Metacosmic intelligent interactive training system

Abstract

The invention belongs to the technical field of meta universe, and provides a meta universe intelligent interaction training system, which comprises a first training module, a second training module and a third training module, and is characterized in that a first data set is constructed according to historical data related to cross-culture experience, and a BERT model is finely adjusted by using the first data set to obtain a cross-culture experience analysis model; the first providing module is used for providing first cross-culture experience data for the first customer in a virtual space constructed based on the meta universe by using the model, and collecting experience feedback data of the first customer; the second training module is used for constructing a second data set according to the experience feedback data and the first cross-culture experience data, and carrying out iterative training on the model according to the second data set; and the second providing module is used for generating second cross-culture experience data for a second customer by using the cross-culture experience analysis model subjected to iterative training, and displaying the second cross-culture experience data to the second customer in the virtual space. According to the invention, the cross-culture experience is combined with the meta-universe technology, so that clients can obtain various cross-culture immersive experiences on line.

Description

Technical Field

[0001] The present invention relates to the field of metaverse technology, and in particular to a metaverse intelligent interactive training system. Background Art

[0002] Cross-cultural experience refers to the learning and adaptation process that individuals or groups go through when interacting with people from different cultural backgrounds. This experience can enhance understanding, respect and appreciation of different cultures, and help cultivate a global perspective and cross-cultural communication skills. Existing cross-cultural experiences are mainly achieved through cross-cultural exchange activities organized offline, or by watching cross-cultural materials such as books and videos. On the one hand, the offline method has a high cost, and on the other hand, it is difficult to cover the experience of many different types of cultures, making it difficult to meet the needs of more people for cross-cultural experience.

[0003] The design of the present invention combines cross-cultural experience with the metaverse, thereby more conveniently providing diverse cross-cultural experiences for more people. Summary of the invention

[0004] In order to solve at least one of the above technical problems, the present invention specifically provides a metaverse intelligent interactive training system, method, electronic device, computer storage medium and computer program product.

[0005] The present invention provides a metaverse intelligent interactive training system, which is applied to an intelligent agent, and the system includes a first training module, a first providing module, a second training module, and a second providing module; The first training module is used to collect historical data related to cross-cultural experience, construct a first data set based on the historical data, and use the first data set to fine-tune the BERT model to obtain a cross-cultural experience analysis model; The first providing module is used to provide first cross-cultural experience data for the first customer in a virtual space built based on the metaverse using the cross-cultural experience analysis model, and collect experience feedback data of the first customer; The second training module is used to construct a second data set according to the experience feedback data and the first cross-cultural experience data, and use the second data set to iteratively train the cross-cultural experience analysis model; The second providing module is used to generate second cross-cultural experience data for a second customer using the iteratively trained cross-cultural experience analysis model, and present the second cross-cultural experience data to the second customer in a virtual space.

[0006] The present invention also provides a metaverse intelligent interaction training method based on the above system, which is applied to an intelligent agent and includes the following steps: Collect historical data related to cross-cultural experience, construct a first data set based on the historical data, and use the first data set to fine-tune the BERT model to obtain a cross-cultural experience analysis model; Using the cross-cultural experience analysis model to provide first cross-cultural experience data for a first customer in a virtual space built based on the metaverse, and collecting experience feedback data from the first customer; constructing a second data set according to the experience feedback data and the first cross-cultural experience data, and iteratively training the cross-cultural experience analysis model using the second data set; The iteratively trained cross-cultural experience analysis model is used to generate second cross-cultural experience data for the second customer, and the data is presented to the second customer in the virtual space.

[0007] Furthermore, the collecting of historical data related to the cross-cultural experience and constructing a first data set based on the historical data include: Determine several data source topics related to cross-cultural experience, and obtain the historical data related to cross-cultural experience by web crawling and manual collection; The historical data is classified and processed according to the population type to obtain several groups of related data, each group of related data includes cross-cultural data and population type labels, and all the related data are constructed as the first data set.

[0008] Further, using the cross-cultural experience analysis model to provide first cross-cultural experience data for the first customer in a virtual space built based on the metaverse, and collecting experience feedback data of the first customer, includes: The cross-cultural experience analysis model receives the target culture type and customer-related data of the first customer, and calls the customer analysis model to perform in-depth analysis on the customer-related data to obtain a population type label of the first customer; Searching for cross-cultural data based on the first customer's population type label and the target culture type, performing format conversion processing on the cross-cultural data to obtain the first cross-cultural experience data, and providing the first cross-cultural experience data to the first customer in a virtual space constructed based on the metaverse; In the process of providing the first cross-cultural experience data, first experience feedback data of the first customer is obtained through voice recognition, motion and expression capture technology; and after the first cross-cultural experience data is provided, the survey questionnaire data of the first customer is received and semantically analyzed to obtain second experience feedback data, and the first experience feedback data and the second experience feedback data are used as the experience feedback data.

[0009] Furthermore, the calling of the customer analysis model to perform in-depth analysis on the customer-related data to obtain a population type label of the first customer includes: The customer association data and population type classification rules are input into the BERT model, and the BERT model outputs the population type label of the first customer obtained after semantic recognition and classification prediction; wherein the customer association data includes the identity information and network publishing information of the first customer, and the identity information includes education level and job type.

[0010] Furthermore, constructing a second data set according to the experience feedback data and the first cross-cultural experience data, and iteratively training the cross-cultural experience analysis model using the second data set includes: Determine the type of cross-cultural experience to which the cross-cultural experience analysis model is applicable, and determine the amount of training according to the type of cross-cultural experience; wherein the type of cross-cultural experience includes cross-national cultural experience and cross-regional cultural experience; The experience feedback data and the first cross-cultural experience data are constructed into a piece of training data and are input into the second data set. When the amount of training data in the second data set reaches the training quantity, the cross-cultural experience analysis model is iteratively trained using the second data set.

[0011] Furthermore, determining the amount of training according to the cross-cultural experience type includes: If the cross-cultural experience type to which the cross-cultural experience analysis model is applicable is a cross-country cultural experience, the training quantity is set to a first value; if the cross-cultural experience type to which the cross-cultural experience analysis model is applicable is a cross-regional cultural experience, the training quantity is set to a second value; Wherein, the first value is greater than the second value.

[0012] The present invention also provides an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the method as described in any of the preceding items.

[0013] The present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, any of the above methods is executed.

[0014] The present invention also provides a computer program product, which includes computer program instructions. When the computer program instructions are executed by a processor of an electronic device, any of the methods described above is implemented.

[0015] The beneficial effects of the present invention are: 1) This invention organically combines cross-cultural experience with metaverse technology, allowing customers to have an immersive cross-cultural experience online, effectively reducing the cost of cross-cultural experience.

[0016] 2) The present invention uses the existing BERT large language model to construct a cross-cultural experience analysis model, thereby obtaining an intelligent agent that can accurately provide customers with cross-cultural experience data. Compared with the existing method that can only provide fixed cross-cultural experience resources, customers can obtain a more personalized experience.

[0017] 3) The present invention also provides iterative upgrade training for the cross-cultural experience analysis model, thereby ensuring that the cross-cultural experience analysis model can always analyze and provide the most accurate cross-cultural experience data, thereby providing customers with the best cross-cultural experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 It is a structural schematic diagram of a metaverse intelligent interactive training system disclosed in an embodiment of the present invention; Figure 2 It is a flowchart of a metaverse intelligent interactive training method disclosed in an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of the intelligent agent disclosed in the embodiment of the present invention. DETAILED DESCRIPTION

[0020] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0021] See also Figure 1 As shown, a metaverse intelligent interaction training system according to an embodiment of the present invention is applied to an intelligent agent, and the system includes a first training module, a first providing module, a second training module, and a second providing module; The first training module is used to collect historical data related to cross-cultural experience, construct a first data set based on the historical data, and use the first data set to fine-tune the BERT model to obtain a cross-cultural experience analysis model; The first providing module is used to provide first cross-cultural experience data for the first customer in a virtual space built based on the metaverse using the cross-cultural experience analysis model, and collect experience feedback data of the first customer; The second training module is used to construct a second data set according to the experience feedback data and the first cross-cultural experience data, and use the second data set to iteratively train the cross-cultural experience analysis model; The second providing module is used to generate second cross-cultural experience data for a second customer using the iteratively trained cross-cultural experience analysis model, and present the second cross-cultural experience data to the second customer in a virtual space.

[0022] See also Figure 2 As shown, an embodiment of the present invention provides a metaverse intelligent interaction training method, which is applied to an intelligent agent and includes the following steps: Collect historical data related to cross-cultural experience, construct a first data set based on the historical data, and use the first data set to fine-tune the BERT model to obtain a cross-cultural experience analysis model; Using the cross-cultural experience analysis model to provide first cross-cultural experience data for a first customer in a virtual space built based on the metaverse, and collecting experience feedback data from the first customer; constructing a second data set according to the experience feedback data and the first cross-cultural experience data, and iteratively training the cross-cultural experience analysis model using the second data set; The iteratively trained cross-cultural experience analysis model is used to generate second cross-cultural experience data for the second customer, and the data is presented to the second customer in the virtual space.

[0023] A virtual space for cross-cultural experience is constructed based on the metaverse, and customers can enter the virtual space through virtual reality equipment. The present invention configures an intelligent agent for providing cross-cultural experience data for entering customers in the virtual space, and the data providing function is realized by the cross-cultural experience analysis model preset in the intelligent agent. Before the intelligent agent is arranged, historical data related to the cross-cultural experience is first collected and a first data set is constructed based on it, and the first data set is used to fine-tune the existing BERT model to obtain a preliminary cross-cultural experience analysis model. The intelligent agent is arranged, and the preliminary cross-cultural experience analysis model is used to provide the first cross-cultural experience data for the customer entering the virtual space, and the experience feedback data of the first customer is collected at the same time. According to the experience feedback data and the first cross-cultural experience data, a second data set can be constructed, and the cross-cultural experience analysis model is trained for multiple iterations using the second data set, that is, the cross-cultural experience analysis model is gradually upgraded, so that more suitable cross-cultural experience data can be provided for the second customer, and a better cross-cultural experience can be obtained.

[0024] It should be noted that the cross-cultural experience in the present invention includes both cross-national cultural experience, such as cultural experience of African culture, Southeast Asian culture, etc.; and cross-regional cultural experience, such as cultural experience of Jiangnan culture and Yunnan-Guizhou culture in China.

[0025] Furthermore, the collecting of historical data related to the cross-cultural experience and constructing a first data set based on the historical data include: Determine several data source topics related to cross-cultural experience, and obtain the historical data related to cross-cultural experience by web crawling and manual collection; The historical data is classified and processed according to the population type to obtain several groups of related data, each group of related data includes cross-cultural data and population type labels, and all the related data are constructed as the first data set.

[0026] In this embodiment, several data source topics related to cross-cultural experience are manually specified in advance, such as the Internet, newspapers, video data of various network platforms during holidays (such as videos of celebrations during the Chinese New Year released by a certain audio platform), etc., and the above data source topics are used as entry points to crawl historical data related to cross-cultural experience from these channels using web crawler technology. At the same time, data that is difficult to obtain through web crawling can also be obtained through manual collection, such as a large amount of paper data stored in libraries and archives. The data obtained from the above two channels are integrated to obtain historical data.

[0027] Next, the above historical data is classified by population. There can be multiple standards for population classification, such as classification by education level (below college level, college level, graduate level, etc.), classification by job type (technical work, cultural work, planting / breeding work, etc.). After classification, multiple groups of related data can be obtained. Each group of related data includes cross-cultural data and population type labels. Each group of related data represents cross-cultural data of interest to a specific type of people. The population type label is set by automatic labeling equipment or manually. After deduplication and deletion of erroneous data, the sorted related data is integrated into the first data set.

[0028] Further, using the cross-cultural experience analysis model to provide first cross-cultural experience data for the first customer in a virtual space built based on the metaverse, and collecting experience feedback data of the first customer, includes: The cross-cultural experience analysis model receives the target culture type and customer-related data of the first customer, and calls the customer analysis model to perform in-depth analysis on the customer-related data to obtain a population type label of the first customer; Searching for cross-cultural data based on the first customer's population type label and the target culture type, performing format conversion processing on the cross-cultural data to obtain the first cross-cultural experience data, and providing the first cross-cultural experience data to the first customer in a virtual space constructed based on the metaverse; In the process of providing the first cross-cultural experience data, first experience feedback data of the first customer is obtained through voice recognition, motion and expression capture technology; and after the first cross-cultural experience data is provided, the survey questionnaire data of the first customer is received and semantically analyzed to obtain second experience feedback data, and the first experience feedback data and the second experience feedback data are used as the experience feedback data.

[0029] In this embodiment, the cross-cultural experience analysis model can receive the target culture type and customer-related data of the first customer, where the target culture type can be manually input by the first customer, such as African culture, Jiangnan culture, etc., and the customer-related data can be retrieved based on the identity information of the first customer. Figure 3As shown, the intelligent agent is also provided with a customer analysis model, and the cross-cultural experience analysis model can call the customer analysis model, and the model performs an in-depth analysis on the above-mentioned customer-related data, thereby obtaining the population type label of the first customer. Based on the determined population type label of the first customer and the specified target culture type, the corresponding cross-cultural data can be searched, and the search can be from the Internet or from a database connected to the intelligent agent. It should be noted that the cross-cultural data searched on the Internet also needs to be converted into an adaptive format for the virtual space, mainly to extract the cross-cultural data from the searched data, and then convert it into a virtual character image, and the virtual character image outputs the cross-cultural data to the customer, for example, the cross-cultural data searched is a piece of opera, and after conversion, a virtual character of the opera is obtained, the customer interacts with the virtual character of the opera, and the virtual character of the opera sings the opera; and the cross-cultural experience data that has completed the format conversion processing is stored in the database.

[0030] In the process of providing the first cross-cultural experience data to the first customer, the satisfaction degree of the first customer with the provided first cross-cultural experience data can be extracted from the first customer's voice, body movements, and facial expressions, that is, the first experience feedback data; and the second experience feedback data can be obtained by semantic analysis of the survey questionnaire data fed back by the first customer afterwards. The first experience feedback data and the second experience feedback data are integrated into experience feedback data, which is used to construct a second data set, and the cross-cultural experience analysis model is iteratively trained using the second data set.

[0031] It should be noted that cross-cultural experience data includes data on various forms of cultural presentation. For example, for opera culture, it includes singing scene data, costume data, opera history data, opera experience data, etc. One or more of them can be selected based on the different population type labels of the customer.

[0032] Furthermore, the calling of the customer analysis model to perform in-depth analysis on the customer-related data to obtain a population type label of the first customer includes: The customer association data and population type classification rules are input into the BERT model, and the BERT model outputs the population type label of the first customer obtained after semantic recognition and classification prediction; wherein the customer association data includes the identity information and network publishing information of the first customer, and the identity information includes education level and job type.

[0033] In this embodiment, the present invention reuses the existing BERT model, but does not need to fine-tune it, that is, the original BERT model is used to perform semantic recognition and classification prediction on customer-related data and crowd type classification rules, so as to obtain the crowd type label of the first customer. Among them, the crowd type classification rules are artificially set methods and standards for dividing customers into groups, such as the aforementioned classification according to education level, classification according to work type, etc. The crowd type classification rules can guide the customer analysis model to accurately analyze the crowd type label of the first customer.

[0034] Customer-related data refers to the identity information and online publishing information of the first customer, wherein the identity information includes education level and job type, and the online publishing information mainly refers to the cross-cultural information published by the first customer on various online platforms. The customer analysis model can determine multiple universal population type labels of people with similar identities based on the identity information, and then select more appropriate population type labels from them considering the personalized hobbies of the first customer.

[0035] Among them, the Transformer deep learning model is based on the self-attention mechanism and is good at processing sequence data, such as text or time series. The present invention preferably builds a customer analysis model based on the Transformer deep learning model, which can achieve accurate semantic analysis and prediction of the population type label of the first customer.

[0036] Furthermore, constructing a second data set according to the experience feedback data and the first cross-cultural experience data, and iteratively training the cross-cultural experience analysis model using the second data set includes: Determine the type of cross-cultural experience to which the cross-cultural experience analysis model is applicable, and determine the amount of training according to the type of cross-cultural experience; wherein the type of cross-cultural experience includes cross-national cultural experience and cross-regional cultural experience; The experience feedback data and the first cross-cultural experience data are constructed as a piece of training data and are put into the second data set. When the amount of training data in the second data set reaches the training quantity, the second data set is used to iteratively train the cross-cultural experience analysis model.

[0037] In this embodiment, when retraining the cross-cultural experience analysis model, it is necessary to consider whether the amount of training data is appropriate. Only a sufficient amount of training data can ensure that the cross-cultural experience analysis model accurately analyzes the cross-cultural experience data suitable for different types of customers. In this regard, the present invention sets the cross-cultural experience type to include the aforementioned cross-national cultural experience and cross-regional cultural experience. Correspondingly, cross-cultural experience analysis models are configured for the above two types of cross-cultural experience. The number of trainings corresponding to each cross-cultural experience analysis model is determined according to the cross-cultural experience type, so that when the amount of training data in the second data set reaches the training number, the cross-cultural experience analysis model can be retrained.

[0038] Furthermore, determining the amount of training according to the cross-cultural experience type includes: If the cross-cultural experience type to which the cross-cultural experience analysis model is applicable is a cross-national cultural experience, the training quantity is set to a first value; if the cross-cultural experience type to which the cross-cultural experience analysis model is applicable is a cross-regional cultural experience, the training quantity is set to a second value; Wherein, the first value is greater than the second value.

[0039] As mentioned above, the present invention sets the cross-cultural experience type as a cross-national cultural experience or a cross-regional cultural experience. For the former, customers generally have less understanding of the culture of other countries, and the first cross-cultural experience data determined by the cross-cultural experience analysis model is more likely to be incompatible with the customer, resulting in the customer's experience feedback data easily containing negative content. Therefore, when the present invention sets the cross-cultural experience analysis model to be applicable to cross-national cultural experience, more training data is used to self-train the cross-cultural experience analysis model, so that the cross-cultural experience analysis model obtained by training can achieve a more comprehensive understanding of the customer's demand rules, thereby outputting more accurate cross-cultural experience data. For the latter, customers generally have more understanding of the culture of different regions in the country (mainly compared to cross-national culture), and the first data set obtained through the aforementioned multiple data sources can achieve a relatively sufficient training of the cross-cultural experience analysis model. Therefore, when the cross-cultural experience analysis model is applicable to cross-regional cultural experience, the present invention sets the use of less training data to self-train the cross-cultural experience analysis model, which can effectively reduce the data load and training time of self-training and improve the efficiency of self-training. For example, the first value is 500 and the second value is 200.

[0040] An embodiment of the present invention further discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the method described in the above embodiment.

[0041] The embodiment of the present invention further discloses a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in the above embodiment is executed.

[0042] An embodiment of the present invention further discloses a computer program product, which includes computer program instructions. When the computer program instructions are executed by a processor of an electronic device, any of the above methods is implemented.

[0043] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0044] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable load balancing device, so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0045] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0046] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0047] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A Metaverse Intelligent Interactive Training System, applied to an intelligent agent, characterized in that: The system comprises a first training module, a first providing module, a second training module, and a second providing module; The first training module is used to collect historical data related to cross-cultural experience, construct a first data set based on the historical data, and use the first data set to fine-tune the BERT model to obtain a cross-cultural experience analysis model; The first providing module is used to provide first cross-cultural experience data for the first customer in a virtual space built based on the metaverse using the cross-cultural experience analysis model, and collect experience feedback data of the first customer; The second training module is used to construct a second data set according to the experience feedback data and the first cross-cultural experience data, and use the second data set to iteratively train the cross-cultural experience analysis model; The second providing module is used to generate second cross-cultural experience data for a second customer using the iteratively trained cross-cultural experience analysis model, and present the second cross-cultural experience data to the second customer in a virtual space.

2. A metaverse intelligent interactive training method based on the system of claim 1, applied to an intelligent agent, characterized in that: The steps include: Collect historical data related to cross-cultural experience, construct a first data set based on the historical data, and use the first data set to fine-tune the BERT model to obtain a cross-cultural experience analysis model; Using the cross-cultural experience analysis model to provide first cross-cultural experience data for a first customer in a virtual space built based on the metaverse, and collecting experience feedback data from the first customer; constructing a second data set according to the experience feedback data and the first cross-cultural experience data, and iteratively training the cross-cultural experience analysis model using the second data set; The iteratively trained cross-cultural experience analysis model is used to generate second cross-cultural experience data for the second customer, and the data is presented to the second customer in the virtual space.

3. A metaverse intelligent interactive training method according to claim 2, characterized in that: Collecting historical data related to cross-cultural experience, and constructing a first data set based on the historical data, including: Determine several data source topics related to cross-cultural experience, and obtain the historical data related to cross-cultural experience by web crawling and manual collection; The historical data is classified and processed according to the population type to obtain several groups of related data, each group of related data includes cross-cultural data and population type labels, and all the related data are constructed as the first data set.

4. A metaverse intelligent interactive training method according to claim 3, characterized in that: Using the cross-cultural experience analysis model to provide first cross-cultural experience data to a first customer in a virtual space built based on the metaverse, and collecting experience feedback data from the first customer, including: The cross-cultural experience analysis model receives the target culture type and customer-related data of the first customer, and calls the customer analysis model to perform in-depth analysis on the customer-related data to obtain a population type label of the first customer; Searching for cross-cultural data based on the first customer's population type label and the target culture type, performing format conversion processing on the cross-cultural data to obtain the first cross-cultural experience data, and providing the first cross-cultural experience data to the first customer in a virtual space constructed based on the metaverse; In the process of providing the first cross-cultural experience data, first experience feedback data of the first customer is obtained through voice recognition, motion and expression capture technology; and after the first cross-cultural experience data is provided, the survey questionnaire data of the first customer is received and semantically analyzed to obtain second experience feedback data, and the first experience feedback data and the second experience feedback data are used as the experience feedback data.

5. A metaverse intelligent interactive training method according to claim 4, characterized in that: The customer analysis model is called to perform in-depth analysis on the customer-related data to obtain a population type label of the first customer, including: The customer association data and population type classification rules are input into the BERT model, and the BERT model outputs the population type label of the first customer obtained after semantic recognition and classification prediction; wherein the customer association data includes the identity information and network publishing information of the first customer, and the identity information includes education level and job type.

6. A Metaverse Intelligent Interaction Training Method according to claim 5, characterized in that: Constructing a second data set according to the experience feedback data and the first cross-cultural experience data, and iteratively training the cross-cultural experience analysis model using the second data set, including: Determine the type of cross-cultural experience to which the cross-cultural experience analysis model is applicable, and determine the amount of training according to the type of cross-cultural experience; wherein the type of cross-cultural experience includes cross-national cultural experience and cross-regional cultural experience; The experience feedback data and the first cross-cultural experience data are constructed into a piece of training data and are input into the second data set. When the amount of training data in the second data set reaches the training quantity, the cross-cultural experience analysis model is iteratively trained using the second data set.

7. A Metaverse Intelligent Interaction Training Method according to claim 6, characterized in that: The amount of training will be determined based on the type of cross-cultural experience being described, and may include: If the cross-cultural experience type to which the cross-cultural experience analysis model is applicable is a cross-country cultural experience, the training quantity is set to a first value; if the cross-cultural experience type to which the cross-cultural experience analysis model is applicable is a cross-regional cultural experience, the training quantity is set to a second value; Wherein, the first value is greater than the second value.

8. An electronic device, comprising: A memory storing executable program code; A processor coupled to the memory; characterized in that: the processor calls the executable program code stored in the memory to execute the method according to any one of claims 2-7.

9. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 2 to 7 is executed.

10. A computer program product, comprising computer program instructions, characterized in that: When the computer program instructions are executed by a processor of an electronic device, the method according to any one of claims 2 to 7 is implemented.

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