CN117372128A - Meta universe virtual shopping center innovation experience platform serving QoS - Google Patents
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Abstract
The invention discloses a QoS-service meta-universe virtual shopping center innovation experience platform, which comprises a user interface module, a resource management module, an interaction optimization module, a commodity recommendation module, a data collection and analysis module, a security and privacy module and a social and interactive module, wherein the user interface module provides an intuitive user interface, the resource management module is responsible for resource allocation of the whole platform and provides seamless shopping experience for users, the interaction optimization module uses a multi-mode interaction optimization algorithm to optimize the experience of users in a virtual shopping center according to interaction data, the commodity recommendation module uses a deep learning recommendation system algorithm to provide commodity recommendation for users, the data collection and analysis module collects browsing history, interaction data and purchase data of the users, the security and privacy module ensures that all user data are encrypted and stored safely, and the social and interactive module allows the users to interact with other users in the virtual shopping center: providing a more accurate market analysis for merchants.
Description
Technical Field
The invention relates to the fields of meta-universe technology and image analysis, in particular to an innovative experience platform of a meta-universe virtual shopping center serving QoS.
Background
With the increasing progress of science and technology, the boundary between the digital world and the real life is gradually blurred, wherein the concept of "meta universe" is paid attention to in recent years, and represents a virtual universe parallel to the real world and has multiple functions of economy, social contact and entertainment, and in the wide digital field, a virtual shopping center is generated, so that the virtual shopping center simulates the real shopping experience and provides an unprecedented novel shopping mode for users;
however, with the rapid expansion of the metauniverse and the increase of the user base, it becomes particularly important to ensure the quality of service (QoS) of a virtual shopping center, and in multi-user and high-concurrency scenarios, including providing a high-quality shopping experience for users while ensuring the smoothness of interactions, the business model of the metauniverse is different from the traditional online platform, users are not just buying goods, but rather experience, exploration and socialization, which requires the shopping center to be not just a simple demonstration platform, but rather an interactive and innovative experience space, so as to meet these requirements, necessarily makes higher demands on technology and design, and management of data flows is different, and a large amount of data is generated by each interaction, each purchase, and even each social behavior of users in the metauniverse, including collecting, analyzing and utilizing such data, providing more accurate and personalized recommendations for users, and being another core, security and privacy problems become particularly prominent in the metauniverse, and security problems are very important due to the fact that the identity in the metauniverse is closely associated with the virtual identity in the metauniverse, and the virtual universe must be abused, and the security problems are seriously included in the world, and the security problems are each has to be assuredly abused, and the security problems are each has to be guaranteed.
The invention considers that the success of the virtual shopping center and the satisfaction of the user can be ensured only by placing QoS at the first place, therefore, the invention combines advanced technology and design ideas, builds a brand-new meta-universe virtual shopping center innovation experience platform facing QoS, hopes to provide unprecedented shopping experience for the user through the platform, and provides new thinking and direction for business modes in meta-universe.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a meta-universe virtual shopping center innovation experience platform serving QoS.
The aim of the invention is realized by the following technical scheme:
the QoS is user service quality, the meta-universe virtual shopping center innovation experience platform comprises a user interface module, a resource management module, an interaction optimization module, a commodity recommendation module, a data collection and analysis module, a security and privacy module and a social and interaction module, wherein the user interface module provides an visual user interface for allowing a user to browse, select and interact with other users in a virtual shopping center, the resource management module is responsible for resource allocation of the whole platform, a dynamic resource allocation algorithm is used for ensuring that system resources comprise a CPU (central processing unit), a GPU (graphics processing unit), a memory and a bandwidth are reasonably allocated so as to provide seamless shopping experience for the user, the interaction optimization module uses a multi-mode interaction optimization algorithm for optimizing the user experience in the virtual shopping center according to interaction data of the user, the commodity recommendation module uses a deep learning recommendation system algorithm for providing commodity recommendation for the user according to browsing history, purchasing history and other related information of the user, the data collection and the data analysis module is responsible for collecting the browsing history, interaction data and purchasing data of the user, the user can provide personal information or conduct transaction in the virtual shopping center, the security and privacy module is ensured to be stored in the virtual shopping center, and the security and interaction relationship is ensured to be safely shared with the social and interaction with the other social shopping center.
Further, the user interface module provides an intuitive user interface, allows a user to browse, select goods and interact with other users in the virtual shopping center, shows a realistic metauniverse virtual shopping center environment for the user through an advanced graphic rendering technology, comprises real-time light effect, reflection and anti-aliasing visual effects, enables the user to have a feeling of being personally on the scene, can freely move and explore in the metauniverse virtual shopping center environment, can directly interact with various displayed goods, and is internally provided with a real-time chat function for increasing shopping instantaneity, the user communicates with merchants or other customers to consult goods details or share shopping experience, and in addition, the user interface module provides language and currency conversion tools to adapt to the users from different regions and cultural backgrounds in consideration of the cross-border of the metauniverse.
Further, the resource management module ensures that all resources of the platform are effectively and efficiently managed and allocated, so that smooth and uninterrupted shopping experience is provided for users, the resource management module firstly classifies and indexes all resources of the platform, including virtual goods, user data, interaction models, graphic textures and audio files, the resources can be quickly searched and called through advanced classification algorithms and database technologies, the virtual environment of the metauniverse is considered to be huge, the resource management module adopts a dynamic resource loading technology, corresponding resources are loaded only when required by users, bandwidth and computing resources are saved, loading speed is improved, for frequently accessed resources, the resource management module caches the frequently accessed resources to local and near-end servers, quick access is ensured, delay is reduced, in order to provide optimal user experience under equipment and network conditions, the resource management module is dynamically optimized according to the quality and resolution of the resources, the resource management module is also responsible for updating and synchronizing the platform resources, the virtual center contains a large number of regular and user data in consideration of shopping centers, the security measures are strictly implemented, and the integrity of each privacy measure is ensured.
Furthermore, the interaction optimization module ensures natural, smooth and visual interaction between the user and the platform, supports various interaction modes, including voice, gesture, touch and virtual reality controllers, can accurately interpret and respond to commands and actions of the user through deep learning and sensor data fusion, provides instant visual, auditory and tactile feedback for enhancing immersion of the user, and can predict the next operation of the user through analyzing historical interaction data and behavior patterns of the user, and perform resource preloading and interface optimization according to the operation modes, so that delay is further reduced and response speed is improved.
Further, for the visual feedback, in the present invention, the gesture of the user is recorded by the interaction optimization module in the form of images, and in a continuous period, the images recorded by the interaction optimization module are continuous, and the light sensitivity of the images is defined as F (x, y, t), wherein the light sensitivity function includes an abscissa pixel x, an ordinate pixel y and a moment t of the images, and for the continuous period, there are: f (x, y, t) =f (x+Δx, y+Δy, t+Δt) holds, where Δx is the drift amount of the abscissa pixel x, Δy is the drift amount of the ordinate pixel y, Δt is the drift amount of the time t, and F (x+Δx, y+Δy, t+Δt) is first-order taylor expansion, and there is:
Wherein,representing F to x first order partial derivative, < ->Representing F to y first order partial derivative, < ->Representing F to t first order partial derivatives, for +.>The updating is carried out by:
wherein,for->Normal vector n is->In a planar rectangular coordinate system, the distance between (u, v) and the origin of coordinates is: />In view of the timeliness of the system, smoothing and GAUSS filtering are performed on each frame image, and for the pixel point P (x, y), there are:
wherein, sigma is the standard deviation of GAUSS, downsampling the image interlayer, and there are:
P (N) (x,y,t)＝G*P (0) (2Nx,2Ny,t)
wherein N is the layer number, G is the Laplace convolution kernel, and P (0) For the initial image, P (N) For downsampling an image of N layers, after interlacing, assuming that the original size of the image is u×v, the current size of the image isWhere U 'V' represents the current size of the image, denoted by P (N) Is the search center, extends to the image pixel range of (2k+1) x (2k+1) in the search domain, where k is a natural number, and the presence is:
wherein x is 1 ，x 2 ，…，x U`×V` Respectively representing the abscissa, y of the 1 st, 2 nd, … th and U 'XV' th images on the U 'XV' size image 1 ，y 2 ，…，y U`×V` Respectively are provided withRepresenting the ordinate, t, of the 1 st, 2 nd, … th and U ' XV ' th images on an image of size U ' XV 1 ，t 2 ，…，t U`×V` Representing the mapping time, satisfy P (N) (x,y,t)＝G*P (0) (2 nx,2ny, t), Indicating the deviation of the velocity vector in the direction of the horizontal axis, < >>Indicating the deviation of the velocity vector in the direction of the vertical axis.
Further, the method is obtained by fitting technology
Wherein,is P (N) For x i To calculate the second order deviation>Is P (N) For x i y i To calculate the second order deviation>Is P (N) For y i And solving a second order partial derivative, wherein i is an index number.
Where ρ is an autocorrelation function, n 0 Is a window function N 0 The window length, beta is a parameter, the shape of the window is controlled, and after first-order expansion, the window is obtained:
furthermore, since the present platform is based on the meta-space technology, considering that the presentation of the virtual commodity has a consistency, for the pixel point P (x, y) and the pixel drift point Δp (Δx, Δy), there is F (P (x, y) - Δp (Δx, Δy), y) =f (P (x, y), t- Δt), and there is:
(P(x,y)-ΔP(Δx,Δy)) T A 1 (P(x,y)-ΔP(Δx,Δy))+B 1 (P(x,y)-ΔP(Δx,Δy))+C 1 ＝P(x,y) T A 2 P(x,y)+B 2 P(x,y)+C 2
wherein A is 1 ，B 1 ，C 1 ，A 2 ，B 2 ，C 2 For the taylor expansion coefficient, the update is based on mathematical derivation:
P(x,y)TA 1 P(x,y)+(B 1 -2A 1 ΔP(Δx,Δy))P(x,y)+ΔP(Δx,Δy) T A 1 ΔP(Δx,Δy)-B 2 ΔP(Δx,Δy)+C 1
＝P(x,y) T A 2 P(x,y)+B 2 P(x,y)+C 2
according to equality of corresponding terms of equation, there is B 1 -2A 1 ΔP(Δx,Δy)＝B 2 This holds, and therefore derives Δp (Δx, Δy) as:
in addition, the interaction optimization module integrates an intelligent assistant, can understand the requirements, questions and commands of the user, then provides relevant information and suggestions, and can provide more personalized and relevant interaction experience according to the current scene and state of the user, including position, time and mood through a context awareness technology.
Further, in order to ensure high-quality recommendation, the commodity recommendation module comprehensively considers the preference, the behavior history, the social network and the real-time feedback of the user, collects and analyzes the behavior data of the user on the platform, including browsing history, purchasing records, search query and residence time, processes the user data and commodity information by using the deep neural network, and uses a collaborative filtering technology to recommend the commodity of interest to the user according to the historical behaviors of the user and other similar users, the commodity recommendation module can collect the feedback in real time and adjust the recommendation strategy so as to better meet the requirements of the user, and the commodity recommendation module can provide relevant and practical recommendation according to the real-time context of the user in consideration of the multidimensional characteristics of the metauniverse.
Further, the data collection and analysis module is responsible for capturing data from each interaction point, including functions of multi-source data collection, data preprocessing, advanced data analysis, data visualization, cloud storage and calculation, and specifically includes the following steps:
(1) A multi-source data collection function comprising:
user interaction data: the method comprises click rate, page browsing time, purchase records and search records;
sensor data: in the meta universe, users use VR/AR glasses, motion capture equipment devices, devices collect eye movement tracking, gestures, and body movement data;
social interaction data: social interactions of users within the platform, including comments, scoring, sharing and communication with other users,
(2) Data preprocessing, including:
data cleaning: removing errors, duplicates, and irrelevant data;
data conversion: converting the raw data into a format for use by an analysis tool;
filling data: reasonably estimating or filling missing or incomplete data;
(3) Advanced data analysis, comprising:
trend analysis: analyzing the trend of the data changing along with time, and predicting future user behaviors and market trends;
and (3) cluster analysis: users are divided into different groups and market segments to provide more targeted services or recommendations,
association rule analysis: finding out purchase relations between commodities;
(4) Data visualization, comprising:
visual charts and instrument panel visualization tools are provided to help merchants and platform managers understand and master key indexes of user behaviors and market trends;
(5) Cloud storage and computing, comprising:
elastic storage and computing power is provided through cloud technology to support the processing and analysis of large-scale user data.
Further, the security and privacy module aims at ensuring the integrity, confidentiality and availability of user data, ensuring that privacy rights of users in meta-universe shopping experience are not violated, ensuring that user data are not sniffed or intercepted in the transmission process, ensuring that user data stored on a server adopt a high-strength encryption algorithm, ensuring that data security, besides a traditional user name and a password, the security and privacy module is further added with biological identification and mobile phone verification codes for second verification, ensuring that users can only access authorized data and resources of the user, removing or replacing information of an identified individual when collecting and analyzing the data, ensuring anonymity of the data, defining the time length of data storage, ensuring that data which are not needed are safely and irrecoverably destroyed, utilizing transparency and irrevocability modification of a block chain, ensuring that security and geographic integrity of transactions are automatically executed through intelligent contracts, enhancing security, enabling the security and privacy module to regularly backup important data, and ensuring that data are quickly restored when a data center fails or is attacked.
Further, the social and interactive module provides an immersive and highly interactive social shopping experience for the user, allows the user to interact with other users in the virtual space, shares the shopping experience, participates in various interactive activities, enables the user to create and customize own virtual images, enables the user to communicate with other users on the platform in real time through voice and text, allows the users to browse commodities jointly and participate in the virtual activities, increases a collaborative dimension for the shopping experience, enables the user to directly share the shopping experience, virtual try-on effect or other interactions to the social media account of the user on the platform, and provides a more immersive shopping experience for the user by utilizing VR and AR technologies, including 3D fitting room and AR commodity preview.
The invention has the beneficial effects that: unlike traditional online shopping platform, the invention adopts advanced artificial intelligence technology, according to user's behavior and preference, real-time adjusts interface content and layout, provide more personalized shopping experience, through intelligent interface, optimized interaction mode and personalized commodity recommendation, greatly improve user's shopping satisfaction, promote user's purchasing will again, consider three-dimensional space characteristics of metauniverse, the invention introduces more natural and intuitive interaction mode, provide instant visual, auditory and tactile feedback, greatly improve user's interaction experience, and record user's gesture behavior in image mode at interaction optimization module, through predicting user's next operation, and according to this, further reduce delay and improve response speed, and the invention introduces the dimension of time t on the basis of traditional consideration pixel point, in order to satisfy the limitation of the invention to the system, take into account the timeliness of the system, smooth USS filtering is carried out on each frame image, utilize Laplace convolution image to convolve, in addition, the invention checks P with QoS, the invention further carries out resource preloading and interface optimization by predicting user's gesture behavior (N) The (x 0, y 0) of the image is taken as a search center, and the image pixel range of the search domain (2k+1) x (2k+1) is expanded, so that the gesture at the moment t with the user is searched in the search domainThe invention adds a window function to the autocorrelation function to eliminate the influence of sidelobe energy on the image. The intelligent commodity recommending system and the intelligent commodity recommending method combine deep learning and user behavior analysis to recommend commodities which are most in line with demands and preferences of users, improve accuracy and satisfaction of shopping, accurately recommend commodities to users, greatly improve conversion rate of commodities and shopping efficiency, collect shopping behavior data of the users, further comprise social and entertainment multidimensional data, and a new mode of social and interaction, so that the users can shop in a virtual shopping center, communicate and share with friends, greatly improve viscosity and liveness of the users, analyze through a big data technology, provide richer and more accurate user portraits, fully respect privacy of the users while guaranteeing safety of the user data, ensure integrity and safety of the user data, and enhanced safety and privacy protection measures, ensure that the user data is not revealed or abused, win trust and support of the users.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation on the invention, and other drawings can be obtained by one of ordinary skill in the art without undue effort from the following drawings.
Fig. 1 is a schematic diagram of the structure of the present invention.
Detailed Description
The invention will be further described with reference to the following examples.
Referring to fig. 1, a meta-universe virtual shopping center innovation experience platform serving QoS is user service quality, and comprises a user interface module, a resource management module, an interaction optimization module, a commodity recommendation module, a data collection and analysis module, a security and privacy module and a social and interaction module, wherein the user interface module provides an intuitive user interface, allows a user to browse, select and interact with other users in a virtual shopping center, the resource management module is responsible for resource allocation of the whole platform, a dynamic resource allocation algorithm is used for ensuring that system resources including a CPU, a GPU, a memory and a bandwidth are reasonably allocated so as to provide seamless shopping experience for the user, the interaction optimization module uses a multi-mode interaction optimization algorithm to optimize the experience of the user in the virtual shopping center according to interaction data of the user, the commodity recommendation module uses a deep learning recommendation system algorithm to provide commodity recommendation for the user according to browsing history, purchasing history and other related information of the user, the data collection and analysis module is responsible for collecting browsing history, interaction data and purchasing data of the user, the user is considered to provide personal information or conduct financial transaction in the virtual shopping center, the security and privacy interaction are ensured to be conducted in the virtual shopping center, and all the security and interaction data are ensured to be safely shared with the social interaction environment of the user and the other social interaction environments.
Specifically, the user interface module provides an intuitive user interface, allows a user to browse, select goods and interact with other users in the virtual shopping center, shows a realistic metauniverse virtual shopping center environment for the user through an advanced graphic rendering technology, comprises real-time light effect, reflection and antialiasing visual effects, enables the user to have a feeling of being personally on the scene, can freely move and explore in the metauniverse virtual shopping center environment, can directly interact with various displayed goods, and is internally provided with a real-time chat function for increasing shopping instantaneity, the user communicates with merchants or other customers to consult goods details or share shopping experience, and in addition, the user interface module provides language and currency conversion tools to adapt to the users from different regions and cultural backgrounds in consideration of the cross-border of the metauniverse.
Specifically, the resource management module ensures that all resources of the platform are effectively and efficiently managed and allocated, so that smooth and uninterrupted shopping experience is provided for users, the resource management module firstly classifies and indexes all resources of the platform, including virtual goods, user data, interaction models, graphic textures and audio files, the resources can be quickly searched and called through advanced classification algorithms and database technologies, the virtual environment of the metauniverse is considered to be huge, the resource management module adopts a dynamic resource loading technology, corresponding resources are loaded only when required by users, bandwidth and computing resources are saved, loading speed is improved, for frequently accessed resources, the resource management module caches the frequently accessed resources to local and near-end servers, quick access is ensured, delay is reduced, in order to provide optimal user experience under equipment and network conditions, the resource management module is dynamically optimized according to the quality and resolution of the resources, the resource management module is also responsible for updating and synchronizing the platform resources, the virtual center contains a large number of regular and user data in consideration of shopping centers, the security measures are strictly implemented by the resource management module, and the privacy integrity of each resource is ensured.
Specifically, the interaction optimization module ensures natural, smooth and visual interaction between the user and the platform, supports various interaction modes including voice, gesture, touch and virtual reality controllers, can accurately interpret and respond to commands and actions of the user through deep learning and sensor data fusion, provides instant visual, auditory and tactile feedback for enhancing immersion of the user, and can predict next operation of the user through analysis of historical interaction data and behavior patterns of the user, and perform resource preloading and interface optimization according to the operation modes, so that delay is further reduced and response speed is improved.
Preferably, for the visual feedback, in the present invention, the gesture of the user is recorded by the interaction optimization module in an image manner, and in a continuous period, the images recorded by the interaction optimization module are continuous, and the light sensitivity of the images is defined as F (x, y, t), wherein the light sensitivity function includes an abscissa pixel x, an ordinate pixel y and a moment t of the images, and for the continuous period, there are: f (x, y, t) =f (x+Δx, y+Δy, t+Δt) holds, where Δx is the drift amount of the abscissa pixel x, Δy is the drift amount of the ordinate pixel y, Δt is the drift amount of the time t, and F (x+Δx, y+Δy, t+Δt) is first-order taylor expansion, and there is:
Wherein,representing F to x first order partial derivative, < ->Representing F to y first order partial derivative, < ->Representing F to t first order partial derivatives, for +.>The updating is carried out by:
wherein,for->Normal vector n is->In a planar rectangular coordinate system, the distance between (u, v) and the origin of coordinates is: />Taking into account the ageing of the systemFor each frame of image, smoothing and GAUSS filtering is performed, and for pixel points P (x, y), there are:
wherein, sigma is the standard deviation of GAUSS, downsampling the image interlayer, and there are:
P (N) (x,y,t)＝G*P (0) (2Nx,2Ny,t)
wherein N is the layer number, G is the Laplace convolution kernel, and P (0) For the initial image, P (N) For downsampling an image of N layers, after interlacing, assuming that the original size of the image is u×v, the current size of the image isWhere U 'V' represents the current size of the image, denoted by P (N) Is the search center, extends to the image pixel range of (2k+1) x (2k+1) in the search domain, where k is a natural number, and the presence is:
wherein x is 1 ，x 2 ，…，x U`×V` Respectively representing the abscissa, y of the 1 st, 2 nd, … th and U 'XV' th images on the U 'XV' size image 1 ，y 2 ，…，y U`×V` Respectively representing the ordinate, t, of the 1 st, 2 nd, … th and U 'XV' th images on the U 'XV' size image 1 ，t 2 ，…，t U`×V` Representing the mapping time, satisfy P (N) (x,y,t)＝G*P (0) (2 nx,2ny, t), Indicating the deviation of the velocity vector in the direction of the horizontal axis, < >>Indicating the deviation of the velocity vector in the direction of the vertical axis.
Preferably, the determination is made by fitting techniques
Wherein,is P (N) For x i To calculate the second order deviation>Is P (N) For x i y i To calculate the second order deviation>Is P (N) For y i And solving a second order partial derivative, wherein i is an index number.
Where ρ is an autocorrelation function, n 0 Is a window function N 0 The window length, beta is a parameter, the shape of the window is controlled, and after first-order expansion, the window is obtained:
furthermore, since the present platform is based on the meta-space technology, considering that the presentation of the virtual commodity has a consistency, for the pixel point P (x, y) and the pixel drift point Δp (Δx, Δy), there is F (P (x, y) - Δp (Δx, Δy), t) =f (P (x, y), t- Δt), and there is:
(P(x,y)-ΔP(Δx,Δy)) T A 1 (P(x,y)-ΔP(Δx,Δy))+B 1 (P(x,y)-ΔP(Δx,Δy))+C 1 ＝P(x,y) T A 2 P(x,y)+B 2 P(x,y)+C 2
wherein A is 1 ，B 1 ，C 1 ，A 2 ，B 2 ，C 2 For the taylor expansion coefficient, the update is based on mathematical derivation:
P(x,y) T A 1 P(x,y)+(B 1 -2A 1 ΔP(Δx,Δy))P(x,y)+ΔP(Δx,Δy) T A 1 ΔP(Δx,Δy)-B 2 ΔP(Δx,Δy)+C 1
＝P(x,y) T A 2 P(x,y)+B 2 P(x,y)+C 2
according to equality of corresponding terms of equation, there is B 1 -2A 1 ΔP(Δx,Δy)＝B 2 This holds, and therefore derives Δp (Δx, Δy) as:
in addition, the interaction optimization module integrates an intelligent assistant, can understand the requirements, questions and commands of the user, then provides relevant information and suggestions, and can provide more personalized and relevant interaction experience according to the current scene and state of the user, including position, time and mood through a context awareness technology.
Specifically, in order to ensure high-quality recommendation, the commodity recommendation module comprehensively considers the preference, the behavior history, the social network and the real-time feedback of the user, collects and analyzes the behavior data of the user on the platform, including browsing history, purchasing records, search queries and residence time, processes the user data and commodity information by using the deep neural network, and uses a collaborative filtering technology to recommend the commodity of interest to the user according to the historical behaviors of the user and other similar users, the commodity recommendation module can collect the feedback in real time and adjust the recommendation strategy so as to better meet the requirements of the user, and the commodity recommendation module can provide relevant and practical recommendation according to the real-time context of the user in consideration of the multidimensional characteristics of the metauniverse.
Specifically, the data collection and analysis module is responsible for capturing data from each interaction point, including functions of multi-source data collection, data preprocessing, advanced data analysis, data visualization, cloud storage and calculation, and specifically includes the following steps:
(1) A multi-source data collection function comprising:
user interaction data: the method comprises click rate, page browsing time, purchase records and search records;
sensor data: in the meta universe, users use VR/AR glasses, motion capture equipment devices, devices collect eye movement tracking, gestures, and body movement data;
social interaction data: social interactions of users within the platform, including comments, scoring, sharing and communication with other users,
(2) Data preprocessing, including:
data cleaning: removing errors, duplicates, and irrelevant data;
data conversion: converting the raw data into a format for use by an analysis tool;
filling data: reasonably estimating or filling missing or incomplete data;
(3) Advanced data analysis, comprising:
trend analysis: analyzing the trend of the data changing along with time, and predicting future user behaviors and market trends;
and (3) cluster analysis: users are divided into different groups and market segments to provide more targeted services or recommendations,
association rule analysis: finding out purchase relations between commodities;
(4) Data visualization, comprising:
visual charts and instrument panel visualization tools are provided to help merchants and platform managers understand and master key indexes of user behaviors and market trends;
(5) Cloud storage and computing, comprising:
elastic storage and computing power is provided through cloud technology to support the processing and analysis of large-scale user data.
Specifically, the security and privacy module aims at ensuring the integrity, confidentiality and availability of user data, ensuring that the privacy rights of users in meta-universe shopping experience are not violated, ensuring that the user data are not sniffed or intercepted in the transmission process, ensuring that the user data stored on a server adopt a high-strength encryption algorithm, ensuring that the data are safe, besides the traditional user name and password, the invention also adds biological identification and mobile phone verification code second verification, ensuring that the users can only access authorized data and resources thereof, removing or replacing information of an identified individual when collecting and analyzing the data, ensuring the anonymity of the data, prescribing the time length of data storage, ensuring that the data which are not needed are safely and irrecoverably destroyed, utilizing the transparency and the irrevocable modification of a block chain, ensuring the security and the geographic integrity of transactions, simultaneously automatically executing preset conditions through intelligent contracts, enhancing the security, ensuring that the security and privacy module can regularly backup important data, and ensuring that the data are quickly restored when a data center fails or is attacked.
Specifically, the social and interactive module provides an immersive and highly interactive social shopping experience for users, allows the users to interact with other users in a virtual space, shares the shopping experience, participates in various interactive activities, enables the users to create and customize own virtual images, enables the users to communicate with other users on a platform in real time through voices and texts, allows a plurality of users to browse commodities jointly and participate in the virtual activities, increases a collaborative dimension for the shopping experience, enables the users to share the shopping experience, virtual try-on effect or other interactions to own social media account directly on the platform, and provides more immersive shopping experience for the users by utilizing VR and AR technologies, including 3D fitting room and AR commodity preview.
The invention has the beneficial effects that: unlike traditional online shopping platform, the invention adopts advanced artificial intelligence technology, according to user's behavior and preference, real-time adjusts interface content and layout, provide more personalized shopping experience, through intelligent interface, optimized interaction mode and personalized commodity recommendation, greatly improve user's shopping satisfaction, promote user's purchasing will again, consider three-dimensional space characteristics of metauniverse, the invention introduces more natural and intuitive interaction mode, provide instant visual, auditory and tactile feedback, greatly improve user's interaction experience, and record user's gesture behavior in image mode at interaction optimization module, through predicting user's next operation, and according to this, further reduce delay and improve response speed, and the invention introduces the dimension of time t on the basis of traditional consideration pixel point, in order to satisfy the limitation of the invention to the system, take into account the timeliness of the system, smooth USS filtering is carried out on each frame image, utilize Laplace convolution image to convolve, in addition, the invention checks P with QoS, the invention further carries out resource preloading and interface optimization by predicting user's gesture behavior (N) The method is characterized in that (x 0, y 0) is taken as a search center, and the search center is expanded to the image pixel point range of (2k+1) x (2k+1) in the search domain, so that a set of points related to the gesture of a user at the moment t is searched in the search domain, and in addition, a window function is added to an autocorrelation function to eliminate the influence of side lobe energy on an image. The invention combines deep learning and user behavior analysis to recommend the commodity which best meets the requirement and preference of the user, improves the accuracy and satisfaction of shopping, and the intelligent commodity recommendation system is exactly usedThe user recommends commodities, the commodity conversion rate and shopping efficiency are greatly improved, shopping behavior data of the user are collected, social and entertainment multidimensional data are also included, a new social and interaction mode is adopted, the user can shop, communicate and share with friends in a virtual shopping center, viscosity and liveness of the user are greatly enhanced, analysis is conducted through a big data technology, more rich and accurate user portraits are provided, the user data safety is guaranteed, the privacy of the user is fully respected, the integrity and safety of the user data are guaranteed, the strengthened safety and privacy protection measures are provided, the user data is prevented from being leaked or abused, and trust and support of the user are won.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the scope of the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications can be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. The QoS is user service quality, the meta-universe virtual shopping center innovation experience platform comprises a user interface module, a resource management module, an interaction optimization module, a commodity recommendation module, a data collection and analysis module, a security and privacy module and a social and interaction module, wherein the user interface module provides an visual user interface for allowing a user to browse, select and interact with other users in a virtual shopping center, the resource management module is responsible for resource allocation of the whole platform, a dynamic resource allocation algorithm is used for ensuring that system resources comprise a CPU (central processing unit), a GPU (graphics processing unit), a memory and a bandwidth are reasonably allocated so as to provide seamless shopping experience for the user, the interaction optimization module uses a multi-mode interaction optimization algorithm for optimizing the user experience in the virtual shopping center according to interaction data of the user, the commodity recommendation module uses a deep learning recommendation system algorithm for providing commodity recommendation for the user according to browsing history, purchasing history and other related information of the user, the data collection and the data analysis module is responsible for collecting the browsing history, interaction data and purchasing data of the user, the user can provide personal information or conduct transaction in the virtual shopping center, the security and privacy module is ensured to be stored in the virtual shopping center, and the security and interaction relationship is ensured to be safely shared with the social and interaction with the other social shopping center.
2. The QoS-serving metauniverse virtual shopping center innovation experience platform of claim 1, wherein the user interface module provides an intuitive user interface, allows a user to browse, select goods and interact with other users in the virtual shopping center, displays a realistic metauniverse virtual shopping center environment for the user through an advanced graphic rendering technology, wherein the realistic metauniverse virtual shopping center environment comprises real-time shadow effects, reflection and antialiasing visual effects, enables the user to have a sense of being personally on the scene, enables the user to freely move and explore in the metauniverse virtual shopping center environment, and can also interact with various displayed goods directly, and in order to increase shopping instantaneity, the user interface is internally provided with a real-time chat function, the user communicates with merchants or other customers, consults goods details or shares shopping experience, and in addition, the user interface module provides language and currency conversion tools to adapt to the user from different regions and cultural backgrounds in consideration of the cross-border of the metauniverse.
3. The metauniverse virtual shopping center innovation experience platform serving QoS as claimed in claim 1, wherein the resource management module ensures that all resources of the platform are effectively and efficiently managed and allocated, so that smooth and uninterrupted shopping experience is provided for users, the resource management module firstly classifies and indexes all resources of the platform, including virtual goods, user data, interaction models, graphic textures and audio files, enables the resources to be quickly searched and called through advanced classification algorithms and database technology, and takes account of huge virtual environment of metauniverse, the resource management module adopts dynamic resource loading technology, corresponding resources are loaded only when needed by users, bandwidth and computing resources are saved, loading speed is improved, the resource management module caches the frequently accessed resources to local and near-end servers, quick access is ensured, delay is reduced, and in order to provide optimal user experience under equipment and network conditions, the resource management module is responsible for updating and synchronizing the resources of the platform, and implementing the strict business and implementing the security of the resources and the security of the user module according to the current equipment performance and network conditions, and the quality and resolution of the resources are also responsible for updating and synchronizing the resources of the platform, and the security of the user module is guaranteed to be strictly and the security of the user is guaranteed.
4. The meta-universe virtual shopping center innovation experience platform for serving QoS according to claim 1, wherein an interaction optimization module ensures natural, smooth and visual interaction between a user and the platform, supports various interaction modes including voice, gesture, touch and virtual reality controllers, can accurately interpret and respond to commands and actions of the user through deep learning and sensor data fusion, provides instant visual, auditory and tactile feedback for enhancing immersion of the user, and can predict the next operation of the user through analyzing historical interaction data and behavior patterns of the user, and accordingly performs resource preloading and interface optimization, thereby further reducing delay and improving response speed.
5. The QoS-serviced metauniverse virtual shopping center innovation experience platform of claim 4, wherein for the visual feedback, gesture behaviors of a user are recorded by an interaction optimization module in a manner of images, the images recorded by the interaction optimization module are continuous in a continuous time period, and light sensitivity of the images is defined as F (x, y, t), wherein a light sensitivity function comprises an abscissa pixel point x, an ordinate pixel point y and a moment t of the images, and for the continuous time, the following steps are included: f (x, y, t) =f (x+Δx, y+Δy, t+Δt) holds, where Δx is the drift amount of the abscissa pixel x, Δy is the drift amount of the ordinate pixel y, Δt is the drift amount of the time t, and F (x+Δx, y+Δy, t+Δt) is first-order taylor expansion, and there is:
Wherein,representing F to x first order partial derivative, < ->Representing F to y first order partial derivative, < ->Representing F to t to obtain first order partial derivativeThe updating is carried out by:
wherein,for->Normal vector n is->In a planar rectangular coordinate system, the distance between (u, v) and the origin of coordinates is: />In view of the timeliness of the system, smoothing and GAUSS filtering are performed on each frame image, and for the pixel point P (x, y), there are:
wherein, sigma is the standard deviation of GAUSS, downsampling the image interlayer, and there are:
P (N) (x，y，t)＝G*P (0) (2Nx,2Ny，t)
wherein N is the layer number, G is the Laplace convolution kernel, and P (0) For the initial image, P (N) For downsampling an image of N layers, after interlacing, assuming that the original size of the image is u×v, the current size of the image isWhere U 'V' represents the current size of the image, denoted by P (N) Is the search center, extends to the image pixel range of (2k+1) x (2k+1) in the search domain, where k is a natural number, and the presence is:
wherein x is 1 ，x 2 ，...，x U`×V` Respectively representing the 1 st, 2 nd, and U 'x V' th numbers on an image of size U 'x V'Image abscissa, y 1 ，y 2 ，...，y U`×V` Representing the ordinate, t, of the 1 st, 2 nd, and U 'x V' th images on the U 'x V' size image, respectively 1 ，t 2 ，...，t U`×V` Representing the mapping time, satisfy P (N) (x，y，t)＝G*P (0) (2 nx,2ny, t), Indicating the deviation of the velocity vector in the direction of the horizontal axis, < >>Indicating the deviation of the velocity vector in the direction of the vertical axis.
6. The QoS-capable meta-universe virtual shopping center innovation experience platform of claim 5, wherein the meta-universe virtual shopping center innovation experience platform is obtained through fitting technology
Wherein,is P (N) For x i To calculate the second order deviation>Is P (N) For x i y i To calculate the second order deviation>Is P (N) For y i And solving a second order partial derivative, wherein i is an index number.
Where ρ is an autocorrelation function, n 0 Is a window function N 0 The window length, beta is a parameter, the shape of the window is controlled, and after first-order expansion, the window is obtained:
furthermore, since the present platform is based on the meta-space technology, considering that the presentation of the virtual commodity has a consistency, for the pixel point P (x, y) and the pixel drift point Δp (Δx, Δy), there is F (P (x, y) - Δp (Δx, Δy), t) =f (P (x, y), t- Δt), and there is:
(P(x，y)-ΔP(Δx，Δy)) T A 1 (P(x，y)-ΔP(Δx，Δy))+B 1 (P(x，y)-ΔP(Δx，Δy))+C 1 ＝P(x，y) T A 2 P(x，y)+B 2 P(x，y)+C 2
wherein A is 1 ，B 1 ，C 1 ，A 2 ，B 2 ，C 2 For the taylor expansion coefficient, the update is based on mathematical derivation:
P(x，y) T A 1 P(x，y)+(B 1 -2A 1 ΔP(Δx，Δy))P(x，y)+ΔP(Δx，Δy) T A 1 ΔP(Δx，Δy)-B 2 ΔP(Δx，Δy)+C 1
＝P(x，y) T A 2 P(x，y)+B 2 P(x，y)+C 2
according to equality of corresponding terms of equation, there is B 1 -2A 1 ΔP(Δx，Δy)＝B 2 Is true, and therefore deduce ΔP (Δx, Δy) as：
In addition, the interaction optimization module integrates an intelligent assistant, can understand the requirements, questions and commands of the user, then provides relevant information and suggestions, and can provide more personalized and relevant interaction experience according to the current scene and state of the user, including position, time and mood through a context awareness technology.
7. The QoS-serving meta-universe virtual shopping center innovation experience platform of claim 1, wherein in order to ensure high-quality recommendation, a commodity recommendation module comprehensively considers preferences, behavior histories, social networks and real-time feedback of users, collects and analyzes behavior data of the users on the platform, including browsing histories, purchase records, search queries and residence time, processes user data and commodity information by using a deep neural network, and uses collaborative filtering technology to recommend interesting commodities for the users according to historic behaviors of the users and other similar users, the commodity recommendation module can collect the feedback in real time and adjust recommendation strategies to better meet the requirements of the users, and the commodity recommendation module can provide relevant and practical recommendation according to real-time context of the users.
8. The QoS-serving metauniverse virtual shopping center innovation experience platform of claim 1, wherein the data collection and analysis module is responsible for capturing data from various interaction points, including functions of multi-source data collection, data preprocessing, advanced data analysis, data visualization, cloud storage and computation, and specifically comprises the following steps:
(1) A multi-source data collection function comprising:
user interaction data: the method comprises click rate, page browsing time, purchase records and search records;
sensor data: in the meta universe, users use VR/AR glasses, motion capture equipment devices, devices collect eye movement tracking, gestures, and body movement data;
social interaction data: social interactions of users within the platform, including comments, scoring, sharing and communication with other users,
(2) Data preprocessing, including:
data cleaning: removing errors, duplicates, and irrelevant data;
data conversion: converting the raw data into a format for use by an analysis tool;
filling data: reasonably estimating or filling missing or incomplete data;
(3) Advanced data analysis, comprising:
trend analysis: analyzing the trend of the data changing along with time, and predicting future user behaviors and market trends;
And (3) cluster analysis: users are divided into different groups and market segments to provide more targeted services or recommendations,
association rule analysis: finding out purchase relations between commodities;
(4) Data visualization, comprising:
visual charts and instrument panel visualization tools are provided to help merchants and platform managers understand and master key indexes of user behaviors and market trends;
(5) Cloud storage and computing, comprising:
elastic storage and computing power is provided through cloud technology to support the processing and analysis of large-scale user data.
9. The QoS-serving meta-universe virtual shopping center innovation experience platform of claim 1, wherein the security and privacy module aims at ensuring the integrity, confidentiality and availability of user data, ensuring that privacy rights of users in meta-universe shopping experiences are not violated, ensuring that user data are not sniffed or intercepted in the transmission process, and the user data stored on a server adopt a high-strength encryption algorithm to ensure data security.
10. The QoS-serving metauniverse virtual shopping center innovation experience platform of claim 1, wherein the social and interaction module provides an immersive, highly interactive social shopping experience for users, allows users to interact with other users in the virtual space, share the shopping experience, and participate in various interactive activities, the users can create and customize their own avatar, the users communicate with other users on the platform in real time through voice, text, the social and interaction module allows multiple users to co-browse merchandise, participate in virtual activities, adds a collaborative dimension to the shopping experience, the users share the shopping experience, virtual try-on effects, or other interactions directly on the platform to their social media accounts, and provides the users with a more immersive shopping experience, including 3D fitting room, AR merchandise preview, using VR and AR technologies.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117611299A (en) * | 2024-01-22 | 2024-02-27 | 青岛威达体育用品有限公司 | Customization method and system for personalized school uniform, electronic equipment and storage medium |
| CN117997959A (en) * | 2024-04-07 | 2024-05-07 | 厦门两万里文化传媒有限公司 | Resource intelligent matching method and system based on meta universe |
| CN118014695A (en) * | 2024-04-09 | 2024-05-10 | 加客云科技(河北)有限公司 | Commodity pushing method and system based on multi-source data screening |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117611299A (en) * | 2024-01-22 | 2024-02-27 | 青岛威达体育用品有限公司 | Customization method and system for personalized school uniform, electronic equipment and storage medium |
| CN117611299B (en) * | 2024-01-22 | 2024-04-26 | 青岛威达体育用品有限公司 | Customization method and system for personalized school uniform, electronic equipment and storage medium |
| CN117997959A (en) * | 2024-04-07 | 2024-05-07 | 厦门两万里文化传媒有限公司 | Resource intelligent matching method and system based on meta universe |
| CN117997959B (en) * | 2024-04-07 | 2024-06-04 | 厦门两万里文化传媒有限公司 | Resource intelligent matching method and system based on meta universe |
| CN118014695A (en) * | 2024-04-09 | 2024-05-10 | 加客云科技(河北)有限公司 | Commodity pushing method and system based on multi-source data screening |
| CN118014695B (en) * | 2024-04-09 | 2024-06-04 | 加客云科技(河北)有限公司 | Commodity pushing method and system based on multi-source data screening |
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