Trusted sharing method for distributed digital twinborn service of air-ground cooperative Internet of Vehicles

Abstract

The invention discloses an air-ground cooperative vehicle networking distributed digital twin service credible sharing method, which comprises the following steps of: firstly, according to an air-ground cooperative traffic data type, performing hierarchical packaging and storage on operation data of a vehicle networking distributed digital twin system; secondly, an authoritative random equilibrium consensus algorithm is used, and a distributed edge digital twinning system of the air-ground cooperative Internet of Vehicles carries out consensus on blocks generated by data and uploads the blocks to a block chain; and finally, requesting a twinning service from the remote digital twinning system at the edge digital twinning system, and verifying the service record through a directed acyclic graph block chain and an interstellar file system, thereby realizing the trusted sharing of the service among the edge digital twinning systems in the air-ground collaborative internet-of-vehicles distributed digital twinning system. According to the method, the authenticity and tamper-proof capability of the operation data of the air-ground cooperative Internet of Vehicles distributed digital twin system in cross-node sharing are ensured, the access efficiency of large-scale data is improved, and technical support is provided for cooperation of the Internet of Vehicles and the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed data management of unmanned aerial vehicles and vehicle networks, and in particular to a trusted sharing method for distributed digital twin services of air-ground collaborative vehicle networks. Background Art

[0002] Digital twin technology achieves two-way interaction between the physical world and the digital world through real-time data, virtual simulation, artificial intelligence and other technical means. It is widely used in manufacturing, transportation, energy and other fields for real-time monitoring, predictive maintenance and optimized management. However, as the scale of application expands, digital twin technology faces a series of challenges in distributed data sharing and data credibility, especially in the collaborative scenario of drones and Internet of Vehicles, which puts higher requirements on the security of data sharing.

[0003] At present, distributed digital twin data sharing technologies are mainly divided into traditional methods based on distributed databases, improved distributed data storage solutions, and technical solutions combined with blockchain. Among them:

[0004] Traditional methods based on distributed databases (such as Cassandra, MongoDB, HBase, etc.) use data sharding, replication, load balancing and other technologies to achieve distributed storage and sharing of data to support the high concurrency and big data processing requirements of distributed digital twins of air-ground collaborative vehicle networks. Although this method can meet the needs of data sharing to a certain extent, the lack of anti-tampering and traceability mechanisms in distributed databases does not perform well in terms of data credibility and consistency. This conflicts with the digital twin system's demand for high-precision data.

[0005] Improved distributed data storage methods (such as IPFS and HDFS) use distributed file systems to store massive amounts of data and ensure data accessibility and storage scalability. However, such methods lack a built-in trusted sharing mechanism, making it difficult to ensure the authenticity of data shared by multiple parties in the IoV digital twin scenario, and there is a risk of malicious data injection.

[0006] Digital twin data sharing solutions combined with blockchain technology. Blockchain technology has become a potential solution for data sharing in digital twin systems due to its decentralization, tamper-proof and traceability characteristics. Common methods of combination include storing data directly on the blockchain, or combining blockchain with the file system to achieve trusted sharing of digital twin data.

[0007] For example, a digital twin data security storage method is authorized in the patent with the authorization announcement number CN 115484032 B. It uses blockchain to store data during the operation of digital twins, which can effectively prevent data from being tampered with and improve the security of data storage. And the digital twin system can read the required data from the blockchain during operation, improving the security of data interaction in the system prototype.

[0008] However, storing massive amounts of data directly on the blockchain is costly, and the on-chain capacity and processing power are limited, which cannot meet the big data and real-time requirements of the distributed digital twin system of the air-ground collaborative vehicle network.

[0009] In summary, existing methods still have problems of insufficient performance and incomplete reliability in distributed digital twin scenarios that support collaboration between drones and Internet of Vehicles. Therefore, there is an urgent need for a digital twin data sharing solution that is highly scalable, high-performance, real-time, and reliable to meet the needs of efficient, secure, and reliable interoperability of massive data in the collaborative scenarios between drones and Internet of Vehicles. Summary of the invention

[0010] In view of the above problems, the present invention provides a method for sharing a distributed digital twin trusted service for air-ground collaborative vehicle networks, which is a distributed digital twin data management method based on the trusted data storage and authoritative balanced adaptive consensus algorithm of the DAG blockchain, as well as a distributed digital twin service trusted sharing method for the collaboration of drones and vehicle networks, which is suitable for the distributed digital twin scenario of the collaboration of drones and vehicle networks, and solves the technical problems of poor data credibility and uneven node usage in data storage and consensus mechanisms in the prior art. By designing an adaptive node consensus algorithm, the present invention ensures efficient storage and trusted sharing of data, and improves the overall performance and security of the system.

[0011] The technical solution adopted by the present invention to solve the above technical problems is:

[0012] A method for sharing a distributed digital twin service of an air-ground collaborative vehicle network includes the following steps:

[0013] S1. Classify and encapsulate the operation data of the distributed digital twin system of the Internet of Vehicles according to the type of air-ground collaborative traffic data.

[0014] S2. The hierarchical packaged data is stored hierarchically on the distributed edge servers to which it belongs; the original data of large-scale data is stored in the InterPlanetary File System (IPFS), and the packaged data is stored on a blockchain based on a directed acyclic graph (DAG).

[0015] S3. Based on the data after hierarchical storage, the authoritative random equilibrium consensus algorithm is used, and the distributed edge digital twin system of the air-ground collaborative vehicle network reaches a consensus on the blocks generated by the data and uploads them to the blockchain to achieve reliable recording of the data.

[0016] S4. After the edge digital twin system requests the twin service from the remote digital twin system, the service record is verified through the directed acyclic graph (DAG) blockchain and IPFS system to achieve trusted sharing of services between the edge digital twin systems in the distributed digital twin system of the air-ground collaborative vehicle network.

[0017] Furthermore, step S1 is specifically as follows: the distributed digital twin system of the UAV and the Internet of Vehicles uses a unified encapsulation format to encapsulate all operating data to standardize the data structure and adapt to data storage and access requirements of different scales. The encapsulation of each piece of data includes four main steps: calculating timestamps, performing system signatures, classifying data, and processing data content. First, the timestamp is calculated, which records the generation or encapsulation time of the air-ground collaborative traffic data in the edge area to ensure that the system can manage and trace the timing of the data; secondly, EDT signs the traffic data, and the signature identifies the source node or subsystem of the data, which is used for data verification and anti-tampering, ensuring the credibility and uniqueness of the traffic data during sharing and transmission; then, the Internet of Vehicles distributed digital twin data can be divided into sensor data, simulation data, derivative data, service request data, control data or status data, etc., to facilitate the system to quickly identify the functions and characteristics of the data. Finally, the encapsulated data content is differentiated according to the size of the data. For large-scale traffic data such as sensor data, simulation data, and derivative data, the data body stores the hash value of the original data or the storage address in the distributed file system (such as IPFS), which not only reduces the direct storage burden of the blockchain, but also ensures the integrity and verifiability of the data through the hash value. For small-scale traffic data such as service request data, control data, and status data, the original data content is directly stored in the data body, which is convenient for direct access by the system, ensuring the credibility of the data while reducing the access delay.

[0018] Furthermore, step S2 is specifically: hierarchically storing the hierarchical packaged data in the distributed edge server network. In this process, the system will classify the data according to the data type to ensure that the system maintains high efficiency and scalability during storage and access.

[0019] For large-scale traffic data such as sensor data, simulation data, and derivative data, the original data will be stored in the IPFS (InterPlanetary File System) distributed file system. As a decentralized distributed storage solution, IPFS can effectively store and share large-scale data, reduce the burden of data storage in traditional blockchain networks, and ensure data availability and persistence through content addressing and redundant storage. In order to ensure the integrity and authenticity of the data, the original data stored in IPFS will generate hash values, and these hash values ​​or storage addresses will be recorded in the blockchain for subsequent rapid positioning and access.

[0020] All packaged data will be stored on a blockchain based on a directed acyclic graph (DAG). Compared with traditional blockchains, DAG blockchains have higher throughput and lower latency, making them very suitable for data transmission and processing in digital twin systems. The DAG structure avoids the bottleneck of traditional chain block storage, improves the efficiency of data on-chain, and ensures fast storage and access to large-scale data. Storing data on the blockchain not only ensures the security and integrity of the data, but also enables easy verification and auditing.

[0021] Furthermore, the DAG-based blockchain uses a voting mechanism to resolve conflicting blocks. The DAG structure may conflict during data upload and block generation, for example, the same data is maliciously modified and uploaded multiple times. A voting mechanism is used to solve this problem. The core of the mechanism is to determine the final selected block through voting between nodes. After each node generates a block, it will initiate a vote to other nodes in the network. The nodes vote on the blocks they receive according to the recursive voting algorithm. The voting results reflect which block has gained a higher degree of trust in the network. When a block conflict occurs, the node will select the block with the majority support based on the voting results and regard it as a trusted block.

[0022] The specific rules of the recursive voting algorithm are as follows: when two blocks conflict, first, the conflicting block votes for itself; second, if the two conflicting blocks are both ancestor blocks of a certain block, the vote of this block is the same as the result of the previous round of voting; then, if only one conflicting block is the ancestor block of a certain block, the block votes for the ancestor conflicting block; finally, if neither of the two conflicting blocks is the ancestor block of a certain block, the vote of this block is determined by the votes of all its descendant nodes. In the end, the block with the most votes will be recognized as a legitimate block, and the other will be marked as a conflicting block.

[0023] Furthermore, the DAG-based blockchain uses a trusted set to represent trusted blocks. A trusted set refers to a group of verified and trusted blocks that are considered reliable through a certain mechanism and can play a key role in the blockchain network. The generation process of the trusted set is as follows: first, traverse all blocks and add conflict-free blocks to the preliminary trusted set; second, add conflicting blocks to the conflict set, use the recursive voting algorithm to vote for the conflicting blocks to select trusted blocks and move them into the preliminary trusted set; finally, filter out blocks whose existence time exceeds the specified threshold from the preliminary trusted set and form a trusted set.

[0024] Furthermore, step S3 is specifically as follows: design and use the Authority of Random Balance (AoRB) consensus algorithm to reach consensus and upload the DAG blocks generated by the data in the distributed digital twin system of the air-ground collaborative vehicle network. In this step, the distributed digital twin system effectively allocates node resources through the AoRB consensus algorithm to ensure that each node has a fair chance to participate in the consensus process, while avoiding excessive load on a single node, improving consensus efficiency and security; and the order of node activity is determined by the system operation status, and the actual order of node activity is random, which enhances anti-attack capability.

[0025] In the distributed digital twin system of drones and IoV, multiple edge digital twins (EDT) work together to build a comprehensive and real-time virtual mapping of the physical system. Among them, the edge digital twin (EDT) is a digital twin system deployed at the edge of the IoV, including several drones, vehicles, roads and other traffic entities and their digital virtual bodies. Each EDT is responsible for running the edge traffic digital twin system. During operation, the data is packaged as described in S2. The packaged data generates blocks of the DAG blockchain through the blockchain node, and is uploaded to the blockchain after reaching consensus with other nodes through the AoRB consensus algorithm.

[0026] Furthermore, the air-ground cooperative vehicle network distributed digital twin system can be expressed as: DDTS = {EDT i |1≤i≤n}, where: DDTS represents the set of distributed edge traffic domain digital twin systems of air-ground collaborative vehicle networks, including n edge traffic domain digital twin (EDT) instances, each EDT i Represents an independent edge digital twin unit.

[0027] The DAG blockchain can be expressed as: G = (B, E), where: B = {b i |1≤i≤m} represents a set of blocks, each b i represents a block; E = {(b i,b j )|b i ,b j ∈B} represents the directed edge between blocks. If there is an edge (b i ,b j ), then it means block b i Directly quote (or confirm) block b j , block b i Block b on the chain j It is the tail block of G.

[0028] The blockchain node numbered i in the AoRB consensus algorithm can be represented as node i (c i ), where c i Indicates the number of blocks generated by the node. The blockchain nodes are divided into two categories: one is the twin node set D j , indicating that it is edge digital twin EDT j The other type is the global free node set F, which represents the nodes not occupied by any EDT. The blockchain node set can be represented as in

[0029] The AoRB consensus algorithm can be divided into the node initialization phase and the runtime consensus phase. The node initialization phase is to initialize the blockchain node after the entire DDTS has completed the preparatory work, specifically: create a blockchain node and set the upload block count to 0 (c i =0), generating a twin node set {D j |1≤j≤n} and the global free node set F.

[0030] The runtime consensus phase of the AoRB consensus algorithm begins when the DDTS is started. The execution process of each EDT is the same. j For example, the specific one is: EDT j Submit the running data to the twin node set D j The least used node in D is node. Node collects data and encapsulates it. When the specified conditions are met, block b is generated and broadcast. j Remove the least used node in F * Move out and move into D j , move node into F; D k,k≠j The nodes in verify the block. If the verification pass rate is not lower than the set verification threshold, the block is uploaded to the DAG blockchain G. Otherwise, it is not uploaded.

[0031] Furthermore, step S4 is specifically as follows: when the edge EDT requests the traffic twin service from the remote EDT system, the service record is verified through the DAG blockchain and IPFS system to ensure the credibility of traffic data sharing across EDTs. Specifically, in order to achieve trusted sharing of services between EDTs, the shared twin service includes the following steps.

[0032] The edge EDT initiates and records the twin service request: The edge EDT sends a twin service request to the remote EDT according to the traffic task requirements, including information such as service type, request parameters, timestamp, and edge EDT signature. The request is encapsulated into a standardized data packet and broadcast through the blockchain network. At the same time, the edge EDT generates the corresponding traffic service access record, including the unique identifier of the request, the hash value and timestamp of the request content, etc., and stores the record at the edge for subsequent verification.

[0033] Remote EDT responds to traffic twin service requests and records them: After receiving the traffic service request, the remote EDT parses the request content and performs the twin service operation according to the specific task. After the service is completed, the remote EDT encapsulates the generated service results, including key information such as the hash value of the response content, the execution timestamp, and the service status, and saves the information record to the DAG blockchain.

[0034] The edge EDT receives and verifies the traffic twin service results: First, after receiving the service response from the remote EDT, the edge EDT extracts the corresponding service record hash value from the DAG blockchain and compares it with the hash value in the response to verify the authenticity of the service response content. Secondly, the edge EDT not only verifies the remote record, but also further verifies whether real traffic data is used in the service process, and performs partial replay or logical checks on key data operations to ensure that the remote service has not tampered with or ignored the use of key data. In addition, the service verification will also combine timestamp information and service execution time range to check whether the remote traffic service completes the task within a reasonable time range, and further confirm the credibility of its service behavior. If the verification passes, the edge EDT will accept the service result and complete the data sharing operation; if the verification fails, the service response will be rejected and the exception will be recorded. At the same time, the service dishonesty behavior may be broadcast to the network to maintain the credibility and security of the entire system.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] (1) A unified encapsulation format improves the standardization of data management for the air-ground collaborative vehicle network digital twin system. The present invention adopts a unified data encapsulation format, and by clarifying the composition of the data type, timestamp, system signature, and data body, the operating data of the air-ground collaborative vehicle network distributed digital twin system has a highly standardized structure when stored and accessed. The data type identification function is clear, which improves the efficiency of traffic data classification and identification, and is particularly suitable for complex and diverse twin system scenarios. The timestamp and system signature not only realize the precise management of the temporal nature of traffic data, but also ensure the authenticity and tamper-proof capability of the data in cross-node sharing, laying a trusted foundation for data flow and traceability.

[0037] (2) Hierarchical storage of traffic twin data to optimize the storage and access efficiency of the Internet of Vehicles. In view of the different scales and types of data generated by the collaboration between drones and the Internet of Vehicles, the present invention achieves efficient utilization of storage resources through a hierarchical storage mechanism. For large-scale traffic twin data, the original data is stored in the IPFS system and uploaded to the chain in the form of hash values; for small-scale traffic twin data, it is directly stored in the blockchain. This hierarchical design reduces the storage pressure of the blockchain, and at the same time, combined with the content addressing and distributed redundancy characteristics of IPFS, it ensures the reliability and scalability of data storage and improves the access efficiency of large-scale data.

[0038] (3) DAG blockchain ensures data credibility and improves throughput and real-time performance in traffic scenarios. The introduction of voting mechanism and trusted set solves the problem of conflicting blocks that may occur in DAG. Through majority node voting and trusted block screening, data consistency and reliability are ensured. The DAG-based blockchain architecture breaks through the performance bottleneck of the traditional chain block structure, supports the parallel generation and storage of multiple blocks, significantly improves the system throughput and processing efficiency, and provides technical support for the collaboration between Internet of Vehicles and drones.

[0039] (4) The authoritative random equilibrium consensus algorithm improves fairness and anti-attack performance. The AoRB consensus algorithm designed by the present invention balances the node load of the blockchain network by dynamically allocating blockchain node resources, while ensuring that all nodes have fair opportunities to participate in the consensus process. This design not only improves the overall efficiency of the system, but also enhances anti-attack performance through random node activity order, reducing the potential threat of malicious nodes. Especially in the scenario where the Internet of Vehicles and drones work together, the AoRB algorithm effectively balances the node load and ensures the stability and security of the twin system.

[0040] (5) Comprehensive recording and verification capabilities of the service process, enhancing the credibility of data sharing. In the verification process of cross-EDT services, the edge EDT not only verifies the hash value and timestamp of the data, but also conducts in-depth inspections of service behaviors by simulating key computing processes. This full-link verification mechanism effectively eliminates data tampering and service fraud. For example, the local EDT can verify whether the drone path planning or fleet dynamic adjustment tasks are actually executed, and perform logical checks on the service results through the original data stored in IPFS combined with blockchain records. This mechanism significantly improves the credibility of the service process in the collaboration between drones and Internet of Vehicles, effectively prevents data tampering and service fraud, and provides reliable service guarantees for intelligent transportation systems.

[0041] The present invention will be explained in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A data encapsulation flow chart of the distributed digital twin system of the air-ground collaborative vehicle network provided by the present invention;

[0043] Figure 2 This is a schematic diagram of data hierarchical storage of the distributed digital twin system of the air-ground collaborative vehicle network of the present invention;

[0044] Figure 3 It is a block structure diagram of the DAG blockchain of the present invention;

[0045] Figure 4 A consensus flow chart of the authoritative random equilibrium consensus algorithm provided by the present invention;

[0046] Figure 5 A trusted sharing flow chart of the distributed digital twin service of the air-ground collaborative vehicle network provided by the present invention. DETAILED DESCRIPTION

[0047] To facilitate understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings, but the present invention can be implemented in different forms and is not limited to the embodiments described in the text. On the contrary, these embodiments are provided to make the content disclosed in the present invention more thorough and comprehensive.

[0048] Embodiment: A method for sharing a distributed digital twin service of an air-ground collaborative vehicle network, comprising:

[0049] Step 1. According to the type of air-ground collaborative traffic data, the operation data of the air-ground collaborative vehicle network distributed digital twin system is hierarchically packaged before storage, and parsed and accessed when the data is used.

[0050] Step 2. Store the traffic data in a hierarchical manner in the distributed edge server network according to the data scale; the raw data of large-scale data is stored in the InterPlanetary File System (IPFS), and the encapsulated data is stored on a blockchain based on a directed acyclic graph (DAG).

[0051] Step 3. Use the authoritative random equilibrium consensus algorithm to realize the upload and consensus of the operating data of the distributed digital twin system of the air-ground collaborative vehicle network on the blockchain.

[0052] Step 4. After the edge digital twin system requests the twin service from the remote digital twin system, the service record is verified through the DAG blockchain and IPFS system to realize the trusted service sharing among the edge digital twin systems in the distributed digital twin system of the air-ground collaborative vehicle network.

[0053] In step 1 above, the distributed digital twin system of drones and IoV is divided into multiple traffic edge digital twin systems, each of which is responsible for data encapsulation of devices in a specific area, as shown in the attached figure. Figure 1 As shown, it mainly includes the steps of generating timestamp, EDT signature, classification and generating encapsulation package.

[0054] Generate data timestamps for data during EDT operation. Timestamps are used to record the exact time of events or data collection, usually in UTC (Coordinated Universal Time) or local time format. Common formats include ISO 8601 standard (for example, 2024-11-20T15:30:00Z) and Unix timestamp (the number of seconds since January 1, 1970). In the Python programming language, you can use the datetime module to generate ISO 8601 timestamps and the time module to generate Unix timestamps.

[0055] Furthermore, to ensure the security and integrity of the data, an EDT signature needs to be generated for each piece of data when it is encapsulated. The EDT signature is a unique identifier generated by an encryption algorithm (such as a hash algorithm) to verify the source of the data and prevent tampering. The EDT signature contains the data source (such as the device ID or EDT ID), timestamp, and data content. The SHA-256 hash algorithm is used to generate a signature in the form of "5d41402abc4b2a76b9719d911017c592", which can ensure the integrity and immutability of the data.

[0056] Furthermore, in the distributed digital twin system of the air-ground collaborative vehicle network, since the data generated by different data sources have their own characteristics, the data sources are pre-coded and classified, and different operations are performed according to the numbers in data encapsulation and parsing. In this embodiment, the data sources can be divided into six types of data: S, M, D, R, C, and T. Among them, S represents sensor data, M represents simulation data, D represents derivative data, R represents request data, C represents control data, and T represents status data. According to this classification, large-scale data include S, M, and D, and small-scale data include R, C, and T. Each type of data is added with a subscript to indicate a more specific type of division, for example, S1 represents the first type (speed) sensor data.

[0057] Furthermore, during the operation of the distributed digital twin system, the packaging process of the raw data needs to be hierarchically operated according to the data type coding. For large-scale data with type coding of S, M, and D, these data are periodically collected and their hash values ​​are calculated using the SHA-256 hash algorithm. The cycle of data collection and hash calculation is set according to system requirements, which can usually be hourly, daily, or other time intervals that meet specific application requirements. For small-scale data with type coding of R, C, and T, these raw data are directly used as the content in the data packaging package without hash calculation.

[0058] Through this hierarchical packaging approach, the system can adopt different storage and processing strategies according to the scale and characteristics of the data, thereby ensuring the integrity, security and real-time performance of the data. The data parsing process is the reverse process of the data packaging process, which is performed when the node or edge digital twin system verifies the data or service.

[0059] In step 2 above, data is stored in different levels according to the data size. During this process, the system will classify the data according to the size and type of the data to ensure that the system maintains efficiency and scalability during storage and access.

[0060] As attached Figure 2 As shown, for large-scale data with type codes of S, M, and D, the original data is stored in the IPFS (InterPlanetary File System) distributed file system. In order to ensure the integrity and authenticity of the data, the original data stored in IPFS will generate a hash value that is convenient for subsequent rapid positioning and access, and the generated hash value is encapsulated by the process described in step 1. For small-scale data with type codes of R, C, and T, they are directly encapsulated. The encapsulated data is stored on a blockchain based on a directed acyclic graph (DAG).

[0061] The DAG blockchain is implemented based on Python's DAG module, uses the networkx library to build the DAG structure, and uses the pycryptodome library to process encryption and signature operations. In this embodiment, each block is represented by a Block class, which includes the size, version, timestamp, data content, hash value of the previous block, hash value (Merkle tree root) of the current block, etc. The block structure is shown in the attached Figure 3 With this information, the block can be connected to other blocks as a node in the DAG graph.

[0062] The DAG blockchain uses a voting mechanism to resolve conflicting blocks. The DAG structure may conflict during data upload and block generation, for example, the same data is maliciously modified and uploaded multiple times. To solve this problem, a voting mechanism is introduced into the DAG blockchain. The voting mechanism checks whether a new block conflicts with an existing block when it is uploaded to the chain. If there is a conflict, the new block and the conflicting block are input, and the existing blocks vote, and the high-priority block is output as a trusted block, and the other block is marked as a conflicting block.

[0063] The DAG blockchain uses a trusted set to represent a trusted block. The trusted set is obtained through a trusted set generation algorithm, which inputs the entire DAG blockchain, generates a trusted set based on a voting algorithm, and outputs it.

[0064] In step 3 above, the Authority of Random Balance (AoRB) consensus algorithm is designed and used to achieve consensus uploading of data in the distributed digital twin system. In this embodiment, the parameters and process settings of the AoRB algorithm are as follows:

[0065] The distributed digital twin system of the air-ground collaborative vehicle network is divided into four edge digital twins, numbered EDT1 to EDT4. The distributed digital twin system can be expressed as: DDTS = {EDT i |1≤i≤4}. When the system is initialized, 25 DAG blockchain network nodes are created, represented as Node={node i |1≤i≤25}, and initialize node for any node i i (c i =0), which is used as a balancing strategy for subsequent node selection.

[0066] Furthermore, after the DAG blockchain network node is initialized, you need to press the Figure 4 As shown in the figure, the twin node set and global free node set of each EDT are generated randomly for the first time. The size of the twin node set of each EDT is set to 5, which means it contains 5 DAG blockchain network nodes. i The twin node set of |D i |=5. The size of the global free node set is also set to 5, expressed as |F|=5.

[0067] Furthermore, when the air-ground collaborative vehicle network distributed digital twin system is running, the AoRB consensus algorithm is also synchronously Figure 4 The authoritative equilibrium consensus cycle shown. Each EDT selects an authoritative node to generate a new block, and the selection condition is the blockchain network node that generates the least number of blocks. For example, the authoritative node selected by D1 Indicates the number of blocks generated in D1 c i The smallest node. x After a new block is generated and broadcast, the least used free node is selected and locked first. Then node x Move from D1 to F, and finally move node y Move from F to D1, and the cycle ends. The next block generation starts from selecting the authoritative node.

[0068] Furthermore, the nodes in the blockchain network i,i≠x Receive node node x After the block is broadcast, press Attach Figure 4 The structure and content of the block are verified as shown. After the verification is passed, the local cached blockchain copy will be updated and the result will be notified to other nodes. Nodes in the blockchain network will regularly broadcast local blockchain copies to ensure the consistency of the blockchain of the entire network.

[0069] The process of the present invention is as follows Figure 5 As shown, in the above step 4, when the edge EDT requests the twin transportation service from the remote EDT, the service record is verified through the DAG blockchain and IPFS system to ensure the credibility of data sharing across EDTs. In this embodiment, it is mainly divided into three steps: the edge initiates the service request, the remote responds to the service request, and the edge receives the result and verifies it.

[0070] In the stage where the edge EDT initiates a twin service request, the edge EDT needs to complete a certain task (for example, real-time analysis of simulation data). When processing locally at the edge, a twin service request will be initiated to the remote EDT. The data analysis service request in this embodiment includes the following contents: service type, request parameters, timestamp, and signature. The values ​​of each item are data analysis, data range, 2024-11-21T12:30:00Z, and SHA256 signature code, respectively. Then, the request is encapsulated into a standardized data packet and broadcast over the DAG blockchain network. Finally, when sending a request, the edge EDT generates a service access record. The record content includes: a unique identifier and a hash value, and the values ​​are REQ-15445 and a SHA256 hash code, respectively.

[0071] Furthermore, in the remote EDT response twin service stage, after receiving the service request, the remote EDT parses the request content and performs specified operations according to the service type and parameters. For example, in the embodiment, upon receiving a traffic data prediction request, the remote EDT will load the specified model and perform simulation prediction on the requested data. After the remote EDT service is executed, a service result is generated, including content hash, timestamp, and service status, with values ​​of SHA256 hash code, UTC time, and success, respectively. At the same time, the results are packaged and stored in a hierarchical manner.

[0072] Furthermore, in the edge EDT verification service phase, the edge EDT uses the DAG blockchain and IPFS file system to verify the received service results. In the embodiment, the DAG blockchain is accessed to obtain the encapsulated data, the hash of the original data is calculated, and the data is compared with the on-chain data to verify the authenticity of the data; in addition, the remote calculation process can also be verified. For example, there is an operation of calculating the difference between two batches of data in the remote service. The edge can sample and calculate the data difference and compare it with the service record for verification.

Claims

1. A trusted sharing method for distributed digital twin services in air-ground collaborative vehicle networking, characterized in that: The steps include: S1. Based on the type of air-ground collaborative traffic data, hierarchical packaging of the operating data of the distributed digital twin system of the Internet of Vehicles; S2, hierarchically storing the hierarchical packaged data in the distributed edge server network; S3: Based on the data after hierarchical storage, the distributed edge digital twin system of the air-ground collaborative vehicle network uses the authoritative random equilibrium consensus algorithm to reach a consensus on the blocks generated by the data and upload them to the blockchain; S4. After the edge digital twin system requests the twin service from the remote digital twin system, the service record is verified through the directed acyclic graph DAG blockchain and IPFS system to achieve trusted sharing of services between the edge digital twin systems in the distributed digital twin system of the air-ground collaborative vehicle network.

2. According to claim 1, a method for sharing a distributed digital twin service of an air-ground collaborative vehicle network is characterized in that: The specific implementation process of step S1 is: the distributed digital twin system of the drone and the Internet of Vehicles uses a unified packaging format to encapsulate all operating data. The encapsulation of each piece of data includes: calculating timestamps, performing system signatures, classifying data, and processing data content, as follows: First, the timestamp is calculated, which records the generation or packaging time of the air-ground collaborative traffic data in the edge area; secondly, EDT signs the traffic data, and the signature identifies the source node or subsystem of the data; then, the distributed digital twin data is classified according to performance, and the distributed digital twin data of the Internet of Vehicles is divided into sensor data, simulation data, derivative data, service request data, control data or status data; finally, the encapsulated data content is determined according to the data scale.

3. According to claim 2, a method for sharing a distributed digital twin service of an air-ground collaborative vehicle network is characterized in that: The specific implementation process of hierarchical storage in step S2 is as follows: For sensor data, simulation data, and derived data, the data body stores the hash value of the original data or the storage address in the distributed file system; for service request data, control data, and status data, the data body directly stores the original data content; For sensor data, simulation data, and derived data, the original data is stored in the IPFS distributed file system; all encapsulated data will be stored on a blockchain based on a directed acyclic graph (DAG).

4. According to claim 3, a method for sharing a distributed digital twin service of an air-ground collaborative vehicle network is characterized in that: The blockchain based on the directed acyclic graph (DAG) uses a voting mechanism to determine the selected block through voting between nodes to resolve conflicting blocks. After generating a block, each node initiates a vote to other nodes in the network, and the nodes vote on the blocks they receive according to the recursive voting algorithm. The DAG-based blockchain uses a trusted set to represent trusted blocks; a trusted set refers to a group of verified and trusted blocks that are considered reliable. The generation process of the trusted set is as follows: first, traverse all blocks and add conflict-free blocks to the preliminary trusted set; second, add conflicting blocks to the conflict set, use the recursive voting algorithm to vote for the conflicting blocks to select trusted blocks and move them into the preliminary trusted set; finally, filter out blocks whose existence time exceeds a specified threshold from the preliminary trusted set and form a trusted set.

5. According to claim 4, a method for sharing a distributed digital twin service of an air-ground collaborative vehicle network is characterized in that: The specific implementation process of step S3 is as follows: in the distributed digital twin system of drones and Internet of Vehicles, multiple edge traffic domain digital twin EDTs work together to build a comprehensive and real-time virtual mapping of the physical system, wherein the edge traffic domain digital twin EDT is a digital twin system deployed at the edge of the Internet of Vehicles, including several drones, vehicles, roads and their digital virtual bodies; each EDT is responsible for running the edge traffic digital twin system, and during operation, the data is packaged as described in S2, and the packaged data generates blocks of the DAG blockchain through the blockchain nodes, and is uploaded to the blockchain after reaching a consensus with other nodes through the AoRB consensus algorithm.

6. The method for sharing a distributed digital twin service of an air-ground collaborative vehicle network according to claim 5 is characterized in that: The specific implementation process of the AoRB consensus algorithm is as follows: The distributed digital twin system of the air-ground cooperative vehicle network is expressed as: DDTS = {EDT i |1≤i≤n}, where: DDTS represents the set of distributed edge traffic digital twin systems of air-ground collaborative vehicle networks, including n edge traffic domain digital twin EDT instances, each EDT i Represents an independent edge digital twin unit; The DAG blockchain is represented as: G = (B, E), where the set of blocks B = {b i |1≤i≤m}, each b i represents a block; E = {(b i ,b j )|b i ,b j ∈B} represents the directed edge between blocks. If there is an edge (b i ,b j ), then it means block b i Directly reference block b j , block b i Block b on the chain j is the tail block of G; The blockchain node numbered i in the AoRB consensus algorithm is represented by node i (c i ), where c i Indicates the number of blocks generated by the node; blockchain nodes are divided into two categories: one is the twin node set D j , indicating that it is edge digital twin EDT j The other type is the global free node set F, which represents the nodes not occupied by any EDT; the blockchain node set is The AoRB consensus algorithm is divided into a node initialization phase and a runtime consensus phase; The node initialization phase is when DDTS initializes the blockchain node, specifically: creating a blockchain node and setting the upload block count to 0 (c i =0), generating a twin node set {D j |1≤j≤n} and the global free node set F; The runtime consensus phase begins when DDTS is started. Each EDT has the same execution process, specifically: j Submit the running data to the twin node set D j The least used node in the D is node. Node collects data and encapsulates it. After meeting the set conditions, it generates block b and broadcasts it. j Remove the least used node in F * Move out and move into D j , move node into F; D k,k≠j The nodes in verify the block. If the verification pass rate is not lower than the set verification threshold, the block is uploaded to the DAG blockchain G, otherwise it is not uploaded.

7. The method for sharing a distributed digital twin service of an air-ground collaborative vehicle network according to claim 6 is characterized in that: The specific implementation process of step S4 is as follows: Step 4.1, the edge EDT initiates a twin service request and records it: the edge EDT sends a twin service request to the remote EDT according to the traffic task requirements, including the service type, request parameters, timestamp, and edge EDT signature; the request is encapsulated into a standardized data packet and broadcasted through the blockchain network, and the edge EDT generates a corresponding traffic service access record, including the unique identifier of the request, the hash value of the request content, and the timestamp, and stores the record at the edge; Step 4.2, remote EDT responds to the traffic twin service request and records it: After receiving the traffic service request, the remote EDT parses the request content and performs the twin service operation according to the task; after the service is completed, the remote EDT encapsulates the generated service result, including the hash value of the response content, the execution timestamp and the service status, and saves the information record to the DAG blockchain; Step 4.3, the edge EDT receives and verifies the traffic twin service result: First, after receiving the service response from the remote EDT, the edge EDT extracts the corresponding service record hash value from the DAG blockchain and compares it with the hash value in the response to verify the authenticity of the service response content; Secondly, the edge EDT also verifies whether real traffic data is used in the service process, and ensures that the remote service has not tampered with or ignored the use of key data by partially replaying or logically checking key data operations. In addition, the service verification also combines timestamp information and service execution time range to check whether the remote service completes the task within a reasonable time range and confirm the credibility of its service behavior. If the verification passes, the edge EDT will accept the service result and complete the data sharing operation. If the verification fails, the service response will be rejected and the exception will be recorded, and the dishonest service behavior will be broadcast to the network.

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