Cross-Cultural Experience Metaverse Interaction Training Method and System

Abstract

The present invention belongs to the fields of the metaverse and educational technology, and provides a cross-cultural experience metaverse interaction training method and system. The method includes: constructing a first data set based on historical data related to cross-cultural experience, and using the first data set to fine-tune and train a 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 constructed based on the metaverse, and collecting the experience feedback data of the first customer; constructing a second data set based on the experience feedback data and the first cross-cultural experience data, and using the second data set to iteratively train the cross-cultural experience analysis model; using the cross-cultural experience analysis model after iterative training to generate second cross-cultural experience data for a second customer and displaying it to the second customer in the virtual space. The present invention combines cross-cultural experience with metaverse technology, and customers can obtain a variety of cross-cultural immersive experiences online.

Description

Technical Field

[0001] The present invention relates to the fields of the metaverse and educational technology, and more particularly, to a cross-cultural experience metaverse interaction training method and system. Background Art

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

[0003] The present invention is designed to combine cross-cultural experience with the metaverse, thereby more conveniently providing diverse cross-cultural experiences for more people. Summary of the Invention

[0004] To solve at least one of the above technical problems, the present invention specifically provides a cross-cultural experience metaverse interaction training method, system, electronic device, computer storage medium, and computer program product.

[0005] The present invention provides a cross-cultural experience metaverse interaction training method, applied to an intelligent agent, including the following steps:

[0006] 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 and train a BERT model to obtain a cross-cultural experience analysis model;

[0007] Use the cross-cultural experience analysis model to provide first cross-cultural experience data for a first customer in a virtual space constructed based on the metaverse, and collect the experience feedback data of the first customer;

[0008] Construct a second data set based on 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;

[0009] Use the cross-cultural experience analysis model after iterative training to generate second cross-cultural experience data for a second customer and display it to the second customer in the virtual space.

[0010] Further, the collecting historical data related to cross-cultural experience and constructing a first data set based on the historical data includes:

[0011] Determine several data source themes related to cross - cultural experiences, and obtain the historical data related to cross - cultural experiences through web crawling and manual collection;

[0012] Classify and process the historical data according to population types to obtain several groups of associated data. Each group of associated data includes cross - cultural data and population type labels, and construct all the associated data into the first data set.

[0013] Furthermore, use the cross - cultural experience analysis model to provide the first cross - cultural experience data for the first customer in a virtual space constructed based on the metaverse, and collect the experience feedback data of the first customer, including:

[0014] The cross - cultural experience analysis model receives the target cultural type and customer - associated data of the first customer, and calls the customer analysis model to deeply analyze the customer - associated data to obtain the population type label of the first customer;

[0015] Search for cross - cultural data according to the population type label and the target cultural type of the first customer, perform format conversion processing on the cross - cultural data to obtain the first cross - cultural experience data, and provide the first cross - cultural experience data for the first customer in a virtual space constructed based on the metaverse;

[0016] During the process of providing the first cross - cultural experience data, obtain the first experience feedback data of the first customer through speech recognition, motion, and expression capture technologies, and after the first cross - cultural experience data is provided, receive and perform semantic analysis on the survey questionnaire data of the first customer to obtain the second experience feedback data, and use the first experience feedback data and the second experience feedback data as the experience feedback data.

[0017] Furthermore, the calling of the customer analysis model to deeply analyze the customer - associated data to obtain the population type label of the first customer includes:

[0018] Input the customer - associated data and population type classification rules into the BERT model, and the BERT model outputs the population type label of the first customer obtained after semantic recognition and classification prediction; among them, the customer - associated data includes the identity information and network publishing information of the first customer, and the identity information includes the education level and work type.

[0019] Furthermore, 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, including:

[0020] Determine the types of cross - cultural experiences applicable to the cross - cultural experience analysis model, and determine the number of trainings according to the types of cross - cultural experiences; wherein, the types of cross - cultural experiences include cross - national cultural experiences and cross - regional cultural experiences.

[0021] Construct the experience feedback data and the first cross - cultural experience data into a piece of training data, and input it into the second data set. When the amount of training data in the second data set reaches the number of trainings, use the second data set to iteratively train the cross - cultural experience analysis model.

[0022] Furthermore, the determining the number of trainings according to the types of cross - cultural experiences includes:

[0023] If the type of cross - cultural experience applicable to the cross - cultural experience analysis model is a cross - national cultural experience, set the number of trainings to a first value; if the type of cross - cultural experience applicable to the cross - cultural experience analysis model is a cross - regional cultural experience, set the number of trainings to a second value;

[0024] Wherein, the first value is greater than the second value.

[0025] The present invention also provides a cross - cultural experience metaverse interaction training system, which is applied to an intelligent agent. The system includes a first training module, a first providing module, a second training module, and a second providing module;

[0026] The first training module is used to collect historical data related to cross - cultural experiences, construct a first data set according to the historical data, and use the first data set to fine - tune and train a BERT model to obtain a cross - cultural experience analysis model;

[0027] The first providing module is used to use the cross - cultural experience analysis model to provide first cross - cultural experience data for a first customer in a virtual space constructed based on the metaverse, and collect the experience feedback data of the first customer;

[0028] 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;

[0029] The second providing module is used to use the cross - cultural experience analysis model after iterative training to generate second cross - cultural experience data for a second customer and display it to the second customer in the virtual space.

[0030] The present invention also provides an electronic device, including: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory and executes the method as described in any one of the preceding items.

[0031] The present invention also provides a computer storage medium, on which a computer program is stored, and when the computer program is run by a processor, it executes the method described in any one of the above.

[0032] The present invention also provides a computer program product, which contains computer program instructions, and when the computer program instructions are executed by a processor of an electronic device, the method described in any one of the above is implemented.

[0033] The beneficial effects of the present invention are as follows:

[0034] 1) The present invention organically combines cross-cultural experiences with metaverse technologies, enabling customers to obtain immersive cross-cultural experiences online and effectively reducing the costs of cross-cultural experiences.

[0035] 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 cross-cultural experience data for customers. Compared with the existing methods that can only provide fixed cross-cultural experience resources, it allows customers to obtain more personalized experiences.

[0036] 3) The present invention also sets up iterative upgrade training for the cross-cultural experience analysis model, thereby ensuring that the cross-cultural experience analysis model can always analyze and obtain the most accurate cross-cultural experience data, so as to provide the best cross-cultural experience for customers. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0038] Figure 1 is a schematic flowchart of a cross-cultural experience metaverse interaction training method disclosed in an embodiment of the present invention;

[0039] Figure 2 is a schematic structural diagram of an intelligent agent disclosed in an embodiment of the present invention;

[0040] Figure 3 is a schematic structural diagram of a cross-cultural experience metaverse interaction training system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The exemplary embodiments of the present disclosure will be described below in conjunction with the accompanying drawings. Various details of the embodiments of the present disclosure are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, descriptions of well-known functions and structures are omitted in the following description for clarity and conciseness.

[0042] Referring to Figure 1 As shown, an embodiment of the present invention provides a cross-cultural experience metaverse interaction training method, which is applied to an intelligent agent and includes the following steps:

[0043] 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 and train a BERT model to obtain a cross-cultural experience analysis model;

[0044] Use the cross-cultural experience analysis model to provide first cross-cultural experience data for a first customer in a virtual space constructed based on the metaverse, and collect the experience feedback data of the first customer;

[0045] Construct a second data set based on 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;

[0046] Use the cross-cultural experience analysis model after iterative training to generate second cross-cultural experience data for a second customer and display it to the second customer in the virtual space.

[0047] A virtual space for cross-cultural experience is constructed based on the metaverse, and customers can enter this virtual space through virtual reality devices. The present invention configures an intelligent agent for this virtual space to provide cross-cultural experience data for the entering customers, and the data providing function is realized by a cross-cultural experience analysis model preset in the intelligent agent. Before the intelligent agent is arranged, first collect historical data related to cross-cultural experience and construct a first data set based on this, and use the first data set to fine-tune and train an existing BERT model to obtain a preliminary cross-cultural experience analysis model. Arrange the intelligent agent, use the preliminary cross-cultural experience analysis model to provide first cross-cultural experience data for the customers entering the virtual space, and at the same time collect the experience feedback data of the first customer. Based on the experience feedback data and the first cross-cultural experience data, a second data set can be constructed, and using this second data set to perform multiple iterative trainings on the cross-cultural experience analysis model, that is, the gradual upgrade of the cross-cultural experience analysis model is realized, so that more suitable cross-cultural experience data can be provided for the second customer and a better cross-cultural experience can be obtained.

[0048] It should be noted that the cross-cultural experience in the present invention includes both cross-national cultural experiences, such as cultural experiences of African culture, Southeast Asian culture, etc.; and cross-regional cultural experiences, such as cultural experiences of Jiangnan culture and Yunnan-Guizhou culture in China.

[0049] Further, collecting historical data related to cross-cultural experience and constructing a first data set according to the historical data includes:

[0050] Determining a number of data source themes related to cross-cultural experience, and obtaining the historical data related to cross-cultural experience through web crawling and manual collection methods;

[0051] Classifying the historical data by population type to obtain several groups of associated data. Each group of associated data includes cross-cultural data and population type labels, and constructing all the associated data into the first data set.

[0052] In this embodiment, a number of data source themes related to cross-cultural experience are artificially specified in advance, such as the Internet, newspapers, video data on various Internet platforms during holidays (such as celebration activity videos released on a certain video platform during the Chinese New Year), etc. Taking the above data source themes as the entry point, the historical data related to cross-cultural experience is crawled 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 methods, such as a large amount of paper data stored in libraries and archives. Integrating the data obtained through the above two channels can obtain the historical data.

[0053] Then, the obtained historical data is classified by population. There can be multiple criteria for population classification, such as classification according to educational level (below university level, university level, postgraduate level, etc.), classification according to work type (technical work, cultural work, planting / farming work, etc.), etc. After classification processing, multiple groups of associated data can be obtained. Each group of associated data includes cross-cultural data and population type labels, and each group of associated data represents the cross-cultural data that specific types of people are interested in. The population type labels therein are set by automatic labeling devices or manually. After processing such as deduplication and deletion of incorrect data for the sorted associated data, they are integrated into the first data set.

[0054] Further, 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 the experience feedback data of the first customer includes:

[0055] The cross-cultural experience analysis model receives the target cultural type and customer associated data of the first customer, and calls the customer analysis model to deeply analyze the customer associated data to obtain the population type label of the first customer;

[0056] Search for cross - cultural data based on the population type label of the first customer and the target cultural type, perform format conversion processing on the cross - cultural data to obtain the first cross - cultural experience data, and provide the first cross - cultural experience data for the first customer in the virtual space constructed based on the metaverse;

[0057] During the process of providing the first cross - cultural experience data, obtain the first experience feedback data of the first customer through speech recognition, motion, and facial expression capture technologies. After the first cross - cultural experience data is provided, receive and perform semantic analysis on the survey questionnaire data of the first customer to obtain the second experience feedback data, and use the first experience feedback data and the second experience feedback data as the experience feedback data.

[0058] In this embodiment, the cross - cultural experience analysis model can receive the target cultural type of the first customer and customer - related data input. The target cultural type can be manually input by the first customer, such as African culture, Jiangnan culture, etc., and the customer - related data can be obtained by retrieving according to the identity information of the first customer. Refer to Figure 2 As shown, a customer analysis model is also set in the intelligent agent. The cross - cultural experience analysis model can call this customer analysis model, and this model can deeply analyze the above - mentioned customer - related data to obtain the population type label of the first customer. Based on the determined population type label of the first customer and the specified target cultural type, the corresponding cross - cultural data can be searched. This search can be from the Internet or from a database connected to the intelligent agent. It should be noted that it is also necessary to convert the cross - cultural data obtained from the Internet to the adaptation format of the virtual space. Mainly, extract the cross - cultural data from the searched data, and then convert it into a virtual character image. The virtual character image outputs the cross - cultural data to the customer. For example, if the searched cross - cultural data is an opera, after conversion, an opera virtual character is obtained, and the customer interacts with this opera virtual character, and the opera virtual character performs this opera; and the cross - cultural experience data that has completed format conversion processing is stored in the database.

[0059] During the process of providing the first cross - cultural experience data for the first customer, the satisfaction degree of the first customer with the provided first cross - cultural experience data, that is, the first experience feedback data, can be obtained from the first customer's voice, body movements, and facial expressions; and semantic analysis of the survey questionnaire data feedback by the first customer afterwards can obtain the second experience feedback data. Integrate the first experience feedback data and the second experience feedback data into the experience feedback data, and this experience feedback data is used to construct the second data set and iteratively train the cross - cultural experience analysis model using the second data set.

[0060] It should be noted that the cross-cultural experience data includes data in various cultural display forms. For example, for opera culture, it includes singing scene data, costume data, opera history data, opera experience data, etc., and one or more of them are selected according to different population type labels of customers.

[0061] Further, the calling of the customer analysis model to deeply analyze the customer association data to obtain the population type label of the first customer includes:

[0062] Inputting the customer association data and the population type classification rules 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 the education level and work type.

[0063] In this embodiment, the present invention re-uses the existing BERT model without the need for fine-tuning training, that is, uses the original BERT model to perform semantic recognition and classification prediction on the customer association data and the population type classification rules, so as to obtain the population type label of the first customer. Among them, the population type classification rules are artificially set ways and standards for classifying customers, such as the aforementioned classification according to education level, classification according to work type, etc. The population type classification rules can guide the customer analysis model to accurately analyze the population type label of the first customer.

[0064] Customer association data refers to the identity information and network publishing information of the first customer. Among them, the identity information includes the education level and work type, and the network publishing information mainly refers to the cross-cultural related information published by the first customer on various network platforms. The customer analysis model can determine multiple general population type labels of the population with the same identity based on the identity information, and then select a more suitable population type label from the personalized hobbies of the first customer.

[0065] 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 constructs a customer analysis model based on the Transformer deep learning model, which can realize accurate semantic analysis and prediction of the population type label of the first customer.

[0066] Further, the construction of the second data set according to the experience feedback data and the first cross-cultural experience data, and the use of the second data set to iteratively train the cross-cultural experience analysis model includes:

[0067] Determine the types of cross-cultural experiences applicable to the cross-cultural experience analysis model, and determine the training quantity according to the types of cross-cultural experiences; wherein, the types of cross-cultural experiences include cross-country cultural experiences and cross-region cultural experiences;

[0068] Construct the experience feedback data and the first cross-cultural experience data into a piece of training data, and input it into the second data set. When the amount of training data in the second data set reaches the training quantity, use the second data set to perform iterative training on the cross-cultural experience analysis model.

[0069] 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. For this reason, the present invention sets the types of cross-cultural experiences to include the aforementioned cross-country cultural experiences and cross-region cultural experiences. Correspondingly, cross-cultural experience analysis models are configured for the above two types of cross-cultural experiences respectively. Determine the training quantity corresponding to each cross-cultural experience analysis model according to the types of cross-cultural experiences, so that when the amount of training data in the second data set reaches this training quantity, retraining of this cross-cultural experience analysis model can be started.

[0070] Further, the determining the training quantity according to the types of cross-cultural experiences includes:

[0071] If the type of cross-cultural experience applicable to the cross-cultural experience analysis model is a cross-country cultural experience, set the training quantity to a first value; if the type of cross-cultural experience applicable to the cross-cultural experience analysis model is a cross-region cultural experience, set the training quantity to a second value;

[0072] Wherein, the first value is greater than the second value.

[0073] As described above, the present invention sets the cross-cultural experience type as cross-country cultural experience or cross-regional cultural experience. For the former, customers generally have less understanding of the cultures of other countries, and the first cross-cultural experience data determined by the cross-cultural experience analysis model is more likely to be mismatched with customers, resulting in the experience feedback data of customers being prone to contain negative content. Therefore, when the cross-cultural experience analysis model of the present invention is applicable to cross-country cultural experience, more training data is used to self-train the cross-cultural experience analysis model. The cross-cultural experience analysis model obtained through such training can achieve a more thorough understanding of the demand patterns of customers, thereby outputting more accurate cross-cultural experience data. For the latter, customers generally have more understanding of the cultures of different regions within the country (mainly compared to cross-country cultures), and the first data set obtained through the aforementioned multiple data sources can already achieve 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 to use less training data to self-train the cross-cultural experience analysis model, which can effectively reduce the data load and training duration of self-training and improve the self-training efficiency. For example, the first value is 500 pieces, and the second value is 200 pieces.

[0074] Refer to Figure 3 As shown, a cross-cultural experience metaverse interaction training system according to an embodiment of the present invention is applied to an intelligent agent. The system includes a first training module, a first providing module, a second training module, and a second providing module;

[0075] The first training module is used to collect historical data related to cross-cultural experience, construct a first data set according to the historical data, and use the first data set to fine-tune and train a BERT model to obtain a cross-cultural experience analysis model;

[0076] The first providing module is used to use the cross-cultural experience analysis model to provide first cross-cultural experience data for a first customer in a virtual space constructed based on the metaverse and collect the experience feedback data of the first customer;

[0077] 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;

[0078] The second providing module is used to use the cross-cultural experience analysis model after iterative training to generate second cross-cultural experience data for a second customer and display it to the second customer in the virtual space.

[0079] An embodiment of the present invention also discloses an electronic device, including: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory and executes the method as described in the foregoing embodiment.

[0080] An embodiment of the present invention also discloses a computer storage medium, on which a computer program is stored, and when the computer program is run by a processor, it executes the method as described in the foregoing embodiment.

[0081] An embodiment of the present invention also discloses a computer program product, which contains computer program instructions, and when the computer program instructions are executed by a processor of an electronic device, they implement the method as described in any one of the above.

[0082] The various embodiments of the systems and technologies described above in this document 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 a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs, and the one or more computer programs can be executed and / or interpreted on a programmable system including at least one programmable processor. The programmable processor can be a dedicated or general programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0083] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable load balancing devices, so that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, executed partially on the machine and partially on a remote machine as an independent software package, or executed entirely on a remote machine or server.

[0084] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0085] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitation is made herein.

[0086] The above specific embodiments do not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A cross-cultural experience metaverse interaction training method, applied to an intelligent agent, characterized in that, It includes the following steps: Collect historical data related to cross-cultural experiences, construct a first data set based on the historical data, and use the first data set to fine-tune and train a BERT model to obtain a cross-cultural experience analysis model; Use 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 collect the experience feedback data of the first customer; Construct a second data set based on 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; Use the cross-cultural experience analysis model that has been iteratively trained to generate second cross-cultural experience data for a second customer and display it to the second customer in the virtual space; Collect historical data related to cross-cultural experiences, construct a first data set based on the historical data, including: Determine several data source topics related to cross-cultural experiences, and obtain the historical data related to cross-cultural experiences through web crawling and manual collection; Classify the historical data by population type to obtain several groups of associated data. Each group of associated data includes cross-cultural data and population type labels, and construct all the associated data into the first data set; Use 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 collect the experience feedback data of the first customer, including: The cross-cultural experience analysis model receives the target cultural type and customer associated data of the first customer, and calls the customer analysis model to deeply analyze the customer associated data to obtain the population type label of the first customer; Search for cross-cultural data according to the population type label and the target cultural type of the first customer, perform format conversion processing on the cross-cultural data to obtain the first cross-cultural experience data, and provide the first cross-cultural experience data for the first customer in a virtual space built based on the metaverse; Obtain the first experience feedback data of the first customer through voice recognition, action and expression capture technologies during the provision of the first cross-cultural experience data, and after the provision of the first cross-cultural experience data is completed, receive and perform semantic analysis on the survey questionnaire data of the first customer to obtain second experience feedback data, and use the first experience feedback data and the second experience feedback data as the experience feedback data.

2. The cross-cultural experience metaverse interaction training method according to claim 1, wherein: Call the customer analysis model to deeply analyze the customer associated data to obtain the population type label of the first customer, including: Input the customer associated data and the population type classification rules 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 associated data includes the identity information and network release information of the first customer, and the identity information includes the education level and work type.

3. The cross-cultural experience metaverse interaction training method according to claim 2, wherein: Construct a second data set based on 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, including: Determine the cross-cultural experience types applicable to the cross-cultural experience analysis model, and determine the training quantity according to the cross-cultural experience types; wherein, the cross-cultural experience types include cross-country cultural experience and cross-region cultural experience; Construct the experience feedback data and the first cross-cultural experience data into a piece of training data, and input it into the second data set. When the amount of training data in the second data set reaches the training quantity, use the second data set to iteratively train the cross-cultural experience analysis model.

4. A cross-cultural experience metaverse interaction training method according to claim 1, characterized in that: Determine the training quantity according to the cross-cultural experience types, including: If the cross-cultural experience type applicable to the cross-cultural experience analysis model is cross-country cultural experience, set the training quantity to a first value; if the cross-cultural experience type applicable to the cross-cultural experience analysis model is cross-region cultural experience, set the training quantity to a second value; Wherein, the first value is greater than the second value.

5. A cross-cultural experience metaverse interaction training system, applied to an intelligent agent, the system is based on the method described in any one of claims 1-4, and is characterized in that: 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 according to the historical data, and use the first data set to fine-tune and train the BERT model to obtain a cross-cultural experience analysis model; The first providing module is used to use the cross-cultural experience analysis model to provide first cross-cultural experience data for a first customer in a virtual space constructed based on the metaverse, and collect the 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 use the cross-cultural experience analysis model after iterative training to generate second cross-cultural experience data for a second customer and display it to the second customer in the virtual space.

6. 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 and executes the method according to any one of claims 1-4.

7. A computer storage medium, on which a computer program is stored, characterized in that: The computer program, when run by a processor, executes the method according to any one of claims 1-4.

8. A computer program product containing 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 1-4 is implemented.

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