Digital member right automatic distribution and management system based on block chain

Abstract

The invention discloses a digital member right automatic distribution and management system based on a block chain, and relates to the technical field of digital member right management, and the system comprises a state fingerprint generation module, a state summary communication module, a consistency feature extraction module, a consistency risk assessment module, and an intelligent contract delay control and rollback module. And the state fingerprint generation module is used for extracting key parameters representing the current block chain state from the chain account book unit and the consensus unit, and performing feature coding, hash compression and instruction stream abstract modeling on the key parameters to form a state fingerprint which uniquely identifies the current node chain state. According to the invention, through state fingerprint comparison and a support vector machine risk assessment mechanism, intelligent delay and rollback control of issuing of key rights and interests on a chain are realized, issuing accuracy and system stability in an abnormal state are improved, and risks of wrong issuing, missed issuing and repeated issuing are effectively prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of digital member rights management, and in particular to a blockchain-based automatic distribution and management system for digital member rights. Background Art

[0002] The automated allocation and management of digital membership benefits based on blockchain leverages the decentralized, traceable, and tamper-proof nature of blockchain technology to automate the management and allocation of digital benefits within the membership system, such as points, discounts, and exclusive services, through smart contracts. In this system, each member's benefits information is recorded on-chain in the form of blocks. Smart contracts automatically determine member level, consumption behavior, activity levels, and other indicators, triggering the corresponding benefits issuance or adjustment operations. This ensures that the entire benefits allocation process is open, transparent, and responsive in real time, preventing human intervention and data falsification. This enhances the credibility and operational efficiency of the membership system and is particularly suitable for cross-platform benefits sharing and refined operational management within multi-party collaboration or alliance systems.

[0003] The existing technology has the following deficiencies:

[0004] In a multi-node private chain environment, due to the delay in state synchronization between nodes, if the on-chain state fork is not effectively resolved in a short period of time, different nodes may produce inconsistent judgment results on the rights status of a member within the same time window, thereby causing the following technical problems: On the one hand, some nodes mistakenly repeatedly issue the same digital rights based on local state judgment, resulting in abnormal release of resources within the system; on the other hand, other nodes, because they have not synchronized with the latest rights status, refuse to recognize the issued rights, resulting in members being unable to uniformly enjoy their legal rights on all nodes. Once the above-mentioned forked states are merged by the on-chain consensus mechanism, the original multi-version rights records will have data conflicts, which are not only difficult to repair automatically, but also require manual intervention to reconstruct the rights status, seriously affecting the stability, fairness and traceability of the system.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide an automatic distribution and management system for digital membership rights based on blockchain. By constructing node status fingerprints, establishing a network-wide status summary comparison mechanism, and introducing a support vector machine model to intelligently evaluate consistency risks, the system further links the smart contract execution dynamic delayed release and rollback control strategy to achieve risk perception, adaptive regulation and fault-tolerant protection of the key rights issuance process on the chain, significantly improving the accuracy of rights issuance, system stability and operation traceability of the blockchain system under abnormal state consistency conditions, and effectively reducing resource loss and trust crises caused by misissuance, missed issuance or repeated issuance, so as to solve the problems in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: a blockchain-based digital membership rights automatic allocation and management system, comprising a state fingerprint generation module, a state summary communication module, a consistency feature extraction module, a consistency risk assessment module, and a smart contract delay control and rollback module;

[0008] The state fingerprint generation module extracts key parameters representing the current blockchain state from the on-chain ledger unit and consensus unit, performs feature encoding, hash compression, and instruction stream abstract modeling on the key parameters to form a state fingerprint that uniquely identifies the current node's on-chain state;

[0009] The state summary communication module, after the node state fingerprint is generated, uses the established point-to-point communication protocol in the consensus network (such as gRPC, libp2p or customized P2P channels) to send a state summary request to all other known consensus nodes on the chain, so that the other nodes generate their own state fingerprints based on the local state, and construct a full-node state summary matrix;

[0010] The consistency feature extraction module compares the status fingerprint of the current node with the status fingerprint of each returned node, extracts key features that reflect the consistency differences between the status fingerprints of the current node and all returned nodes, analyzes the extracted features, and uses the analyzed indicators as the basic feature vector to measure the consistency difference between the current node status and the status of all nodes in the entire network;

[0011] The consistency risk assessment module uses the extracted basic feature vector as input into a support vector machine (SVM) model trained with large-scale on-chain state anomaly data to assess the consistency risk between the current node's on-chain state and the states of all returned nodes.

[0012] The smart contract delay control and rollback module, when it identifies the existence of consistency risk between the on-chain state of the current node and the state of all returned nodes, dynamically quantifies the severity of the consistency risk, and accordingly enables a delayed release strategy for the on-chain issuance process of key rights and interests (such as points, tokens or NFTs); specifically: according to the severity of the consistency risk, the release delay time of key rights and interests is automatically adjusted through the smart contract to achieve intelligent regulation of on-chain operations; during the delay period, if the node state restores consistency, the smart contract process automatically continues to execute; if the consistency state confirmation fails during the delay period, the smart contract automatically triggers the rollback mechanism and cancels the pending equity issuance operation, thereby effectively preventing the risk of accidental issuance in the fork state.

[0013] Preferably, feature encoding is to convert different types of state parameters (numeric, Boolean, structure, etc.) into vector expressions of unified dimensions to ensure their consistency in subsequent processing; hash compression is to compress high-dimensional state data into a fixed-length summary through an encrypted hash function, retaining uniqueness while reducing computational overhead; instruction stream abstract modeling is to extract logical dependencies and evolution paths from the changing trajectory of state parameters to form a traceable state behavior template.

[0014] Preferably, key features reflecting the consistency differences between the state fingerprints of the current node and all the returned nodes are extracted, wherein the extracted features include the hash difference between the state roots of the ledgers of the current node and the returned node and the difference in the number of confirmations of the latest M consensus blocks between the current node and the returned node. After analyzing the extracted features, a state root divergence index and a consensus confirmation difference index are generated respectively, and the state root divergence index and the consensus confirmation difference index are used as basic feature vectors to measure the consistency difference between the current node state and the node state of the entire network.

[0015] Preferably, the extracted state root divergence index and consensus confirmation difference index are input as input vectors into a support vector machine (SVM) model that has been trained with large-scale on-chain state abnormality data. A risk assessment coefficient is generated after comprehensive analysis based on the support vector machine model, and the consistency risk between the current node on-chain state and the states of all returned nodes is evaluated using the risk assessment coefficient.

[0016] Preferably, the risk assessment coefficient generated when evaluating the consistency risk between the current node's on-chain state and the states of all returned nodes through the support vector machine model is compared with a pre-set reference threshold of the risk assessment coefficient to determine whether there is a consistency risk between the current node's on-chain state and the states of all returned nodes. The specific determination steps are as follows:

[0017] If the risk assessment coefficient is greater than the risk assessment coefficient reference threshold, it is determined that there is a consistency risk between the on-chain state of the current node and the state of all returned nodes; if the risk assessment coefficient is less than or equal to the risk assessment coefficient reference threshold, it is determined that there is no consistency risk between the on-chain state of the current node and the state of all returned nodes.

[0018] Preferably, the specific steps of generating the state root divergence index after analyzing the hash difference between the current node and the returned node ledger state root are as follows:

[0019] Assume that the state root hash of the current node is H0, and the state root hashes of the remaining N return nodes in the network are H1, H2, ..., H N , by introducing the binary hash distance function to calculate the state root difference between the current node and each returned node, the calculation expression is as follows:

[0020]

[0021] ,in: represents the kth bit of the root hash of the state of the i-th node (in binary form); Indicates the kth bit of the root hash of the current node state; L is the hash length (e.g. 256 bits); D(H i , H0) is the normalized Hamming distance, which represents the difference ratio between the state root of the i-th node and the state root of the current node at the binary bit level, D(H i , H0)∈[0,1];

[0022] In order to emphasize the impact of extreme divergence nodes on the overall consistency judgment, a risk amplification index mapping function is introduced to generate the final state root divergence index. The generation expression of the state root divergence index is:

[0023]

[0024] , where: γ is the difference amplification factor, which is used to enhance the weight of high-difference nodes (usually set to 3-10); r is a nonlinear control parameter, which adjusts the sensitivity of the state root divergence index (it is recommended to be set to 2 or 3); tanh(·) is the hyperbolic tangent function, which is used to compress the weight result to the range of [0, 1] and has gradient buffering and boundary controllability; S rdi is the final generated state root divergence index, S rdi ∈[0, 1], the closer the value is to 1, the more significant the difference between the current node status and the entire network.

[0025] Preferably, the specific steps of generating a consensus confirmation difference index after analyzing the difference in the number of confirmations of the latest M consensus blocks between the current node and the return node are as follows:

[0026] Select the latest M consensus blocks, record the confirmation status of the current node and N return nodes for the consensus blocks, and represent the confirmation status of each node for each consensus block as a binary value (1 for confirmed, 0 for unconfirmed). Then, calculate the number of confirmation differences between the current node and each return node on the M consensus blocks one by one to form a difference vector. The difference vector represents the degree of synchronization deviation between the two nodes in the consensus history. The calculation expression is:

[0027] Where 1≤u≤N

[0028] , where: C 0，j Indicates whether the current node n0 has confirmed the j-th consensus block (1 for confirmation, 0 for unconfirmed); C u，j Indicates returning node n u Whether the j-th consensus block is confirmed; D u For the current node n0 and the returned node n u The number of confirmation differences between the two nodes in the most recent M consensus blocks, ranging from 0 to M; j and u represent the indexes of the consensus block and the returned node respectively;

[0029] After getting all returned nodes n u The number of confirmed differences D u Finally, the maximum value of the number of confirmed differences is extracted to establish a normalized benchmark. Then, each difference value is transformed using the square compression function to increase the consistency weight of the low-difference node, and finally a consensus confirmation difference index is generated. The generation expression of the consensus confirmation difference index is:

[0030]

[0031] , where: D max The maximum value of all confirmed differences, plus 1 to prevent the denominator from being 0; C cgi It is the consensus confirmation difference indicator.

[0032] Preferably, when it is identified that there is a consistency risk between the on-chain state of the current node and the states of all returned nodes, the risk assessment coefficient generated by the support vector machine model is compared with a pre-set risk assessment coefficient reference threshold to construct a consistency risk level function for quantifying the degree of current state deviation. The expression of the consistency risk level function is:

[0033] Δ risk =tanh(κ·(γ risk -γ thresh ))

[0034] , where: γ riskis the risk assessment coefficient output by the support vector machine model. A larger value indicates a higher consistency risk between the on-chain state of the current node and the states of all returned nodes. thresh is the reference threshold of the risk assessment coefficient set by the system based on the historical fault tolerance characteristics; κ is the risk sensitivity adjustment factor, which controls the steepness of the risk level function, and κ>0; the tangent function tanh(·) is used to map the difference into a continuous risk level indicator within a finite interval; Δ risk is the risk level value generated by the risk assessment coefficient and the risk assessment coefficient reference threshold;

[0035] According to the calculated risk level value Δ risk Automatically generate the delayed release time parameters for key rights and interests, and apply them to the pending release operation through the smart contract interface. The delay time control formula is as follows:

[0036] T delay =T min +α·(Δ risk )η

[0037] , where: T delay T is the on-chain release delay time of the current equity; min is the basic delay time, applicable to the risk-free state (i.e., the risk assessment coefficient is less than or equal to the reference threshold of the risk assessment coefficient); α is the delay amplification coefficient, indicating the maximum possible delay interval; η is the risk nonlinear amplification coefficient, used to enhance the penalty intensity under high risk conditions, η>1;

[0038] During the on-chain release delay, the state fingerprint of the current node and the feedback of the state fingerprint of each returning node are continuously monitored. The consistency risk between the on-chain state of the current node and the states of all returning nodes is evaluated, and the consistency recovery judgment conditions are defined:

[0039]

[0040] ,in: is the risk assessment coefficient output by the support vector machine model after the delay period. If this condition is met, it means that the state has been restored to consistency after the delay period, and the smart contract automatically unlocks and continues to execute the original equity issuance operation; otherwise, The system triggers the "on-chain equity issuance rollback mechanism" to cancel the operation and ensure that the system does not make irreversible asset changes in a high-risk environment.

[0041] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0042] The present invention constructs node status fingerprints, establishes a network-wide status summary comparison mechanism, and introduces a support vector machine model to intelligently assess consistency risks. It further links the smart contract execution with dynamic delayed release and rollback control strategies, thereby realizing risk perception, adaptive regulation and fault-tolerant protection of the key rights and interests issuance process on the chain. It significantly improves the accuracy of rights and interests issuance, system stability and operation traceability of the blockchain system under abnormal state consistency conditions, and effectively reduces resource loss and trust crisis caused by misissuance, missed issuance or repeated issuance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0044] Figure 1 This is a module diagram of the blockchain-based automatic distribution and management system for digital membership rights in the present invention. DETAILED DESCRIPTION

[0045] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0046] The present invention provides Figure 1 The blockchain-based automatic digital membership rights distribution and management system shown includes a state fingerprint generation module, a state summary communication module, a consistency feature extraction module, a consistency risk assessment module, and a smart contract delay control and rollback module;

[0047] The state fingerprint generation module extracts key parameters representing the current blockchain state from the on-chain ledger unit and consensus unit, performs feature encoding, hash compression, and instruction stream abstract modeling on the key parameters to form a state fingerprint that uniquely identifies the current node's on-chain state;

[0048] These key status parameters include but are not limited to: current block height, latest block hash, transaction pool summary, equity account mapping table, contract execution status summary, etc.

[0049] Feature encoding, hash compression, and instruction stream abstraction modeling of key parameters extracted from on-chain ledger units and consensus units involve structurally transforming and semantically condensing raw blockchain state data (such as account balances, the latest block hash, transaction pool content, and smart contract variables) to provide comparable, computable, and identifiable unique identifiers. Feature encoding converts different types of state parameters (numeric, Boolean, and structured) into vector representations of uniform dimensions to ensure consistency in subsequent processing. Hash compression compresses high-dimensional state data into fixed-length summaries using cryptographic hash functions, preserving uniqueness while reducing computational overhead. Instruction stream abstraction modeling extracts logical dependencies and evolutionary paths from the trajectory of state parameter changes, forming a traceable state behavior template.

[0050] Through the above processing, the final "state fingerprint" can be used to compare state consistency across nodes and quickly detect state forks or abnormal changes. It is a key intermediate variable for realizing on-chain state consistency assessment, anomaly detection and fork prevention.

[0051] The state summary communication module, after the node state fingerprint is generated, uses the established point-to-point communication protocol in the consensus network (such as gRPC, libp2p or customized P2P channels) to send a state summary request to all other known consensus nodes on the chain, so that the other nodes generate their own state fingerprints based on the local state, and construct a full-node state summary matrix;

[0052] The status summary request contains the identity of the local node, public key signature, current status fingerprint, and block height mark, ensuring that the status request has tamper-proof and time-series verification functions. This step initiates the cross-node status synchronization comparison process, laying the communication foundation for building a full network status view, while ensuring the authenticity and timeliness of the request. When the request is received by each known node, the other node will generate its own status fingerprint based on the local state, and return the response data including its identity, public key signature, current status fingerprint and timestamp to the requesting node. This node unpacks and time-aligns all received status summaries, and constructs a "full-node status summary matrix" for subsequent consistency feature extraction and analysis.

[0053] The consistency feature extraction module compares the status fingerprint of the current node with the status fingerprint of each returned node, extracts key features that reflect the consistency differences between the status fingerprints of the current node and all returned nodes, analyzes the extracted features, and uses the analyzed indicators as the basic feature vector to measure the consistency difference between the current node status and the status of all nodes in the entire network;

[0054] Extract key features that reflect the consistency differences between the state fingerprints of the current node and all returned nodes. The extracted features include the hash difference between the state root of the ledger of the current node and the returned node and the difference in the number of confirmations of the latest M consensus blocks between the current node and the returned node. After analyzing the extracted features, generate the state root divergence index and the consensus confirmation difference index respectively. The state root divergence index and the consensus confirmation difference index are used as the basic feature vectors to measure the consistency difference between the current node state and the node state of the entire network.

[0055] When comparing the state fingerprint of the current node with the state fingerprint of each returned node, a higher hash difference between the state root (i.e., the State Root) of the current node and the returned node generally indicates a greater divergence between the current node's records in the on-chain global state (including account balances, contract variables, storage structures, etc.) and those of other nodes. The state root is a global hash summary obtained by Merkle tree-structured processing of all on-chain state data. Any slight data difference will cause a significant change in the final hash value (with an avalanche effect). Therefore, the greater the hash difference between the state roots (such as the Hamming distance of the hash value, the bit difference ratio, etc.), the more significant the difference in the consistency of the underlying state, reflecting a stronger inconsistency between the state fingerprint of the current node and the other returned nodes. Such differences may arise from issues such as transaction execution discrepancies, synchronization delays, data tampering, or chain reorganizations, and are important indicators for determining state forks and consistency risks.

[0056] The specific steps for generating the state root divergence indicator after analyzing the hash difference between the current node and the returned node's ledger state root are as follows:

[0057] Assume that the state root hash of the current node is H0, and the state root hashes of the remaining N return nodes in the network are H1, H2, ..., H N , by introducing the binary hash distance function to calculate the state root difference between the current node and each returned node, the calculation expression is as follows:

[0058]

[0059] ,in: represents the kth bit of the root hash of the state of the i-th node (in binary form); Indicates the kth bit of the root hash of the current node state; L is the hash length (e.g. 256 bits); D(H i , H0) is the normalized Hamming distance, which represents the difference ratio between the state root of the i-th node and the state root of the current node at the binary bit level, D(H i , H0)∈[0,1];

[0060] The purpose of this step is to construct a discrete difference set of multi-node state roots, reveal the degree of structural divergence of state consistency, and introduce in-situ bit-level distance as the core measurement method.

[0061] In order to emphasize the impact of extreme divergence nodes on the overall consistency judgment, a risk amplification index mapping function is introduced to generate the final state root divergence index. The generation expression of the state root divergence index is:

[0062]

[0063] , where: γ is the difference amplification factor, which is used to enhance the weight of high-difference nodes (usually set to 3-10); r is a nonlinear control parameter, which adjusts the sensitivity of the state root divergence index (it is recommended to be set to 2 or 3); tanh(·) is the hyperbolic tangent function, which is used to compress the weight result to the range of [0, 1] and has gradient buffering and boundary controllability; S rdi is the final generated state root divergence index, S rdi ∈[0, 1], the closer the value is to 1, the more significant the difference between the current node status and the entire network;

[0064] The purpose of this step is to achieve nonlinear quantitative enhancement of state consistency risk, especially to amplify local extreme differences under state bifurcation, to ensure that even if the state of a small number of nodes deviates seriously, a risk warning can be triggered.

[0065] The state root divergence indicator shows that the larger the performance value of the state root divergence indicator, which is generated after analyzing the hash difference between the current node and the returned node's ledger state root, the greater the difference in state fingerprint consistency between the current node and all returned nodes. Conversely, the smaller the difference in state fingerprint consistency between the current node and all returned nodes. The reason is that the state root divergence indicator is based on the bit-level hash difference and uses a nonlinear amplification function (such as the hyperbolic tangent function) to weightedly integrate the structural deviations between multiple nodes. This can significantly amplify the differences between nodes that deviate significantly from the mainstream state root, thereby reflecting the relative isolation or consistency risk of the node from the perspective of the entire network. Since the state root is a global summary of the entire state on the chain, any difference in on-chain data (such as transaction results, contract variables, etc.) will affect the state root. Therefore, the numerical changes in the state root divergence indicator can highly sensitively reflect structural divergences in state consistency.

[0066] A higher difference in the number of confirmations between the current node and the returning node over the last M consensus blocks generally indicates a more significant difference between the current node's state fingerprint and the state fingerprints of all returning nodes. This is because in a blockchain system, the consensus confirmation process for each new block (such as the commit phase in PBFT or log submission in RAFT) directly affects the state update path of its local ledger. If the current node's confirmation count for the last M consensus blocks is significantly lower than that of other nodes, it indicates that it failed to receive or respond to consensus messages in a timely manner during multiple consensus rounds, potentially missing several state writes, causing its ledger state to gradually diverge from the main chain or the majority of nodes. The state fingerprint is an aggregated representation of key ledger states. Therefore, a persistent difference in confirmation counts actually reflects a deviation in the state evolution path caused by insufficient participation in the consensus layer, ultimately manifesting as a significant difference in the state fingerprint. This phenomenon often indicates potential issues with the node, such as network latency, consensus packet loss, synchronization lag, or isolation, and is a key warning sign of state consistency risks.

[0067] The specific steps for generating the consensus confirmation difference index after analyzing the difference in the number of confirmations of the latest M consensus blocks between the current node and the returned node are as follows:

[0068] Select the latest M consensus blocks, record the confirmation status of the current node and N return nodes for the consensus blocks, and represent the confirmation status of each node for each consensus block as a binary value (1 for confirmed, 0 for unconfirmed). Then, calculate the number of confirmation differences between the current node and each return node on the M consensus blocks one by one to form a difference vector. The difference vector represents the degree of synchronization deviation between the two nodes in the consensus history. The calculation expression is:

[0069] Where 1≤u≤N

[0070] , where: C 0，j Indicates whether the current node n0 has confirmed the j-th consensus block (1 for confirmation, 0 for unconfirmed); C u，j Indicates returning node n u Whether the j-th consensus block is confirmed; D u For the current node n0 and the returned node n u The number of confirmation differences between the two nodes in the most recent M consensus blocks, ranging from 0 to M; j and u represent the indexes of the consensus block and the returned node respectively;

[0071] The purpose of this step is to extract the behavioral differences of nodes on the consensus history, construct a structural difference vector, and identify asymmetric behaviors participating in the consensus.

[0072] After getting all returned nodes n u The number of confirmed differences D uFinally, the maximum value of the number of confirmed differences is extracted to establish a normalized benchmark. Then, each difference value is transformed using the square compression function to increase the consistency weight of the low-difference node, and finally a consensus confirmation difference index is generated. The generation expression of the consensus confirmation difference index is:

[0073]

[0074] , where: D max The maximum value of all confirmed differences, plus 1 to prevent the denominator from being 0; C cgi Confirm the difference index for consensus;

[0075] This nonlinear compression formula can amplify the influence of highly consistent nodes while reducing the interference of extremely different nodes on the overall consensus confirmation difference index, accurately reflecting the "degree of synchronization" of the current node in the overall consensus behavior.

[0076] From the consensus confirmation difference index, we can see that the larger the performance value of the consensus confirmation difference index generated after analyzing the difference in the number of confirmations of the latest M consensus blocks between the current node and the return node, the greater the difference in the consistency of the state fingerprints of the current node and all the return nodes, and vice versa. The reason is that the consensus confirmation difference index uses the square form of the normalized difference value for nonlinear amplification processing, and each item Both reflect the degree of deviation between the current node and a particular returning node in the consensus history. The greater the difference, the larger the item, and the higher the consensus confirmation difference index obtained after the final cumulative average. Therefore, the value of the consensus confirmation difference index is essentially a measure of the deviation of node behavior. The larger the performance value, the more likely the current node is out of sync with the majority of nodes in the ledger state, resulting in a greater risk of inconsistent state fingerprints.

[0077] The consistency risk assessment module uses the extracted basic feature vector as input into a support vector machine (SVM) model trained with large-scale on-chain state anomaly data to assess the consistency risk between the current node's on-chain state and the states of all returned nodes.

[0078] The extracted state root divergence index and consensus confirmation difference index are input as input vectors into the support vector machine (SVM) model that has been trained with large-scale on-chain state abnormal data. A risk assessment coefficient is generated after comprehensive analysis based on the support vector machine model. The risk assessment coefficient is used to evaluate the consistency risk between the current node on-chain state and the states of all returned nodes.

[0079] The support vector machine (SVM) model, trained on large-scale on-chain state anomaly data, is a machine learning model constructed using real or simulated anomaly data from historical blockchain networks, such as node state inconsistencies, consensus failures, forks, and transaction misexecutions. Its core is to leverage the SVM's classification boundary learning capabilities to capture the discriminative patterns of consistency risk caused by node state discrepancies in blockchain systems. During the model's construction, two structural features with strong discriminative power in state consistency detection, the state root divergence indicator and the consensus confirmation difference indicator, are used to establish a two-dimensional or high-dimensional input space. Supervised training is performed using a large number of positive and negative samples (i.e., historical instances of consistent and inconsistent states). During training, kernel function mapping is used to map nonlinear features to a high-dimensional space, maximizing the margins of classification boundaries. This results in a discriminative model with strong generalization and precision control capabilities for state consistency risks.

[0080] Specifically, the state root divergence metric primarily quantifies the degree of structural divergence between the current node's state tree (such as a Merkle tree or account state tree) and other nodes. In most fork or state rollback events, changes in this parameter often precede surface manifestations, providing strong early warning capabilities. The consensus confirmation divergence metric, on the other hand, measures whether the current node has deviated from the network's mainstream path during the consensus process over the past few blocks, based on metrics such as the number of transaction confirmations, participation in consensus rounds, and the number of commit messages submitted by the node. This parameter can reflect the risk of dynamic consensus disharmony and is particularly important in BFT consensus algorithms. By training the SVM model with a large number of on-chain anomaly event samples (such as node isolation, delayed confirmations, and double signing), a two-dimensional vector model with the inputs [state root divergence metric and consensus confirmation divergence metric] can be constructed, forming a clear spatial classification boundary between "consistent" and "inconsistent" states. After the model training is completed, the state root divergence index and consensus confirmation difference index values currently collected by any node can be input, and the SVM can output a risk assessment coefficient (such as a continuous value from 0 to 1 or a graded risk label). The risk assessment coefficient represents the consistency risk level of the current node in the entire network.

[0081] In addition, another significant advantage of using large-scale historical data for training is the ability to achieve complex pattern recognition and improve robustness. In reality, the cause of state consistency differences may not be caused by a single exponential change, but by an imbalance in the linkage between the state root and the consensus process. For example, in certain attack scenarios, malicious nodes may maintain the consensus view unchanged (the consensus confirmation difference indicator is normal), but cause the state tree structure to mutate (the state root divergence indicator is abnormal) through state rollback or local contract injection. At this time, relying solely on consensus indicators cannot provide timely warnings. SVM achieves multi-dimensional collaborative discrimination in more complex scenarios by learning this "nonlinear coupling pattern" in historical data.

[0082] In practical applications, this model can further enhance its generalization performance and marginal fault tolerance through cross-validation, grid search, and kernel function tuning. Furthermore, to meet the high concurrency and real-time requirements of blockchain systems, the SVM model is deployed in a lightweight node-side module, enabling millisecond-level inference after receiving state index input, ensuring no additional performance bottlenecks. Ultimately, the risk assessment coefficient not only serves as a basis for determining delayed equity issuance but can also be reported to off-chain regulatory platforms or monitoring services through interfaces, enabling visual auditing and on-chain operational health assessment.

[0083] In summary, the support vector machine model, trained on large-scale on-chain state anomaly data, is a core component for blockchain state consistency risk assessment, integrating data-driven, model-based, engineering deployment, and intelligent reasoning capabilities. Through deep learning and boundary recognition of key features of the state root divergence indicator and consensus confirmation divergence indicator, it enables the system to automatically identify risks and dynamically respond to complex distributed events such as state forks and consensus deviations, significantly improving the security, accuracy, and controllability of on-chain smart contract execution.

[0084] The smart contract delay control and rollback module, when it identifies a consistency risk between the on-chain state of the current node and the state of all returned nodes, dynamically quantifies the severity of the consistency risk and accordingly enables a delayed release strategy for the on-chain issuance process of key rights (such as points, tokens, or NFTs). Specifically, according to the severity of the consistency risk, the release delay time of key rights is automatically adjusted through the smart contract to achieve intelligent regulation of on-chain operations. During the delay period, if the node state recovers consistency, the smart contract process automatically continues to execute. If the consistency state confirmation fails during the delay period, the smart contract automatically triggers the rollback mechanism and cancels the pending equity issuance operation, thereby effectively preventing the risk of accidental issuance in the fork state.

[0085] The risk assessment coefficient generated when evaluating the consistency risk between the current node's on-chain status and the status of all returned nodes through the support vector machine model is compared with the pre-set reference threshold of the risk assessment coefficient to determine whether there is a consistency risk between the current node's on-chain status and the status of all returned nodes. The specific judgment steps are as follows:

[0086] If the risk assessment coefficient is greater than the risk assessment coefficient reference threshold, it is determined that there is a consistency risk between the on-chain state of the current node and the state of all returned nodes; if the risk assessment coefficient is less than or equal to the risk assessment coefficient reference threshold, it is determined that there is no consistency risk between the on-chain state of the current node and the state of all returned nodes.

[0087] When a consistency risk is identified between the on-chain state of the current node and the states of all returned nodes, the risk assessment coefficient generated by the support vector machine model is compared with the pre-set risk assessment coefficient reference threshold to construct a consistency risk level function to quantify the degree of current state deviation. The expression of the consistency risk level function is:

[0088] Δ risk =tanh(κ·(γ risk -γ thresh ))

[0089] , where: γ risk is the risk assessment coefficient output by the support vector machine model. A larger value indicates a higher consistency risk between the on-chain state of the current node and the states of all returned nodes. thresh is the reference threshold of the risk assessment coefficient set by the system based on the historical fault tolerance characteristics; κ is the risk sensitivity adjustment factor, which controls the steepness of the risk level function, and κ>0; the tangent function tanh(·) is used to map the difference into a continuous risk level indicator within a finite interval; Δ risk is the risk level value generated by the risk assessment coefficient and the risk assessment coefficient reference threshold;

[0090] This step establishes a continuously adjustable risk response model that can dynamically adjust the subsequent parameters of the control strategy according to the risk level to avoid system jams caused by overprotection or misjudgment.

[0091] According to the calculated risk level value Δ risk Automatically generate the delayed release time parameters for key rights and interests, and apply them to the pending release operation through the smart contract interface. The delay time control formula is as follows:

[0092] T delay =T min +α·(Δ risk ) η

[0093] , where: T delay T is the on-chain release delay time of the current equity; min is the basic delay time, applicable to the risk-free state (i.e., the risk assessment coefficient is less than or equal to the reference threshold of the risk assessment coefficient); α is the delay amplification coefficient, indicating the maximum possible delay interval; η is the risk nonlinear amplification coefficient, used to enhance the penalty intensity under high risk conditions, η>1;

[0094] This mechanism, embedded in the delayed triggering logic of smart contracts, locks the corresponding operation hash during the waiting period and prevents it from being broadcast to the chain. This ensures that subsequent operations can only be executed after the on-chain state stabilizes or recovers consistency. This step, based on risk nonlinear enhancement logic, adaptively extends the stake release period, cools down and buffers potential consistency deviations, prevents irreversible operations from being triggered before the state is settled, and improves on-chain fault tolerance.

[0095] During the on-chain release delay, the state fingerprint of the current node and the feedback of the state fingerprint of each returning node are continuously monitored. The consistency risk between the on-chain state of the current node and the states of all returning nodes is evaluated, and the consistency recovery judgment conditions are defined:

[0096]

[0097] ,in: is the risk assessment coefficient output by the support vector machine model after the delay period. If this condition is met, it means that the state has been restored to consistency after the delay period, and the smart contract automatically unlocks and continues to execute the original equity issuance operation; otherwise, The system triggers the "on-chain equity issuance rollback mechanism" to cancel the operation, ensuring that the system does not make irreversible asset changes in high-risk environments;

[0098] This step ensures that key equity operations are only released when the system state reaches controllable consistency by building an on-chain risk closed-loop execution process. It also has the ability to "self-destructively block" issuance requests under unstable conditions, effectively preventing core risks such as erroneous transfers, double-spending attacks, and repeated equity issuance under fork conditions.

[0099] The above-mentioned blockchain-based automatic digital membership rights distribution and management system can effectively solve the problem of inconsistent rights status caused by state synchronization delays or untimely merging of on-chain state forks in a multi-node private chain environment. By constructing node state fingerprints, establishing a network-wide state summary comparison mechanism, and introducing a support vector machine model to intelligently assess consistency risks, the system further links the smart contract execution dynamic delay release and rollback control strategy, achieving risk perception, adaptive regulation and fault-tolerant protection for the key rights issuance process on the chain. It significantly improves the accuracy of rights issuance, system stability and operational traceability of the blockchain system in the event of abnormal state consistency, effectively reduces resource loss and trust crises caused by misissuance, omission or duplicate issuance, and has good technological advancement and engineering practical value.

[0100] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0101] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0102] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The blockchain-based automatic distribution and management system for digital membership rights is characterized by: It includes a state fingerprint generation module, a state summary communication module, a consistency feature extraction module, a consistency risk assessment module, and a smart contract delay control and rollback module; The state fingerprint generation module extracts key parameters representing the current blockchain state from the on-chain ledger unit and consensus unit, performs feature encoding, hash compression, and instruction stream abstract modeling on the key parameters to form a state fingerprint that uniquely identifies the current node's on-chain state; The state summary communication module, after the node state fingerprint is generated, uses the established point-to-point communication protocol in the consensus network to send a state summary request to all other known consensus nodes on the chain, so that the other nodes generate their own state fingerprints based on the local state, and construct a full-node state summary matrix; The consistency feature extraction module compares the status fingerprint of the current node with the status fingerprint of each returned node, extracts key features that reflect the consistency differences between the status fingerprints of the current node and all returned nodes, analyzes the extracted features, and uses the analyzed indicators as the basic feature vector to measure the consistency difference between the current node status and the status of all nodes in the entire network; The consistency risk assessment module uses the extracted basic feature vector as input into a support vector machine model trained with large-scale on-chain state anomaly data to assess the consistency risk between the current node's on-chain state and the states of all returned nodes. The smart contract delay control and rollback module, when it identifies a consistency risk between the on-chain state of the current node and the state of all returned nodes, dynamically quantifies the severity of the consistency risk, and accordingly enables a delayed release strategy for the on-chain issuance process of key rights and interests; specifically: according to the severity of the consistency risk, the release delay time of key rights and interests is automatically adjusted through the smart contract to achieve intelligent regulation of on-chain operations; during the delay period, if the node state restores consistency, the smart contract process automatically continues to execute; if the consistency state confirmation fails during the delay period, the smart contract automatically triggers the rollback mechanism and cancels the pending equity issuance operation.

2. The blockchain-based automatic distribution and management system for digital membership rights according to claim 1 is characterized in that: Feature encoding is the process of converting different types of state parameters into vector expressions of uniform dimensions; Hash compression is to compress high-dimensional state data into a fixed-length summary through a cryptographic hash function; Instruction flow abstract modeling is to extract logical dependencies and evolution paths from the change trajectory of state parameters to form a traceable state behavior template.

3. The blockchain-based digital membership rights automatic distribution and management system according to claim 1 is characterized in that: Extract key features that reflect the consistency differences between the state fingerprints of the current node and all returned nodes. The extracted features include the hash difference between the state root of the ledger of the current node and the returned node and the difference in the number of confirmations of the latest M consensus blocks between the current node and the returned node. After analyzing the extracted features, generate the state root divergence index and the consensus confirmation difference index respectively. The state root divergence index and the consensus confirmation difference index are used as the basic feature vectors to measure the consistency difference between the current node state and the node state of the entire network.

4. The blockchain-based automatic distribution and management system for digital membership rights according to claim 3 is characterized in that: The extracted state root divergence index and consensus confirmation difference index are input as input vectors into the support vector machine model that has been trained with large-scale on-chain state abnormal data. A risk assessment coefficient is generated after comprehensive analysis based on the support vector machine model. The risk assessment coefficient is used to evaluate the consistency risk between the current node on-chain state and the states of all returned nodes.

5. The blockchain-based digital membership rights automatic distribution and management system according to claim 4 is characterized in that: The risk assessment coefficient generated when evaluating the consistency risk between the current node's on-chain status and the status of all returned nodes through the support vector machine model is compared with the pre-set reference threshold of the risk assessment coefficient to determine whether there is a consistency risk between the current node's on-chain status and the status of all returned nodes. The specific judgment steps are as follows: If the risk assessment coefficient is greater than the risk assessment coefficient reference threshold, it is determined that there is a consistency risk between the on-chain state of the current node and the state of all returned nodes; if the risk assessment coefficient is less than or equal to the risk assessment coefficient reference threshold, it is determined that there is no consistency risk between the on-chain state of the current node and the state of all returned nodes.

6. The blockchain-based automatic distribution and management system for digital membership rights according to claim 3 is characterized in that: The specific steps for generating the state root divergence indicator after analyzing the hash difference between the current node and the returned node's ledger state root are as follows: Assume that the state root hash of the current node is H0, and the state root hashes of the remaining N return nodes in the network are H1, H2, ..., H N , by introducing the binary hash distance function to calculate the state root difference between the current node and each returned node, the calculation expression is as follows: ,in: Represents the kth bit of the root hash of the state of the i-th node; Indicates the kth bit of the root hash of the current node state; L is the hash length; D(H i , H0) is the normalized Hamming distance, which represents the difference ratio between the state root of the i-th node and the state root of the current node at the binary bit level, D(H i , H0)∈[0,1]; The risk amplification index mapping function is introduced to generate the final state root divergence index. The generation expression of the state root divergence index is: , where: γ is the difference amplification coefficient, which is used to enhance the weight of high-difference nodes; r is the nonlinear control parameter, which adjusts the sensitivity of the state root divergence index; S rdi is the final generated state root divergence index, S rdi ∈[0, 1].

7. The blockchain-based digital membership rights automatic distribution and management system according to claim 6 is characterized in that: The specific steps for generating the consensus confirmation difference index after analyzing the difference in the number of confirmations of the latest M consensus blocks between the current node and the returned node are as follows: Select the most recent M consensus blocks, record the confirmation status of the current node and N return nodes for the consensus blocks, and represent the confirmation status of each node for each consensus block as a binary value; Calculate the number of confirmation differences between the current node and each returned node on M consensus blocks one by one to form a difference vector. The calculation expression is: , where: C 0，j Indicates whether the current node n0 has confirmed the j-th consensus block; C u，j Indicates returning node n u Whether the j-th consensus block is confirmed; D u For the current node n0 and the returned node n u The number of confirmation differences between the two nodes in the most recent M consensus blocks, ranging from 0 to M; j and u represent the indexes of the consensus block and the returned node respectively; After getting all returned nodes n u The number of confirmed differences D u Finally, the maximum value of the number of confirmed differences is extracted to establish a normalized benchmark, and each difference value is transformed using the square compression function to finally generate a consensus confirmation difference index. The generation expression of the consensus confirmation difference index is: , where: D max is the maximum value among all confirmed differences; C cgi It is the consensus confirmation difference indicator.

8. The blockchain-based automatic distribution and management system for digital membership rights according to claim 5 is characterized in that: When a consistency risk is identified between the on-chain state of the current node and the states of all returned nodes, the risk assessment coefficient generated by the support vector machine model is compared with the pre-set risk assessment coefficient reference threshold to construct a consistency risk level function to quantify the degree of current state deviation. The expression of the consistency risk level function is: Δ risk =tanh(k·(γ risk -γ thresh )) , where: γ risk is the risk assessment coefficient output by the support vector machine model; γ thresh is the reference threshold of the risk assessment coefficient; κ is the risk sensitivity adjustment factor, which controls the steepness of the risk level function, κ>0; Δ risk is the risk level value generated by the risk assessment coefficient and the risk assessment coefficient reference threshold; According to the calculated risk level value Δ risk Automatically generate the delayed release time parameters for key rights and interests, and apply them to the pending release operation through the smart contract interface. The delay time control formula is as follows: T delay =T min +a·(D risk ) η , where: T delay T is the on-chain release delay time of the current equity; min is the basic delay time; α is the delay amplification coefficient; η is the risk nonlinear amplification coefficient, which is used to enhance the penalty intensity under high risk, η>1; During the on-chain release delay, the state fingerprint of the current node and the feedback of the state fingerprint of each returning node are continuously monitored. The consistency risk between the on-chain state of the current node and the states of all returning nodes is evaluated, and the consistency recovery judgment conditions are defined: ,in: is the risk assessment coefficient output by the support vector machine model after the delay period. If this condition is met, it means that the state has been restored to consistency after the delay period, and the smart contract automatically unlocks and continues to execute the original equity issuance operation; otherwise, Trigger the "on-chain equity issuance rollback mechanism" to cancel the operation.

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