Space remote sensing data double-chain transaction auditing and tracing method and system based on DRAC-PBFT consensus

Abstract

This invention discloses a dual-chain transaction auditing and traceability method for aerospace remote sensing data based on DRAC-PBFT consensus. The method outputs a comprehensive reputation score for each node based on its response timeliness, historical consensus accuracy, and historical reputation stability. Based on this comprehensive reputation score, network nodes are dynamically divided into an upper-layer high-reputation consensus layer that initiates consensus on received transactions and generates blocks, and a lower-layer ordinary verification layer that receives and verifies the block results to generate blocks after consensus confirmation. The blocks after consensus confirmation are categorized and written as follows: aerospace remote sensing data transaction transactions are written to the main transaction chain, and audit events are written to the audit traceability chain. A dual-chain association index is established, and end-to-end traceability auditing is achieved through this index. This invention also discloses a dual-chain transaction auditing and traceability system for aerospace remote sensing data based on DRAC-PBFT consensus. This method and system can achieve efficient consensus confirmation of transactions and transparent, traceable auditing throughout the entire data lifecycle.

Description

Technical Field

[0001] This invention belongs to the field of blockchain technology, and specifically relates to a method and system for auditing and tracing transactions of aerospace remote sensing data based on DRAC-PBFT consensus. Background Technology

[0002] Blockchain technology is a distributed ledger technology that records transaction information in a decentralized manner. Its core characteristics lie in the immutability, traceability, and multi-party consensus verification of data. In recent years, it has been widely used in areas such as ownership verification and storage of aerospace remote sensing data, traceability of remote sensing results transactions, and auditing of multi-source data sharing. Introducing blockchain technology into aerospace remote sensing data transactions can effectively solve the problems of transaction transparency and audit traceability in multi-party participation scenarios, and is one of the important directions of current research.

[0003] At the consensus mechanism level, Byzantine Fault Tolerance (PBFT) is one of the most widely used consensus algorithms in consortium blockchain scenarios. Its consensus process consists of three phases: Pre-Prepare, Prepare, and Commit. It can achieve correct consensus even when no more than one-third of the nodes are malicious or faulty, and it requires no mining process, achieving transaction confirmation in seconds, making it suitable for permissioned deployment scenarios in consortium blockchains. However, the communication complexity of standard PBFT is... This means that each round of consensus requires full broadcasting among all nodes, and the number of messages increases quadratically with the number of nodes, causing a sharp drop in throughput as the number of nodes increases. Furthermore, the standard PBFT master node election mechanism is simple and lacks dynamic management of node trust levels, making it difficult to identify and remove malicious or low-quality nodes in real time, severely impacting the system's reliability and security. To address these issues, researchers have proposed various improvement schemes such as hierarchical architecture and adaptive node selection. However, existing schemes have a single reputation assessment dimension and static, fixed hierarchical strategies, failing to adapt to the dynamic changes in node behavior in the space-air remote sensing trading network.

[0004] At the chain structure level, existing blockchain-based data auditing and traceability solutions generally adopt a single-chain architecture, mixing high-frequency ordinary transactions (such as data downloads and algorithm calls) with low-frequency, high-value audit events (such as data ownership confirmation and ownership changes) on the same chain. This leads to a continuous accumulation of on-chain load and low traceability efficiency. More critically, when regulatory agencies conduct independent audits of specific data, the single-chain architecture cannot provide a dedicated access path for the audit scenario. Auditors are forced to rely on platform intermediary services, undermining the foundation of decentralized trust. Although some research has begun to explore dual-chain or multi-chain parallel architectures to separate system responsibilities, the existing solutions' inter-chain responsibility division is not specifically designed for the needs of data transaction auditing and traceability, and lack cross-chain anchoring and query interfaces that support independent verification by regulatory agencies.

[0005] Existing blockchain auditing and tracing schemes based on PBFT consensus ignore the significant impact of differences in node trust levels on consensus efficiency and system security. While existing improved PBFT schemes reduce communication overhead to some extent through layering or grouping strategies, they fail to establish a multi-dimensional dynamic reputation assessment mechanism. They cannot comprehensively quantify a node's historical behavior, response timeliness, and consensus accuracy, allowing low-reputation and malicious nodes to continue participating in the consensus process, reducing system reliability and throughput. Furthermore, the node layering strategies in existing improved schemes are static and fixed, unable to adjust node roles in real time according to dynamic changes in node behavior within the network. This makes them unsuitable for real-world scenarios in aerospace remote sensing data trading networks where the participating entities, such as satellite operators, data processors, and buyers, frequently change.

[0006] Existing blockchain-based auditing and traceability solutions for aerospace remote sensing data transactions overlook the crucial difference in storage and retrieval requirements between high-frequency ordinary transactions and low-frequency audit events. While existing single-chain architectures achieve on-chain notarization of remote sensing data transaction records, storing ordinary transactions such as data downloads and algorithm calls alongside audit events like rights confirmation and arbitration on the same chain leads to a continuous accumulation of on-chain load, resulting in increased block generation delays, low traceability query efficiency, and reduced overall system performance. Furthermore, single-chain architectures cannot provide remote sensing data regulatory agencies with an independent and verifiable dedicated audit access path; auditors must rely on platform intermediary services to complete audits, making them unsuitable for aerospace remote sensing data regulatory scenarios requiring independent verification from multiple parties such as aerospace authorities and copyright agencies. Summary of the Invention

[0007] The purpose of this invention is to provide a dual-chain transaction audit and traceability method and system for aerospace remote sensing data based on DRAC-PBFT (Dynamic Reputation Adaptive Clustering PBFT) consensus. Through a multi-dimensional reputation assessment-driven hierarchical consensus mechanism and a dual-chain architecture decoupled from the main transaction chain / audit traceability chain, it achieves efficient consensus confirmation of transactions and transparent and traceable auditing of the entire data lifecycle while ensuring decentralized trust.

[0008] To achieve the above-mentioned objectives, the present invention provides the following technical solution: A method for auditing and tracing transactions of aerospace remote sensing data based on DRAC-PBFT consensus, the method comprising: S1. Perform multi-dimensional reputation quantification calculation on all nodes in the network based on the node's response timeliness score, historical consensus accuracy rate, and reputation historical stability score, and output the comprehensive reputation score of each node. S2. Based on the comprehensive reputation score of each node, the network nodes are dynamically divided into an upper high reputation consensus layer and a lower ordinary verification layer. The upper high reputation consensus layer initiates consensus on the received transactions and generates block results. The lower ordinary verification layer receives and verifies the block results and generates blocks after consensus confirmation. S3. After consensus confirmation, the blocks are classified and written into the main transaction chain or audit traceability chain according to the transaction type: the aerospace remote sensing data transaction is written into the main transaction chain, the audit event is written into the audit traceability chain, and a dual-chain association index is established through the cross-chain anchoring mechanism, and the full-link traceability audit is achieved through the dual-chain association index.

[0009] In S1, the comprehensive reputation score Represented as: ; in: For nodes Response timeliness score For nodes The accuracy of historical consensus For nodes Reputation history stability score Let be the weight hyperparameter, satisfying .

[0010] Furthermore, response timeliness score The calculation method is as follows: ; in: For nodes In recent Average response time in consensus rounds, The maximum tolerable response time set for the system; Historical consensus accuracy Calculation method: ; in: For nodes Number of times historical consensus is correct For nodes Total number of times historical participation reached a consensus.

[0011] In S2, the specific steps include: S2-1. Nodes with a comprehensive reputation score greater than the reputation stratification threshold are included in the upper high reputation consensus layer, and the remaining nodes are assigned to the lower ordinary verification layer. S2-2, The upper-layer high-reputation consensus layer includes master nodes and slave nodes, with a total number of nodes. m The upper-level high-reputation consensus layer executes the improved DRAC-PBFT consensus protocol: S2-2-1, Pre-preparation phase: The master node performs hash calculation on the transaction and broadcasts the preparation message to all slave nodes; S2-2-2, Preparation Phase: For any slave node, after verifying the legitimacy of the broadcast preparation message, it broadcasts the preparation message to the master and slave nodes in the other upper-level high-reputation consensus layer (excluding itself). When the node receives After submitting the completed and valid information, proceed to the submission stage; S2-2-3, Commit Phase: Each node broadcasts a commit message. When the master or slave node participating in the DRAC-PBFT consensus receives the commit message... After a valid submission message is received, the transaction reaches a consensus and a block result is generated; S2-3. The upper high-reputation consensus layer broadcasts the confirmed block results to the lower ordinary verification layer. After the lower ordinary verification layer verifies the hash validity of the block results, it generates a consensus-confirmed block and updates the local ledger copy.

[0012] Furthermore, the election of master nodes in the upper-level high-reputation consensus layer adopts a weighted random strategy, with an election probability of... It is positively correlated with reputation score.

[0013] In S3, the specific steps include: S3-1. Determine the type of transactions in the block after consensus confirmation. If the transaction type is a space-air remote sensing data transaction including data download or algorithm call, it is routed to the main transaction chain; if the transaction type is an audit event of data confirmation, ownership change or dispute arbitration, it is routed to the audit traceability chain. S3-2. Encapsulate the aerospace remote sensing data transaction into a transaction record, including the transaction hash, anonymization identifiers of both parties, resource hash, and timestamp, and write it into the current block of the main transaction chain; S3-3. Encapsulate the audit event into an audit record and write it into the audit tracing chain. At the same time, calculate the anchor hash and write it synchronously into the anchor field of the corresponding block in the audit tracing chain. S3-4. Establish a correlation index between the main transaction chain and the audit traceability chain; S3-5. When regulatory agencies or users input data hashes or transaction IDs, the system first locates the corresponding transaction record in the main transaction chain, and then retrieves the complete lifecycle record of the transaction record from confirmation of rights, transaction to the entire circulation in the audit traceability chain through the dual-chain association index, so as to achieve full-chain traceability audit.

[0014] Furthermore, the method includes: after step S2, dynamically updating the overall reputation score based on the node's performance in this round. in, As a decay factor, if the node behaves normally in this round of consensus, then If a node is determined to be engaging in Byzantine behavior, then This triggers the node degradation mechanism.

[0015] This invention also provides a dual-chain transaction audit and traceability system for aerospace remote sensing data based on DRAC-PBFT consensus, the system comprising: The node reputation assessment module performs multi-dimensional reputation quantification calculations on all nodes in the network based on the node's response timeliness score, historical consensus accuracy rate, and reputation historical stability score, and outputs a comprehensive reputation score for each node. The layered consensus module dynamically divides network nodes into an upper high-reputation consensus layer and a lower ordinary verification layer based on the comprehensive reputation score of each node. The upper high-reputation consensus layer initiates consensus on received transactions and generates block results, while the lower ordinary verification layer receives and verifies the block results and generates blocks after consensus confirmation. The dual-chain storage and anchoring module categorizes and writes the consensus-confirmed blocks into the main transaction chain or the audit traceability chain according to transaction type: aerospace remote sensing data transaction transactions are written into the main transaction chain, and audit events are written into the audit traceability chain. A dual-chain association index is established through a cross-chain anchoring mechanism, and full-chain traceability audit is achieved through the dual-chain association index.

[0016] The present invention also provides a computing device, including a memory and one or more processors, wherein the memory stores an executable program, and the one or more processors implement the above-mentioned method for auditing and tracing transactions of aerospace remote sensing data based on DRAC-PBFT consensus when executing the program.

[0017] The present invention also provides a computer-readable storage medium storing a computer program thereon, characterized in that, when the computer program is processed and executed, it implements the above-mentioned method for auditing and tracing transactions of dual-chain aerospace remote sensing data based on DRAC-PBFT consensus.

[0018] This invention, building upon existing PBFT consortium blockchain consensus and single-chain auditing and tracing schemes, proposes a Dynamic Reputation-based Adaptive Clustering (DRAC) mechanism for electing consensus nodes. DRAC quantifies the historical behavior, response timeliness, and consensus accuracy of nodes in the network using a multi-dimensional node reputation scoring model (MNRS) based on response timeliness, consensus accuracy, and historical stability. This dynamically increases the participation weight and master node election probability of high-reputation nodes in the consensus process, while restricting low-reputation nodes to the verification and forwarding layer, eliminating their direct influence on consensus decisions. This reduces the communication complexity of full-node broadcasting from... Down to ( This approach enhances consensus efficiency and system reliability while ensuring Byzantine fault tolerance. Simultaneously, it provides a dual-chain storage architecture (DCSA) that decouples the main transaction chain from the audit traceability chain. This architecture routes high-frequency ordinary transactions and low-frequency audit events to the main transaction chain and audit traceability chain for independent storage, respectively. A cross-chain anchored hash is used to establish a dual-chain association index, enabling regulatory agencies to independently retrieve complete lifecycle records of data from ownership confirmation and transactions to the entire circulation process without relying on platform intermediaries. This avoids the problems of on-chain load accumulation and low traceability efficiency caused by single-chain hybrid storage.

[0019] When reaching consensus on transactions, selecting nodes with high reputation scores and reliable historical behavior to participate in core consensus decisions helps the system reduce communication overhead and increase throughput while ensuring Byzantine fault tolerance. Nodes with low reputations or abnormal behavior are often detrimental to consensus efficiency and system security. Therefore, when organizing a consortium blockchain consensus network, it is necessary to reduce the direct impact of low-reputation nodes on consensus decisions and enhance the system's adaptive fault tolerance. When storing audit traceability data, routing high-frequency ordinary transactions and low-frequency audit events to different on-chain storage locations based on semantic classification helps reduce on-chain load and improve traceability query efficiency. Mixing the two types of events in a single chain often leads to continuous accumulation of on-chain load and a lack of independence in audit channels.

[0020] In contrast, this invention introduces a multi-dimensional reputation assessment-driven layered consensus mechanism and a decoupled dual-chain architecture (DRAC-PBFT dual-chain audit traceability architecture) when performing consortium blockchain consensus and audit traceability storage. This reduces consensus communication overhead while improving the transparency of transaction records and the traceability efficiency of the entire data lifecycle. Attached Figure Description

[0021] Figure 1 This is a structural diagram of a dual-chain transaction audit and traceability system for aerospace remote sensing data based on the DRAC-PBFT consensus. Figure 2 This is a diagram of the hierarchical consensus node structure. Figure 3 This is a diagram of a double-chain cross-chain anchoring structure. Detailed Implementation

[0022] The technical solution of the present invention will be further described below with reference to the accompanying drawings, but the present invention is not limited to the following embodiments.

[0023] In this embodiment, a given is a A network composed of consortium blockchain nodes Each node Record of behavior with historical consensus Response time series and historical reputation value The system needs to handle transactions related to aerospace remote sensing data. (Including high-frequency trading such as data download and algorithm calls) and audit events (Including low-frequency events such as data ownership confirmation, ownership transfer, and dispute arbitration) The DRAC-PBFT framework aims to achieve consensus and evidence preservation through a multi-dimensional reputation-driven hierarchical consensus mechanism. The above transactions will be efficiently written into the main transaction chain. With audit traceability chain This enables the reliable recording of transactions and transparent traceability of data throughout its entire lifecycle.

[0024] The node devices of the consortium blockchain network can use general server hardware without special restrictions. For example, they can use standard x86 servers with a configuration of no less than 8 CPU cores, 32GB of memory, and 1TB of storage. The nodes communicate with each other via the TCP / IP protocol.

[0025] The method for auditing and tracing transactions of aerospace remote sensing data based on DRAC-PBFT consensus provided in this embodiment specifically includes: S1. Perform multi-dimensional reputation quantification calculation on all nodes in the network based on the node's response timeliness score, historical consensus accuracy, and reputation historical stability score, and output the comprehensive reputation score for each node. In S1, all nodes in the network Perform multi-dimensional reputation quantification calculations and output the comprehensive reputation score for each node. This serves as the basis for subsequent elections of hierarchical consensus nodes.

[0026] Execution steps: S1-1, Multidimensional Reputation Score Calculation First, based on nodes Based on historical behavioral records, their comprehensive reputation score is calculated from three dimensions: ; in: For nodes Response timeliness score For nodes The accuracy of historical consensus For nodes Reputation history stability score Weight hyperparameters (satisfying) ).

[0027] In this embodiment, the reputation score calculation method in the node reputation evaluation module can adopt a general weighted linear scoring model without special restrictions, and the weight hyperparameters... This can be adjusted according to the actual deployment scenario; the example value is... .

[0028] In this embodiment, response timeliness scoring The calculation method can be ( For nodes In recent Average response time in consensus rounds, (Maximum tolerable response time set for the system) ; In this embodiment, the historical consensus accuracy rate The calculation method can be ( For nodes Number of times historical consensus is correct For nodes (Total number of times historical participation in consensus) ; S1-1, Reputation Dynamic Update At the end of each consensus cycle, the reputation value is dynamically updated based on the node's performance in that cycle. This is the attenuation factor; example values ​​are provided. ; (Increased rewards and punishments for consensus-based behavior in this round) ; If a node behaves normally in this round of consensus, then If a node is determined to be engaging in Byzantine behavior, then This triggers the node degradation mechanism.

[0029] In this embodiment, reputation decay factor Example value .

[0030] S2. Based on the comprehensive reputation score of each node, the network nodes are dynamically divided into an upper high-reputation consensus layer and a lower ordinary verification layer. The upper high-reputation consensus layer initiates consensus on received transactions and generates block results. The lower ordinary verification layer receives and verifies the block results, generating blocks after consensus confirmation. In S2, the reputation score is based on the output of S1. The network nodes are dynamically divided into an upper high-reputation consensus layer and a lower ordinary verification layer (such as...). Figure 2 As shown), and executes the improved DRAC-PBFT consensus protocol between upper-layer nodes, reducing communication complexity from Down to ( , (This refers to the number of upper-layer consensus nodes).

[0031] The upper layer is a set of high-reputation consensus nodes. It is responsible for consensus decisions in the three phases of Pre-Prepare, Prepare, and Commit; the lower layer consists of a set of ordinary validator nodes. It only receives and verifies the block results broadcast by the upper layer, and does not participate in the full consensus broadcast. Reputation stratification threshold (hyperparameter, reputation stratification threshold) The value can be adjusted according to network size and security requirements. In this embodiment, the value is [value to be inserted here]. ).

[0032] Execution steps: S2-1, Hierarchical Node Election Based on current reputation score , will satisfy The nodes are incorporated into the upper-level high-reputation consensus layer. (referred to as the upper layer), the remaining nodes are assigned to the lower ordinary verification layer. (Referred to as the lower layer); the upper high-reputation consensus layer includes master nodes and slave nodes, with the master node... The election adopts a weighted random strategy, and the election probability is... Positively correlated with reputation score: .

[0033] S2-2, DRAC-PBFT three-phase consensus S2-2-1, Master Node For received transactions Initiating consensus involves three stages: a) Pre-Prepare Phase: Master Node For transactions Perform hash calculation to obtain Broadcast preparation message To all upper-level secondary nodes, among which Number the view. For serial number; b) Preparation Phase: Each upper-level secondary node After verifying the validity of the message, broadcast the preparation message to all other upper-level nodes. In this embodiment, when the node receives... After submitting the completed and valid information, proceed to the submission stage; c) Commit Phase: Each node broadcasts a commit message. When the node receives After a valid commit message is received, the transaction... Reach consensus and generate blocks .

[0034] S2-3, Issuance of consensus results The upper-level consensus nodes will confirm the blocks Broadcast to all lower-level verification nodes After the lower-level nodes verify the validity of the block hash, they update their local ledger copy.

[0035] The DRAC-PBFT consensus protocol in S2 can adopt a general Byzantine fault-tolerant consensus implementation framework without special restrictions. For example, it can be deployed and further developed based on open-source consortium blockchain platforms such as Hyperledger Fabric or FISCO BCOS; the number of upper-layer consensus nodes... The number of network nodes N can be dynamically adjusted based on the fault tolerance requirements. The system requires that the number of Byzantine nodes in the upper-layer consensus nodes does not exceed [a certain threshold]. .

[0036] S3. Write the consensus-confirmed blocks into the main transaction chain or audit traceability chain according to transaction type: write aerospace remote sensing data transaction transactions into the main transaction chain, and write audit events into the audit traceability chain. Establish a dual-chain association index through a cross-chain anchoring mechanism, and achieve full-chain traceability auditing through the dual-chain association index. In S3, the blocks confirmed through consensus will be... Classified by transaction type and written into the main transaction chain. Or audit traceability chain And establish a correlation index between the two chains through a cross-chain anchoring mechanism (such as...) Figure 3 As shown in the figure, it enables efficient storage of high-frequency trading and independent, verifiable traceability of audit events.

[0037] Main transaction chain Responsible for storing high-frequency trading summaries; auditing the traceability chain. Responsible for storing low-frequency audit events; the two chains are anchored by hashes. Establish cross-chain index relationships.

[0038] Execution steps: S3-1, Transaction Classification Routing Blocks after consensus confirmation The transaction type is determined within the transaction. Then it will be routed to the main transaction chain. If the transaction type Then the route is routed to the audit tracing chain. ; S3-2, Main Transaction Chain Writing High-frequency trading transactions are structured and encapsulated into transaction records. , containing transaction hash Anonymization of both parties Resource hash With timestamp Write to the current block of the main transaction chain: ; S3-3, Audit Traceability Chain Writing and Cross-Chain Anchoring Encapsulate audit events into audit records. Write to the audit traceability chain and calculate the anchor hash. This information is then synchronously written into the anchor field of the corresponding block in the main transaction chain to establish a dual-chain association index. S3-4, Source Tracing Inquiry Regulatory bodies or users input data hashes or transaction IDs, and the system first processes them on the main transaction chain. Locate the corresponding transaction record in the middle, and then through Cross-chain retrieval audit traceability chain This data records the entire lifecycle of ownership, transactions, and the complete transfer of information, enabling full-chain traceability and auditing.

[0039] The blockchain storage structure in S3 can adopt a general chained block data structure without special restrictions; the hash algorithm can use standard hash functions such as SHA-256 or SM3; the cross-chain anchoring interface can be implemented based on a general cross-chain protocol, such as the IBC protocol or the Polkadot cross-chain message passing mechanism; the encrypted storage of on-chain data can use a general symmetric encryption algorithm such as AES-256 without special restrictions.

[0040] To further demonstrate the technical effects of the present invention, the DRAC-PBFT scheme of the present invention is compared with the existing PBFT and its improved schemes. Experimental environment: 20-node consortium blockchain network, where the proportion of malicious nodes is 10%, and the transaction load is 500 transactions / second.

[0041] Table 1 shows the comparison results of key performance indicators between the DRAC-PBFT scheme of the present invention and the existing PBFT and its improved schemes: TPS represents the transaction throughput per second, Latency represents the average consensus latency, CommOverhead represents the total number of consensus communication messages in a single round, FaultTolerance represents whether the system maintains correct consensus when the proportion of malicious nodes is 10%, and AuditQuery represents the average query response time for audit tracing.

[0042] Table 1. Comparison of key performance indicators between the DRAC-PBFT scheme of this invention and existing PBFT and its improved schemes. Existing PBFT-based consortium blockchain consensus schemes typically employ a single-layer consensus structure with full node participation. This means all n nodes in the network participate in the full message broadcast during the pre-preparation, preparation, and commit phases. This approach fails to adequately consider the impact of reputational differences among nodes on consensus efficiency and system security. Consequently, low-reputation or abnormal nodes may continue to participate in the core consensus process, maintaining high communication complexity during the consensus phase. This weakens system throughput and reliability. In contrast, this invention dynamically divides network nodes into an upper high-reputation consensus layer and a lower ordinary verification layer based on a comprehensive node reputation score, which is determined solely by... Several high-reputation consensus nodes at the upper layer participated in the DRAC-PBFT core consensus broadcast, among which The number of high-reputation consensus nodes at the upper level and < Lower-level nodes only receive and verify the consensus results. Therefore, this invention can reduce the communication complexity of the core consensus phase from... Reduce to Considering the broadcast of consensus results to lower-level nodes, the overall communication overhead for a single round is approximately [missing information]. This improves the scalability and consensus efficiency of the consortium blockchain system.

[0043] Existing blockchain auditing and traceability solutions, such as single-chain storage structures (i.e., the original form of Module 3), ignore the fundamental differences in storage and query requirements between high-frequency transactions and low-frequency audit events. This leads to accumulated on-chain load, low traceability efficiency, and an inability to provide regulatory agencies with an independent and verifiable dedicated audit access path. In contrast, this invention routes high-frequency ordinary transactions and low-frequency audit events to different on-chain storage locations based on semantic classification during auditing and traceability storage. This helps reduce on-chain load and improve traceability query efficiency.

[0044] The throughput and communication overhead of the present invention and the standard PBFT are compared under different node scales, as shown in Table 2. CommOverhead is the number of consensus messages in a single round.

[0045] Table 2 Comparison of throughput and communication overhead between the present invention and standard PBFT at different node scales As shown in Table 2, compared to the standard PBFT+single-chain solution, this invention achieves significant improvements in four metrics: TPS, consensus latency, communication overhead, and audit query response time. TPS is increased by approximately 101.9%, average consensus latency is reduced by approximately 66.1%, the number of communication messages per consensus round is reduced by approximately 74.2%, and audit traceability query response time is shortened by approximately 82.6%. Table 2 also shows that as the node size expands from 10 to 80, the communication overhead of standard PBFT increases quadratically (from 90 to 6320 messages), while this invention, benefiting from its layered consensus mechanism, maintains a lower communication overhead (from 36 to 120 messages), demonstrating good scalability.

[0046] Based on the same inventive concept, the embodiments provide a dual-chain transaction audit and traceability system for aerospace remote sensing data based on DRAC-PBFT consensus, such as... Figure 1 As shown, the system consists of three core modules: node reputation evaluation module, hierarchical consensus module, and dual-chain storage and anchoring module.

[0047] The node reputation assessment module performs multi-dimensional reputation quantification calculations on all nodes in the network based on the node's response timeliness score, historical consensus accuracy rate, and reputation historical stability score, and outputs a comprehensive reputation score for each node. The layered consensus module dynamically divides network nodes into an upper high-reputation consensus layer and a lower ordinary verification layer based on the comprehensive reputation score of each node. The upper high-reputation consensus layer initiates consensus on received transactions and generates block results, while the lower ordinary verification layer receives and verifies the block results and generates blocks after consensus confirmation. The dual-chain storage and anchoring module categorizes and writes the consensus-confirmed blocks into the main transaction chain or the audit traceability chain according to transaction type: aerospace remote sensing data transaction transactions are written into the main transaction chain, and audit events are written into the audit traceability chain. A dual-chain association index is established through a cross-chain anchoring mechanism, and full-chain traceability audit is achieved through the dual-chain association index.

[0048] Based on the same inventive concept, the embodiment also provides a computing device, including a memory and one or more processors, wherein the memory stores an executable program, and the one or more processors implement the above-mentioned method for auditing and tracing transactions of aerospace remote sensing data dual-chain based on DRAC-PBFT consensus when executing the program.

[0049] Based on the same inventive concept, the embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when processed and executed, implements the above-mentioned method for auditing and tracing transactions of dual-chain aerospace remote sensing data based on DRAC-PBFT consensus.

[0050] It should be noted that the electronic devices and computer-readable storage media provided in the above embodiments belong to the same inventive concept as the method and system for auditing and tracing transactions of dual-chain transactions of aerospace remote sensing data based on DRAC-PBFT consensus. For details of their specific implementation process, please refer to the embodiment of the system for auditing and tracing transactions of dual-chain transactions of aerospace remote sensing data based on DRAC-PBFT consensus executing the above dual-chain transaction auditing and tracing method, which will not be repeated here.

[0051] The specific embodiments described above illustrate the technical solution and beneficial effects of the present invention in detail. It should be understood that the above description is only the most preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, additions, and equivalent substitutions made within the scope of the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for auditing and tracing transactions of dual-chain aerospace remote sensing data based on DRAC-PBFT consensus, characterized in that: The method includes: S1. Perform multi-dimensional reputation quantification calculation on all nodes in the network based on the node's response timeliness score, historical consensus accuracy rate, and reputation historical stability score, and output the comprehensive reputation score of each node. S2. Based on the comprehensive reputation score of each node, the network nodes are dynamically divided into an upper high reputation consensus layer and a lower ordinary verification layer. The upper high reputation consensus layer initiates consensus on the received transactions and generates block results. The lower ordinary verification layer receives and verifies the block results and generates blocks after consensus confirmation. S3. After consensus confirmation, the blocks are classified and written into the main transaction chain or audit traceability chain according to the transaction type: the aerospace remote sensing data transaction is written into the main transaction chain, the audit event is written into the audit traceability chain, and a dual-chain association index is established through the cross-chain anchoring mechanism, and the full-link traceability audit is achieved through the dual-chain association index.

2. The method according to claim 1, characterized in that, In S1, the comprehensive reputation score Represented as: ； in: For nodes Response timeliness score For nodes The accuracy of historical consensus For nodes Reputation history stability score Let be the weight hyperparameter, satisfying .

3. The method according to claim 2, characterized in that, Response timeliness score The calculation method is as follows: ； in: For nodes In recent Average response time in consensus rounds, The maximum tolerable response time set for the system; Historical consensus accuracy Calculation method: ； in: For nodes Number of times historical consensus is correct For nodes Total number of times historical participation reached a consensus.

4. The method according to claim 1, characterized in that, In S2, the specific steps include: S2-1. Nodes with a comprehensive reputation score greater than the reputation stratification threshold are included in the upper high reputation consensus layer, and the remaining nodes are assigned to the lower ordinary verification layer. S2-2, The upper-layer high-reputation consensus layer includes master nodes and slave nodes, with a total number of nodes. m The upper-level high-reputation consensus layer executes the improved DRAC-PBFT consensus protocol: S2-2-1, Pre-preparation phase: The master node performs hash calculation on the transaction and broadcasts the preparation message to all slave nodes; S2-2-2, Preparation Phase: For any slave node, after verifying the legitimacy of the broadcast preparation message, it broadcasts the preparation message to the master and slave nodes in the other upper-level high-reputation consensus layer (excluding itself). When the node receives After submitting the completed and valid information, proceed to the submission stage; S2-2-3, Commit Phase: Each node broadcasts a commit message. When the master or slave node participating in the DRAC-PBFT consensus receives the commit message... After a valid submission message is received, the transaction reaches a consensus and a block result is generated; S2-3. The upper high-reputation consensus layer broadcasts the confirmed block results to the lower ordinary verification layer. After the lower ordinary verification layer verifies the hash validity of the block results, it generates a consensus-confirmed block and updates the local ledger copy.

5. The method according to claim 4, characterized in that, The election of master nodes in the upper high-reputation consensus layer adopts a weighted random strategy, with an election probability of... It is positively correlated with reputation score.

6. The method according to claim 1, characterized in that, In S3, the specific steps include: S3-1. Determine the type of transactions in the block after consensus confirmation. If the transaction type is a space-air remote sensing data transaction including data download or algorithm call, it is routed to the main transaction chain; if the transaction type is an audit event of data confirmation, ownership change or dispute arbitration, it is routed to the audit traceability chain. S3-2. Encapsulate the aerospace remote sensing data transaction into a transaction record, including the transaction hash, anonymization identifiers of both parties, resource hash, and timestamp, and write it into the current block of the main transaction chain; S3-3. Encapsulate the audit event into an audit record and write it into the audit tracing chain. At the same time, calculate the anchor hash and write it synchronously into the anchor field of the corresponding block in the audit tracing chain. S3-4. Establish a correlation index between the main transaction chain and the audit traceability chain; S3-5. When regulatory agencies or users input data hashes or transaction IDs, the system first locates the corresponding transaction record in the main transaction chain, and then retrieves the complete lifecycle record of the transaction record from confirmation of rights, transaction to the entire circulation in the audit traceability chain through the dual-chain association index, so as to achieve full-chain traceability audit.

7. The method according to claim 1, characterized in that, The method includes: after step S2, dynamically updating the overall reputation score based on the node's performance in this round. in, As a decay factor, if the node behaves normally in this round of consensus, then If a node is determined to be engaging in Byzantine behavior, then This triggers the node degradation mechanism.

8. A dual-chain transaction audit and traceability system for aerospace remote sensing data based on DRAC-PBFT consensus, characterized in that: The system includes: The node reputation assessment module performs multi-dimensional reputation quantification calculations on all nodes in the network based on the node's response timeliness score, historical consensus accuracy rate, and reputation historical stability score, and outputs a comprehensive reputation score for each node. The layered consensus module dynamically divides network nodes into an upper high-reputation consensus layer and a lower ordinary verification layer based on the comprehensive reputation score of each node. The upper high-reputation consensus layer initiates consensus on received transactions and generates block results, while the lower ordinary verification layer receives and verifies the block results and generates blocks after consensus confirmation. The dual-chain storage and anchoring module categorizes and writes the consensus-confirmed blocks into the main transaction chain or the audit traceability chain according to transaction type: aerospace remote sensing data transaction transactions are written into the main transaction chain, and audit events are written into the audit traceability chain. A dual-chain association index is established through a cross-chain anchoring mechanism, and full-chain traceability audit is achieved through the dual-chain association index.

9. A computing device, characterized in that, The device includes a memory and one or more processors, wherein the memory stores an executable program, and the one or more processors, when executing the program, implement the method for auditing and tracing transactions of dual-chain space-air remote sensing data based on DRAC-PBFT consensus as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is processed and executed, it implements the method for auditing and tracing transactions of dual-chain aerospace remote sensing data based on DRAC-PBFT consensus as described in any one of claims 1-7.

Images