An AR tourism real-time cultural interpretation system and method

Abstract

This invention discloses an AR tourism real-time cultural interpretation system and method. In the field of AR tourism technology, it addresses the technical problem of how to provide personalized interpretation methods based on the age, interests, and other characteristics of different tourists, thereby improving tourist satisfaction with the interpretation content. This invention categorizes users according to their age, calculates the feedback standards for different interpretation methods corresponding to different age groups, sorts the feedback standards by age group from highest to lowest, and selects the interpretation method with the highest feedback standard. Simultaneously, it introduces user interest factors, calculates the similarity between user interests and the same standard method, and combines dynamically adjusted weight coefficients to calculate recommendation values, generating personalized interpretation information to meet the needs of users of different ages and interests. It also obtains real-time user feedback and adjusts and analyzes the interpretation information based on the feedback, thereby improving user satisfaction with the interpretation content.

Description

Technical Field

[0001] This invention relates to the field of AR tourism technology, specifically to an AR tourism real-time cultural interpretation system and method. Background Technology

[0002] With the development of the tourism industry, tourists have increasingly higher demands for their travel experiences. During their travels, tourists not only hope to appreciate the natural scenery of scenic spots, but also yearn to gain a deeper understanding of the cultural connotations of these places.

[0003] Currently, while some methods exist for interpreting tourism culture, they suffer from numerous shortcomings. For example, information collection is not comprehensive enough, with insufficient depth of research into cultural heritage, historical stories, and folk customs; the accuracy of text conversion and information processing needs improvement, potentially leading to errors or inaccuracies; positioning technology is not precise enough, affecting tourists' ability to obtain cultural interpretation information for their specific location; the interpretation style lacks personalization, failing to meet the needs of tourists of different ages and interests; and the real-time feedback and adjustment mechanisms are inadequate, making it impossible to adjust the content and style of interpretation promptly based on tourist feedback. Furthermore, when using AR technology for tourism culture interpretation, the processing of 3D modeling and voice narration needs further optimization to enhance the immersive experience for tourists. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an AR tourism real-time cultural interpretation system and method, which solves the problem of how to provide personalized interpretation methods based on the age, interests, and other characteristics of different tourists, thereby improving tourists' satisfaction with the interpretation content.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an AR tourism real-time cultural interpretation system, comprising:

[0006] The user location correction analysis unit calculates the difference between the user's location and the system's location, compares it with a preset value to determine whether correction is needed. If correction is needed, it uses landmarks around the user to accurately locate the user, generates user location information, and transmits it to the real-time interpretation and analysis unit.

[0007] The real-time interpretation and analysis unit calculates the feedback standard of each interpretation method in different age groups based on user age classification, selects the interpretation method with the highest feedback standard, determines whether the current user's age can match the interpretation method with the same standard, generates the same or different analysis signal, and transmits it to the feedback adjustment and processing unit.

[0008] The feedback adjustment processing unit is used to analyze the same or different analysis signals separately. When analyzing the same signals, it calculates the similarity between the user's interest and the same standard interpretation method, calculates the recommendation value with the formula, and determines the final interpretation method information based on the highest recommendation value. When analyzing different signals, it directly selects the interpretation method with the largest feedback standard to generate information.

[0009] The explanation method is adjusted based on real-time user feedback. If the feedback is unsatisfactory, the explanation method information is reselected and generated in sequence, and finally transmitted to the interpretation information display unit.

[0010] As a further embodiment of the present invention, the system also includes a tourist attraction information database establishment unit and an interpretation information display unit;

[0011] The tourist attraction information database establishment unit is used to correct and convert tourist attraction information into text data, while recording narration and creating 3D models to generate a tourist attraction information database, which is then transmitted to the user positioning correction and analysis unit.

[0012] The interpretation information display unit is used to provide real-time interpretation based on the obtained interpretation information and present it to the user.

[0013] As a further aspect of the present invention, the user location analysis unit generates user location information in the following specific manner:

[0014] After obtaining user information and real-time location, the system compares the user location and the system location, calculates the difference in their location ranges, and if the difference is greater than a preset value, generates a location correction signal and transmits it to the real-time interpretation and analysis unit; if the difference is less than the preset value, it generates a location correct signal.

[0015] When a positioning correction signal is received, images of surrounding landmarks are collected with the user's location as the origin. These images are then compared with a pre-stored image library to accurately determine the user's location. After generating user positioning information, this information is also transmitted to the real-time interpretation and analysis unit.

[0016] As a further aspect of the present invention, the specific method by which the real-time interpretation and analysis unit calculates the feedback standards of each interpretation method in different age groups is as follows:

[0017] Obtain user information, categorize by age, collect historical usage data, and count all interpretation methods labeled i, where i = 1 to j, the number of users and their feedback ratings for different age groups. Calculate the average feedback rating for each interpretation method for the same age group, and use this as the feedback standard for the corresponding interpretation method for that age group.

[0018] As a further aspect of the present invention, the specific method by which the real-time interpretation and analysis unit generates the same or different analysis signals is as follows:

[0019] The feedback standards for each interpretation method are sorted in descending order by age group. The age group corresponding to the highest value is taken. If the highest feedback standards for multiple interpretation methods belong to the same age group, they are marked as the same standard method.

[0020] Based on the current user age matching interpretation method, if there is a matching standard method with the same result, the same analysis signal is generated; otherwise, different analysis signals are generated, and both are transmitted to the feedback adjustment processing unit.

[0021] As a further aspect of the present invention, the specific method by which the feedback adjustment processing unit analyzes the same signal is as follows:

[0022] Obtain the same standard method and its feedback standard, current user information, calculate the similarity between user interests and the same standard method, calculate the recommendation value of each method according to the formula "recommendation value = feedback standard × first weight coefficient + similarity × second weight coefficient", and take the method corresponding to the maximum value to generate the explanation method information.

[0023] As a further aspect of the present invention, the specific method by which the feedback adjustment processing unit reselects and generates explanation method information in sequence is as follows:

[0024] Get real-time user feedback. If the user is satisfied, keep the existing explanation method; if the user is not satisfied, reselect the explanation obtained through the same standard method according to the recommendation value from largest to smallest.

[0025] If there is no identical standard method, the selection will be made from largest to smallest according to the maximum feedback standard. After the selection, feedback adjustment information will be generated and sent to the interpretation information display unit along with the explanation method information.

[0026] As a further aspect of the present invention, the specific method by which the tourist attraction information database establishment unit generates the tourist attraction information database is as follows:

[0027] Information on cultural heritage and historical stories of scenic spots is collected through research, transformed into written materials and corrected, and audio recordings of explanations are produced. At the same time, 3D modeling is used to restore scenic spots and buildings, scenic area maps are digitally processed and their locations are marked, and a tourism scenic area information database is built and transmitted to the user positioning correction and analysis unit.

[0028] A method for real-time cultural interpretation in AR tourism, which specifically includes the following steps:

[0029] Step S1: Correct and convert tourist attraction information into textual data, and simultaneously record narration and create 3D models to generate a tourist attraction information database.

[0030] Step S2: Calculate the difference in positioning range between the user's location and the system's location, compare it with a preset value, generate a positioning correction signal, and at the same time determine the user's location based on landmarks around the user to generate user positioning information.

[0031] Step S3: Classify users by age and calculate the feedback criteria for different explanation methods for users of different age groups. At the same time, filter the explanation methods based on the highest feedback criteria to obtain the same standard methods.

[0032] Step S4: Based on the current user's age, perform matching judgment using the same standard method to generate the same or different analysis signals. At the same time, process the different analysis signals to obtain the explanation method information of the maximum feedback standard.

[0033] Step S5: Process the same analysis signal, calculate the similarity between the current user's interest and the same standard method, and calculate the recommendation value of the same standard method according to the formula. At the same time, generate explanation method information based on the maximum recommendation value.

[0034] Step S6: Adjust and analyze the narration method information based on the real-time feedback from the current user. If the real-time feedback is not satisfactory, select the appropriate methods in the order of selection to generate narration method information.

[0035] This invention provides an AR (Augmented Real-Time) Cultural Interpretation System and Method for Tourism. Compared with existing technologies, it has the following advantages:

[0036] This invention employs advanced speech recognition and image text recognition technologies for text conversion, introduces natural language processing technology to construct a tourism culture knowledge graph, and establishes a dual mechanism combining manual review and intelligent error correction to ensure the accuracy and authority of information.

[0037] This invention improves positioning accuracy and reduces positioning deviation by calculating the difference between user positioning and system positioning, combined with multi-dimensional data fusion and intelligent image semantic positioning system, thus providing users with accurate location information.

[0038] This invention categorizes users based on age, calculates the feedback criteria for different explanation methods corresponding to users of different ages, sorts the feedback criteria by age group from highest to lowest, and selects the explanation method with the highest feedback criteria. Simultaneously, it incorporates user interest factors, calculates the similarity between user interests and the same standard method, and combines this with dynamically adjusted weighting coefficients to calculate a recommendation value, generating personalized explanation method information to meet the needs of users of different ages and interests.

[0039] This invention obtains real-time user feedback and adjusts and analyzes the narration method information based on this feedback. For unsatisfactory feedback, narration methods are selected sequentially according to the selection order, and narration method information and feedback adjustment information are regenerated to improve user satisfaction with the narration content. Attached Figure Description

[0040] Figure 1This is a block diagram illustrating the system principle of the present invention;

[0041] Figure 2 This is a diagram illustrating the steps and methods of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Example 1

[0044] Please see Figure 1 This application provides an AR tourism real-time cultural interpretation system, including a tourist attraction information database establishment unit, a user positioning correction and analysis unit, a real-time interpretation analysis unit, a feedback adjustment and processing unit, and an interpretation information display unit, and combined with Figure 1 It can be seen that the above functional units are connected electrically in one direction.

[0045] The tourism scenic area information database establishment unit is used to conduct in-depth research and collection on the cultural heritage, historical stories, and folk customs of tourist attractions. It also converts the collected information into textual data, corrects errors in the textual data, records narration of the corrected textual data, and uses 3D modeling technology to virtually reconstruct important scenic spots, historical buildings, and cultural relics within the scenic area, creating corresponding 3D virtual scenes. Simultaneously, it digitizes the map data of the scenic area, marking the location information and extent of each scenic spot, establishing a tourism scenic area information database, and then transmits it to the user location correction and analysis unit.

[0046] The collected information is converted into text using advanced speech recognition and image text recognition technologies to improve efficiency and accuracy. Natural Language Processing (NLP) technology is introduced to construct a specialized tourism and cultural knowledge graph for in-depth analysis of the converted text materials. The system automatically identifies errors in the text, including factual errors, grammatical errors, and ambiguous expressions. For example, by comparing with authoritative historical databases, errors in the timing of historical events and relationships between figures are corrected; semantic analysis is used to correct unclear or ambiguous content. Simultaneously, a dual mechanism combining manual review and intelligent error correction is established, inviting experts in history, culture, tourism, and other fields to review key information to ensure its accuracy and authority.

[0047] Based on the characteristics of different tourist groups, multiple versions of audio guides are produced. For child tourists, a lively and engaging language style is used, incorporating fun stories and cartoon sound effects to create a children's version. For elderly tourists, a steady and clear speaking pace is used, combined with nostalgic elements to create content suitable for them. For international tourists, multilingual audio guides are provided, with cultural differences fully considered during translation to ensure the accurate conveyance of the scenic area's cultural essence. Furthermore, speech synthesis technology is used to allow users to customize their audio guides, enabling tourists to choose different voice actors and adjust the speaking speed according to their preferences.

[0048] In terms of 3D modeling, technologies such as oblique photogrammetry and BIM (Building Information Modeling) are used to create detailed models of important scenic spots, historical buildings, and cultural relics within the area. This not only restores the external appearance but also realistically simulates details such as building structure and interior decoration. For example, for ancient buildings, the mortise and tenon structure and painted patterns are accurately modeled; for cultural relics, their material textures and craftsmanship details are displayed. In map digitization, a combination of high-precision satellite imagery and on-site surveying is used to ensure the accuracy of the map data.

[0049] The user positioning correction and analysis unit is used to acquire user information and determine the user's real-time positioning. At the same time, it performs correction judgment on the user's real-time positioning, compares the user positioning with the system positioning, calculates the difference in positioning range between the two, if the difference in positioning range is greater than a preset value, it indicates that there is a deviation between the two positioning and generates a positioning correction signal; otherwise, if the difference in positioning range is less than the preset value, it indicates that there is no deviation between the two positioning and generates a positioning correct signal, which is then transmitted to the real-time interpretation and analysis unit.

[0050] Next, the positioning correction signal is analyzed. Taking the user as the origin, the landmark buildings around the origin are acquired and their corresponding images are obtained. The images are then compared with the pre-stored image library to determine the user's location and generate user positioning information, which is then transmitted to the real-time interpretation and analysis unit.

[0051] For example, in a historical and cultural city scenic area, tourists used an AR navigation system to explore the ancient city streets. Due to the dense architecture of the ancient city, satellite signals were interrupted multiple times, and the system initially positioned the tourist on another street about 150 meters away from their actual location. The user positioning correction and analysis unit activated a multi-dimensional data fusion mechanism, combining the location information of nearby Bluetooth beacons and the walking direction and number of steps recorded by the phone's sensors to initially determine that the user was on the correct street. Subsequently, the system automatically used the phone's camera to capture the surrounding environment, and used an intelligent image semantic positioning system to identify the unique brick carving patterns of the ancient city archway in the image. It compared these features with a pre-stored image library, and combined this with the geographical coordinates of the archway and its relationship with surrounding buildings in a knowledge graph, to accurately determine that the tourist was located 20 meters west of the archway in front of a cultural and creative shop.

[0052] The real-time interpretation and analysis unit is used to recommend and analyze real-time interpretation methods based on the acquired user location information and the tourist attraction information database. The specific recommendation and analysis methods are as follows:

[0053] The process involves acquiring user information, specifically including user age and interests, and classifying users according to their age. Next, historical usage data is obtained, and all explanation methods are labeled i (i = 1, 2, ..., j), where j represents the type of explanation method. Historical users corresponding to explanation method i are then classified by age, specifically into children, teenagers, young adults, middle-aged, and elderly users. Children are 0-13 years old, teenagers are 14-19 years old, young adults are 19-35 years old, middle-aged are 35-59 years old, and elderly are 60-74 years old. The usage volume for each age group is obtained, and usage feedback for the same age group is collected and comprehensively rated. Specifically, all usage feedback ratings for the same age group are obtained, and the average rating is calculated. This average rating is used as the feedback standard for the corresponding age group, and so on, for all age groups.

[0054] Similarly, the feedback standards for different age groups corresponding to all explanation methods i are calculated and sorted from largest to smallest according to the feedback standards of different age groups. Then, the age group with the largest feedback standard in explanation method i is selected, and it is determined whether there is a common situation. If there is, the explanation methods corresponding to the common situation are obtained and recorded as the same standard method. Otherwise, if there is no common situation, no processing is performed. The common situation here means that there are multiple explanation methods with the largest values ​​in the same age group. For example, the number of young people using explanation methods 1 and 3 is the largest, so explanation methods 1 and 3 are marked as the same standard method.

[0055] Next, the interpretation method i is filtered based on the user's age, and it is determined whether the current user's age has the same standard method. If it does, the same analysis signal is generated. For example, if the current user's age is in the middle age group, and there are two interpretation methods for the corresponding middle age group, namely interpretation method 2 and interpretation method 4, then the same analysis signal is generated accordingly. Otherwise, if it does not exist, different analysis signals are generated, and both are transmitted to the feedback adjustment processing unit.

[0056] The feedback adjustment processing unit is used to analyze the acquired identical and dissimilar analysis signals separately, and the specific analysis methods are as follows:

[0057] The obtained identical analysis signals are analyzed and processed to obtain all identical standard methods and corresponding feedback standards. Simultaneously, user information for the current user is acquired, and the similarity between user interests and identical standard methods is calculated. For example, if a user's interest tag is "ancient architecture and carving art," and identical standard methods include "ancient architecture VR tour explanation" and "traditional carving craft explanation," the degree of association between these methods in concepts such as "ancient architecture" and "carving art" is analyzed using a knowledge graph. Combined with the weight relationships between nodes, semantic similarity is calculated. Then, the recommendation value is calculated using the formula: Recommendation Value = Feedback Standard × First Weight Coefficient + Similarity × Second Weight Coefficient. The weight coefficients here are dynamically adjusted based on user profiles and real-time scenarios. For new users, due to a lack of historical feedback data, the second weight coefficient corresponding to similarity is appropriately increased, focusing more on matching user interests. For returning users, the ratio of the two weight coefficients is dynamically adjusted based on their historical emphasis on feedback standards. The recommendation value corresponding to the identical standard method is calculated, and the identical standard method with the highest recommendation value is selected as the standard to generate explanation method information. User activity and scenario complexity are introduced as adjustment factors when calculating the recommendation value. User activity is measured by metrics such as user movement speed within the scenic area and frequency of interaction with the system. Highly active users are more likely to quickly access comprehensive information, so the feedback criteria should be given greater weight in the recommendation calculation. Scene complexity is determined based on the user's current location and environmental information. For example, in information-dense museum exhibition halls, similarity weight is increased to accurately match user interests.

[0058] The different analysis signals are analyzed and processed. Based on the current user's age, the corresponding explanation method for the maximum feedback standard is obtained, and explanation method information is generated.

[0059] Simultaneously, real-time feedback from the current user is acquired, and the explanation method information is adjusted and analyzed based on the real-time feedback. If the real-time feedback is satisfactory, the current explanation method information is retained without processing. Conversely, if the real-time feedback is unsatisfactory, selections are made sequentially according to the corresponding selection order, where the selection order is represented by the recommended value or the maximum feedback standard. For unsatisfactory results obtained by selecting according to the same standard, selections are made sequentially from largest to smallest based on the recommended value. For unsatisfactory results where no matching standard exists, selections are made sequentially from largest to smallest based on the maximum feedback standard, and feedback adjustment information is generated. At the same time, the feedback adjustment information and explanation method information are transmitted to the interpretation information display unit.

[0060] When young tourists enter the scenic area, the system determines their interest tag as "Instagrammable spots and photography" based on their search keywords such as "popular photo spots" and "creative photography tips." The system generates identical analysis signals, with two standard methods: "AR navigation explanation of popular routes" and "on-site teaching explanation by photography experts," receiving feedback scores of 4.5 and 4.3 respectively. Through multi-dimensional similarity calculation, the similarity between "AR navigation explanation of popular routes" and the young tourists' interests is 0.85, while the similarity between "on-site teaching explanation by photography experts" and "on-site teaching explanation by photography experts" is 0.92. Considering that the young tourists are new users, the system sets the first weight coefficient to 0.4 and the second weight coefficient to 0.6. Combined with the young tourists' high activity level within the scenic area (frequent map viewing and system interaction), the user activity factor is 1.1; currently located in a concentrated area of ​​popular photo spots, the scene complexity factor is 1.0. The recommended value is calculated as follows:

[0061] Recommended value for "AR navigation and commentary on popular routes": 0.4×4.5×1.1+0.6×0.85×1.0=2.37;

[0062] Recommended value for "Photography expert on-site teaching and explanation": 0.4×4.3×1.1+0.6×0.92×1.0=2.42;

[0063] The system selected "On-site tutorials and explanations by photography experts" to generate interpretation information. A young tourist gave "dissatisfaction" feedback after using it. The system traced the cause and found that the tutorial time conflicted with her planned itinerary. The system then selected "Short video explanations of photography techniques at popular photo spots" from the alternative interpretation method library. This method allows for flexible viewing and does not occupy a fixed time. The system regenerated the interpretation method information and feedback adjustment information and transmitted them to the interpretation information display unit.

[0064] The interpretation information display unit is used to select the appropriate interpretation method for real-time interpretation based on the feedback adjustment information and interpretation method information obtained.

[0065] Example 2

[0066] Please see Figure 2 This application provides a method for real-time cultural interpretation in AR tourism, which specifically includes the following steps:

[0067] Step S1: Correct and convert the tourist attraction information into text data, and at the same time, record the narration and create a 3D model to generate a tourist attraction information database. The specific processing method is the same as that of the tourist attraction information database establishment unit in Implementation Example 1.

[0068] Step S2: Calculate the difference between the user's location and the system's location range, compare it with a preset value, generate a location correction signal, and at the same time determine the user's location based on the landmarks around the user to generate user location information. The processing method here is the same as that of the user location correction analysis unit in Implementation Example 1.

[0069] Step S3: Classify users according to age and calculate the feedback standard for different age groups corresponding to different interpretation methods. At the same time, filter the interpretation methods with the highest feedback standard to obtain the same standard method. The processing method here is the same as the processing method of the real-time interpretation and analysis unit in Implementation Example 1.

[0070] Step S4: Based on the current user's age, perform matching judgment using the same standard method to generate the same or different analysis signals. At the same time, process the different analysis signals to obtain the interpretation method of the maximum feedback standard and generate interpretation method information. The processing method here is the same as the processing method of the real-time interpretation and analysis unit in Embodiment 1.

[0071] Step S5: Process the same analysis signal, calculate the similarity between the current user interest and the same standard method, and calculate the recommendation value of the same standard method according to the formula. At the same time, generate the interpretation method information based on the maximum recommendation value. The processing method here is the same as the processing method of the real-time interpretation analysis unit in Embodiment 1.

[0072] Step S6: Adjust and analyze the explanation method information based on the real-time feedback from the current user. If the real-time feedback is not satisfactory, select the appropriate explanation method information in the order of selection to generate explanation method information. The processing method here is the same as that of the real-time interpretation and analysis unit in Implementation Example 1.

[0073] The data in the above formulas are all calculated using numerical values, without substituting the units of the parameters. In addition, the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0074] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An AR tourism real-time cultural interpretation system, characterized in that, include: The user location correction analysis unit calculates the difference between the user's location and the system's location, compares it with a preset value to determine whether correction is needed. If correction is needed, it uses landmarks around the user to accurately locate the user, generates user location information, and transmits it to the real-time interpretation and analysis unit. The real-time interpretation and analysis unit obtains user information, classifies users by age, collects historical usage data, and counts all interpretation methods labeled as i, where i = 1 to j. For different age groups, it calculates the number of uses and feedback scores, calculates the average feedback score of each interpretation method for the same age group, and uses this as the feedback standard for the corresponding interpretation method for that age group. It also calculates the feedback standard for different age groups for all interpretation methods. The feedback standards for each interpretation method are sorted in descending order by age group. The age group corresponding to the highest value is taken. If the highest feedback standards for multiple interpretation methods belong to the same age group, they are marked as the same standard method. Based on the current user age matching interpretation method, if there is a matching standard method with the same method, the same analysis signal is generated; otherwise, different analysis signals are generated, and the generated same or different analysis signals are transmitted to the feedback adjustment processing unit. The feedback adjustment processing unit is used to analyze the same or different analysis signals separately. When analyzing the same analysis signals, it obtains the same standard method and its feedback standard, calculates the similarity between the user's interest and the same standard method, and calculates the recommendation value of each method according to the formula "recommendation value = feedback standard × first weight coefficient + similarity × second weight coefficient". The highest recommendation value determines the final interpretation method information. User activity and scene complexity are introduced as adjustment factors when calculating the recommendation value. When analyzing different analysis signals, the interpretation method with the largest feedback standard is directly selected to generate information. The explanation method is adjusted based on real-time user feedback. If the feedback is unsatisfactory, the explanation method information is reselected and generated in sequence, and finally transmitted to the interpretation information display unit.

2. The AR tourism real-time cultural interpretation system according to claim 1, characterized in that, The system also includes a tourist attraction information database establishment unit and an interpretation information display unit; The tourist attraction information database establishment unit is used to correct and convert tourist attraction information into text data, while recording narration and creating 3D models to generate a tourist attraction information database, which is then transmitted to the user positioning correction and analysis unit. The interpretation information display unit is used to provide real-time interpretation based on the obtained interpretation information and present it to the user.

3. The AR tourism real-time cultural interpretation system according to claim 1, characterized in that, The specific method by which the user location correction and analysis unit generates user location information is as follows: After obtaining user information and real-time location, the system compares the user location and the system location, calculates the difference in their location ranges, and if the difference is greater than a preset value, generates a location correction signal and transmits it to the real-time interpretation and analysis unit; if the difference is less than the preset value, it generates a location correct signal. When a positioning correction signal is received, images of surrounding landmarks are collected with the user's location as the origin. These images are then compared with a pre-stored image library to accurately determine the user's location. After generating user positioning information, this information is also transmitted to the real-time interpretation and analysis unit.

4. The AR tourism real-time cultural interpretation system according to claim 1, characterized in that, The specific method by which the feedback adjustment processing unit reselects and generates explanation method information in sequence is as follows: Get real-time user feedback. If the user is satisfied, keep the existing explanation method; if the user is not satisfied, reselect the explanation obtained through the same standard method according to the recommendation value from largest to smallest. If there is no identical standard method, the feedback standard will be reselected from largest to smallest. After reselection, feedback adjustment information will be generated and sent to the interpretation information display unit along with the explanation method information.

5. The AR tourism real-time cultural interpretation system according to claim 2, characterized in that, The specific method by which the tourist attraction information database is generated by the aforementioned tourist attraction information database establishment unit is as follows: Information on cultural heritage and historical stories of scenic spots is collected through research, transformed into written materials and corrected, and audio recordings of narration are produced. At the same time, 3D modeling is used to restore scenic spots and buildings, scenic area maps are digitally processed and their locations are marked, and a tourism scenic area information database is built and transmitted to the user location correction and analysis unit.

6. A method for real-time cultural interpretation in AR tourism, executed by an AR real-time cultural interpretation system for tourism as described in any one of claims 1-5, characterized in that, The method specifically includes the following steps: Step S1: Correct and convert tourist attraction information into textual data, and simultaneously record narration and create 3D models to generate a tourist attraction information database. Step S2: Calculate the difference in positioning range between the user's location and the system's location, compare it with a preset value, generate a positioning correction signal, and at the same time determine the user's location based on landmarks around the user to generate user positioning information. Step S3: Classify users by age and calculate the feedback criteria for different explanation methods for users of different age groups. At the same time, filter the explanation methods based on the highest feedback criteria to obtain the same standard methods. Step S4: Based on the current user's age, perform matching judgment using the same standard method to generate the same or different analysis signals. Process the different analysis signals to obtain the explanation method information of the maximum feedback standard. Process the same analysis signals to calculate the similarity between the current user's interests and the same standard method, and calculate the recommendation value of the same standard method according to the formula. At the same time, generate explanation method information based on the maximum recommendation value. Step S5: Adjust and analyze the narration method information based on the real-time feedback from the current user. If the real-time feedback is not satisfactory, reselect and generate the narration method information in sequence.

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