VR intelligent advertisement putting method and system

Abstract

The invention relates to a VR intelligent advertisement putting method and system, and the method comprises the steps: employing a Pancake optical scheme to project a wide-viewing-angle image through a folding optical path, and combining with an LED to render a high-precision image, and constructing a VR advertisement scene; behavior data of the user in the VR environment are collected in real time, the behavior data comprise gestures, sight focuses and facial expressions, and a multi-dimensional behavior data set is formed; generating personalized advertisement content based on the user behavior data, and rendering the personalized advertisement content to a VR advertisement scene in real time; the user behavior data are integrated to construct a dynamic user interest model, the user behavior data are analyzed in real time, an advertisement putting strategy is dynamically adjusted, and accurate pushing of advertisement content is achieved. According to the method, the dynamic user interest model is constructed to dynamically adjust the putting strategy, immersive and personalized VR advertisement experience is provided, the accuracy and attraction of advertisements are improved, and the user participation degree is improved; and meanwhile, the advertisement putting effect is optimized through data analysis, so that the advertisement conversion rate is improved.

Description

Technical Field

[0001] The present invention relates to the field of VR advertising technology, and in particular to a VR smart advertising delivery method and system. Background Art

[0002] Although traditional advertising methods such as television advertising, newspaper advertising, and outdoor advertising have played a huge role in information dissemination, they have gradually failed to meet consumers' needs for interactivity and personalization.

[0003] The rapid development of virtual reality (VR) technology has brought new opportunities to the advertising industry. VR technology offers immersive experiences, allowing users to experience virtual scenes as if they were there, providing a new platform for advertising. However, some existing VR advertising systems have shortcomings in terms of display quality, failing to provide a sufficiently wide field of view and clear images, which hinders user immersion. Furthermore, in terms of interactivity, many VR advertising systems only support simple gesture recognition and are unable to accurately capture detailed information such as user eye movements and facial expressions. This results in an interactive experience that is less natural and in-depth, failing to meet users' expectations for high interactivity.

[0004] Therefore, it is necessary to provide a new VR smart advertising method and system. Summary of the Invention

[0005] Based on the above-mentioned problems existing in the prior art, the purpose of the embodiments of the present invention is to provide a VR intelligent advertising delivery method and system. The present invention provides an immersive and personalized VR advertising experience by dynamically adjusting the delivery strategy by constructing a dynamic user interest model, thereby improving the accuracy and attractiveness of advertisements and enhancing user engagement; at the same time, it optimizes the advertising delivery effect through data analysis, thereby improving the conversion rate of advertisements.

[0006] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is to provide a VR smart advertising method, comprising:

[0007] S1, using Pancake optical solution to project wide-viewing angle images through folded optical paths and combined with LED rendering of high-precision images to create VR advertising scenes;

[0008] S2 collects user behavior data in the VR environment in real time. The behavior data includes gestures, gaze focus, and facial expressions, forming a multi-dimensional behavior data set;

[0009] S3 generates personalized advertising content based on user behavior data and renders it into the VR advertising scene in real time;

[0010] S4 integrates user behavior data to build a dynamic user interest model, analyzes user behavior data in real time, dynamically adjusts advertising delivery strategies, and achieves accurate push of advertising content.

[0011] Furthermore, the Pancake optical solution effectively reduces the thickness and weight of the VR head display device by folding the optical path while providing a field of view of at least 90 degrees.

[0012] Furthermore, the LED adopts a Micro OLED display unit or a Micro LED display unit, which has high resolution, high contrast and fast response time, and is used to provide clear and vivid images to enhance the visual appeal of advertising content.

[0013] Furthermore, the real-time collection of user behavior data in the VR environment includes gestures, eye focus, and facial expressions, forming a multi-dimensional behavior data set, including:

[0014] Step S21, collecting user gesture data by fusing an inertial measurement unit and a capacitive sensor;

[0015] Step S22, using an eye tracking sensor to collect user's sight focus data;

[0016] Step S23, collecting the user's facial expression data through a camera, and recognizing the user's facial expression based on deep learning technology;

[0017] In step S24, the collected gesture, eye focus, and facial expression data are integrated to form a multi-dimensional behavioral data set, and metadata such as timestamp, user ID, and scene ID are added to each data sample to facilitate data association and analysis.

[0018] Furthermore, the method of generating personalized advertising content based on user behavior data and rendering it to the VR advertising scene in real time includes:

[0019] Step S31, using the K-Means clustering algorithm to analyze the multi-dimensional behavior data set to divide and group the user's behavior patterns;

[0020] Step S32: semantically interpret each cluster obtained by clustering, analyze the specific user interests and behavior characteristics corresponding to each cluster, and establish a mapping relationship between user interests and behaviors;

[0021] Step S33: Generate personalized advertising content based on the user interest preferences obtained through cluster analysis, and render it into the VR advertising scene in real time.

[0022] Furthermore, the K-Means clustering algorithm divides the data into K clusters through iterative optimization, so that the data similarity within each cluster is high, while the data similarity between different clusters is low. During the clustering process, the algorithm divides users with similar gestures, gaze focus, and facial expression characteristics into the same cluster based on the similarity of the data. Each cluster represents a specific comprehensive behavioral pattern, which is used to reflect the different interests and interaction preferences of users in the VR environment.

[0023] Furthermore, the integration of user behavior data to build a dynamic user interest model, real-time analysis of user behavior data, and dynamic adjustment of advertising delivery strategies to achieve accurate push of advertising content include:

[0024] Step S41: Based on the user behavior pattern and interest mapping relationship generated by cluster analysis, a long short-term memory network model is used to model the user's behavior data characteristics. By analyzing the dynamic changes of the user's behavior data in the VR scene, the user's interest tag weight is updated in real time to build a dynamic user interest model;

[0025] Step S42, cleaning and feature extraction of the user's gestures, eye focus, and facial expression data collected in real time;

[0026] Step S43: input the real-time features into a pre-trained multi-objective optimization model to dynamically generate an advertising delivery strategy;

[0027] In step S44, the optimized advertising content is pushed to the user end through real-time cloud rendering technology, and the delivery effect data is recorded simultaneously. The delivery strategy is further adjusted based on the evaluation of the delivery effect data.

[0028] A VR smart advertising delivery system, applied to the above-mentioned VR smart advertising delivery method, comprises:

[0029] The optical display module uses a Pancake optical solution to project wide-viewing angle images through a folded optical path and combines it with LEDs to render high-precision images, creating VR advertising scenes.

[0030] The interactive collection module is used to collect real-time behavioral data of users in the VR environment. The behavioral data includes gestures, eye focus, and facial expressions, forming a multi-dimensional behavioral data set;

[0031] The content generation module is used to generate personalized advertising content based on user behavior data and render it into the VR advertising scene in real time;

[0032] The data analysis module is used to integrate user behavior data to build a dynamic user interest model, analyze user behavior data in real time, dynamically adjust advertising delivery strategies, and achieve accurate push of advertising content.

[0033] The embodiment of the present invention further provides a network-side server, comprising:

[0034] At least one processor; and a memory in communication with the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-mentioned VR smart advertising delivery method.

[0035] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which implements the above-mentioned VR smart advertising delivery method when executed by a processor.

[0036] The beneficial effects of the present invention are as follows: a VR intelligent advertising delivery method of the present invention comprises: using a Pancake optical solution to project a wide-viewing angle image through a folded optical path and combining it with LED rendering of high-precision images to construct a VR advertising scene; real-time collection of user behavior data in the VR environment, the behavior data including gestures, eye focus, and facial expressions, to form a multi-dimensional behavior data set; generating personalized advertising content based on the user behavior data and rendering it into the VR advertising scene in real time; integrating the user behavior data to build a dynamic user interest model, analyzing the user behavior data in real time, and dynamically adjusting the advertising delivery strategy to achieve accurate push of advertising content. The VR intelligent advertising delivery method of the present invention uses a Pancake optical solution and a specific LED display unit to build a lightweight VR advertising scene with a wide viewing angle and high-precision images, and uses multiple sensors to collect and integrate user multi-dimensional behavior data in real time. Based on clustering algorithms and other methods, it mines user interest preferences, generates and pushes personalized advertisements with the help of AIGC technology, annotation point rendering, and real-time cloud rendering. At the same time, a dynamic user interest model is built to dynamically adjust the delivery strategy, providing an immersive and personalized VR advertising experience, improving the accuracy and appeal of advertisements, and enhancing user engagement; at the same time, the advertising delivery effect is optimized through data analysis, thereby improving the conversion rate of advertisements. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below with reference to the accompanying drawings and examples.

[0038] In the picture:

[0039] Figure 1 This is a flowchart of the VR smart advertising delivery method provided by the first embodiment of the present invention;

[0040] Figure 2 A schematic diagram of the modules of the VR intelligent advertising delivery system provided by the second embodiment of the present invention;

[0041] Figure 3It is a structural diagram of a network-side server provided according to a third embodiment of the present invention. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0043] First embodiment:

[0044] The first embodiment of the present invention provides a VR smart advertising delivery method, comprising: using a Pancake optical solution to project a wide-viewing angle image through a folded optical path and combining it with LED rendering for high-precision imagery to construct a VR advertising scene; real-time collection of user behavior data in the VR environment, including gestures, gaze focus, and facial expressions, to form a multi-dimensional behavior dataset; generating personalized advertising content based on the user behavior data and rendering it into the VR advertising scene in real time; integrating the user behavior data to construct a dynamic user interest model, analyzing the user behavior data in real time, and dynamically adjusting the advertising delivery strategy to achieve precise delivery of advertising content. The VR smart advertising delivery method of the present invention uses a Pancake optical solution and a specific LED display unit to construct a lightweight VR advertising scene with a wide viewing angle and high-precision imagery. It also uses multiple sensors to collect and integrate user multi-dimensional behavior data in real time, analyzes it, and mines user interest preferences based on clustering algorithms. It generates and delivers personalized advertisements using AIGC technology, annotation point rendering, and real-time cloud rendering. It also constructs a dynamic user interest model to dynamically adjust the delivery strategy, providing an immersive and personalized VR advertising experience, improving the accuracy and appeal of advertisements, and enhancing user engagement. Furthermore, it optimizes the advertising delivery effect through data analysis, thereby improving the conversion rate of advertisements.

[0045] The following is a detailed description of the implementation details of the VR smart advertising method of this embodiment. The following content is only for the convenience of understanding the implementation details, and is not necessary for the implementation of this solution. The specific process of this embodiment is as follows: Figure 1 shown.

[0046] Step S1: Pancake optical solution is used to project a wide-viewing angle image through a folded optical path and combined with LED rendering to create a high-precision image to construct a VR advertising scene.

[0047] Specifically, the Pancake optical solution, adopted as the core display technology for the VR smart advertising system, effectively reduces the thickness and weight of the VR headset by folding the optical path, making the device lighter and easier to use. It also provides a field of view (FOV) of at least 90 degrees, giving users a wide visual experience. Furthermore, the Pancake optical solution utilizes multi-layer reflective lenses and polarizing films to reflect and fold light multiple times, achieving a wider field of view and higher light efficiency within a limited space, significantly improving image clarity and color saturation.

[0048] The LEDs use Micro OLED display units or Micro LED display units. Micro OLED display units or Micro LED display units have high resolution, high contrast and fast response time, which are used to provide clear and vivid images and enhance the visual appeal of advertising content.

[0049] Use professional 3D modeling software to build a variety of VR advertising scenes.

[0050] Step S2: collect the user's behavioral data in the VR environment in real time. The behavioral data includes gestures, eye focus, and facial expressions to form a multi-dimensional behavioral data set.

[0051] Specifically, the real-time collection of user behavior data in a VR environment, including gestures, eye focus, and facial expressions, to form a multi-dimensional behavior data set includes the following specific steps:

[0052] Step S21 : collecting user gesture data by fusing an inertial measurement unit and a capacitive sensor.

[0053] Specifically, an inertial measurement unit (IMU) is used to measure hand acceleration, angular velocity, and posture information. This IMU provides more stable and continuous gesture data, enabling user gesture recognition. Capacitive sensors can detect hand proximity and touch, further enriching gesture interaction methods, enabling air touch operations and improving the naturalness and convenience of interaction.

[0054] Step S22: using an eye tracking sensor to collect the user's sight focus data.

[0055] The eye tracking sensor is based on the principle of infrared light reflection. It emits infrared light to illuminate the user's eyes, and then calculates the eye movement trajectory and line of sight based on the reflection of the infrared light on the eye surface. The collection of line of sight focus data is not only used to determine the user's gaze position in the VR scene, but also to deeply interact with advertising content.

[0056] For example, when a user's gaze is focused on a product for a period of time, detailed information about the product will automatically pop up; or the display content and order of advertisements will be dynamically adjusted according to the user's gaze movement trajectory to achieve personalized advertising recommendations.

[0057] Step S23: Collect the user's facial expression data through the camera and recognize the user's facial expression based on deep learning technology.

[0058] A deep learning convolutional neural network is used to analyze and process the collected facial images. First, a facial feature point detection algorithm is used to extract key facial feature points, such as the eyes, eyebrows, mouth, nose, etc. Then, the user's facial expressions are classified and recognized based on the position changes and shape deformations of these feature points. It can accurately identify common facial expressions such as smiling, frowning, surprise, and anger.

[0059] In step S24, the collected gesture, eye focus, and facial expression data are integrated to form a multi-dimensional behavioral data set, and metadata such as timestamp, user ID, and scene ID are added to each data sample to facilitate data association and analysis.

[0060] Step S3: Generate personalized advertising content based on user behavior data and render it into the VR advertising scene in real time.

[0061] Specifically, the steps of generating personalized advertising content based on user behavior data and rendering it in real time into the VR advertising scene include:

[0062] Step S31: Use K-Means clustering algorithm to analyze the multi-dimensional behavior data set to divide and group the user's behavior patterns.

[0063] The K-Means clustering algorithm divides data into K clusters through iterative optimization, ensuring high data similarity within each cluster and low data similarity between clusters. During the clustering process, the algorithm groups users with similar gestures, gaze focus, and facial expressions into the same cluster based on data similarity. Each cluster represents a specific, comprehensive behavioral pattern that reflects users' diverse interests and interaction preferences in VR environments.

[0064] Step S32 , performing semantic interpretation on each cluster obtained by clustering, analyzing the specific user interests and behavior characteristics corresponding to each cluster, and establishing a mapping relationship between user interests and behaviors.

[0065] Specifically, semantic interpretation is performed on each cluster obtained through clustering, analyzing the specific user interests and behavioral characteristics corresponding to each cluster. Based on the semantic interpretation of the clusters, a mapping relationship is established between user behavior patterns and interests. Each cluster is associated with the corresponding user interest tag, forming a mapping relationship from user behavior to interest. When new user behavior data enters the system, cluster analysis is used to determine the cluster to which it belongs. This allows for the rapid and accurate prediction of the user's interests and preferences, providing a basis for the generation and precise delivery of personalized ads.

[0066] For example, users in a certain cluster frequently perform gestures that are mainly focused on browsing operations, their gazes are focused on products of certain specific brands, and their facial expressions are mostly smiling. Therefore, it can be inferred that users in this cluster have a high interest in browsing advertising content and these specific brands of products.

[0067] Step S33: Generate personalized advertising content based on the user interest preferences obtained through cluster analysis, and render it into the VR advertising scene in real time.

[0068] Specifically, based on user interest preferences, artificial intelligence generated content (AIGC) technology is used to generate advertising content that matches user interests. That is, advertising copy, 3D models and scenes are automatically generated based on deep learning models to adapt to user preferences. Subsequently, annotation point rendering technology is used to dynamically enhance rendering details in key areas of the user's field of view (such as near the gaze point), reduce global computing power consumption, and prioritize optimizing the picture quality in areas of user focus. Real-time cloud rendering technology is used to distribute complex rendering tasks to cloud servers, and only compressed streaming media data is received locally to reduce hardware burden. The global scene is rendered in real time through the cloud to ensure a smooth experience.

[0069] Step S4: Integrate user behavior data to build a dynamic user interest model, analyze user behavior data in real time, dynamically adjust advertising delivery strategies, and achieve accurate push of advertising content.

[0070] Specifically, the steps of integrating user behavior data to build a user interest model, analyzing user behavior data in real time, and dynamically adjusting advertising delivery strategies to achieve accurate push of advertising content include:

[0071] In step S41, based on the user behavior pattern and interest mapping relationship generated by cluster analysis, the user's behavior data characteristics are modeled using a long short-term memory network model. By analyzing the dynamic changes of the user's behavior data in the VR scene, the user's interest tag weight is updated in real time to build a dynamic user interest model.

[0072] Step S42: Cleaning and feature extraction of the user's gestures, eye focus, and facial expression data collected in real time.

[0073] Specifically, the extracted features include interaction intensity index, interest preference index and temporal correlation feature.

[0074] Interaction intensity indicators include gesture frequency, gaze duration, and expression change amplitude. Interest preference indicators include high-frequency gaze area labels, ad content categories that trigger interaction, and positive and negative expression feedback scores. Temporal correlation features include the temporal periodicity of behavior patterns (such as activity within a specific time period) and interest migration paths (such as the conversion link from browsing to purchase).

[0075] Step S43: input the real-time features into the pre-trained multi-objective optimization model to dynamically generate an advertising delivery strategy.

[0076] Specifically, based on the user's current interest tags, the most matching content is screened from the advertising library, and the AIGC technology is combined to generate adaptive copy and 3D scenes in real time; then, according to the user's real-time interaction intensity, the frequency and duration of advertising display are dynamically adjusted, for example, increasing exposure during high-activity periods and reducing interference during low-activity periods; the attention mechanism is used to predict the user's potential attention areas, and high-value advertising content is rendered preferentially near the gaze point; at the same time, user behavioral feedback on advertisements such as clicks, stays, and purchases is collected in real time, and model parameters are updated through online learning.

[0077] In step S44, the optimized advertising content is pushed to the user end through real-time cloud rendering technology, and the delivery effect data is recorded simultaneously. The delivery strategy is further adjusted based on the evaluation of the delivery effect data.

[0078] Specifically, we use multi-dimensional evaluation metrics, such as click-through rate, conversion rate, and user satisfaction ratings, to quantitatively analyze ad effectiveness. Based on these evaluation results, we further adjust delivery strategy parameters to ensure maximum efficiency in allocating ad resources.

[0079] The present invention discloses a VR intelligent advertising delivery method, comprising: using a Pancake optical solution to project a wide-viewing angle image through a folded optical path and combining it with LED rendering for high-precision imagery to construct a VR advertising scene; collecting user behavioral data in the VR environment in real time, including gestures, eye focus, and facial expressions, to form a multi-dimensional behavioral dataset; generating personalized advertising content based on the user behavioral data and rendering it into the VR advertising scene in real time; integrating the user behavioral data to construct a dynamic user interest model, analyzing the user behavioral data in real time, and dynamically adjusting the advertising delivery strategy to achieve precise delivery of advertising content. The VR intelligent advertising delivery method adopts a Pancake optical solution and a specific LED display unit to construct a lightweight VR advertising scene with a wide viewing angle and high-precision imagery. It also utilizes multiple sensors to collect and integrate user multi-dimensional behavioral data in real time, analyzes it, and mines user interest preferences based on clustering algorithms. It generates and delivers personalized advertisements using AIGC technology, annotation point rendering, and real-time cloud rendering. It also constructs a dynamic user interest model to dynamically adjust the delivery strategy, providing an immersive and personalized VR advertising experience, improving the accuracy and appeal of advertisements, and enhancing user engagement. Furthermore, it optimizes the advertising delivery effect through data analysis, thereby improving the conversion rate of advertisements.

[0080] Second embodiment:

[0081] like Figure 2 As shown, the second embodiment of the present invention provides a VR intelligent advertising delivery system, which includes: an optical display module 201, an interactive acquisition module 202, a content generation module 203, and a data analysis module 204.

[0082] Specifically, the optical display module 201 is used to use the Pancake optical solution to project a wide-viewing angle image through a folded optical path and combine it with LED rendering to render high-precision images, thereby constructing a VR advertising scene; the interactive acquisition module 202 is used to collect user behavior data in the VR environment in real time. The behavior data includes gestures, visual focus, and facial expressions to form a multi-dimensional behavior data set; the content generation module 203 is used to generate personalized advertising content based on user behavior data and render it into the VR advertising scene in real time; the data analysis module 204 is used to integrate user behavior data to construct a dynamic user interest model, analyze user behavior data in real time, dynamically adjust the advertising delivery strategy, and achieve accurate push of advertising content.

[0083] It is not difficult to find that this embodiment is a system embodiment corresponding to the first embodiment, and this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the first embodiment.

[0084] It is worth noting that all modules involved in this embodiment are logical modules. In actual applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovations of the present invention, this embodiment does not include units that are not closely related to solving the technical problems proposed by the present invention. However, this does not mean that other units do not exist in this embodiment.

[0085] The third embodiment of the present invention relates to a network side server, such as Figure 3 As shown, it includes at least one processor 302; and a memory 301 that is communicatively connected to the at least one processor 302; wherein the memory 301 stores instructions that can be executed by the at least one processor 302, and the instructions are executed by the at least one processor 302 to enable the at least one processor 302 to execute the above-mentioned data processing method.

[0086] Memory 301 and processor 302 are connected using a bus. The bus can include any number of interconnected buses and bridges, connecting various circuits of one or more processors 302 and memory 301. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits. These are all well known in the art and are therefore not described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 302 is transmitted over a wireless medium via an antenna. Furthermore, the antenna receives data and transmits it to processor 302.

[0087] The processor 302 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. The memory 301 can be used to store data used by the processor 302 when performing operations.

[0088] A fourth embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for delivering VR smart advertisements in the first embodiment is implemented.

[0089] That is, those skilled in the art will understand that all or part of the steps in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a program, which is stored in a storage medium and includes a number of instructions for causing a device (which may be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., various media that can store program code.

[0090] The above is only an embodiment of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the field are aware of all common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the field can improve and implement this scheme in combination with their own abilities under the inspiration given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A VR smart advertising method, characterized in that: include: S1, using Pancake optical solution to project wide-viewing angle images through folded optical paths and combined with LED rendering of high-precision images to create VR advertising scenes; S2 collects user behavior data in the VR environment in real time. The behavior data includes gestures, gaze focus, and facial expressions, forming a multi-dimensional behavior data set; S3 generates personalized advertising content based on user behavior data and renders it into the VR advertising scene in real time; S4 integrates user behavior data to build a dynamic user interest model, analyzes user behavior data in real time, dynamically adjusts advertising delivery strategies, and achieves accurate push of advertising content.

2. The VR smart advertising method according to claim 1, characterized in that: The Pancake optical solution effectively reduces the thickness and weight of the VR headset by folding the optical path while providing a field of view of at least 90 degrees.

3. The VR smart advertising delivery method according to claim 1, characterized in that: The LED uses a Micro OLED display unit or a Micro LED display unit, which has high resolution, high contrast and fast response time, and is used to provide clear and vivid images to enhance the visual appeal of advertising content.

4. The VR smart advertising method according to claim 1, characterized in that: The real-time collection of user behavior data in the VR environment includes gestures, eye focus, and facial expressions, forming a multi-dimensional behavior data set, including: Step S21, collecting user gesture data by fusing an inertial measurement unit and a capacitive sensor; Step S22, using an eye tracking sensor to collect user's sight focus data; Step S23, collecting the user's facial expression data through a camera, and recognizing the user's facial expression based on deep learning technology; In step S24, the collected gesture, eye focus, and facial expression data are integrated to form a multi-dimensional behavioral data set, and metadata such as timestamp, user ID, and scene ID are added to each data sample to facilitate data association and analysis.

5. The VR smart advertising delivery method according to claim 1, characterized in that: The process of generating personalized advertising content based on user behavior data and rendering it to a VR advertising scene in real time includes: Step S31, using the K-Means clustering algorithm to analyze the multi-dimensional behavior data set to divide and group the user's behavior patterns; Step S32: semantically interpret each cluster obtained by clustering, analyze the specific user interests and behavior characteristics corresponding to each cluster, and establish a mapping relationship between user interests and behaviors; Step S33: Generate personalized advertising content based on the user interest preferences obtained through cluster analysis, and render it into the VR advertising scene in real time.

6. The VR smart advertising delivery method according to claim 5, characterized in that: The K-Means clustering algorithm divides data into K clusters through iterative optimization, so that the data similarity within each cluster is high, while the data similarity between different clusters is low. During the clustering process, the algorithm groups users with similar gestures, gaze focus, and facial expressions into the same cluster based on data similarity. Each cluster represents a specific comprehensive behavioral pattern, which is used to reflect the different interests and interaction preferences of users in the VR environment.

7. The VR smart advertising method according to claim 1, characterized in that: The aforementioned integration of user behavior data to build a dynamic user interest model, real-time analysis of user behavior data, dynamic adjustment of advertising delivery strategies, and accurate push of advertising content include: Step S41: Based on the user behavior pattern and interest mapping relationship generated by cluster analysis, a long short-term memory network model is used to model the user's behavior data characteristics. By analyzing the dynamic changes of the user's behavior data in the VR scene, the user's interest tag weight is updated in real time to build a dynamic user interest model; Step S42, cleaning and feature extraction of the user's gestures, eye focus, and facial expression data collected in real time; Step S43: input the real-time features into a pre-trained multi-objective optimization model to dynamically generate an advertising delivery strategy; In step S44, the optimized advertising content is pushed to the user end through real-time cloud rendering technology, and the delivery effect data is recorded simultaneously. The delivery strategy is further adjusted based on the evaluation of the delivery effect data.

8. A VR intelligent advertising delivery system, characterized in that: The VR smart advertising method according to claims 1 to 7 is applied to the system comprising: The optical display module uses a Pancake optical solution to project wide-viewing angle images through a folded optical path and combines it with LEDs to render high-precision images, creating VR advertising scenes. The interactive collection module is used to collect real-time behavioral data of users in the VR environment. The behavioral data includes gestures, eye focus, and facial expressions, forming a multi-dimensional behavioral data set; The content generation module is used to generate personalized advertising content based on user behavior data and render it into the VR advertising scene in real time; The data analysis module is used to integrate user behavior data to build a dynamic user interest model, analyze user behavior data in real time, dynamically adjust advertising delivery strategies, and achieve accurate push of advertising content.

9. A network-side server, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the VR smart advertising delivery method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the VR smart advertising delivery method according to any one of claims 1 to 7 is implemented.

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