Digital content tracing system based on blockchain technology

Abstract

The application discloses a digital content tracing system based on a blockchain technology, and particularly relates to the field of the blockchain technology; by analyzing multi-node data synchronization behavior, tampering risk of digital content in a distributed storage process is evaluated; a transaction backlog risk judgment module evaluates transaction backlog and local congestion hidden danger according to a transaction verification rate of a propagation process; a reliability evaluation module combines tampering risk and local congestion hidden danger to determine reliability of a digital content tracing chain. An abnormal risk evaluation module analyzes operation abnormality through a smart contract log, a conflict possibility evaluation module detects consistency deviation of a flow record; a potential risk evaluation module comprehensively evaluates multi-dimensional risk to generate a potential risk evaluation result of the tracing chain, and decides whether to trigger an on-chain responsible node traceability verification mechanism, which can comprehensively identify potential hidden dangers in the tracing chain, and provides an efficient and reliable solution for blockchain digital content tracing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blockchain, and more particularly, to a digital content tracing system based on blockchain technology. BACKGROUND

[0002] In the traditional digital content tracing system, data tampering detection and the legality of operation behavior often rely on centralized management, which has the problems of single point failure and tampering difficulty to trace. The distributed storage and tamper-proof characteristics of blockchain provide a new technical means for digital content tracing. In practical application, the blockchain network faces challenges such as multi-node data synchronization delay, transaction backlog, consensus conflict, and abnormal execution of smart contracts. These problems may lead to the unreliability of the digital content tracing chain, affecting the credibility and security of the overall digital content tracing system. In addition, with the complexity of blockchain applications, how to comprehensively evaluate the tampering risk, network congestion risk, and operation behavior anomaly in the chain, accurately identify potential risks and trigger dynamic traceability verification has become a key problem for the further development of blockchain technology.

[0003] To solve the above problems, a technical solution is provided. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present application provide a digital content tracing system based on blockchain technology to solve the problems raised in the background art.

[0005] To achieve the above object, the present application provides the following technical scheme:

[0006] The digital content tracing system based on blockchain technology comprises a tampering risk assessment module, a transaction backlog risk judgment module, a reliability evaluation module, an abnormal risk assessment module, a conflict possibility evaluation module and a potential risk assessment module.

[0007] The tampering risk assessment module: by analyzing the multi-node data synchronization behavior of the digital content in the blockchain network, the tampering risk degree of the digital content in the distributed storage process is evaluated.

[0008] The transaction backlog risk judgment module: by measuring the transaction verification rate of the digital content in the propagation process in the blockchain network, it is judged whether there is a transaction backlog risk in the blockchain network; when there is no transaction backlog risk in the blockchain network, the local congestion risk hidden danger degree of the blockchain network is evaluated.

[0009] The reliability evaluation module: based on the tampering risk degree of the digital content in the distributed storage process and the local congestion risk hidden danger degree of the blockchain network, the reliability of the digital content tracing chain in the blockchain network is evaluated.

[0010] When the reliability of the digital content traceability chain is low: the anomaly risk assessment module assesses the degree of anomaly risk in digital content operation behavior by analyzing the smart contract execution logs between blockchain nodes; the conflict probability assessment module assesses the conflict probability of digital content circulation behavior based on the timestamp of the digital content traceability record and the consistency deviation in the consensus process between nodes.

[0011] Potential Risk Assessment Module: This module comprehensively analyzes the degree of tampering risk of digital content during distributed storage, the degree of abnormal risk of digital content operation behavior, and the possibility of conflict in digital content circulation behavior. It assesses the potential risks of the digital content traceability chain in the blockchain network and decides whether to trigger the traceability verification mechanism of the responsible nodes on the chain.

[0012] In a preferred embodiment, the degree of tampering risk of digital content during distributed storage is assessed by analyzing the multi-node data synchronization behavior of digital content in the blockchain network, specifically as follows:

[0013] Analyze the distributed storage structure of digital content and extract multi-node synchronization rules;

[0014] Collect multi-node data synchronization logs, recording synchronization status and hash check values;

[0015] By comparing the hash values ​​of each node, data inconsistency was detected.

[0016] The tampering risk index is calculated to assess the risk of digital content being tampered with during distributed storage. The expression for the tampering risk index is:

[0017] Where TRI is the tampering risk index; n represents the total number of fragments; ΔH k This is the hash difference value for shard k.

[0018] In a preferred embodiment, the risk of transaction backlog within the blockchain network is determined by measuring the transaction verification rate during the propagation of digital content within the blockchain network. Specifically:

[0019] To determine the transaction verification rate, the average time interval for each node to process a transaction is calculated: record the transaction reception time and verification completion time, and calculate the transaction verification time interval; sum up the verification time intervals of all transactions to calculate the node's average transaction verification time. The reciprocal of the average transaction verification time is defined as the node's transaction verification rate.

[0020] comparing the transaction verification rate of the node with a preset transaction verification rate threshold value, to determine whether there is a risk of transaction backlog in the blockchain network: after obtaining the transaction verification rate of the node, the transaction verification rate threshold value is compared; when the transaction verification rate of the node is lower than the transaction verification rate threshold value, there is a risk of transaction backlog in the blockchain network.

[0021] In a preferred embodiment, when there is no risk of transaction backlog in the blockchain network, the degree of risk of local congestion of the blockchain network is evaluated, specifically:

[0022] When there is no risk of transaction backlog in the blockchain network, the spatiotemporal distribution characteristics of the transaction processing delay of the node are analyzed;

[0023] The local congestion risk index is calculated to quantify the degree of risk of local congestion of the blockchain network, and the expression of the local congestion risk index is:

[0024] Wherein, LCRI is the local congestion risk index; D is the total number of nodes in the blockchain network; is the average verification delay time of all nodes; is the average delay time of node j.

[0025] In a preferred embodiment, based on the degree of tampering risk of digital content in the distributed storage process and the degree of risk of local congestion of the blockchain network, the reliability of the digital content traceability chain in the blockchain network is evaluated, specifically:

[0026] A preset tampering risk index threshold value is compared with the tampering risk index:

[0027] When the tampering risk index is less than or equal to the tampering risk index threshold value, the reliability of the digital content traceability chain in the blockchain network is high reliability;

[0028] A preset local congestion risk index threshold value is compared with the local congestion risk index:

[0029] When the local congestion risk index is less than or equal to the local congestion risk index threshold value, the reliability of the digital content traceability chain in the blockchain network is high reliability;

[0030] When the tampering risk index is less than or equal to the tampering risk index threshold value, and the local congestion risk index is less than or equal to the local congestion risk index threshold value, the reliability of the digital content traceability chain in the blockchain network is high reliability; except that the tampering risk index is less than or equal to the tampering risk index threshold value, and the local congestion risk index is less than or equal to the local congestion risk index threshold value, the reliability of the digital content traceability chain in the blockchain network is low reliability.

[0031] In a preferred embodiment, the abnormal risk degree of the digital content operation behavior is evaluated by analyzing the smart contract execution logs between the blockchain nodes, specifically:

[0032] Collecting the smart contract execution logs, extracting the core parameters of the digital content operation behavior;

[0033] Analyzing the compliance of the operation behavior with the smart contract rules, marking the abnormal operation records;

[0034] Counting the abnormal operation frequency, calculating the abnormal operation risk index, the expression of the abnormal operation risk index:

[0035] Wherein, AORI is the abnormal operation risk index; M is the total number of all nodes of the smart contract execution records; f i is the abnormal operation frequency of node j; is the average abnormal operation frequency of all nodes of the smart contract execution records.

[0036] In a preferred embodiment, the conflict possibility of the digital content circulation behavior is evaluated based on the timestamp of the digital content traceability record and the consistency deviation in the consensus process between the nodes, specifically:

[0037] Extracting the digital content traceability record, parsing the timestamp information of the circulation behavior;

[0038] Comparing the timestamp order and the consensus records between the nodes, detecting the abnormal circulation behavior;

[0039] Calculating the consensus consistency deviation, the consensus consistency deviation refers to the confirmation time difference of the same circulation operation on different nodes;

[0040] Combining the timestamp abnormality and the consensus deviation results, calculating the conflict possibility index;

[0041] The expression of the conflict possibility index is:

[0042] Wherein, CLI is the conflict possibility index; L is the total number of the circulation operations of the digital content; is the consensus deviation of the hth circulation operation; ΔR avg is the average consensus deviation of all circulation operations; ΔS h is the time interval of the hth circulation operation; ε is a positive number to prevent the denominator from being zero.

[0043] In a preferred embodiment, the tampering risk degree of the digital content in the distributed storage process, the abnormal risk degree of the digital content operation behavior and the conflict possibility of the digital content circulation behavior are comprehensively analyzed, the potential risk of the digital content traceability chain in the blockchain network is evaluated, and it is decided whether to trigger the traceability verification mechanism of the on-chain responsible node, specifically:

[0044] The tampering risk index corresponding to the tampering risk degree of the digital content in the distributed storage process, the abnormal operation risk index corresponding to the abnormal risk degree of the digital content operation behavior and the conflict possibility index corresponding to the conflict possibility of the digital content circulation behavior are normalized respectively, and the tampering risk index, the abnormal operation risk index and the conflict possibility index obtained after normalization are calculated to obtain a risk index.

[0045] The calculation formula of the risk index is:

[0046] Wherein, RI is the risk index; TRI is the tampering risk index; AORI is the abnormal operation risk index; CLI is the conflict possibility index; and alpha, beta and lambda are weight coefficients of the tampering risk index, the abnormal operation risk index and the conflict possibility index respectively.

[0047] A preset risk index threshold is compared with the risk index: when the risk index is greater than or equal to the risk index threshold, it indicates that the digital content traceability chain in the blockchain network has potential risks, and the traceability verification mechanism of the on-chain responsible node needs to be triggered.

[0048] When the risk index is less than the risk index threshold, it indicates that the digital content traceability chain in the blockchain network is running normally, the potential risk is within a controllable range, and the traceability verification mechanism of the on-chain responsible node does not need to be triggered.

[0049] The technical effects and advantages of the digital content traceability system based on the blockchain technology are as follows:

[0050] Through the analysis of the multi-node data synchronization behavior of the tampering risk assessment module, the effective evaluation of the tampering risk of the digital content in the distributed storage process is ensured. The transaction backlog risk judgment module combines the transaction verification rate and the analysis of the local congestion risk to provide comprehensive monitoring capability for the performance state of the blockchain network. The reliability evaluation module integrates the tampering risk and the congestion risk to determine the reliability of the digital content traceability chain. When the reliability of the digital content traceability chain is low, the abnormal risk assessment module and the conflict possibility assessment module work together to analyze the potential problems in the operation behavior and the flow record by analyzing the smart contract execution log and the consensus consistency deviation. The potential risk assessment module provides decision support for dynamic traceability verification by integrating multi-dimensional risks, ensuring the safety and stability of the traceability chain and providing an efficient and reliable digital content traceability mechanism for blockchain applications. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 The structure diagram of the digital content traceability system based on the blockchain technology of the present application is given. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0053] EMBODIMENT

[0054] Figure 1 The structure diagram of the digital content traceability system based on the blockchain technology of the present application is given. The digital content traceability system based on the blockchain technology includes a tampering risk assessment module, a transaction backlog risk judgment module, a reliability evaluation module, an abnormal risk assessment module, a conflict possibility assessment module, and a potential risk assessment module.

[0055] The tampering risk assessment module: by analyzing the multi-node data synchronization behavior of the digital content in the blockchain network, the tampering risk degree of the digital content in the distributed storage process is evaluated.

[0056] The transaction backlog risk judgment module: by measuring the transaction verification rate of the digital content in the propagation process in the blockchain network, it is judged whether there is a transaction backlog risk in the blockchain network. When there is no transaction backlog risk in the blockchain network, the local congestion risk hidden danger degree of the blockchain network is evaluated.

[0057] Reliability evaluation module: based on the tampering risk degree of digital content in the distributed storage process and the local congestion risk degree of the blockchain network, evaluate the reliability of the digital content traceability chain in the blockchain network;

[0058] When the reliability of the digital content traceability chain is low: the anomaly risk evaluation module evaluates the anomaly risk degree of the digital content operation behavior by analyzing the smart contract execution log between the blockchain nodes; the conflict possibility evaluation module evaluates the conflict possibility of the digital content circulation behavior based on the timestamp of the digital content traceability record and the consistency deviation in the consensus process between the nodes;

[0059] Potential risk evaluation module: comprehensively analyze the tampering risk degree of digital content in the distributed storage process, the anomaly risk degree of digital content operation behavior and the conflict possibility of digital content circulation behavior, evaluate the potential risk of digital content traceability chain in the blockchain network, and decide whether to trigger the traceability verification mechanism of the on-chain responsible node.

[0060] By analyzing the multi-node data synchronization behavior of digital content in the blockchain network, the tampering risk degree of digital content in the distributed storage process is evaluated, which is specifically:

[0061] Parse the distributed storage structure of digital content and extract the multi-node synchronization rule: in the blockchain network, digital content is saved in a distributed storage manner on multiple nodes. Each node maintains a copy of part or all data to ensure data security and consistency. First, parse the distributed storage structure of the target digital content, including the sharding, replication mechanism and synchronization strategy of the blockchain. The parsing process of the distributed storage structure mainly includes the following tasks:

[0062] Extract storage shard information: confirm the sharding distribution of digital content in the network, each shard is stored in multiple replica nodes to realize redundant storage and fault tolerance function.

[0063] Synchronization rule analysis: according to the consensus mechanism of the blockchain network (such as PoW, PoS or PBFT), extract the time interval, verification method and consistency protocol of multi-node data synchronization.

[0064] Association mapping: establish the mapping relationship between digital content and corresponding storage nodes, record the storage path of each shard on different nodes.

[0065] Suppose the digital content is divided into 4 shards {C1, C2, C3, C4}, respectively stored in node sets {N 11 ,N 12}、{N 21 ,N 22}、{N 31 ,N 32}、{N41 ,N 42} in the method. The mapping relationship is: M(C x ) = {N x1 ,N x2}, x = 1, 2, 3, 4.

[0066] wherein M(C x ) is the node mapping of the shard C x ; N x1 , N x2 are two node replicas storing the shard C x .

[0067] Collecting multi-node data synchronization logs to record the synchronization state and hash check value: in each data synchronization process, the node generates a synchronization log to record the key information in the synchronization process, including whether the synchronization is successful and the check value. The collected log data content includes:

[0068] Synchronization state: records the identification of successful and failed synchronization between nodes, indicating whether the data is consistent;

[0069] Hash check value: each node performs hash operation on the stored shard data to generate a check value to verify the data integrity.

[0070] The generation formula of the hash check value is: H k = Hash(D k );

[0071] wherein H k is the hash check value of the shard k in a certain node; D k is the data content of the shard k; Hash is a hash function used to generate a unique identification of the data.

[0072] Comparing the hash values of each node to detect data inconsistency: after collecting the hash check value of each node, the hash values between nodes are compared to determine whether the data is consistent. If the hash values of different nodes are different, it indicates that there may be abnormal or tampering behavior in the data synchronization.

[0073] The data consistency detection formula is: ΔH k = H k1 -H k2 ;

[0074] wherein ΔH k is the hash difference value of the shard k between nodes {N k1 , N k2}; H k1 , H k2 are the hash check values of the shard k on nodes {N k1 , N k2}.

[0075] When ΔH k equals 0, it indicates that the data consistency is normal, and the shard k has not been tampered with or lost;

[0076] When ΔH k is not equal to 0, it indicates that there is an exception in the storage or synchronization process of shard k, and tampering or loss may occur.

[0077] Calculate the tampering risk index: after completing the data consistency detection, calculate the tampering risk index to evaluate the tampering risk of digital content in the distributed storage process. The expression of tampering risk index is:

[0078]

[0079] Where TRI is the tampering risk index; n represents the total number of shards; ΔH k is the hash difference value of shard k.

[0080] The larger the tampering risk index, the higher the degree of tampering risk of digital content in the distributed storage process. When the tampering risk index is large, it indicates that multiple shards have data inconsistency phenomena in the synchronization process, such as hash value difference, data loss or unauthorized modification. This risk may damage the integrity of the blockchain and pose a threat to the security of stored digital content. At the same time, a high tampering risk index may indicate potential attack behavior, device failure or execution problems of synchronization rules in the network, which requires immediate measures to investigate the cause of the anomaly and repair the data.

[0081] By measuring the transaction verification rate of digital content in the propagation process of the blockchain network, it is determined whether there is a transaction backlog risk in the blockchain network; when there is no transaction backlog risk in the blockchain network, the degree of local congestion risk hidden danger of the blockchain network is evaluated, which is:

[0082] Measure the transaction verification rate and calculate the average time interval of node transaction processing: the transaction verification rate is a core indicator of measuring the processing capacity of nodes in the blockchain network, reflecting the processing efficiency of nodes on transaction requests. By monitoring the transaction processing process of each node in the blockchain network, record the receiving time and verification completion time of transactions, and calculate the verification time interval of transactions. For a node, the average transaction verification time of the node is calculated by summarizing the verification time interval of all transactions. The reciprocal of the average transaction verification time is defined as the transaction verification rate of the node, which reflects the processing capacity of the node on transaction requests.

[0083] Comparing the transaction verification rate with the preset transaction verification rate threshold to determine whether there is a transaction backlog risk: after obtaining the node transaction verification rate, it is compared with the preset transaction verification rate threshold. The transaction verification rate threshold is the minimum rate requirement set according to the normal operation state of the blockchain network, indicating the minimum processing capacity that the node needs to achieve under the condition of no backlog. When the transaction verification rate of the node is lower than the transaction verification rate threshold, it indicates whether there is a transaction backlog risk in the blockchain network, which cannot process the received transaction request in time.

[0084] When there is no transaction backlog risk in the blockchain network, analyze the spatio-temporal distribution characteristics of node transaction processing delay: when there is no transaction backlog risk in the blockchain network, further analyze the transaction processing delay of each node to determine whether there is a local congestion phenomenon. Local congestion usually manifests as the transaction verification delay of a specific node or region being higher than the average level of the network. By statistically analyzing the transaction verification time interval of all nodes, combined with the time and space dimensions, the average delay time value of the node is calculated to identify the node with higher delay.

[0085] Delay distribution analysis can reveal the imbalance of processing capacity among nodes, and help locate the potential causes of local congestion. For example, some nodes may have higher processing delay due to hardware performance limitations or insufficient network bandwidth. Through the analysis of the spatio-temporal distribution characteristics of delay data, data support is provided for quantifying the degree of local congestion risk.

[0086] Calculate the local congestion risk index to quantify the degree of local congestion risk hidden danger of the blockchain network: after completing the delay distribution analysis, the local congestion risk index is calculated by quantifying the delay difference among nodes, which is used to quantify the degree of local congestion risk hidden danger of the blockchain network.

[0087] The expression of the local congestion risk index is:

[0088] Wherein, LCRI is the local congestion risk index; D is the total number of nodes in the blockchain network; is the average verification delay time of all nodes; is the average delay time of node j.

[0089] The greater the local congestion risk index, the more obvious the difference in transaction processing delay between nodes in the blockchain network, indicating that the degree of risk of local congestion in the blockchain network is greater. The transaction verification time of some nodes is higher than that of other nodes, and the increase in delay difference will affect the overall propagation efficiency of transactions, which may cause local bottlenecks in transaction processing, thereby reducing the overall performance and reliability of the blockchain network. At the same time, a larger local congestion risk index also indicates that local congestion may further spread and affect the normal operation of more nodes. At this time, optimization measures need to be taken in a timely manner, such as redistributing transaction loads, improving the computing power or bandwidth resources of congested nodes, to alleviate local congestion and ensure the efficient operation and stability of the blockchain network.

[0090] Based on the tampering risk degree of digital content in the distributed storage process and the local congestion risk degree of the blockchain network, the reliability of the digital content traceability chain in the blockchain network is evaluated, specifically:

[0091] A tampering risk index threshold is set, and the tampering risk index is compared with the tampering risk index threshold:

[0092] When the tampering risk index is less than or equal to the tampering risk index threshold, it indicates that the digital content in the blockchain network has not been tampered with during the distributed storage process, and the storage integrity of the digital content is within a safe range. At this time, the reliability of the digital content traceability chain in the blockchain network is high reliability.

[0093] When the tampering risk index is greater than the tampering risk index threshold, it indicates that there is a tampering risk in the storage process of the digital content, which may lead to a decrease in the reliability of the traceability chain.

[0094] The tampering risk index threshold is set according to the security level requirements of the blockchain network. For example, the tampering risk index threshold for high-security-level applications (such as financial data) is lower, and the tampering risk index threshold for low-sensitivity scenarios (such as log data) is higher.

[0095] A local congestion risk index threshold is set, and the local congestion risk index is compared with the local congestion risk index threshold:

[0096] When the local congestion risk index is less than or equal to the local congestion risk index threshold, it indicates that the blockchain transaction processing capacity meets the current demand, and the degree of risk of local congestion in the blockchain network is small. At this time, the reliability of the digital content traceability chain in the blockchain network is high reliability.

[0097] When the local congestion risk index is greater than the local congestion risk index threshold, it indicates that the blockchain transaction processing capacity cannot meet the current demand, and the degree of risk of local congestion in the blockchain network is large.

[0098] The local congestion risk index threshold is set according to the application scenario of the blockchain network. For example, a high-frequency transaction network (such as a payment system) has high real-time requirements, so the local congestion risk index threshold is low, and a non-real-time scenario such as log storage has high delay tolerance, so the local congestion risk index threshold is high.

[0099] When the tampering risk index is less than or equal to the tampering risk index threshold, and the local congestion risk index is less than or equal to the local congestion risk index threshold, the reliability of the digital content traceability chain in the blockchain network is high. Except that the tampering risk index is less than or equal to the tampering risk index threshold, and the local congestion risk index is less than or equal to the local congestion risk index threshold, the reliability of the digital content traceability chain in the blockchain network is low.

[0100] By analyzing the smart contract execution logs between blockchain nodes, the abnormal risk degree of digital content operation behavior is evaluated, specifically:

[0101] Collecting smart contract execution logs, extracting core parameters of digital content operation behavior: the execution log of the smart contract records the operation behavior, parameter value and execution result involved in each contract invocation process, which is an important data source for analyzing digital content operation behavior. Through the interface, the execution log of the smart contract is collected from the blockchain node, the key fields are extracted, and the core parameter set of the digital content operation behavior is established.

[0102] The smart contract execution log usually includes the following key contents:

[0103] Caller identity (user address): records the account identifier of the execution operation;

[0104] Operation type: indicates the current digital content operation behavior (such as creation, modification, transfer);

[0105] Parameter set: records the input parameters related to the operation, such as content ID, operation target address, modification field, etc.

[0106] Execution result: including whether it is successful and the returned hash value, used to verify the operation result.

[0107] The core parameter set includes the type of operation, the target of digital content involved in the operation, and the execution result (success or failure) of the operation.

[0108] Analyze the compliance of the operation behavior with the smart contract rules, and mark the abnormal operation records: after extracting the core parameters, compare the actual operation behavior with the pre-set rules of the smart contract, detect and mark the abnormal operations that do not comply with the rules. The smart contract rule is a pre-defined logical constraint, which is used to ensure the legality and security of the operation of digital content.

[0109] The smart contract rules include operation permissions, input parameter ranges, operation sequences, and the like. For example, when a user performs a digital content transfer operation, the following rules must be met:

[0110] The caller is the content owner; the transfer target address is valid and registered on the chain; and the digital content state is transferable.

[0111] Check whether the core parameters of the actual operation behavior meet the rules. For example, verify whether the caller's permission is valid and the operation target is correct. Mark the operation record as normal through the check, and mark the operation record as abnormal if it fails, and record the abnormal reason, such as insufficient permission or invalid operation target.

[0112] Statistical abnormal operation frequency, quantifying the difference in operation behavior between nodes: count the abnormal operation records of each node, divide the number of abnormal operations of the node by the total number of operations of the node, and calculate the abnormal operation frequency to quantify the difference in operation behavior between nodes.

[0113] Calculate the abnormal operation risk index to assess the abnormal risk level of the operation behavior: combine the abnormal operation frequency between nodes to calculate the abnormal operation risk index to quantify the abnormal risk level of the operation behavior. The expression of the abnormal operation risk index is:

[0114] Where AORI is the abnormal operation risk index; M is the total number of all nodes in the smart contract execution record; f i is the abnormal operation frequency of node j; is the average abnormal operation frequency of all nodes in the smart contract execution record.

[0115] The larger the abnormal operation risk index, the higher the abnormal risk level of the operation behavior, indicating that some nodes may frequently trigger illegal operations, non-compliant calls, and have a high failure rate, increasing the uncertainty of network operation. A larger abnormal operation risk index indicates that abnormal operations may be concentrated in a specific area or node, posing potential security risks such as malicious operations, resource abuse, or smart contract vulnerabilities. This will weaken the stability and transaction reliability of the network, and high-risk nodes need to be focused on, abnormal sources need to be promptly investigated, and smart contract rules and execution logic need to be optimized to reduce the overall network operation risk and ensure the legality and security of digital content operations.

[0116] Based on the timestamps of the digital content traceability records and the consistency deviation in the consensus process between nodes, the conflict possibility of the digital content flow behavior is evaluated, specifically:

[0117] Extracting digital content trace records and analyzing timestamp information of flow behavior: Digital content trace records are the core data in the blockchain that records each content flow behavior, including operation time, operation node, operation type, and other key information. Extract all trace records of the target digital content from the blockchain, establish a complete flow path, and analyze the timestamp information of each flow behavior, including:

[0118] Trace record analysis: Obtain the trace record set of the digital content, where each record contains timestamp, operation node, and flow target fields;

[0119] Timestamp information extraction: Extract the timestamp from each record to establish a time series;

[0120] Flow path construction: According to the timestamp order, establish a complete flow path of the digital content from the source node to the target node.

[0121] Compare the timestamp order and the consensus record between nodes to detect flow behavior anomalies: By comparing the timestamp order and the consensus record between nodes, detect whether the flow behavior has time anomalies or consensus conflicts. Consensus conflicts may manifest as inconsistent timestamps between flow operation and node confirmation, or multiple flows of the same content occurring within a short period of time.

[0122] Timestamp order check: Time sequence verification: Ensure that the time sequence is monotonically increasing, and if there is a violation of the order, record the abnormal flow operation; Repeat flow detection: Calculate the time interval between adjacent timestamps of flow operations, and if it is less than the preset minimum time interval, mark it as a repeated flow risk.

[0123] Consensus record comparison: Extract the confirmation time of each flow operation from the node consensus log, verify the consistency of the timestamp and the node confirmation time, record the differences and mark the abnormal nodes.

[0124] Calculate consensus consistency deviation: Consensus consistency deviation refers to the difference in confirmation time of the same flow operation on different nodes. Let R h h be the timestamp of the hth flow operation, and the confirmation times on the node set {Q1, Q2,... Qm} participating in consensus be {Rh,1, Rh,2,... Rh,m}, the consensus consistency deviation calculation formula is:

[0125] Where, is the consensus deviation of the hth flow operation; R h,b is the confirmation time of the hth flow operation on node Q b ; b is the index value from 1 to m; m is the total number of nodes participating in consensus; max{R h,1 , R h,2 ,... R h,m} is the maximum value of the confirmation time; min{R h,1 ,R h,2 ,...R h,m} is the minimum value of the confirmation time.

[0126] A conflict possibility index is calculated to evaluate the conflict possibility of the digital content flow behavior: the conflict possibility index is calculated in combination with the timestamp anomaly and the consensus deviation result.

[0127] The expression of the conflict possibility index is:

[0128] wherein, CLI is the conflict possibility index; L is the total number of flow operations of the digital content; is the consensus deviation of the hth flow operation; ΔR avg is the average consensus deviation of all flow operations; ΔS h is the time interval of the hth flow operation; ε is a positive number for preventing the denominator from being zero.

[0129] The greater the conflict possibility index, the greater the possibility of conflict of the digital content in the flow process, which is specifically manifested in the abnormality of the time interval of the flow operation or the increase of the consensus consistency deviation between nodes, indicating that the digital content may experience repeated flow, illegal operation or node synchronization problem, resulting in the increase of the operation conflict possibility index. The greater conflict possibility index also reflects that some nodes in the blockchain network have low processing efficiency or malicious behavior, affecting the normal order of the flow behavior.

[0130] The tampering risk degree of the digital content in the distributed storage process, the abnormal risk degree of the digital content operation behavior and the conflict possibility of the digital content flow behavior are comprehensively analyzed to evaluate the potential risk of the digital content traceability chain in the blockchain network, and it is determined whether to trigger the traceability verification mechanism of the on-chain responsible node according to the risk level, specifically:

[0131] The tampering risk index corresponding to the tampering risk degree of the digital content in the distributed storage process, the abnormal operation risk index corresponding to the abnormal risk degree of the digital content operation behavior and the conflict possibility index corresponding to the conflict possibility of the digital content flow behavior are respectively normalized, and the tampering risk index, the abnormal operation risk index and the conflict possibility index obtained after the normalization are calculated to obtain a risk index, through which the potential risk of the digital content traceability chain in the blockchain network is evaluated, and it is determined whether to trigger the traceability verification mechanism of the on-chain responsible node.

[0132] The calculation formula of the risk index is:

[0133] Wherein, RI is the risk index; TRI is the tampering risk index; AORI is the abnormal operation risk index; CLI is the conflict possibility index; a, b, and l are the weight coefficients of the tampering risk index, the abnormal operation risk index, and the conflict possibility index, respectively.

[0134] A preset risk index threshold is compared with the risk index.

[0135] When the risk index is greater than or equal to the risk index threshold, it indicates that there is a potential risk in the digital content traceability chain in the blockchain network, which may include tampering, abnormal operation, or conflict problems. At this time, the security of the chain is threatened, which may affect the authenticity and traceability effectiveness of the digital content, and the traceability verification mechanism of the on-chain responsible node needs to be triggered.

[0136] The traceability verification mechanism of the on-chain responsible node includes a comprehensive check of the operation records, data integrity, and operation sequence of each node in the digital content traceability chain. First, the traceability verification mechanism verifies the operation log of each node, including timestamp, operation type, participant identity, and execution result, etc., to ensure that all records meet the preset rules and detect whether there is abnormal behavior or illegal operation. Secondly, by checking the content hash value stored on the chain, it is ensured that the digital content has not been tampered with in the transmission and storage process, and the data integrity and consistency are guaranteed.

[0137] In addition, the traceability verification mechanism also checks the consensus behavior of the responsible node, evaluates its participation and response time in the digital content circulation process, and ensures that the consensus results between nodes are unbiased. At the same time, combined with the time series data of the traceability chain, it verifies whether the operation sequence conforms to the logic, and excludes the conflict problems caused by delay or repeated operation.

[0138] When the risk index is less than the risk index threshold, it indicates that the digital content traceability chain in the blockchain network is running normally, and the potential risk is within a controllable range. At this time, the security of the chain is not significantly threatened, the operation behavior of the node meets the expectation, and the traceability information of the digital content is highly reliable, and the traceability verification mechanism of the on-chain responsible node does not need to be triggered.

[0139] The above formulas are dimensionless and the numerical values are calculated. The formula is obtained by collecting a large amount of data to simulate the recent real situation. The preset parameters and threshold values in the formula are set by the person skilled in the art according to the actual situation.

[0140] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center through a wired (for example, infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0141] Those of ordinary skill in the art can realize that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0142] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device, and module can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0143] In several embodiments provided in the present application, it should be understood that the disclosed system, device, and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed ones can be indirect coupling or communication connection through some interfaces, devices, or modules, which can be electrical, mechanical, or other forms.

[0144] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0145] In addition, the functional modules in each embodiment of the present application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0146] The functions, if realized in the form of software functional modules and sold or used as independent products, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0147] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0148] Finally, the above is only the preferred embodiment of the present application and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A digital content traceability system based on blockchain technology, characterized in that, The tampering risk assessment module, the transaction backlog risk judgment module, the reliability assessment module, the abnormal risk assessment module, the conflict possibility assessment module, and the potential risk assessment module are included. The tampering risk assessment module: by analyzing the multi-node data synchronization behavior of the digital content in the blockchain network, the tampering risk degree of the digital content in the distributed storage process is evaluated. The transaction backlog risk judgment module: by measuring the transaction verification rate of the digital content in the propagation process in the blockchain network, it is judged whether there is a transaction backlog risk in the blockchain network; when there is no transaction backlog risk in the blockchain network, the local congestion risk hidden degree of the blockchain network is evaluated. When there is no transaction backlog risk in the blockchain network, the spatiotemporal distribution characteristics of node transaction processing delay are analyzed. A local congestion risk index is calculated to quantify the degree of local congestion risk hidden danger of the blockchain network, and the expression of the local congestion risk index is: ; wherein, is a local congestion risk index; is a total number of nodes in the blockchain network; is an average verification delay time of all nodes; is an average delay time of nodes . The reliability assessment module: based on the tampering risk degree of the digital content in the distributed storage process and the local congestion risk hidden degree of the blockchain network, the reliability of the digital content traceability chain in the blockchain network is evaluated. When the reliability of the digital content traceability chain is low: the abnormal risk assessment module evaluates the abnormal risk degree of the digital content operation behavior by analyzing the smart contract execution log between the blockchain nodes; the conflict possibility assessment module evaluates the conflict possibility of the digital content flow behavior based on the timestamp of the digital content traceability record and the consistency deviation in the consensus process between the nodes. Extract the digital content traceability record and analyze the timestamp information of the flow behavior. Compare the timestamp order and the consensus record between the nodes to detect the abnormal flow behavior. Calculate the consensus consistency deviation, which refers to the confirmation time difference of the same flow operation on different nodes. Combine the timestamp anomaly and consensus deviation results to calculate the conflict possibility index. The expression of the conflict likelihood index is: ; wherein, is a conflict likelihood index; is a total number of flow-through operations of the digital content; is a consensus deviation of the th flow-through operation; is an average consensus deviation of all flow-through operations; is a consensus deviation of the th flow-through operation; is a positive number to prevent the denominator from being zero; The potential risk assessment module: the tampering risk degree of the digital content in the distributed storage process, the abnormal risk degree of the digital content operation behavior, and the conflict possibility of the digital content flow behavior are comprehensively analyzed to evaluate the potential risk of the digital content traceability chain in the blockchain network, and it is decided whether to trigger the traceability verification mechanism of the on-chain responsible node. 2.The blockchain technology-based digital content tracing system according to claim 1, wherein, By analyzing the multi-node data synchronization behavior of the digital content in the blockchain network, the tampering risk degree of the digital content in the distributed storage process is evaluated, specifically: Analyze the distributed storage structure of the digital content and extract the multi-node synchronization rules. Collect multi-node data synchronization logs to record synchronization status and hash check values. Compare the hash values of each node to detect data inconsistency. A tamper risk index is calculated to assess the tamper risk of the digital content in the distributed storage process, and the expression of the tamper risk index is: ; wherein, is a tampering risk index; represents the total number of shards; is a hash difference value of the shard . 3.The blockchain technology-based digital content tracing system according to claim 1, wherein, By measuring the transaction verification rate of the digital content in the propagation process in the blockchain network, it is judged whether there is a transaction backlog risk in the blockchain network, specifically: Determine the transaction verification rate and calculate the average time interval of node transaction processing: record the reception time and verification completion time of the transaction, and calculate the verification time interval of the transaction; after the verification time intervals of all transactions are summarized, the average transaction verification time of the node is calculated; the reciprocal of the average transaction verification time is defined as the transaction verification rate of the node; The transaction verification rate is compared with the preset transaction verification rate threshold to determine whether there is a transaction backlog risk: after obtaining the node transaction verification rate, the node transaction verification rate is compared with the preset transaction verification rate threshold; when the node transaction verification rate is lower than the transaction verification rate threshold, there is a transaction backlog risk in the blockchain network. 4.The blockchain technology-based digital content tracing system according to claim 1, wherein, Based on the tampering risk degree of the digital content in the distributed storage process and the local congestion risk degree of the blockchain network, the reliability of the digital content traceability chain in the blockchain network is evaluated, specifically: A preset tampering risk index threshold is set, and the tampering risk index is compared with the tampering risk index threshold: When the tampering risk index is less than or equal to the tampering risk index threshold, the reliability of the digital content traceability chain in the blockchain network is high reliability; A preset local congestion risk index threshold is set, and the local congestion risk index is compared with the local congestion risk index threshold: When the local congestion risk index is less than or equal to the local congestion risk index threshold, the reliability of the digital content traceability chain in the blockchain network is high reliability; When the tampering risk index is less than or equal to the tampering risk index threshold, and the local congestion risk index is less than or equal to the local congestion risk index threshold, the reliability of the digital content traceability chain in the blockchain network is high reliability; in other cases, the reliability of the digital content traceability chain in the blockchain network is low reliability. 5.The blockchain technology-based digital content tracing system according to claim 1, wherein, By analyzing the smart contract execution logs between blockchain nodes, the abnormal risk degree of digital content operation behavior is evaluated, specifically: Collect smart contract execution logs and extract core parameters of digital content operation behavior; Analyze the compliance of operation behavior with smart contract rules and mark abnormal operation records; Statistical abnormal operation frequency, calculate abnormal operation risk index, the expression of abnormal operation risk index: ; wherein, is an abnormal operation risk index; is the total number of all nodes of the smart contract execution record; is the abnormal operation frequency of the node ; is the average abnormal operation frequency of all nodes of the smart contract execution record. 6.The blockchain technology-based digital content tracing system according to claim 1, wherein, The tampering risk degree of the digital content in the distributed storage process, the abnormal risk degree of the digital content operation behavior, and the conflict possibility of the digital content flow behavior are comprehensively analyzed to evaluate the potential risk of the digital content traceability chain in the blockchain network, and it is determined whether to trigger the traceability verification mechanism of the on-chain responsible node, specifically: The tampering risk index corresponding to the tampering risk degree of the digital content in the distributed storage process, the abnormal operation risk index corresponding to the abnormal risk degree of the digital content operation behavior, and the conflict possibility index corresponding to the conflict possibility of the digital content flow behavior are normalized respectively, and the tampering risk index, the abnormal operation risk index, and the conflict possibility index obtained after normalization are calculated to obtain a risk index; The formula for calculating the risk index is: ; wherein, is a risk index; is a tampering risk index; is an abnormal operation risk index; is a conflict likelihood index; , , are weight coefficients of the tampering risk index, the abnormal operation risk index, and the conflict likelihood index, respectively. A preset risk index threshold is set, and the risk index is compared with the risk index threshold: when the risk index is greater than or equal to the risk index threshold, it indicates that the digital content traceability chain in the blockchain network has potential risks, and the traceability verification mechanism of the on-chain responsible node needs to be triggered; When the risk index is less than the risk index threshold, it indicates that the digital content traceability chain in the blockchain network is running normally, and the potential risk is within a controllable range, and the traceability verification mechanism of the on-chain responsible node does not need to be triggered.

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