Sea transportation data periodic acquisition and scheduling system based on block chain

Abstract

The invention, which relates to the technical field of marine transportation data analysis, discloses a block chain-based marine transportation data periodic acquisition and scheduling system comprising a data scheduling management center, the data scheduling management center being in communication connection with a data acquisition module, a request management module, a scheduling optimization module and a cross-chain interaction module, the modules are in electric signal connection; the data acquisition module is used for collecting and preprocessing sea transportation data from a plurality of data sources, and performing distributed storage of the sea transportation data in combination with a block chain technology. The block chain side chain and fragment technology is adopted, sea transportation data processing is dispersed to different side chains and fragments, the load of a main chain is effectively reduced, the throughput of the system is improved, meanwhile, the application of a hierarchical consensus mechanism carries out hierarchical processing on the consensus process, consensus delay is reduced, and the system performance is improved. The hierarchical processing mode not only balances the security and the efficiency, but also significantly reduces the system bottleneck and transaction confirmation delay during multi-user concurrent requests.

Description

Technical Field

[0001] The present invention relates to the technical field of maritime data analysis, and particularly relates to a periodic acquisition and scheduling system for maritime data based on blockchain. Background Art

[0002] With the development of globalization, the maritime industry plays a crucial role in international trade. As one of the main modes of transportation in international trade, maritime transportation undertakes more than 80% of the cargo transportation volume. The maritime industry involves a large number of participants, including shipping companies, freight forwarders, ports, customs, shippers and other stakeholders. Maritime transportation is a business highly dependent on time and space. Periodically collecting and scheduling maritime data can ensure real-time mastery of information such as ships, goods, ports, and transportation routes, so as to make decisions and adjustments in a timely manner.

[0003] For example, a periodic acquisition and scheduling system for maritime data applied to international logistics disclosed in Chinese Patent Publication No.: CN119579040A. The operation end can set corresponding scheduling rules for each tenant according to the data acquisition requirements of different tenants, and save the set scheduling rules to the scheduling rule table.

[0004] In the prior art, the next scheduling time stored is updated according to the scheduling rules and the current acquisition time, reducing the server resource cost and system pressure. However, due to the large amount of maritime data, when a large amount of data is collected and processed, data delay and insufficient throughput will occur. Moreover, when multiple users participate in requests, it will further cause system bottlenecks and transaction confirmation delays. Therefore, how to combine the blockchain side chain and sharding technology, and adopt a hierarchical consensus mechanism to reduce the consensus delay of periodic acquisition and scheduling of maritime data and avoid network bottlenecks is the problem to be solved by the present invention. For this reason, a periodic acquisition and scheduling system for maritime data based on blockchain is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a periodic acquisition and scheduling system for maritime data based on blockchain to solve the problems raised in the above background art.

[0006] To solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A periodic acquisition and scheduling system for maritime data based on blockchain, including a data scheduling management center, the data scheduling management center is communicatively connected with a data acquisition module, a request management module, a scheduling optimization module, and a cross-chain interaction module, wherein, the modules are electrically connected to each other;

[0008] The data acquisition module is used to collect and preprocess maritime data from multiple data sources, and perform distributed storage of maritime data in combination with blockchain technology;

[0009] The request management module is used to perform sharding and splitting processing on the preprocessed maritime data through the blockchain side chain and sharding technology, and introduce a hierarchical consensus structure of the main chain and side chain in combination with a hierarchical consensus mechanism to adapt to multi-user concurrent requests;

[0010] The scheduling optimization module is used to write and deploy smart contracts, dynamically adjust the maritime scheduling strategy based on historical maritime data and real-time status using a reinforcement learning algorithm, and automatically execute scheduling instructions in combination with blockchain smart contracts to reduce manual intervention and improve the accuracy and reliability of system operation;

[0011] The cross-chain interaction module is used to perform data synchronization and interaction between the main chain, side chains, and shards using hash locking and atomic swap technologies to ensure the atomicity of cross-chain transactions and break data islands.

[0012] A further improvement of the technical solution of the present invention lies in that: the data acquisition module includes a data collection and processing unit and a distributed storage unit;

[0013] Among them, the data collection and processing unit is used to periodically collect maritime data from each node in the maritime field using automated tools, and perform preprocessing operations of cleaning and filtering on the collected maritime data to provide an accurate maritime data source and ensure the timeliness and integrity of the data;

[0014] The distributed storage unit is used to use the distributed storage technology of the blockchain to disperse the preprocessed maritime data and store it on multiple nodes, and add timestamps and authentication information to each data block to improve data traceability and ensure the security and reliability of the data.

[0015] A further improvement of the technical solution of the present invention lies in that: the data collection and processing unit specifically includes:

[0016] Deploy automated tools at each node in the maritime field including ships, ports, and freight forwarders to form a data collection network covering all nodes in the maritime field, and integrate the ship AIS system, port management system (TOS), and logistics ERP system, preset the collection frequency and trigger conditions, obtain raw maritime data from multiple data sources, and then store the collected raw maritime data in a temporary buffer. Among them, the raw maritime data includes ship position, cargo status, and port operations. The automated tools include Internet of Things sensors, RFID devices, and GPS trackers, etc., covering ship position, cargo status (temperature and humidity, weight, packaging integrity), and port operations (loading and unloading efficiency, equipment status);

[0017] Perform preprocessing on the collected raw maritime data, including the operation processes of cleaning and filtering;

[0018] Verify and integrate the cleaned and filtered maritime data to ensure its accuracy and integrity, and then uniformly process the maritime data from different data sources, eliminate data islands, form a standardized data format, record the conversion log, and establish a version traceability mechanism.

[0019] A further improvement of the technical solution of the present invention lies in that: the distributed storage unit specifically includes:

[0020] Perform sharding processing on the preprocessed maritime data, split it into multiple data blocks according to the data type, attach timestamp information to each data block, record the time node when the data is generated or stored, embed authentication information, covering the data source node identifier (node ID) and the identity of the operator, and then bind the timestamp and identity information to the data block to build a complete identity profile for each data block, so that the data has a clear time context and a clear source attribution;

[0021] After the data block carries the timestamp and identity information, relying on the distributed characteristics of the blockchain, distribute the sharded data blocks to multiple storage nodes in the network, and store 3 copies of each data block on multiple nodes;

[0022] Verify the stored maritime data, combine the distributed ledger technology of the blockchain, cross-verify the records of multiple nodes to ensure that the data has not been tampered with, and reassemble the verified data blocks according to the timestamp to form a complete data set for subsequent query and use.

[0023] A further improvement of the technical solution of the present invention lies in that: the request management module includes a side chain sharding unit and a hierarchical consensus unit;

[0024] Among them, the side chain sharding unit is used to disperse the processing of maritime data to different side chains and shards through the blockchain side chain and sharding technology, reduce the load on the main chain, improve the throughput of the system, relieve data latency and network bottleneck problems, and enhance the overall performance of the system;

[0025] The hierarchical consensus unit is used to divide into a global layer, a regional layer and an edge layer, and process the consensus process in layers to reduce the consensus latency. Among them, the main chain of the global layer adopts PBFT consensus to ensure global consistency, the PoS consensus is adopted within the shards of the regional layer to quickly verify the transactions within the shards, and the PoA is adopted for the side chain of the edge layer to achieve millisecond-level confirmation, balance security and efficiency, reduce the consensus latency, and adapt to multi-user concurrent requests.

[0026] A further improvement of the technical solution of the present invention lies in that: the side chain sharding unit specifically includes:

[0027] Construct a side-chain network architecture that interacts securely with the main chain. Divide the side chains according to the types of maritime data and business scenarios, and based on the data volume dimension, conduct sharding planning within each side chain. Each side chain is divided into multiple logical shards, and the data scope and processing logic responsible for each shard are clarified to form a hierarchical data processing system. Among them, side-chain nodes achieve lightweight verification through the consortium chain consensus mechanism (PoA), reducing the burden on the main chain;

[0028] Establish a full-performance monitoring system to monitor the performance indicators of the load conditions, processing efficiency, and network latency of each side chain and shard in real time. When maritime data enters the system, obtain the side-chain allocation score according to the performance indicator information, and allocate the maritime data to the corresponding side chain;

[0029] After receiving the data, each shard uses its own computing resources to process the allocated maritime data in parallel, performing operations including data verification and computational analysis.

[0030] A further improvement of the technical solution of the present invention lies in that: the calculation process of the side-chain allocation score is as follows:

[0031] For each side chain, obtain the current load of the side chain, compare the current load with the maximum value of the load, and take the smaller value to avoid excessive influence on the score when the load exceeds the maximum value. Then divide the compared load value by the maximum value of the load to obtain the relative load value, which represents the proportion of the load in the maximum value. Furthermore, multiply the relative load value by the weight to obtain the weighted value of the load part;

[0032] For each side chain, obtain the network latency of the side chain, perform attenuation processing on the network latency using an exponential function combined with the attenuation coefficient of the latency to obtain the exponential attenuation value of the latency. Then multiply the weighted value of the load part by the exponential attenuation value of the latency to obtain the comprehensive weighted value of the load and latency;

[0033] For each side chain, obtain the processing efficiency of the side chain, compare the processing efficiency with the maximum value of the processing efficiency, and take the larger value to avoid excessive influence on the score when the efficiency exceeds the maximum value. Then divide the compared efficiency value by the maximum value of the processing efficiency to obtain the relative value of the processing efficiency, which represents the proportion of the efficiency in the maximum value. Furthermore, subtract the relative value of the processing efficiency from 1 to normalize the processing efficiency to the range of 0 to 1, and obtain the contribution value of the processing efficiency part to calculate the contribution of the processing efficiency to the score;

[0034] Sum up the comprehensive weighted values of the load and latency of all side chains to obtain the total score of the load and latency. Then multiply the total score of the load and latency by the contribution value of the processing efficiency part to obtain the final side-chain allocation score. According to the calculated side-chain allocation score, allocate the maritime data to the side chain with the highest score. The higher the score, the better the performance of the side chain and the more suitable it is for data allocation.

[0035] A further improvement of the technical solution of the present invention lies in that: the hierarchical consensus unit specifically includes:

[0036] Construct a three-layer architecture system of the global layer, regional layer, and edge layer, clarify the functional positioning and role division of each layer. Among them, the global layer serves as the core layer, responsible for maintaining the consistency of cross-regional data. The regional layer is divided into multiple shards, undertaking local data verification and processing. The edge layer deploys side chains to achieve fast response close to the data source. Each layer conducts data interaction and collaboration through interface protocols to form a hierarchical consensus network;

[0037] Adopt the PBFT (Practical Byzantine Fault Tolerance) consensus algorithm on the main chain of the global layer. Through multiple rounds of message interaction among nodes, ensure the consistency of data within the global scope. Among them, the main node initiates a consensus proposal, and each node verifies and votes on the proposal. When more than 2 / 3 of the nodes reach an agreement, the consensus is completed, and the result is written into the global ledger;

[0038] Adopt the PoS (Proof of Stake) consensus mechanism within the shards of the regional layer. Nodes participate in transaction verification according to the held rights (storage resources). Nodes within the shard quickly verify the transactions and package them into blocks, and determine the validity of the blocks through weighted voting of rights, ensuring the consistency and fast processing of data within the shard;

[0039] Adopt the PoA (Proof of Authority) consensus on the side chain of the edge layer. The validity of transactions is directly confirmed by trusted nodes, achieving millisecond-level confirmation. After the side chain nodes receive the maritime data, they immediately conduct local verification and broadcast the confirmation result without requiring network-wide consensus, reducing latency. Among them, the trusted nodes are port management agencies;

[0040] Through the hierarchical consensus coordination mechanism, the consensus results between the global layer, regional layer, and edge layer are mutually verified and synchronized, and the consensus results are transmitted between different layers to ensure the consistency and integrity of data. Among them, the transmission of consensus results is divided into from the edge layer to the regional layer, from the regional layer to the global layer, and from the global layer to the regional layer / edge layer.

[0041] A further improvement of the technical solution of the present invention lies in that: the scheduling optimization module specifically includes:

[0042] Write smart contracts to define the rules and logic of maritime scheduling. Among them, the smart contracts include the automated execution rules for ship scheduling, cargo loading and unloading, and port operation business processes. After writing, compile the smart contracts into bytecodes, deploy them to the blockchain network, and configure the contract permissions so that only authorized nodes can call the scheduling function to ensure system security and reduce manual intervention;

[0043] Integrate historical maritime transport data. Using the Deep Q-Network (DQN) algorithm based on Deep Reinforcement Learning (DRL) as a framework, train a scheduling policy model with the historical maritime transport data as the training set. Initialize the model parameters of the DQN algorithm, including the state space, action space, and reward function. The state space defines the current states of ships and ports, the action space defines the existing scheduling decisions, and the reward function gives a reward value based on the scheduling effect. Furthermore, define the loss function of the DQN algorithm to measure the difference between the predicted Q value and the target Q value. Combine the input of real-time maritime transport data, dynamically generate a scheduling policy, simulate the impact of different scheduling decisions on the global efficiency, and iteratively optimize the policy parameters. Among them, the historical maritime transport data includes route efficiency (voyage time, fuel consumption), port throughput (cargo handling volume per hour), and ship status (failure rate, load capacity);

[0044] Obtain real-time maritime transport data from the maritime transport data source and record the timestamp. Compare the real-time maritime transport data with the rule benchmark values defined in the smart contract, analyze the deviation of each monitoring index, and then calculate the anomaly monitoring score in combination with the set time decay coefficient. When the anomaly monitoring score exceeds the preset anomaly threshold, it is judged as an abnormal situation, and the feedback mechanism is automatically triggered. Input the real-time maritime transport data into the reinforcement learning model, dynamically adjust the scheduling policy, respond to environmental changes in a timely manner, and improve the adaptability and accuracy of the system;

[0045] According to the optimized scheduling policy, the smart contract automatically executes the scheduling instructions, including ship route adjustment, optimization of the cargo loading and unloading sequence, and port resource allocation. The execution process of the instructions is recorded by the blockchain to ensure that every operation is traceable and tamper-proof, and the execution result is fed back into the system to further optimize the scheduling policy model and form a closed-loop optimization.

[0046] A further improvement of the technical solution of the present invention lies in that: the cross-chain interaction module specifically includes:

[0047] When cross-chain interaction is required, the cross-chain interaction is triggered by the user or application. The initiator creates a transaction request on the main chain or side chain and generates a unique hash value, which serves as the core voucher for transaction locking. At the same time, set the locking conditions (time window, unlocking key). The initiator broadcasts the hash value and related transaction parameters to the target chain (side chain or shard). After receiving it, the target chain verifies the legitimacy of the transaction and locks the equivalent assets based on the hash value to ensure that neither party can unilaterally transfer the assets before the condition verification is completed;

[0048] After hash locking, data synchronization between the main chain and the side chain, shards is carried out through the relay node. The relay node monitors the state changes of the two chains in real time, converts the transaction events into a standardized format and broadcasts them to the target chain. The target chain verifies the data consistency (hash value matching, time window valid) to ensure that the states of the two chains are synchronized without deviation;

[0049] After the two-chain status verification passes, the cross-chain interaction module starts the atomic swap protocol. The recipient submits a key (private key signature) that matches the initiator's hash value on the target chain, triggering the execution condition unlocking of the smart contract. The contract automatically verifies the validity of the key and completes the two-way transfer of assets between the two chains. If any condition is not met, that is, the time-out or the key is incorrect, the assets will be automatically returned to the original chain to ensure the atomicity of the transaction;

[0050] After the atomic swap is completed, the cross-chain interaction module records the transaction result in the distributed ledgers of the two chains and triggers a status update. The relay node aggregates the cross-chain transaction data and generates a global consistency proof (Merkle tree root hash) for other nodes to verify.

[0051] Due to the adoption of the above technical solutions, the technical progress achieved by the present invention compared with the prior art is as follows:

[0052] 1. The present invention provides a blockchain-based periodic collection and scheduling system for maritime data. By using an automated tool to collect maritime data from multiple data sources and leveraging the distributed storage technology of the blockchain, the real-time nature and integrity of the data are ensured. The application of distributed storage and consensus mechanisms further enhances the reliability and security of the data, making the data collection and processing process more efficient and accurate.

[0053] 2. The present invention provides a blockchain-based periodic collection and scheduling system for maritime data. By adopting blockchain side-chain and sharding technologies, the processing of maritime data is decentralized to different side-chains and shards, effectively reducing the load on the main chain and improving the throughput of the system. At the same time, the application of a hierarchical consensus mechanism processes the consensus process in layers, reducing the consensus delay. This hierarchical processing method not only balances security and efficiency but also significantly reduces the system bottleneck and transaction confirmation delay during multi-user concurrent requests.

[0054] 3. The present invention provides a blockchain-based periodic collection and scheduling system for maritime data. By writing and deploying smart contracts, based on historical maritime data and real-time status, and using a reinforcement learning algorithm to dynamically adjust the maritime scheduling strategy, it can automatically execute scheduling instructions, reduce manual intervention, improve the accuracy and reliability of system operation, and thus better adapt to the dynamic changes in the maritime market, ensuring the accuracy and traceability of data collection and scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0056] Figure 1 Schematic diagram of the system function modules of the present invention;

[0057] Figure 2 Schematic diagram of the working process of the scheduling optimization module of the present invention. Specific implementation manners

[0058] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0059] Embodiment 1, as Figure 1 shown, the present invention provides a blockchain-based periodic collection scheduling system for maritime data, including a data scheduling management center, which is communicatively connected to a data acquisition module, a request management module, a scheduling optimization module, and a cross-chain interaction module. Among them, the modules are electrically connected to each other;

[0060] The data acquisition module is used to collect and preprocess maritime data from multiple data sources, and perform distributed storage of maritime data in combination with blockchain technology. The data acquisition module includes a data acquisition and processing unit and a distributed storage unit;

[0061] Among them, the data acquisition and processing unit is used to periodically collect maritime data from various nodes in the maritime transportation field using automated tools, and perform preprocessing operations such as cleaning and filtering on the collected maritime data to provide accurate maritime data sources, ensuring the timeliness and integrity of the data. Automated tools are deployed at various nodes including ships, ports, and freight forwarders in the maritime transportation field to form a data acquisition network covering all nodes in the maritime transportation field, and integrate the ship AIS system, port management system (TOS), and logistics ERP system. The acquisition frequency and trigger conditions are preset to obtain raw maritime data from multiple data sources, and then the collected raw maritime data is stored in a temporary buffer. Among them, the raw maritime data includes ship position, cargo status, and port operations. The automated tools include Internet of Things sensors, RFID devices, and GPS trackers, etc., covering ship position, cargo status (temperature and humidity, weight, packaging integrity), port operations (loading and unloading efficiency, equipment status). Ship position data is collected in real time (every minute), cargo status data is dynamically adjusted according to cargo types (hazardous goods every 5 minutes, general cargo every hour), and the trigger conditions are immediate collection triggered by abnormal cargo status, ship deviation from the route, and port equipment failure events. Preprocessing is performed on the collected raw maritime data, including the operation processes of cleaning and filtering. Among them, invalid or duplicate data records are detected and removed through business rules (ship position must be within a longitude and latitude range, cargo weight cannot be negative), missing values are filled or marked, and outliers are detected and corrected to ensure the integrity and consistency of the data. Based on business requirements and data quality dimensions, multi-dimensional filtering is performed on the cleaned maritime data. By constructing a dynamic rule engine and configuring screening conditions, including parameters such as time window, geographical scope, and data type, the cleaned and filtered maritime data is verified and integrated to ensure its accuracy and integrity. Furthermore, the maritime data from different data sources is uniformly processed to eliminate data islands, form a standardized data format, and record the conversion log to establish a version traceability mechanism;

[0062] Distributed storage unit, which uses the distributed storage technology of blockchain to disperse and store the preprocessed maritime data on multiple nodes, adds timestamps and authentication information to each data block to improve data traceability and ensure data security and reliability. It performs sharding processing on the preprocessed maritime data, splits it into multiple data blocks according to data types, attaches timestamp information to each data block to record the time node when the data is generated or stored, embeds authentication information covering the data source node identifier (node ID) and the operator's identity, and then binds the timestamp and identity information to the data block to build a complete identity profile for each data block, enabling the data to have a clear time context and a clear source attribution. After the data block carries the timestamp and identity information, relying on the distributed characteristics of blockchain, the sharded data blocks are allocated to multiple storage nodes in the network. Each data block is stored with 3 copies on multiple nodes, and the data consistency is ensured through the consensus mechanism. When a single node fails, other nodes can provide data services to ensure data availability. Among them, each node undertakes the storage task of some data blocks to achieve the dispersed storage of data, reduce the risk of data loss caused by a single node failure, and ensure the stability and availability of data storage. It verifies the stored maritime data, combines the distributed ledger technology of blockchain, and cross-verifies the records of multiple nodes to ensure that the data has not been tampered with. Among them, through the distributed ledger technology of blockchain, the hash value of each data block is recorded on multiple nodes, and the hash value of the data block is associated with the hash value of the previous data block to form a chain structure. The data blocks that pass the verification are marked as "valid", and the data blocks that do not pass are marked as "abnormal". The verified data blocks are reassembled according to the timestamp to form a complete data set for subsequent query and use;

[0063] Request management module, which uses the side chain and sharding technology of blockchain to perform shunt splitting processing on the preprocessed maritime data, and combines the hierarchical consensus mechanism to introduce the hierarchical consensus structure of the main chain and the side chain to adapt to multi-user concurrent requests. The request management module includes a side chain sharding unit and a hierarchical consensus unit;

[0064] Among them, the side-chain sharding unit is used to disperse the processing of maritime data to different side-chains and shards through the blockchain side-chain and sharding technology, reduce the load of the main chain, improve the throughput of the system, alleviate data latency and network bottleneck problems, enhance the overall performance of the system, construct a side-chain network architecture that securely interacts with the main chain, divide side-chains according to maritime data types and business scenarios, and based on the data volume dimension, conduct sharding planning within the side-chains, divide each side-chain into multiple logical shards, clarify the data scope and processing logic responsible for each shard, and form a hierarchical data processing system. Among them, side-chain nodes achieve lightweight verification through the consortium chain consensus mechanism (PoA), reduce the burden on the main chain, establish a full-performance monitoring system, and real-time monitor the performance indicators such as the load conditions, processing efficiency, and network latency of each side-chain and shard. When maritime data enters the system, obtain the side-chain allocation score according to the performance indicator information, and allocate the maritime data to the corresponding side-chain. Inside the side-chain, further allocate the data to a specific shard for processing according to the sharding rules to ensure that the data can be quickly located to the appropriate processing unit, avoid data accumulation on the main chain, effectively reduce the load of the main chain. After receiving the data, each shard uses its own computing resources to process the allocated maritime data in parallel, and perform operations including data verification and computational analysis;

[0065] The calculation process of the side-chain allocation score is as follows:

[0066] For each side-chain, obtain the current load of the side-chain, compare the current load with the maximum value of the load, and take the smaller value to avoid excessive influence on the score when the load exceeds the maximum value. Then divide the compared load value by the maximum value of the load to obtain the relative load value, which represents the proportion of the load in the maximum value. Furthermore, multiply the relative load value by the weight to obtain the weighted value of the load part. For each side-chain, obtain the network latency of the side-chain, perform decay processing on the network latency using the exponential function combined with the decay coefficient of the latency to obtain the exponential decay value of the latency. Then multiply the weighted value of the load part by the exponential decay value of the latency to obtain the comprehensive weighted value of the load and latency. For each side-chain, obtain the processing efficiency of the side-chain, compare the processing efficiency with the maximum value of the processing efficiency, and take the larger value to avoid excessive influence on the score when the efficiency exceeds the maximum value. Then divide the compared efficiency value by the maximum value of the processing efficiency to obtain the relative value of the processing efficiency, which represents the proportion of the efficiency in the maximum value. Furthermore, subtract the relative value of the processing efficiency from 1 to normalize the processing efficiency to the range of 0 to 1 to obtain the contribution value of the processing efficiency part. Calculate the contribution of the processing efficiency to the score, sum up the comprehensive weighted values of the load and latency of all side-chains to obtain the total score of the load and latency. Then multiply the total score of the load and latency by the contribution value of the processing efficiency part to obtain the final side-chain allocation score. According to the calculated side-chain allocation score, allocate the maritime data to the side-chain with the highest score. The higher the score, the better the performance of the side-chain and the more suitable it is for data allocation;

[0067] The expression for the side-chain allocation score is as follows:

[0068]

[0069] In the formula, S is the side-chain allocation score, representing the comprehensive performance score of the side chain, n is the number of performance indicators, w i is the weight of the i-th performance indicator, indicating the importance of this indicator in the score, L i is the load situation (percentage, from 0 to 100) of the i-th side chain, L max is the maximum value of the load (usually 100), D i is the network latency of the i-th side chain, k is the decay coefficient of the latency, used to adjust the influence degree of the latency on the score, E is the processing efficiency of the side chain (the amount of data processed per second), E max is the maximum value of the processing efficiency (determined according to the system design). The value range of S is from 0 to 1. 0 indicates that the side-chain performance is extremely poor and not suitable for data allocation, and 1 indicates that the side-chain performance is extremely excellent and very suitable for data allocation;

[0070] Hierarchical consensus unit, used to divide the global layer, regional layer, and edge layer, process the consensus process in layers, reduce consensus latency. Among them, the main chain of the global layer adopts PBFT consensus to ensure global consistency; the shards within the regional layer adopt PoS consensus to quickly verify transactions within the shards; the side chain of the edge layer adopts PoA to achieve millisecond-level confirmation, balance security and efficiency, reduce consensus latency, and adapt to multi-user concurrent requests. A three-layer architecture system of the global layer, regional layer, and edge layer is constructed, and the functional positioning and role division of each layer are clarified. Among them, the global layer serves as the core layer, responsible for maintaining the consistency of cross-regional data; the regional layer is divided into multiple shards, undertaking local data verification and processing; the edge layer deploys side chains to achieve fast response close to the data source. Each layer conducts data interaction and collaboration through interface protocols to form a hierarchical consensus network. On the main chain of the global layer, the PBFT (Practical Byzantine Fault Tolerance) consensus algorithm is adopted. Through multiple rounds of message interaction among nodes, the consistency of data within the global scope is ensured. Among them, the primary node initiates a consensus proposal, and each node verifies and votes on the proposal. When more than 2 / 3 of the nodes reach an agreement, the consensus is completed, and the result is written into the global ledger. Within the shards of the regional layer, the PoS (Proof of Stake) consensus mechanism is adopted. Nodes participate in transaction verification according to the held stake (storage resources). Nodes within the shard quickly verify the transactions and package them into blocks, and the validity of the blocks is determined by voting based on the stake weight to ensure the consistency and fast processing of data within the shard. On the side chain of the edge layer, the PoA (Proof of Authority) consensus is adopted. The validity of transactions is directly confirmed by trusted nodes to achieve millisecond-level confirmation. After the side chain nodes receive maritime data, they immediately conduct local verification and broadcast the confirmation result without requiring network-wide consensus, reducing latency. Among them, the trusted node is the port management agency. Through the hierarchical consensus coordination mechanism, the consensus results among the global layer, regional layer, and edge layer are mutually verified and synchronized, and the consensus results are transmitted between different layers to ensure the consistency and integrity of data. Among them, the transmission of consensus results is divided into from the edge layer to the regional layer, from the regional layer to the global layer, and from the global layer to the regional layer / edge layer. For the transmission from the edge layer to the regional layer, after the edge layer transaction is confirmed, the result is submitted to the corresponding regional layer shard for further verification. For the transmission from the regional layer to the global layer, after the transactions within the regional layer shard are confirmed, the result is submitted to the global layer for final confirmation. For the transmission from the global layer to the regional layer / edge layer, after the global layer confirms the result, it feeds back to the regional layer and the edge layer to ensure data consistency. The process of the coordination mechanism includes transaction submission, global confirmation, and result feedback. For transaction submission, when the transactions of the edge layer or regional layer require global confirmation, the transaction results are submitted to the global layer. For global confirmation, the global layer uses the PBFT consensus algorithm to finally confirm the submitted transaction results. For result feedback, after the global layer confirms the result, it feeds back to the regional layer and the edge layer to update the local ledger;

[0071] The scheduling optimization module is used to write and deploy smart contracts. Based on historical maritime transportation data and real-time status, it dynamically adjusts the maritime transportation scheduling strategy using reinforcement learning algorithms, and combines with blockchain smart contracts to automatically execute scheduling instructions, reducing manual intervention, improving the accuracy and reliability of system operation, and ensuring the accuracy and traceability of data collection and scheduling;

[0072] The cross-chain interaction module is used to synchronize and interact data between the main chain, side chains, and shards using hash locking and atomic swap technologies, ensuring the atomicity of cross-chain transactions, breaking data silos, achieving global data consistency, and supporting multi-agent collaboration.

[0073] Embodiment 2, as Figure 2 shown, based on Embodiment 1, the present invention provides a technical solution: Preferably, the scheduling optimization module specifically includes:

[0074] Write an intelligent contract to define the rules and logic for maritime shipping scheduling. The intelligent contract includes the automated execution rules for the business processes of ship scheduling, cargo loading and unloading, and port operations. After writing, compile the intelligent contract into bytecode, deploy it to the blockchain network, and configure the contract permissions. Only authorized nodes can call the scheduling function to ensure system security and reduce manual intervention. The ship scheduling rule is to automatically allocate routes and docking times based on the ship's status (load, fuel level, maintenance cycle) and port operation plans. The cargo loading and unloading rule is to optimize the loading and unloading sequence and resource allocation based on the cargo priority (timeliness, dangerous goods level) and port equipment status. The port operation rule is to dynamically adjust the use of resources such as berths, cranes, and storage to avoid conflicts and improve throughput efficiency. Integrate historical maritime shipping data, use the Deep Q-Network (DQN) algorithm based on Deep Reinforcement Learning (DRL) as the framework, and train a scheduling strategy model with historical maritime shipping data as the training set. Initialize the model parameters of the Deep Q-Network algorithm, including the state space, action space, and reward function. The state space defines the current state of the ship and the port, the action space defines the existing scheduling decisions, and the reward function gives a reward value according to the scheduling effect. Furthermore, define the loss function of the Deep Q-Network algorithm to measure the difference between the predicted Q value and the target Q value. Combine the input of real-time maritime shipping data, dynamically generate a scheduling strategy, simulate the impact of different scheduling decisions on the global efficiency, and iteratively optimize the strategy parameters. Among them, the historical maritime shipping data includes route efficiency (voyage time, fuel consumption), port throughput (cargo handling volume per hour), and ship status (failure rate, load capacity). Obtain real-time maritime shipping data from the maritime shipping data source and record the timestamp. Compare the real-time maritime shipping data with the rule benchmark values defined in the intelligent contract, analyze the deviation of each monitoring indicator, and then calculate the anomaly monitoring score in combination with the set time decay coefficient. When the anomaly monitoring score exceeds the preset anomaly threshold, it is judged as an abnormal situation, and the feedback mechanism is automatically triggered. Input the real-time maritime shipping data into the reinforcement learning model, dynamically adjust the scheduling strategy, respond to environmental changes in a timely manner, and improve the adaptability and accuracy of the system. According to the optimized scheduling strategy, the intelligent contract automatically executes the scheduling instructions, including ship route adjustment, optimization of cargo loading and unloading sequence, and port resource allocation. The instruction execution process is recorded by the blockchain to ensure that every operation is traceable and tamper-proof, and the execution result is fed back into the system to further optimize the scheduling strategy model, forming a closed-loop optimization;

[0075] The expression of the loss function of the Deep Q-Network algorithm is:

[0076]

[0077] Where, \(L(\theta)\) is the loss function, which is used to measure the difference between the current Q value and the target Q value. \(\theta\) is the parameter of the current Q network, which is used to approximate the Q value function. \(s\) is the current state, including the load of the ship, the fuel quantity, the throughput of the port, etc. \(a\) is the action taken in state \(s\), including the route adjustment of the ship, the loading and unloading sequence of the goods, etc. \(r\) is the immediate reward obtained after executing action \(a\), including the efficiency improvement or cost reduction of completing a goods loading and unloading. \(s\) - is the next state transferred to after executing action \(a\). \(D\) is the experience replay buffer, which stores the experiences of the interaction between the agent and the environment, including the tuples of state, action, reward, and the next state. \(y\) is the target Q value. \(\gamma\) is the discount factor, which is used to weigh the importance of the current reward and the future reward, and its value range is \(0\leqslant\gamma\lt1\). \(\theta^-\) is the parameter of the target network, which is used to stabilize the training process. \(L(\theta)\geqslant0\). As the training progresses, the value of the loss function should gradually decrease, indicating that the difference between the predicted Q value of the model and the target Q value is decreasing, and the performance of the model is improving;

[0078] The expression for the anomaly monitoring score is:

[0079]

[0080] Where, \(A\) is the anomaly monitoring score, which is used to measure the deviation degree between the real-time data and the smart contract rules. \(m\) is the number of monitoring indicators, including the ship state, the cargo state, the port operation, etc. \(D\) j is the real-time data value of the \(j\)-th monitoring indicator, \(B\) j is the reference value of the \(j\)-th monitoring indicator, that is, the rule threshold defined in the smart contract. \(N\) j is the normalization coefficient of the \(j\)-th monitoring indicator, which is used to normalize the data with different dimensions to the same range, and its value is a certain proportion of the reference value. \(\lambda\) is the time decay coefficient, which is used to adjust the influence degree of time on the anomaly monitoring score. \(T\) j is the real-time timestamp of the \(j\)-th monitoring indicator, \(T\) ref is the reference timestamp, including the inspection time point or data update time point stipulated in the smart contract. \(A\geqslant0\). The smaller the value, the smaller the deviation between the real-time data and the smart contract rules, and the more normal the system operation; the larger the value, the greater the deviation, and there may be abnormal situations;

[0081] The cross-chain interaction module specifically includes:

[0082] When cross-chain interaction is required, it is triggered by the user or application. The initiator creates a transaction request on the main chain or side chain and generates a unique hash value, which serves as the core credential for transaction locking. At the same time, locking conditions (time window, unlocking key) are set. The initiator broadcasts the hash value and related transaction parameters to the target chain (side chain or shard). After receiving the broadcast, the target chain verifies the legitimacy of the transaction and locks equivalent assets based on the hash value, ensuring that neither party can unilaterally transfer assets before the condition verification is completed. After hash locking, data synchronization between the main chain and the side chain or shard is carried out through relay nodes. The relay nodes continuously monitor the state changes of the two chains, convert the transaction events into a standardized format and broadcast them to the target chain. The target chain verifies the data consistency (hash value matching, valid time window) to ensure that the states of the two chains are synchronized without deviation. When the state verification of the two chains passes, the cross-chain interaction module starts the atomic swap protocol. The recipient submits a key (private key signature) that matches the initiator's hash value on the target chain, triggering the execution of the smart contract to unlock the condition. The contract automatically verifies the validity of the key and completes the two-way transfer of assets between the two chains. If any condition is not met, i.e., the time expires or the key is incorrect, the assets will automatically be returned to the original chain to ensure the atomicity of the transaction. After the atomic swap is completed, the cross-chain interaction module records the transaction result in the distributed ledgers of the two chains and triggers a status update. The relay nodes aggregate the cross-chain transaction data and generate a global consistency proof (Merkle tree root hash) for other nodes to verify.

[0083] As described above, it is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

Claims

1. A periodic acquisition scheduling system for maritime transport data based on blockchain, including a data scheduling management center, characterized in that: The data scheduling and management center is communicatively connected to a data acquisition module, a request management module, a scheduling optimization module, and a cross-chain interaction module. Among them, the modules are electrically connected to each other; The data acquisition module is used to collect and preprocess maritime data from multiple data sources, and perform distributed storage of maritime data in combination with blockchain technology; The request management module is used to perform sharding and splitting processing on the preprocessed maritime data through the blockchain side chain and sharding technology, and introduce a hierarchical consensus structure of the main chain and the side chain in combination with the hierarchical consensus mechanism to adapt to multi-user concurrent requests; The scheduling optimization module is used to write and deploy smart contracts, dynamically adjust the maritime scheduling strategy based on historical maritime data and real-time status by using the reinforcement learning algorithm, and automatically execute the scheduling instructions in combination with the blockchain smart contract; The cross-chain interaction module is used to perform data synchronization and interaction between the main chain and the side chain, and shards by using the hash lock and atomic swap technology.

2. The periodic acquisition scheduling system for maritime transport data based on blockchain according to claim 1, wherein: The data acquisition module includes a data collection and processing unit and a distributed storage unit; Among them, the data collection and processing unit is used to periodically collect maritime data from each node in the maritime field by using an automated tool, and perform preprocessing operations on the collected maritime data; The distributed storage unit is used to disperse and store the preprocessed maritime data on multiple nodes by using the distributed storage technology of the blockchain, and add a time stamp and authentication information to each data block.

3. The periodic acquisition and scheduling system for maritime transport data based on blockchain according to claim 2, characterized in that: The data collection and processing unit specifically includes: Deploy automated tools at each node in the maritime field including ships, ports, and freight forwarders to form a data collection network covering all nodes in the maritime field, integrate the ship AIS system, port management system, and logistics ERP system, preset the collection frequency and trigger conditions, obtain the original maritime data from multiple data sources, and then store the collected original maritime data in a temporary buffer; Perform preprocessing on the collected original maritime data, including the operation processes of cleaning and filtering; Verify and integrate the cleaned and filtered maritime data, and then uniformly process the maritime data from different data sources to form a standardized data format, record the conversion log, and establish a version traceability mechanism.

4. A periodic acquisition scheduling system for maritime data based on blockchain according to claim 2, characterized in that: The distributed storage unit specifically includes: Perform sharding processing on the preprocessed maritime data, split it into multiple data blocks according to the data type, attach time stamp information to each data block, record the time node when the data is generated or stored, embed authentication information, covering the data source node identifier and the operator's identity, and then bind the time stamp and identity information to the data block to build a complete identity profile for each data block; After the data block carries the time stamp and identity information, by virtue of the distributed characteristics of the blockchain, distribute the sharded data blocks to multiple storage nodes in the network, and store 3 copies of each data block on multiple nodes; Verify the stored maritime data, and cross-verify the records of multiple nodes in combination with the distributed ledger technology of the blockchain, and reassemble the verified data blocks according to the time stamp to form a complete data set.

5. A periodic acquisition scheduling system for maritime data based on blockchain according to claim 1, characterized in that: The request management module includes a side chain sharding unit and a hierarchical consensus unit; Among them, the side-chain sharding unit is used to decentralize the processing of maritime data to different side-chains and shards through the blockchain side-chain and sharding technology; The hierarchical consensus unit is used to divide into a global layer, a regional layer, and an edge layer, and process the consensus process in layers. Among them, the main chain of the global layer adopts PBFT consensus, the PoS consensus is adopted within the shards of the regional layer, and the side-chain of the edge layer adopts PoA to adapt to multi-user concurrent requests.

6. The periodic acquisition scheduling system for maritime data based on blockchain according to claim 5, wherein: The side-chain sharding unit specifically includes: Construct a side-chain network architecture that securely interacts with the main chain, divide side-chains according to the types of maritime data and business scenarios, and based on the data volume dimension, conduct sharding planning within the side-chains, divide each side-chain into multiple logical shards, clarify the data scope and processing logic responsible for each shard, and form a hierarchical data processing system; Establish a full-performance monitoring system to continuously monitor the performance indicators of the load, processing efficiency, and network latency of each side-chain and shard. When maritime data enters the system, obtain the side-chain allocation score according to the performance indicator information, and allocate the maritime data to the corresponding side-chain; After receiving the data, each shard uses its own computing resources to process the allocated maritime data in parallel, and performs operations including data verification and calculation analysis.

7. The periodic acquisition scheduling system for maritime transport data based on blockchain according to claim 6, characterized in that: The calculation process of the side-chain allocation score is as follows: For each side-chain, obtain the current load of the side-chain, compare the current load with the maximum value of the load, take the smaller value, and divide the compared load value by the maximum value of the load to obtain the relative load value. Furthermore, multiply the relative load value by the weight to obtain the weighted value of the load part; For each side-chain, obtain the network latency of the side-chain, perform attenuation processing on the network latency using an exponential function combined with the attenuation coefficient of the latency to obtain the exponentially attenuated value of the latency. Furthermore, multiply the weighted value of the load part by the exponentially attenuated value of the latency to obtain the comprehensive weighted value of the load and latency; For each side-chain, obtain the processing efficiency of the side-chain, compare the processing efficiency with the maximum value of the processing efficiency, take the larger value to avoid excessive influence on the score when the efficiency exceeds the maximum value, and divide the compared efficiency value by the maximum value of the processing efficiency to obtain the relative value of the processing efficiency. Furthermore, subtract the relative value of the processing efficiency from 1 to normalize the processing efficiency to the range of 0 to 1 to obtain the contribution value of the processing efficiency part, and calculate the contribution of the processing efficiency to the score; Sum up the comprehensive weighted values of the load and latency of all side-chains to obtain the total score of the load and latency. Furthermore, multiply the total score of the load and latency by the contribution value of the processing efficiency part to obtain the final side-chain allocation score.

8. A periodic acquisition scheduling system for maritime data based on blockchain according to claim 5, characterized in that: The hierarchical consensus unit specifically includes: Construct a three-layer architecture system of the global layer, regional layer, and edge layer, clarify the functional positioning and role division of each layer, and each layer conducts data interaction and collaboration through interface protocols to form a hierarchical consensus network; Adopt the PBFT consensus algorithm on the main chain of the global layer, and through multiple rounds of message interaction between nodes. Among them, the main node initiates a consensus proposal, and each node verifies and votes on the proposal. When more than 2 / 3 of the nodes reach an agreement, the consensus is completed, and the result is written into the global ledger; Within the shards of the regional layer, the PoS consensus mechanism is adopted. Nodes participate in transaction verification based on the held stake. Nodes within the shards quickly verify transactions and package them into blocks. On the side chain of the edge layer, the PoA consensus is adopted. Trusted nodes directly confirm the validity of transactions. After the side chain nodes receive maritime transportation data, they immediately conduct local verification and broadcast the confirmation results. Among them, the trusted nodes are port management agencies. Through the hierarchical consensus coordination mechanism, the consensus results between the global layer, regional layer, and edge layer are mutually verified and synchronized, and the consensus results are transmitted between different layers. Among them, the transmission of consensus results is divided into from the edge layer to the regional layer, from the regional layer to the global layer, and from the global layer to the regional layer / edge layer.

9. The periodic acquisition scheduling system for maritime transport data based on blockchain according to claim 1, characterized in that: The said scheduling optimization module specifically includes: Write smart contracts to define the rules and logic of maritime transportation scheduling. Among them, the smart contracts include the automated execution rules for ship scheduling, cargo loading and unloading, and port operation business processes. After writing, compile the smart contracts into bytecode, deploy them to the blockchain network, and configure the contract permissions so that only authorized nodes can call the scheduling function. Integrate historical maritime transportation data. Based on the deep Q-network algorithm of deep reinforcement learning as the framework, train a scheduling strategy model with historical maritime transportation data as the training set. Initialize the model parameters of the deep Q-network algorithm, including the state space, action space, and reward function. Then define the loss function of the deep Q-network algorithm. Combine the input of real-time maritime transportation data to dynamically generate a scheduling strategy, simulate the impact of different scheduling decisions on the global efficiency, and iteratively optimize the strategy parameters. Among them, the historical maritime transportation data includes route efficiency, port throughput, and ship status. Obtain real-time maritime transportation data from the maritime transportation data source and record the timestamp. Compare the real-time maritime transportation data with the rule benchmark values defined in the smart contract, analyze the deviation of each monitoring index, and then combine the set time decay coefficient to calculate the anomaly monitoring score. When the anomaly monitoring score exceeds the preset anomaly threshold, it is judged as an abnormal situation, and the feedback mechanism is automatically triggered. Input the real-time maritime transportation data into the reinforcement learning model to dynamically adjust the scheduling strategy. According to the optimized scheduling strategy, the smart contract automatically executes the scheduling instructions. The scheduling instructions include ship route adjustment, optimization of cargo loading and unloading sequence, and port resource allocation. The execution process of the instructions is recorded by the blockchain, and the execution results are fed back into the system to further optimize the scheduling strategy model, forming a closed-loop optimization.

10. A periodic acquisition scheduling system for maritime data based on blockchain according to claim 1, characterized in that: The said cross-chain interaction module specifically includes: When cross-chain interaction is required, it is triggered by the user or application. The initiator creates a transaction request on the main chain or side chain and generates a unique hash value. This hash value serves as the core voucher for transaction locking. At the same time, set the locking conditions. The initiator broadcasts the hash value and related transaction parameters to the target chain. The target chain verifies the transaction legality after receiving it and locks equivalent assets based on the hash value. After hash locking, data synchronization between the main chain and side chain, and shards is carried out through relay nodes. The relay nodes continuously monitor the state changes of the two chains, convert the transaction events into a standardized format and broadcast them to the target chain. After the two-chain status verification passes, the cross-chain interaction module starts the atomic swap protocol. The recipient submits a key on the target chain that matches the initiator's hash value, triggering the execution condition unlocking of the smart contract and completing the two-way transfer of assets between the two chains. If any condition is not met, i.e., the time-out or the key is incorrect, the assets will be automatically returned to the original chain; After the atomic swap is completed, the cross-chain interaction module records the transaction results in the distributed ledgers of the two chains and triggers a status update. The relay node aggregates the cross-chain transaction data and generates a global consistency proof for other nodes to verify.

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