' charm. Second, existing systems often lack sufficient creativity and interactivity in plot design and triggering mechanisms. The scripts are monotonous, and the triggering conditions are simple, making it difficult to inspire visitors' enthusiasm for participation and desire to explore. Furthermore, existing systems also have shortcomings in the collection and analysis of user behavior data, making it difficult to accurately understand visitors' needs and preferences, making it difficult to provide personalized recommendations and services.

[0004] In this regard, the inventor proposes a personalized tourism treasure hunting system and business model to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a personalized tourism treasure hunting system and business model to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A personalized tourism treasure hunting system, comprising:

[0008] Scenic spot module: obtains scenic spot research data and creates a material collection based on the research data;

[0009] 3D model building module: building a three-dimensional model of the scenic spot based on the material collection;

[0010] Screenwriting module: Based on the material collection, combined with field investigations, online searches, books and documents, and consultation and interview methods, write a field script and set the plot content and triggering conditions of the script;

[0011] Material production module: produces prop materials and plot materials according to the plot content, including real-life photos, videos, CG animations, AR scenes and physical props, and stores the produced prop materials and plot materials on the server;

[0012] Treasure hunting plot trigger module: includes a treasure hunting unit and a plot trigger unit;

[0013] The treasure hunting unit provides at least one detection item to detect virtual items or plots around the current location;

[0014] The plot trigger unit: triggers the plot according to user positioning, item use and completion of specific conditions;

[0015] Data analysis module, obtains user behavior data, combines the user behavior data with a geographic coordinate mapping algorithm to achieve precise positioning of the user's location and plot triggering, combines a prop matching algorithm to match plot nodes with props, combines a recommendation algorithm to recommend nearby interactive plots or tasks to the user, conducts data analysis, obtains an analysis result, and provides system optimization suggestions and user feedback channels according to the data analysis result;

[0016] Prop exchange module: includes two prop exchange methods, online and offline, and uses specific props obtained in the plot to exchange for corresponding prizes in the mall;

[0017] Business model module: provides business models of content charging, cooperative promotion, advertising revenue and membership system.

[0018] Preferably, the scenic spot research data includes the cultural scenery, story legends, and landmark building information of the scenic area, divides the scenic area into different plot nodes, and each node carries a single story background, interactive task or treasure.

[0019] Preferably, the plot content includes main plot, side plot, branch plot and multiple endings.

[0020] Preferably, the triggering conditions include location triggering, prop use triggering, multi-condition combined triggering and completion of specific conditions triggering.

[0021] Preferably, the geographic coordinate mapping algorithm maps the scenic spot location to the virtual scene map to precisely locate the user's current location, and its expression is:

[0022]

[0023] Among them, d represents the distance between the user and the trigger point. When d < R, where R is the trigger radius, the system will automatically trigger the plot.

[0024] Preferably, the prop matching algorithm matches between plot nodes and props by means of label matching, and the labels of props or plots are generated according to the following logic:

[0025]

[0026] Among them, w i represents the weight, f i Represents a label feature. The plot will be triggered only when the score reaches the set threshold.

[0027] Preferably, the recommendation algorithm uses a recommendation algorithm based on collaborative filtering to recommend nearby interactive plots or tasks to users, where the expression is:

[0028]

[0029] Among them, μ is the global average rating, bu and bi are the biases of users and items, and qi and pu are the vector representations of users and items.

[0030] Preferably, the detection range and effect of the detection prop are determined according to the level or type, and the detection range calculation formula is:

[0031] Re=Rb×(1+k·L)

[0032] Among them, Re is the final detection radius, Rb is the basic detection range, L is the item level, and k is the level influence coefficient.

[0033] The present invention also provides a personalized tourism treasure hunting business model, including the personalized tourism treasure hunting system as described above, and the business model further includes:

[0034] Content charging model: Provide users with basic free plots, and charge for specific advanced plots or props;

[0035] Cooperative promotion model: cooperate with scenic spots and surrounding businesses to link the treasure hunting system with scenic spots, bringing more traffic and value-added services to scenic spots;

[0036] Advertising revenue model: embed advertisements of surrounding businesses, including restaurants and souvenir shops, into the system to generate additional revenue;

[0037] Membership system model: set up membership functions, members enjoy special plot content and prop discounts, and increase long-term user retention.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] (1) This invention uses advanced technologies such as AR / VR to create a personalized tourism treasure hunt experience, increase tourists' immersion and interest, and encourage tourists to explore different areas of the scenic area. In combination with local cultural stories, historical legends, etc., it brings tourists a new way of traveling by "walking, playing and learning".

[0040] (2) The system of the present invention promotes local culture through storytelling, virtual props, and characters, allowing tourists to learn about local history and cultural background through game interaction and form deeper memories. Scenic spots can increase brand exposure through this system and enhance the popularity and influence of tourism brands. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a block diagram of a personalized tourism treasure hunting system of the present invention. DETAILED DESCRIPTION

[0042] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0043] Example 1:

[0044] See also Figure 1 As shown, a personalized tourism treasure hunting system includes:

[0045] Scenarios module: Obtain scenic spot research data and create a collection of materials based on the research data to provide materials for subsequent scriptwriting and model building;

[0046] 3D model building module: building a three-dimensional model of the scenic spot based on the material collection;

[0047] Screenwriting module: Based on the material collection, combined with field investigations, online searches, books and literature, and consultations and interviews, write a field script, set the plot content and triggering conditions of the script, and rewrite it according to local location coordinates and scenic spots to ensure the richness and appeal of the plot;

[0048] Material production module: produces props and plot materials based on the plot content, including real-life photos, videos, CG animations, AR scenes, and physical props, and stores the produced props and plot materials on the server to ensure the security and accessibility of the materials;

[0049] Treasure hunting plot trigger module: including treasure hunting unit and plot trigger unit;

[0050] The treasure hunting unit provides at least one detection prop to detect virtual objects or plots around the current location, and the detection range is determined by the properties of the detection prop.

[0051] The plot triggering unit triggers the plot according to the user's location, prop usage and completion of specific conditions, ensuring that the user can immerse themselves in the plot and enjoy the fun of treasure hunting.

[0052] The data analysis module acquires user behavior data and uses this data in conjunction with a geographic coordinate mapping algorithm to accurately locate the user's location and trigger plots. It also uses an item matching algorithm to match plot nodes with items. It also uses a recommendation algorithm to recommend nearby interactive plots or tasks to the user, including location information, item usage history, and plot triggering status. This data is then analyzed to generate results to assess system performance and user satisfaction. Based on these data analysis results, system optimization suggestions and user feedback channels are provided to ensure continuous system improvement and meet user needs.

[0053] Prop exchange module: includes two prop exchange methods: online and offline. Specific props obtained in the plot can be used to exchange corresponding prizes in the mall, including virtual items and physical prizes.

[0054] Reward module: Set up a reward mechanism to encourage users to participate in treasure hunt activities and improve user stickiness and activity

[0055] Business model module: Provides content charging, cooperative promotion, advertising revenue and membership system business models to bring traffic and revenue to scenic spots and cooperating merchants.

[0056] As can be seen above, using advanced technologies like AR / VR to create a personalized treasure hunt experience increases visitors' immersion and interest, encouraging them to explore different areas of the scenic area. Incorporating local cultural stories and historical legends, this offers visitors a new way to travel, "playing and learning."

[0057] The system promotes local culture through storytelling, virtual props, and characters, allowing visitors to learn about local history and cultural background through interactive gameplay, forming deeper memories. Scenic spots can use this system to increase brand exposure and enhance the visibility and influence of their tourism brands.

[0058] Example 2:

[0059] refer to Figure 1 As shown, the scenic spot survey data includes the cultural landscape, stories and legends, and landmark building information of the scenic area, dividing the scenic area into different plot nodes, each of which carries a single story background, interactive task or treasure.

[0060] Specifically, the plot content includes main plot, sub-plots, branch plots and multiple endings.

[0061] Specifically, the trigger conditions include location trigger, item usage trigger, multiple condition combined trigger and completion of specific condition trigger.

[0062] Specifically, the geographic coordinate mapping algorithm maps the location of scenic spots to the virtual scene map, accurately positioning the user's current location, making the location trigger or prop trigger highly accurate. Its expression is:

[0063]

[0064] Among them, d represents the distance between the user and the trigger point. When d < R, where R is the trigger radius, the system will automatically trigger the plot.

[0065] Specifically, the prop matching algorithm matches between the plot nodes and props through label matching. The labels of props or plots are generated according to the following logic:

[0066]

[0067] Among them, w i represents the weight, and f i represents the label feature. Only when the score reaches a certain threshold is the plot trigger allowed.

[0068] Specifically, the recommendation algorithm uses a collaborative filtering-based recommendation algorithm to recommend nearby interactive plots or tasks to users. Its expression is:

[0069]

[0070] Among them, μ is the global average rating, bu and bi are the biases of the user and the item, qi and pu are the vector representations of the user and the item. Through this recommendation system, personalized interactive task suggestions can be provided to users.

[0071] Specifically, the detection prop determines the detection range and effect according to the level or type. The calculation formula for the detection range is:

[0072] Re = Rb × (1 + k·L)

[0073] Among them, Re is the final detection radius, Rb is the basic detection range, L is the prop level, and k is the level influence coefficient.

[0074] As can be seen from the above, the treasure hunt task and prop collection system guide tourists to explore different locations in depth, increasing the stay time of tourists in the scenic area. The completed plots and tasks can unlock more props or rewards, attracting tourists to enter the park again to experience the unfinished tasks and avoiding repeated visits.

[0075] The prop mall, advertisement push and online-offline exchange mode in the system effectively connect tourists, scenic areas and merchants. Tourists can obtain discount coupons or exchange goods through props, promoting more consumption and increasing the revenue of scenic areas and merchants. At the same time, merchants can analyze the advertising effect based on data and improve the precision marketing and the input-output ratio of advertising.

[0076] The system uses location data, heat map analysis, visitor path analysis, and other detailed data to accurately analyze visitor behavior, helping scenic spots rationally plan task nodes and prop settings, and optimize the distribution of scenic spots. It also helps scenic spots identify potential congested areas or areas of low interest in advance, allowing for timely adjustments and resource allocation.

[0077] Example 3:

[0078] This design is specifically applied to the tourism industry. The tourism treasure hunt system can be applied to various cultural tourism scenic spots, such as historical sites, museums, ancient town scenic spots, etc., integrating cultural content into tasks, props and plot design, realizing the deep integration of culture and tourism, and providing a new tourism model that combines education and entertainment.

[0079] Furthermore, taking the landscape culture theme treasure hunt tourism system as an example:

[0080] This system will be implemented in a scenic area with rich natural landscapes and unique historical and cultural backgrounds, such as the famous "Legend of the Mountain and the Sea." This example will demonstrate how to integrate modules such as framing, screenwriting, material production, and plot triggering to provide tourists with a customized treasure hunting experience.

[0081] Implementation steps

[0082] 1. Framing

[0083] The system operation team conducted on-site research on the scenic area, collecting information on the area's landmark buildings, landscape features, historical legends, and special businesses around the scenic area.

[0084] Survey data:

[0085] Coordinate data: The longitude and latitude of major scenic spots (such as mountains and pools), used for positioning and triggering plots.

[0086] Cultural materials: local folk tales, such as the story of "Longtan Secret Realm", legends related to historical relics, etc.

[0087] Landmark buildings: temples, ancient bridges, stone sculptures, etc. on the top of the mountain, used as plot nodes and prop trigger points.

[0088] Landscape features: such as stone forests and seas of clouds, which will be used for AR / VR production later.

[0089] 3D model construction

[0090] The collected data is used to construct subsequent plots, props, and tasks.

[0091] 2. Screenwriter

[0092] Combining local historical legends and geographical features, a main story with the theme of "Secret Treasures of Mountains and Seas" is designed. Visitors need to complete tasks in different locations to unlock clues related to the treasure.

[0093] Plot:

[0094] Main storyline: Visitors need to find various "spiritual stones", each of which is hidden in different scenic spots. Obtaining the spirit stones can unlock the final location of the treasure.

[0095] Side quests: Complete challenges in certain areas of the scenic area (such as solving puzzles or AR interactions) to obtain the virtual prop "ancient map", which can provide tourists with more clues.

[0096] 3. Material production

[0097] 3D models of various attractions are created using 3D modeling software, and AR technology is used to display virtual scenes at designated locations. For example, when visitors open the mobile app at a certain location, they can see an AR-based "dragon shadow," further enhancing the immersive experience.

[0098] material:

[0099] AR scene: The "shadow of a dragon" is displayed at the location of Longtan.

[0100] Prop materials: virtual keys, treasure chests, puzzle pieces, etc. picked up at different locations. Some props are displayed in AR form.

[0101] Data storage and trigger settings

[0102] All scene materials and prop information are stored in the cloud server, and trigger conditions are set in the system to ensure that the system can trigger corresponding plots based on the user's geographic location, prop status and other conditions.

[0103] 4. Treasure Hunt plot trigger:

[0104] Location trigger: When the user approaches the Longtan area, the coordinate range of the Longtan area is [(x1,y1),(x2,y2)], the "Dragon's Shadow" plot is triggered.

[0105] Prop trigger: When the user uses the "Ancient Map" prop, the system automatically marks the next task location on the map.

[0106] Multi-condition triggering: At a specific location, users must first complete identity authentication (face recognition or QR code scanning) and use props before they can unlock the task.

[0107] 5. Data Analysis

[0108] Location data: records the user's current location for plot triggering and navigation.

[0109] Item possession status: records the virtual items that the user has obtained and tracks their usage.

[0110] Interaction data: records the tasks completed by users and the plots unlocked, which facilitates the analysis of user preferences.

[0111] Content Data

[0112] Landmark location: The coordinates of each key location, used for geographic triggering.

[0113] Plot nodes: trigger conditions, content description, material ID, etc. of each plot.

[0114] Item attributes: the purpose, triggering conditions, acquisition conditions, etc. of each item.

[0115] Algorithm support data

[0116] Heat analysis: Record the frequency of user visits to each location to adjust task difficulty or item distribution.

[0117] User preference analysis: Optimize recommended content based on user interactions, such as the number of tasks completed and item usage.

[0118] The specific implementation effect evaluation is as follows:

[0119] 1. Visitor experience feedback

[0120] Collect feedback from tourists during the treasure hunt through questionnaires or the in-app evaluation system. For example:

[0121] Satisfaction: experience rating and suggestion feedback.

[0122] Depth of experience: number of tasks completed by users, number of interactions, task duration, etc.

[0123] 2. Commercial benefits

[0124] The system cooperates with merchants around the scenic area to provide tourists with props exchange services. For example:

[0125] Merchant ad click-through rate: The number of times users view advertisements for nearby restaurants and souvenir shops in the app.

[0126] Prop redemption volume: Counts the number of prizes obtained by tourists through prop redemption, which is used to evaluate the attractiveness of prop rewards.

[0127] 3. Data analysis results

[0128] Through data analysis, the system's content distribution, plot difficulty and recommended content are optimized to further enhance the tourists' immersive experience.

[0129] Heat map analysis: Use heat maps to show the distribution of tourists' interactive locations in the scenic area, identify high-frequency and low-frequency locations, and facilitate the adjustment of task difficulty and trigger conditions.

[0130] User path analysis: Analyze the activity paths of tourists in the scenic area to provide support for the system to improve the layout of plot trigger points.

[0131] The effect after specific implementation is shown:

[0132] Visitor participation: Due to the introduction of rich interactive plots and prop rewards in the system, visitors’ play time and participation in the scenic area have increased significantly, especially the number of visits from young tourists.

[0133] Immersion and repeat visit rate: The personalized and interactive experience brought by the system has enhanced the attractiveness of the scenic spot. About 40% of tourists expressed their hope to participate in more side quests and unlock hidden plots during their next visit.

[0134] Increased commercial revenue: Ad click-through rates and item redemption rates for surrounding businesses increased significantly, boosting the scenic area's overall revenue. Furthermore, the physical prizes or discounts tourists received through item redemptions increased customer traffic to offline stores.

[0135] Data-driven optimization: Based on data analysis heat maps and user path analysis, the operations team adjusted the locations of some tasks, optimized the plot triggering mechanism, and further enhanced the user's interactive experience.

[0136] By utilizing user behavior data to continuously adjust content and reward mechanisms, the system not only enhances the visitor experience, but also significantly increases the entertainment and attractiveness of the scenic area, bringing higher visitor volume and commercial value to the scenic area.

[0137] In the description of this specification, the reference terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0138] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0139] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A personalized tourism treasure hunting system, characterized in that: Comprising: Viewing module: Obtain scenic spot research data and establish a material collection according to the research data; 3D model construction module: Construct a three-dimensional model of the scenic area according to the material collection; Scriptwriting module: Compile a field script according to the material collection, combining on-site inspections, online searches, books and literature, and consultation interviews, and set the plot content and trigger conditions of the script; Material production module: Produce prop materials and plot materials according to the plot content, including real-scene photos, videos, CG animations, AR scenes, and physical props, and store the produced prop materials and plot materials in the server; Treasure hunt plot trigger module: Including a treasure hunt unit and a plot trigger unit; The treasure hunt unit provides at least one detection prop to detect virtual items or plots around the current location; The plot trigger unit: Trigger the plot according to user positioning, prop use, and completion of specific conditions; Data analysis module, obtain user behavior data, combine the user behavior data with a geographic coordinate mapping algorithm to achieve precise positioning of the user's location and plot trigger, combine a prop matching algorithm to match plot nodes with props, combine a recommendation algorithm to recommend nearby interactive plots or tasks to the user, perform data analysis, obtain an analysis result, and provide system optimization suggestions and user feedback channels according to the data analysis result; Prop exchange module: Including two prop exchange methods, online and offline, and use specific props obtained in the plot to exchange corresponding prizes in the mall; Business model module: Provide business models of content charging, cooperative promotion, advertising revenue, and membership system.

2. A personalized tourism treasure hunting system according to claim 1, characterized in that: The scenic spot research data includes the cultural scenery, story legends, and landmark building information of the scenic area, and the scenic area is divided into different plot nodes, and each node carries a single story background, interactive task, or treasure.

3. The personalized tourism treasure hunting system according to claim 1, characterized in that: The plot content includes main plot, side plot, branch plot, and multiple endings.

4. The personalized tourism treasure hunting system according to claim 1, characterized in that: The trigger conditions include location trigger, prop use trigger, multi-condition combination trigger, and completion of specific conditions trigger.

5. The personalized tourism treasure hunting system according to claim 1, characterized in that: The geographic coordinate mapping algorithm maps the scenic spot location to the virtual scene map to precisely locate the user's current location, and its expression is: Where d represents the distance between the user and the trigger point. When d < R, where R is the trigger radius, the system will automatically trigger the plot.

6. The personalized tourism treasure hunting system according to claim 1, characterized in that: The prop matching algorithm matches between plot nodes and props by means of label matching, and the labels of props or plots are generated according to the following logic: Among them, w i represents the weight, f i Represents a label feature. The plot will be triggered only when the score reaches the set threshold.

7. The personalized tourism treasure hunting system according to claim 1, characterized in that: The recommendation algorithm uses a recommendation algorithm based on collaborative filtering to recommend nearby interactive plots or tasks to the user, and its expression is: Where μ is the global average score, bu and bi are the biases of the user and the item, and qi and pu are the vector representations of the user and the item.

8. The personalized tourism treasure hunting system according to claim 1, characterized in that: The detection range of the detection prop is determined according to the level or type, and the detection range calculation formula is: Re = Rb × (1 + k·L) Where Re is the final detection radius, Rb is the basic detection range, L is the prop level, and k is the level influence coefficient.

9. A personalized tourism treasure hunt business model, characterized by: Including a personalized tourism treasure hunt system according to any one of claims 1-8, the business model further includes: Content charging model: Provide users with basic free plots, and charge for specific advanced plots or props; Cooperative promotion model: cooperate with scenic spots and surrounding businesses to link the treasure hunting system with scenic spots, bringing more traffic and value-added services to scenic spots; Advertising revenue model: embed advertisements of surrounding businesses, including restaurants and souvenir shops, into the system; Membership system mode: set up membership functions, and members can enjoy special plot content and prop discounts.

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