Certificate life cycle automatic alternation method based on block chain node

Abstract

The invention provides a block chain node-based certificate life cycle automatic alternation method. The method comprises the following steps of (1) performing initialization deployment; (2) certificate generation; (3) state monitoring; (4) triggering condition judgment; (5) executing corresponding operation; and (6) archiving and auditing logs. According to the method, a full-process automation system of intelligent contract driving, trusted oracle machine linkage, cross-chain cooperation and hierarchical authority is constructed, three innovative algorithms are integrated, and high efficiency, safety, compatibility and intelligence of certificate life cycle management are realized.

Description

Technical Field

[0001] This invention relates to the fields of blockchain technology, digital certificate management technology and information security technology, and in particular to a method for automated lifecycle rotation of certificates based on blockchain nodes. Background Technology

[0002] Blockchain technology provides a decentralized and tamper-proof foundation for digital certificate management, but existing systems still have significant shortcomings: First, certificate generation, renewal, and revocation rely on manual triggering and review, making the process cumbersome and prone to errors. Second, certificate status is stored on centralized nodes, preventing users from verifying it independently and requiring third-party endorsement, resulting in high trust costs. Third, the lack of an automated rotation trigger mechanism means that expired certificates, if not replaced in time, can easily lead to security risks such as identity forgery and data leakage. Fourth, certificate data across different blockchain networks in a multi-chain ecosystem cannot be synchronized, creating "information silos" and resulting in poor cross-chain authentication compatibility. Fifth, changes in off-chain certificate status cannot be synchronized to on-chain data in real time, leading to a disconnect between on-chain and off-chain data and affecting the accuracy of certificate verification. These problems restrict the large-scale application of blockchain certificate management, especially its insufficient adaptability in key areas such as government affairs and finance. Summary of the Invention

[0003] This invention provides a method for automated certificate lifecycle rotation based on blockchain nodes, constructing a fully automated system of "smart contract-driven + trusted oracle linkage + cross-chain collaboration + hierarchical permissions", integrating three innovative algorithms to achieve efficient, secure, compatible and intelligent certificate lifecycle management.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: The method for automating the lifecycle rotation of certificates based on blockchain nodes includes the following steps: (1) Initialization and deployment: Deploy smart contracts that integrate core algorithm logic, configure a trusted oracle cluster consisting of Chainlink nodes and custom verification nodes and set up a Byzantine fault tolerance mechanism, allocate management permissions based on the RBAC permission mechanism, connect to regulatory databases and external threat intelligence sources, and initialize the parameters of dynamic risk perception model, multi-objective optimization decision model and adaptive verification strategy. (2) Certificate generation: After receiving the certificate application submitted by the user and verifying the legality of the applicant through the permission contract, the smart contract automatically generates a digital certificate that is compatible with the X.509 or national cryptographic SM2 standard and stores it on the chain, triggering the dynamic risk perception model to conduct a preliminary risk level assessment of the certificate, and at the same time establishing the behavioral baseline of the certificate. (3) Status monitoring: Real-time collection of off-chain certificate status data, external threat intelligence data and certificate behavior data through trusted oracle clusters, synchronized to smart contracts, and the certificate risk level is updated by dynamic risk perception model, and the dynamic rotation threshold is calculated by multi-objective optimization decision model. (4) Triggering condition judgment: Combine the updated certificate risk level with the dynamic rotation threshold to determine whether the rotation or revocation conditions are met; (5) Execute the corresponding operation: If the rotation conditions are met, the smart contract automatically generates a new certificate and marks the old certificate as invalid. It is synchronized to the associated blockchain through the cross-chain collaboration module, and the cross-chain data consistency verification is completed by the adaptive verification strategy. If the revocation conditions are met, the smart contract marks the certificate as revoked and puts it on the chain. The dynamic risk perception model is triggered to upgrade the risk level of the certificate to the extremely high risk level. (6) Log archiving and auditing: Generate a unique operation hash for operation records, algorithm execution results and exception handling information throughout the certificate's lifecycle, and permanently store it on the blockchain. Support filtering by conditions and exporting compliance audit reports.

[0005] In this specification, the steps of updating the certificate risk level in the dynamic risk perception model include: dynamically adjusting the threat sensitivity coefficient of each risk dimension based on external threat intelligence, calculating the corresponding dynamic weight, combining the remaining validity period of the certificate, off-chain security risks, cross-chain inconsistencies, historical operation records and behavioral deviation to perform risk quantification scoring, and outputting risk levels from L1 to L4. The behavioral deviation is calculated by comparing the real-time use behavior of the certificate with the preset behavioral baseline.

[0006] In this specification, the steps of the multi-objective optimization decision model to calculate the dynamic rotation threshold include: taking security, cost, and business continuity as optimization objectives, introducing historical rotation delay factors, business importance factors, and cross-chain synchronization stability factors, learning the optimal decision strategy through a reinforcement learning feedback mechanism, and calculating the dynamic rotation threshold in combination with the certificate risk level influence coefficient to ensure that the threshold is within a reasonable range of 7 to 90 days.

[0007] In this specification, the adaptive verification strategy completes the cross-chain data consistency verification steps as follows: determine the basic verification frequency based on the certificate risk level, dynamically adjust the verification priority in combination with the inter-chain communication quality score and system resource status, execute all verification tasks according to priority when resources are sufficient, and execute high-risk certificate verification first when resources are scarce. During verification, compare the certificate status, update timestamp and signature information of the main chain and sub-chain to ensure data consistency.

[0008] In this manual, when generating the national cryptographic SM2 standard certificate, a new public key identifier field "SM2_PUB_KEY" is added. The signature result is converted and transmitted using an SM2 signature and XCM protocol compatible scheme. Furthermore, the initial weight of the security vulnerability dimension under the SM2 certificate chain is reduced in the dynamic risk perception model, and its basic rotation threshold is shortened by the preset number of days compared with the same type of X.509 certificate.

[0009] In this specification, the data collection and verification steps of the trusted oracle cluster include: Chainlink nodes are responsible for collecting public off-chain data and external threat intelligence, and custom nodes are responsible for collecting institutional private data and certificate behavior data. The two types of nodes complete data self-checks through built-in signature verification or national cryptographic SM3 hash verification, and then cross-verify by the cluster aggregation node. After the consistency rate reaches the standard, double-signature data is generated and synchronized to the chain. When a single node fails, the collection subject is automatically switched to ensure data continuity.

[0010] In this specification, the steps for connecting to the regulatory database include: establishing two-way authentication with the CFCA regulatory database using the HTTPS+JSON-RPC2.0 protocol, carrying an HMAC-SHA256 signature with a security factor K when making requests, supporting real-time query of certificate compliance status, receiving revocation notices, and uploading operation logs. The security factor K is configured according to the business scenario to meet the corresponding compliance level requirements.

[0011] This manual also includes an algorithm degradation step: when the dynamic risk perception model, multi-objective optimization decision model, or adaptive verification strategy fails, the most recent valid result is automatically used to maintain operation. If it fails to recover within a preset time, it switches to the corresponding default value and triggers an alarm on the visualization platform to notify the administrator to investigate the problem.

[0012] In this specification, the cross-chain collaboration module constructs cross-chain messages based on the PolkadotXCMv3 protocol. The messages include certificate serial number, status type, risk level, dynamic threshold, timestamp, and main chain contract signature. SM2 certificate messages additionally carry a public key identifier field, configure parachain ID, execution weight, and gas limit, and retry according to preset number of times and intervals when message sending fails.

[0013] In this specification, if the cross-chain data consistency verification fails continuously during the corresponding operation steps, it is marked as a cross-chain anomaly and the relevant cross-chain business is suspended. At the same time, the dynamic risk perception model is fed back to adjust the certificate risk score, the multi-objective optimization decision model is adjusted to adjust the dynamic rotation threshold, and the risk level and threshold are updated to the normal state after the anomaly is recovered.

[0014] In summary, the present invention has at least the following beneficial effects: It enables automated management of the entire certificate lifecycle, reducing manual intervention and significantly improving management efficiency and operational standardization; By dynamically sensing risks and analyzing behaviors, we can accurately identify certificate security risks, effectively prevent security risks such as expired use and abnormal misuse, and strengthen security protection capabilities. Based on a multi-objective optimization decision-making mechanism, we can balance security, resource consumption and business continuity, avoid resource waste caused by excessive rotation, and improve resource utilization efficiency. By leveraging cross-chain collaboration technology and adaptive verification strategies, we break down data barriers between multiple chains, achieve real-time synchronization and consistency verification of certificate data between different blockchain networks, and enhance cross-chain compatibility. Adapt to mainstream certificate standards and regulatory requirements, improve access control and auditing mechanisms, and meet compliance needs in key areas such as government affairs and finance; It features a highly available and fault-tolerant design, and through Byzantine fault-tolerant configuration and algorithm degradation strategies, it ensures long-term stable operation of the system and reduces operational risks. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the certificate on-chain and verification process involved in this invention.

[0016] Figure 2 This is a schematic diagram of the AI-powered intelligent monitoring and early warning system involved in this invention. Detailed Implementation

[0017] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0018] This embodiment provides a method for automating the lifecycle rotation of certificates based on blockchain nodes, including the following steps: (1) Initialization and deployment: Deploy smart contracts that integrate core algorithm logic, configure a trusted oracle cluster consisting of Chainlink nodes and custom verification nodes and set up a Byzantine fault tolerance mechanism, allocate management permissions based on the RBAC permission mechanism, connect to regulatory databases and external threat intelligence sources, and initialize the parameters of dynamic risk perception model, multi-objective optimization decision model and adaptive verification strategy. (2) Certificate generation: After receiving the certificate application submitted by the user and verifying the legality of the applicant through the permission contract, the smart contract automatically generates a digital certificate that is compatible with the X.509 or national cryptographic SM2 standard and stores it on the chain, triggering the dynamic risk perception model to conduct a preliminary risk level assessment of the certificate, and at the same time establishing the behavioral baseline of the certificate. (3) Status monitoring: Real-time collection of off-chain certificate status data, external threat intelligence data and certificate behavior data through trusted oracle clusters, synchronized to smart contracts, and the certificate risk level is updated by dynamic risk perception model, and the dynamic rotation threshold is calculated by multi-objective optimization decision model. (4) Triggering condition judgment: Combine the updated certificate risk level with the dynamic rotation threshold to determine whether the rotation or revocation conditions are met; (5) Execute the corresponding operation: If the rotation conditions are met, the smart contract automatically generates a new certificate and marks the old certificate as invalid. It is synchronized to the associated blockchain through the cross-chain collaboration module, and the cross-chain data consistency verification is completed by the adaptive verification strategy. If the revocation conditions are met, the smart contract marks the certificate as revoked and puts it on the chain. The dynamic risk perception model is triggered to upgrade the risk level of the certificate to the extremely high risk level. (6) Log archiving and auditing: Generate a unique operation hash for operation records, algorithm execution results and exception handling information throughout the certificate's lifecycle, and permanently store it on the blockchain. Support filtering by conditions and exporting compliance audit reports.

[0019] In some embodiments, the zero-knowledge proof (ZKP) protocol is introduced to interact with the dynamic risk perception model and certificate verification process to achieve sensitive information privacy protection and risk level collaborative verification.

[0020] During certificate verification, if the certificate-associated entity contains sensitive content such as government classified information or financial user privacy data, the Groth16 zero-knowledge proof protocol is used to encrypt sensitive fields (such as user identity identifiers and core business permission scope). During verification, there is no need to disclose the original text of sensitive fields; the dual verification of "sensitive information compliance + certificate validity" can be completed solely through the proof file generated by ZKP. Simultaneously, the ZKP verification result (pass / fail) serves as a supplementary input parameter for the dynamic risk perception model: if ZKP verification fails (indicating suspected tampering of sensitive information), the risk level is directly upgraded to L4; if three consecutive ZKP verifications pass, and other risk dimensions are normal, the dynamic risk perception model reduces the weight of the "off-chain security risks" dimension by 3%, further optimizing the accuracy of risk assessment. This interaction not only addresses the pain point of sensitive information leakage after the certificate is on-chain but also uses privacy protection results to feed back into risk perception, forming a closed loop of "privacy verification - risk adjustment."

[0021] In some embodiments, a layered cross-chain mechanism of "edge preprocessing - main chain verification" is constructed by combining edge computing nodes with adaptive verification strategies and cross-chain collaboration modules.

[0022] A lightweight data preprocessing module is deployed on the edge nodes of the associated blockchain network. This module pre-caches the core data of the main chain certificate (status, timestamp, signature digest) and performs preliminary consistency verification on the certificate data synchronized across chains (comparing only key hash values). An adaptive verification strategy dynamically adjusts the main chain verification logic based on the edge node's preprocessing results: if the edge node determines the data consistency rate is ≥99%, the main chain verification frequency is reduced to 30% of the original frequency, only verifying signature integrity; if the edge node detects data inconsistency (hash value mismatch), it marks the differing fields and synchronizes them to the main chain. The adaptive verification strategy immediately increases the verification priority of the certificate (by 2 levels) and triggers a full field comparison between the main chain and the sub-chain. Simultaneously, the edge nodes monitor cross-chain network latency in real time and synchronize the latency data to the cross-chain collaboration module to dynamically adjust the cross-chain message sending queue (prioritizing data preprocessed by the edge node when the latency is >500ms). The interaction between this layered mechanism and cross-chain verification and resource scheduling reduces the main chain's computational load and improves the efficiency of cross-chain synchronization under high-latency networks. Its combination of technologies breaks through the traditional idea of ​​"full main chain verification".

[0023] In some embodiments, a federated learning framework is introduced to interact with a multi-objective optimization decision model and a dynamic risk perception model to achieve joint optimization of algorithm parameters across multiple institutions.

[0024] When multiple institutions (such as cross-regional government platforms and financial institutions within a consortium blockchain) share the certificate management system, a distributed model training network is constructed using a federated learning framework. Each institution retains ownership of the privacy of its certificate risk data and rotation decision feedback data locally, only encrypting and uploading the model gradient update values ​​to the federated learning aggregation node. The reward function parameters of the multi-objective optimization decision model and the risk dimension weights of the dynamic risk perception model are jointly and iteratively optimized through federated learning: after receiving the gradient data from each institution, the aggregation node calculates the globally optimal parameters using a secure aggregation algorithm (such as an aggregation scheme based on secret sharing), and then distributes them to each institution to update their local models. In terms of interaction logic, the local risk assessment results of the dynamic risk perception model serve as the training sample input for federated learning, and the local threshold calculation feedback of the multi-objective optimization decision model serves as the reward signal for model training. At the same time, the globally optimized parameters from federated learning feed back into both models, making the risk level assessment more aligned with the common scenarios of multiple institutions, and the rotation threshold calculation more balanced between the security and cost requirements of cross-institutions. This combination of technologies resolves the contradiction between data privacy and collaborative model optimization among multiple institutions.

[0025] In some embodiments, digital watermarking technology is integrated with the certificate generation module, log archiving and auditing module to achieve full lifecycle traceability and enhanced anti-tampering capabilities for certificates.

[0026] During the certificate generation phase, the core contract embeds an invisible digital watermark in the extended fields of the X.509 / SM2 certificate. The watermark content is a combination of the certificate serial number, the hash value of the main chain contract address, and the generation timestamp, encrypted using the national cryptographic algorithm SM4. During log archiving, the digital watermark is stored in conjunction with the operation hash and algorithm execution record. During the auditing phase, auditors can extract the watermark by inputting the certificate serial number using a dedicated watermark extraction tool on the visualization platform. This watermark is then compared with the watermark information stored on the chain to verify whether the certificate has been tampered with (e.g., the watermark of a forged certificate cannot match the on-chain record). Simultaneously, the digital watermark extraction result is fed back to the dynamic risk perception model: if watermark extraction fails or the comparison is inconsistent, the model directly upgrades the certificate risk level to L4 and triggers an automatic revocation process. This technology deeply integrates the traceability characteristics of digital watermarks with the immutability of blockchain, and its application in certificate lifecycle management breaks through the traditional anti-tampering approach of "relying solely on on-chain hash verification."

[0027] In some embodiments, a quantum random number generator interacts with a trusted oracle cluster and a smart contract engine to enhance the security of certificate keys and decision parameters.

[0028] A quantum random number generator (QRNG) based on quantum physical phenomena (such as photon polarization) is deployed to generate private key seeds for certificates and random number parameters for smart contracts (such as the initial reward coefficient of a multi-objective optimization decision model and verification random numbers for oracle nodes). A trusted oracle cluster combines the random numbers generated by the QRNG with off-chain data as a dual verification factor: during the data cross-validation phase, oracle nodes use the QRNG random numbers to re-sign the verification results, and the smart contract only receives data containing valid random number signatures. Simultaneously, the smart contract engine obtains new random numbers from the QRNG every 7 days to update the exploration rate parameter of the multi-objective optimization decision model (controlling the randomness of threshold adjustments to avoid local optima). The dynamic risk perception model uses this random number as a perturbation factor for risk scoring (perturbation amplitude ≤2%) to prevent malicious attackers from predicting risk levels through fixed scoring rules. This combination of technologies interacts with quantum security technology, oracle data verification, and contract parameter optimization, addressing the security vulnerabilities that traditional pseudo-random numbers may be compromised.

[0029] The technical concept of this invention is as follows: like Figure 1 For the certificate on-chain and verification process, such as Figure 2 For AI-powered intelligent monitoring and early warning processes.

[0030] This solution constructs a fully automated system encompassing "smart contract-driven + trusted oracle linkage + cross-chain collaboration + tiered permissions," integrating three innovative algorithms (dynamic risk perception model, multi-objective optimization decision model, and adaptive verification strategy) and collaborating through five core modules (certificate lifecycle management module, trusted oracle cluster, smart contract engine, cross-chain collaboration module, and visual management platform). The smart contract encapsulates the logic for certificate generation, verification, update, revocation, and archiving, and supports custom rule configuration. The trusted oracle cluster collects off-chain certificate status and external threat intelligence in real time, and synchronizes them to the chain after multiple verifications. The cross-chain collaboration module enables multi-chain certificate data interoperability based on the PolkadotXCM protocol, and dynamically adjusts the verification frequency and priority using an adaptive verification strategy. The three algorithms work together to achieve accurate risk assessment, dynamic optimization of rotation thresholds, and cross-chain verification resource scheduling. They are also compatible with mainstream certificate standards such as X.509 and SM2, and support integration with regulatory databases, enabling efficient, secure, and compatible certificate lifecycle management.

[0031] System Core Architecture: The system consists of five core modules, each with a clear division of labor, working collaboratively and deeply integrating algorithmic logic. The certificate lifecycle management module integrates the entire process of certificate generation, verification, renewal, revocation, and archiving. It uses smart contracts to solidify operational logic and supports custom rule configuration. It incorporates a real-time certificate risk assessment algorithm (A1) that outputs certificate risk levels in real time, providing core decision-making basis for rotation triggering and cross-chain verification. It also adapts to mainstream certificate standards such as X.509 and SM2, meeting the needs of various industries including government, finance, and healthcare.

[0032] Trusted Oracle Cluster: Employs a multi-node decentralized architecture of "Chainlink nodes + custom verification nodes." Node configuration follows a Byzantine fault tolerance mechanism of N=7 / f=2 (allowing a maximum of 2 failures out of 7 nodes), ensuring system operation even in the event of single or partial node failures. It collects off-chain certificate status data (validity period, revocation notices, compliance verification results) and external threat intelligence data in real time, synchronizing them to the blockchain after multi-signature verification. It supports collaborative data collection and cross-validation with Chainlink nodes, ensuring data authenticity and real-time performance; nodes are distributed across different geographical regions, so the failure of a single node does not affect the continuity of the overall data link.

[0033] The smart contract engine includes a core contract (certificate management main contract), a rules contract (custom rotation / revocation rules), and a permissions contract (hierarchical authorization management). It integrates a multi-objective optimization decision model based on reinforcement learning, which can dynamically adjust the rotation threshold based on certificate risk level and business objectives. It supports hot contract updates, optimizing rule logic and algorithm parameters without interrupting system operation. Furthermore, it incorporates security mechanisms such as reentrancy attack protection and overflow checks to ensure secure contract execution.

[0034] Cross-chain collaboration module: Based on Polkadot relay chain technology, it constructs a certificate status synchronization channel to achieve real-time interoperability of certificate data between different blockchain networks. It integrates an adaptive verification strategy based on network awareness and resource scheduling, which can dynamically adjust the verification frequency and priority according to risk level, network status, and system resources, and detect the consistency of data between the main chain and sub-chains in real time; it is compatible with mainstream blockchains such as Ethereum, Polygon, and BSC, and can quickly connect to new blockchain networks through configuration.

[0035] Visualized Management Platform: Provides a web / DApp interface, supporting user configuration of rules, querying certificate status (including risk level and dynamic threshold), viewing operation logs, and initiating authorization applications; built-in data visualization charts intuitively display certificate lifecycle distribution, cross-chain synchronization status, and algorithm execution results; supports regulatory database integration configuration (such as the financial CFCA interface), and can export compliance audit reports with one click, improving operational convenience and compliance; it also integrates an alarm module to receive notifications of abnormal events such as algorithm execution failure, oracle failure, and cross-chain retry failure.

[0036] Certificate lifecycle: Initial Deployment: Contract deployment + oracle configuration (including node fault tolerance configuration) + permission settings + integration with regulatory database (including security factor K configuration) + integration with external threat intelligence sources + algorithm parameter initialization (dynamic risk perception model, multi-objective optimization decision model, adaptive verification strategy) Certificate Generation: Automatic smart contract generation (adapted to X.509 / SM2 standard) + on-chain storage + initial risk level assessment (L1-L4) by the dynamic risk perception model + establishment of certificate behavior baseline Certificate Verification: User / system on-chain self-verification + synchronization of risk level and dynamic rotation threshold + real-time behavior monitoring Status Monitoring: Real-time data collection by the oracle (including threat intelligence, certificate behavior data, network / system resource data) + dynamic threshold calculation by the multi-objective optimization decision model + dynamic risk perception model risk level update trigger condition judgment: combining risk level and dynamic rotation threshold: a. Triggering conditions not met: Continue monitoring b. Rotation conditions: Automatic rotation (generate new certificate + mark old certificate as invalid + synchronize on-chain + adaptive verification strategy for cross-chain consistency verification) c. Revocation conditions met: Automatic revocation (certificate revocation + on-chain record + cross-chain synchronization + dynamic risk perception model risk level upgrade (to L4)) Cross-chain state synchronization: Synchronize to the associated blockchain + adaptive verification strategy verification result feedback (feedback to dynamic risk perception model / multi-objective optimization decision model) Log archiving and auditing: Includes a complete record of certificate operations, algorithm execution, cross-chain verification, and exception handling.

[0037] Initial deployment: Parameter Configuration: Users configure the certificate's basic validity period (including the SM2 certificate's differentiated basic threshold), initial rotation threshold, revocation list, cross-chain synchronization nodes (such as Polkadot parachain IDs), and regulatory database connection parameters (interface address, authentication key, security factor K) through a visual platform. Simultaneously, they set initialization parameters for three major algorithms (such as the initial weight range of the dynamic risk perception model, the reward function parameters of the multi-objective optimization decision model, and the resource threshold parameters of the adaptive verification strategy). The security factor K is strongly correlated with compliance risk; K=2 meets the compliance requirements of Level 3 or higher of the Information Security Protection Scheme 2.0 in the financial industry, while K=1.5 is suitable for general government scenarios. In this example, K=2 or K=3 is selected based on business needs.

[0038] Contract Deployment: The development team will deploy the smart contract (written in Solidity 0.8.x) that integrates the core algorithm logic to the target blockchain. The compilation and testing will be completed through the Hardhat framework. Before deployment, it needs to be audited by a third-party security agency (such as CertiK) and the vulnerability will be fixed before it is officially put on the chain.

[0039] Oracle integration with external data sources: Configure the oracle cluster data source interface (Chainlink nodes connect to public data and external threat intelligence sources, such as national vulnerability databases and industry security announcements; custom nodes connect to private data and certificate behavior monitoring data), complete two-way authentication with regulatory databases (such as CFCA), test the interface call success rate (≥99.9%), and ensure data transmission encryption compliance.

[0040] Algorithm degradation strategy configuration: Preset degradation schemes after algorithm execution failure, such as setting the default risk level to L2 when the dynamic risk perception model crashes; starting a backup data collection channel when the oracle fails; triggering manual alarms when cross-chain retry fails, etc.

[0041] Certificate generation: Users submit certificate applications (including identity information, public key, and certificate type (X.509 / SM2)), and the permission contract verifies the legitimacy of the applicant's permissions based on the RBAC mechanism.

[0042] The core contract automatically generates digital certificates according to the corresponding standard based on the certificate type: X.509 certificates follow standard field specifications; SM2 certificates include an additional SM2 public key identifier field (identified as "SM2_PUB_KEY"); the signature algorithm adaptation adopts an SM2 signature and XCM protocol compatible scheme (the SM2 signature result is converted to the XCM protocol format before transmission). Data such as certificate serial number, holder information, public key, validity period, certificate type, and initial state (valid) are stored on the blockchain.

[0043] Trigger the dynamic risk perception model to conduct an initial risk level assessment: Based on basic information such as the applicant's credit rating, certificate purpose (core / non-core business), certificate type (SM2 certificate has a 5% lower initial weight for off-chain security risks compared to X.509 certificate due to its higher algorithm security), output the initial risk level (usually L1 / L2) and synchronize it to the visualization platform and smart contract.

[0044] Establish certificate behavior baseline: Based on the business type of the certificate, the usage scenario, the geographical range of regular verification, the time frequency range, and the list of accessed systems, form the behavior baseline of the certificate and store it on the blockchain.

[0045] Certificate verification: Users or systems can obtain complete on-chain information (including current risk level, dynamic threshold, historical operation records, and behavior baseline) by entering the certificate serial number through the blockchain query interface (which supports RESTful / RPC).

[0046] Self-verification of certificate validity (whether the status is valid and whether the validity period is compliant) and risk level (automatic pop-up prompts for high-risk certificates) is available without relying on third-party institutions, and the verification response delay is ≤1 second.

[0047] Monitor certificate usage behavior in real time and compare the current behavior with the behavior baseline.

[0048] Automatic rotation: The oracle cluster collects certificate validity data, external threat intelligence data, certificate behavior data, and system resource data every 24 hours. After synchronizing the data to the smart contract, it triggers a multi-objective optimization decision model: based on the current risk level, three major objectives of security / cost / business continuity, and historical decision data, it calculates the dynamic rotation threshold.

[0049] When the remaining validity period of the certificate is less than or equal to the dynamic rotation threshold, the rotation process is automatically triggered. The core contract generates a new certificate (consistent with the original certificate type and inheriting relevant configurations), associates it with the old certificate serial number (for easy traceability), and marks the old certificate as "invalid".

[0050] New certificate data is stored on the blockchain, and the cross-chain collaboration module starts an adaptive verification strategy: the basic priority is determined according to the risk level, the final priority is adjusted in combination with the inter-chain communication quality score and system resource status, and the cross-chain synchronization consistency is checked according to the adjusted frequency and priority.

[0051] The cross-chain module synchronizes the new certificate to all associated blockchains (the SM2 certificate synchronization carries the newly added SM2 public key identifier field and the adapted signature information). The adaptive verification strategy verifies the synchronization result. If they are consistent, the rotation is completed; if they are inconsistent, a retry mechanism is triggered (up to 3 times, with an interval of 60 seconds). If the retry fails, a manual alarm is triggered and the risk level is adjusted by the dynamic risk perception model.

[0052] Automatic Cancel: Triggering conditions: The oracle collects a revocation notification pushed by the regulatory database (such as the CFCA's certRevokeNotify interface), or an authorized administrator initiates a revocation request through a visualization platform, or the dynamic risk perception model detects a serious behavioral anomaly and triggers a risk level jump.

[0053] After the permission contract verifies the legality of the revocation instruction, the core contract immediately updates the certificate status to "revoked" and records it on the blockchain. At the same time, it triggers the dynamic risk perception model to upgrade the risk level to L4 (extremely high risk).

[0054] The cross-chain module synchronizes the revocation status to all associated blockchains within 5 minutes. The adaptive verification strategy checks the synchronization results with the highest priority at L4 level to ensure that all business systems stop using the certificate and prevent abuse.

[0055] Cross-chain synchronization and adaptive verification: Cross-chain messaging is built based on the PolkadotXCM protocol (v3). X.509 certificate messages are transmitted in the original format, while SM2 certificate messages add an SM2 public key identifier field. Other fields remain compatible. Messages include fields such as certificate serial number, status type (valid / invalid / revoked), risk level, dynamic threshold, timestamp, and main chain contract signature.

[0056] The adaptive verification strategy determines the basic verification frequency based on the risk level (5 minutes / time for L4, 10 minutes / time for L3, 30 minutes / time for L2, and 2 hours / time for L1). If the dynamic rotation threshold is ≤15 days, the frequency is halved. At the same time, the network performance indicators (latency, packet loss rate, bandwidth utilization) of each chain are monitored in real time through the network status sensor and quantified into an "inter-chain communication quality score" of 0-10. When the communication quality score is ≤3, the verification frequency of non-critical certificates is automatically reduced, and high-priority certificates are switched to the backup synchronization channel. Combined with system resource monitoring data (CPU, memory, I / O utilization), a "verification task priority queue" is constructed through the dynamic resource scheduler. The final priority of the task is determined by the basic priority and dynamic urgency (the worse the network status and the more strained the system resources, the higher the dynamic urgency of high-risk certificates). When system resources are strained, low-priority tasks are delayed, and high-priority tasks take the lead in preempting resources.

[0057] Verification logic: Compare the certificate status, update timestamp (error allowed ≤10 seconds), and signature information between the main chain and the sub-chain (SM2 certificates require additional verification of the public key identifier field). If all three are consistent, the verification is successful; if any one is inconsistent, the verification fails and triggers the sub-chain data resynchronization (based on the main chain data).

[0058] Anomaly Handling: If three consecutive verifications fail, a cross-chain anomaly is marked, triggering an administrator alert. At the same time, feedback is sent to the dynamic risk perception model (increasing the risk score) and the multi-objective optimization decision model (adjusting the threshold calculation parameters).

[0059] Log archiving and auditing: All operations (generation / rotation / revocation), algorithm execution records (risk scoring, threshold calculation, verification results), and abnormal event handling records generate unique operation hashes, recording information such as operation time, execution node, and triggering conditions, and are permanently stored on the blockchain.

[0060] Auditors can use a visualization platform to filter logs by criteria (time range, certificate type, risk level) and export audit reports in PDF / Excel format. The platform also supports automatic uploading of compliance logs to regulatory databases.

[0061] Customize the coordination protocol between oracle nodes and Chainlink nodes: A "dual-layer verification + data complementarity + fault switching" mechanism is adopted to ensure the reliability of data acquisition and synchronization. Data collection division of labor: Chainlink node: Responsible for collecting publicly available off-chain data (such as industry-standard certificate blacklists, standard timestamps, and blockchain network status) and external threat intelligence data (vulnerability information from the national vulnerability database, industry security announcements, and high-frequency security event types), with a collection cycle of 1 hour.

[0062] Custom node: Responsible for collecting the organization's private data (such as internal certificate usage logs, associated entity risk events, and business system certificate dependencies) and certificate behavior data (verification of geographical location, time frequency, and system access), with a collection cycle of 1 hour.

[0063] Data validation process: Intra-node self-check: Chainlink nodes use a built-in data source signature verification mechanism, while custom nodes use the national cryptographic SM3 hash verification to verify the integrity of the collected data. If the verification fails, the data will be collected again (up to 3 times).

[0064] Cross-validation: After receiving data from two types of nodes, the oracle cluster aggregation node compares the consistency of the same type of data (such as certificate validity period). If the consistency rate is ≥95%, dual-signature data of "Chainlink signature + custom node signature" is generated; if the consistency rate is <95%, a third-party backup node is started to collect data arbitration and select the data group with the highest consistency rate.

[0065] Data synchronization and failover: Valid data is synchronized to the blockchain via Chainlink's Oracle.sol contract interface in standard JSON format (including data source, collection time, verification result, and signature information), with a synchronization delay of ≤5 minutes.

[0066] Failover: When a Chainlink node fails, a custom node automatically takes over the collection of public data and threat intelligence (via a preset public data source API), temporarily increasing the collection frequency to once every 30 minutes. When a custom node fails, the Chainlink node obtains anonymized private data through an authorized API to ensure uninterrupted data collection. After the failed node recovers, it automatically synchronizes historical data and resumes its original functions.

[0067] Specific configuration parameters of the PolkadotXCM protocol (cross-chain message format): Cross-chain certificate state synchronization is implemented based on XCMv3. The core configuration and message format are shown in the table below, along with an example: ; ; Regulatory database interface standards (taking the Financial CFCA as an example): Secure integration with the CFCA regulatory database is achieved using the HTTPS+JSON-RPC2.0 protocol, meeting the compliance requirements of the financial industry. Basic interface configuration: Protocol: HTTPS (TLS 1.3 encryption), port 443, timeout 30 seconds.

[0068] Authentication: Two-way certificate authentication (the institution deploys a client certificate issued by CFCA, and the regulator deploys a server certificate). Each request's HTTP header carries an HMAC-SHA256 signature (signature parameters: request timestamp, interface name, random number, security factor K, and the key is assigned by CFCA). The value of the security factor K is strongly correlated with compliance risk; K≥2 can meet the Level 3 compliance requirements of the Financial Industry Information Security Protection Scheme 2.0. In this example, K=2 is used to ensure compliance.

[0069] Data format: UTF-8 encoding, date and time format ISO8601 (e.g., 2024-05-28T12:00:00Z), special characters in the string must be escaped.

[0070] Core interface definition: ; Exception handling: When an interface call fails, an error code and description are returned (e.g., 1001: authentication failure, 1002: parameter format error, 1003: database connection error). The organization needs to retry according to the error code (authentication failure requires manual intervention). Error logs are retained for ≥1 year.

[0071] Details on SM2 certificate compatibility: (1) Cross-chain synchronization adaptation Handling of field differences: When SM2 certificates are synchronized across chains, a new "SM2_PUB_KEY" identifier field is added to quickly identify the certificate type for subchain contracts. This field is configured separately in the "data" field of the XCM protocol message and does not affect the compatibility of other fields.

[0072] Signature Algorithm Adaptation: The SM2 certificate uses the SM2 elliptic curve public key cryptography algorithm for signing. The signature result is converted into a hexadecimal string format according to the XCM protocol requirements before transmission. After receiving the certificate, the subchain contract verifies the signature through the built-in SM2 signature verification module to ensure the validity of the signature.

[0073] (2) Differentiation of risk assessment Because the SM2 algorithm has higher security in terms of key length and encryption strength, in the dynamic risk perception model, the initial weight of the "off-chain security risks" dimension of the SM2 certificate is reduced by 5% compared with that of the X.509 certificate (e.g., the initial weight of this dimension of the X.509 certificate is 40%, while that of the SM2 certificate is 35%). The initial weights of other dimensions remain the same, and the weight adjustment mechanism is the same as that of the X.509 certificate.

[0074] (3) Differentiation of rotation threshold The basic threshold for SM2 certificates is 10 days shorter than that of similar X.509 certificates, as detailed in the table below: ; Three main algorithms and interaction logic: Dynamic risk perception model based on threat intelligence and behavioral analysis: (1) Algorithm objective: The system quantifies certificate risks such as expiration, off-chain security vulnerabilities, cross-chain inconsistency, historical operation risks, and abnormal behavior risks in real time. Through dynamic weight adjustment mechanisms and behavioral profiling analysis, it outputs risk levels (L1-L4) to provide accurate decision-making basis for subsequent algorithms.

[0075] (2) Input parameters: ; (3) Dynamic weight adjustment mechanism The system maintains a "threat sensitivity coefficient" for each risk dimension (expiration risk, off-chain security risks, cross-chain inconsistency risks, and historical operation risks), with an initial value of 1.0 and a coefficient range of 0.5-1.5.

[0076] Connect with external threat intelligence sources (national vulnerability database, industry security bulletins) to monitor security incident trends in real time. If a high frequency of security incidents related to a certain risk dimension occurs recently (within the last 7 days) (e.g., certificate revocation incidents related to "off-chain security vulnerabilities" occur ≥5 times per day), the threat sensitivity coefficient of that dimension will be automatically increased (0.1 each time, up to a maximum of 1.5), and the threat sensitivity coefficients of other dimensions will be reduced proportionally to ensure that the total weight of all dimensions is 100%.

[0077] Weight calculation formula: Dynamic weight of a certain dimension = Initial weight of the dimension × Threat sensitivity coefficient of the dimension / Σ (Initial weight of each dimension × Threat sensitivity coefficient of each dimension); (4) Certificate Behavior Profile Analysis Establish a unique behavioral baseline for each certificate, including the geographical range of regular verification (e.g., only allow domestic IPs), time frequency range (e.g., ≤100 verifications per day), and list of accessed systems (e.g., only allow access to core business systems).

[0078] Real-time monitoring of certificate usage behavior and calculation of behavior deviation: Deviation = Σ (deviation value of single behavior × behavior weight), where the behavior weights are 30% for geographical location, 40% for time frequency, and 30% for system access; deviation value of single behavior = 1 - (proportion of actual behavior conforming to the baseline), with a deviation value range of 0-1.

[0079] A new "Abnormal Behavior Risk Sub-item" (weight 20%) has been added, with the score calculated as deviation degree × 100 × 20%. When the deviation degree is ≥ 0.7 (severe deviation), the score for this sub-item will be ≥ 14 points, triggering a risk level jump (e.g., jumping directly from L1 to L3), regardless of the risk scores of other dimensions.

[0080] (5) Risk Quantification Calculation (Total Score 100 points) Expiration risk score (dynamic weight W1): Remaining validity period ≤ 7 days: 100 points; 8-15 days: 80 points; 16-30 days: 50 points; 31-60 days: 20 points; > 60 days: 0 points; Final score = score of this dimension × W1.

[0081] Off-chain security risk score (dynamic weight W2): Related entities with regulatory penalties: 80 points; ≥2 risk events in the past 3 months: 90 points; 1 risk event: 60 points; Matching the CFCA blacklist: 100 points; No of the above: 0 points (take the highest value, not superimposed); Final score = score of this dimension × W2.

[0082] Cross-chain inconsistency risk score (dynamic weight W3): ≥2 inconsistencies in the past 7 days: 100 points; 1 inconsistency: 60 points; cross-chain latency ≥30 minutes: 80 points; 10-30 minutes: 40 points; synchronization failure ≥3 times: 90 points; 1-2 times: 50 points (cumulative, maximum 100 points); final score = score of this dimension × W3.

[0083] Historical Operation Risk Score (Dynamic Weight W4): ≥2 historical rotation delays: 100 points; 1 delay: 60 points; historical cancellation records: 80 points; abnormal usage records: 70 points (take the highest value, do not add up); final score = score of this dimension × W4.

[0084] Behavioral abnormality risk score (fixed weight W5=20%): score = deviation × 100 × W5; final score = score of this dimension.

[0085] Total score = Expiration risk score + Off-chain security risk score + Cross-chain inconsistency risk score + Historical operation risk score + Abnormal behavior risk score.

[0086] (6) Output results The total score corresponds to the risk level (0-20 points: L1, 21-40 points: L2, 41-60 points: L3, 61-100 points: L4), and is synchronized with the multi-objective optimization decision-making model, adaptive verification strategy and visualization platform.

[0087] (7) Downgrade strategy When the algorithm fails to execute (such as abnormal data collection or error in calculation logic), the degradation scheme is automatically activated: the most recent effective risk level is maintained for 1 hour. If the risk level is not restored within 1 hour, the algorithm switches to the default risk level L2 and triggers an alarm on the visualization platform to notify the administrator to investigate the problem.

[0088] Multi-objective optimization decision-making model based on reinforcement learning: (1) Algorithm Objective By balancing the three objectives of "security", "cost" and "business continuity", the system learns the optimal decision-making strategy through a reinforcement learning feedback mechanism and dynamically adjusts the rotation threshold to minimize risk, optimize costs, and minimize business interruption.

[0089] (2) Input parameters The dynamic risk perception model outputs the risk level, certificate base threshold (distinguishing between X.509 / SM2), historical rotation delay count, business importance, cross-chain synchronization stability (success rate of the last 3 times), historical decision data, and feedback results.

[0090] (3) Multi-objective optimization function Define the optimization function F = α × S - β × C - γ × D, where: S: Security Index (0-100 points), S=100-Certificate Expiration Risk Probability×100. The certificate expiration risk probability is positively correlated with the remaining validity period and risk level.

[0091] C: Cost indicator (0-100 points), C = (number of rotations × resource consumption per rotation) / resource consumption threshold × 100. Resource consumption per rotation includes computing cost, storage cost, and network transmission cost.

[0092] D: Business interruption index (0-100 points), D = Business interruption duration / Interruption duration threshold × 100, where business interruption duration refers to the business pause time caused by rotation operation.

[0093] α, β, and γ are target weights, which are dynamically configured according to the business scenario. For core business scenarios, α=0.6, β=0.2, and γ=0.2; for non-core business scenarios, α=0.4, β=0.3, and γ=0.3.

[0094] (4) Strengthen the learning feedback mechanism Intelligent agent: The multi-objective optimization decision-making model itself.

[0095] State space: includes certificate risk level (L1-L4), certificate type (X.509 / SM2), business type (core / non-core), cross-chain synchronization stability (0-10 points), and system resource status (0-10 points).

[0096] Action space: The selectable range of dynamic rotation thresholds (7 days - 90 days).

[0097] Reward function: R = λ × F, where λ is the reward coefficient (fixed at 1.0), and F is the result calculated by the multi-objective optimization function above. R is positive when no security event occurs after rotation, resource consumption is below the threshold, and there is no service interruption; otherwise, it is negative.

[0098] Learning process: The algorithm is trained using historical data (decision-feedback data from the past 6 months). After each round of decision-making, the data of "state-action-reward-next state" is stored in the experience pool. The Q-Learning algorithm is used to update the Q-value table and learn the threshold selection strategy that can obtain the maximum long-term cumulative reward in different states.

[0099] (5) Adjustment factor and the basis for selecting the value Historical rotation delay factor: +5 days for 1 rotation delay, +10 days for 2 or more delays. Selection criteria: Experimental verification shows that adding 5 days after 1 delay reduces the subsequent delay rate to below 0.1%, and adding 10 days after 2 or more delays stabilizes the delay rate below 0.05%, balancing security with excessive rotation costs.

[0100] Business Importance Factor: Core business certificates +10 days, non-core business certificates -5 days. Selection criteria: Core businesses have higher security requirements; rotating certificates 10 days in advance can reduce the expiration risk to near zero, and core businesses are less sensitive to rotation costs. For non-core businesses, appropriately shortening the threshold can reduce unnecessary resource consumption.

[0101] Cross-chain synchronization stability factor: -3 days for the last 3 successful synchronizations, +3 days for 1 failure, and +8 days for 2 or more failures. The selection criteria are as follows: a high synchronization success rate reduces the number of rotations and lowers costs by shortening the time between synchronization failures; adding 3 days for 1 synchronization failure allows sufficient synchronization time; and adding 8 days for 2 or more failures prevents rotation failures due to synchronization issues, ensuring security.

[0102] (6) Threshold calculation formula and constraints Dynamic rotation threshold = base threshold × risk level impact coefficient + historical factor + business factor + cross-chain factor. Constraints: 7 days ≤ dynamic threshold ≤ 90 days (if exceeded, the boundary value is taken).

[0103] (7) Risk level impact coefficient and basis ; (8) Output results The current dynamic rotation threshold (days) is synchronized to the smart contract triggering module and adaptive verification strategy.

[0104] (9) Degradation strategy When the algorithm fails to execute, it automatically uses the most recently calculated valid threshold to continue running and triggers an alarm. If it does not recover within 1 hour, it switches to the basic threshold corresponding to the certificate type to ensure that the rotation mechanism is not interrupted.

[0105] Adaptive verification strategy based on network awareness and resource scheduling: (1) Algorithm Objective The verification frequency and priority are dynamically adjusted based on risk level, network status, and system resources to optimize resource utilization and avoid network congestion or resource waste while ensuring cross-chain data consistency.

[0106] (2) Input parameters The risk level output by the dynamic risk perception model, the dynamic threshold output by the multi-objective optimization decision model, cross-chain synchronization data (main chain / sub-chain certificate status, timestamp, signature), cross-chain node health status, network performance indicators (latency, packet loss rate, bandwidth utilization), and system resource status (CPU, memory, I / O utilization).

[0107] (3) Implementation logic of network state sensor Real-time monitoring of network performance metrics for inter-chain communication: latency (milliseconds), packet loss rate (%), and bandwidth utilization (%).

[0108] Construct an inter-chain communication quality score calculation model: Communication quality score = 80 - delay / 100 × 20 - packet loss rate × 2 - bandwidth utilization rate × 0.5, with a score range of 0-10.

[0109] A communication quality score of ≥8 is excellent, 5-7 is good, and ≤4 is poor. When the communication quality score is poor, the verification frequency of non-core business certificates (L1 / L2 level) is automatically reduced (to 50% of the original frequency), and the core business certificates (L3 / L4 level) are switched to the backup synchronization channel (a preset low-latency, high-reliability network channel).

[0110] (4) Implementation logic of dynamic resource scheduler Real-time monitoring of system resource status: CPU utilization, memory utilization, and I / O utilization. When all three indicators are ≤70%, resources are considered sufficient; 70%-85% indicates resource strain; and ≥85% indicates resource scarcity.

[0111] Construct a priority queue for verification tasks: The basic priority of a task is determined by its risk level and dynamic threshold (the higher the risk level and the lower the threshold, the higher the basic priority), and the dynamic urgency is determined by its network communication quality score and system resource status (the lower the communication quality score and the more strained the resources, the higher the dynamic urgency of a high-risk task).

[0112] The final priority of a task is calculated as: base priority × 0.6 + dynamic urgency × 0.4. When system resources are sufficient, all verification tasks are executed in priority order. When resources are scarce, only the top 60% of tasks are executed, and the remaining tasks are delayed. When resources are limited, only L3 / L4 certificate verification tasks are retained to ensure that critical verifications are not interrupted.

[0113] (5) Verification frequency setting and basis ; (6) Consistency check logic The certificate status, update timestamp (error allowed ≤10 seconds), and signature information (SM2 certificate additionally verifies the public key identifier field) of the main chain and the sub-chain are compared. If all three are consistent, the test is successful; if any one is inconsistent, the test fails and the sub-chain data is resynchronized (based on the main chain data).

[0114] (7) Exception handling If three consecutive verifications fail, a cross-chain anomaly is marked, triggering an administrator alert. Simultaneously, feedback is sent to the dynamic risk perception model (increasing the risk score) and the multi-objective optimization decision model (adjusting the threshold calculation parameters).

[0115] Cross-chain retry failure handling: If the retry fails after 3 attempts, an alarm will be triggered on the visualization platform, and the certificate will be marked as "cross-chain abnormal". Related cross-chain services will be suspended until the administrator handles it manually or the system automatically resumes synchronization.

[0116] (8) Output results The verification results (pass / fail), inconsistency fields (in case of failure), cross-chain synchronization stability score (1-10 points), and communication quality score (1-10 points) are fed back to the dynamic risk perception model and the multi-objective optimization decision model.

[0117] (9) Degradation strategy When the algorithm fails, it automatically performs a verification task at the basic verification frequency (regardless of network and resource status) to ensure that cross-chain consistency verification is not interrupted, and triggers an alarm to notify the administrator.

[0118] Three major algorithm interaction logics: (1) Interaction between dynamic risk perception model and multi-objective optimization decision model Positive: The risk level output by the dynamic risk perception model determines the risk level influence coefficient of the multi-objective optimization decision model (e.g., 1.2-1.5 for L3 level), which directly affects the threshold calculation; when the score of the abnormal behavior risk item is ≥14, the multi-objective optimization decision model is triggered to immediately recalculate the threshold.

[0119] Conversely: The dynamic threshold of the multi-objective optimization decision-making model is used as an adjustment factor for the "expiration risk" of the dynamic risk perception model. If the threshold is greater than the basic threshold (early rotation), the expiration risk score is reduced by 5 points; if the threshold is less than the basic threshold, the expiration risk score is increased by 3 points.

[0120] (2) Dynamic risk perception model ↔ Adaptive verification strategy interaction Positive: The risk level output by the dynamic risk perception model determines the basic priority and basic verification frequency of the adaptive verification strategy (L4 level 5 minutes / time).

[0121] Conversely: When the cross-chain synchronization stability score output by the adaptive verification strategy is less than 6, the cross-chain inconsistency risk score of the dynamic risk perception model is increased by 5; if the verification fails 3 times in a row, the risk level of the dynamic risk perception model is upgraded by one level (e.g., L2→L3).

[0122] (3) Multi-objective optimization decision model ↔ Adaptive verification strategy interaction Positive: The dynamic threshold output by the multi-objective optimization decision model determines the adjustment of the verification frequency of the adaptive verification strategy (the frequency is halved if the threshold is ≤15 days) and the basic priority (the lower the threshold, the higher the priority).

[0123] Conversely: When the adaptive verification strategy detects inconsistency, the cross-chain stability factor of the multi-objective optimization decision model is increased by 8 points (the threshold is increased urgently), and the impact coefficient is increased by 0.1 in the next calculation (e.g., 1.2 → 1.3).

[0124] The advantages of the present invention are as follows: 1. Full-process automation + intelligent algorithm-driven, achieving dual improvement in efficiency and intelligence: From certificate generation to archiving, everything is executed automatically by smart contracts. Three upgraded smart algorithms enable intelligent linkage between risk perception, threshold optimization, and cross-chain verification. The certificate renewal cycle has been shortened from several days to hours, reducing operational costs by over 80%. (Case study: After implementation by a bank, the renewal time for a single certificate decreased from 3 business days to 2 hours, and the average annual operational cost decreased from 2 million yuan to 300,000 yuan, with manual operation steps reduced by 100%) 2. Dynamic risk perception for more precise security protection: By introducing threat intelligence and behavioral analysis mechanisms, risk weights are dynamically adjusted according to the security situation, abnormal behavior is detected in real time, and the accuracy of risk level assessment is improved to 99.5%. Risks such as expired certificate use, abuse of expired certificates, and abnormal impersonation are reduced to zero. (Case study: After application on a certain government platform, the incidence of certificate expiration events decreased from 15% to 0. No security incidents caused by certificates occurred in 2024, and three instances of abnormal certificate use were successfully intercepted.) 3. Multi-objective optimization decision-making leads to more efficient resource utilization: By balancing the three objectives of security, cost, and business continuity through reinforcement learning, we can avoid the waste of resources caused by excessive rotation, while ensuring uninterrupted business operations. The cost of core business rotation has been reduced by 30%, and the business interruption rate has approached zero.

[0125] 4. Cross-chain collaboration and compatibility, adaptable to multi-chain ecosystems: Based on the PolkadotXCM protocol and adaptive verification strategy, this system achieves multi-chain certificate data synchronization and consistency verification, compatible with 10+ mainstream blockchains including Ethereum and Polygon. It supports cross-chain interoperability between X.509 and SM2 certificates, achieving a cross-chain synchronization success rate of ≥99.5%, thus solving the "information silo" problem. (Case study: After application by a cross-border payment company, the cross-chain data inconsistency rate decreased from 1% to 0.1%, and the transaction failure rate significantly reduced, demonstrating excellent cross-chain synchronization adaptation of SM2 certificates.) 5. Strong compliance and adaptability to key sectors: It introduces RBAC access control, supports integration with regulatory databases such as CFCA, and its explicit configuration of the security factor K meets the requirements of the Financial Industry Information Security Protection Standard 2.0. Algorithm execution records and operation logs can serve as evidence for compliance audits, making it suitable for critical sectors such as government affairs, finance, and healthcare. (Case study: A bank passed two special inspections by regulatory authorities without any compliance issues; the audit time was reduced from 2 days to 2 hours.) 6. High availability and fault tolerance, ensuring system stability: The oracle cluster adopts a Byzantine fault-tolerant configuration of N=7 / f=2, and all three algorithms are equipped with degradation strategies. Cross-chain retry failures trigger alarms, the overall system availability is ≥99.99%, and it can run continuously and stably for more than 180 days without human intervention.

[0126] Taking the certificate management requirements of a commercial bank (across the Ethereum main chain and the Polygon side chain) as an example, the bank needs to manage three types of certificates: API interface authentication certificates (core business), user identity authentication certificates (core business), and internal testing certificates (non-core business). It also supports both X.509 and SM2 certificate standards. The implementation process is as follows: 1. Initial deployment phase (1-2 weeks) Contract Development and Auditing: The development team used Solidity 0.8.19 to write a smart contract integrating three core algorithm logics (certificate management main contract and permission contract). After testing with the Hardhat framework, CertiK was invited to complete three rounds of auditing, fixing two low-risk vulnerabilities (overflow risk and lax permission verification). Finally, it was deployed to the Ethereum mainnet (parachain ID2000) and the Polygon sidechain (ID2001).

[0127] Oracle integration with external data sources: A 7-node oracle cluster is deployed (following the N=7 / f=2 Byzantine fault tolerance mechanism). Three Chainlink nodes connect to threat intelligence sources such as the CFCA blacklist, standard timestamps, national vulnerability databases, and industry security bulletins. Four custom nodes connect to the bank's internal risk event database, business system logs, and certificate behavior monitoring data. Two-way authentication with the CFCA regulatory database is completed, with a security factor of K=2. Tests show a 100% success rate for API calls such as certStatusQuery, and data transmission is encrypted and compliant.

[0128] Parameter and permission configuration: Basic certificate thresholds: X.509 certificate (API certificate 30 days, identity certificate 45 days, test certificate 90 days); SM2 certificate (API certificate 20 days, identity certificate 35 days, test certificate 80 days).

[0129] Algorithm parameters: Initial weights of the dynamic risk perception model (expiration 30%, off-chain security 40%, cross-chain 20%, history 10%), SM2 certificate off-chain security weight reduced by 5%; target weights of the multi-objective optimization decision model (α=0.6, β=0.2, γ=0.2); resource thresholds of the adaptive verification strategy (CPU≤85%, memory≤85%, I / O≤85%).

[0130] RBAC permissions: Administrators (2 people, modify rules), Operators (5 people, apply for certificates), Auditors (3 people, check logs), Regulatory liaisons (2 people, assign interfaces). The permission matrix shall be filed with the Compliance Department.

[0131] The first batch of certificates generated includes 50 X.509 API certificates, 50 SM2 API certificates, 100 X.509 identity certificates, 100 SM2 identity certificates, and 50 X.509 test certificates. The initial A1 rating is L1 level. The behavior baseline of each certificate is established, and the certificates are synchronized across chains to the Polygon sidechain. The A3 verification shows a 100% consistency rate, and the certificate data is stored on the blockchain.

[0132] Degradation strategy configuration: Preset algorithm degradation scheme, such as defaulting to L2 level when the dynamic risk perception model fails, and triggering email and SMS alerts when cross-chain retry fails.

[0133] 2. Daily Operation Phase The algorithm is executed routinely: the oracle collects data every hour, the dynamic risk perception model updates the risk level every 24 hours (initially all are L1), and the weights are dynamically adjusted based on threat intelligence; the multi-objective optimization decision model calculates dynamic thresholds (X.509 API certificate 24-30 days, SM2 API certificate 16-20 days); the adaptive verification strategy verifies cross-chain data at an L1 level frequency (2 hours / time), with a 100% synchronization success rate.

[0134] Automatic Rotation Case (SM2API Certificate): When an SM2API certificate (serial number CERT20240601001-SM2) has 18 days remaining, the dynamic risk perception model detects a recent surge in off-chain revocation events through threat intelligence. This increases the weight of off-chain security risks from 35% to 45%. Simultaneously, it detects high-frequency verification initiated by the certificate from an unknown IP at 2 AM (behavior significantly deviating from the baseline, deviation degree 0.8), resulting in an abnormal behavior risk score of 16 points and a total score of 42 points, raising the risk level to L3. The multi-objective optimization decision model immediately calculates the threshold: 20 days × 1.3 (L3 coefficient) + 10 days (core business) - 3 days (cross-chain stability) = 33 days. Since the remaining 18 days ≤ 33 days, rotation is triggered. Generate a new SM2 certificate (CERT20240601001-SM2-New), mark the old certificate as invalid and put it on the blockchain. The new certificate carries the SM2 public key identifier field.

[0135] Cross-chain synchronization to the Polygon sidechain, adaptive verification strategy verifies at L3 level frequency (10 minutes / time), communication quality score of 9 (excellent), sufficient system resources, synchronization delay of 3 minutes, consistent pass.

[0136] The new certificate was automatically pushed to the trading system without interruption of business. The dynamic risk perception model re-evaluated the new certificate as Level L1 (score of 10 points), and the multi-objective optimization decision model adjusted the threshold to 20×0.9+10-3=25 days.

[0137] Compliance audit: Auditors query logs (including algorithm execution records, weight adjustment records, and behavior monitoring data) weekly through a visualization platform and export an Excel audit report; regulatory liaisons upload compliance logs monthly through the CFCA interface, with a 100% success rate for interface calls and no issues reported by regulators.

[0138] (3) Emergency Response Phase Certificate revocation case (X.509 identity certificate): A user's identity certificate (CERT20240610050-X509) was associated with an account where fraudulent transactions were detected. CFCA pushed a revocation notification through the certRevokeNotify interface. The oracle receives the notification, cross-validates it, and synchronizes it to the blockchain. The authorization contract verifies its legitimacy.

[0139] The core contract certificate has been marked as "revoked," and the dynamic risk perception model has upgraded the risk level to L4 (score 85 points).

[0140] The cross-chain module synchronizes to the Polygon sidechain within 5 minutes. The adaptive verification strategy verifies at an L4 level frequency (5 minutes / time). If all verifications pass, all business systems stop using the certificate.

[0141] The multi-objective optimization decision model adjusts the thresholds for other identity certificates: the impact coefficient is uniformly increased by 0.1 (L1: 0.9-1.1) to ensure early rotation in high-risk environments.

[0142] Cross-chain exception handling case (X.509 test certificate): An X.509 test certificate (CERT20240615020-X509) failed to synchronize with the Polygon sidechain due to a node failure, and the adaptive verification strategy detected an inconsistency. The adaptive verification strategy marks an anomaly and triggers a retry (the second synchronization is successful), while simultaneously feeding back to the dynamic risk perception model and the multi-objective optimization decision model.

[0143] The dynamic risk perception model scores +5 points for cross-chain risk, bringing the total score from 8 to 23 points, and the risk level from L1 to L2.

[0144] The multi-objective optimization decision model adjusts the threshold as follows: the basic threshold is 90 days × 1.1 (L2 coefficient) - 5 days (non-core) + 8 days (cross-chain anomalies) = 102 days, and the constraint is to take 90 days.

[0145] After the anomaly was recovered, the stability score of the adaptive verification strategy rose to 9 points, the risk level of the dynamic risk perception model recovered to L1, and the threshold of the multi-objective optimization decision model was adjusted to 90×0.9-5-3=73 days.

[0146] Algorithm downgrade case: Due to the failure of the external threat intelligence source interface, the dynamic risk perception model data collection was abnormal, and the downgrade scheme was automatically activated, using the most recent effective risk level (L1) to maintain for 1 hour. During this period, the administrator repaired the interface failure, the algorithm returned to normal, and the certificate management process was not affected.

[0147] (4) Implementation effect data (3 months later) ; The implementation results show that this invention effectively solves the problems of low efficiency, high security risks, and cross-chain incompatibility in traditional certificate management. Through algorithm upgrades and adaptation mechanisms, it meets the needs of multiple certificate standards and multiple business objectives, meets the compliance requirements of the financial industry, and has the value for large-scale promotion.

Claims

1. A method for automated certificate lifecycle rotation based on blockchain nodes, characterized in that, include: (1) Initialization and deployment: Deploy smart contracts that integrate core algorithm logic, configure a trusted oracle cluster consisting of Chainlink nodes and custom verification nodes and set up a Byzantine fault tolerance mechanism, allocate management permissions based on the RBAC permission mechanism, connect to regulatory databases and external threat intelligence sources, and initialize the parameters of dynamic risk perception model, multi-objective optimization decision model and adaptive verification strategy. (2) Certificate generation: After receiving the certificate application submitted by the user and verifying the legality of the applicant through the permission contract, the smart contract automatically generates a digital certificate that conforms to the standard and stores it on the chain, triggering the dynamic risk perception model to conduct a preliminary risk level assessment of the certificate, and at the same time establishing the behavioral baseline of the certificate. (3) Status monitoring: Real-time collection of off-chain certificate status data, external threat intelligence data and certificate behavior data through trusted oracle clusters, synchronized to smart contracts, and the certificate risk level is updated by dynamic risk perception model, and the dynamic rotation threshold is calculated by multi-objective optimization decision model. (4) Triggering condition judgment: Combine the updated certificate risk level with the dynamic rotation threshold to determine whether the rotation or revocation conditions are met; (5) Perform the corresponding operation: If the rotation conditions are met, the smart contract automatically generates a new certificate and marks the old certificate as invalid. It is synchronized to the associated blockchain through the cross-chain collaboration module, and the cross-chain data consistency verification is completed by the adaptive verification strategy. If the revocation conditions are met, the smart contract marks the certificate as revoked and puts it on the chain. The dynamic risk perception model is triggered to upgrade the risk level of the certificate to the extremely high risk level.

2. The method for automated certificate lifecycle rotation based on blockchain nodes according to claim 1, characterized in that, It also includes (6) Log archiving and auditing: generating a unique operation hash from the operation records, algorithm execution results and exception handling information throughout the certificate's lifecycle, permanently storing it on the blockchain, and supporting filtering and exporting compliance audit reports by conditions.

3. The method for automated certificate lifecycle rotation based on blockchain nodes according to claim 1, characterized in that, The steps of updating the certificate risk level in the dynamic risk perception model include: dynamically adjusting the threat sensitivity coefficient of each risk dimension based on external threat intelligence, calculating the corresponding dynamic weight, combining the remaining validity period of the certificate, off-chain security risks, cross-chain inconsistencies, historical operation records and behavioral deviation to perform risk quantification scoring, and outputting risk levels from L1 to L4. The behavioral deviation is calculated by comparing the real-time use behavior of the certificate with the preset behavioral baseline.

4. The method for automated certificate lifecycle rotation based on blockchain nodes according to claim 1, characterized in that, The steps of the multi-objective optimization decision model to calculate the dynamic rotation threshold include: taking security, cost, and business continuity as optimization objectives, introducing historical rotation delay factors, business importance factors, and cross-chain synchronization stability factors, learning the optimal decision strategy through a reinforcement learning feedback mechanism, and calculating the dynamic rotation threshold in combination with the certificate risk level influence coefficient to ensure that the threshold is within a reasonable range of 7 to 90 days.

5. The method for automated certificate lifecycle rotation based on blockchain nodes according to claim 1, characterized in that, The adaptive verification strategy completes the cross-chain data consistency verification steps as follows: determine the basic verification frequency based on the certificate risk level, dynamically adjust the verification priority in combination with the inter-chain communication quality score and system resource status, execute all verification tasks according to priority when resources are sufficient, and prioritize the verification of high-risk certificates when resources are scarce. During verification, compare the certificate status, update timestamp and signature information of the main chain and sub-chain to ensure data consistency.

6. The method for automated certificate lifecycle rotation based on blockchain nodes according to claim 1, characterized in that, The compatibility standards include X.509 and the national cryptographic standard SM2. When generating a national cryptographic standard SM2 certificate, a new SM2_PUB_KEY public key identifier field is added. The signature result is converted and transmitted using an SM2 signature and XCM protocol compatible scheme. In the dynamic risk perception model, the initial weight of the security vulnerability dimension under the SM2 certificate chain is reduced, and its basic rotation threshold is shortened by the preset number of days compared with the same type of X.509 certificate.

7. The method for automated certificate lifecycle rotation based on blockchain nodes according to claim 1, characterized in that, The data collection and verification steps of the trusted oracle cluster include: Chainlink nodes are responsible for collecting public off-chain data and external threat intelligence, while custom nodes are responsible for collecting institutional private data and certificate behavior data. Both types of nodes complete data self-checks through built-in signature verification or national cryptographic SM3 hash verification, and then cross-verify by the cluster aggregation node. After the consistency rate reaches the standard, double-signature data is generated and synchronized to the chain. When a single node fails, the collection subject is automatically switched to ensure data continuity.

8. The method for automated certificate lifecycle rotation based on blockchain nodes according to claim 1, characterized in that, The steps for connecting to the regulatory database include: establishing two-way authentication with the CFCA regulatory database using the HTTPS+JSON-RPC2.0 protocol, carrying an HMAC-SHA256 signature with a security factor K when making requests, supporting real-time query of certificate compliance status, receiving revocation notices, and uploading operation logs. The security factor K is configured according to the business scenario to meet the corresponding compliance level requirements.

9. The method for automated certificate lifecycle rotation based on blockchain nodes according to claim 1, characterized in that, It also includes an algorithm degradation step: when the dynamic risk perception model, multi-objective optimization decision model or adaptive verification strategy fails, it automatically uses the most recent valid result to maintain operation. If it fails to recover within a preset time, it switches to the corresponding default value and triggers an alarm on the visualization platform to notify the administrator to investigate the problem.

10. The method for automated certificate lifecycle rotation based on blockchain nodes according to claim 6, characterized in that, The cross-chain collaboration module constructs cross-chain messages based on the PolkadotXCMv3 protocol. The messages include certificate serial number, status type, risk level, dynamic threshold, timestamp, and main chain contract signature. SM2 certificate messages additionally carry a public key identifier field, configure parachain ID, execution weight, and gas limit, and retry according to preset number of times and intervals when message sending fails.

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