Metacosm tourism interaction environment regulation and control method and system based on AI solution mechanism

Abstract

The invention belongs to the technical field of computers and artificial intelligence, and provides a meta-universe tourism interaction environment regulation and control method and system based on an AI solution mechanism. The method comprises the following steps: acquiring a plurality of virtual images configured in a visual field area of a target user; performing deep analysis on the historical tourism data to obtain a plurality of tourism interest labels of the target user; light features are extracted from the real light environment data corresponding to the view area; analyzing and processing the light characteristics and the tourism interest labels by using a regulation and control module to obtain environment follow-up coefficients of the virtual images; and regulating and controlling each virtual image according to the environment follow-up coefficient. According to the method and the device, different response degrees of the virtual images to the light change of the real world are controlled, so that some unnecessary virtual images can be fused with the real world to a higher degree, the number of obvious virtual images observed by a target user is reduced, and the experience of the target user in viewing scenery is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer and artificial intelligence technology, and in particular to a method and system for regulating a metaverse tourism interactive environment based on an AI solution mechanism. Background Art

[0002] Metaverse is a new type of Internet application and social form that integrates multiple new technologies and provides an immersive experience based on extended reality technology. Applying Metaverse technology to tourism scenes will bring tourists a better tourism experience. For example, through VR glasses and helmets, tourists can see virtual images projected in real sightseeing scenes. By interacting with these virtual images, tourists can obtain various service information such as scenic spot introductions, sightseeing questions and answers, games / check-in rewards, etc.

[0003] The existing technologies all use the same high brightness, high virtuality, etc. to uniformly process virtual images, so that these virtual images are more prominent than real objects in the scene, and tourists can find these virtual images more clearly. However, the disadvantage of such a setting is that there are too many virtual images in the tourists' field of vision, and these virtual images are too prominent, which will affect the tourists' appreciation of the real scenery of the scenic spot. The present invention aims to improve this technical problem. Summary of the invention

[0004] In order to solve at least one of the above technical problems, the present invention specifically provides a metaverse tourism interactive environment control method, system, electronic device, computer storage medium and computer program product based on AI solution mechanism.

[0005] The present invention provides a method for regulating the interactive environment of metaverse tourism based on an AI solution mechanism, comprising the following steps:

[0006] Determine a field of view of a target user, and obtain a plurality of virtual images configured in the field of view;

[0007] Acquire historical travel data of the target user, and perform in-depth analysis on the historical travel data to obtain several travel interest tags of the target user;

[0008] Acquire real light environment data corresponding to the visual field area, and extract light features from the real light environment data;

[0009] The light characteristics and the tourism interest tags are analyzed and processed by a control module to obtain an environmental following coefficient of each virtual image; wherein the environmental following coefficient represents the degree of proximity between the visual change response value of the virtual image to the real light environment data and the theoretical value of the visual change response; and the control module is configured with an AI solution mechanism;

[0010] Each of the virtual images is regulated according to the environment following coefficient.

[0011] Furthermore, the acquiring of the historical travel data of the target user and performing in-depth analysis on the historical travel data to obtain a number of travel interest tags of the target user include:

[0012] Acquire travel-related data from multiple platforms corresponding to the target user through web crawling technology, and integrate the data into the historical travel data;

[0013] The historical tourism data is imported into a fine-tuned and trained GPT analysis model, and the GPT analysis model outputs a number of tourism interest tags.

[0014] Furthermore, the acquiring of real light environment data corresponding to the field of view area and extracting light features from the real light environment data includes:

[0015] Using a high-definition camera to shoot a first high-definition image, and extracting second high-definition image data corresponding to the field of view area from the first high-definition image;

[0016] Determine the sub-region images corresponding to each of the virtual images in the second high-definition image data, identify the light intensity of the real light environment data contained in each of the sub-region images, and pair each of the light intensities with the corresponding virtual images to obtain the light characteristics.

[0017] Furthermore, the use control module analyzes and processes the light characteristics and each of the tourism interest tags to obtain the environmental following coefficient of each of the virtual images, including:

[0018] The light characteristics and the tourism interest tags are input into the control module, and the control module calls the AI ​​solution mechanism to implement the following processing flow:

[0019] Performing matching analysis on each of the tourism interest tags and each of the virtual images to obtain a plurality of first virtual images that match each of the tourism interest tags and a plurality of second virtual images that do not match each of the tourism interest tags;

[0020] The environment following coefficient of each of the first virtual images is set to a first value, and a second value corresponding to the environment following coefficient of each of the second virtual images is calculated according to the light characteristics; wherein the first value is lower than the second value.

[0021] Furthermore, the calculating according to the light characteristics to obtain a second value corresponding to the environment following coefficient of each second virtual image includes:

[0022] Calculate a third value corresponding to the environment following coefficient of each second virtual image according to the light characteristics;

[0023] Calculate the ratio of the target user's real field of view to the field of view, and correct the third value to the second value according to the ratio; wherein the real field of view is determined according to the eyesight of the target user, and the field of view is determined according to the openness of the tour area.

[0024] Further, the correcting the third value to the second value according to the ratio includes:

[0025] Dividing each of the second virtual images into a first type of second virtual image located in the real visual field area and a second type of second virtual image not located in the real visual field area based on the configuration position of each of the second virtual images;

[0026] The third value corresponding to the first type second virtual image is corrected according to the ratio and a ratio coefficient greater than 1 to obtain the corresponding second value; and the third value corresponding to the second type second virtual image is corrected according to the ratio and a ratio coefficient less than 1 to obtain the corresponding second value.

[0027] The present invention also provides a metaverse tourism interactive environment control system based on an AI solution mechanism, the system comprising a first processing module, a second processing module, a third processing module, a fourth processing module, and a control module;

[0028] The first processing module is used to determine the visual field area of ​​the target user and obtain a plurality of virtual images configured in the visual field area;

[0029] The second processing module is used to obtain the historical travel data of the target user, and perform in-depth analysis on the historical travel data to obtain a number of travel interest tags of the target user;

[0030] The third processing module is used to obtain real light environment data corresponding to the visual field area, and extract light features from the real light environment data;

[0031] The fourth processing module is used to use the control module to analyze and process the light characteristics and each of the tourism interest tags to obtain the environment following coefficient of each of the virtual images; wherein the environment following coefficient refers to the degree of deviation between the visual change response value of the virtual image to the real light environment data and the visual change response theoretical value; and the control module is configured with an AI solution mechanism;

[0032] The control module is used to control each of the virtual images according to the environment following coefficient.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] The beneficial effect of the present invention is that by controlling each virtual image to respond to light changes in the real world to different degrees, some unnecessary virtual images can be integrated with the real world to a higher degree, thereby reducing the number of significant virtual images observed by the target user, thereby improving the target user's viewing experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] 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.

[0038] Figure 1 It is a flow chart of a method for controlling the interactive environment of metaverse tourism based on an AI solution mechanism disclosed in an embodiment of the present invention;

[0039] Figure 2 is a schematic diagram of the relative relationship between the real visual field area and the visual field area of ​​the target user disclosed in an embodiment of the present invention;

[0040] Figure 3 It is a structural schematic diagram of a metaverse tourism interactive environment control system based on an AI solution mechanism disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] 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.

[0042] See also Figure 1 As shown, the embodiment of the present invention provides a method for regulating the interactive environment of metaverse tourism based on an AI solution mechanism, comprising the following steps:

[0043] Determine a field of view of a target user, and obtain a plurality of virtual images configured in the field of view;

[0044] Acquire historical travel data of the target user, and perform in-depth analysis on the historical travel data to obtain several travel interest tags of the target user;

[0045] Acquire real light environment data corresponding to the visual field area, and extract light features from the real light environment data;

[0046] The light characteristics and the tourism interest tags are analyzed and processed by a control module to obtain an environmental following coefficient of each virtual image; wherein the environmental following coefficient represents the degree of proximity between the visual change response value of the virtual image to the real light environment data and the theoretical value of the visual change response; and the control module is configured with an AI solution mechanism;

[0047] Each of the virtual images is regulated according to the environment following coefficient.

[0048] In the present invention, the target user wears VR glasses and helmets and other equipment in the tour area, that is, accesses the metaverse system. VR glasses and helmets can detect the eye orientation of the target user, thereby determining its field of view. At the same time, virtual images are pre-configured at multiple points in the tour area, and these virtual images allow the target user to interact with them to obtain corresponding service information. The user's historical travel data is obtained, and several travel interest tags of the target user can be obtained by in-depth analysis of its historical travel data. Each travel interest tag reflects what types of things the target user is interested in in the tour area, such as introductions to famous people, introductions to tour routes, guidance for rest / shopping areas, and rewards for tours and check-ins. In addition, the real light environment data in the field of view is also obtained. The real light environment data can be extracted from the real scene image taken by the high-definition camera on the VR glasses or helmet, and the light characteristics of each area are specifically extracted from the real light environment data. The light characteristics and each travel interest tag are input into the control module for analysis and processing, and the environmental following coefficient of each virtual image can be obtained. The environmental following coefficient will affect the response degree of the virtual image seen by the user to the real light change, that is, the visual change response value. Therefore, by controlling the different degrees of response of each virtual image to the light changes in the real world, it is possible to achieve a higher degree of integration of some unnecessary virtual images with the real world, thereby reducing the number of significant virtual images observed by the target user, thereby improving the target user's viewing experience.

[0049] It should be noted that objects in the real world will undergo visual changes as light changes. For example, when light shines on an object, it will become brighter, and objects in the shadow will become darker. The degree of brightening and darkening can be measured in advance, that is, the theoretical value of the visual change response is obtained; however, the present invention sets an environmental following coefficient for each virtual image. Under the intervention of the environmental following coefficient, different virtual images have different degrees of response to light changes. The degree of response refers to the degree of proximity between the actual response value of the virtual image and the theoretical value of the response. The higher the degree of proximity, the higher the environmental following coefficient, and vice versa.

[0050] Furthermore, the acquiring of the historical travel data of the target user and performing in-depth analysis on the historical travel data to obtain a number of travel interest tags of the target user include:

[0051] Acquire travel-related data from multiple platforms corresponding to the target user through web crawling technology, and integrate them into the historical travel data;

[0052] The historical tourism data is imported into a fine-tuned and trained GPT analysis model, and the GPT analysis model outputs a number of tourism interest tags.

[0053] In this embodiment, the target user needs to submit relevant data information of himself when using the Metaverse system, such as name, mobile phone number, email address, etc. Based on these data, the target user's account and the relevant information published in the account can be crawled from multiple other platforms. For example, the target user publishes his travel experiences in multiple places on Weibo and Xiaohongshu, which contain text, videos, images, etc. By integrating the above data crawled from each platform, historical travel data can be obtained. Then, the preset GPT analysis model is used to perform semantic analysis and semantic classification on the crawled historical travel data, and multiple travel interest tags of the target user can be obtained, including the aforementioned introduction of famous people, introduction of tour routes, guide to rest / shopping areas, and rewards for sightseeing / punching in.

[0054] Among them, GPT (Generative Pre-traveled Transformer) is a language model based on artificial intelligence, which uses deep learning technology and can achieve deep semantic analysis and understanding through training of a large amount of text data. The present invention uses GPT to construct the above-mentioned analysis model to extract the travel interest tags of the target user. In addition, the GPT analysis model of the present invention can be obtained by fine-tuning the existing general GPT model, that is, using the training data corresponding to the travel interest tags to train the general GPT model to a certain extent (different from the deep training of the traditional analysis model), the general GPT model can be fine-tuned to the GPT analysis model of the present invention.

[0055] Furthermore, the acquiring of real light environment data corresponding to the field of view area and extracting light features from the real light environment data includes:

[0056] Using a high-definition camera to shoot a first high-definition image, and extracting second high-definition image data corresponding to the field of view area from the first high-definition image;

[0057] Determine the sub-region images corresponding to each of the virtual images in the second high-definition image data, identify the light intensity of the real light environment data contained in each of the sub-region images, and pair each of the light intensities with the corresponding virtual images to obtain the light characteristics.

[0058] In this embodiment, the high-definition camera configured on the VR glasses or helmet can capture the first high-definition image in front, and then extract the second high-definition image data belonging to the field of view of the target user. Then, according to the configuration position (predicted) of each virtual image, the corresponding sub-region image is determined from the second high-definition image data. Specifically, the VR glasses or helmet will include a positioning device. The positioning information of the target user obtained by the positioning device and the predicted configuration position of each virtual image can be converted to obtain the sub-region image corresponding to each virtual image in the second high-definition image data. Finally, the light intensity of each sub-region image is identified, and the identified light intensities and the corresponding virtual images are paired to obtain the above-mentioned light characteristics.

[0059] The light characteristics include the light intensity at multiple different moments, and may also include the difference between adjacent light intensities.

[0060] Furthermore, the use control module analyzes and processes the light characteristics and each of the tourism interest tags to obtain the environmental following coefficient of each of the virtual images, including:

[0061] The light characteristics and the tourism interest tags are input into the control module, and the control module calls the AI ​​solution mechanism to implement the following processing flow:

[0062] Performing matching analysis on each of the tourism interest tags and each of the virtual images to obtain a plurality of first virtual images that match each of the tourism interest tags and a plurality of second virtual images that do not match each of the tourism interest tags;

[0063] The environment following coefficient of each of the first virtual images is set to a first value, and a second value corresponding to the environment following coefficient of each of the second virtual images is calculated according to the light characteristics; wherein the first value is lower than the second value.

[0064] In this embodiment, an AI solution mechanism is configured in the control module of the present invention. The AI ​​solution mechanism can be nested in the core processing module of the control module, or can be set in other non-core processing models, and can be called when needed. Specifically, the control module calls the AI ​​solution mechanism to analyze the environmental tracking coefficient of each virtual image: first, each tourism interest tag is matched and analyzed with each virtual image (each virtual image is also configured with a corresponding tag), that is, it is determined which of the tourism interest tags of the target user correspond to the virtual image in the field of view, so as to obtain a number of first virtual images that match each tourism interest tag, and a number of second virtual images that do not match each tourism interest tag. For each first virtual image that the target user is interested in, its environmental tracking coefficient is set to a low value, that is, the first virtual image seen by the target user in the metaverse does not change with the change of ambient light or changes in response to a very slight degree, so as to ensure that the virtual image that the target user is interested in is always visible to the target user in an obvious and prominent form. For the second virtual images that the target user is not interested in, the environment following coefficient of each second virtual image is calculated according to the corresponding light characteristics. The environment following coefficient is higher than that of the aforementioned first virtual image. That is, the second virtual images that the target user is not interested in are set to respond to the light changes in the real environment in a more active manner, so that they can be visually viewed by the target user in a less obvious and less prominent form.

[0065] Through the above-mentioned control, it can be achieved that the field of view seen by the target user only contains a few virtual images that the user is interested in. These virtual images are the ones that the user is likely to need to interact with, and other virtual images that are not of interest become more integrated with the environment by strongly following the ambient light. For example, virtual image A corresponds to the travel interest tag of the target user, and virtual image B does not belong to the travel interest tag of the target user. When the real ambient light at the virtual image A changes, virtual image A does not change its brightness, and when the real ambient light at the virtual image B changes, virtual image B responds to the change in ambient light to a corresponding degree according to the determined second value. For example, if the ambient light intensity increases by 1 unit, the brightness of virtual image B increases by 0.5 units.

[0066] In addition to adjusting the brightness, the color, texture, etc. of the virtual image can also be adjusted at the same time. For example, the color and texture of the virtual image can be adjusted to be closer to the real environment of the area.

[0067] Furthermore, the calculating according to the light characteristics to obtain a second value corresponding to the environment following coefficient of each second virtual image includes:

[0068] Calculate a third value corresponding to the environment following coefficient of each second virtual image according to the light characteristics;

[0069] Calculate the ratio of the target user's real field of view to the field of view, and correct the third value to the second value according to the ratio; wherein the real field of view is determined according to the eyesight of the target user, and the field of view is determined according to the openness of the tour area.

[0070] In this embodiment, a comparison table of light characteristics and the environment tracking coefficient of the second virtual image is pre-set, and the comparison table is provided with multiple sets of one-to-one matching light intensity / light intensity change value-third value. When the ambient light changes, the third value can be determined by looking up the table. Figure 2 As shown, the present invention also considers the deviation between the real visual field range based on the eyesight of the target user and the aforementioned visual field range. The aforementioned visual field range is determined according to the openness of the tourist area. When the tourist area is an ancient town, its openness is low, when the tourist area is a famous mountain, its openness is medium, when the tourist area is a seaside, its openness is high, and when the tourist area is indoor (such as a museum, a science and technology museum, and a celebrity's former residence), its openness is even lower. The openness of the above different tourist areas can be preset. The above deviation degree is described by the ratio of the real visual field range of the target user to the visual field range. A correction coefficient can be determined based on the ratio, and the correction coefficient is used to correct the third value obtained by the above comparison, thereby obtaining the final second value.

[0071] The target user's vision is preset or periodically updated by the target user. The better the vision, the larger the corresponding real visual field, and vice versa.

[0072] Further, the correcting the third value to the second value according to the ratio includes:

[0073] Dividing each of the second virtual images into a first type of second virtual image located in the real visual field area and a second type of second virtual image not located in the real visual field area based on the configuration position of each of the second virtual images;

[0074] The third value corresponding to the first type second virtual image is corrected according to the ratio and a ratio coefficient greater than 1 to obtain the corresponding second value; and the third value corresponding to the second type second virtual image is corrected according to the ratio and a ratio coefficient less than 1 to obtain the corresponding second value.

[0075] In this embodiment, after determining the real field of view of the target user according to the target user's vision, it is possible to further know whether the virtual images that are not of interest to the target user are located within the real field of view, that is, to obtain the above-mentioned first-category second virtual images and second-category second virtual images.

[0076] For the first type of second virtual image, the third value obtained by looking up the table is directly corrected to the second value according to the ratio obtained above. When the ratio obtained above is larger, it means that the target user's vision is more compatible with the openness of the current tour scene, that is, the target user can see more virtual images in the tour scene. At this time, it is necessary to set the environmental tracking coefficient of the first type of second virtual image located in the real visual field area to be larger (ratio multiplied by a ratio coefficient greater than 1) to better integrate it with the things in the real scene, so that the target user is less likely to observe them. For the second type of second virtual image that is not located in the real visual field area, a ratio coefficient less than 1 (ratio multiplied by a ratio coefficient less than 1) is used to reduce the aforementioned ratio, and then the reduced ratio is used to correct the third value corresponding to the second type of second virtual image to a smaller second value. Since the second type of second virtual images that are not located in the real field of view are beyond the visual range of the target user, the target user is actually unable to perceive their existence. Therefore, at this time, these second type of second virtual images are set to be less responsive to changes in ambient light (or even unresponsive like the first virtual images). Such a setting can reduce the data processing load.

[0077] See also Figure 3 As shown, a metaverse tourism interactive environment control system based on an AI solution mechanism according to an embodiment of the present invention comprises a first processing module, a second processing module, a third processing module, a fourth processing module, and a control module;

[0078] The first processing module is used to determine the visual field area of ​​the target user and obtain a plurality of virtual images configured in the visual field area;

[0079] The second processing module is used to obtain the historical travel data of the target user, and perform in-depth analysis on the historical travel data to obtain a number of travel interest tags of the target user;

[0080] The third processing module is used to obtain real light environment data corresponding to the visual field area, and extract light features from the real light environment data;

[0081] The fourth processing module is used to use the control module to analyze and process the light characteristics and each of the tourism interest tags to obtain the environment following coefficient of each of the virtual images; wherein the environment following coefficient refers to the degree of deviation between the visual change response value of the virtual image to the real light environment data and the theoretical value of the visual change response; and the control module is configured with an AI solution mechanism;

[0082] The control module is used to control each of the virtual images according to the environment following coefficient.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] Various embodiments 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 embodiments may include: being implemented in one or more computer programs, which may be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general programmable processor, which may 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.

[0087] 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.

[0088] 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.

[0089] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0090] 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.

[0091] 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 method for regulating the interactive environment of metaverse tourism based on AI solution mechanism, characterized in that: The steps include: Determine a field of view of a target user, and obtain a plurality of virtual images configured in the field of view; Acquire historical travel data of the target user, and perform in-depth analysis on the historical travel data to obtain several travel interest tags of the target user; Acquire real light environment data corresponding to the visual field area, and extract light features from the real light environment data; The light characteristics and the tourism interest tags are analyzed and processed by a control module to obtain an environmental following coefficient of each virtual image; wherein the environmental following coefficient represents the degree of proximity between the visual change response value of the virtual image to the real light environment data and the theoretical value of the visual change response; and the control module is configured with an AI solution mechanism; Each of the virtual images is regulated according to the environment following coefficient.

2. According to claim 1, a method for controlling the interactive environment of metaverse tourism based on an AI solution mechanism is characterized by: Acquire the historical travel data of the target user, and perform in-depth analysis on the historical travel data to obtain several travel interest tags of the target user, including: Acquire travel-related data from multiple platforms corresponding to the target user through web crawling technology, and integrate the data into the historical travel data; The historical tourism data is imported into a fine-tuned and trained GPT analysis model, and the GPT analysis model outputs a number of tourism interest tags.

3. According to claim 1, a method for controlling the interactive environment of metaverse tourism based on an AI solution mechanism is characterized by: Acquiring real light environment data corresponding to the field of view area, and extracting light features from the real light environment data, including: Using a high-definition camera to shoot a first high-definition image, and extracting second high-definition image data corresponding to the field of view area from the first high-definition image; Determine the sub-region images corresponding to each of the virtual images in the second high-definition image data, identify the light intensity of the real light environment data contained in each of the sub-region images, and pair each of the light intensities with the corresponding virtual images to obtain the light characteristics.

4. According to claim 3, a method for controlling the interactive environment of metaverse tourism based on an AI solution mechanism is characterized in that: The light characteristics and the tourism interest tags are analyzed and processed by using a control module to obtain the environmental following coefficient of each virtual image, including: The light characteristics and the tourism interest tags are input into the control module, and the control module calls the AI ​​solution mechanism to implement the following processing flow: Performing matching analysis on each of the tourism interest tags and each of the virtual images to obtain a plurality of first virtual images that match each of the tourism interest tags and a plurality of second virtual images that do not match each of the tourism interest tags; The environment following coefficient of each of the first virtual images is set to a first value, and a second value corresponding to the environment following coefficient of each of the second virtual images is calculated according to the light characteristics; wherein the first value is lower than the second value.

5. According to claim 4, a method for controlling the interactive environment of metaverse tourism based on an AI solution mechanism is characterized in that: Calculating and obtaining a second value corresponding to the environment following coefficient of each second virtual image according to the light characteristics includes: Calculate a third value corresponding to the environment following coefficient of each second virtual image according to the light characteristics; Calculate the ratio of the target user's real field of view to the field of view, and correct the third value to the second value according to the ratio; wherein the real field of view is determined according to the eyesight of the target user, and the field of view is determined according to the openness of the tour area.

6. The method for controlling the interactive environment of metaverse tourism based on AI solution mechanism according to claim 5 is characterized by: Correcting the third value to the second value according to the ratio includes: Dividing each of the second virtual images into a first type of second virtual image located in the real visual field area and a second type of second virtual image not located in the real visual field area based on the configuration position of each of the second virtual images; The third value corresponding to the first type second virtual image is corrected according to the ratio and the ratio coefficient greater than 1 to obtain the corresponding second value; and the third value corresponding to the second type second virtual image is corrected according to the ratio and the ratio coefficient less than 1 to obtain the corresponding second value.

7. A metaverse tourism interactive environment control system based on AI solution mechanism, the system comprising a first processing module, a second processing module, a third processing module, a fourth processing module, and a control module; characterized in that: The first processing module is used to determine the visual field area of ​​the target user and obtain a plurality of virtual images configured in the visual field area; The second processing module is used to obtain the historical travel data of the target user, and perform in-depth analysis on the historical travel data to obtain a number of travel interest tags of the target user; The third processing module is used to obtain real light environment data corresponding to the visual field area, and extract light features from the real light environment data; The fourth processing module is used to use the control module to analyze and process the light characteristics and each of the tourism interest tags to obtain the environment following coefficient of each of the virtual images; wherein the environment following coefficient refers to the degree of deviation between the visual change response value of the virtual image to the real light environment data and the visual change response theoretical value; and the control module is configured with an AI solution mechanism; The control module is used to control each of the virtual images according to the environment following coefficient.

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 1-6.

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 1 to 6 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 1 to 6 is implemented.

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