Vent travel item evaluation system and method based on meta universe

Abstract

The invention discloses a vocabulary travel item evaluation system and method based on meta universe, relates to the technical field of intelligent recommendation, and solves the technical problem of poor experience feeling when a user experiences a vocabulary travel item due to low individuation degree of existing vocabulary travel item recommendation. The data acquisition module is used for acquiring user data and experience data through data acquisition equipment connected with the data acquisition module; the project recommendation module is used for generating user feature data according to the user data; obtaining item feature data corresponding to the plurality of text travel items; according to the user feature data and the item feature data, generating a plurality of test text travel items and a recommended text travel item list; the item scoring module is used for generating item scores according to the experience data and adjusting a recommended text travel item list according to the item scores of the test text travel items; generating a recommendation score of the current travel item according to the item score of each recommended travel item; the personalized degree of text travel item recommendation is improved, and then the experience feeling of the user is improved.

Description

Technical Field

[0001] The present application belongs to the field of intelligent recommendation technology and relates to intelligent recommendation technology, specifically a cultural and tourism project evaluation system and method based on the metaverse. Background Art

[0002] As the evolutionary form of the next generation of the Internet, the Metaverse integrates cutting-edge technologies such as virtual reality, augmented reality, and blockchain to build a three-dimensional digital ecological space where the virtual and the real coexist. This system breaks through the limitations of physical time and space, realizes real-time mapping and two-way interaction between the real world and the digital space, and provides innovative solutions for the digital transformation of the tourism industry. In the field of tourism services, Metaverse technology can perform full-dimensional digital modeling of destination landscapes, cultural scenes, and characteristic elements. By building a high-precision virtual tourism space, tourists can experience immersive roaming before traveling: view the full view of the scenic spot in real time, participate in virtual guided tours, and experience special tourism projects. The system supports personalized scene customization. Users can choose special modules such as seasonal landscape switching and historical scene restoration according to their interests. They can also use digital avatars to interact with each other in virtual social interaction and equipment fitting.

[0003] Existing cultural and tourism project recommendations often recommend related Metaverse cultural and tourism projects to users based on the popularity of cultural and tourism projects in the park. Since different users have different personalities and prefer different cultural and tourism projects, cultural and tourism project recommendations are made based on the popularity of Metaverse cultural and tourism projects by the public, resulting in a low degree of personalization in cultural and tourism project recommendations, which leads to a poor user experience when experiencing Metaverse cultural and tourism projects. Therefore, a Metaverse-based cultural and tourism project evaluation system and method is needed. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes a cultural and tourism project evaluation system and method based on the Metaverse, which is used to solve the technical problem that the existing cultural and tourism project recommendations have a low degree of personalization, resulting in a poor user experience when experiencing Metaverse cultural and tourism projects.

[0005] To achieve the above-mentioned purpose, the first aspect of the present application provides a cultural tourism project evaluation system based on the metaverse, including: a data collection module, an interaction module, a project recommendation module, a project scoring module and a database;

[0006] The data acquisition module is used to acquire user data and experience data through a data acquisition device connected thereto;

[0007] The project recommendation module: generates user characteristic data according to user data; obtains project characteristic data corresponding to a number of cultural and tourism projects; generates a list of a number of tested cultural and tourism projects and a list of recommended cultural and tourism projects according to the user characteristic data and the project characteristic data;

[0008] The project scoring module: generates a project score based on the experience data, adjusts the recommended cultural and tourism project list according to the project scores of the tested cultural and tourism projects; generates a recommended score for this cultural and tourism project based on the project scores of each recommended cultural and tourism project.

[0009] This application obtains user data, generates user feature data according to the user data, and obtains the project feature data of several cultural and tourism projects; generates several tested cultural and tourism projects and a recommended cultural and tourism project list according to the user feature data and the project feature data; generates a project score for the tested cultural and tourism projects according to the experience data; adjusts the recommended cultural and tourism project list according to the project scores; displays each recommended cultural and tourism project in sequence according to the order of the recommended cultural and tourism project list; obtains the experience data of each recommended cultural and tourism project, generates a corresponding project score according to the experience data, and generates a recommended score for this cultural and tourism project according to the project scores of each recommended cultural and tourism project; selects cultural and tourism projects that the user is interested in according to the user data and recommends them to the user for experience, improving the personalization degree of the cultural and tourism project recommendation, and further enhancing the user experience when experiencing the metaverse cultural and tourism projects.

[0010] Preferably, generating the user feature data according to the user data includes:

[0011] Extract several feature images from the user data, and input the feature images into a feature recognition model to obtain several feature keywords; the feature recognition model is trained through an artificial intelligence model;

[0012] Obtain several feature keywords corresponding to each feature image; obtain the number of times each feature keyword appears; perform normalization processing on the number of times each feature keyword appears to obtain a keyword score corresponding to each feature keyword; integrate each feature keyword and the corresponding keyword score into user feature data.

[0013] Preferably, the feature recognition model is trained through an artificial intelligence model, including:

[0014] Obtain several feature images through a database, and the feature keywords corresponding to each feature image, where the feature keywords are keywords set by experts according to the feature images, including keywords of projects that the user is interested in judged through the feature images; integrate the feature images and their corresponding feature keywords into several training data and test data;

[0015] Use the training data to train the artificial intelligence model, use the test data to test the trained artificial intelligence model, and finally obtain a feature recognition model with the input being the feature image and the output being the corresponding several feature keywords; the artificial intelligence model at least includes a BP neural network model and an RBF neural network model.

[0016] Preferably, generating a number of test cultural and tourism projects and a recommended cultural and tourism project list based on user characteristic data and project characteristic data includes:

[0017] Extracting a number of project keywords from the project characteristic data, as well as a number of characteristic keywords and their corresponding keyword scores from the user characteristic data; vectorizing the project keywords and the characteristic keywords to obtain a project keyword vector and a characteristic keyword vector, calculating the cosine similarity between the characteristic keyword vector and each project keyword vector, and marking the cosine similarity as the keyword matching degree;

[0018] Generating a matching degree score between the cultural and tourism project and the user according to a number of keyword matching degrees;

[0019] Obtaining cultural and tourism projects with a matching degree score greater than a set matching degree threshold, marking the cultural and tourism projects as recommended cultural and tourism projects; obtaining the project recommended duration in the project characteristic data corresponding to each recommended cultural and tourism project, marking the recommended cultural and tourism project with the shortest project recommended duration as the test cultural and tourism project, and arranging the remaining recommended cultural and tourism projects in descending order of the matching degree score to obtain a recommended cultural and tourism project list.

[0020] By selecting the recommended cultural and tourism project with the shortest project recommended duration as the test project in this application, it is possible to quickly judge and analyze whether each recommended cultural and tourism project obtained is a cultural and tourism project that the user is interested in; it is convenient to modify the recommended cultural and tourism project in a timely manner.

[0021] Preferably, generating the matching degree score between the cultural and tourism project and the user according to a number of keyword matching degrees includes:

[0022] Obtaining the keyword matching degree between each project keyword in the project characteristic data corresponding to the cultural and tourism project and a number of characteristic keywords, and marking it as GPij; i is the number of the project keyword, and j is the number of the characteristic keyword;

[0023] Through the formula Calculating to obtain the matching degree score PP of the cultural and tourism project; where XPj is the keyword score of the characteristic keyword; αi is the weight coefficient of the project keyword numbered i; the specific value can be set according to expert experience; i = 1, 2,..., I, I is the total number of project keywords; j = 1, 2,..., J, J is the total number of characteristic keywords.

[0024] Preferably, the interaction module: displays the test cultural and tourism project and displays the recommended cultural and tourism projects in the order of the recommended cultural and tourism project list.

[0025] Preferably, generating a project score according to the experience data includes:

[0026] Extracting the user action data, user expression data, and user voice data in the experience data;

[0027] Extract each interaction action and action moment in the action data, and generate a positive score for each interaction action according to the action moment of the activity action;

[0028] Extract each expression image and its corresponding image label in the user expression data, obtain the number of expression images with a positive image label and the number of expression images with a negative image expression, and mark them as BZS and BFS respectively;

[0029] Extract each keyword and its corresponding keyword label in the user voice data, obtain the number of keywords with a positive keyword label and the number of keywords with a negative keyword label, and mark them as GZS and GFS respectively;

[0030] Through the formula Calculate the project score XP; where, JPn is the positive score of the interaction action numbered n, n = 1, 2,..., N, and N is the total number of interaction actions in the tested cultural and tourism project; β1, β2, and β3 are proportionality coefficients used to adjust the proportional relationship between different parameters, and the specific values are set according to expert experience.

[0031] Preferably, the generating a positive score for each interaction action according to the action moment of the interaction action includes:

[0032] Obtain the similarity between the interaction action and its corresponding standard action, and mark it as XDn; obtain the action moment of the interaction action and the standard moment of the standard action corresponding to the interaction action, and mark them as HTn and BTn respectively;

[0033] Through the formula Calculate the positive score of the interaction action numbered n, where DT is the unit time; γ is an adjustment coefficient used to adjust the range of the positive score obtained by calculation, and the specific value is set according to experience.

[0034] Preferably, the adjusting the recommended cultural and tourism project list according to the project score of the tested cultural and tourism project includes:

[0035] Obtain the project score of the tested cultural and tourism project, and judge whether the project score is greater than the set project score threshold;

[0036] If so, obtain a number of project keywords for each recommended cultural and tourism project in the recommended cultural and tourism project list, as well as a number of project keywords for the test cultural and tourism project; calculate the keyword matching degree between each project keyword of the recommended cultural and tourism project and each project keyword of the test cultural and tourism project; and mark it as GPkg, where g is the number of the project keyword of the recommended cultural and tourism project, and k is the number of the project keyword of the test cultural and tourism project. Specifically, vectorize each project keyword to obtain a relevant keyword vector, calculate the cosine similarity between each project keyword vector of the recommended cultural and tourism project and each project keyword vector of the test cultural and tourism project, and mark the cosine similarity as the keyword matching degree; through the formula Calculate the relevance score XGP between the recommended cultural and tourism project and the test cultural and tourism project; re - sort each recommended cultural and tourism project in descending order of the relevance score to obtain an adjusted recommended cultural and tourism project list;

[0037] If not, do not adjust the recommended cultural and tourism project list.

[0038] Preferably, generating the recommended score of this cultural and tourism project according to the project scores of each recommended cultural and tourism project includes:

[0039] Obtain the matching degree scores of each recommended cultural and tourism project, normalize the matching degree scores of each recommended cultural and tourism project, and mark the normalized matching degree scores as the weight coefficients of the corresponding recommended cultural and tourism projects;

[0040] Obtain the project scores of each recommended cultural and tourism project, and perform weighted summation on the project scores corresponding to each recommended cultural and tourism project according to the weight coefficients of each recommended cultural and tourism project to obtain the recommended score of this cultural and tourism project.

[0041] Another aspect of this application provides a method for evaluating cultural and tourism projects based on the meta - universe, including the following steps:

[0042] Step 1: Obtain user data and generate user feature data according to the user data;

[0043] Step 2: Obtain the project feature data of a number of cultural and tourism projects; generate a number of test cultural and tourism projects and a recommended cultural and tourism project list according to the user feature data and the project feature data;

[0044] Step 3: Display the test cultural and tourism projects and obtain the user's experience data;

[0045] Step 4: Generate the project scores of the test cultural and tourism projects according to the experience data; adjust the recommended cultural and tourism project list according to the project scores;

[0046] Step 5: Display each recommended cultural and tourism project in turn according to the order of the recommended cultural and tourism project list;

[0047] Step 6: Obtain the experience data of each recommended cultural and tourism project, generate corresponding project scores according to the experience data, and generate the recommended score of this cultural and tourism project according to the project scores of each recommended cultural and tourism project.

[0048] Compared with the prior art, the beneficial effects of this application are as follows:

[0049] 1. This application obtains user data, generates user feature data according to the user data, and obtains the project feature data of several cultural and tourism projects; generates several test cultural and tourism projects and a list of recommended cultural and tourism projects according to the user feature data and the project feature data; generates the project scores of the test cultural and tourism projects according to the experience data; adjusts the list of recommended cultural and tourism projects according to the project scores; displays each recommended cultural and tourism project in sequence according to the order of the list of recommended cultural and tourism projects; obtains the experience data of each recommended cultural and tourism project, generates corresponding project scores according to the experience data, and generates the recommended score of this cultural and tourism project according to the project scores of each recommended cultural and tourism project; selects the cultural and tourism projects that the user is interested in according to the user data and recommends them to the user for experience, improving the personalization degree of the cultural and tourism project recommendation, and further enhancing the user experience when experiencing the metaverse cultural and tourism project.

[0050] 2. This application selects the recommended cultural and tourism project with the shortest project recommendation duration as the test project, which can quickly judge and analyze whether each recommended cultural and tourism project obtained is a cultural and tourism project that the user is interested in; it is convenient to modify the recommended cultural and tourism project in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0052] Figure 1 It is a schematic diagram of the module connection of the cultural and tourism project evaluation system in this application;

[0053] Figure 2 It is a schematic diagram of the steps of the cultural and tourism project evaluation method in this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The following will clearly and completely describe the technical solutions of this application in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of this application, rather than all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0055] Please refer toFigure 1 In the first aspect of the present application, a cultural and tourism project evaluation system based on the metaverse is provided, including: a data collection module, an interaction module, a project recommendation module, a project scoring module, and a database;

[0056] Data collection module: Obtain user data and experience data through data collection devices connected thereto; the data collection devices include cameras, etc.; the user data includes image data of several users, which is convenient for analyzing the preferences and interests of users; the experience data is data related to users collected when users experience the cultural and tourism projects in the metaverse. It can be understood that both the user data and the experience data are obtained after obtaining the authorization of the users, and will be deleted when the users end the play of the cultural and tourism projects in the metaverse;

[0057] Project recommendation module: Generate user characteristic data based on the user data, where the user characteristic data is characteristic data related to users obtained by analyzing the user data; obtain the project characteristic data corresponding to several cultural and tourism projects, where the project characteristic data is the relevant characteristic data of the cultural and tourism projects, including several keywords of the cultural and tourism projects. For example, if the cultural and tourism project is the Mogao Grottoes in Dunhuang, the corresponding project characteristic data includes project keywords such as historical site, murals, Buddhist art, grotto architecture, cultural heritage, and academic research; generate several test cultural and tourism projects and a recommended cultural and tourism project list based on the user characteristic data and the project characteristic data; the test cultural and tourism projects are the cultural and tourism projects that are matched with the user's interests and have the shortest time obtained by analyzing the user characteristic data and each project characteristic data, and are used to test whether the analysis results are accurate; the recommended cultural and tourism project list includes several cultural and tourism projects that are matched with the user's interests obtained by analyzing the user characteristic data and each project characteristic data;

[0058] Project scoring module: Generate a project score based on the experience data, where the project score is the score given to the user's experience of the cultural and tourism project according to the user's performance when the user experiences the cultural and tourism project in the metaverse; adjust the recommended cultural and tourism project list according to the project score of the test cultural and tourism project; generate a recommended score for the current cultural and tourism project based on the project scores of each recommended cultural and tourism project;

[0059] Interaction module: Display the test cultural and tourism projects and display the recommended cultural and tourism projects in the order of the recommended cultural and tourism project list.

[0060] In this embodiment, by obtaining user data, generating user feature data according to the user data, and obtaining the project feature data of several cultural and tourism projects; generating several test cultural and tourism projects and a recommended cultural and tourism project list according to the user feature data and the project feature data; generating a project score for the test cultural and tourism projects according to the experience data; adjusting the recommended cultural and tourism project list according to the project score; sequentially displaying each recommended cultural and tourism project in the order of the recommended cultural and tourism project list; obtaining the experience data of each recommended cultural and tourism project, generating a corresponding project score according to the experience data, and generating a recommended score for this cultural and tourism project according to the project scores of each recommended cultural and tourism project; selecting cultural and tourism projects that the user is interested in according to the user data and recommending them to the user for experience, which improves the personalization degree of the cultural and tourism project recommendation, and further enhances the user experience when experiencing the metaverse cultural and tourism projects.

[0061] Generating user feature data according to user data includes: extracting several feature images from the user data, where the feature images are images related to the user provided through the interaction module before the user plays the metaverse cultural and tourism project, such as images of scenic spots that the user is interested in taken by the user before; inputting the feature images into a feature recognition model to obtain several feature keywords; the feature recognition model is trained by an artificial intelligence model; specifically, obtaining several feature images and the corresponding feature keywords through a database, where the feature keywords are keywords set by experts according to the feature images, including keywords of projects that the user is interested in judged through the feature images; integrating the feature images and their corresponding feature keywords into several training data and test data.

[0062] Training the artificial intelligence model with the training data and testing the trained artificial intelligence model with the test data to finally obtain a feature recognition model with feature images as the input and corresponding several feature keywords as the output; the artificial intelligence model at least includes a BP neural network model and an RBF neural network model.

[0063] Obtaining several feature keywords corresponding to each feature image; obtaining the number of times each feature keyword appears; normalizing the number of times each feature keyword appears to obtain the keyword score corresponding to each feature keyword; integrating each feature keyword and the corresponding keyword score into user feature data.

[0064] Generate a list of several test cultural and tourism projects and recommended cultural and tourism projects based on user characteristic data and project characteristic data, including: extract several project keywords from the project characteristic data, as well as several characteristic keywords and their corresponding keyword scores in the user characteristic data; vectorize the project keywords and characteristic keywords to obtain project keyword vectors and characteristic keyword vectors, calculate the cosine similarity between the characteristic keyword vectors and each project keyword vector, and mark the cosine similarity as the keyword matching degree; it should be noted that the method of training an artificial intelligence model can also be used to generate the correlation degree between keywords, and use this correlation degree as the keyword matching degree between keywords; specifically, obtain several keywords and the keyword matching degree between them, integrate several pairs of keywords and their corresponding keyword matching degrees into training data and test data, use the training data to train the artificial intelligence model, and use the test data to test the trained artificial intelligence model, and finally obtain a matching degree generation model that inputs two keywords and outputs the corresponding keyword matching degree; among them, the higher the keyword matching degree, the higher the correlation degree between the two keywords.

[0065] Generate a matching degree score between the cultural and tourism project and the user according to several keyword matching degrees.

[0066] Obtain the cultural and tourism projects with the matching degree score greater than the set matching degree threshold, and mark the cultural and tourism projects as recommended cultural and tourism projects; obtain the project recommended duration in the project characteristic data corresponding to each recommended cultural and tourism project, mark the recommended cultural and tourism project with the shortest project recommended duration as the test cultural and tourism project, and arrange the remaining recommended cultural and tourism projects in descending order of the matching degree score to obtain a list of recommended cultural and tourism projects.

[0067] In this embodiment, by selecting the recommended cultural and tourism project with the shortest project recommended duration as the test project, it is possible to quickly judge and analyze whether each recommended cultural and tourism project obtained is a cultural and tourism project that the user is interested in; it is convenient to modify the recommended cultural and tourism project in a timely manner.

[0068] Generate a matching degree score between the cultural and tourism project and the user according to several keyword matching degrees, including: obtain the keyword matching degree between each project keyword in the project characteristic data corresponding to the cultural and tourism project and several characteristic keywords, and mark it as GPij; i is the number of the project keyword, and j is the number of the characteristic keyword.

[0069] Through the formula Calculate the matching degree score PP of the cultural and tourism project; where XPj is the keyword score of the characteristic keyword; αi is the weight coefficient of the project keyword numbered i; the specific value can be set according to expert experience; i = 1, 2,..., I, I is the total number of project keywords; j = 1, 2,..., J, J is the total number of characteristic keywords.

[0070] In this embodiment, the matching degree scores between each cultural and tourism project and the corresponding user are calculated through the above formula. The higher the matching degree between the characteristic keywords of the user and the project characteristic keywords of the cultural and tourism project, the greater the possibility that the user is interested in the cultural and tourism project, and the greater the corresponding matching degree score is set. The corresponding recommended cultural and tourism projects are set according to the matching degree scores, so that the recommended cultural and tourism projects are more in line with the user's interests, and thus the user experience is better.

[0071] Generate project scores based on the experience data, including: extracting user action data, user expression data, and user voice data from the experience data;

[0072] Extract each interaction action and action moment in the action data, and generate positive scores for each interaction action according to the action moment of the activity action;

[0073] Extract each expression image and its corresponding image label in the user expression data, obtain the number of expression images with a positive image label, and the number of expression images with a negative image expression, and mark them as BZS and BFS respectively;

[0074] Extract each keyword and its corresponding keyword label in the user voice data, obtain the number of keywords with a positive keyword label, and the number of keywords with a negative keyword label, and mark them as GZS and GFS respectively;

[0075] In this embodiment, a recognition model is trained to recognize the actions, expressions, and voices of users; it can be understood that the recognition model trained in this embodiment includes an action recognition model, an expression recognition model, and a voice recognition model; the action recognition model is used to recognize the actions of users, the expression recognition is used to recognize the expressions of users, and the voice recognition model is used to recognize the keywords in the voice of users and generate corresponding keyword labels; the image labels are the labels corresponding to each expression set in advance by experts, including positive and negative; the keyword labels are the labels set by experts according to the relevance between the project keywords of specific cultural and tourism projects and the keywords in the voice. If the project keywords and the keywords in the voice are positively correlated, the corresponding keyword label is positive; otherwise, it is negative; when the image label or keyword label is positive, it indicates that the user is more interested in the cultural and tourism project in terms of expression and language at this time; when the image label or keyword label is negative, it indicates that the user is not interested in the cultural and tourism project in terms of expression and language at this time;

[0076] Through the formula Calculate the project score XP of the project; where JPn is the positive score of the interaction action numbered n, n = 1, 2, …, N, and N is the total number of interaction actions in the tested cultural and tourism project; β1, β2, and β3 are proportionality coefficients used to adjust the proportional relationship between different parameters, and the specific values are set according to expert experience.

[0077] In this embodiment, the project score of the cultural and tourism project is calculated through the above formula; the higher the positive score of each action of the user, the more satisfied the user is with the experience of the cultural and tourism project; the larger the corresponding project score is set; the more the number of positive expressions or positive keywords of the user, the more satisfied the user is with the experience of the cultural and tourism project; the larger the corresponding project score is set.

[0078] Generate the positive score of each interaction action according to the action time of the interaction action, including: obtaining the similarity between the interaction action and its corresponding standard action, and marking it as XDn; obtaining the action time of the interaction action, and the standard time of the corresponding standard action of the interaction action, and marking them as HTn and BTn respectively; the interaction action is the action performed by the user, the standard action is the action set in the cultural and tourism project, the action time is the time when the user performs the interaction action, that is, the duration between the time when the user performs the action and the start time of the cultural and tourism project; the standard time is the duration between the set action in the cultural and tourism project and the start time of the cultural and tourism project; the similarity between the interaction action and the standard action can be obtained by training an action recognition model. Specifically, obtain several groups of interaction action images and standard action images, and their corresponding similarities. The similarity is set by experts according to the similarity degree between the interaction action image and the standard action image. The larger the value of the similarity, the closer the interaction action is to the standard action; integrate several groups of interaction action images and standard action images, and their corresponding similarities into training data and test data, use the training data to train the artificial intelligence model, use the test data to test the trained artificial intelligence model, and finally obtain an action recognition model that inputs the interaction action image and the standard action image and outputs the corresponding similarity.

[0079] Through the formula Calculate the positive score of the interaction action numbered n, where DT is the unit time; γ is an adjustment coefficient used to adjust the range of the positive score calculated, and the specific value is set according to experience.

[0080] In this embodiment, the positive scores of each interaction action are calculated through the above formula. When the similarity between the interaction action and the corresponding standard action is higher, it indicates that the user's enthusiasm during the experience is higher. When the moment when the interaction action is made is closer to the moment required by the corresponding standard action, it also indicates that the user's enthusiasm during the experience is higher, and the corresponding positive score is set larger; the higher the positive score, the stronger the user's enthusiasm for participating in the corresponding cultural and tourism project, the more interested the user is in the corresponding cultural and tourism project, and further indicates that the recommended cultural and tourism project has a higher degree of true matching with the user; thus making the user's experience better.

[0081] Adjust the recommended cultural and tourism project list according to the project score of the tested cultural and tourism project, including: obtaining the project score of the tested cultural and tourism project, and determining whether the project score is greater than the set project score threshold;

[0082] If so, obtain several project keywords of each recommended cultural and tourism project in the recommended cultural and tourism project list, and several project keywords of the tested cultural and tourism project; calculate the keyword matching degree between each project keyword of the recommended cultural and tourism project and each project keyword of the tested cultural and tourism project; and mark it as GPkg; where g is the number of the project keyword of the recommended cultural and tourism project, and k is the number of the project keyword of the tested cultural and tourism project; specifically, vectorize each project keyword to obtain the relevant keyword vectors, calculate the cosine similarity between each project keyword vector of the recommended cultural and tourism project and each project keyword vector of the tested cultural and tourism project, and mark the cosine similarity as the keyword matching degree; through the formula Calculate the relevance score XGP between the recommended cultural and tourism project and the tested cultural and tourism project; reorder each recommended cultural and tourism project in descending order of the relevance score to obtain the adjusted recommended cultural and tourism project list;

[0083] If not, do not adjust the recommended cultural and tourism project list.

[0084] In this embodiment, the recommended cultural and tourism project list is adjusted according to the project score of the tested cultural and tourism project, so that the recommended project list is more matched with the user. When the project score of the user during the experience of the tested cultural and tourism project is higher, it indicates that the user's interest in the tested cultural and tourism project is higher. At this time, the order of the recommended cultural and tourism projects with a high degree of relevance to the tested cultural and tourism project in the recommended project list is improved; let the user give priority to experiencing the recommended cultural and tourism projects with a high degree of relevance to the tested cultural and tourism project; increase the user's satisfaction.

[0085] Generate the recommended score of this cultural and tourism project according to the project scores of each recommended cultural and tourism project, including: obtaining the matching degree scores of each recommended cultural and tourism project, normalizing the matching degree scores of each recommended cultural and tourism project, and marking the normalized matching degree scores as the weight coefficients of the corresponding recommended cultural and tourism projects;

[0086] Obtain the project scores of each recommended cultural and tourism project, and perform weighted summation on the project scores corresponding to each recommended cultural and tourism project according to the weight coefficients of each recommended cultural and tourism project to obtain the recommended score of this cultural and tourism project.

[0087] In this embodiment, the project scores of each recommended cultural and tourism project are used to comprehensively score the recommendation of this cultural and tourism project to obtain the recommended score. The recommended score is used to measure the effect of the recommendation of this cultural and tourism project. When the recommended score is greater than the set recommended score threshold, it indicates that the recommendation effect of this cultural and tourism project is good and can obtain good user feedback. When the recommended score is less than the set recommended score threshold, it indicates that the recommendation effect of this cultural and tourism project is poor and the recommendation mechanism needs to be adjusted.

[0088] Please refer to Figure 2 , another aspect of this application provides a method for evaluating cultural and tourism projects based on the metaverse, including the following steps:

[0089] Step 1: Obtain user data and generate user feature data according to the user data;

[0090] Step 2: Obtain the project feature data of several cultural and tourism projects; generate a list of several test cultural and tourism projects and recommended cultural and tourism projects according to the user feature data and the project feature data;

[0091] Step 3: Display the test cultural and tourism projects and obtain the experience data of the user;

[0092] Step 4: Generate the project scores of the test cultural and tourism projects according to the experience data; adjust the list of recommended cultural and tourism projects according to the project scores;

[0093] Step 5: Display each recommended cultural and tourism project in sequence according to the order of the list of recommended cultural and tourism projects;

[0094] Step 6: Obtain the experience data of each recommended cultural and tourism project, generate the corresponding project scores according to the experience data, and generate the recommended score of this cultural and tourism project according to the project scores of each recommended cultural and tourism project.

[0095] Some of the data in the above formula are calculated by removing the dimension and taking their numerical values. The formula is obtained by software simulation of a large amount of collected data to obtain a formula that is closest to the actual situation; the preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0096] The working principle of this application:

[0097] This application generates user characteristic data based on the acquired user data, and acquires the project characteristic data of several cultural and tourism projects; generates a list of test cultural and tourism projects and recommended cultural and tourism projects according to the user characteristic data and the project characteristic data; generates a project score for the test cultural and tourism projects according to the experience data; adjusts the list of recommended cultural and tourism projects according to the project score; displays each recommended cultural and tourism project in sequence according to the order of the list of recommended cultural and tourism projects; acquires the experience data of each recommended cultural and tourism project, generates the corresponding project score according to the experience data, and generates the recommended score of this cultural and tourism project according to the project scores of each recommended cultural and tourism project; selects the cultural and tourism projects that the user is interested in according to the user data and recommends them to the user for experience, which improves the personalization degree of the cultural and tourism project recommendation, and further enhances the user experience when experiencing the metaverse cultural and tourism project.

[0098] The above embodiments are only used to illustrate the technical method of this application and not to limit it. Although this application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of this application can be modified or equivalently replaced without departing from the spirit and scope of the technical method of this application.

Claims

1. A cultural tourism project evaluation system based on Metaverse, comprising: The data collection module, the interaction module, the project recommendation module, the project scoring module and the database are characterized in that: The data acquisition module is used to acquire user data and experience data through a data acquisition device connected thereto; The project recommendation module: generates user characteristic data according to user data; obtains project characteristic data corresponding to a number of cultural and tourism projects; generates a list of a number of tested cultural and tourism projects and a list of recommended cultural and tourism projects according to the user characteristic data and the project characteristic data; The project scoring module generates project scores based on the experience data, adjusts the list of recommended cultural and tourism projects based on the project scores of the tested cultural and tourism projects, and generates a recommended score for this cultural and tourism project based on the project scores of each recommended cultural and tourism project.

2. According to the metaverse-based cultural tourism project evaluation system of claim 1, it is characterized in that: Generating user characteristic data according to user data includes: Extracting several feature images from user data, and inputting the feature images into a feature recognition model to obtain several feature keywords; the feature recognition model is obtained by training an artificial intelligence model; Acquire several feature keywords corresponding to each feature image; acquire the number of occurrences of each feature keyword; normalize the number of occurrences of each feature keyword to obtain a keyword score corresponding to each feature keyword; integrate each feature keyword and the corresponding feature keyword score into user feature data.

3. According to the metaverse-based cultural tourism project evaluation system of claim 2, it is characterized in that: The feature recognition model is obtained through artificial intelligence model training, including: Acquire a number of feature images and feature keywords corresponding to each feature image through a database; integrate the feature images and their corresponding feature keywords into a number of training data and test data; The artificial intelligence model is trained using training data, and the trained artificial intelligence model is tested using test data, and finally a feature recognition model is obtained with a feature image as input and a corresponding number of feature keywords as output; the artificial intelligence model includes at least a BP neural network model and an RBF neural network model.

4. According to the metaverse-based cultural tourism project evaluation system of claim 1, it is characterized in that: The generating of a number of test cultural tourism projects and a list of recommended cultural tourism projects according to the user characteristic data and the project characteristic data includes: Extracting several project keywords from the project feature data, as well as several feature keywords and their corresponding keyword scores from the user feature data; vectorizing the project keywords and feature keywords to obtain project keyword vectors and feature keyword vectors, calculating the cosine similarity between the feature keyword vectors and each project keyword vector, and marking the cosine similarity as the keyword matching degree; Generate a matching score between the cultural tourism project and the user based on the matching degree of several keywords; Obtain cultural and tourism projects whose matching scores are greater than the set matching threshold, and mark the cultural and tourism projects as recommended cultural and tourism projects; obtain the project recommendation duration in the project feature data corresponding to each recommended cultural and tourism project, and mark the recommended cultural and tourism project with the shortest project recommendation duration as a test cultural and tourism project, and arrange the remaining recommended cultural and tourism projects in descending order according to the matching scores to obtain a list of recommended cultural and tourism projects.

5. According to the metaverse-based cultural tourism project evaluation system of claim 4, it is characterized in that: The matching scores between the cultural tourism project and the user are generated based on the matching of several keywords, including: Obtain the keyword matching degree between each project keyword and several feature keywords in the project feature data corresponding to the cultural tourism project, and mark it as GPij; i is the number of the project keyword, and j is the number of the feature keyword; By formula The matching score PP of the cultural tourism project is calculated; wherein XPj is the keyword score of the characteristic keyword; αi is the weight coefficient of the project keyword numbered i; i=1, 2, ..., I, I is the total number of project keywords; j=1, 2, ..., J, J is the total number of characteristic keywords.

6. According to the metaverse-based cultural tourism project evaluation system of claim 1, it is characterized in that: Generating a project score based on the experience data includes: Extract user action data, user expression data and user voice data from experience data; Extract each interactive action and action moment in the action data, and generate a positive score for each interactive action according to the action moment of the activity action; Extract each expression image and its corresponding image label in the user expression data, obtain the number of expression images with positive image labels and the number of expression images with negative image labels, and mark them as BZS and BFS respectively; Extract each keyword and its corresponding keyword label in the user voice data, obtain the number of keywords with positive keyword labels and the number of keywords with negative keyword labels, and mark them as GZS and GFS respectively; By formula The project score XP is calculated; wherein JPn is the positive score of the interactive action numbered n, n=1, 2, ..., N, N is the total number of interactive actions in the test cultural tourism project; β1, β2 and β3 are proportional coefficients.

7. A cultural tourism project evaluation system based on metaverse according to claim 6, characterized in that: The generating of a positive score for each interactive action according to the action moment of the interactive action includes: Obtain the similarity between the interactive action and its corresponding standard action, and mark it as XDn; obtain the action time of the interactive action and the standard time of the interactive action corresponding to the standard action, and mark them as HTn and BTn respectively; By formula The positive score of the interactive action numbered n is calculated, where DT is the unit time and γ is the adjustment coefficient.

8. According to the metaverse-based cultural tourism project evaluation system of claim 1, it is characterized in that: The list of recommended cultural tourism projects is adjusted according to the project scores of the tested cultural tourism projects, including: Obtain the project score of the test cultural tourism project, and determine whether the project score is greater than a set project score threshold; If yes, obtain several project keywords of each recommended cultural tourism project in the recommended cultural tourism project list, as well as several project keywords of the test cultural tourism project; calculate the keyword matching degree of each project keyword of the recommended cultural tourism project and each project keyword of the test cultural tourism project; and mark it as GPkg; where g is the project keyword number of the recommended cultural tourism project, and k is the project keyword number of the test cultural tourism project; through the formula Calculate and obtain the relevance score XGP between the recommended cultural tourism project and the test cultural tourism project; re-sort the recommended cultural tourism projects in descending order of relevance score to obtain an adjusted recommended cultural tourism project list; If no, the list of recommended cultural and tourism projects will not be adjusted.

9. The cultural tourism project evaluation system based on the metaverse according to claim 1 is characterized in that: The recommended score of this cultural tourism project is generated according to the project scores of each recommended cultural tourism project, including: Obtain the matching scores of each recommended cultural tourism project, normalize the matching scores of each recommended cultural tourism project, and mark the normalized matching scores as weight coefficients of the corresponding recommended cultural tourism projects; The project score of each recommended cultural tourism project is obtained, and the project score corresponding to each recommended cultural tourism project is weightedly summed according to the weight coefficient of each recommended cultural tourism project to obtain the recommended score of this cultural tourism project.

10. A method for evaluating cultural and tourism projects based on the Metaverse, applied to the operation of a cultural and tourism project evaluation system based on the Metaverse as described in any one of claims 1 to 9; characterized in that: The following steps are involved: Step 1: Obtain user data and generate user feature data based on the user data; Step 2: Obtain project feature data of several cultural tourism projects; generate a list of several test cultural tourism projects and recommended cultural tourism projects based on the user feature data and project feature data; Step 3: Display the test cultural tourism project and obtain the user's experience data; Step 4: Generate a project score for the test cultural tourism project based on the experience data; and adjust the list of recommended cultural tourism projects based on the project score; Step 5: Display each recommended cultural and tourism project in the order of the recommended cultural and tourism project list; Step 6: Obtain the experience data of each recommended cultural tourism project, generate a corresponding project score based on the experience data, and generate a recommended score for this cultural tourism project based on the project score of each recommended cultural tourism project.

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