Intangible cultural heritage personalized interaction method and system based on virtual reality

Abstract

The invention relates to the field of virtual reality, in particular to an intangible cultural heritage personalized interaction method and system based on virtual reality, and the method comprises the steps: receiving an instruction inputted by a user side in real time; analyzing the instruction input by the user, and determining a non-missing type of the user side according to an analysis result; according to the non-permissive type of the user side, matching target non-permissive data in a non-permissive database; generating initial virtual data based on the target non-perpetual data; receiving behavior data of a user side in real time, inputting the behavior data into a preset interaction model, and outputting switching parameters through the interaction model; and adjusting the initial virtual data according to the switching parameter to obtain virtual data after user interaction response. All aspects of traditional culture are directly contacted and experienced in an interactive mode. According to the input instruction and the behavior data of the user, the non-abandoned content in the virtual environment is accurately matched and adjusted, and each user is made to have interest and requirements for non-abandoned culture according to the own interest and requirements for the non-abandoned culture.

Description

Technical Field

[0001] The present application relates to the field of virtual reality technology, and in particular to a method and system for personalized interaction with intangible cultural heritage based on virtual reality. Background Art

[0002] Virtual reality simulation of intangible cultural heritage is a way to apply virtual reality technology to the display, inheritance and interaction of intangible cultural heritage. Intangible cultural heritage usually refers to some cultural heritage that cannot be touched or displayed, such as traditional dance, music, handicrafts, festivals, languages, etc. Through virtual reality (VR) technology, these cultural heritages can be digitally simulated and allow users to experience them in an interactive way.

[0003] In the existing technology, the simulation and interactive methods of intangible cultural heritage mainly build virtual environments to allow users to interactively learn and experience intangible cultural heritage in virtual scenes. Existing technologies often adopt unified learning paths and interactive links, ignoring the cultural background, learning interests and cognitive differences of users, and not taking into account that different users may have different concentrations and interests in different aspects of intangible cultural heritage. For example, users may have completed a certain production link, but it does not mean that they understand the culture and craftsmanship behind it. Due to the inability to fully understand the content that users are interested in, it is difficult for the virtual learning environment to adjust the teaching content and methods in a targeted manner. Especially in the scenario of intangible cultural heritage learning, users are prone to boredom and reduced enthusiasm due to the difficulty in understanding the intangible cultural heritage process.

[0004] Therefore, the prior art has defects and needs to be improved. Summary of the invention

[0005] In order to solve one or several problems in the prior art, the main purpose of this application is to provide a personalized interaction method and system for intangible cultural heritage based on virtual reality.

[0006] In order to achieve the above invention objectives, the present application proposes a personalized interaction method for intangible cultural heritage based on virtual reality, the method comprising:

[0007] Receive instructions input by the user in real time;

[0008] Parsing the instruction input by the user, and determining the type of intangible cultural heritage that the user terminal is learning according to the result of the parsing;

[0009] According to the intangible cultural heritage type of the user, match the target intangible cultural heritage data in the intangible cultural heritage database;

[0010] Based on the target intangible cultural heritage data, generating initial virtual data;

[0011] Receive the behavior data of the user terminal in real time, input the behavior data into a preset interaction model, and output a switching parameter through the interaction model;

[0012] Adjust the initial virtual data according to the switching parameter to obtain the virtual data after the user interaction response.

[0013] An embodiment of the present application further provides a personalized interaction system for intangible cultural heritage based on virtual reality, including:

[0014] A receiving module, configured to receive the instructions input by the user terminal in real time;

[0015] An analysis module, configured to analyze the instructions input by the user, and determine the intangible cultural heritage type of the user terminal according to the analysis result;

[0016] A matching module, configured to match the target intangible cultural heritage data in the intangible cultural heritage database according to the intangible cultural heritage type of the user terminal;

[0017] A production module, configured to generate initial virtual data based on the target intangible cultural heritage data;

[0018] An input module, configured to receive the behavior data of the user terminal in real time, input the behavior data into a preset interaction model, and output a switching parameter through the interaction model;

[0019] An adjustment module, configured to adjust the initial virtual data according to the switching parameter to obtain the virtual data after the user interaction response.

[0020] The present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0021] The present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0022] The personalized interaction method and system for intangible cultural heritage based on virtual reality in the embodiments of the present application utilize virtual reality technology, enabling users to immerse themselves in the learning process of intangible cultural heritage and directly contact and experience all aspects of traditional culture through interactive means. According to the input instructions and behavior data of users, the intangible cultural heritage content in the virtual environment is accurately matched and adjusted to support personalized learning paths and interaction methods. This customized interaction method can effectively solve the one-size-fits-all problem in traditional learning, allowing each user to obtain the behavior data according to their own interests and needs and providing intelligent feedback based on their interactions to adjust the display method of virtual data or learning content. This intelligent adaptation mechanism can not only maintain users' high attention to intangible cultural heritage content but also provide timely prompts or guidance during the learning process to help users deeply understand and master intangible cultural heritage. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic flowchart of the personalized interaction method for intangible cultural heritage based on virtual reality according to an embodiment of the present application;

[0024] Figure 2 It is a schematic flowchart of the personalized interaction method for intangible cultural heritage based on virtual reality according to an embodiment of the present application;

[0025] Figure 3 It is a schematic block diagram of the structure of the personalized interaction system for intangible cultural heritage based on virtual reality according to an embodiment of the present application;

[0026] Figure 4 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present application.

[0027] The realization of the purpose of the present application, functional features, and advantages will be further described in conjunction with the embodiments with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] In order to make the purpose, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0029] Refer to Figure 1 , in the embodiments of the present application, a personalized interaction method for intangible cultural heritage based on virtual reality is provided, and the method includes:

[0030] S1. Receive the instructions input by the user terminal in real time;

[0031] S2. Analyze the instructions input by the user, and determine the type of intangible cultural heritage learned by the user terminal according to the analysis result;

[0032] S3. Match the target intangible cultural heritage data in the intangible cultural heritage database according to the intangible cultural heritage type of the user terminal;

[0033] S4. Generate initial virtual data based on the target intangible cultural heritage data;

[0034] S5. Real-time receive the behavior data of the user terminal, input the behavior data into a preset interaction model, and output a switching parameter through the interaction model;

[0035] S6. Adjust the initial virtual data according to the switching parameter to obtain the virtual data after user interaction response.

[0036] As described in the above steps S1 - S3, the real-time reception of instructions is the starting process of interacting with the user through virtual reality devices (such as joysticks, eye trackers, touch screens, etc.). The user sends instructions through these devices, and the system needs to receive and process them immediately. The core of this step is the real-time nature of the data, ensuring that the system can quickly respond to the user's input and avoid delays that affect the interaction experience. The user's operations can be immediately reflected in the system. Instruction parsing is to convert the original signal input by the user into an operation that the computer can understand. The purpose of instruction parsing is to determine the user's intention based on the content or action input by the user. By parsing the user's operations, the system can judge the type of intangible cultural heritage that the user wants to understand or interact with. For example, if the user selects relevant content of a certain cultural heritage through gesture or voice input, the system needs to accurately correspond the instruction with the specific intangible cultural heritage item in the intangible cultural heritage database through instruction parsing. By accurately parsing the user's instructions, the system can accurately identify the user's needs and filter out relevant content from the intangible cultural heritage data. Each instruction input by the user can directly affect the displayed content, making each user's interaction experience unique. The process of matching intangible cultural heritage data is based on an intangible cultural heritage database that contains detailed information on various intangible cultural heritages. The system filters out the target intangible cultural heritage data that meets the user's needs from the database according to the user's input instructions (such as the selected intangible cultural heritage category). This process relies on classification algorithms and data indexing technologies to ensure the accuracy of the matching. The database is designed to support fast queries to ensure that the user's input instructions can find the corresponding data in the shortest time. The system can accurately and efficiently extract the target data from the vast intangible cultural heritage database, optimizing the user's experience in virtual reality. By providing diverse intangible cultural heritage data, the user's selection range and exploration depth can be enhanced, thereby improving the quality of learning and interaction.

[0037] As described in the above steps S4 - S6, the process of generating initial virtual data is to transform the intangible cultural heritage data into content that can be presented in virtual reality. These target intangible cultural heritage data can include images, audio, 3D models, animations, etc. According to the characteristics of the target data, the system transforms it into a form suitable for display in virtual reality and generates a preliminary virtual environment based on these data. Ensure that the intangible cultural heritage can be presented in an intuitive and highly interactive way. By generating initial virtual data that conforms to user needs, the realism and immersion of the virtual reality environment are enhanced. Users can interact with the cultural heritage, increasing their understanding and interest in the culture. The generated virtual data is user - input - oriented, ensuring that each interaction presents content that meets user needs, enhancing the flexibility and adaptability of the system. Real - time receiving of behavior data means that the system needs to continuously monitor and receive the behavior data of users in the virtual environment, including users' movements, eye movements, voices, gestures, etc. Then these data are input into an interaction model, and the interaction model will dynamically adjust the presentation of virtual data according to user behavior. The interaction model can adjust the presentation method of virtual content according to the real - time behavior of users. For example, if a user stays in front of a certain virtual object for a long time, the interaction model can infer that the object is attractive to the user and thus make appropriate content switching. Adjusting the presentation method of virtual reality in real - time according to user behavior enhances the user's interaction experience, making it more personalized and intelligent. By reacting in real - time to user behavior, the system can accurately perceive the user's points of interest, thus optimizing the display of interactive content and giving users a greater sense of participation and control. The process of switching parameter adjustment is to adjust the content or presentation method of virtual data according to the parameters output by the interaction model. For example, if the interaction model identifies that a user shows a high level of interest in a certain virtual intangible cultural heritage item (such as long - time gazing or frequent clicking), the system will increase the detailed information of this item or launch a new interaction level. The adjustment may include adding animation effects, magnifying a certain virtual object, or introducing new interaction tasks, etc. The core of this process is to respond in real - time to user interactions, ensuring that the virtual reality environment can make flexible changes according to user behavior.

[0038] As mentioned above, by using virtual reality technology, users can immerse themselves in the learning process of intangible cultural heritage and directly contact and experience all aspects of traditional culture through interactive means. According to the user's input instructions and behavior data, the intangible cultural heritage content in the virtual environment is accurately matched and adjusted, supporting personalized learning paths and interaction methods. This customized interaction method can effectively solve the one - size - fits - all problem in traditional learning, allowing each user to obtain the user's behavior data and receive intelligent feedback based on their interactions, adjusting the display method of virtual data or learning content. This intelligent adaptation mechanism can not only maintain users' high attention to intangible cultural heritage content but also provide timely tips or guidance during the learning process to help users deeply understand and master intangible cultural heritage.

[0039] It is worth mentioning that assume we are using virtual reality technology to teach traditional Chinese paper-cutting art. The user follows a preset tutorial in the virtual environment to learn the basic techniques of paper-cutting, such as paper folding, cutting, and pattern design. After the tutorial, the user completes a fixed paper-cutting work, and the system gives a score according to the completion degree. After the user enters the virtual environment, the system first understands the user's understanding of paper-cutting art, interest preferences (such as animals, flowers, story scenes, etc.), and paper-cutting experience through a series of interactive questions and answers. Paper-cutting art contains rich patterns and techniques, and different users may have higher interests in different patterns or techniques. The learning of intangible cultural heritage is not only the inheritance of skills, but also the transmission of culture. Therefore, it is necessary to customize the learning content according to the user's background and interests to maintain the attraction of learning and the diversity of culture. According to the user's answers, the system recommends different paper-cutting tutorials. For example, it recommends basic patterns for beginners and complex patterns for experienced users. During the paper-cutting process, the system can adjust the difficulty and guidance in real time according to the user's operations. The learning of intangible cultural heritage skills often requires repeated practice and personalized guidance. The dynamic interactive session can ensure that users learn at a difficulty level and rhythm suitable for themselves, and can solve the problems encountered in practice in a timely manner, which is crucial for maintaining learning motivation and the precise inheritance of skills. During the user's paper-cutting process, the system not only records the completed works, but also analyzes the user's cutting techniques, pattern innovation, and understanding of paper-cutting culture, and provides personalized feedback through a virtual tutor. The learning of intangible cultural heritage is not only the replication of skills, but more importantly, understanding and innovation. In-depth learning understanding monitoring can help educators understand whether users have truly absorbed the cultural connotations of paper-cutting and innovate on this basis. Such a setting in the intangible cultural heritage scenario is because the uniqueness of intangible cultural heritage lies in the depth and diversity of its cultural connotations. Compared with other learning fields, the learning of intangible cultural heritage relies more on the understanding of cultural backgrounds and the personalized inheritance of skills. Therefore, the application of virtual reality technology in intangible cultural heritage education needs to pay more attention to personalization, interactivity, and the depth of cultural transmission.

[0040] Refer to Figure 2 , in one embodiment, after the step of adjusting the initial virtual data according to the switching parameter to obtain the virtual data after the user interaction response, the method includes:

[0041] S61. Obtain the historical behavior data set of the user terminal;

[0042] S62. Analyze the user terminal's mastery of intangible cultural heritage knowledge in the virtual scene according to the intangible cultural heritage type and the historical behavior data set of the user terminal;

[0043] S63. Quantify the mastery degree according to the analysis result to obtain a degree coefficient;

[0044] S64. Analyze the historical behavior dataset and analyze the interaction habits of the user side;

[0045] S65. Based on the interaction habits and degree coefficients, predict the demand parameters of the user side in the target intangible cultural heritage data;

[0046] S66. Based on the prediction result, adjust the virtual data according to the demand parameters.

[0047] As described in the above steps, the historical behavior dataset refers to the data on users' past behaviors collected from the user side, which may include operation records, interaction patterns, learning progress, participation, etc. of users in a virtual environment. Through these data, a comprehensive understanding of users' interaction habits and learning methods can be obtained. During the process of intangible cultural heritage learning, users' behavior data can provide important feedback information. Historical behavior data can help the system understand users' interests, learning tendencies, and behavior patterns, thus laying a foundation for subsequent personalized adjustments. For example, if a user repeatedly encounters difficulties in certain operations (such as staying at a certain step of an embroidery technique for a long time), it can be inferred that the user has problems understanding this part and needs more guidance. The collected historical behavior data can effectively provide data support for subsequent personalized adjustments, enabling the system to optimize precisely according to the real situation of users. The type of intangible cultural heritage refers to the traditional handicrafts or cultural categories that users learn (such as embroidery, pottery, etc.), which is crucial for customizing learning paths. By analyzing the interaction information in the historical behavior dataset, the system can evaluate the user's mastery of knowledge about this type of intangible cultural heritage. This analysis is based on factors such as the user's operation frequency, error frequency, and completion rate, combined with the knowledge points of relevant learning content, to analyze the user's mastery of a certain knowledge point or skill. Each user has different learning progress and knowledge mastery when learning intangible cultural heritage. Therefore, accurately evaluating each user's mastery can provide a basis for customizing personalized learning paths. For example, if a user has already mastered some basic embroidery techniques, the system can adjust the learning path to progress to higher-level skills or complex patterns. Precise mastery of the user's learning level enables the virtual learning system to flexibly adjust content and difficulty, avoiding ineffective learning or excessive challenges. The degree coefficient is a quantitative indicator used to represent the user's mastery of a certain intangible cultural heritage knowledge or skill. It is usually calculated through a certain scoring standard or algorithm and can be comprehensively evaluated by combining the learning time, success rate, error feedback, etc. in the user's historical behavior. The quantification methods include techniques such as using weighted averages, grading, and regression analysis to convert complex behavior data into quantifiable numerical results. In a virtual learning system, quantifying the mastery degree can simplify the decision-making process, enabling the system to make more accurate personalized adjustments. For example, if the degree coefficient is low, the system may recommend more basic knowledge; if the degree coefficient is high, the system can recommend advanced content or challenges. Through the degree coefficient, the system can precisely understand the user's current learning state, and then make reasonable adjustments to the learning progress, thereby optimizing the learning experience. The analysis of interaction habits focuses on the interaction patterns of users in a virtual environment. For example, how users operate, which types of guidance they prefer (such as videos, texts, diagrams, etc.), and whether they need more hints or reviews when encountering difficulties during the learning process.By analyzing the user's interaction behavior data, the system can identify the user's learning habits and then optimize and adjust accordingly. For example, some users like to browse quickly, while others like to follow each step slowly. The analysis of interaction habits can help the system better adapt to the personalized needs of users. For example, some users may tend to master skills through repeated practice, while other users may prefer to watch demonstration operations. Understanding these habits helps to provide each user with a suitable learning method and interaction style. By identifying and adjusting the user's interaction habits, the system can provide a learning experience that better meets the needs of each user, increasing user engagement and learning efficiency. Based on the analyzed interaction habits and the user's proficiency level, the system can predict the user's possible future learning needs. For example, if a user shows strong interest in a specific intangible cultural heritage project, the system can predict that the user may need more detailed guidance or more difficult exercises. The prediction model can combine various data factors such as the user's historical behavior, proficiency level, interest points, and learning frequency, and use machine learning and other methods to perform data modeling to predict the user's future demand parameters. Predicting user needs can prepare resources and content in advance for the system, thus improving the fluency of learning. The addition of the prediction function can make the system more intelligent and flexible, adapting to the learning changes of users in advance. Predicting user needs in advance enables the system to provide more personalized learning content for users, avoiding redundancy or inappropriateness of content and improving learning effects. Based on the predicted demand parameters, the system can adjust the presentation method of virtual data in real time, such as adjusting the complexity of the virtual scene, recommending the content of learning tasks, and controlling the operation difficulty, etc. This adjustment usually relies on the feedback mechanism of the system, dynamically monitoring the user's behavior and appropriately adjusting the content according to the predicted demand. Dynamically adjusting virtual data can help the system better track the user's learning progress and demand changes, preventing users from getting stuck in overly simple or overly complex content. If a user shows a high demand for a certain skill or task, the system can increase the corresponding practice and guidance; conversely, if a user has mastered a certain skill, the challenge difficulty can be increased. By flexibly adjusting virtual data, the system can adapt to user needs in real time, making the virtual learning experience more fluent and personalized, and helping to improve the user's learning interest and efficiency.

[0048] In one embodiment, for matching target intangible cultural heritage data in the intangible cultural heritage database according to the type of intangible cultural heritage learned by the user terminal, the method includes:

[0049] Analyze the type of intangible cultural heritage. When the type of intangible cultural heritage is traditional handicraft, match the data set corresponding to the traditional handicraft label in the intangible cultural heritage database;

[0050] Based on the matching result, obtain the target intangible cultural heritage data of traditional handicrafts, including interaction parameters, virtual scene parameters corresponding to the interaction parameters, and sound effect parameters;

[0051] When the intangible cultural heritage type is traditional performance, match the dataset corresponding to the traditional performance label in the intangible cultural heritage database;

[0052] Based on the matching result, obtain the target intangible cultural heritage data of traditional performance, including interaction parameters, virtual scene parameters corresponding to the interaction parameters, and sound effect parameters;

[0053] When the intangible cultural heritage type is intangible cultural heritage documentary video, match the dataset corresponding to the intangible cultural heritage documentary video label in the intangible cultural heritage database;

[0054] Based on the matching result, obtain the target intangible cultural heritage data of traditional performance, including virtual scene parameters and sound effect parameters corresponding to the virtual scene parameters.

[0055] As described above, by parsing the intangible cultural heritage types provided by the client (such as traditional handicrafts, traditional performances, intangible cultural heritage documentary videos, etc.), the corresponding datasets in the intangible cultural heritage database are selected. In the parsing process, corresponding tags are selected according to the intangible cultural heritage type input by the user, so as to locate and retrieve the relevant intangible cultural heritage data stored in the database. For example, the intangible cultural heritage dataset of traditional handicrafts will be connected to content related to handicrafts, while traditional performances will be linked to content related to performances. When the intangible cultural heritage type is "traditional handicrafts", the system will match the dataset marked as "traditional handicrafts" in the database. This dataset contains information related to the production, techniques, tools, etc. of handicrafts. For intangible cultural heritage projects of the handicraft category, the system can obtain interaction parameters, which refer to the interaction methods or parameters between the user and the handicraft process; virtual scene parameters, which are virtual environment settings related to the handicraft process; and sound effect parameters may involve the sound effects during manual operations, which can enhance the immersion of the virtual experience. When the intangible cultural heritage type is "traditional performances", the system will match the dataset marked as "traditional performances". Such datasets contain content related to performing arts, such as dance, drama, music performances, etc. For traditional performances, the system will provide interaction parameters, such as the way the audience participates in the performance or the interaction method in a virtual performance; the virtual scene parameters may involve content such as scene design (such as the stage, background), etc.; and the sound effect parameters may correspond to the music, audience reactions, etc. in the stage performance. When the intangible cultural heritage type is "intangible cultural heritage documentary videos", the system will match the dataset marked as "intangible cultural heritage documentary videos" in the database. Such datasets generally contain real records and video materials of traditional culture, art activities or techniques. In the documentary video data, the system will display a virtualized version of the real scene through virtual scene parameters, which may include the background setting of the video or the virtual reproduction of the shooting venue. At the same time, the sound effect parameters may include the background sounds of the real scene, such as natural sounds, dialogue sound effects, etc. In this way, an immersive virtual reality experience can be provided to reproduce and enrich the video content of traditional culture. The three parameters of interaction parameters, virtual scene parameters, and sound effect parameters all belong to the detailed part of the data matching results, and are responsible for the interaction between the user and the data, the presentation of the virtual scene, and the coordination of the sound effects respectively. Interaction parameters involve the interaction methods between the user and the virtual scene and virtual objects. For example, in the traditional handicrafts category, the user may perform virtual handicraft operations by clicking, dragging, etc. The virtual environment settings related to intangible cultural heritage determine how the virtual scene of intangible cultural heritage display is presented. For example, intangible cultural heritage of the performance category may have a virtual stage background, while intangible cultural heritage of the handicraft category may simulate a workshop scene. For each type of intangible cultural heritage, there will be different sound effect requirements. The sound effect parameters define the sound effect performance when interacting with the scene. For example, there may be the sound of tool operations when making handicrafts, music and actor's lines in performances, and background sound effects and environmental sounds in documentary videos.Through the combination of these parameters, the system can bring the digital display of each type of intangible cultural heritage into a more realistic and rich virtual environment, enhancing the user experience and immersion.

[0056] In one embodiment, after the step of adjusting the virtual data according to the demand parameters, the method further includes:

[0057] Obtaining the user's behavior data in real time, and judging whether the demand parameters meet the user's needs according to the behavior data;

[0058] Inputting the adjusted virtual data and behavior data into a preset matching model, analyzing the participation frequency of the user side in the adjusted virtual data through the matching model, and outputting the analysis result;

[0059] Obtaining a historical behavior data set, calculating the historical average participation frequency according to the historical behavior data set, and setting a frequency threshold according to the historical average participation frequency;

[0060] If the participation frequency of the adjusted virtual data is greater than or equal to the frequency threshold, it is determined that the demand parameters meet the user's needs.

[0061] As described above, the behavior data of users is continuously monitored and collected. The behavior data of users can include the ways, frequencies, durations, etc. in which they interact with virtual data. For example, a user clicks on a certain manual operation, participates in a certain performance segment, watches a certain video clip, etc. Obtaining behavior data in real time can ensure that the system dynamically understands the interaction status between users and virtual content. This feedback mechanism enables the system to more precisely understand the interests, needs, and preferences of users, thereby providing a basis for subsequent adjustments. Real-time feedback also means that the system can continuously optimize the experience during the user's use process and enhance the user's sense of participation. By analyzing the behavior data of users, the system will evaluate whether the current virtual data meets the needs of users. This process usually includes the identification of user behavior patterns and the comparison of demand parameters. Demand parameters may include indicators such as the depth of user interaction, participation frequency, and preference for virtual data. The significance of this judgment step is to enable the system to have an adaptive ability. If the system detects that the current virtual data does not reach the expected participation level or interaction quality of users, it will make adjustments to optimize the user experience. This mechanism enables the system to meet the needs of different users in a personalized manner. After adjusting the virtual data, the system will input the adjusted data and the user behavior data into a preset matching model for analysis. The matching model is usually based on machine learning algorithms and can identify which features and patterns are related to high participation from a large amount of data. Through this analysis step, the system can obtain the correlation between virtual data and user behavior and analyze whether the virtual data has generated sufficient attraction for users. For example, if a certain adjusted virtual scene or sound effect enhances the user's participation frequency, the matching model can identify this pattern and provide feedback. This also helps to further optimize the adjustment of virtual data. Analyzing the participation frequency is a way to quantify the interaction behavior between users and virtual data. The system determines the popularity of certain adjusted virtual data by calculating the number of interactions of users with it. Frequency analysis can help the system discover which adjustments are appropriate and which may still need further optimization. For example, if the participation frequency of a certain virtual data is very high, it indicates that the data meets the needs of users, otherwise it may indicate that further adjustments are needed. This function enables the system to automatically optimize and improve user satisfaction during long-term use. By collecting and analyzing historical behavior data, the system can understand the long-term behavior patterns and needs of users. These historical data may include the user's past operation records, participation situations, and preference trends, etc. The historical behavior data set provides a macroscopic perspective for the system to help the system understand the long-term behavior trends of users, rather than just real-time short-term data. Through historical data, the system can provide more accurate adjustments when facing changes in user needs. For example, some users may gradually tend to have a higher frequency of interaction, while other users may prefer static viewing. By analyzing historical data, the system can better predict and adapt to changes in user needs.By analyzing the historical behavior dataset, the system can calculate the user's historical average participation frequency. This frequency reflects the average activity level of the user in past interactions, which may include the number of times or duration of their participation in virtual data, etc. The historical average participation frequency provides a baseline value for the system to compare with the current participation frequency. If the current participation frequency in virtual data matches or exceeds this value, it indicates that the user's participation in the virtual data meets expectations; if it is lower than the historical average, it may indicate that the needs are not met and further optimization is required. Based on the historical dataset and the calculated historical average participation frequency, the system sets a "frequency threshold" as a judgment criterion. When the user's participation frequency is greater than or equal to this threshold, the system considers that the demand parameter has met the user's needs. The purpose of setting the frequency threshold is to provide a clear criterion for the system to determine whether the virtual data has successfully attracted the user's participation. When the participation frequency reaches or exceeds the set threshold, the system can consider that the adjustment of the virtual data is successful and the user's needs are met. This threshold can also be flexibly adjusted according to different historical data or group characteristics of the user, enabling the system to adapt to the needs of different user groups. Finally, the system makes a judgment based on the participation frequency and the set frequency threshold. If the participation frequency of the adjusted virtual data at the user end reaches or exceeds the set frequency threshold, the system will consider that the virtual data meets the user's needs and the demand parameter has been satisfied. This judgment mechanism ensures that the system can continuously optimize based on real-time feedback and will not over-adjust the virtual data. Only when the user truly shows sufficient participation interest will the system consider that the virtual data has met the user's needs.

[0062] In one embodiment, after the step of adjusting the virtual data according to the demand parameter, the method further includes:

[0063] If the participation frequency of the adjusted virtual data is less than the frequency threshold, it is determined that the demand parameter does not meet the user's needs;

[0064] Based on the judgment result, re-predict the demand parameter.

[0065] As described above, if the adjusted virtual data fails to attract sufficient user participation, the system determines whether the virtual data meets the user's needs by comparing the actual participation frequency with a pre-set frequency threshold. When the participation frequency is less than the threshold, it indicates that the user's interest or engagement with the virtual data is low, which may be due to the virtual data itself failing to provide the experience expected by the user or the adjustment being insufficient. The system regards this situation as the unmet demand. The system can promptly identify the deficiencies of the adjusted virtual data. After determining that the demand parameter does not meet the user's needs, the system avoids continuing to rely on the current virtual data to prevent the decline of the user experience. This helps to ensure that users obtain continuous attraction and satisfaction during use. When the system discovers that the adjusted virtual data fails to meet the user's needs, it re-predicts the demand parameter of the user based on the judgment result that the participation frequency does not reach the threshold. This process usually relies on the analysis of the current user behavior and combines historical behavior data to make a new demand prediction. The purpose of predicting the demand parameter is to find out the virtual data characteristics that the user truly needs. For example, the user may prefer a certain type of interaction, a specific virtual scenario, or have strong demands for certain visual or audio elements. The system adjusts the demand parameter based on real-time feedback to improve the satisfaction. Re-predicting the demand parameter helps the system to flexibly adapt to the changes in user needs. This mechanism ensures that the system does not simply rely on previous data or fixed assumptions, but adjusts the virtual data according to the user's latest behavior. By predicting the demand parameter, the system can more accurately adjust the virtual content.

[0066] In one embodiment, based on the judgment result, re-predicting the demand parameter, the method includes:

[0067] Obtain the historical behavior data set of the user terminal, extract the participation frequencies of different time periods of the user terminal's history according to the historical behavior data set, and analyze the participation frequencies of different time periods;

[0068] According to the analysis result, determine whether the participation frequency of the user terminal has a time attribute;

[0069] If the participation frequency of the user terminal does not have a time attribute, obtain the current physiological parameters of the user terminal;

[0070] Re-predict the demand parameter of the user terminal according to the current virtual data, intangible cultural heritage type, and physiological parameters.

[0071] As described above, historical behavior data of the client is collected to understand the user's past behavior patterns. Historical behavior data usually includes information about the user's activities during different time periods, such as login frequency, usage duration, number of clicks or interactions, etc. By extracting the participation frequencies for different time periods (such as daily, weekly, monthly, etc.), the system can obtain a time series data that reflects the user's activity level during these time periods. This step helps the system identify the patterns of the user's participation in virtual data, especially whether there are periodic or time attributes. This analysis can reveal the user's activity in different time periods and help the system understand whether the user's participation behavior is affected by time. Through this step, the system can identify peak and trough periods, which helps optimize the push and adjustment of virtual data. By analyzing the data of participation frequencies for different time periods, the system can identify the time periods when the user's participation frequency is high and those when it is low. For example, the user may be more active in the afternoon on weekdays and less active on weekend evenings. This temporal analysis helps the system identify whether there are obvious time attributes in the participation frequency (such as periodic or time-of-day changes). By analyzing the participation frequencies for different time periods, the system can determine whether the user shows a high level of participation during certain time periods. This provides important clues for subsequent demand prediction. If the system finds that the user's participation frequency changes over time (e.g., active in the morning and inactive in the evening), it can develop different virtual data optimization strategies for these time periods. Based on the analysis of historical behavior data, the system needs to determine whether the participation frequency is affected by time attributes. Time attributes may refer to the association between the user's behavior and certain time periods (such as a certain time of day, days of the week, etc.). If the user's behavior frequency shows obvious differences in different time periods, the system can infer the existence of time attributes. If the system finds that the user's participation frequency is significantly affected by time, it can adjust the push time or type of virtual data according to this time attribute to maximize the user's participation. Conversely, if there are no obvious time patterns, the system can make adjustments in other ways. If the system determines that there are no obvious time attributes in the participation frequency of the client, it means that time itself has little impact on the user's behavior. At this time, the system turns to consider other factors, such as the user's physiological state. Obtaining the user's current physiological parameters (such as heart rate, body temperature, stress level, etc.) can provide new information for predicting demand parameters. Physiological parameters are usually closely related to the user's physiological needs and emotional states and can reflect the user's physical condition at a specific moment. These parameters may affect the user's demand for virtual data. For example, when fatigued, the user may need a more relaxing experience, while when excited, they may need more interactive or challenging content. After introducing physiological parameters, the system can adjust the presentation of virtual data according to the user's physical state.For example, when the user's physiological parameters indicate that they are in a stressed state, the system may recommend relaxation virtual experiences, and vice versa, it may recommend more stimulating and interactive content. This approach improves the personalization and accuracy of the user experience. By integrating current virtual data, intangible cultural heritage types (which may refer to data forms related to traditional culture or heritage), and the user's physiological parameters, the system re-predicts the user's demand parameters. The selection of intangible cultural heritage types may be related to the user's interests or preferences, so the user's preferences for certain specific content (such as cultural heritage content) need to be considered when predicting demand. This process involves a comprehensive analysis of multiple factors, and the system predicts the user's needs based on the current virtual data, the user's physiological condition, and the intangible cultural heritage types. This method takes into account the user's immediate needs rather than simply relying on past behavioral data.

[0072] In another embodiment, after the step when there is no time attribute for the participation frequency of the user terminal, the method further includes:

[0073] Obtain the historical behavior data of the user terminal and the corresponding historical virtual data;

[0074] According to the historical behavior data of the user terminal and the corresponding historical virtual data, analyze the residence time, interaction frequency, and interaction completion progress of the user terminal for different historical virtual data;

[0075] According to the analysis results, calculate the concentration level of the user terminal for different historical virtual data;

[0076] Based on the concentration levels of different historical virtual data, re-predict the demand parameters of the user terminal in the target intangible cultural heritage data;

[0077] Based on the prediction results, adjust the virtual data according to the demand parameters.

[0078] As mentioned above, historical behaviors and virtual data are the basis for understanding user interests and habits. By collecting this data, the system can establish the association between users and virtual data, laying the foundation for subsequent personalized analysis. This step provides rich raw data for subsequent analysis and prediction, helping the system understand the interaction history between users and virtual content, and thus making more accurate demand predictions. The duration of user stay in virtual data. A longer user stay indicates that they are interested in or more engaged with the virtual data. The number of interactions between users and virtual data, reflecting the user's participation and interaction depth. Whether users have completed the predefined interaction tasks or goals (such as game levels, video viewing completion, etc.). This indicator reflects the user's participation completion degree and the attractiveness of virtual data. These indicators can directly reflect the user's focus, interest level, and interaction behavior towards virtual content, thus helping the system analyze user preferences more accurately. By analyzing the stay time, interaction frequency, and interaction completion progress, the system can understand the user's preferences, focus points, and depth of participation in different virtual data. This provides support for subsequent focus assessment and demand prediction. The focus level is calculated by comprehensively analyzing the stay time, interaction frequency, and interaction completion progress of users towards different virtual data. Focus reflects the degree of user engagement when contacting virtual data: users stay for a long time, interact frequently, and complete most of the interaction tasks. Users have a short stay time, low interaction frequency, and poor completion of interaction tasks. By calculating the focus of users on virtual data, the system can quantify the user's interest and engagement level with the content, providing more intuitive data support for subsequent personalized recommendations and demand predictions. The focus level can reveal the depth of user interest in specific virtual data, thus helping the system adjust the presentation method of virtual data according to the user's actual interests. High-focus content may mean that users like a certain type of interaction or experience, and the system can further optimize recommendations based on this. At this stage, the system will combine the focus levels of users on different virtual data with target intangible cultural heritage data (such as virtual data related to cultural heritage) to re-predict the user's demand parameters. Virtual data with a higher focus level may indicate that users have a higher interest in certain types of intangible cultural heritage data (such as cultural activities, artworks, etc.). Based on these predicted demand parameters, the system adjusts the presentation method of the target intangible cultural heritage data. The focus level reflects the user's interest and participation degree in the content. Combining this information, the system can more accurately infer the user's demand for the target intangible cultural heritage data, thereby improving the content matching degree and user satisfaction. Through the prediction of the focus level, the system can more accurately predict the user's needs in intangible cultural heritage data, ensure that the recommended content meets the user's interests, and can improve the user's participation and satisfaction. Finally, the system will adjust the presentation method of virtual data according to the predicted demand parameters. The adjustment can involve the type, display form, interaction method, etc. of virtual data, aiming to make the virtual data more in line with the user's current needs.Through precise demand forecasting, the system can provide more personalized virtual data according to users' interests and needs, thus enhancing the user experience and engagement. For example, if a user shows a high degree of focus on a certain type of intangible cultural heritage data, the system can push similar content or add elements with strong interactivity.

[0079] In one embodiment, before the step of matching the target intangible cultural heritage data in the intangible cultural heritage database, the method further includes constructing an intangible cultural heritage database, and the steps include:

[0080] Collect three-dimensional data of the physical tools and dynamic behavior data of intangible cultural heritage inheritors through multi-dimensional sensors;

[0081] Obtain the gesture trajectories, force parameters, and tool movement paths during the implementation of intangible cultural heritage techniques through motion capture devices;

[0082] Collect the ambient sound effects and voice commentary data of intangible cultural heritage performance projects through a stereo field recording device;

[0083] Perform spatio-temporal alignment processing on the three-dimensional data, dynamic behavior data, gesture trajectories, tool movement paths, and sound effect data to generate an intangible cultural heritage digital model with multi-modal features;

[0084] Associate and store the digital model with the process step parameters and cultural background data in the intangible cultural heritage knowledge graph to obtain the constructed intangible cultural heritage database.

[0085] As described above, multi-dimensional sensors usually include depth cameras, accelerometers, gyroscopes, etc. These devices can obtain data such as the position, movement direction, and speed of objects in real time. When applied to the physical tools of intangible cultural heritage inheritors, they can accurately capture the shape, movement trajectory, and usage method of the tools. This method can provide accurate three-dimensional data for the tools in the virtual environment, making the operation simulation of the tools in virtual reality more realistic. In addition, the collection of dynamic behavior data can help analyze and reconstruct the details of intangible cultural heritage techniques, providing a more interactive and immersive experience. Motion capture devices track the movement trajectories of the human body or tools through sensors or markers, including the movements of gestures, changes in force, and the movement paths of tools in space. Through precise motion analysis, these data can capture every detail in the operation of intangible cultural heritage techniques. By obtaining gesture trajectories, force parameters, and tool movement paths, the simulation of intangible cultural heritage techniques can be made more detailed, and the force, direction, and details of each action can be realistically reproduced, which plays an important role in the learning, inheritance, and display of intangible cultural heritage techniques. Stereo field recording devices use multiple microphones to collect sounds from different directions in space, thus simulating the sound effects in the natural environment. It can capture sound information such as environmental sound effects, voice commentaries in performances, music, natural sounds, etc., and accurately record the spatial distribution of audio. The collection of sound effects and voice data can provide a more vivid and realistic auditory experience for intangible cultural heritage projects, making the virtual display more immersive and touching. It can not only enhance the cultural atmosphere of performance projects but also help learners understand the background stories, backgrounds, and meanings behind intangible cultural heritage techniques through voice commentaries. Temporal and spatial alignment processing refers to integrating multi-modal data from different sources (such as three-dimensional data, gesture trajectories, tool paths, sound effects, etc.) into the same time dimension for synchronous processing. Through precise alignment algorithms, the consistency in time and space between different data is ensured. Through temporal and spatial alignment, it can be ensured that various types of data are presented synchronously, and the generated digital model is more accurate and multi-dimensional. Finally, the generated digital model contains information at multiple levels such as actions, sounds, tools, and environments, enhancing the integrity and authenticity of the digital representation of intangible cultural heritage and making its interactivity and learning effect better in the virtual environment. The intangible cultural heritage knowledge graph is a structured data storage method that contains various types of information about intangible cultural heritage, such as technological steps, historical backgrounds, cultural connotations, etc. These data are stored in association with the generated digital model, and various data of intangible cultural heritage can be effectively integrated through association technologies and database management systems. By associating the digital model with the technological steps and cultural background data in the knowledge graph, a comprehensive intangible cultural heritage database can be formed. This not only makes the intangible cultural heritage information better organized and stored but also provides more accurate guidance and reference for users in future learning and inheritance, ensuring the comprehensiveness and accuracy of intangible cultural heritage knowledge and techniques.

[0086] In another embodiment, the method for generating the virtual scene parameters includes:

[0087] When the intangible cultural heritage type is traditional handicrafts, construct a progressive interaction model for material processing, tool use, and finished product assembly, where:

[0088] Set tactile feedback parameters and physical deformation algorithms in the material processing stage;

[0089] Associate gesture recognition thresholds with process standard parameters in the tool use stage;

[0090] Configure combination logic rules and error tolerance limits in the finished product assembly stage;

[0091] When the intangible cultural heritage type is traditional performances, construct a multi-level interaction model for role-playing, action imitation, and rhythm coordination, where:

[0092] Set clothing replacement logic and role behavior constraints in the role-playing layer;

[0093] Configure bone mapping algorithms and similarity evaluation matrices in the action imitation layer;

[0094] Associate audio waveform analysis with action timing deviation detection in the rhythm coordination layer;

[0095] When the intangible cultural heritage type is intangible cultural heritage documentary videos, construct a visual interaction model for spatio-temporal marking, cultural annotation, and inheritance context, where:

[0096] Set geographical information overlay and timeline zoom controls in the spatio-temporal marking layer;

[0097] Configure semantic association engines and multi-language commentary channels in the cultural annotation layer;

[0098] Generate inheritor relationship diagrams and visual paths for the evolution of skills in the inheritance context layer.

[0099] As described above, haptic feedback parameters can simulate the physical touch sensations during material processing, such as the hardness, elasticity, etc. of the material. The physical deformation algorithm is used to simulate the shape changes that occur to the material during processing due to external forces, such as folding, stretching, cutting, etc. The combination of these technologies enables users to experience real physical sensations in a virtual environment. The gesture recognition threshold is set to a specific action standard, enabling the system to recognize the correctness of the user's hand movements and tool operations. At the same time, process standard parameters refer to the norms that need to be followed when performing a specific skill, such as force, angle, etc. By combining gestures with process standards, the system can determine whether the user's operations meet the requirements. The combinatorial logic rules help the system judge and optimize the combination methods of multiple components, while the error tolerance limit sets the acceptable error range during the assembly process. For example, during the splicing process, the system can tolerate a certain degree of angular or positional deviation without forcing a perfect fit. This configuration makes the assembly of virtual models more fault-tolerant, reduces the restrictions on learning imposed by overly strict standards, and also guides users to pay attention to the balance between details and precision when completing traditional manual skills. The clothing replacement logic controls through algorithms that users can select different clothing in the virtual environment to simulate different cultural roles. The role behavior constraints are used to ensure that under a specific role, the behavior is consistent with the cultural background, avoiding inappropriate actions or behaviors and ensuring cultural accuracy. The bone mapping algorithm matches the user's actions with the skeletal system of the virtual character to ensure that the user's actions can be accurately mapped to the virtual character. The similarity evaluation matrix is used to measure the similarity between the user's actions and the actions of traditional skills, helping the system to provide real-time feedback on the user's performance. Through precise bone mapping and similarity evaluation, users can accurately imitate the actions of intangible cultural heritage performances in a virtual environment. This function helps to improve the action imitation ability of learners, enabling them to get closer to the standard actions of traditional skills and enhancing the learning effect. The audio waveform analysis technology ensures the temporal consistency with the performer's actions by analyzing the rhythm waveforms of music or performances. The action timing deviation detection corrects the timing deviation of the user's actions by detecting the time differences in the user's actions, synchronizing the actions with the rhythm. This layer helps users master the coordination of actions and rhythm, enabling them to maintain precise rhythm and action consistency during performances and enhancing the learning effect of performance-based intangible cultural heritage skills. The geographic information overlay technology provides a sense of spatial dimension by combining the geographic location data in the video with a virtual map. At the same time, the timeline zoom control allows users to review and view events or processes at different time points by operating the zoomed timeline. The semantic association engine generates annotations for the cultural content in the video in an automatic or semi-automatic manner, associating the information such as images, texts, and voices in the video with the cultural background. The multilingual commentary channels provide appropriate voice commentaries for users of different languages.The inheritor relationship map shows the relationships between different inheritors and the inheritance process in a visual way, while the visual path of the evolution of skills shows how intangible cultural heritage skills change over time and evolve in different cultural backgrounds. This hierarchical design enables learners to understand the historical inheritance process of intangible cultural heritage skills, observe the evolution and development of skills, and gain a deeper understanding of the background and evolution trajectory of intangible cultural heritage skills.

[0100] Referring to Figure 3 , an embodiment of the present application also provides a personalized interaction system for intangible cultural heritage based on virtual reality, including:

[0101] A receiving module 1 for receiving instructions input by the user terminal in real time;

[0102] An analysis module 2 for analyzing the instructions input by the user and determining the type of intangible cultural heritage of the user terminal according to the analysis result;

[0103] A matching module 3 for matching target intangible cultural heritage data in the intangible cultural heritage database according to the type of intangible cultural heritage of the user terminal;

[0104] A production module 4 for generating initial virtual data based on the target intangible cultural heritage data;

[0105] An input module 5 for receiving the behavior data of the user terminal in real time, inputting the behavior data into a preset interaction model, and outputting switching parameters through the interaction model;

[0106] An adjustment module 6 for adjusting the initial virtual data according to the switching parameters to obtain the virtual data after user interaction response.

[0107] As described above, it can be understood that each component of the personalized interaction system for intangible cultural heritage based on virtual reality proposed in the present application can implement the functions of any one of the above-mentioned personalized interaction methods for intangible cultural heritage based on virtual reality, and the specific structure will not be elaborated.

[0108] Referring to Figure 4 , an embodiment of the present application also provides a computer device, which can be a server, and its internal structure can be as Figure 4As shown in the figure. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device is used to store data such as monitoring data. The network interface of the computer device is used to communicate with external terminals via a network connection. The computer program, when executed by the processor, implements a personalized interaction method for intangible cultural heritage based on virtual reality.

[0109] The above-mentioned processor executes the above-mentioned personalized interaction method for intangible cultural heritage based on virtual reality, including: receiving in real time the instructions input by the user terminal; parsing the instructions input by the user, and determining the type of intangible cultural heritage of the user terminal according to the parsing result; matching the target intangible cultural heritage data in the intangible cultural heritage database according to the type of intangible cultural heritage of the user terminal; generating initial virtual data based on the target intangible cultural heritage data; receiving in real time the behavior data of the user terminal, inputting the behavior data into a preset interaction model, and outputting a switching parameter through the interaction model; adjusting the initial virtual data according to the switching parameter to obtain the virtual data after the user's interaction response.

[0110] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a personalized interaction method for intangible cultural heritage based on virtual reality, including the steps of: receiving in real time the instructions input by the user terminal; parsing the instructions input by the user, and determining the type of intangible cultural heritage of the user terminal according to the parsing result; matching the target intangible cultural heritage data in the intangible cultural heritage database according to the type of intangible cultural heritage of the user terminal; generating initial virtual data based on the target intangible cultural heritage data; receiving in real time the behavior data of the user terminal, inputting the behavior data into a preset interaction model, and outputting a switching parameter through the interaction model; adjusting the initial virtual data according to the switching parameter to obtain the virtual data after the user's interaction response.

[0111] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in this application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0112] It should be noted that in this article, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, device, article, or method including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such a process, device, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, device, article, or method including that element.

[0113] The above are only the preferred embodiments of this application, and do not limit the patent scope of this application accordingly. Any equivalent structural or equivalent process transformation made by using the specification and drawings of this application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of this application.

Claims

1. A personalized interaction method for intangible cultural heritage based on virtual reality, characterized in that The method includes: Receiving the instructions input by the user terminal in real time; Parsing the instructions input by the user, and determining the type of intangible cultural heritage learned by the user terminal according to the parsing result; Matching the target intangible cultural heritage data in the intangible cultural heritage database according to the type of intangible cultural heritage learned by the user terminal; Generating initial virtual data based on the target intangible cultural heritage data; Receiving the behavior data of the user terminal in real time, inputting the behavior data into a preset interaction model, and outputting a switching parameter through the interaction model; Adjusting the initial virtual data according to the switching parameter to obtain the virtual data after user interaction response.

2. The personalized interaction method for intangible cultural heritage based on virtual reality according to claim 1, characterized in that After the step of adjusting the initial virtual data according to the switching parameter to obtain the virtual data after user interaction response, the method includes: Obtaining the historical behavior data set of the user terminal; Analyzing the user's mastery of the intangible cultural heritage knowledge in the virtual scene according to the type of intangible cultural heritage and the historical behavior data set of the user terminal; Quantifying the mastery degree according to the analysis result to obtain a degree coefficient; Parsing the historical behavior data set and analyzing the interaction habits of the user terminal; Predicting the demand parameters of the user terminal in the target intangible cultural heritage data based on the interaction habits and the degree coefficient; Adjusting the virtual data according to the demand parameters based on the prediction result.

3. The personalized interaction method for intangible cultural heritage based on virtual reality according to claim 1, characterized in that, For the step of matching the target intangible cultural heritage data in the intangible cultural heritage database according to the type of intangible cultural heritage learned by the user terminal, the method includes: Parsing the type of intangible cultural heritage. When the type of intangible cultural heritage is traditional handicraft, matching the data set corresponding to the traditional handicraft label in the intangible cultural heritage database; Based on the matching result, obtaining the target intangible cultural heritage data of traditional handicraft, including interaction parameters, virtual scene parameters corresponding to the interaction parameters, and sound effect parameters; When the type of intangible cultural heritage is traditional performance, matching the data set corresponding to the traditional performance label in the intangible cultural heritage database; Based on the matching result, obtaining the target intangible cultural heritage data of traditional performance, including interaction parameters, virtual scene parameters corresponding to the interaction parameters, and sound effect parameters; When the type of intangible cultural heritage is intangible cultural heritage documentary video, matching the data set corresponding to the intangible cultural heritage documentary video label in the intangible cultural heritage database; Based on the matching result, obtaining the target intangible cultural heritage data of intangible cultural heritage documentary video, including virtual scene parameters and sound effect parameters corresponding to the virtual scene parameters.

4. The personalized interaction method for intangible cultural heritage based on virtual reality according to claim 2, wherein After the step of adjusting the virtual data according to the demand parameters, the method further includes: Obtaining the behavior data of the user in real time, and judging whether the demand parameters meet the user's needs according to the behavior data; Inputting the adjusted virtual data and behavior data into a preset matching model, analyzing the participation frequency of the user terminal in the adjusted virtual data through the matching model, and outputting the analysis result; Obtaining the historical behavior data set, calculating the historical average participation frequency according to the historical behavior data set, and setting a frequency threshold according to the historical average participation frequency; If the participation frequency of the adjusted virtual data is greater than or equal to the frequency threshold, it is determined that the demand parameters meet the user's needs.

5. The personalized interaction method for intangible cultural heritage based on virtual reality according to claim 4, characterized in that After the step of adjusting the virtual data according to the demand parameters, the method further includes: If the participation frequency of the adjusted virtual data is less than the frequency threshold, it is determined that the demand parameter does not meet the user's demand; Based on the judgment result, re-predict the demand parameter.

6. The personalized interaction method for intangible cultural heritage based on virtual reality according to claim 5, characterized in that, The method of re-predicting the demand parameter based on the judgment result includes: Obtain the historical behavior data set of the user terminal, extract the participation frequencies of different historical time periods of the user terminal according to the historical behavior data set, and analyze the participation frequencies of different time periods; According to the analysis result, judge whether the participation frequency of the user terminal has a time attribute; If the participation frequency of the user terminal does not have a time attribute, obtain the current physiological parameters of the user terminal; Re-predict the demand parameter of the user terminal according to the current virtual data, intangible cultural heritage type and physiological parameters.

7. The personalized interaction method for intangible cultural heritage based on virtual reality according to claim 3, characterized in that, Before the step of matching the target intangible cultural heritage data in the intangible cultural heritage database, the method further includes constructing an intangible cultural heritage database, and the steps include: Collect the three-dimensional data of the physical tools and dynamic behavior data of the intangible cultural heritage inheritor through a multi-dimensional sensor; Obtain the gesture trajectory, force parameter and tool movement path during the implementation of the intangible cultural heritage skill through a motion capture device; Collect the environmental sound effects and voice commentary data of the intangible cultural heritage performance projects through a stereo field recording device; Perform spatio-temporal alignment processing on the three-dimensional data, dynamic behavior data, gesture trajectory, tool movement path and sound effect data to generate an intangible cultural heritage digital model with multi-modal features; Associate and store the digital model with the process step parameters and cultural background data in the intangible cultural heritage knowledge graph to obtain the constructed intangible cultural heritage database.

8. An intangible cultural heritage personalized interaction system based on virtual reality, characterized in that, Including: A receiving module, configured to receive the instruction input by the user terminal in real time; An analysis module, configured to analyze the instruction input by the user, and determine the intangible cultural heritage type of the user terminal according to the analysis result; A matching module, configured to match the target intangible cultural heritage data in the intangible cultural heritage database according to the intangible cultural heritage type of the user terminal; A production module, configured to generate initial virtual data based on the target intangible cultural heritage data; An input module, configured to receive the behavior data of the user terminal in real time, input the behavior data into a preset interaction model, and output a switching parameter through the interaction model; An adjustment module, configured to adjust the initial virtual data according to the switching parameter to obtain the virtual data after user interaction response.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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