Block chain multi-node secure communication method of industrial internet

Abstract

The invention discloses a block chain multi-node secure communication method for an industrial internet, and the method comprises the steps: constructing a multi-chain cooperation mechanism, and obtaining an inter-chain interaction path table; determining a task partition optimization scheme and inter-chain interaction path update data based on the inter-chain interaction path table; matching an adaptive node resource for each task according to a task partition optimization scheme; determining an encryption mechanism between a cross-chain protocol and a block chain according to the security requirement of the task; constructing a multi-level block chain node hierarchical architecture and an intra-chain and inter-chain communication architecture, and formulating a parallel communication scheduling scheme; formulating a dynamic load balancing strategy based on the node resource use condition and the task execution state; and obtaining the task execution progress and the resource use condition of each block chain node, and adjusting the task scheduling and cross-chain communication path. Through the multi-dimensional collaborative design, the execution efficiency, the resource utilization rate and the communication security of the industrial internet are remarkably improved, and the stability and the reliability in a complex multi-chain collaborative scene are ensured.

Description

Technical Field

[0001] The present invention relates to the field of Internet communication security, and in particular to a blockchain multi-node secure communication method for the industrial Internet. Background Art

[0002] With the rapid development of the industrial Internet, the demand for real-time transmission, storage and collaborative processing of industrial data has increased significantly. However, the existing centralized system has been unable to meet the growing requirements of industrial scenarios for data security, real-time and efficiency. Blockchain technology has shown great potential in the field of industrial Internet due to its decentralization, transparency and data immutability. However, the current industrial blockchain still faces many problems in practical applications, which hinders its large-scale deployment in complex industrial scenarios. First, the diversity and complexity of industrial tasks make task allocation and scheduling challenging. Industrial tasks usually have significant differences, including high-frequency trading tasks, low-latency real-time tasks, and big data storage tasks. Different tasks have different requirements for computing resources, storage capacity and communication bandwidth. The traditional static task allocation method lacks dynamic perception of the resource status of blockchain nodes, resulting in inefficient task allocation and frequent waste or overload of node resources. In addition, multi-chain collaboration scenarios are becoming more and more common in the industrial Internet, including different types of blockchains such as public chains, private chains and consortium chains. However, due to the significant performance differences between chains, the uncertainty of inter-chain communication paths and the lack of cross-chain data sharing mechanisms, the complexity of multi-chain collaboration has increased significantly, manifested in increased communication delays, low data transmission efficiency and the inability to guarantee the order of task execution. In practical applications, the heterogeneity of blockchain nodes further exacerbates the difficulty of resource allocation. The differences in hardware performance of different nodes, such as CPU, memory and bandwidth, and the dynamic characteristics of task load changes often lead to uneven resource allocation, overload bottlenecks in some nodes, and insufficient resource utilization of other nodes. In addition, the security of cross-chain communication is also a core challenge in industrial scenarios. The high sensitivity of industrial data places extremely high demands on the security of transmission and storage, and the existing cross-chain communication protocols show great limitations in dealing with the complex needs of industrial environments, such as the inability to flexibly balance the security and performance of encryption algorithms, and the difficulty in real-time dynamic evaluation of path security. Finally, the monitoring and optimization methods of industrial blockchain systems also need to be improved. Traditional monitoring systems often cannot fully and real-time grasp the resource usage status of nodes, task execution progress and inter-chain communication status, lack intelligent optimization capabilities based on real-time data, and cannot quickly respond to complex scenarios of multi-task concurrency in the industrial Internet. Therefore, these technical bottlenecks make it difficult for the current industrial blockchain system to simultaneously meet the high efficiency of task allocation, the security of communication, and the balance of resource utilization. New technical solutions are urgently needed to solve these problems so as to realize the widespread application of blockchain in the industrial Internet. Summary of the invention

[0003] The present invention solves the problems existing in the above-mentioned prior art and provides a blockchain multi-node secure communication method for industrial Internet, which mainly includes:

[0004] According to the business characteristics and security requirements of the task, determine the appropriate blockchain type for task partitioning, combine the dependencies between tasks on the chains, build a multi-chain coordination mechanism, and obtain the inter-chain interaction path table;

[0005] Based on the inter-chain interaction path table, a topological sorting algorithm is used to linearize the dependencies between tasks, and to determine the task partition optimization scheme and the inter-chain interaction path update data;

[0006] According to the task partition optimization scheme, match the appropriate node resources for each task, and optimize the task allocation based on the processing capacity and current load of each node;

[0007] Based on the task allocation results, determine the cross-chain communication requirements and optimal routing paths between tasks, and determine the cross-chain protocol and encryption mechanism between blockchains based on the security requirements of the tasks;

[0008] According to the priority and dependency of tasks, we build a task scheduling plan, and based on the load, task type and communication requirements of different blockchain nodes, we build a multi-level blockchain node hierarchical architecture and intra-chain and inter-chain communication architecture, and formulate a parallel communication scheduling plan;

[0009] Based on the node resource usage and task execution status, a dynamic load balancing strategy is formulated to evaluate the optimized task execution time and data transmission efficiency, and obtain the optimized task scheduling solution;

[0010] Through the distributed task monitoring module, the task execution progress and node resource usage status of each blockchain node can be obtained in real time, a dynamic optimization model based on the Q-learning algorithm can be built, and task scheduling and cross-chain communication paths can be dynamically adjusted.

[0011] Furthermore, according to the business characteristics and security requirements list of the task, the adapted blockchain type is determined to partition the task, and the multi-chain coordination mechanism is constructed in combination with the dependencies between the tasks of the chains to obtain the inter-chain interaction path table, including:

[0012] According to the task list of the current industrial blockchain system, obtain all pending task relationship data, business characteristics and security requirements list. Task relationship data includes task priority, data flow path and dependency table. Business characteristics include high-frequency tasks, low-latency tasks and big data storage tasks. The security requirements list includes task data encryption requirements, storage requirements and transmission conditions. According to the business characteristics and security requirements list of the task, determine the appropriate blockchain type for task partitioning. The blockchain types include public chain, private chain and alliance chain. Combined with the business characteristics of the task, determine the optimal blockchain allocation strategy by calculating the throughput, latency and storage capacity of the blockchain. ; Use network simulation tools to simulate task allocation results, verify whether task allocation meets performance and security requirements, and obtain optimized task allocation table and chain type matching results; According to the task allocation table and chain type matching results, obtain the performance indicators of each chain, including throughput, latency and security, adopt cross-chain communication protocols, combine the dependencies between chain tasks, and build a multi-chain collaboration mechanism, including but not limited to cross-chain communication protocols and inter-chain data sharing; Use network simulation tools to simulate cross-chain task interactions, evaluate the latency and throughput performance of communication protocols, obtain the optimization scheme of the multi-chain collaboration mechanism and the inter-chain interaction path table, and determine the priority and scheduling strategy of cross-chain data sharing.

[0013] Furthermore, based on the inter-chain interaction path table, a topological sorting algorithm is used to linearize the dependencies between tasks, and determine the task partition optimization scheme and the inter-chain interaction path update data, including:

[0014] Based on the inter-chain interaction path table, the dependency information in the task allocation table is obtained, and a directed acyclic graph is constructed to represent the dependency structure of the task; a topological sorting algorithm is used, starting from the node with in-degree 0, the nodes are recursively removed and added to the sorting queue, and the execution order of the tasks and the inter-chain interaction point identification table are obtained by running the algorithm; a network simulation tool is used to simulate the task allocation and execution process in a multi-chain environment, and by monitoring the execution time of each task, the inter-chain communication delay and the data transmission security, the statistical data of the throughput and task completion time are recorded, and the integrity of data encryption, transmission and storage is evaluated, and the execution efficiency evaluation report and security analysis results are obtained; according to the execution efficiency evaluation report and Based on the security analysis results, a collaborative optimization algorithm is used to adjust the task allocation and inter-chain communication strategy; the task execution process is re-simulated through a network simulation tool, the throughput, latency and security indicators under the optimization scheme are calculated, and it is determined whether the optimization effect meets the business needs. Based on the adjustment results, the task partition optimization scheme and the inter-chain interaction path update data are obtained; based on the task partition optimization scheme and the inter-chain interaction path update data, a multi-chain collaborative simulation test environment is constructed, and by executing all task chains, the execution order of tasks, inter-chain communication paths and data security performance are verified item by item, and the latency, throughput and task completion rate in the simulation test are monitored to determine the rationality of the multi-chain collaborative partitioning.

[0015] Furthermore, the task partition optimization scheme is used to match the node resources suitable for each task, and the task allocation is optimized based on the processing capacity and current load of each node, including:

[0016] According to the task partition optimization plan, the computing resource requirements, storage requirements, and bandwidth requirements of each task on different blockchains are evaluated, and the hardware performance, bandwidth status, and network latency of the blockchain nodes are combined to match the appropriate node resources for each task; according to the processing power and current load of each node, a weighted polling algorithm is used to schedule tasks, allocate computing resources, and optimize task allocation.

[0017] Furthermore, based on the task allocation results, the cross-chain communication requirements and optimal routing paths between tasks are determined, and according to the security requirements of the tasks, the cross-chain protocol and the encryption mechanism between blockchains are determined, including:

[0018] Based on the task allocation results, determine the cross-chain communication requirements between tasks, and use the inter-chain messaging protocol for cross-chain communication; based on the bandwidth, latency and data encryption mechanism of different chains, use the Dijkstra algorithm to optimize the cross-chain communication links between tasks and determine the optimal routing path; according to the cross-chain communication requirements and security requirements of the tasks, select the appropriate cross-chain protocol, including the IBC protocol and Polkadot's shared security protocol; formulate encryption mechanisms between blockchains, and use encryption algorithms, including but not limited to hashing, digital signatures, and zero-knowledge proofs, for cross-chain encrypted communication; simulate routing delays and throughput predictions through network simulation tools to verify whether the routing strategy meets the needs of time-sensitive tasks.

[0019] Furthermore, the task scheduling scheme is constructed according to the priority and dependency of the tasks, and a multi-level blockchain node hierarchical architecture and intra-chain and inter-chain communication architecture are constructed based on the load, task type and communication requirements of different blockchain nodes, and a parallel communication scheduling scheme is formulated, including:

[0020] Based on the priority and dependency of tasks, a task scheduling scheme is constructed to process tasks in parallel on blockchain nodes; according to the task scheduling scheme, the data traffic distribution between nodes is obtained, and the communication frequency and data size within and between each chain are determined; by adopting a distributed queue model, data transmission queues between nodes are formulated, and the capacity, priority rules and number of parallel transmission channels of each queue are determined; according to the sensitivity and real-time requirements of the tasks, queue priority rules are constructed, the priority processing strategy for sensitive data is determined, and tasks in the message queue are sorted; according to the load, task type and communication requirements of different blockchain nodes, a multi-level blockchain node hierarchical architecture and intra-chain and inter-chain communication architecture are constructed by adopting a distributed graph algorithm, and a parallel communication scheduling scheme is formulated; through network simulation tools, task scheduling and cross-chain communication scenarios are simulated, and the node load, packet loss rate and communication delay in the parallel communication scheduling simulation process are counted to evaluate whether the parallel communication scheduling scheme optimizes the task execution time, and verify the encryption and integrity of sensitive data during transmission.

[0021] Furthermore, the method of formulating a dynamic load balancing strategy based on the node resource usage status and task execution status, evaluating the optimized task execution time and data transmission efficiency, and obtaining an optimized task scheduling solution includes:

[0022] According to the node resource usage and task execution status, a dynamic load balancing strategy is formulated to adjust the load distribution of each node and optimize resource utilization. Based on the implementation results of the load balancing strategy, the Q-learning algorithm is used to adjust the dynamic load balancing strategy according to the real-time status of the task, including task execution time, communication delay and node load, to optimize the load distribution and resource utilization efficiency between nodes. By evaluating the optimized task execution time and data transmission efficiency, the optimized task scheduling plan is obtained.

[0023] Furthermore, the distributed task monitoring module is used to obtain the task execution progress and node resource usage status of each blockchain node in real time, build a dynamic optimization model based on the Q-learning algorithm, and dynamically adjust task scheduling and cross-chain communication paths, including:

[0024] Through the distributed task monitoring module, the task execution progress of each blockchain node, the node resource usage status, and the data transmission delay and traffic distribution during the cross-chain communication process are obtained in real time. The node resource usage status includes CPU usage, memory usage, and network bandwidth usage. The statistical analysis method is used to evaluate the task load of each blockchain node and the real-time delay of the communication link, and identify high-load nodes, communication bottleneck links, and inefficient task allocation. According to the high-load nodes, communication bottleneck links, and inefficient task allocation, a dynamic optimization model based on the Q-learning algorithm is constructed, and the state space is set as the node task load, communication link delay, and resource usage, and the action space is set as the task redistribution. The task scheduling and communication path adjustment are carried out by setting the reward function to minimize the delay and load balancing, dynamically adjusting the task scheduling and cross-chain communication path, and obtaining the optimized task allocation plan and communication path planning according to the dynamic optimization model; according to the optimized task allocation plan, the resource usage of the node is monitored in real time, and the tasks are reallocated based on the priority of the task and the dynamic changes of the node resources; by dynamically monitoring the cross-chain communication link, the data transmission path is adjusted according to the delay and bandwidth utilization of the current link, and the real-time adjusted task allocation and communication path strategy is obtained; by adjusting the selection and parameter configuration of the encryption algorithm, the security and efficiency of data transmission are optimized, and the improvement results of the encryption algorithm selection and data transmission efficiency are recorded.

[0025] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0026] The present invention provides a blockchain multi-node secure communication method for the industrial Internet. The present invention combines the business characteristics of the task and the security requirements list, accurately matches the adapted blockchain type to perform task partitioning, realizes a high degree of adaptation between the task and the blockchain characteristics, and significantly improves the accuracy and execution efficiency of task allocation. The inter-chain interaction path table and the topological sorting algorithm are used to linearize and optimize the task dependency relationship, ensure the correctness of the task sequence, and reduce the inter-chain communication delay. Based on the task partition optimization scheme, the present invention dynamically matches the node resources and load status, optimizes the task allocation strategy, improves resource utilization and avoids node overload. In the cross-chain communication scenario, by combining the task security requirements, selecting the adapted cross-chain protocol and dynamic encryption mechanism, the security of data transmission and the efficiency of the communication path are improved, and the integrity and privacy of industrial data are guaranteed. The present invention significantly improves the overall efficiency of task execution time and system throughput by constructing a task scheduling scheme and a parallel communication scheduling strategy. Through the multi-level node hierarchical architecture design and the dynamic load balancing strategy, the present invention realizes efficient resource scheduling and load balancing between nodes, avoiding performance bottlenecks and resource waste problems. The present invention realizes the efficient collaboration of multi-node task allocation, scheduling, cross-chain communication and resource management of blockchain in the Industrial Internet through multi-dimensional collaborative design of task partitioning, scheduling optimization, dynamic load balancing and cross-chain communication security mechanism. It not only significantly improves the execution efficiency, resource utilization and communication security of the system, but also ensures the stability and reliability in complex multi-chain collaboration scenarios, meets the strict requirements of industrial scenarios for high performance and high security, and lays the foundation for the widespread application of blockchain technology in the Industrial Internet. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flowchart of a multi-node secure communication method of blockchain for industrial Internet according to the present invention;

[0028] Figure 2 A schematic diagram of a multi-node secure communication method of blockchain for industrial Internet according to the present invention;

[0029] Figure 3 This is another schematic diagram of a blockchain multi-node secure communication method for the industrial Internet of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] like Figure 1-3 In this embodiment, a blockchain multi-node secure communication method for industrial Internet may specifically include:

[0032] Step S101, according to the business characteristics and security requirements list of the task, determine the adapted blockchain type for task partitioning, combine the dependencies between inter-chain tasks, build a multi-chain coordination mechanism, and obtain the inter-chain interaction path table.

[0033] According to the task list of the current industrial blockchain system, all pending task relationship data, business characteristics and security requirements list are obtained. Task relationship data includes task priority, data flow path and dependency table. Business characteristics include high-frequency tasks, low-latency tasks and big data storage tasks. The security requirements list includes data encryption requirements, storage requirements and transmission conditions of tasks. According to the business characteristics and security requirements list of tasks, the adapted blockchain type is determined for task partitioning. Blockchain types include public chain, private chain and alliance chain. Combined with the business characteristics of the task, the optimal blockchain allocation strategy is determined by calculating the throughput, latency and storage capacity of the blockchain. The task allocation results are simulated by network simulation tools to verify whether the task allocation meets the performance and security requirements, and the optimized task allocation table and chain type matching results are obtained. According to the task allocation table and chain type matching results, the performance indicators of each chain are obtained, including throughput, latency and security. A cross-chain communication protocol is adopted, combined with the dependency relationship of tasks between chains, and a multi-chain collaboration mechanism is constructed, including but not limited to cross-chain communication protocol and inter-chain data sharing. Through network simulation tools, cross-chain task interactions are simulated, the latency and throughput performance of the communication protocol are evaluated, the optimization scheme of the multi-chain collaboration mechanism and the inter-chain interaction path table are obtained, and the priority and scheduling strategy of cross-chain data sharing are determined.

[0034] For example, in an industrial blockchain system, the task list includes Task A, Task B, and Task C. According to the task relationship data, Task A has the highest priority and is a high-frequency task. It needs to process 1,500 transactions per second, and the data flow path is Chain A → Chain B. Task B is a medium-priority task and a low-latency task. The latency requirement is less than 15ms. It can only be transmitted on Chain B → Chain C after the result of Task A. Task C is the lowest priority task and belongs to a big data storage task. It needs to store 3TB of data and run independently. The data flow path is Chain A → Chain D. According to the security requirements list, Task A requires AES-256 encryption to ensure data transmission security. Task B involves sensitive data and needs to use TLS protocol for transmission and zero-knowledge proof for verification. Task C only requires basic SHA-256 hash verification to ensure data integrity. Task A has low data storage requirements but requires high-speed transmission. Task B requires high transmission bandwidth and low latency conditions, and Task C requires high storage capacity. Based on the business characteristics and security requirements of the task, Task A is assigned to the alliance chain A, which has a throughput of 3,000 transactions per second and a latency of 20ms, meeting the requirements of high-frequency tasks. Task B is assigned to the low-latency private chain B and chain C, with a latency of 10ms and a bandwidth of 30MB / s, and fast data transmission is achieved through the inter-chain shared security protocol. Task C is assigned to the public chain D with high storage capacity, which supports 20MB of data storage operations per second to meet the requirements of big data tasks. The task allocation results are simulated by the network simulation tool. The execution time of Task A is 400ms, and the data is accurately transferred to Chain B. The latency of Task B is stable at 10ms, and the total completion time is 300ms. The storage completion time of Task C is 2.5 seconds. In the simulation of cross-chain task interaction through the network simulation tool, the communication path latency of Chain A→Chain B is 12ms, the throughput is 2,800 transactions per second, the latency of Chain B→Chain C is 8ms, the bandwidth utilization is 95%, and the data integrity verification of Chain A→Chain D is passed with zero packet loss. The final optimization results show that the task allocation scheme accurately matches the task characteristics and chain resources. The inter-chain interaction path table clarifies the high-priority data flow of task A→task B. Task C is set as the lowest priority as an independent storage task, ensuring resource utilization and security. The system throughput is increased by 20%, and the data transmission security and storage integrity have reached industrial standards.

[0035] Step S102: Based on the inter-chain interaction path table, a topological sorting algorithm is used to linearize the dependencies between tasks, and the task partition optimization scheme and inter-chain interaction path update data are determined.

[0036] Based on the inter-chain interaction path table, the dependency information in the task allocation table is obtained, and a directed acyclic graph is constructed to represent the dependency structure of the task. Using the topological sorting algorithm, starting from the node with in-degree 0, the node is recursively removed and added to the sorting queue. Through the algorithm operation, the execution order of the task and the identification table of the inter-chain interaction point are obtained. The network simulation tool is used to simulate the distribution and execution process of tasks in a multi-chain environment, and by monitoring the execution time of each task, the inter-chain communication delay and the data transmission security, the statistical data of the throughput and task completion time are recorded, and the integrity of data encryption, transmission and storage is evaluated to obtain the execution efficiency evaluation report and security analysis results. According to the execution efficiency evaluation report and the security analysis results, the collaborative optimization algorithm is used to adjust the task allocation and inter-chain communication strategy. The task execution process is re-simulated by the network simulation tool, and the throughput, delay and security indicators under the optimization scheme are calculated to determine whether the optimization effect meets the business needs. According to the adjustment results, the task partition optimization scheme and the inter-chain interaction path update data are obtained. According to the task partition optimization plan and the inter-chain interaction path update data, a multi-chain collaborative simulation test environment is built. By executing all task chains, the execution order of tasks, inter-chain communication paths and data security performance are verified item by item. The latency, throughput and task completion rate in the simulation test are monitored to determine the rationality of the multi-chain collaborative partitioning.

[0037] Exemplarily, in a multi-chain environment, the task allocation table shows the dependency relationship as follows: Task H needs to be completed first, and then the result is passed to Task I and Task J. The output of Task I is the input of Task W, and Task J is executed independently without further data transmission. A directed acyclic graph is constructed through the dependency relationship. Task H is defined as the starting node with an in-degree of 0. Task I and Task J are the output nodes of Task H, respectively, and Task W is the output node of Task I. Using the topological sorting algorithm, the execution order of the tasks is H→I→W and H→J, where the output of Task H is the cross-chain interaction point. The task allocation and execution process is simulated by the network simulation tool. Task H is assigned to the alliance chain, Task I and Task W are assigned to the private chain, and Task J is assigned to the public chain. During the simulation process, the completion time of Task H is 180ms, and its result is passed to Task I and Task J through the cross-chain communication protocol. The communication delay is 12ms. Data encryption uses AES-256 to verify the success of transmission integrity. The execution time of task I is 90ms, the execution time of task J is 130ms, and the execution time of task W is 140ms. The total throughput of the entire task chain is monitored to be 55 transactions per second, and the total task completion time is 452ms. Through security analysis, it is found that there is no packet loss or tampering in the task transmission data in all inter-chain communications, the storage integrity verification is passed, and the security of inter-chain interaction meets the requirements. However, the execution efficiency evaluation report shows that the delay of task W is higher than expected, resulting in a longer completion time for the entire task chain. After the collaborative optimization algorithm is used to adjust the strategy, task W is reallocated to the alliance chain with higher throughput, and the cross-chain communication path of task H is optimized to avoid passing through the link with higher latency. After re-simulating the execution through the network simulation tool, the throughput is increased to 70 transactions per second, the task chain completion time is shortened to 400ms, the communication delay is reduced to 9ms, and the security indicators remain stable. According to the optimized task partitioning scheme and the inter-chain interaction path update data, a multi-chain collaborative simulation test environment is constructed to verify the optimization effect of the task execution order and the inter-chain communication path. The throughput recorded in the test was 72 transactions per second, the average task completion time was 395ms, the task completion rate reached 100%, and the security of inter-chain communication and data storage was verified. The evaluation results show that the multi-chain collaborative partition is reasonable and can meet business needs, while improving the overall execution efficiency and security.

[0038] Step S103, according to the task partition optimization scheme, matching the node resources suitable for each task, and optimizing the task allocation based on the processing capacity and current load of each node.

[0039] According to the task partition optimization plan, the computing resource requirements, storage requirements, and bandwidth requirements of each task on different blockchains are evaluated, and the hardware performance, bandwidth status, and network latency of the blockchain nodes are combined to match the appropriate node resources for each task. According to the processing power and current load of each node, a weighted polling algorithm is used to schedule tasks, allocate computing resources, and optimize task allocation.

[0040] For example, in an industrial blockchain system, the task partition optimization scheme determines the task requirements, including that task P requires high computing resources, occupies 80% of the CPU and 2GB of memory, and requires 10MB of bandwidth per second, and the storage requirement is 500MB. Task Q is a low computing resource task, which only requires 30% of the CPU and 1GB of memory, with a bandwidth requirement of 5MB per second and a storage requirement of 100MB. Task R requires higher storage capacity, occupies 5GB of storage space, has a bandwidth requirement of 8MB per second, and only 50% of the CPU is occupied. According to the hardware performance of the blockchain node, node N1 has a 16-core CPU, 32GB of memory, a bandwidth of 100MB per second, and a network delay of 10ms. Node N2 has an 8-core CPU, 16GB of memory, a bandwidth of 50MB per second, and a network delay of 20ms. Node N3 has a 4-core CPU, 8GB of memory, a bandwidth of 30MB per second, and a network delay of 15ms. Tasks are scheduled by weighted round-robin algorithm. According to the hardware performance and load of the nodes, task P is assigned to node N1 because it requires high computing resources and N1 has sufficient CPU and memory. Task Q is assigned to node N2 because it has low computing and bandwidth requirements and is suitable for tasks with small loads. Task R is assigned to node N3 because it has high storage requirements and N3 bandwidth and storage capacity meet the conditions. During the task execution process, dynamic monitoring found that the bandwidth utilization of node N3 reached 90%, which may become a performance bottleneck. After optimizing the task allocation, task R is migrated to node N2, and the load balancing strategy of node N2 is adjusted to increase the priority of its processing tasks to ensure balanced utilization of bandwidth resources. The final optimization results show that all tasks are completed on schedule, the average network delay is 12ms, the total throughput is increased to 95MB per second, the node resource utilization is balanced, and the task allocation meets the hardware performance requirements.

[0041] Step S104: Based on the task allocation results, determine the cross-chain communication requirements and optimal routing paths between tasks, and determine the encryption mechanism between the cross-chain protocol and the blockchain according to the security requirements of the tasks.

[0042] Based on the task allocation results, determine the cross-chain communication requirements between tasks, and use the inter-chain messaging protocol for cross-chain communication. Based on the bandwidth, latency, and data encryption mechanism of different chains, use the Dijkstra algorithm to optimize the cross-chain communication link between tasks and determine the optimal routing path. According to the cross-chain communication requirements and security requirements of the task, select the appropriate cross-chain protocol, including the IBC protocol and Polkadot's shared security protocol. Develop an encryption mechanism between blockchains and use encryption algorithms, including but not limited to hashing, digital signatures, and zero-knowledge proofs, for cross-chain encrypted communication. Use network simulation tools to simulate routing delays and throughput predictions to verify whether the routing strategy meets the needs of time-sensitive tasks.

[0043] For example, in an industrial blockchain system, Task M and Task N are assigned to Chain A and Chain B respectively, and Task V is assigned to Chain C. Task M needs to process 10MB of real-time transaction data and pass the processing results to Task N for analysis. The results of Task N need to be further passed to Task V on Chain C for long-term storage. The communication requirements between Task M and Task N have the highest priority. Data transmission must be completed within 50ms, and the encryption security of the transmission must be ensured. The communication between Task N and Task V is of secondary priority, with a data volume of 100MB, mainly focusing on data integrity and long-term storage security. To meet the cross-chain communication requirements, the transmission from Task M to Task N adopts the IBC protocol, which realizes efficient communication between Chain A and Chain B, ensuring the rapidity of message transmission and data consistency. The communication from Task N to Task V adopts Polkadot's shared security protocol, and the integrity and tamper-proofness of Task V's data storage are guaranteed through a shared security verification mechanism. According to the performance data of chain A, chain B and chain C, the bandwidth of chain A is 50MB / s and the delay is 15ms; the bandwidth of chain B is 40MB / s and the delay is 20ms; the bandwidth of chain C is 30MB / s and the delay is 25ms. The optimal communication path is calculated using the Dijkstra algorithm, and the total delay of the path from chain A→chain B→chain C is determined to be 55ms, of which the path delay from chain A to chain B is 20ms and the path delay from chain B to chain C is 35ms. For the high-priority data transmission of task M, the AES-256 encryption algorithm is used to ensure the encryption security of the data; for the large amount of stored data from task N to task V, zero-knowledge proof is used to verify the integrity of the data, and the hash algorithm is used to quickly verify the correctness of the data block. The cross-chain communication process is simulated by the network simulation tool. The results show that the throughput from chain A to chain B is 48MB / s, the total delay is 18ms, and the throughput from chain B to chain C is 28MB / s, and the total delay is 37ms. During the communication process, the data integrity and encryption security of Task M were verified, and the storage data from Task N to Task V was verified successfully. The optimized communication routing and encryption mechanism ensure the high efficiency of cross-chain communication, the overall throughput is increased by 15%, and the latency and security of inter-chain data transmission meet the requirements of industrial-grade applications.

[0044] Step S105, build a task scheduling plan according to the priority and dependency of the tasks, and build a multi-level blockchain node hierarchical architecture and intra-chain and inter-chain communication architecture based on the load, task type and communication requirements of different blockchain nodes, and formulate a parallel communication scheduling plan.

[0045] Based on the priority and dependency of tasks, a task scheduling scheme is constructed to process tasks in parallel on blockchain nodes. According to the task scheduling scheme, the data traffic distribution between nodes is obtained, and the communication frequency and data size within and between each chain are determined. By adopting a distributed queue model, data transmission queues between nodes are formulated, and the capacity, priority rules and number of parallel transmission channels of each queue are determined. According to the sensitivity and real-time requirements of the task, queue priority rules are constructed, the priority processing strategy for sensitive data is determined, and the tasks in the message queue are sorted. According to the load, task type and communication requirements of different blockchain nodes, a multi-level blockchain node hierarchical architecture and intra-chain and inter-chain communication architecture are constructed by adopting a distributed graph algorithm, and a parallel communication scheduling scheme is formulated. Through network simulation tools, task scheduling and cross-chain communication scenarios are simulated, and node load, packet loss rate and communication delay in the parallel communication scheduling simulation process are counted to evaluate whether the parallel communication scheduling scheme optimizes the task execution time, and verify the encryption and integrity of sensitive data during transmission.

[0046] For example, in an industrial blockchain system, the task scheduling scheme shows that task X and task Y can be processed in parallel, and task Z depends on the result of task Y to be executed. Task X is a high-priority task, which needs to transmit 5MB of data to chain A and complete the processing on chain A, with an execution time of 200ms. Task Y is a medium-priority task, which needs to transmit the result to chain B, with a data size of 3MB and an execution time of 150ms. Task Z is a low-priority task, with a data volume of 8MB, which needs to be transmitted to chain C, with an execution time of 300ms. According to the scheduling scheme, the data traffic distribution between nodes is obtained. The communication frequency of chain A is 20 times per second, chain B is 15 times per second, and chain C is 10 times per second. The size of each data transmission is between 1MB and 5MB. The distributed queue model is used to formulate the data transmission queue between nodes. The queue capacity of chain A is set to 50MB, and that of chain B and chain C is set to 30MB and 40MB respectively, giving priority to the data of high-priority tasks. Combined with the sensitivity and real-time nature of the tasks, the queue priority rule gives priority to the data of task X and ensures that task Y is transmitted in sequence after task X. Task Z starts after Task Y is transmitted because of its low priority. After the queue is sorted, the data of Task X first enters Chain A for processing, followed by the Chain B transmission data of Task Y, and finally the Chain C data of Task Z. According to the load, task type and communication requirements of different blockchain nodes, a distributed graph algorithm is used to build a multi-level blockchain node hierarchical architecture. The nodes are divided into three levels. The first level is the data storage node, which is responsible for processing big data storage tasks, such as the nodes on Chain Z. The second level is the computing node, which is mainly responsible for transaction verification and block generation tasks, such as the nodes on Chain A. The third level is the communication node, which is responsible for processing cross-chain communication tasks, such as the nodes on Chain B. The entire task scheduling and cross-chain communication scenario is simulated by the network simulation tool, and the node load during the simulation is recorded as 70% for Chain A, 50% for Chain B, and 40% for Chain C. The packet loss rate is 0.5%, and the communication delay is 20ms for Chain A, 30ms for Chain B, and 50ms for Chain C. The evaluation results show that the current architecture meets the requirements of the task scheduling solution, the encrypted transmission and integrity verification of sensitive data have been passed, the task execution time is in line with expectations, and the architecture stability and throughput meet business requirements.

[0047] Step S106, based on the node resource usage status and task execution status, a dynamic load balancing strategy is formulated, the optimized task execution time and data transmission efficiency are evaluated, and an optimized task scheduling solution is obtained.

[0048] According to the node resource usage and task execution status, a dynamic load balancing strategy is formulated to adjust the load distribution of each node and optimize resource utilization. Based on the implementation results of the load balancing strategy, the Q-learning algorithm is used to adjust the dynamic load balancing strategy according to the real-time status of the task, including task execution time, communication delay and node load, to optimize the load distribution and resource utilization efficiency between nodes. By evaluating the optimized task execution time and data transmission efficiency, the optimized task scheduling solution is obtained.

[0049] For example, in an industrial blockchain system, node E belongs to the data storage layer, with 2TB storage capacity, 16-core GPU and 100MB bandwidth per second, node F belongs to the consensus layer, with 8-core GPU, 500GB storage capacity and 50MB bandwidth per second, and node G belongs to the communication layer, with 4-core GPU, 200GB storage capacity and 30MB bandwidth per second. In the task allocation process, the storage task K1 requires 500GB storage capacity, 50% GPU utilization, and 10MB / s bandwidth, and is assigned to node E. The transaction verification task K2 requires 70% GPU utilization and 15MB bandwidth, and is assigned to node F. The cross-chain communication task K3 requires 20MB bandwidth and low latency, and is assigned to node G. The initial allocation plan shows that the load of node E is 60%, the load of node F is 75%, and the load of node G is 80%. Based on the real-time load of the node and the task execution status, a dynamic load balancing strategy is formulated. When it is monitored that the load of node G continues to exceed 90%, part of the cross-chain communication task K3 is reallocated to node F with lower latency, and task K2 is optimized and scheduled to give priority to high-priority transactions. On this basis, the Q-learning algorithm is used to optimize the load balancing strategy. The model inputs the real-time status of the task, including execution time, latency, and bandwidth occupancy, and outputs a dynamically adjusted task allocation plan. After adjustment, the load of node E is reduced to 50%, the load of node F is stabilized at 70%, and the load of node G is reduced to 75%. By evaluating the optimized task execution time and data transmission efficiency, the completion time of task K1 is 200ms, task K2 is 150ms, and task K3 is 120ms. The average inter-chain communication delay is reduced to 12ms, the total system resource utilization rate is increased by 15%, the task allocation is balanced and meets the real-time requirements, and the optimized task scheduling plan is obtained.

[0050] Step S107, through the distributed task monitoring module, the task execution progress and node resource usage status of each blockchain node are obtained in real time, a dynamic optimization model based on the Q-learning algorithm is constructed, and task scheduling and cross-chain communication paths are dynamically adjusted.

[0051] Through the distributed task monitoring module, the task execution progress of each blockchain node, the node resource usage status, and the data transmission delay and traffic distribution during the cross-chain communication process are obtained in real time. The node resource usage status includes CPU usage, memory usage, and network bandwidth usage. The statistical analysis method is used to evaluate the task load of each blockchain node and the real-time delay of the communication link, and identify high-load nodes, communication bottleneck links, and inefficient task allocation. According to the high-load nodes, communication bottleneck links, and inefficient task allocation, a dynamic optimization model based on the Q-learning algorithm is constructed. The state space is set as the node task load, communication link delay, and resource usage status, and the action space is task redistribution and communication path adjustment. By setting the reward function to minimize delay and load balancing, the task scheduling and cross-chain communication path are dynamically adjusted. According to the dynamic optimization model, the optimized task allocation scheme and communication path planning are obtained. According to the optimized task allocation scheme, the resource usage of the node is monitored in real time, and the tasks are reallocated based on the priority of the task and the dynamic changes of the node resources. By dynamically monitoring the cross-chain communication link, the data transmission path is adjusted according to the delay and bandwidth utilization of the current link, and the task allocation and communication path strategy adjusted in real time are obtained. By adjusting the selection and parameter configuration of the encryption algorithm, the security and efficiency of data transmission can be optimized, and the improvement results of encryption algorithm selection and data transmission efficiency can be recorded.

[0052] Exemplarily, in an industrial blockchain system, the distributed task monitoring module obtains the resource usage and communication delay data of the nodes in real time. The CPU usage of node R is 85%, the memory usage is 70%, and the bandwidth usage is 90%. The CPU usage of node S is 50%, the memory usage is 40%, and the bandwidth usage is 60%. The CPU usage of node T is 40%, the memory usage is 35%, and the bandwidth usage is 50%. The delay of the cross-chain communication link R→S is 30ms, and the bandwidth is 50MB per second. The delay of link S→T is 20ms, and the bandwidth is 40MB per second. The delay of link R→T is 50ms, and the bandwidth is 30MB per second. After using statistical analysis methods to evaluate the task load and link performance of each node, it is found that node R is a high-load node with low task allocation efficiency. The communication link R→T has low data transmission efficiency due to high latency and obvious bottlenecks. A dynamic optimization model based on Q-learning is constructed, and the node task load, communication link latency and resource usage are used as the state space. The action space includes reallocating tasks and adjusting communication paths. The reward function is set to minimize task latency and load balancing. After the optimization model runs, task U1 is reallocated from node R to node S, and task U2 remains at node T, but its communication path is adjusted from R→T to R→S→T. The optimized task allocation scheme shows that the CPU utilization rate of node R drops to 60%, the memory utilization rate is 50%, and the bandwidth utilization rate drops to 70%. The CPU utilization rate of node S rises to 65%, the memory utilization rate is 55%, and the bandwidth utilization rate rises to 75%. The resource usage of node T remains stable. The delay of the cross-chain communication link is re-evaluated as R→S30ms and S→T20ms. The total path delay is reduced from 50ms to 45ms, and the bandwidth utilization is balanced. On this basis, the data transmission encryption algorithm is adjusted. The data of task U1 is encrypted with AES-128, and the bandwidth overhead is reduced to 5MB per second. Task U2 is adjusted to RSA encryption because it involves highly sensitive information. The security is improved but the bandwidth occupancy is 10MB per second. The final record shows that the completion time of task U1 is optimized from 500ms to 450ms, and the completion time of task U2 is optimized from 700ms to 620ms. The integrity verification of encrypted transmission has passed, and the system data transmission efficiency has increased by 20%. The optimization scheme has achieved balanced task allocation, efficient communication paths, and enhanced security.

[0053] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the concept of the present application. For example, the above features are replaced with the technical features with similar functions disclosed in the present application (but not limited to) to form a technical solution.

Claims

1. A blockchain multi-node secure communication method for industrial Internet, characterized in that: The method comprises: According to the business characteristics and security requirements of the task, determine the appropriate blockchain type for task partitioning, combine the dependencies between tasks on the chains, build a multi-chain coordination mechanism, and obtain the inter-chain interaction path table; Based on the inter-chain interaction path table, a topological sorting algorithm is used to linearize the dependencies between tasks, and to determine the task partition optimization scheme and the inter-chain interaction path update data; According to the task partition optimization scheme, match the appropriate node resources for each task, and optimize the task allocation based on the processing capacity and current load of each node; Based on the task allocation results, determine the cross-chain communication requirements and optimal routing paths between tasks, and determine the cross-chain protocol and encryption mechanism between blockchains based on the security requirements of the tasks; According to the priority and dependency of tasks, we build a task scheduling plan, and based on the load, task type and communication requirements of different blockchain nodes, we build a multi-level blockchain node hierarchical architecture and intra-chain and inter-chain communication architecture, and formulate a parallel communication scheduling plan; Based on the node resource usage and task execution status, a dynamic load balancing strategy is formulated to evaluate the optimized task execution time and data transmission efficiency, and obtain the optimized task scheduling solution; Through the distributed task monitoring module, the task execution progress and node resource usage status of each blockchain node can be obtained in real time, a dynamic optimization model based on the Q-learning algorithm can be built, and task scheduling and cross-chain communication paths can be dynamically adjusted.

2. The method according to claim 1, wherein: According to the business characteristics and security requirements list of the task, the adapted blockchain type is determined to partition the task, and the multi-chain coordination mechanism is constructed in combination with the dependencies between the tasks of the chains to obtain the inter-chain interaction path table, including: According to the task list of the current industrial blockchain system, obtain all pending task relationship data, business characteristics and security requirements list. Task relationship data includes task priority, data flow path and dependency table. Business characteristics include high-frequency tasks, low-latency tasks and big data storage tasks. The security requirements list includes task data encryption requirements, storage requirements and transmission conditions. According to the business characteristics and security requirements list of the task, determine the appropriate blockchain type for task partitioning. The blockchain types include public chain, private chain and alliance chain. Combined with the business characteristics of the task, determine the optimal blockchain allocation strategy by calculating the throughput, latency and storage capacity of the blockchain. ; Use network simulation tools to simulate task allocation results, verify whether task allocation meets performance and security requirements, and obtain optimized task allocation table and chain type matching results; According to the task allocation table and chain type matching results, obtain the performance indicators of each chain, including throughput, latency and security, adopt cross-chain communication protocols, combine the dependencies between chain tasks, and build a multi-chain collaboration mechanism, including but not limited to cross-chain communication protocols and inter-chain data sharing; Use network simulation tools to simulate cross-chain task interactions, evaluate the latency and throughput performance of communication protocols, obtain the optimization scheme of the multi-chain collaboration mechanism and the inter-chain interaction path table, and determine the priority and scheduling strategy of cross-chain data sharing.

3. The method according to claim 1, wherein: Based on the inter-chain interaction path table, a topological sorting algorithm is used to linearize the dependencies between tasks, and to determine the task partition optimization scheme and the inter-chain interaction path update data, including: Based on the inter-chain interaction path table, the dependency information in the task allocation table is obtained, and a directed acyclic graph is constructed to represent the dependency structure of the task; a topological sorting algorithm is used, starting from the node with in-degree 0, the node is recursively removed and added to the sorting queue, and the execution order of the task and the inter-chain interaction point identification table are obtained by running the algorithm; a network simulation tool is used to simulate the task allocation and execution process in a multi-chain environment, and by monitoring the execution time of each task, the inter-chain communication delay and the data transmission security, the statistical data of the throughput and task completion time are recorded, and the integrity of data encryption, transmission and storage is evaluated to obtain the execution efficiency evaluation report and security analysis results; according to the execution efficiency evaluation report and the security analysis results, a collaborative optimization algorithm is used to adjust the task allocation and inter-chain communication strategy; Re-simulate the task execution process through network simulation tools, calculate the throughput, latency and security indicators under the optimization plan, judge whether the optimization effect meets the business needs, and obtain the task partition optimization plan and inter-chain interaction path update data based on the adjustment results; build a multi-chain collaborative simulation test environment based on the task partition optimization plan and inter-chain interaction path update data, and verify the execution order of tasks, inter-chain communication paths and data security performance item by item by executing all task chains, and monitor the latency, throughput and task completion rate in the simulation test to judge the rationality of multi-chain collaborative partitioning.

4. The method according to claim 1, wherein: The task partition optimization scheme is used to match the node resources suitable for each task, and the task allocation is optimized based on the processing capacity and current load of each node, including: According to the task partition optimization plan, the computing resource requirements, storage requirements, and bandwidth requirements of each task on different blockchains are evaluated, and the hardware performance, bandwidth status, and network latency of the blockchain nodes are combined to match the appropriate node resources for each task; according to the processing power and current load of each node, a weighted polling algorithm is used to schedule tasks, allocate computing resources, and optimize task allocation.

5. The method according to claim 1, wherein: Based on the task allocation results, the cross-chain communication requirements and optimal routing paths between tasks are determined, and the cross-chain protocol and encryption mechanism between blockchains are determined according to the security requirements of the tasks, including: Based on the task allocation results, determine the cross-chain communication requirements between tasks, and use the inter-chain messaging protocol for cross-chain communication; based on the bandwidth, latency, and data encryption mechanism of different chains, use the Dijkstra algorithm to optimize the cross-chain communication links between tasks and determine the optimal routing path; According to the cross-chain communication needs and security requirements of the task, select the appropriate cross-chain protocol, including the IBC protocol and Polkadot's shared security protocol; Develop encryption mechanisms between blockchains and use encryption algorithms, including but not limited to hashing, digital signatures, and zero-knowledge proofs, to conduct cross-chain encrypted communications; The network simulation tool is used to simulate routing delay and throughput prediction to verify whether the routing strategy meets the requirements of time-sensitive tasks.

6. The method according to claim 1, wherein: According to the task priority and dependency, a task scheduling scheme is constructed, and based on the load, task type and communication requirements of different blockchain nodes, a multi-level blockchain node hierarchical architecture and intra-chain and inter-chain communication architecture are constructed, and a parallel communication scheduling scheme is formulated, including: Based on the priority and dependency of tasks, a task scheduling scheme is constructed to process tasks in parallel on blockchain nodes; according to the task scheduling scheme, the data traffic distribution between nodes is obtained, and the communication frequency and data size within and between each chain are determined; by adopting a distributed queue model, data transmission queues between nodes are formulated, and the capacity, priority rules and number of parallel transmission channels of each queue are determined; according to the sensitivity and real-time requirements of the tasks, queue priority rules are constructed, the priority processing strategy for sensitive data is determined, and tasks in the message queue are sorted; according to the load, task type and communication requirements of different blockchain nodes, a multi-level blockchain node hierarchical architecture and intra-chain and inter-chain communication architecture are constructed by adopting a distributed graph algorithm, and a parallel communication scheduling scheme is formulated; through network simulation tools, task scheduling and cross-chain communication scenarios are simulated, and the node load, packet loss rate and communication delay in the parallel communication scheduling simulation process are counted to evaluate whether the parallel communication scheduling scheme optimizes the task execution time, and verify the encryption and integrity of sensitive data during transmission.

7. The method according to claim 1, wherein: The method of formulating a dynamic load balancing strategy based on the node resource usage status and task execution status, evaluating the optimized task execution time and data transmission efficiency, and obtaining an optimized task scheduling solution includes: According to the node resource usage and task execution status, formulate dynamic load balancing strategies, adjust the load distribution of each node, and optimize resource utilization; Based on the implementation results of the load balancing strategy, the Q-learning algorithm is used to adjust the dynamic load balancing strategy according to the real-time status of the task, including task execution time, communication delay and node load, to optimize the load distribution and resource utilization efficiency among nodes; by evaluating the optimized task execution time and data transmission efficiency, the optimized task scheduling plan is obtained.

8. The method according to claim 1, wherein: The distributed task monitoring module is used to obtain the task execution progress and node resource usage status of each blockchain node in real time, build a dynamic optimization model based on the Q-learning algorithm, and dynamically adjust task scheduling and cross-chain communication paths, including: Through the distributed task monitoring module, the task execution progress of each blockchain node, the node resource usage status, and the data transmission delay and traffic distribution during the cross-chain communication process are obtained in real time. The node resource usage status includes CPU usage, memory usage, and network bandwidth usage; Statistical analysis methods are used to evaluate the task load of each blockchain node and the real-time delay of the communication link, and to identify high-load nodes, communication bottleneck links, and inefficient task allocation. According to high-load nodes, communication bottleneck links, and inefficient task allocation, a dynamic optimization model based on the Q-learning algorithm is constructed, and the state space is set as the node task load, communication link delay, and resource usage, and the action space is task reallocation and communication path adjustment. By setting the reward function to minimize delay and load balancing, the task scheduling and cross-chain communication path are dynamically adjusted. According to the dynamic optimization model, the optimized task allocation scheme and communication path planning are obtained. According to the optimized task allocation scheme, the resource usage of the node is monitored in real time, and the tasks are reallocated based on the priority of the task and the dynamic changes of the node resources. By dynamically monitoring the cross-chain communication link, the data transmission path is adjusted according to the delay and bandwidth utilization of the current link, and the real-time adjusted task allocation and communication path strategy is obtained. By adjusting the selection and parameter configuration of the encryption algorithm, the security and efficiency of data transmission are optimized, and the improvement results of the encryption algorithm selection and data transmission efficiency are recorded.

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