Metacosmic digital asset source right confirmation method and equipment for robot cluster monitoring

Abstract

The invention discloses a robot cluster monitoring meta-universe digital asset source right confirmation method and device, and the method comprises the steps: monitoring and observing the state of a target physical asset through a local sensor of each robot, obtaining real-time monitoring data, uploading the real-time monitoring data to a block chain network for distributed storage, and carrying out the real-time monitoring of the real-time monitoring data; the real-time supervision data comprises self-collected data and associated verification data of other robots in the cluster; realizing message transmission communication of real-time supervision data between the robots by adopting an asymmetric encryption mechanism; and executing three-stage authenticity verification by adopting an intelligent contract to obtain a reputation value of each data acquisition source, and finally determining a source right confirmation result based on the reputation value. According to the invention, the robustness, the expandability and the active fraud prevention capability of the meta-universe digital asset source right confirmation process are improved.

Description

Technical Field

[0001] This invention relates to the intersection of intelligent robot technology and blockchain technology, and in particular to a method and device for confirming the source of digital assets in the metaverse through robot swarm monitoring. Background Technology

[0002] With the deepening development of the metaverse digital economy, ensuring that the digital mapping of physical assets in the metaverse has a legally credible source has become a core requirement. During the creation of digital assets, if some data collection nodes exhibit Byzantine behavior (such as uploading tampered data or falsely reporting asset status) due to hardware failures or malicious attacks, it will directly undermine the trust foundation of the digital asset system and lead to disputes over on-chain asset ownership. Existing solutions mainly rely on centralized institution authentication or fixed sensor data collection, which presents the following technical problems: It requires a pre-defined scale of trusted nodes and lacks dynamic monitoring capabilities and adaptive fault tolerance mechanisms. The source of anti-counterfeiting capabilities is weak, making it impossible to achieve real-time fraud detection and proactive defense linkage; The single dimension of supervision makes it difficult to support the multi-dimensional credible mapping and judicial traceability requirements of high-value assets in the metaverse; Security vulnerabilities, lack of proactive defense mechanisms, and the ability to preserve the integrity of source data through judicial means.

[0003] Therefore, there is an urgent need for a solution that can automatically, decentralizedly, and proactively verify the authenticity of source data and solidify it in the judicial process, so as to improve the robustness, scalability, and judicial credibility of the source confirmation process of metaverse digital assets. Summary of the Invention

[0004] This invention provides a method, apparatus, computer equipment, and storage medium for confirming the source of metaverse digital assets through robot swarm monitoring, in order to improve the robustness, scalability, and judicial evidentiary effectiveness of confirming the source of metaverse digital assets.

[0005] To address the aforementioned technical problems, this application provides a method for confirming the source ownership of metaverse digital assets through robot swarm monitoring, including: S1: Construct a hybrid monitoring architecture: Monitor the status of the target physical asset through the local sensors of each robot to obtain real-time monitoring data, and upload the real-time monitoring data to the blockchain network for distributed storage. The monitoring data includes the data collected by the robot itself and the associated verification data of other robots in the cluster. S2: Establish an encrypted communication channel: Employ an asymmetric encryption mechanism to achieve real-time monitoring data message transmission and communication between robots. Each robot generates a public / private key pair. During message transmission, the receiver's public key is used to encrypt the message payload, and the sender's private key is used to generate a digital signature. S3: Execute a smart contract-driven three-stage fault detection: Phase 1: Extract the supervision data of all robots from the blockchain distributed storage to generate an asset status data frame set; Phase Two involves conducting multi-perspective inconsistency analysis on the asset status data frame set stored on the blockchain to obtain the analysis results. Phase 3: Based on the analysis results, dynamically adjust the reputation value of the data collection source and store it in the blockchain; S4: Based on the reputation value, determine the source ownership confirmation result, which serves as the legal basis for the creation of metaverse digital assets.

[0006] Optionally, in step S1: The blockchain network is a decentralized storage architecture composed of the Hashgraph programs of each robot node. Real-time monitoring data within the robot cluster is transmitted via encrypted communication channels and written to the blockchain; Each robot has the same communication range and monitoring distance to ensure an overlapping monitoring network for the target asset, enabling cross-validation. The monitoring coverage must be such that each physical asset is monitored by at least three or more robots simultaneously.

[0007] Optionally, the robot achieves asset status supervision and observation through the fusion of visual sensor and LiDAR data; the communication range is ≤10m, and the supervision and observation distance is matched with the communication range to ensure that each asset is within the common supervision field of view of multiple robots, realizing real-time supervision coverage of "observation is verification"; the visual sensor uses the YOLOv5 algorithm to identify asset feature points and detect abnormal interference objects in the environment, and the LiDAR monitors the asset position offset through point cloud clustering.

[0008] Optionally, each data frame in the asset status data frame set includes {robot ID, timestamp, target asset identifier, self-supervision data, list of supervision verification data of other robots in the cluster, and supervision confidence score}. The asset status data frame set stored on the blockchain undergoes multi-perspective inconsistency analysis, yielding analysis results including: For the target physical asset, iterate through all robot records that monitor that asset; For any supervisory robot j, the dynamic Bayesian consensus algorithm is used to calculate the posterior probability of the target asset state observed by j and the consensus state within the cluster. If the posterior probability is greater than a preset threshold, the supervisory robot j is marked as a trusted node; otherwise, it is marked as an abnormal node. The analysis results are determined based on the number of trusted nodes and the number of abnormal nodes.

[0009] Optionally, determining the analysis results based on the number of trusted nodes and the number of abnormal nodes includes: If the number of abnormal nodes exceeds the number of trustworthy nodes, or the supervision confidence score is lower than the judicial evidence preservation standard, the analysis result will determine that the batch of supervision data is unreliable, triggering a source fraud alarm and suspending the digital asset creation process.

[0010] Optionally, dynamically adjusting the reputation value based on the analysis results includes: If the analysis result of a certain robot node is determined to be providing abnormal data, the reputation value of that node will be reduced by δ with a penalty coefficient α; otherwise, it will be increased by δ with a reward coefficient β, where δ is a preset adjustment parameter and α>β>0. If the reputation value is lower than the preset threshold, the corresponding node will be actively isolated, its dereliction of duty will be recorded in the blockchain for evidence storage, and the node will be added to the supervision blacklist.

[0011] Optionally, the method further includes: All robots' reputation values ​​are initialized to the same baseline value; The preset threshold is a dynamically adjustable parameter; Once an isolated node enters the observation period, it must undergo system auditing or manual review before it can rejoin the monitoring task. Upon rejoining the team, its reputation value will be reset according to the decay coefficient γ.

[0012] To address the aforementioned technical problems, this application also provides a device for confirming the source of digital assets in the metaverse through robot swarm monitoring, comprising: Robot Cluster Monitoring Module: Composed of mobile robots equipped with visual sensors, LiDAR, and Hashgraph nodes, it is used to perform asset status monitoring and observation and blockchain data storage. The module has a built-in judicial-grade timestamp service. Encrypted communication module: Implements asymmetric encryption and digital signature verification based on the ROS framework, used to perform encrypted communication; Smart contract review and verification module: Deployed on the blockchain network, it is used to perform three-stage authenticity verification and reputation value management, realizing automated compliance review of source data and permission control for digital asset minting.

[0013] To address the aforementioned technical problems, this application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned method for confirming the source of digital assets in the metaverse through robot cluster monitoring.

[0014] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the steps of the aforementioned method for confirming the source of digital assets in the metaverse through robot cluster monitoring.

[0015] The present invention provides a method, apparatus, computer equipment, and storage medium for source confirmation of metaverse digital assets using robot swarm monitoring. Each robot monitors the state of the target physical asset through its local sensors, obtaining real-time monitoring data, which is then uploaded to a blockchain network for distributed storage. This real-time monitoring data includes data collected by the robot itself and associated verification data from other robots within the swarm. An asymmetric encryption mechanism is used to achieve message transmission and communication of the real-time monitoring data between robots. A three-stage authenticity verification is then performed using smart contracts to obtain the reputation value of each data source. Finally, based on the reputation value, the source confirmation result is determined. This achieves a closed-loop technology of hybrid monitoring architecture, encrypted communication channels, and three-stage smart contract verification. It establishes a multi-node cross-monitoring mechanism at the source of digital asset generation, reducing communication overhead during the confirmation process while improving accuracy, thus enhancing the robustness, scalability, and proactive fraud prevention capabilities of the metaverse digital asset source confirmation system. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is an exemplary system architecture diagram to which this application can be applied; Figure 2 This is a flowchart of an embodiment of the method for confirming the source of digital assets in the metaverse through robot swarm monitoring in this application; Figure 3 This is a schematic diagram of a structure of an embodiment of the metaverse digital asset source confirmation device for robot cluster monitoring according to this application; Figure 4 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0019] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 ,like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0022] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc.

[0023] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0024] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0025] It should be noted that the method for confirming the source of digital assets in the metaverse by monitoring robot clusters provided in this application embodiment is executed by the server, and correspondingly, the multi-robot consistency Byzantine fault detection device is set in the server.

[0026] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included. The terminal devices 101, 102, and 103 in this embodiment can specifically correspond to application systems in actual production.

[0027] Please see Figure 2 , Figure 2 This invention illustrates a method for determining the source of digital assets in the metaverse through robot swarm monitoring, as provided in an embodiment of the present invention. This method is then applied to… Figure 1 Taking the server-side as an example, the details are as follows: S1: Construct a hybrid monitoring architecture: Monitor the status of the target physical asset through the local sensors of each robot to obtain real-time monitoring data, and upload the real-time monitoring data to the blockchain network for distributed storage. The monitoring data includes the data collected by the robot itself and the correlation verification data of other robots in the cluster.

[0028] The self-collected data consists of asset images, dimensions, textures, and other feature data collected by sensors, while the associated verification data from other robots within the cluster consists of a summary of cross-supervision data of the same asset obtained through communication between other robots.

[0029] In one specific optional implementation, in step S1: The blockchain network is a decentralized storage architecture composed of the Hashgraph programs of each robot node; Real-time monitoring data within the robot cluster is transmitted via encrypted communication channels and written to the blockchain; Each robot has the same communication range and supervision observation distance to ensure that an overlapping supervision network is formed over the target asset, enabling multi-node cross-verification. The supervision coverage must meet the requirement that each physical asset is supervised by at least three or more robots simultaneously.

[0030] In one alternative implementation, the blockchain architecture includes: The blockchain network is a decentralized storage architecture composed of the Hashgraph programs of each robot node; Hashgraph's asynchronous Byzantine Fault Tolerance (aBFT) consensus mechanism ensures the consistency of distributed storage data. Data storage structure design: Each block contains {timestamp, robot ID, supervision data hash value, previous block hash, supervision verification flag, judicial evidence preservation flag}.

[0031] Optionally, the robot achieves asset status supervision and observation through the fusion of visual sensor and LiDAR data; the communication range is ≤10m, and the supervision and observation distance matches the communication range to achieve real-time supervision coverage of "observation is verification"; the visual sensor uses the YOLOv5 algorithm to identify asset feature points and detect abnormal interference objects in the environment, and the LiDAR monitors the asset position offset through point cloud clustering.

[0032] In a specific example, a blockchain-based distributed asset data collection and monitoring network is constructed as follows: A monitoring cluster of five mobile robots is deployed within an exhibition hall housing artworks awaiting ownership verification. Each robot is equipped with a visual sensor and a LiDAR, enabling multi-angle simultaneous monitoring of the artworks through the fusion of visual and LiDAR data. A Hashgraph blockchain application is deployed on each robot node to store data collected by the robot itself and verification data from other robots within the cluster acquired through communication. The robot communication range is set to equal the monitoring observation distance, ensuring overlapping monitoring horizons and that each asset is monitored simultaneously by at least three robots. The robots within the cluster maintain communication connections via Wi-Fi and collaboratively monitor the artworks' status using visual cameras. Furthermore, this embodiment also includes system integration and testing. The encrypted communication module, blockchain network, and verification algorithm are integrated into a unified framework. System testing is conducted in the exhibition hall, setting up different data collection scenarios, including normal data collection and simulated abnormal situations such as asset swapping and node data tampering. The focus is on testing the response time and detection accuracy of the monitoring cluster against fraudulent activities. Performance indicators such as the accuracy of rights confirmation and system tolerance are recorded.

[0033] It should be noted that this embodiment adopts a hybrid monitoring architecture, combining local robot supervision and global blockchain verification. It uses Hashgraph technology to realize a distributed data collection and supervision network, ensuring data reliability and system scalability. It establishes an immutable supervision record at the source of physical asset digitization, providing technical support for subsequent judicial evidence preservation.

[0034] Furthermore, in this embodiment, the communication range and observation distance of each robot are the same, ensuring that neighboring robots within the communication range can observe each other. This embodiment adopts the following sensor fusion scheme: Visual sensor: Employs YOLOv5 for real-time target detection, identifying specific markers and their relative positions on artworks (accuracy ±5cm), and detecting abnormal interference. LiDAR: Extracts the 3D contours of artworks using point cloud clustering algorithms (sampling rate 10Hz) to monitor asset position shifts; Fusion Algorithm: Extended Kalman Filter (EKF) fuses visual and laser data to output an artwork state vector, such as key point coordinates and dimensions, and generates a supervised confidence score.

[0035] S2: Establish an encrypted communication channel: Use an asymmetric encryption mechanism to realize message transmission communication of real-time observation data between robots. Each robot generates a public key / private key pair. When transmitting messages, the receiver's public key is used to encrypt the message payload, and the sender's private key is used to generate a digital signature.

[0036] Specifically, in this embodiment, an asymmetric encryption algorithm is used to generate an RSA 2048-bit public / private key pair for each robot to construct an encrypted ROS communication channel. The encryption process is as follows: The sender uses the receiver's public key to encrypt the message payload, generates a digital signature using its own private key, and constructs the encrypted message {sender ID, timestamp, encrypted payload, digital signature, supervisory task identifier, judicial timestamp}.

[0037] The transmission process is implemented through the ROS communication framework, and all messages are encrypted and signed.

[0038] After receiving the message, the recipient first uses the sender's public key to verify the digital signature, confirming the message's origin and integrity, and then uses its own private key to decrypt the message payload.

[0039] Upon receiving the message, the recipient first verifies the digital signature using the sender's public key to confirm the message's origin and integrity. Then, the recipient decrypts the message payload using their own private key. This entire encrypted communication process ensures the confidentiality, integrity, and authenticity of the message, preventing man-in-the-middle attacks, data tampering, and the injection of false monitoring information by malicious nodes.

[0040] S3: Perform a three-phase authenticity verification driven by the smart contract: Phase 1: Extract the supervision data of all robots from the blockchain distributed storage to generate an asset status data frame set; Phase Two: Perform multi-perspective inconsistency analysis on the asset state data frame set stored on the blockchain to obtain the analysis results; Phase 3: Dynamically adjust the reputation value based on the analysis results and store it on the blockchain.

[0041] Specifically, this embodiment implements a three-stage verification process for preventing fraud at the source: First, all supervision data is reshaped. The smart contract automatically extracts the current and historical supervision data of all robots gathered on the local blockchain, and classifies and sorts the asset status data according to robot ID and time sequence to form an asset status data frame set. Each asset status data frame contains {robot ID, timestamp, target asset ID, its own supervision data, a list of supervision verification data of other robots in the cluster, supervision consensus flag, and supervision confidence score}.

[0042] Then, the supervision results of different robots are audited through smart contract rules. For each robot j that supervises an artwork, the dynamic Bayesian consensus algorithm is used to calculate the posterior probability of the artwork state (such as size) observed by robot j and the mainstream state obtained through consensus within the cluster. If the posterior probability is greater than a preset threshold, robot j is listed as a trusted node in this supervision; otherwise, it is listed as an abnormal node.

[0043] Audit the supervisory data of all data frames according to the above rules, count the number of trusted nodes and abnormal nodes for the same asset state, and use the dynamic Bayesian consensus algorithm to determine whether the state has been trusted to collect data.

[0044] Finally, dynamic reputation adjustment is performed. The robot's reputation value is updated based on the verification results. If the verification results indicate that the robot provides abnormal data, its reputation value is significantly reduced by the penalty coefficient α. Otherwise, its reputation value is slightly increased by the reward coefficient β. When the robot's reputation value is lower than the threshold, it is determined to be a low-reputation node, and the robot is actively isolated. It will no longer be scheduled to participate in subsequent data collection tasks, and its abnormal behavior will be recorded on the blockchain for evidence storage and included in the supervision blacklist.

[0045] In one specific optional implementation, each data frame in the asset status data frame set contains {robot ID, timestamp, target asset ID, self-supervision data, list of supervision verification data of other robots in the cluster, and supervision confidence score}. Multi-perspective inconsistency analysis is performed on the asset status data frame set stored on the blockchain, and the analysis results include: For the target physical asset, iterate through all robot records that monitor that asset; For any supervisory robot j, the dynamic Bayesian consensus algorithm is used to calculate the posterior probability of the key state of the asset observed by j and the consensus state within the cluster. If the posterior probability is greater than a preset threshold, the supervisory robot j is marked as a trusted node; otherwise, it is marked as an abnormal node. The analysis results are determined based on the number of trusted nodes and the number of abnormal nodes.

[0046] In one specific optional implementation, the analysis results are determined based on the number of trusted nodes and the number of abnormal nodes, including: If the number of abnormal nodes exceeds the number of trustworthy nodes, or the supervision confidence score is lower than the judicial evidence preservation standard, the analysis result is determined to be that the batch of supervision data is unreliable and there is a risk of source fraud. An early warning will be triggered immediately and the digital asset creation process will be suspended.

[0047] In one specific optional implementation, dynamically adjusting the reputation value based on the analysis results includes: If the analysis result of a certain robot node is determined to be providing abnormal data, the reputation value of that node will be reduced by δ with a penalty coefficient α; otherwise, it will be increased by δ with a reward coefficient β, where δ is a preset adjustment parameter and α>β>0. If the reputation value is lower than the preset threshold, the corresponding node will be actively isolated and added to the supervision blacklist.

[0048] In one specific optional implementation, the method further includes: The reputation values ​​of all robot nodes are initialized to the same baseline value; The preset threshold is a dynamically adjustable parameter; Once an isolated node enters the observation period, it must undergo system auditing or manual review before it can rejoin the monitoring task. Upon rejoining the team, its reputation value will be reset according to the decay coefficient γ.

[0049] S4: Determine the source ownership confirmation result based on reputation value.

[0050] Specifically, the reputation score is combined with the consistency analysis results to determine the source ownership confirmation result. If the monitoring cluster consensus determines that the asset status is trustworthy and the proportion of high-reputation nodes exceeds 80%, a source ownership confirmation certificate is issued, which serves as the legal basis for the minting of metaverse digital asset NFTs.

[0051] Furthermore, if the confirmation result indicates a risk of source fraud, pre-set measures will be adopted for emergency handling. These pre-set measures include, but are not limited to: immediately isolating the corresponding node, sending an alarm to the control terminal, suspending the current digital asset creation process, and initiating a manual audit procedure.

[0052] Furthermore, the system displays the real-time status of the rights confirmation results, including robot location, monitoring data, and reputation value. It records the rights confirmation results and generates a detailed report, including confirmation time, anomalies, and judgment criteria. Based on test results, it optimizes system parameters to improve rights confirmation performance. It also provides monitoring effectiveness analysis suggestions to help users optimize robot cluster deployment strategies.

[0053] It should be noted that the global state verification of smart contracts on the blockchain aggregates historical monitoring data from global robots on the blockchain and uses smart contract auditing rules to analyze and audit global asset state information, thereby identifying abnormal data nodes. The innovative three-stage verification process significantly improves the accuracy of source ownership confirmation through historical data reshaping, multi-perspective analysis, and dynamic reputation adjustment. The multi-perspective comparison implemented by smart contracts enhances the system's fault tolerance, and the dynamic reputation adjustment mechanism effectively isolates abnormal nodes, building a proactive defense system at the source of digital asset generation.

[0054] In this embodiment, the status of the target physical asset is monitored and observed through the local sensors of each robot to obtain real-time monitoring data. This real-time monitoring data is then uploaded to a blockchain network for distributed storage. The real-time monitoring data includes its own collected data and the associated verification data from other robots within the cluster. An asymmetric encryption mechanism is then used to achieve message transmission and communication of the real-time monitoring data between robots. Subsequently, a three-stage authenticity verification is performed using smart contracts to obtain the reputation value of each data collection source. Finally, based on the reputation value, the source ownership confirmation result is determined. This achieves a technical closed loop of hybrid monitoring architecture, encrypted communication channels, and three-stage smart contract verification. It establishes a multi-node cross-monitoring mechanism at the source of digital asset generation, reducing communication overhead in the ownership confirmation process while improving the accuracy of ownership confirmation. This enhances the robustness, scalability, and proactive fraud prevention capabilities of the Metaverse digital asset source ownership confirmation system.

[0055] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0056] Figure 3 This diagram illustrates the principle block diagram of a robot swarm monitoring device for determining the source ownership of metaverse digital assets, corresponding one-to-one with the robot swarm monitoring method for determining the source ownership of metaverse digital assets described in the above embodiments. (See diagram for example.) Figure 3 As shown, this multi-robot swarm monitoring device for confirming the source of digital assets in the metaverse includes a robot swarm monitoring module 31, an encrypted communication module 32, and a smart contract review and verification module 33. Detailed descriptions of each functional module are as follows: The robot cluster monitoring module 31 consists of a mobile robot equipped with a visual sensor, a lidar and a Hashgraph node. It is used to perform asset status monitoring and observation and blockchain data storage. The module has a built-in judicial-grade timestamp service. The encrypted communication module 32 implements asymmetric encryption and digital signature verification based on the ROS framework and is used to perform encrypted communication. The smart contract review and verification module 33 is deployed on the blockchain network to perform three-stage authenticity verification and reputation value management, thereby achieving automated compliance review of source data and control over the minting permissions of digital assets.

[0057] Specific limitations regarding the source ownership confirmation device for metaverse digital assets monitored by robot swarms can be found in the limitations of the source ownership confirmation method for metaverse digital assets monitored by robot swarms mentioned above, and will not be repeated here. Each module in the aforementioned source ownership confirmation device for metaverse digital assets monitored by robot swarms can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0058] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0059] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components connected to the memory 41, processor 42, and network interface 43 is shown in the figure; however, it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0060] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0061] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or D-interface display memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as the program code of the metaverse digital asset source confirmation method for robot swarm monitoring, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0062] In some embodiments, the processor 42 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run program code stored in the memory 41 or process data, such as running program code for a method for confirming the source of digital assets in a metaverse monitored by a robot swarm.

[0063] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0064] This application also provides another embodiment, namely, a computer-readable storage medium storing an interface display program that can be executed by at least one processor to cause the at least one processor to perform the steps of the above-described method for confirming the source of metaverse digital assets by monitoring robot swarms.

[0065] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0066] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for confirming the source of digital assets in the metaverse through robot swarm monitoring, characterized in that, include: S1: Construct a hybrid monitoring architecture: Monitor the status of the target physical asset through the local sensors of each robot to obtain real-time monitoring data, and upload the real-time monitoring data to the blockchain network for distributed storage. The monitoring data includes the data collected by the robot itself and the associated verification data of other robots in the cluster. S2: Establish an encrypted communication channel: Use an asymmetric encryption mechanism to realize the message transmission and communication of real-time monitoring data between robots. Each robot generates a public key / private key pair. When transmitting messages, the receiver's public key is used to encrypt the message payload, and the sender's private key is used to generate a digital signature. S3: Perform a three-phase authenticity verification driven by the smart contract: Phase 1: Extract the supervision data of all robots from the blockchain distributed storage to generate an asset status data frame set; Phase Two involves conducting multi-perspective inconsistency analysis on the asset status data frame set stored on the blockchain to obtain the analysis results. Phase 3: Based on the analysis results, dynamically adjust the reputation value of the data collection source and store it in the blockchain; S4: Based on the reputation value, determine the source ownership confirmation result, which is used for the creation and judicial preservation of metaverse digital assets.

2. The method as described in claim 1, characterized in that, In step S1: The blockchain network is a decentralized storage architecture composed of the Hashgraph programs of each robot node. Real-time monitoring data within the robot cluster is transmitted via encrypted communication channels and written to the blockchain; Each robot has the same communication range and monitoring distance to ensure an overlapping monitoring network for the target asset, enabling cross-validation. The monitoring coverage must be such that each physical asset is monitored by at least three or more robots simultaneously.

3. The method as described in claim 2, characterized in that, The robot achieves asset status supervision and observation through the fusion of visual sensor and LiDAR data; the communication range is ≤10m, and the supervision and observation distance is matched with the communication range to ensure real-time supervision coverage of observation and verification; the visual sensor uses the YOLOv5 algorithm to identify asset feature points and detect abnormal interference objects in the environment, and the LiDAR monitors asset position offset through point cloud clustering.

4. The method as described in claim 1, characterized in that, Each data frame in the asset status data frame set contains {robot ID, timestamp, target asset identifier, self-supervision data, list of supervision verification data of other robots in the cluster, and supervision confidence score}. The asset status data frame set stored on the blockchain undergoes multi-perspective inconsistency analysis, yielding the following results: For target robot i, iterate through the observation records of its neighboring robots; For any supervisory robot j, the dynamic Bayesian consensus algorithm is used to calculate the posterior probability of the target asset state observed by j and the consensus state within the cluster. If the posterior probability is greater than a preset threshold, the supervisory robot j is marked as a trusted node; otherwise, it is marked as an abnormal node. The analysis results are determined based on the number of trusted nodes and the number of abnormal nodes.

5. The method as described in claim 4, characterized in that, The analysis results determined based on the number of trusted nodes and the number of abnormal nodes include: If the number of abnormal nodes exceeds the number of trustworthy nodes, or the supervision confidence score is lower than the judicial evidence preservation standard, the analysis result will determine that the batch of supervision data is unreliable, triggering a source fraud alarm and suspending the digital asset creation process.

6. The method as described in claim 5, characterized in that, The dynamic adjustment of the reputation value based on the analysis results includes: If the analysis result of a certain robot node is determined to be providing abnormal data, the reputation value of that node will be reduced by δ with a penalty coefficient α; otherwise, it will be increased by δ with a reward coefficient β, where δ is a preset adjustment parameter and α>β>0. If the reputation value is lower than the preset threshold, the corresponding node will be actively isolated, its dereliction of duty will be recorded in the blockchain for evidence storage, and the node will be added to the supervision blacklist.

7. The method as described in claim 6, characterized in that, The method further includes: All robots' reputation values ​​are initialized to the same baseline value; The preset threshold is a dynamically adjustable parameter; Once an isolated node enters the observation period, it must undergo system auditing or manual review before it can rejoin the monitoring task. Upon rejoining the team, its reputation value will be reset according to the decay coefficient γ.

8. A device for confirming the source of digital assets in the metaverse through robot swarm monitoring, characterized in that, The apparatus for performing the method for confirming the source of digital assets in the metaverse through robot swarm monitoring as described in any one of claims 1 to 7, the apparatus comprising: Robot Cluster Monitoring Module: Composed of mobile robots equipped with visual sensors, LiDAR, and Hashgraph nodes, this module performs asset status monitoring and observation, and stores blockchain data. It also includes a built-in judicial-grade timestamp service. Encrypted communication module: Based on the ROS framework, it implements asymmetric encryption and digital signature verification, and is used to perform encrypted communication in step S2; Smart contract review and verification module: Deployed on the blockchain network, it is used to perform the three-stage authenticity verification and reputation value management of step S3, realizing automated compliance review of source data and control of digital asset minting permissions.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for confirming the source of metaverse digital assets by monitoring robot clusters as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for confirming the source of metaverse digital assets by monitoring robot swarms as described in any one of claims 1 to 7.

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