Intelligent wearable device and universe identity synchronization system

Abstract

The invention belongs to the technical field of digital identity authentication, and particularly relates to an intelligent wearable device and meta universe identity synchronization system, which is characterized in that a three-dimensional virtual avatar is generated by constructing a center-edge secondary identity authentication information packet and fusing multi-dimensional user information; comprehensive protection of user identity information and high synchronization of virtuality and reality are realized; secondly, a private key and public key verification valve is adopted to ensure the safety of the identity information chain, and cross-platform seamless switching and verification are supported; thirdly, user postures, micro expressions and emotional states are mapped in real time, and the sense of reality and immersion of virtual interaction are enhanced; meanwhile, health monitoring and abnormity relieving scene generation functions are integrated, so that the health management level and the emergency response capability of the user are improved; synchronous mapping of non-local node verification and dynamic three-dimensional user virtual avatar is achieved through a single user sharing identifier, and continuity and safety of the user in the meta universe are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital identity authentication, and in particular relates to a smart wearable device and metaverse identity synchronization system. Background Art

[0002] The Metaverse is a virtual space that integrates multiple advanced technologies. It allows users to create and experience new ways of living, working, and entertaining in the virtual world. In this virtual space, users usually have one or more digital identities, which represent the user's image and behavior in the virtual world. In order to enhance the user experience, smart wearable devices are widely used in the Metaverse. They can collect users' physiological and behavioral data and synchronize these data to the user's virtual identity, making the virtual identity closer to the real user's status; however, there are multiple challenges in the current smart wearable device and Metaverse identity synchronization technology: the identity authentication mechanism is simple and vulnerable to attacks, and lacks unified standards; data synchronization is poor in real time and accuracy; virtual identities are difficult to adjust dynamically, affecting immersion; cross-platform compatibility is poor, and user experience is inconsistent; intelligent interaction technology is not yet mature, and feedback mechanism is lacking, which overall affects the security and user experience of the system.

[0003] For example, the Chinese patent with publication number CN116449958A discloses a virtual office system based on the metaverse. Although the user experience is improved through inertial motion capture and 3D virtual characters, there are still problems in actual application, such as a single identity authentication mechanism, insufficient real-time data synchronization, and limited personalized adjustment of virtual identities; the Chinese patent with authorization announcement number CN117221633B discloses a virtual reality live broadcast system based on the metaverse and digital twin technology. Although it uses laser radar and tracking equipment to achieve high-precision three-dimensional reconstruction and motion capture, its hardware configuration is complex and costly, and there may be delays in data transmission and processing, affecting real-time performance. In addition, both systems lack effective mechanisms for privacy protection, the security of user data cannot be fully guaranteed, and cross-platform compatibility and intelligent interactive experience still need to be improved.

[0004] The above existing technologies have the following problems: the existing technologies can only authenticate identity at a single node, and there are still some problems with simultaneous authentication on multiple platforms and multiple nodes and data synchronization and security. In order to solve the above problems, the present invention proposes a smart wearable device and metaverse identity synchronization system. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a smart wearable device and metaverse identity synchronization system, which achieves comprehensive protection of user identity information and high synchronization between virtual and reality by constructing a center-edge secondary identity authentication information package and integrating multi-dimensional user information to generate a three-dimensional virtual avatar; secondly, private key and public key verification valves are used to ensure the security of the identity information chain and support seamless switching and verification across platforms; thirdly, user posture, micro-expression and emotional state are mapped in real time to enhance the realism and immersion of virtual interaction; at the same time, health monitoring and abnormal mitigation scenario generation functions are integrated to improve user health management level and emergency response capabilities; non-local node verification and dynamic three-dimensional user virtual avatar synchronous mapping are realized through a single user shared identifier to ensure the continuity and security of users in the metaverse.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A smart wearable device and metaverse identity synchronization system, comprising: an identity authentication module, a data synchronization module, a data mapping module and an interaction module;

[0008] The identity authentication module includes an identity building unit, a verification chain building unit and an identity authentication unit;

[0009] The identity construction unit is used to construct a center-edge secondary identity authentication information package through a federated learning algorithm based on the obtained user information; the verification chain construction unit is used to construct a center identity information chain and an edge dual identity information chain based on the obtained center-edge secondary identity authentication information package, and build the constructed center identity information chain and edge dual identity information chain into the identity authentication unit; the identity authentication unit is used to perform local or non-local login identity authentication and social identity authentication based on the constructed center identity information chain and edge dual identity information chain and the login verification information input by the user;

[0010] The data synchronization module includes a data acquisition unit, a data compression unit, a data encryption unit and a synchronization sharing unit;

[0011] The data acquisition unit is used to obtain multi-dimensional user information through the smart wearable device; the synchronization sharing unit is used to share the verification information verified by the identity authentication unit and the multi-dimensional user valid data compression package obtained by the data encryption unit and the data compression unit to the non-local edge node using the constructed edge dual identity information chain, and at the same time, a single user shared identifier is generated on the non-local edge node, and the generated single user shared identifier is fed back to the edge node and central node corresponding to the user's current location for storage.

[0012] Specifically, the data mapping module includes a posture mapping unit, a micro-expression mapping unit, a voice emotion unit, a scene mapping unit, and an anomaly monitoring unit;

[0013] A posture mapping unit is used to construct a model through three-dimensional posture according to the effective data of multi-dimensional users to obtain an initial three-dimensional dynamic user virtual avatar; a micro-expression mapping unit is used to obtain the micro-expression information of the corresponding user through a micro-expression recognition model according to the obtained micro-expression information; a voice emotion unit is used to recognize the emotional information in the voice through a voice emotion recognition model according to the obtained user voice information, and map the recognized emotional information, micro-expression information and edge dual identity information chain to the initial three-dimensional dynamic user virtual avatar to obtain a dynamic three-dimensional user virtual avatar; a scene mapping unit is used to generate a scene corresponding to the user's needs through a scene generation model according to the synchronized user demand data and the user health status monitored by the abnormal monitoring unit;

[0014] The interaction module includes an input unit and a voice interaction unit;

[0015] Input unit, used for inputting user login verification information and user demand information;

[0016] The voice interaction unit is used to recognize and convert voice commands during the interaction between the user and the Metaverse, and feeds back the converted voice commands to the data mapping module to perform corresponding conversions and updates on the user's gestures, micro-expressions or scenes.

[0017] Specifically, the edge dual identity information chain includes a single-user edge identity information verification chain and a single-node multi-user edge identity information verification chain, and information verification valves are configured between different sub-nodes in the single-node multi-user edge identity information verification chain to perform information interaction verification between users corresponding to different sub-nodes in a single node;

[0018] A single user edge identity verification chain for user local or non-local login authentication;

[0019] A single-node multi-user edge identity information verification chain for user local or non-local social identity authentication.

[0020] Specifically, the steps of constructing the center-edge secondary authentication information package include:

[0021] A1. Obtain basic attribute information and preprocess it, and construct a basic attribute information package using the preprocessed basic attribute information and the configured effective verification time;

[0022] A2. Generate a private key and a public key corresponding to the basic attribute information package through an encryption algorithm according to the basic attribute information package obtained;

[0023] A3. The generated private key is built into the basic attribute information package to generate a central identity authentication information package and saved in the central node, and the obtained public key is built into the basic attribute information package to generate an edge identity authentication information package and saved in a distributed manner to all edge nodes connected to the central node;

[0024] A4. Build an update verification model based on reinforcement learning, and embed the update verification model into the central node and all corresponding edge nodes. Use the corresponding working status data of the central node and all edge nodes to initialize the update verification model built into each node, and start the test verification process.

[0025] Specifically, the steps of constructing the center-edge secondary authentication information package also include:

[0026] A5. Set the effective verification time update interval and start the test process, collect the operating parameters and operating efficiency data corresponding to the central node and all edge nodes in real time, and pre-process the collected data;

[0027] A6. Use the preprocessed operating parameters and operating efficiency data to construct an input sequence pair (Xi, Yi), and input (Xi, Yi) into the random forest algorithm to obtain the operating parameters and effective verification time update interval corresponding to the initial optimal operating efficiency; where Xi represents the operating parameter sequence corresponding to the central node and all edge nodes under the i-th effective verification time update interval, and Yi represents the operating efficiency sequence corresponding to the central node and all edge nodes under the i-th effective verification time update interval;

[0028] A7. Feedback the effective verification time update interval corresponding to the optimal operation efficiency to the central identity authentication information package and edge identity authentication information package corresponding to the verified user in the central node and all edge nodes, and at the same time feed back the obtained operation parameters to the update verification model built into the central node and all edge nodes, and start the secondary test;

[0029] A8. Obtain the optimal operating efficiency corresponding to the secondary test and compare it with the initial optimal operating efficiency. If it is less than the initial optimal operating efficiency, continue to perform the update test. If it is greater than the initial optimal operating efficiency, use the operating parameters corresponding to the optimal operating efficiency of the secondary test and the effective verification time update interval to update the central identity authentication information package, the edge identity authentication information package, and the update verification model built into the central node and all edge nodes;

[0030] A9. Set the test stop iteration threshold. When the optimal running efficiency of m consecutive tests is in a declining state and m is greater than the test stop iteration threshold, stop the test iteration.

[0031] A10, embed the effective verification time update interval corresponding to the maximum optimal operation efficiency of continuous testing m times into the central identity authentication information package and the edge identity authentication information package, obtain the central-edge secondary identity authentication information package, and update the corresponding operation parameters to the update verification model built into the central node and all edge nodes, obtain the updated verification model in the federated mode after training, and integrate the A7-A9 parameter iteration process into all edge nodes;

[0032] A11. When the optimal operating efficiency drops again, repeat the A7-A10 process to continuously update the update verification model and effective verification time update interval built into all edge nodes.

[0033] Specifically, the specific steps of constructing a dynamic three-dimensional user virtual avatar include:

[0034] B1. Obtain user facial image data, voice data, health status data and video motion data through smart wearable devices, and pre-process the acquired data;

[0035] B2. Process the acquired video action data into video frames to obtain point cloud position data of each joint point of the user at different time points, limb image data, and micro-expression image data corresponding to different time points;

[0036] B3. Build a human 3D generation model based on the pre-trained VIBE model, and input the corresponding point cloud position data and limb image data of each joint point of the user at different time points into the human 3D generation model to generate a dynamic limb 3D avatar;

[0037] B4. Input the user's facial image data into the pre-trained lightweight VIT model to extract the user's facial features and obtain the user's static facial features;

[0038] B5. Map the acquired static facial features of the user to the facial position in the dynamic limb three-dimensional avatar, and use the point cloud matrix constructed by the facial position point cloud data in the dynamic limb three-dimensional avatar and the matrix composed of the corresponding position pixel points in the user's static facial features to perform facial matching to obtain the initial three-dimensional dynamic user virtual avatar.

[0039] Specifically, the specific steps of constructing a dynamic three-dimensional user virtual avatar also include:

[0040] B6. Construct a micro-expression recognition model based on multi-scale convolution and attention network, input the acquired micro-expression image data corresponding to different time points into the micro-expression recognition model, and obtain a micro-expression change feature sequence with time characteristics;

[0041] B7, aligning the time features in the micro-expression change feature sequence with the time information in the dynamic limb three-dimensional avatar, and mapping the aligned micro-expression change feature sequence to the facial position of the initial three-dimensional dynamic user virtual avatar corresponding to different time points to obtain a secondary three-dimensional dynamic user virtual avatar;

[0042] B8. After the speech data is labeled with emotion type and intensity, it is input into the speech emotion recognition model to obtain the speech emotion type and emotion intensity corresponding to different time points;

[0043] B9. Using the speech emotion type, emotion intensity and micro-expression change feature sequence with time characteristics corresponding to different time points, a speech-to-facial emotion mapping function is constructed through a support vector machine;

[0044] B10, according to the constructed voice-facial emotion mapping function, mapping the voice emotion type and emotion intensity corresponding to different time points to the micro-expressions of the secondary three-dimensional dynamic user virtual avatar, to obtain the final dynamic three-dimensional user virtual avatar;

[0045] B11. Based on the health status data, micro-expressions and emotional information corresponding to the final dynamic three-dimensional user virtual avatar, the health status score and abnormal status type corresponding to the current user are obtained through the constructed abnormality monitoring model;

[0046] B12, obtaining the health status score and abnormal status type corresponding to the user, and embedding them into the final dynamic three-dimensional user virtual avatar through an encryption and hiding algorithm;

[0047] B13. When the user successfully logs in through authentication, the dynamic three-dimensional user virtual avatar is updated in real time through the multi-dimensional user valid data compression package obtained in real time.

[0048] Specifically, the workflow of the scene mapping unit includes:

[0049] C1. Obtain the user's real-time voice data, health status data and micro-expression image data, and input the acquired real-time voice data and micro-expression image data into the voice emotion unit and the micro-expression mapping unit in sequence to obtain the real-time emotion type, emotion intensity score and micro-expression type and corresponding evaluation score;

[0050] C2. Setting an abnormal emotion score threshold, and using the obtained emotion intensity score and micro-expression type evaluation score to obtain an abnormal emotion comprehensive evaluation score. If the abnormal emotion comprehensive evaluation score is less than the abnormal emotion score threshold, it is judged that the first evaluation parameter of the corresponding user is abnormal. Otherwise, the first evaluation parameter of the corresponding user is in a normal state.

[0051] C3. Setting a physiological abnormality score threshold, inputting the acquired emotional abnormality comprehensive evaluation score and health status data into the abnormality monitoring unit, and obtaining the corresponding user physiological evaluation score;

[0052] C4. If the user's physiological assessment score is less than the physiological abnormality score threshold, it is determined that the user's second assessment parameter is abnormal; otherwise, the second assessment parameter is normal.

[0053] Specifically, the workflow of the scene mapping unit includes:

[0054] C5. When the second evaluation parameter of the corresponding user is abnormal, a private mapping scene space 12 is generated through a scene mapping unit, a verification public key is configured on the generated private mapping scene space 12, and the configured verification public key is mapped to the central identity authentication information package of the corresponding user in the central node;

[0055] C6. Based on the user's physiological abnormality information, the system searches for the doctor user corresponding to the user's physiological abnormality through the constructed central multi-user identity information chain, and issues an alarm through the emergency rescue program configured by the system;

[0056] C7. If a doctor user with physiological abnormality of the corresponding user is searched, the identity construction unit generates a corresponding verification pass code according to the verification public key configured in the private mapping scene space 12, and the generated verification pass code is assigned to the corresponding doctor user. The doctor user enters the constructed private mapping scene space 12 through the verification pass code and the single-user edge identity information verification chain or the single-node multi-user edge identity information verification chain to provide self-help guidance to the user with physiological abnormality;

[0057] C8, when the corresponding user is only abnormal for the first evaluation parameter, then according to the micro-expression and emotion abnormality comprehensive evaluation score obtained by the micro-expression mapping unit and the voice emotion unit at the current moment and the user demand input by the user or acquired by voice, the abnormality mitigation scene space 11 is generated by the scene mapping unit;

[0058] C9. When there is no abnormality in the corresponding user, the scene space 2 corresponding to the user's needs is generated through the scene mapping unit according to the user demand information input by the user, and displayed in real time through the display unit. At the same time, during the interaction with the metaverse, the voice interaction unit captures the voice commands issued by the user in real time, and switches the user's current scene in real time.

[0059] Specifically, the steps of switching the user's current scene in real time include:

[0060] C10. When the user switches the non-local node scene, the corresponding non-local node is first verified through the constructed single user shared identifier. When the verification is passed, the constructed dynamic three-dimensional user virtual avatar is mapped to the corresponding non-local node through the synchronous sharing unit, and the locally generated scene is synchronously switched to the corresponding non-local node through the synchronous sharing unit according to the user voice command information;

[0061] C11. When the user needs to communicate privately with other users, a private room 21 is constructed through the scene mapping unit according to the user demand information input by the input unit, and the private room 21 is verified and configured using the private key in the identity authentication information corresponding to the user created and stored by the central node;

[0062] C12. The public key corresponding to the configured private key is assigned to the non-creating user who is allowed by the creating user to enter the private room 21. The non-creating user performs identity authentication through the assigned public key and the single-user edge identity information verification chain, or the public key and the single-node multi-user edge identity information verification chain, and enters the private room 21 after passing the authentication.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] In view of the deficiencies of the prior art, the present invention generates a center-edge secondary identity authentication information package by using a federated learning algorithm through an identity construction unit, wherein the private key is stored in the central node and the public key is distributed and stored in multiple edge nodes. This design not only ensures the security of identity information, but also improves the flexibility of verification. The central identity information chain and the edge dual identity information chain constructed by the verification chain construction unit enable users to perform efficient identity authentication in local or non-local environments. In particular, through the information verification valve in the single-node multi-user edge identity information verification chain, interactive verification of private information between different users is realized, and privacy protection in social scenarios is enhanced. In addition, the data synchronization module realizes real-time data synchronization between smart wearable devices and the metaverse system through functions such as data acquisition, compression, encryption and synchronous sharing, and ensures secure data sharing through the edge dual identity information chain. This multi-layer verification and synchronization mechanism not only solves the limitations of single-node authentication in the prior art, but also greatly improves the authentication efficiency and data security between multiple platforms and multiple nodes, providing users with a more convenient, secure and reliable metaverse experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a module diagram of a smart wearable device and Metaverse identity synchronization system in Example 1 of the present invention;

[0066] Figure 2 This is a flowchart of the corresponding units of a smart wearable device and a metaverse identity synchronization system according to Example 2 of the present invention. DETAILED DESCRIPTION

[0067] Example 1

[0068] See also Figure 1 , an embodiment provided by the present invention: a smart wearable device and metaverse identity synchronization system, comprising: an identity authentication module, a data synchronization module, a data mapping module and an interaction module;

[0069] The identity authentication module is used to authenticate the information input by the user through the smart wearable device by constructing an identity authentication strategy; the identity authentication module includes an identity construction unit, a verification chain construction unit and an identity authentication unit;

[0070] The identity construction unit is used to construct a center-edge secondary identity authentication information package through a federated learning algorithm based on the acquired user information; the identity authentication information package includes a private key and a public key constructed by the identity authentication information; the private key is stored in the central node, and the public key is stored in multiple edge nodes in a distributed manner; further, the edge nodes in this embodiment are different edge login platforms, and the central node is a central service platform.

[0071] The steps to construct the center-edge secondary authentication information package include:

[0072] A1. Obtain basic attribute information and preprocess it, and construct a basic attribute information package using the preprocessed basic attribute information and the configured effective verification time;

[0073] A2. Generate a private key and a public key corresponding to the basic attribute information package through an encryption algorithm according to the basic attribute information package obtained;

[0074] A3. The generated private key is built into the basic attribute information package to generate a central identity authentication information package and saved in the central node, and the obtained public key is built into the basic attribute information package to generate an edge identity authentication information package and saved in a distributed manner to all edge nodes connected to the central node;

[0075] A4. Build an update verification model based on reinforcement learning, and embed the update verification model into the central node and all corresponding edge nodes. Use the corresponding working status data of the central node and all edge nodes to initialize the update verification model embedded into each node, and start the test verification process.

[0076] A5. Set the effective verification time update interval and start the test process, collect the operating parameters and operating efficiency data corresponding to the central node and all edge nodes in real time, and pre-process the collected data; further, in this embodiment, the operating efficiency data includes network delay, data transmission rate and resource consumption, etc.;

[0077] A6. Use the preprocessed operating parameters and operating efficiency data to construct an input sequence pair (Xi, Yi), and input (Xi, Yi) into the random forest algorithm to obtain the operating parameters and effective verification time update interval corresponding to the initial optimal operating efficiency; wherein Xi represents the operating parameter sequence corresponding to the central node and all edge nodes under the i-th effective verification time update interval, and Yi represents the operating efficiency sequence corresponding to the central node and all edge nodes under the i-th effective verification time update interval; further, in this embodiment, the optimal operating efficiency is the test result of the test process corresponding to the shortest network delay, the fastest data transmission rate and the least resource consumption;

[0078] A7. Feedback the effective verification time update interval corresponding to the optimal operation efficiency to the central identity authentication information package and edge identity authentication information package corresponding to the verified user in the central node and all edge nodes, and at the same time feed back the obtained operation parameters to the update verification model built into the central node and all edge nodes, and start the secondary test;

[0079] A8. Obtain the optimal operating efficiency corresponding to the secondary test and compare it with the initial optimal operating efficiency. If it is less than the initial optimal operating efficiency, continue to perform the update test. If it is greater than the initial optimal operating efficiency, use the operating parameters corresponding to the optimal operating efficiency of the secondary test and the effective verification time update interval to update the central identity authentication information package, the edge identity authentication information package, and the update verification model built into the central node and all edge nodes;

[0080] A9. Set the test stop iteration threshold. When the optimal running efficiency of m consecutive tests is in a declining state and m is greater than the test stop iteration threshold, stop the test iteration.

[0081] A10, embed the effective verification time update interval corresponding to the maximum optimal operation efficiency of continuous testing m times into the central identity authentication information package and the edge identity authentication information package, obtain the central-edge secondary identity authentication information package, and update the corresponding operation parameters to the update verification model built into the central node and all edge nodes, obtain the updated verification model in the federated mode after training, and integrate the A7-A9 parameter iteration process into all edge nodes;

[0082] A11. When the optimal operating efficiency drops again, repeat the A7-A10 process to continuously update the update verification model and effective verification time update interval built into all edge nodes.

[0083] This process builds an authentication information package through a federated learning algorithm. The private key is stored in the central node, while the public key is distributed and stored in multiple edge nodes. This design not only ensures data security, but also improves the robustness and availability of the system. Secondly, the update verification model built based on reinforcement learning can self-optimize according to the system operation status data, thereby continuously improving the verification efficiency and accuracy. In addition, by setting the effective verification time update interval and continuously iterating and optimizing, the system can dynamically adjust the verification strategy to ensure that efficient verification performance can be maintained under different operating environments.

[0084] The verification chain construction unit is used to construct a central multi-user identity information chain and an edge dual identity information chain according to the obtained central-edge secondary identity authentication information package, and embed the constructed central multi-user identity information chain and edge dual identity information chain into the identity authentication unit;

[0085] The central multi-user identity information chain is constructed through a blockchain algorithm based on the identity authentication information package containing a private key corresponding to each user stored in the central node; further, in this embodiment, the node consisting of the private key information corresponding to each user in the central multi-user identity information chain is a sub-node of the central multi-user identity information chain.

[0086] The identity verification unit is used to perform local or non-local login identity verification and social identity verification according to the constructed central multi-user identity information chain and edge dual identity information chain and the login verification information input by the user;

[0087] The edge dual identity information chain includes a single-user edge identity information verification chain and a single-node multi-user edge identity information verification chain, and information verification valves are configured between different sub-nodes in the single-node multi-user edge identity information verification chain to perform information interaction verification between different users; further, the single-user edge identity information verification chain in this embodiment is constructed by the identity authentication information package containing public key information stored in each edge node through a distributed ledger algorithm; the information verification valve is verified by configuring verification questions and answers;

[0088] The single-node multi-user edge identity information verification chain is constructed by the blockchain algorithm using the identity verification information packages containing public key information corresponding to different users stored in a single edge node;

[0089] The single-user edge identity information verification chain is used for user local or non-local login identity authentication; the single-node multi-user edge identity information verification chain is used for user local or non-local social identity authentication.

[0090] Furthermore, in this embodiment, local or non-local social identity authentication is specifically: when a user needs to communicate privately or display private information to other users, identity information verification is performed through the constructed single-node multi-user edge identity information verification chain. When the corresponding user passes the verification, private communication is carried out or private information is displayed to him.

[0091] The data synchronization module is used to realize real-time data synchronization between the smart wearable device and the Metaverse system; the data synchronization module includes a data acquisition unit, a data compression unit, a data encryption unit and a synchronization sharing unit;

[0092] The data acquisition unit is used to acquire multi-dimensional user information through the smart wearable device; the multi-dimensional user information includes user basic attribute information, user posture information, health status information, and micro-expression information;

[0093] Furthermore, in this embodiment, the basic attribute information of the user includes user name, user ID, biometric information, contact information, security questions and answers, authority level, authentication certificate, and wearable device information; biometric information includes fingerprint, iris scan, and facial recognition data; authority levels include ordinary users and VIP users; authentication certificates include digital certificates and public key certificates; wearable device information includes the model and hardware identifier of the smart wearable device used by the user, etc.

[0094] The data compression unit is used to filter and compress the acquired multidimensional user information through a data filtering and compression algorithm to obtain a multidimensional user valid data compression package; further, in this embodiment, the acquired multidimensional user information is filtered through a principal component analysis algorithm to filter the data, and the user information with a cumulative contribution rate exceeding 80% in the multidimensional user information is retained to obtain a multidimensional user valid data compression package;

[0095] The data encryption unit is used to encrypt the multi-dimensional user valid data compression package through an encryption algorithm and upload it to the local edge node;

[0096] The synchronous sharing unit is used to share the verification information and the multi-dimensional user valid data compression package verified by the identity authentication unit to the non-local edge node using the constructed edge dual identity information chain, and at the same time generate a corresponding single user shared identifier on the non-local edge node, and feed back the generated single user shared identifier to the edge node and central node corresponding to the user's current location for storage.

[0097] The data mapping module is used to construct a dynamic user virtual avatar and virtual scene based on the synchronized multi-dimensional user effective data, and to monitor and warn the user health data in real time; the data mapping module includes a posture mapping unit, a micro-expression mapping unit, a voice emotion unit, a scene mapping unit and an abnormality monitoring unit;

[0098] The posture mapping unit is used to construct a model through three-dimensional posture according to the multi-dimensional user effective data to obtain a three-dimensional dynamic user virtual avatar; the three-dimensional posture includes upper limb posture movements and lower limb gait movements;

[0099] The micro-expression mapping unit is used to obtain micro-expression information of the corresponding user through a micro-expression recognition model according to the obtained micro-expression information;

[0100] The speech emotion unit is used to identify the emotion information in the speech through the speech emotion recognition model according to the acquired user speech information, and map the identified emotion information, micro-expression information and edge dual identity information chain to the initial three-dimensional dynamic user virtual avatar to obtain a dynamic three-dimensional user virtual avatar;

[0101] Furthermore, in this embodiment, the specific steps of constructing a dynamic three-dimensional user virtual avatar include:

[0102] B1. Obtain user facial image data, voice data, health status data and video motion data through smart wearable devices, and pre-process the acquired data;

[0103] B2. Process the acquired video action data into video frames to obtain point cloud position data of each joint point of the user at different time points, limb image data, and micro-expression image data corresponding to different time points;

[0104] B3. Build a human 3D generation model based on the pre-trained VIBE model, and input the corresponding point cloud position data and limb image data of each joint point of the user at different time points into the human 3D generation model to generate a dynamic limb 3D avatar;

[0105] B4. Input the user's facial image data into the pre-trained lightweight VIT model to extract the user's facial features and obtain the user's static facial features;

[0106] B5. Map the acquired static facial features of the user to the facial position in the dynamic limb three-dimensional avatar, and use the point cloud matrix constructed by the facial position point cloud data in the dynamic limb three-dimensional avatar and the matrix composed of the corresponding position pixel points in the user's static facial features to perform facial matching to obtain the initial three-dimensional dynamic user virtual avatar.

[0107] B6. Construct a micro-expression recognition model based on multi-scale convolution and attention network, input the acquired micro-expression image data corresponding to different time points into the micro-expression recognition model, and obtain a micro-expression change feature sequence with time characteristics;

[0108] B7, aligning the time features in the micro-expression change feature sequence with the time information in the dynamic limb three-dimensional avatar, and mapping the aligned micro-expression change feature sequence to the facial position of the initial three-dimensional dynamic user virtual avatar corresponding to different time points to obtain a secondary three-dimensional dynamic user virtual avatar;

[0109] B8. After the speech data is labeled with emotion type and intensity, it is input into the speech emotion recognition model to obtain the speech emotion type and emotion intensity corresponding to different time points;

[0110] B9. Using the speech emotion type, emotion intensity and micro-expression change feature sequence with time characteristics corresponding to different time points, a speech-to-facial emotion mapping function is constructed through a support vector machine;

[0111] B10, according to the constructed voice-facial emotion mapping function, mapping the voice emotion type and emotion intensity corresponding to different time points to the micro-expressions of the secondary three-dimensional dynamic user virtual avatar, to obtain the final dynamic three-dimensional user virtual avatar;

[0112] B11. Based on the health status data, micro-expressions and emotional information corresponding to the final dynamic three-dimensional user virtual avatar, the health status score and abnormal status type corresponding to the current user are obtained through the constructed abnormality monitoring model;

[0113] B12, obtaining the health status score and abnormal status type corresponding to the user, and embedding them into the final dynamic three-dimensional user virtual avatar through an encryption and hiding algorithm;

[0114] B13. When the user successfully logs in through authentication, the dynamic three-dimensional user virtual avatar is updated in real time through the multi-dimensional user valid data compression package obtained in real time.

[0115] This process uses a three-dimensional human body generation model and a micro-expression recognition model to process the user's motion data and facial micro-expression data respectively, generates a dynamic limb three-dimensional avatar and maps facial features in real time to ensure that the virtual avatar's movements and expressions are natural and smooth; secondly, the speech emotion recognition model is used to identify the emotional information in the user's voice, and combined with the characteristic sequence of micro-expression changes, a speech-to-facial emotion mapping function is constructed, so that the virtual avatar can show expression changes that are consistent with the user's emotional state; finally, the system evaluates the user's health status through an abnormality monitoring model, and encrypts and hides the relevant information in the virtual avatar to ensure data security and privacy protection.

[0116] The abnormal monitoring unit is used to obtain the user health status according to the acquired user health status data through the configured abnormal evaluation and recognition model, and to determine whether the current user comprehensive status is abnormal by obtaining the user health status, the voice emotion recognition result and the micro-expression recognition result. If abnormal, the abnormal user comprehensive status obtained is fed back to the synchronization sharing unit and the scene mapping unit, and an alarm is processed at the same time;

[0117] The scenario mapping unit is used to generate a scenario corresponding to the user's needs through a scenario generation model according to the synchronized user demand data and the user health status monitored by the abnormality monitoring unit;

[0118] The scenarios corresponding to the user's needs include public scene mode and private scene mode; the public scene mode is the original scene space of the metaverse virtual space; the private scene mode is a private scene with user verification information generated by the metaverse space according to user demand information, and the private scene generates a private verification key and a corresponding public key through an encryption algorithm through the edge identity authentication information package corresponding to the user; the encryption algorithms in this embodiment include RSA and ECC, etc.

[0119] Furthermore, the workflow of the scene mapping unit in this embodiment includes:

[0120] C1. Obtain the user's real-time voice data, health status data and micro-expression image data, and input the acquired real-time voice data and micro-expression image data into the voice emotion unit and the micro-expression mapping unit in sequence to obtain the real-time emotion type, emotion intensity score and micro-expression type and corresponding evaluation score;

[0121] C2. Setting an abnormal emotion score threshold, and using the obtained emotion intensity score and micro-expression type evaluation score to obtain an abnormal emotion comprehensive evaluation score. If the abnormal emotion comprehensive evaluation score is less than the abnormal emotion score threshold, it is judged that the first evaluation parameter of the corresponding user is abnormal. Otherwise, the first evaluation parameter of the corresponding user is in a normal state.

[0122] C3. Setting a physiological abnormality score threshold, inputting the acquired emotional abnormality comprehensive evaluation score and health status data into the abnormality monitoring unit, and obtaining the corresponding user physiological evaluation score;

[0123] C4. If the user's physiological assessment score is less than the physiological abnormality score threshold, it is determined that the user's second assessment parameter is abnormal; otherwise, the second assessment parameter is normal.

[0124] C5. When the second evaluation parameter of the corresponding user is abnormal, a private mapping scene space 12 is generated through a scene mapping unit, a verification public key is configured on the generated private mapping scene space 12, and the configured verification public key is mapped to the central identity authentication information package of the corresponding user in the central node;

[0125] C6. Based on the user's physiological abnormality information, the system searches for the doctor user corresponding to the user's physiological abnormality through the constructed central multi-user identity information chain, and issues an alarm through the emergency rescue program configured by the system;

[0126] C7. If a doctor user with physiological abnormality of the corresponding user is searched, the identity construction unit generates a corresponding verification pass code according to the verification public key configured in the private mapping scene space 12, and the generated verification pass code is assigned to the corresponding doctor user. The doctor user enters the constructed private mapping scene space 12 through the verification pass code and the single-user edge identity information verification chain or the single-node multi-user edge identity information verification chain to provide self-help guidance to the user with physiological abnormality;

[0127] C8, when the corresponding user is only abnormal for the first evaluation parameter, then according to the micro-expression and emotion abnormality comprehensive evaluation score obtained by the micro-expression mapping unit and the voice emotion unit at the current moment and the user demand input by the user or acquired by voice, the abnormality mitigation scene space 11 is generated by the scene mapping unit;

[0128] C9. When there is no abnormality in the corresponding user, the scene space 2 corresponding to the user's needs is generated through the scene mapping unit according to the user demand information input by the user, and displayed in real time through the display unit. At the same time, during the interaction with the metaverse, the voice interaction unit captures the voice commands issued by the user in real time, and switches the user's current scene in real time.

[0129] Furthermore, the step of switching the user's current scene in real time in this embodiment includes:

[0130] C10. When the user switches the non-local node scene, the corresponding non-local node is first verified through the constructed single user shared identifier. When the verification is passed, the constructed dynamic three-dimensional user virtual avatar is mapped to the corresponding non-local node through the synchronous sharing unit, and the locally generated scene is synchronously switched to the corresponding non-local node through the synchronous sharing unit according to the user voice command information;

[0131] C11. When the user needs to communicate privately with other users, a private room 21 is constructed through the scene mapping unit according to the user demand information input by the input unit, and the private room 21 is verified and configured using the private key in the identity authentication information corresponding to the user created and stored by the central node;

[0132] C12. The public key corresponding to the configured private key is assigned to the non-creating user who is allowed by the creating user to enter the private room 21. The non-creating user performs identity authentication through the assigned public key and the single-user edge identity information verification chain, or the public key and the single-node multi-user edge identity information verification chain, and enters the private room 21 after passing the authentication.

[0133] The interaction module is used for real-time interaction between the user and the metaverse and private scene configuration; the interaction module includes a display unit, an input unit and a voice interaction unit;

[0134] The display unit is used to visually display the scene entered by the user on the smart wearable device; the input unit is used to input user login verification information and user demand information;

[0135] The voice interaction unit is used to recognize and convert voice commands during the interaction between the user and the metaverse, and feeds back the converted voice commands to the data mapping module to perform real-time conversion and update of the posture, micro-expressions or scenes of the user's corresponding dynamic three-dimensional user virtual avatar.

[0136] This process uses the configured abnormal assessment and recognition model to analyze the user's health and emotional state in real time. Once an abnormality is found, a private scene is immediately generated and an alarm is processed, and doctors or emergency rescue personnel are notified in time to intervene to ensure user safety. Secondly, the system can automatically generate private scenes or abnormal relief scenes based on the user's physiological and emotional abnormalities, providing users with personalized virtual spaces to help them relieve stress or self-regulate. In addition, by using encryption algorithms to generate private verification keys, the system ensures the security and privacy protection of private scenes. When users need to communicate privately with other users, the system can dynamically generate private rooms and use identity authentication mechanisms to ensure that only authorized users can enter, further enhancing the security of the system.

[0137] Example 2

[0138] See also Figure 2 , another embodiment provided by the present invention: a corresponding unit workflow of a smart wearable device and a metaverse identity synchronization system includes:

[0139] In the user identity information construction stage, first, the system transmits the basic attribute information in the multi-dimensional user information obtained by the data acquisition unit to the identity construction unit, and obtains the center-edge secondary identity authentication information package;

[0140] Second, the verification chain construction unit constructs a central multi-user identity information chain in the central node according to the obtained central identity authentication information package, and adds a private key verification valve between the sub-nodes corresponding to different users in the constructed multi-user identity information chain for identity information verification between different sub-nodes in the central multi-user identity information chain;

[0141] Third, a single user edge identity information verification chain is constructed using the edge identity authentication information package of a single user distributed on multiple edge nodes, and a unique verification identifier is created and stored in the corresponding edge nodes and central nodes respectively;

[0142] Fourth, a single-node multi-user edge identity information verification chain is constructed using the multi-user edge identity authentication information package stored in a single edge node, and a public key verification valve is added between the sub-nodes corresponding to different users in the single-node multi-user edge identity information verification chain;

[0143] Fifth, the user posture information and micro-expression information in the multi-dimensional user information acquired by the data acquisition unit are input into the posture mapping unit and the micro-expression mapping unit to obtain a three-dimensional user virtual avatar and corresponding micro-expressions, and the acquired micro-expressions are mapped to the three-dimensional user virtual avatar to obtain an initial three-dimensional user virtual avatar;

[0144] Sixth, the constructed initial three-dimensional user virtual avatar is mapped and bound to the corresponding sub-node in the single user edge identity information verification chain to obtain the initial three-dimensional user virtual avatar with verification information;

[0145] In the application stage, seventh, the user inputs the login verification information through the input unit, and feeds the login verification information back to the identity verification unit, and uses the corresponding user edge identity verification information package stored in the single user edge identity information verification chain in the local edge node for identity verification. If the verification is successful, the verified information is shared to the non-local node corresponding to the user through the synchronization sharing unit, and the created single user shared identifier is activated;

[0146] Eighth, if the verification fails, the login verification information is fed back to the central node, and the identity is authenticated using the user-centered identity verification information package stored in the central multi-user identity information chain. If the verification passes, the verification information is directly shared with all edge nodes where the user is located through the central node, and the created single user shared identifier is activated; if the verification fails, a verification error is returned and the login information is re-generated;

[0147] Ninth, when the verification is passed, the initial three-dimensional user virtual avatar is synchronously started, and the multi-dimensional user information of the user at the current moment is obtained through the data acquisition unit, and the obtained multi-dimensional user information of the user is encrypted and filtered and compressed in turn through the data encryption unit and the data compression unit to obtain a multi-dimensional user valid data compression package, and the multi-dimensional user valid data compression package is synchronized to the local node for decompression;

[0148] Tenth, the user posture information, micro-expression information, and voice emotion data in the decompressed multi-dimensional user valid data are input into the posture mapping unit, the micro-expression mapping unit, and the voice emotion unit respectively, to obtain the upper and lower limb action features, micro-expression state features, and emotion type features corresponding to the user at the current moment, as well as the corresponding micro-expression and emotion abnormality evaluation comprehensive scores;

[0149] Eleventh, the upper and lower limb movement features, micro-expression state features and emotion type features corresponding to the current moment are mapped to the initial three-dimensional user virtual avatar with verification information to obtain a dynamic three-dimensional user virtual avatar, so that the dynamic three-dimensional user virtual avatar is consistent with the user's real state at the current moment in real time, and is displayed in real time through the display unit, and at the same time, the health status information in the multi-dimensional user effective data is input into the abnormality monitoring unit to monitor the user's health status, and the user's current health status is judged by using the health status monitoring results and the micro-expressions and emotion types monitored at the current moment. If there is an abnormality, the user's current abnormal state information is fed back to the scene mapping unit to generate a corresponding abnormality mitigation scene 1, and the acquired abnormality is mapped to the dynamic three-dimensional user virtual avatar and encrypted and hidden at the same time; if there is no abnormality, the corresponding scene 2 is generated through the scene mapping unit according to the user's demand information input by the user through the input unit or the voice interaction unit;

[0150] Twelfth, the abnormal current health state of the user includes abnormal physiological state and abnormal psychological state; when the current health state of the user is only abnormal psychological state, an abnormality mitigation scene space 11 is generated by a scene mapping unit;

[0151] Thirteenth, when the user's current health status is an abnormal physiological state, a private mapping scene space 12 is generated through the scene mapping unit and a verification public key is configured on the generated private mapping scene space 12, and the configured verification public key is mapped to the central identity authentication information package of the corresponding user in the central node. At the same time, the system searches for the doctor user corresponding to the abnormal physiological state of the user through the constructed central multi-user identity information chain, and issues an alarm through the emergency rescue program configured by the system;

[0152] Fourteenth, if a doctor user with physiological abnormalities corresponding to the user is searched, the identity construction unit generates a corresponding verification pass code according to the verification public key configured in the private mapping scene space 12, and through the constructed single user edge identity information verification chain or single node multi-user edge identity information verification chain, the doctor user uses the generated verification pass code to enter the constructed private mapping scene space 12 to provide self-help guidance to the user with physiological abnormalities;

[0153] The abnormality relief scene space 11 judges the user's psychological abnormality based on the comprehensive scores of micro-expression and emotional abnormality assessment obtained by the micro-expression mapping unit and the voice emotion unit at the current moment, and through the emotional abnormality score threshold set in the abnormality relief scene space. If the user is psychologically abnormal, the system generates an abnormality relief scene space 11 with different encouragement scenes through the scene mapping unit based on the obtained user micro-expressions, emotional state information and user demand information, and generates comforting and encouraging voices through the configured voice equipment to encourage the corresponding users.

[0154] Fifteenth, if there is no abnormality in the user, the scene space 2 corresponding to the user's needs is generated through the scene mapping unit according to the user demand information input by the user, and is displayed in real time through the display unit. At the same time, during the interaction with the metaverse, the voice interaction unit captures the voice commands issued by the user in real time, and switches the user's current scene in real time. When the user switches the non-local node scene, the corresponding non-local node verification is first performed through the constructed single user shared identifier. When the verification is performed, the constructed dynamic three-dimensional user virtual avatar is mapped to the corresponding non-local node through the synchronous sharing unit, and the scene is synchronously switched according to the user's voice command information. When the user needs to communicate privately with other users, a private room 21 is constructed according to the more complete user demand information input by the input unit, and the private room 21 is verified and configured using the private key in the user's corresponding identity authentication information stored in the central node, and the public key corresponding to the private key is distributed to other users allowed by the user to enter the private room 21. Other users enter the private room 21 after verification through the distributed public key and the single user edge identity information verification chain or the public key and the single-node multi-user edge identity information verification chain.

[0155] The system firstly builds a secondary authentication information package between the center and the edge, and establishes identity information chains at the center node and the edge node respectively, thus ensuring the integrity and privacy of the user's identity information. At the same time, the security of identity information verification between different nodes is enhanced through private key verification valves and public key verification valves. This design not only reduces the risk of identity forgery, but also improves the system's anti-attack capability through a distributed verification mechanism, allowing users to seamlessly switch between different metaverse platforms without frequent repeated logins. Secondly, the system uses multi-dimensional user information to generate a three-dimensional user virtual avatar, and updates the user's posture, micro-expression and emotional state in real time, so that the virtual avatar is consistent with the user's real state, enhancing the realism and immersion of virtual interaction. By mapping the user's posture information and micro-expression information to the three-dimensional virtual avatar, the system can reflect the user's movements and emotional changes in real time, providing users with a more vivid and realistic virtual identity experience. At the same time, by mapping and binding with the user's edge identity information verification chain, the security and credibility of the virtual avatar are ensured. In addition, the system also has a health status monitoring function, which can detect the user's physiological or psychological abnormalities in a timely manner, and provide targeted help by generating abnormality mitigation scenarios, thus improving the user's health management level. When physiological abnormalities are detected, the system can automatically find a professional doctor for remote guidance, enhancing the response capability in emergency situations; this real-time monitoring and response mechanism not only helps to improve users' health awareness, but also can take quick action in critical moments to ensure users' life safety; finally, through non-local node verification and synchronous mapping of dynamic three-dimensional user virtual avatars, the system supports users' seamless switching between different scenes, further improving the convenience and flexibility of interaction; when users switch between non-local node scenes, the corresponding non-local node verification is first performed through the constructed single user shared identifier, ensuring the consistency and security of the user's identity; after the verification is passed, the dynamic three-dimensional user virtual avatar is mapped to the corresponding non-local node through the synchronous sharing unit, and synchronously switched according to the user's voice command information, ensuring the consistency of the user's experience in different virtual scenes.

[0156] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the scope of protection of the purpose of the present invention and the claims, and all of these are within the protection of the present invention.

[0157] If the disclosed technical solution involves personal information, the product using the disclosed technical solution has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the disclosed technical solution involves sensitive personal information, the product using the disclosed technical solution has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A smart wearable device and metaverse identity synchronization system, characterized in that: include: Authentication module, data synchronization module, data mapping module and interaction module; The identity verification module includes an identity building unit, a verification chain building unit and an identity verification unit; The identity construction unit is used to construct a center-edge secondary identity authentication information package through a federated learning algorithm based on the acquired user information; the verification chain construction unit is used to construct a center identity information chain and an edge dual identity information chain based on the acquired center-edge secondary identity authentication information package, and build the constructed center identity information chain and edge dual identity information chain into the identity authentication unit; the identity authentication unit is used to perform local or non-local login identity authentication and social identity authentication based on the constructed center identity information chain and edge dual identity information chain and the login verification information input by the user; The data synchronization module includes a data acquisition unit, a data compression unit, a data encryption unit and a synchronization sharing unit; The data acquisition unit is used to acquire multi-dimensional user information through the smart wearable device; The synchronous sharing unit is used to share the verification information verified by the identity authentication unit and the multi-dimensional user valid data compression package obtained by the data encryption unit and the data compression unit to the non-local edge node using the constructed edge dual identity information chain, and at the same time generate a corresponding single user shared identifier on the non-local edge node, and feed back the generated single user shared identifier to the edge node and central node corresponding to the user's current location for storage.

2. A smart wearable device and metaverse identity synchronization system as claimed in claim 1, characterized in that: The data mapping module includes a posture mapping unit, a micro-expression mapping unit, a voice emotion unit, a scene mapping unit and an abnormality monitoring unit; The posture mapping unit is used to construct a model through three-dimensional posture according to the effective data of the multi-dimensional user to obtain an initial three-dimensional dynamic user virtual avatar; the micro-expression mapping unit is used to obtain the micro-expression information of the corresponding user through a micro-expression recognition model according to the acquired micro-expression information; the voice emotion unit is used to recognize the emotional information in the voice through a voice emotion recognition model according to the acquired user voice information, and map the recognized emotional information, micro-expression information and edge dual identity information chain to the initial three-dimensional dynamic user virtual avatar to obtain a dynamic three-dimensional user virtual avatar; the scene mapping unit is used to generate a scene corresponding to the user's needs through a scene generation model according to the synchronized user demand data and the user health status monitored by the abnormal monitoring unit; The interaction module includes an input unit and a voice interaction unit; The input unit is used to input user login verification information and user demand information; The voice interaction unit is used to recognize and convert voice instructions during the interaction between the user and the metaverse, and feeds back the converted voice instructions to the data mapping module to perform corresponding conversion and update on the user's gestures, micro-expressions or scenes.

3. A smart wearable device and metaverse identity synchronization system as claimed in claim 1, characterized in that: The edge dual identity information chain includes a single-user edge identity information verification chain and a single-node multi-user edge identity information verification chain, and information verification valves are configured between different sub-nodes in the single-node multi-user edge identity information verification chain to perform information interaction verification between users corresponding to different sub-nodes in a single node; The single user edge identity information verification chain is used for user local or non-local login identity authentication; The single-node multi-user edge identity information verification chain is used for user local or non-local social identity authentication.

4. A smart wearable device and metaverse identity synchronization system as claimed in claim 3, characterized in that: The steps of constructing the center-edge secondary authentication information package include: A1. Obtain basic attribute information and preprocess it, and construct a basic attribute information package using the preprocessed basic attribute information and the configured effective verification time; A2. Generate a private key and a public key corresponding to the basic attribute information package through an encryption algorithm according to the basic attribute information package obtained; A3. The generated private key is built into the basic attribute information package to generate a central identity authentication information package and saved in the central node, and the obtained public key is built into the basic attribute information package to generate an edge identity authentication information package and saved in a distributed manner to all edge nodes connected to the central node; A4. Build an update verification model based on reinforcement learning, and embed the update verification model into the central node and all corresponding edge nodes. Use the corresponding working status data of the central node and all edge nodes to initialize the update verification model built into each node, and start the test verification process.

5. A smart wearable device and metaverse identity synchronization system as claimed in claim 4, characterized in that: The step of constructing the center-edge secondary authentication information package also includes: A5. Set the effective verification time update interval and start the test process, collect the operating parameters and operating efficiency data corresponding to the central node and all edge nodes in real time, and pre-process the collected data; A6. Use the preprocessed operating parameters and operating efficiency data to construct an input sequence pair (Xi, Yi), and input (Xi, Yi) into the random forest algorithm to obtain the operating parameters and effective verification time update interval corresponding to the initial optimal operating efficiency; where Xi represents the operating parameter sequence corresponding to the central node and all edge nodes under the i-th effective verification time update interval, and Yi represents the operating efficiency sequence corresponding to the central node and all edge nodes under the i-th effective verification time update interval; A7. Feedback the effective verification time update interval corresponding to the optimal operation efficiency to the central identity authentication information package and edge identity authentication information package corresponding to the verified user in the central node and all edge nodes, and at the same time feed back the obtained operation parameters to the update verification model built into the central node and all edge nodes, and start the secondary test; A8. Obtain the optimal operating efficiency corresponding to the secondary test and compare it with the initial optimal operating efficiency. If it is less than the initial optimal operating efficiency, continue to perform the update test. If it is greater than the initial optimal operating efficiency, use the operating parameters corresponding to the optimal operating efficiency of the secondary test and the effective verification time update interval to update the central identity authentication information package, the edge identity authentication information package, and the update verification model built into the central node and all edge nodes; A9. Set the test stop iteration threshold. When the optimal running efficiency of m consecutive tests is in a declining state and m is greater than the test stop iteration threshold, stop the test iteration. A10, embed the effective verification time update interval corresponding to the maximum optimal operation efficiency of continuous testing m times into the central identity authentication information package and the edge identity authentication information package, obtain the central-edge secondary identity authentication information package, and update the corresponding operation parameters to the update verification model built into the central node and all edge nodes, obtain the updated verification model in the federated mode after training, and integrate the A7-A9 parameter iteration process into all edge nodes; A11. When the optimal operating efficiency drops again, repeat the A7-A10 process to continuously update the update verification model and effective verification time update interval built into all edge nodes.

6. A smart wearable device and metaverse identity synchronization system as claimed in claim 5, characterized in that: The specific steps of constructing the dynamic three-dimensional user virtual avatar include: B1. Obtain user facial image data, voice data, health status data and video motion data through smart wearable devices, and pre-process the acquired data; B2. Process the acquired video action data into video frames to obtain point cloud position data of each joint point of the user at different time points, limb image data, and micro-expression image data corresponding to different time points; B3. Build a human 3D generation model based on the pre-trained VIBE model, and input the corresponding point cloud position data and limb image data of each joint point of the user at different time points into the human 3D generation model to generate a dynamic limb 3D avatar; B4. Input the user's facial image data into the pre-trained lightweight VIT model to extract the user's facial features and obtain the user's static facial features; B5. Map the acquired static facial features of the user to the facial position in the dynamic limb three-dimensional avatar, and use the point cloud matrix constructed by the facial position point cloud data in the dynamic limb three-dimensional avatar and the matrix composed of the corresponding position pixel points in the user's static facial features to perform facial matching to obtain the initial three-dimensional dynamic user virtual avatar.

7. A smart wearable device and metaverse identity synchronization system as claimed in claim 6, characterized in that: The specific steps of constructing the dynamic three-dimensional user virtual avatar also include: B6. Construct a micro-expression recognition model based on multi-scale convolution and attention network, input the acquired micro-expression image data corresponding to different time points into the micro-expression recognition model, and obtain a micro-expression change feature sequence with time characteristics; B7, aligning the time features in the micro-expression change feature sequence with the time information in the dynamic limb three-dimensional avatar, and mapping the aligned micro-expression change feature sequence to the facial position of the initial three-dimensional dynamic user virtual avatar corresponding to different time points to obtain a secondary three-dimensional dynamic user virtual avatar; B8. After the speech data is labeled with emotion type and intensity, it is input into the speech emotion recognition model to obtain the speech emotion type and emotion intensity corresponding to different time points; B9. Using the speech emotion type, emotion intensity and micro-expression change feature sequence with time characteristics corresponding to different time points, a speech-to-facial emotion mapping function is constructed through a support vector machine; B10, according to the constructed voice-facial emotion mapping function, mapping the voice emotion type and emotion intensity corresponding to different time points to the micro-expressions of the secondary three-dimensional dynamic user virtual avatar, to obtain the final dynamic three-dimensional user virtual avatar; B11. Based on the health status data, micro-expressions and emotional information corresponding to the final dynamic three-dimensional user virtual avatar, the health status score and abnormal status type corresponding to the current user are obtained through the constructed abnormality monitoring model; B12, obtaining the health status score and abnormal status type corresponding to the user, and embedding them into the final dynamic three-dimensional user virtual avatar through an encryption and hiding algorithm; B13. When the user successfully logs in through authentication, the dynamic three-dimensional user virtual avatar is updated in real time through the multi-dimensional user valid data compression package obtained in real time.

8. A smart wearable device and metaverse identity synchronization system as claimed in claim 7, characterized in that: The workflow of the scene mapping unit includes: C1. Obtain the user's real-time voice data, health status data and micro-expression image data, and input the acquired real-time voice data and micro-expression image data into the voice emotion unit and the micro-expression mapping unit in sequence to obtain the real-time emotion type, emotion intensity score and micro-expression type and corresponding evaluation score; C2. Setting an abnormal emotion score threshold, and using the obtained emotion intensity score and micro-expression type evaluation score to obtain an abnormal emotion comprehensive evaluation score. If the abnormal emotion comprehensive evaluation score is less than the abnormal emotion score threshold, it is judged that the first evaluation parameter of the corresponding user is abnormal. Otherwise, the first evaluation parameter of the corresponding user is in a normal state. C3. Setting a physiological abnormality score threshold, inputting the acquired emotional abnormality comprehensive evaluation score and health status data into the abnormality monitoring unit, and obtaining the corresponding user physiological evaluation score; C4. If the user's physiological assessment score is less than the physiological abnormality score threshold, it is determined that the user's second assessment parameter is abnormal; otherwise, the second assessment parameter is normal.

9. A smart wearable device and metaverse identity synchronization system as claimed in claim 8, characterized in that: The workflow of the scene mapping unit includes: C5. When the second evaluation parameter of the corresponding user is abnormal, a private mapping scene space 12 is generated through a scene mapping unit, a verification public key is configured on the generated private mapping scene space 12, and the configured verification public key is mapped to the central identity authentication information package of the corresponding user in the central node; C6. Based on the user's physiological abnormality information, the system searches for the doctor user corresponding to the user's physiological abnormality through the constructed central multi-user identity information chain, and issues an alarm through the emergency rescue program configured by the system; C7. If a doctor user with physiological abnormality of the corresponding user is searched, the identity construction unit generates a corresponding verification pass code according to the verification public key configured in the private mapping scene space 12, and the generated verification pass code is assigned to the corresponding doctor user. The doctor user enters the constructed private mapping scene space 12 through the verification pass code and the single-user edge identity information verification chain or the single-node multi-user edge identity information verification chain to provide self-help guidance to the user with physiological abnormality; C8, when the corresponding user is only abnormal for the first evaluation parameter, then according to the micro-expression and emotion abnormality comprehensive evaluation score obtained by the micro-expression mapping unit and the voice emotion unit at the current moment and the user demand input by the user or acquired by voice, the abnormality mitigation scene space 11 is generated by the scene mapping unit; C9. When there is no abnormality in the corresponding user, the scene space 2 corresponding to the user's needs is generated through the scene mapping unit according to the user demand information input by the user, and displayed in real time through the display unit. At the same time, during the interaction with the metaverse, the voice interaction unit captures the voice commands issued by the user in real time, and switches the user's current scene in real time.

10. A smart wearable device and metaverse identity synchronization system as claimed in claim 9, characterized in that: The step of switching the user's current scene in real time includes: C10. When the user switches the non-local node scene, the corresponding non-local node is first verified through the constructed single user shared identifier. When the verification is passed, the constructed dynamic three-dimensional user virtual avatar is mapped to the corresponding non-local node through the synchronous sharing unit, and the locally generated scene is synchronously switched to the corresponding non-local node through the synchronous sharing unit according to the user voice command information; C11. When the user needs to communicate privately with other users, a private room 21 is constructed through the scene mapping unit according to the user demand information input by the input unit, and the private room 21 is verified and configured using the private key in the identity authentication information corresponding to the user created and stored by the central node; C12. The public key corresponding to the configured private key is assigned to the non-creating user who is allowed by the creating user to enter the private room 21. The non-creating user performs identity authentication through the assigned public key and the single-user edge identity information verification chain, or the public key and the single-node multi-user edge identity information verification chain, and enters the private room 21 after passing the authentication.

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