Intelligent decision optimization model design based on distributed blockchain and federated reinforcement learning
The proposed intelligent decision optimization model employs a multi-layered architecture that seamlessly integrates distributed blockchain technology with federated reinforcement learning to address the complex requirements of ship traffic collaborative supervision. The system architecture consists of four distinct but interconnected layers: the data layer, blockchain layer, federated learning layer, and decision layer, each designed to fulfill specific functional requirements while maintaining overall system coherence and operational efficiency.
As depicted in Fig. 2, the system overall architecture demonstrates the hierarchical organization of functional modules and their interdependencies within the collaborative supervision framework. The architecture ensures scalable deployment across multiple maritime authorities while maintaining data sovereignty and enabling intelligent collaborative decision-making through advanced machine learning techniques.
The data layer forms the foundation of the system architecture, responsible for collecting, preprocessing, and managing heterogeneous maritime data from diverse sources including vessel Automatic Identification Systems (AIS), radar networks, satellite surveillance, and port management systems. This layer implements standardized data formats and communication protocols to ensure interoperability between different data sources and maritime authorities. The data processing efficiency can be mathematically expressed as:
where represents the data volume from source , denotes the data quality index, is the processing time, and indicates the computational cost.
The blockchain layer provides the secure, decentralized infrastructure for data sharing and transaction recording among participating maritime authorities. This layer implements a customized Practical Byzantine Fault Tolerance (PBFT) variant specifically optimized for maritime applications, with a validator set size of 7-21 nodes to ensure fault tolerance where f represents the maximum number of Byzantine nodes. The consensus mechanism operates with configurable parameters: block size of 2 MB, block generation interval of 3 s, endorsement timeout of 5 s, and ordering service batch timeout of 2 s. This layer implements a customized Practical Byzantine Fault Tolerance (PBFT) variant specifically optimized for maritime applications, with detailed specifications outlined in Table 2.
where represents the probability of successful consensus from validator , and is the total number of validators in the network.
Figure 3 illustrates the detailed interaction flows between different system modules, showcasing how data flows from collection through processing, blockchain verification, federated learning, and ultimately to decision execution. The diagram emphasizes the bidirectional communication patterns and feedback loops that enable continuous system improvement and adaptation.
The federated learning layer orchestrates collaborative model training across distributed maritime authorities while preserving data privacy and local autonomy. This layer implements advanced aggregation algorithms that combine local model updates from participating nodes to create globally optimized decision models. The federated learning framework employs differential privacy mechanisms and secure aggregation protocols to ensure that sensitive operational data remains protected throughout the collaborative training process. The global model convergence rate can be expressed as:
where represents the global model parameters, denotes local parameters from participant , is the local dataset size, is the total dataset size, and introduces differential privacy noise.
The decision layer synthesizes information from lower layers to generate intelligent recommendations and automated responses for ship traffic management scenarios. This layer incorporates sophisticated reinforcement learning algorithms that continuously adapt to changing maritime conditions and learn from historical decision outcomes. The decision layer interfaces with existing maritime management systems to provide seamless integration with current operational workflows while enhancing decision-making capabilities through artificial intelligence.
The architectural design ensures system security through multiple complementary mechanisms including blockchain-based access control, cryptographic data protection, and distributed consensus validation. The system addresses specific security threats including: (1) Sybil attacks where malicious nodes create multiple false identities, mitigated through verified credential requirements; (2) 51% attacks attempting to control consensus mechanisms, prevented by diversified validator networks; (3) Data poisoning attacks targeting federated learning models, countered through robust aggregation algorithms and outlier detection; (4) Privacy inference attacks attempting to extract sensitive information, addressed via differential privacy mechanisms.
Security is further enhanced through the federated learning approach that minimizes data exposure risks by keeping sensitive information locally stored while enabling collaborative intelligence development. The multi-layered security model provides defense in depth against various cyber threats and ensures system resilience against node failures or malicious attacks.
System efficiency is optimized through intelligent load balancing, adaptive resource allocation, and dynamic consensus mechanisms that adjust to varying network conditions and traffic demands. The architecture supports horizontal scaling to accommodate growing numbers of participating authorities and increasing data volumes without compromising performance. Real-time processing capabilities ensure that time-critical decisions can be made within acceptable latency constraints, while batch processing modes handle non-urgent analytical tasks efficiently.
The modular design philosophy enables flexible deployment configurations that can be customized to meet specific regional requirements and regulatory frameworks while maintaining interoperability with the global supervision network. This approach facilitates gradual system adoption and allows maritime authorities to integrate new capabilities incrementally without disrupting existing operations.
The distributed blockchain data management mechanism forms the core infrastructure for secure and trustworthy data exchange among multiple maritime authorities, implementing sophisticated data governance protocols that ensure data integrity, confidentiality, and availability across the collaborative supervision network. The mechanism employs a multi-tiered data storage strategy that categorizes maritime information based on sensitivity levels, access requirements, and operational criticality to optimize both security and performance characteristics of the blockchain-based data management system.
The data on-chain strategy implements a hybrid approach that distinguishes between on-chain and off-chain storage to balance security requirements with storage efficiency and transaction costs. Critical metadata, transaction records, and verification hashes are stored directly on the blockchain to ensure immutability and auditability, while large-volume operational data such as radar images and detailed vessel tracking information are stored in distributed off-chain storage systems with cryptographic references maintained on the blockchain. The data integrity verification process can be mathematically expressed as:
where represents the verification hash, denotes the data segment , is the corresponding encryption key, and is a large prime number used for modular arithmetic.
As shown in Table 3, the blockchain data structure specification defines the storage strategy, access control mechanism and verification protocol for different types of ship traffic data, ensuring that all types of data can be efficiently shared and collaboratively processed across departments while meeting security requirements.
The smart contract design adopts a modular architecture, including core components such as data access control contract, verification logic contract, permission management contract and audit trail contract. The data access control contract implements role-based fine-grained permission management to ensure that only authorized maritime authorities can access specific types of sensitive information. The verification logic contract is responsible for performing complex data integrity checks and business rule validation, automatically handling data quality assessment and anomaly detection tasks. The permission management contract provides dynamic permission allocation and revocation functions, supporting temporary permission escalation and multi-party authorization mechanisms in emergency situations.
The data verification protocol implements a multi-level security mechanism, combining cryptography technology and distributed consensus algorithms to ensure the authenticity and integrity of data. The verification process uses zero-knowledge proof technology, allowing data providers to prove the validity of data without disclosing specific data content, effectively protecting commercial sensitive information. The probability of data verification can be expressed as:
Where represents the reliability probability of verification node j, represents the success probability of attack type , and are the number of verification nodes and the number of potential attack types, respectively.
The data sharing protocol establishes a standardized interface and communication mechanism to support seamless data exchange and collaborative operations between heterogeneous systems. The protocol defines data format standards, transmission encryption specifications, identity authentication processes, and access log requirements to ensure the security and traceability of cross-organizational data sharing. The sharing protocol supports both real-time data streaming and batch data transmission modes, and automatically selects the optimal transmission strategy based on business needs and network conditions.
The decentralized data governance system achieves multi-party data management decision-making through a distributed governance mechanism, avoiding the risk of a single authority monopolizing data control. The governance system establishes a data policy formulation process based on a voting mechanism, and important data management decisions require the consensus of the majority of participants before they can be implemented. The governance framework also includes functional modules such as dispute resolution mechanisms, data quality standard formulation, and privacy protection policy updates to ensure that the data management system can adapt to changing regulatory requirements and technological development trends.
This mechanism establishes a highly trusted data management environment through the tamper-proof characteristics of blockchain and the automatic execution capabilities of smart contracts, providing a solid technical foundation for collaborative supervision among maritime authorities. The distributed architecture eliminates the single point failure risk of traditional centralized systems, improves the reliability and anti-attack capabilities of the overall system, and ensures the equal status and decision-making weight of all participants in the data governance process.
The federated reinforcement learning collaborative decision algorithm establishes a sophisticated multi-agent framework that enables distributed maritime authorities to jointly optimize ship traffic management decisions while preserving data privacy and operational autonomy. The algorithm architecture employs a hierarchical learning structure where local agents at each maritime authority independently interact with their respective environments while contributing to a global knowledge base through privacy-preserving parameter sharing mechanisms.
The state space design encompasses comprehensive maritime situational awareness information that captures the dynamic characteristics of ship traffic environments across multiple jurisdictions. The state vector is formally defined as:
where represents vessel positions and trajectories, denotes weather and environmental conditions, captures traffic density and flow patterns, includes regulatory compliance status, and encompasses communication and coordination states between participating authorities. Each state component is normalized and encoded to ensure compatibility across different regional systems and data formats.
The action space framework defines the comprehensive set of supervisory interventions available to maritime authorities for ship traffic management and risk mitigation. The action space incorporates both direct control actions and collaborative coordination mechanisms:
where includes traffic routing recommendations, speed advisories, and port allocation decisions, encompasses multi-authority collaborative actions such as joint search and rescue operations, and represents information sharing and communication protocols between participating agencies.
As shown in Table 4, the federated reinforcement learning parameter configuration specification defines the value range and functional characteristics of the key parameters of the algorithm. The reasonable configuration of these parameters is of great significance to ensuring the convergence of the algorithm, the effect of privacy protection and the quality of collaborative decision-making.
The reward function is designed using a multi-objective optimization strategy, taking into account key performance indicators such as safety, efficiency, and coordination. The mathematical expression of the composite reward function is:
Among them, measures the degree of reduction of ship safety risks, evaluates the traffic flow optimization effect, quantifies the cross-departmental synergy benefits, represents the penalty item for violations, and the weight parameter w_i is dynamically adjusted according to the specific application scenario.
The distributed training strategy implements a collaborative learning mechanism under privacy protection conditions. Each participant independently optimizes the decision strategy in the local training environment, and then shares the model parameter update information through a secure aggregation protocol. The local model update process follows the standard Q-learning algorithm:
The subscript represents the participant ID, and is the local learning rate, which ensures that each participant can adaptively adjust the learning strategy according to the local environment characteristics.
The model aggregation method uses secure multi-party computing technology to achieve global model parameter fusion under differential privacy protection. The aggregation process introduces a noise perturbation mechanism to protect the privacy of the participants:
Where represents the global model parameters, is the local model parameter of participant , is the Gaussian noise that meets the requirements of differential privacy, and the noise variance is dynamically adjusted according to the privacy budget and data sensitivity.
The algorithm implements an adaptive convergence detection mechanism, which determines the training convergence status by monitoring the changes in the global loss function and model performance indicators. The convergence condition is defined as:
Where and are the convergence thresholds of parameter changes and loss function changes, respectively. This mechanism ensures that the algorithm stops training in time when it reaches a stable state, avoiding overfitting problems and improving computational efficiency.
The collaborative decision-making mechanism achieves a balance between knowledge sharing and privacy protection through a federated learning framework, enabling maritime authorities to jointly improve the intelligence level and collaborative effect of ship traffic management without leaking sensitive data. To address adversarial conditions, the system implements robust aggregation algorithms including trimmed mean and median-based approaches that automatically detect and filter outlier model updates that deviate beyond 2.5 standard deviations from the ensemble mean. For noisy data scenarios, the framework employs adaptive noise injection with variance scheduling where T represents the total training rounds, ensuring model convergence while maintaining differential privacy guarantees. The distributed nature of the algorithm ensures the scalability and fault tolerance of the system, and can adapt to the deployment requirements of maritime supervision networks of different scales and complexities.
