Industrial internet performance improvement method based on intelligent block chain fragmentation

Abstract

The invention discloses an industrial internet performance improvement method based on intelligent block chain fragmentation, and belongs to the technical field of communication networks. According to the invention, a block chain intelligent fragmentation and adaptive consensus protocol switching method based on a reputation mechanism is adopted, so that the expandability of a block chain system is greatly improved, and the security and decentration of the block chain system can be effectively protected. In addition, cloud side end cooperative computing is introduced to provide computing power support for the fragmented block chain, and parallel computing unloading and terahertz communication technologies are adopted to enhance the collaboration among three layers of cloud side end networks. And finally, an optimization problem is modeled as a Markov decision process, and a near-end strategy optimization algorithm is adopted for solving. A simulation result shows that the system total delay and the block chain transaction throughput of the method are superior to those of other methods.

Description

Technical Field

[0001] The present invention relates to an Industrial Internet of Things (IIoT) performance improvement method based on intelligent blockchain sharding, and belongs to the technical field of communication networks. Background Art

[0002] In today's ever-changing Internet era, the traditional industrial field is undergoing a profound transformation, and its boundaries are constantly expanding, moving from simple digitization to a new stage of intelligence. This historic leap has strongly driven the vigorous development of the cutting-edge field of Industrial Internet of Things (IIoT). As a product of the deep integration of industry and information technology, IIoT aims to build a highly interconnected, intelligent decision-making, and automated industrial ecosystem through the comprehensive interconnection of industrial equipment, work scenarios, and personnel. However, while enjoying the unlimited convenience and flexibility brought by open networks, this architecture may expose industrial data to potential security risks and reliability challenges.

[0003] Blockchain is a hash-proof storage structure that integrates distributed storage, consensus mechanism and encryption algorithm. It can provide innovative solutions to the data security and reliability problems that have long plagued IIoT. The uniqueness of blockchain technology lies in its decentralized architecture design, which not only gives the system a high degree of openness and transparency, but also ensures that the data cannot be tampered with. However, blockchain technology faces a severe "trilemma" in the process of pursuing decentralization, security and scalability at the same time. In addition, blockchain consensus protocols often require huge computing resources as support, and this feature is particularly prominent in sharded blockchain systems, resulting in limited transaction throughput and a significant increase in latency, which seriously restricts the widespread deployment and application of sharded blockchain systems.

[0004] Sharding technology can significantly enhance the scalability of blockchain without affecting the decentralization and security of blockchain, thus cleverly solving the "trilemma". In recent years, with the continuous advancement of technology, a variety of sharding algorithms have emerged and have been innovatively integrated into the IIoT system that supports blockchain, aiming to fundamentally solve the bottleneck of scalability. However, it is worth noting that although sharding technology has shown great potential, its adaptability to the highly dynamic characteristics of IIoT is often overlooked. On the other hand, in response to the heavy burden of blockchain consensus protocols on computing resource consumption, researchers have proposed a series of new consensus mechanisms tailored for dynamic IIoT scenarios. These innovative attempts have undoubtedly opened up new paths to alleviate resource pressure and improve system efficiency. However, considering the continuous dynamic changes of IIoT, each consensus protocol has its specific applicable scenarios and limitations. Therefore, a single consensus protocol cannot provide a suitable solution for IIoT.

[0005] In addition, industrial equipment generally faces the challenge of limited computing resources, making it difficult for them to independently undertake the complex and heavy computing and verification tasks in the sharded blockchain system, which greatly restricts the performance of the IIoT system based on the sharded blockchain. To this end, the cloud-edge-end collaborative computing architecture has become a key strategy for dealing with massive data task processing with its flexibility and powerful real-time computing capabilities. At present, many methods have focused on enhancing the computing power of blockchain-based IIoT with the help of cloud-edge-end collaborative architecture. However, in view of the current situation of scarce computing resources and insufficient processing power of devices in IIoT scenarios, the method of deeply integrating cloud-edge-end collaborative computing with sharded blockchain technology is still in the exploratory stage, and relevant results are not yet rich. In addition, the existing methods are still insufficient in optimizing the computing offloading efficiency and data transmission rate between the cloud layer and the edge layer, and have failed to fully tap the potential advantages of cloud-edge collaboration to achieve efficient configuration of computing resources and rapid transmission of industrial data.

[0006] In summary, in view of the above challenges, we propose an industrial Internet performance improvement method based on intelligent blockchain sharding, aiming to improve the overall response speed (i.e., reduce the total latency) and transaction processing capability (i.e., enhance transaction throughput) of the IIoT system based on sharded blockchain. To achieve this goal, this method formulates the above joint optimization problem as a Markov decision process and solves it through the proximal policy optimization (PPO) algorithm. Summary of the invention

[0007] The present invention is aimed at the highly complex and dynamic Industrial Internet of Things (IIoT) system, which is characterized by its huge scale, wide distribution of equipment, and integration of multi-blockchain sharding network and edge intelligent server, supplemented by a single cloud server as computing support. In this complex scenario, the present invention uses the dynamic switching mechanism of intelligent blockchain sharding and adaptive consensus protocol, with the minimization of the total delay of the IIoT system and the maximization of the blockchain transaction throughput as the joint optimization goal, to model and optimize the scenario problem. Specifically, this method deeply considers the interconnection and interoperability of massive industrial equipment, the collaborative consensus between multiple blockchain sharding networks, and the collaborative computing of multiple edge intelligent servers and cloud servers, and introduces the advanced proximal policy optimization (PPO) algorithm to perform deep iterative learning and optimization on the constructed complex system model. This process not only promotes the optimization of resource allocation, but also ensures the efficient and secure sharing of industrial data in the IIoT network, while significantly reducing the overall response delay of the system and greatly improving the processing efficiency of blockchain transactions. In summary, the present invention adopts blockchain intelligent sharding based on reputation mechanism and adaptive switching of multi-blockchain consensus protocol, which can effectively ensure its security and decentralization while greatly improving the scalability of blockchain system. In addition, this method introduces a cloud-edge-end collaborative computing framework to enhance the computing power of the sharded blockchain system, and adopts parallel computing offloading methods and terahertz communication technology to enhance the synergy between the three-layer network of cloud-edge-end. It provides strong technical support and solutions for the intelligent transformation and sustainable development of IIoT field.

[0008] The IIoT model of cloud-edge-end collaborative computing enabling blockchain intelligent sharding adapted by the present invention is shown in Figure 1 .

[0009] The system operation principle flow chart in the technical solution of the present invention is shown in Figure 2 .

[0010] The relationship between the blockchain transaction throughput and the average transaction size is shown in the figure Figure 3 .

[0011] The relationship between the blockchain transaction throughput and the number of IIoT devices in this invention is shown in Figure 4 .

[0012] The relationship between the total delay of the system of the present invention and the number of blockchain shards is shown in Figure 5 .

[0013] like Figure 1As shown in the figure, the present invention constructs the IIoT industrial network into a three-layer collaborative computing network mode of cloud, edge and end. The architecture is composed of the device layer, edge layer and cloud layer from bottom to top. In the edge layer, M edge intelligent servers are deployed. Although these servers have limited computing resources, they undertake key tasks such as data processing and transaction consensus. M = {1, 2, ..., M} represents the set of all edge intelligent servers. At the same time, we introduce the set H = {1, 2, ..., H} to dynamically reflect the current available computing resources of each edge intelligent server.

[0014] At the device layer, a large number of intelligent devices are deployed. These devices are like the tentacles of the industrial network, widely distributed in various production links, collecting and transmitting real-time critical industrial data such as environmental status and production parameters. In order to systematically manage these devices, we define their node set as N = {1, 2, ..., N}.

[0015] The blockchain network consists of edge nodes and device nodes. In view of the two different transaction types, cross-shard transaction (CST) and intra-shard transaction (IST), data tasks in the IIoT environment can be flexibly allocated, which can be verified by edge intelligent server nodes or undertaken by device nodes. In order to further optimize the efficiency and security of transaction processing, the present invention innovatively introduces a blockchain sharding mechanism based on the reputation level of device nodes. Under this mechanism, all device nodes can be evenly divided into multiple blockchain shards according to the reputation level, and each shard processes IST independently and in parallel, effectively improving the concurrent processing capability of the blockchain system. The set of these shards is represented by K = {1,2,...,K}. It is worth noting that the edge server node plays a key role in the system, specifically responsible for processing more complex CSTs to ensure the security and efficiency of cross-shard transactions. At the same time, in order to maintain the efficiency of the sharded blockchain architecture, the edge server node does not directly participate in the blockchain sharding. This design not only ensures the scalability of the blockchain system, but also enhances its flexibility and robustness in processing diversified IIoT data tasks.

[0016] When the system receives an IIoT data processing task, the intelligent agent will configure the sharding model, blockchain consensus model, data transmission model and computing model accordingly based on the real-time environmental conditions to adapt to data task characteristics such as transaction batch size and data task size. Subsequently, the intelligent agent constructs a deep reinforcement learning framework, which specifically includes designing a state space to reflect the system state, defining an action space to cover potential optimization strategies, and establishing a reward function to quantify the effectiveness of the strategy and motivate the intelligent agent to conduct optimal exploration. In addition, based on the constructed system model, the intelligent agent will first initialize the parameters of the training network and start the iterative learning process. In this process, the deep neural network interacts with the environment to continuously update and adjust its network parameters based on environmental feedback to achieve the optimal IIoT resource optimization strategy with low system latency and high blockchain transaction throughput. Specifically, follow the following steps in sequence:

[0017] In step (1), the IIoT industrial device requests a data task and uploads the data task to the edge layer. The edge intelligent server decides the number of blockchain shards based on the transaction batch size of the data task, the size of the data task, and other information, and executes blockchain sharding based on the reputation mechanism. The sharding strategy should meet the following rules: all device nodes with the same reputation level should be evenly divided into each partition, and the master node in each partition should be elected based on the reputation level as the only indicator; all partitions should maintain similar reputation levels and similar ratios of honest nodes and malicious nodes; nodes with extremely low reputation levels will be restricted from participating in the consensus verification of data tasks.

[0018] In the present invention, the reputation level of a device node is represented by R, which is expressed as a finite state Markov process. In addition, R is split into r values ​​and represented as

[0019] X={x1,x2,...,x r}

[0020] In addition, R will evolve to the next state according to the state transition probability matrix, expressed as

[0021]

[0022] Therefore, the r×r state transition matrix of the device node reputation level R is expressed as

[0023]

[0024] Before each round of transactions, the controller will send a shard permission signal to the blockchain system. Then, all device nodes will be assigned to each blockchain shard according to the reputation mechanism and wait for the assignment of data tasks. The specific steps are as follows.

[0025] Step (1.1), first, the intelligent agent obtains relevant environmental information to determine the number of blockchain shards.

[0026] In step (1.2), the intelligent agent obtains the reputation level of all device nodes and records it on the reputation blockchain.

[0027] In step (1.3), each device node will be assigned to the shard with the smallest current size according to its reputation level to balance the size of each shard and maintain the ratio of honest and dishonest nodes in each block.

[0028] In step (1.4), the device nodes with the lowest reputation level ranking will be warned, and the device nodes with the lowest reputation level ranking will be restricted from participating in the consensus verification process. In addition, the blockchain system will be resharded after each round of transactions.

[0029] In step (2), blockchain intelligent sharding divides the transaction processing types of the blockchain into two types: intra-shard transactions and cross-shard transactions. Intra-shard transactions are verified and processed by the IIoT device nodes in each shard, while cross-shard transactions are jointly responsible for consensus verification by the transaction parties (device nodes) and the connected edge intelligent servers.

[0030] Due to the dynamic characteristics of IIoT, it is difficult for a single blockchain consensus protocol to be applied to IIoT data tasks. In this case, the present invention considers a dynamic switching model of multiple blockchain consensus protocols to match the most appropriate consensus protocol for each data task. There are three blockchain consensus protocols used, as shown below:

[0031] Step (2.1), Practical Byzantine Fault Tolerance (PBFT) was proposed by M.Castro et al. in 1999 and improved in 2002. PBFT reduces the algorithm complexity to the polynomial level based on Byzantine Fault Tolerance (BFT), making it feasible in practical applications such as Hyper-ledger Fabric. In addition, PBFT is highly robust because consensus verification can be performed without errors when the number of Byzantine nodes does not exceed one-third. However, due to the complex node broadcasting process, PBFT consumes higher latency and computational overhead. Assume that the number of consensus nodes is N c , the signature calculation period is χ, and the message authentication code (MAC) calculation period is δ, then the PBFT consensus process is as follows:

[0032] Request: The client first submits a consensus request, then packages the transaction data into blocks, and the signature and MAC are verified by the master node. Therefore, the computation cycle of this process is expressed as

[0033]

[0034] Among them, B(t) is the transaction batch size and A(t) is the average transaction size.

[0035] Pre-preparation: Initially, a signature and N c -1 MAC is generated and forwarded to all replica nodes participating in block verification. Each replica node then verifies the received signature and MAC. Therefore, the computation cycle of the master node is the time required to generate the signature and MAC and forward them to the replica, while the computation cycle of the replica is the time required to verify the received signature and MAC. As mentioned above, the computation cycle of the master node is

[0036] c2 p (t) = x + (N c -1)δ

[0037] The calculation period of the replica node is

[0038]

[0039] Among them, ρ is the probability of correct transaction verification.

[0040] Preparation: The preparation phase involves the process of completing information broadcasting between consensus nodes. Specifically, the replica nodes sign the verified blocks and generate N c -1 MAC to broadcast to all nodes including the master node. It is worth noting that when the number of authentication nodes is not less than 2ζ (where ζ = (N c -1) / 3), PBFT can ensure the effectiveness of the consensus verification process. Therefore, the calculation cycle of the master node in this process is

[0041] c3 p (t) = 2ζ(χ+δ)

[0042] The calculation period of the replica node is

[0043] c3 r (t) = 2ζ(χ+δ)+χ+(N c -1)δ

[0044] Submit: In the submission phase, all nodes need to exchange and verify block information. Specifically, a signature and N c -1 MAC, and verify the received signature and MAC. Therefore, the calculation cycle of this stage can be given by the following formula

[0045] c4(t)=2ζ(χ+δ)+χ+(N c -1)δ

[0046] Reply: In this phase, the consensus node replies with a verification message to the client. In addition, if the client receives more than 2ζ reply messages, the consensus verification process is considered valid. At this point, the newly generated block will be appended to the blockchain. Therefore, the calculation formula for the calculation cycle of this phase is

[0047] c5(t)=2ζ(χ+δ)+(χ+δ)

[0048] In summary, the computational cycle required for PBFT is

[0049]

[0050] Step (2.2), Zyzzyva algorithm. Byzantine faults are recognized as the most serious faults in distributed systems. The BFT algorithm provides an effective method for dealing with Byzantine faults, but its highly redundant verification procedures consume a lot of computing resources and affect the transaction efficiency of the blockchain. Since computer systems rarely encounter errors now, speculative BFT consensus protocols such as Zyzzyva have received widespread attention. The Zyzzyva consensus protocol can skip the call of the fault-tolerant mechanism in non-faulty situations, thereby significantly improving the performance of the blockchain. Similar to PBFT, Zyzzyva can ensure the correctness of transactions when the number of honest nodes exceeds 2ζ. When all nodes are honest, Zyzzyva can skip node broadcasts and directly perform speculative execution. Otherwise, if there are malicious nodes, Zyzzyva will execute the speculative execution at t r During this time, the recovery process is started.

[0051] In the fast-case scenario, Zyzzyva’s authentication process is divided into three phases, namely, request, service request, and speculative reply. Therefore, the computation cycle can be expressed as

[0052]

[0053] In the two-phase case, Zyzzyva's authentication process is divided into five phases, namely, request, service request, speculative reply, submission, and reply. Therefore, the computation cycle in the two-phase case is expressed as

[0054]

[0055] In step (2.3), the Quorum consensus protocol uses the pigeonhole principle to implement a simple communication model. Specifically, it only requires one message interaction between the client and the replica to complete the transaction verification. The consensus process of the Quorum consensus protocol consists of two steps, namely request and reply. It should be noted that the reply information includes the historical summary of the replica. If the client receives less than N replies, c If the number of quorums is less than 1, the panic mechanism will be called and the verification process will be terminated. Therefore, the calculation cycle of Quorum is expressed as

[0056]

[0057] In addition, the data integrity and network robustness of the blockchain require that the proportion of malicious nodes is within the safety range of the specific consensus protocol (PBFT, Zyzzyva, and Quorum can tolerate up to one-third of malicious validators). Therefore, to ensure the security of the proposed architecture, the following constraints are imposed:

[0058] 3N p +1≤N c

[0059] Among them, N p is the number of Byzantine nodes, N c is the number of consensus nodes.

[0060] For intra-shard transactions, the consensus node consists of device nodes within a single shard, so N c =N / K. Obviously, the number of shards K must obey the following constraints:

[0061]

[0062] Step (3), terahertz communication technology is a wireless communication technology that uses terahertz frequency band electromagnetic waves as carriers. The terahertz frequency band can support a transmission rate of more than 10Gbps and has excellent confidentiality and anti-interference capabilities. Therefore, the present invention uses terahertz communication technology to further optimize the computing offloading link from the edge layer to the cloud layer.

[0063] Assuming that the average distance of the unloading link is l, when the terahertz electromagnetic wave with a frequency of ω propagates in the medium, the molecular absorption loss caused by the interaction between the electromagnetic wave and various molecules and atoms in the medium is:

[0064]

[0065] Here, τ represents the transmittance and κ(ω) is the absorption coefficient.

[0066] Propagation loss, also known as free space loss, refers to the loss caused by electromagnetic waves penetrating a medium. In terahertz communication technology, the formula for calculating propagation loss is:

[0067]

[0068] Where c = 2.9979 × 10 8 m / s represents the speed of light in a vacuum (the speed of light).

[0069] The total loss of terahertz communication technology is the sum of molecular absorption loss and free space loss, expressed as:

[0070]

[0071] In the terahertz transmission link, the noise temperature of the cloud receiver mainly comes from the molecular absorption noise. The equivalent noise temperature generated by molecular absorption is expressed as:

[0072] N abs T =N0 T ε

[0073] Among them, N0 T = 296 K refers to the reference temperature, ε = 1-τ represents the emissivity of the calculated unloading channel, and τ represents the transmittance.

[0074] Therefore, for a given bandwidth B, the noise power absorbed by the molecule is given by:

[0075]

[0076] According to the above analysis, the signal-to-noise ratio can be expressed as:

[0077]

[0078] Among them, P t (t) is the transmission power of the edge intelligent server. In addition, and Refers to the antenna gain of the transmitting end and the receiving end respectively.

[0079] Therefore, the data transmission rate based on terahertz communication technology can be calculated as follows:

[0080]

[0081] Step (4), in the framework proposed by the present invention, the edge intelligent server undertakes heavy verification and calculation tasks. Therefore, in order to better assign tasks to the edge intelligent server, we need to obtain the computing resources of the edge intelligent server at any time.

[0082] Step (4.1), in this case, we model the computing resources H of the edge intelligent server as a Markov process and divide it into h discrete values, which can be expressed as:

[0083] Y={y1,y2,...,y h}

[0084] In addition, the computing resources of the edge intelligent server evolve to the next state according to the state transition probability matrix, as shown in the following formula:

[0085]

[0086] Therefore, the state transition probability matrix can be expressed as:

[0087]

[0088] Step (4.2), since the computing resources of the edge intelligent server are relatively limited, it is extremely challenging to perform multiple tasks including cross-shard consensus verification and data calculation. Therefore, in order to reduce the edge burden and improve the computing efficiency of the system, we consider a parallel offloading strategy from the edge layer to the cloud layer. Assuming that the computing offloading ratio is O∈[0,1], the computing delay of the edge intelligent server and the cloud server can be expressed as:

[0089]

[0090] and

[0091]

[0092] Among them, G(t) refers to the calculation period, F m and F c denote the computing frequencies of the edge intelligence server and the cloud server, respectively. In addition, D(t) represents the data task size and λ(t) refers to the data transmission rate.

[0093] Therefore, according to the barrel effect, the total computational delay of parallel computing can be given by the following formula:

[0094] t1(t)=max{t1 m (t),t1 c (t)}

[0095] Step (4.3), in order to efficiently execute the data tasks of IIoT, it is necessary to clearly classify the transaction types based on the blockchain sharding. As we all know, the transaction types in IIoT supported by sharded blockchain can be roughly divided into two types, namely IST and CST.

[0096] The consensus delay of IST can be expressed as:

[0097]

[0098] Among them, the value of C(t) comes from the set {CP (t),C Z 1 (t),C Z 2 (t),C Q (t)}. In addition, T I represents the block interval, F d Refers to the frequency of the device, t b Represents the broadcast delay between consensus nodes.

[0099] The consensus delay of CST can be expressed as:

[0100]

[0101] In summary, the total delay is calculated as:

[0102] T(t)=t1(t)+t2(t)

[0103] Among them, t1(t) represents the calculation delay, and its value is t1 m (t) or t1 c (t). In addition, t2(t) is the consensus delay, which depends on the transaction type of the blockchain and is taken as t2 d (t) or t2 m (t).

[0104] Step (4.4), in addition, the present invention evaluates the transaction throughput of the blockchain system by calculating the number of transactions completed per unit time. In addition, since all shards verify the IIoT data tasks in parallel, the throughput Ξ is positively correlated with the number of blockchain shards K. Specifically, the calculation formula for the transaction throughput Ξ is:

[0105]

[0106] Step (5): According to steps (1)-(4), combined with the environment and optimization objectives, set the state space, action space and reward function of the PPO algorithm. The specific steps are as follows:

[0107] Step (5.1), the state space defined in this invention consists of five key variables, namely, data task size D(t), transaction batch size B(t), transmission rate λ(t), computing resources H of edge intelligent server and reputation level R of all device nodes. Therefore, the state space is set as:

[0108] S(t)={D(t),B(t),λ(t),H,R}

[0109] The data task size D(t) ranges from 4MB to 8MB, and the transaction batch size B(t) ranges from 1MB to 2MB. Due to the diversity and dynamic characteristics of the wireless link conditions of computational offloading, the data transmission rate is modeled as a finite state Markov decision process. The data transmission rate assigned to each computational offloading task comes from the set {125, 100, 75, 50} Mbps, and its probability transfer matrix is ​​expressed as:

[0110]

[0111] In step (5.2), in order to obtain better long-term returns, it is necessary to flexibly adjust the action strategy according to environmental changes. Therefore, the action space is set as follows:

[0112] A(t)={S B ,T I ,O,C(t),K}

[0113] Among them, S B = {1, 2, ..., B} represents the block size, T I ={0.5,1,...,I} represents the block interval. In addition, O∈[0,1] represents the computation offloading ratio. C(t) represents the decision factor of the consensus protocol. In addition, K represents the number of shards.

[0114] In step (5.3), in order to jointly optimize transaction throughput and total latency, the reward function is defined as the weighted sum of the inverse of throughput and total latency, and its formula is:

[0115]

[0116] stC1:D(t)≤S B

[0117] C2:T(t)≤T I

[0118] C3:K≤N / (3N p +1)

[0119] Among them, α1+α2=1, ν is a reward and punishment parameter.

[0120] Step (6), based on the state space, action space and reward function constructed in step (5), complete the setting of parameters such as the PPO algorithm execution strategy and neural network training.

[0121] PPO is a deep reinforcement learning algorithm proposed by Open-AI in 2017 and is considered to be one of the most widely applicable algorithms. The main idea of ​​PPO is to control the update amplitude of the policy within a controllable range. In addition, the PPO algorithm updates the policy by maximizing the objective function, which consists of two parts, namely the expected return of the policy and the constraint term of the policy update amplitude. Specifically, the PPO algorithm estimates the expected return of the policy by sampling multiple trajectories. The PPO algorithm has many advantages, such as excellent stability and convergence. In addition, PPO can also balance the exploration and exploitation of the policy by adjusting the weight of the constraint term, so as to better adapt to different environments.

[0122] Considering the dynamic characteristics of IIoT, in this method, cloud servers with super computing power are considered as intelligent agents of the PPO network. In addition, the output of the actor network is a strategy π that follows a normal distribution. θ , whose mean and variance are μ(s t ) and σ(s t ). The intelligent agent follows a normal distribution policy π θ The initial action is sampled in the environment and fed back to the environment to obtain information such as the next state.

[0123] To ensure the consistency of policy optimization, the cut generation objective function (CSOF) is introduced in PPO, and then the mini-batch stochastic gradient ascent method is used to optimize it. The calculation formula of CSOF is:

[0124]

[0125] Among them, ε represents the shear index, r(θ) represents the ratio of the previous strategy to the new strategy, which is used to ensure the difference between the new and old strategies, as follows:

[0126]

[0127] also, represents the generalized advantage estimation function, where V φ (s t ) represents the approximate value function. In addition, the critic network updates the strategy by gradient descent of the loss function, which can be expressed as:

[0128]

[0129] The advantage of an action at a given time t can be quantified to some extent, which is defined as:

[0130]

[0131] in and Represent the value functions of actions and states respectively.

[0132] Finally, the mini-batch stochastic optimization algorithm is used to update the performer and critic parameters θ and φ respectively through gradient ascent and gradient descent as follows:

[0133]

[0134] Step (7), according to the deep neural network trained in step (6), the optimal strategy for the optional actions in each state is obtained, the action generated by the strategy is used as the optimal action in the state, and the optimal action in each state is continuously executed until the execution of the instruction is completed.

[0135] The advantage of the present invention is that it integrates multiple blockchain sharding networks and edge intelligent servers for highly complex, large-scale, widely distributed and dynamic industrial Internet of Things (IIoT) systems, supplemented by a single cloud server for computing support. The present invention uses intelligent blockchain sharding and adaptive consensus protocol dynamic switching mechanism, with the minimization of the total delay of the IIoT system and the maximization of blockchain transaction throughput as joint optimization goals, to model and optimize scenario problems. Specifically, this method deeply considers the interconnection and interoperability of massive industrial equipment, the collaborative consensus between multiple blockchain sharding networks, and the collaborative computing of multiple edge intelligent servers and cloud servers, and introduces advanced proximal policy optimization (PPO) algorithms to perform deep iterative learning and optimization on the constructed complex system model. This process not only promotes the optimization of resource allocation, but also ensures the efficient and secure sharing of industrial data in the IIoT network, while significantly reducing the overall response delay of the system and greatly improving the processing efficiency of blockchain transactions. In summary, the present invention successfully solves key issues such as how to balance data processing speed and system stability in a resource-intensive and network-heterogeneous IIoT environment, and how to achieve efficient transmission and processing of industrial data while ensuring data security, providing strong technical support and solutions for the intelligent transformation and sustainable development of the IIoT field. BRIEF DESCRIPTION OF THE DRAWINGS

[0136] Figure 1 ,The communication scenario model includes the structural diagram of IIoT intelligent industrial equipment, ,intelligent edge server, controller, cloud computing server and blockchain system.

[0137] Figure 2 , Flowchart for designing the performance improvement method of industrial Internet based on intelligent blockchain sharding.

[0138] Figure 3 , a graph showing the relationship between blockchain transaction throughput and average transaction size, in which a cross represents the method of the present invention, a diamond represents a method for fixing the number of blockchain shards, a circle represents a binary computation offloading method (non-parallel offloading), and a square represents a single blockchain consensus protocol method (non-dynamic switching of multiple blockchain consensus protocols).

[0139] Figure 4 , a relationship diagram between blockchain transaction throughput and the number of IIoT devices, in which the cross represents the method described in the present invention, the diamond represents the method based on the deep reinforcement learning algorithm of A3C, the circle represents the method with a fixed number of blockchain shards, and the square represents the non-blockchain sharding method.

[0140] Figure 5 , a graph showing the relationship between the total system delay and the number of blockchain shards. In the graph, the diamond represents a single blockchain consensus protocol method (not dynamic switching of multiple blockchain consensus protocols), the square represents a binary computation offloading method (not parallel offloading), the circle represents a fixed number of blockchain shards method, and the cross represents the method of the present invention. DETAILED DESCRIPTION

[0141] The following is a further explanation of the technical solution of the industrial Internet performance improvement method based on intelligent blockchain sharding with reference to the accompanying drawings and examples.

[0142] This method adopts blockchain intelligent sharding based on reputation mechanism and adaptive switching of multi-blockchain consensus protocol, which can greatly improve the scalability of blockchain system while effectively ensuring its security and decentralization. Due to the scarce computing resources of sharded blockchain system, this method introduces cloud-edge-end collaborative computing framework to enhance the computing power of sharded blockchain system, and adopts parallel computing offloading method and terahertz communication technology to enhance the synergy between the three-layer network of cloud-edge-end. In addition, considering the dynamic characteristics of IIoT, this method models the joint optimization problem of total system delay and blockchain transaction throughput as a Markov decision process (MDP) and solves it through the proximal policy optimization (PPO) algorithm.

[0143] The process flow chart of the present invention is as follows: Figure 2 As shown, the following steps are included:

[0144] Step 1: Set the number of industrial equipment nodes and the number of intelligent edge server nodes, set the number of blockchain shards according to the data task size, transaction batch size and the number of equipment nodes, and execute the intelligent blockchain sharding algorithm based on the reputation mechanism;

[0145] Step 2: Classify the transaction type (intra-shard transaction / cross-shard transaction) for each data task, and match the corresponding blockchain consensus protocol (PBFT / Zyzzyva / Quorum) to complete the consensus verification procedure for the transaction;

[0146] Step 3: Use terahertz communication technology to increase the data transmission rate of the link from the edge layer to the cloud layer for computing offload;

[0147] Step 4: According to the data task size and the computing resources of the intelligent edge server, the computing offload ratio from the edge layer to the cloud layer is intelligently determined, and the consensus verification delay, transmission delay, and computing delay are calculated and summarized to obtain the total system delay. The blockchain transaction throughput is calculated based on the number of blockchain shards, average transaction size, block size, and block interval;

[0148] Step 5: Establish the optimization problem and model the Markov decision process, setting the state space, action space and reward function;

[0149] Step 6: Complete the setting of algorithm execution strategy and neural network training parameters according to the PPO algorithm;

[0150] Step seven: Select the optimal action based on the optimal strategy obtained in each state to obtain the maximum benefit.

[0151] Figure 3 It is a relationship diagram between blockchain transaction throughput and average transaction size. It can be noted that as the average transaction size increases, the blockchain transaction throughput gradually decreases. In addition, the transaction throughput of different schemes is compared vertically. Under different average transaction sizes, the scheme proposed in the present invention can always achieve higher transaction throughput. For example, when the average transaction size is 200B, the transaction throughput achieved by the method of the present invention can reach 2390TPS, while the transaction throughput of the other methods can only reach 1790B at most. This is due to our comprehensive consideration of parallel computing offloading and intelligent sharding blockchain, as well as the use of the PPO algorithm with excellent training efficiency. Parallel computing offloading can reasonably allocate the computing resources of edge intelligent servers, thereby significantly improving computing efficiency. In addition, the intelligent sharding blockchain method can better adapt to the data tasks of IIoT by increasing the number of independent blockchain networks (number of blockchain shards).

[0152] Figure 4It is a relationship diagram between blockchain transaction throughput and the number of IIoT devices. As can be seen from the figure, the scheme proposed by the present invention achieves a higher transaction throughput than other methods, and this advantage becomes more and more obvious as the number of device nodes increases. When the number of device nodes is 70, the transaction throughput corresponding to the method of the present invention is as high as 4446TPS, while the transaction throughput of other methods can only reach 3621TPS at most. In addition, except for the fixed sharding method and the blockchain-free sharding method, the transaction throughput of all schemes increases linearly with the increase in the number of device nodes. This is because as the number of device nodes increases, the intelligent agent tends to create more shards.

[0153] Figure 5 This is a graph showing the relationship between the total system latency and the number of blockchain shards. Figure 5 The total system delay obtained by different methods is compared with the number of blockchain shards as the independent variable. It can be observed that the scheme proposed in the present invention always maintains a low delay under different numbers of blockchain shards. For example, when the number of blockchain shards is 8, the scheme proposed in the present invention can obtain a total delay as low as 2.21s, while other schemes require a minimum total system delay of 3.83s. In addition, when using the method of fixed number of shards (the number of shards is manually adjusted with the independent variable), the delay decreases almost linearly with the increase of the number of shards. This is because the consensus delay consumed by the blockchain during transaction verification decreases linearly with the increase of the number of shards.

[0154] In addition, except for the method with a fixed number of shards, the total latency of all methods does not change significantly with the increase in the number of shards. This is because in the smart sharding blockchain method, the optimal number of blockchain shards is determined by the intelligent agent rather than manually set. In this case, our proposed method can organically combine the blockchain system with the highly dynamic Internet of Things.

Claims

1. A method for improving industrial Internet performance based on intelligent blockchain sharding, characterized in that: The method comprises the following steps: Step 1: Set the number of industrial equipment nodes and the number of intelligent edge server nodes, set the number of blockchain shards according to the data task size, transaction batch size and the number of equipment nodes, and execute the intelligent blockchain sharding algorithm based on the reputation mechanism; Step 2: Classify the transaction type for each data task, i.e. intra-shard transaction / cross-shard transaction, and match the corresponding blockchain consensus protocol to complete the consensus verification procedure for the transaction; Step 3: Use terahertz communication technology to increase the data transmission rate of the link from the edge layer to the cloud layer for computing offload; Step 4: According to the data task size and the computing resources of the intelligent edge server, the computing offload ratio from the edge layer to the cloud layer is intelligently determined, and the consensus verification delay, transmission delay, and computing delay are calculated and summarized to obtain the total system delay; The blockchain transaction throughput is calculated based on the number of blockchain shards, average transaction size, block size, and block interval; Step 5: Establish the optimization problem and model the Markov decision process, setting the state space, action space and reward function; Step 6: Complete the setting of algorithm execution strategy and neural network training parameters according to the PPO algorithm; Step seven: Select the optimal action based on the optimal strategy obtained in each state to obtain the maximum benefit.

2. The method for improving industrial Internet performance based on intelligent blockchain sharding according to claim 1 is characterized in that: In step 1, the number of industrial equipment nodes and the number of intelligent edge server nodes are first set, and the number of blockchain shards is set according to the data task size, transaction batch size and the number of equipment nodes. Then, the intelligent blockchain sharding algorithm based on the reputation mechanism is executed to perform blockchain sharding. The specific steps are as follows: IIoT industrial equipment requests data tasks and uploads them to the edge layer. The edge intelligent server decides the number of blockchain shards based on the transaction batch size and data task size of the data task and executes blockchain sharding based on the reputation mechanism. The sharding strategy meets the following rules: All device nodes with the same reputation level should be evenly divided into various partitions, and the master node in each partition is elected based on the reputation level as the only indicator; all partitions should maintain similar reputation levels and similar ratios of honest nodes and malicious nodes; nodes with extremely low reputation levels will be restricted from participating in the consensus verification of data tasks; The reputation level of a device node is represented by R, which is expressed as a finite state Markov process; in addition, R is split into r values ​​and expressed as X={x1,x2,...,x r } Where X represents the set of different states of reputation level R split in the finite state Markov process, x1, x2, ..., x r They represent the first, second, and up to the rth split states of the credibility level R respectively; In addition, R will evolve to the next state according to the state transition probability matrix, expressed as Among them, x i and x j Indicates the different states of the device node reputation level R, Reputation level R from x i The state evolves to x j The state transition probability matrix of the state, Pr represents the probability, R(t) and R(t+1) represent the device node reputation level R at the current moment and the next moment respectively; The r×r state transition matrix of the device node reputation level R Expressed as Before each round of transactions, the controller sends a sharding permission signal to the blockchain system; then, all device nodes will be assigned to each blockchain shard according to the reputation mechanism and wait for the assignment of data tasks; the details are as follows: First, the intelligent agent obtains relevant environmental information to determine the number of blockchain shards; second, the intelligent agent obtains the reputation level of all device nodes and records it on the reputation blockchain; each device node will be assigned to the smallest shard according to its reputation level to balance the size of each shard and maintain the ratio of honest and dishonest nodes in each block; the bottom 20% of device nodes in the reputation level ranking will be warned, and the bottom 5% of device nodes will be restricted from participating in the consensus verification process; in addition, the blockchain system will be re-sharded after each round of transactions.

3. The method for improving industrial Internet performance based on intelligent blockchain sharding according to claim 2 is characterized in that: In step 2, each data task is divided into transaction types and matched with the corresponding blockchain consensus protocol to complete the consensus verification procedure for the transaction; the details are as follows: Blockchain smart sharding divides blockchain transaction processing types into two types: intra-shard transactions and cross-shard transactions. Intra-shard transactions are verified and processed by IIoT device nodes in each shard, while cross-shard transactions are jointly completed by the transaction parties and the connected edge intelligent servers for consensus verification. Due to the dynamic characteristics of IIoT, it is difficult for a single blockchain consensus protocol to be applicable to IIoT data tasks. In this case, a dynamic switching model of multiple blockchain consensus protocols is considered to match the most appropriate consensus protocol for each data task. There are three blockchain consensus protocols used, as shown below: Practical Byzantine Fault Tolerance PBFT: Assuming the number of consensus nodes is N c , the calculation period of the signature is χ, and the calculation period of the message authentication code MAC is δ, then the consensus process of PBFT is as follows: Request: The client first submits a consensus request, then packages the transaction data into blocks, and the signature and MAC are verified by the master node. The calculation cycle is expressed as: Among them, the calculation period of the signature is χ, and the calculation period of the message authentication code MAC is δ; B(t) is the transaction batch size, and A(t) is the average transaction size; Pre-preparation: Initially, a signature and N c -1 MAC is generated and forwarded to all replica nodes participating in block verification; then, each replica node verifies the received signature and MAC; therefore, the computational cycle of the master node is the time required to generate the signature and MAC and forward them to the replica, while the computational cycle of the replica is the time required to verify the received signature and MAC; as mentioned above, the computational cycle of the master node is c2 p (t)=x+(N c -1)d The calculation period of the replica node is Among them, N c is the number of consensus nodes, ρ is the probability of correct transaction verification; Preparation: The preparation phase involves the process of completing the information broadcast between consensus nodes; specifically, the replica nodes will sign the verified blocks and generate N c -1 MAC to broadcast to all nodes including the master node; when the number of authentication nodes is not less than 2ζ, where ζ=(N c -1) / 3, PBFT ensures the effectiveness of the consensus verification process; the calculation cycle of the master node is c3 p (t)=2ζ(x+d) The calculation period of the replica node is c3 r (t)=2ζ(χ+δ)+χ+(N c -1)d Submit: In the submission phase, all nodes need to exchange and verify block information. Specifically, a signature and N c -1 MAC and verify the received signature and MAC; therefore, the computation cycle of the submission phase is given by c4(t)=2ζ(χ+δ)+χ+(N c -1)d Reply: In the reply phase, the consensus node replies with a verification message to the client. In addition, if the client receives more than 2ζ reply messages, the consensus verification process is considered valid. At this time, the newly generated block will be attached to the blockchain. The calculation formula for the calculation period of the reply phase is c5(t)=2ζ(χ+δ)+(χ+δ) In summary, the computation cycle required for PBFT is Zyzzyva consensus protocol: When the number of honest nodes exceeds 2ζ, Zyzzyva ensures the correctness of the transaction; when all nodes are honest, Zyzzyva skips node broadcasting and directly performs speculative execution; otherwise, if there are malicious nodes, Zyzzyva will r During this period, the recovery process is initiated; In the fast-case scenario, Zyzzyva’s authentication process is divided into three phases, namely, request, service request, and speculative reply; therefore, the computation cycle is expressed as In the two-phase case, Zyzzyva's authentication process is divided into five phases, namely, request, service request, speculative reply, submission, and reply; therefore, the computation cycle in the two-phase case is expressed as Among them, t r The delay from the start of the recovery procedure to the successful recovery of the consensus process; Quorum consensus protocol: The consensus process of the Quorum consensus protocol consists of two steps, namely request and reply; the reply information includes the historical summary of the replica; if the client receives less than N replies c If a panic mechanism is called, the verification process will be terminated; therefore, the calculation cycle of Quorum is expressed as In addition, the data integrity and network robustness of the blockchain require that the proportion of malicious nodes is within the safety range of the specific consensus protocol; therefore, to ensure the security of the proposed architecture, the following constraints are imposed: <h2 style=";text-align:left;direction:ltr">3N<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> +1≤N<h2 style=";text-align:left;direction:ltr"> c Among them, N p is the number of Byzantine nodes, N c is the number of consensus nodes; For intra-shard transactions, the consensus node consists of device nodes within a single shard, so N c =N / K; Obviously, the number of shards K must obey the following constraints:

4. The method for improving industrial Internet performance based on intelligent blockchain sharding according to claim 3 is characterized in that: In step three, terahertz communication technology is used to increase the data transmission rate of the link for computing offload from the edge layer to the cloud layer; Apply terahertz communication to optimize the computational offloading link from the edge layer to the cloud layer; The average distance of the unloading link is l. When the terahertz electromagnetic wave with a frequency of ω propagates in the medium, the molecular absorption loss caused by the interaction between the electromagnetic wave and various molecules and atoms in the medium is: Where τ represents the transmittance and κ(ω) is the absorption coefficient; Propagation loss, also known as free space loss, refers to the loss caused by electromagnetic waves penetrating the medium. In terahertz communication technology, the calculation formula for propagation loss is: Where c = 2.9979 × 10 8 m / s represents the speed of light in a vacuum; The total loss of terahertz communication technology is the sum of molecular absorption loss and free space loss, expressed as: In the terahertz transmission link, the noise temperature of the cloud receiver mainly comes from the molecular absorption noise; the equivalent noise temperature generated by molecular absorption is expressed as: N abs T =N0 T e Among them, N0 T = 296K refers to the reference temperature, ε = 1-τ represents the emissivity of the calculated unloading channel, and τ represents the transmittance; Therefore, for a given bandwidth B, the noise power absorbed by the molecule is given by: P n =s B N abs T dω According to the above analysis, the signal-to-noise ratio is expressed as: Among them, P t (t) is the transmission power of the edge intelligent server; in addition, and Refers to the antenna gain of the transmitting end and the receiving end respectively; Therefore, the data transmission rate based on terahertz communication technology is calculated as follows:

5. The method for improving industrial Internet performance based on intelligent blockchain sharding according to claim 4 is characterized in that: In step 4, the computing offload ratio from the edge layer to the cloud layer is intelligently determined based on the data task size and the computing resources of the intelligent edge server. The consensus verification delay, transmission delay, and computing delay are calculated and summarized to obtain the total system delay. The blockchain transaction throughput is calculated based on the number of blockchain shards, average transaction size, block size, and block interval, as follows: The computing resources H of the edge intelligent server are modeled as a Markov process and divided into h discrete values, expressed as: Y={y1,y2,...,y h } Where Y represents the set of different states of the computing resources H of the split edge intelligent server in the finite state Markov process, y1,y2,...,y h They represent the first, second, and up to the hth split states of the computing resource H of the edge intelligent server respectively; In addition, the computing resources of the edge intelligent server evolve to the next state according to the state transition probability matrix, as shown in the following formula: Among them, y i and j Indicates the different states of the computing resources H of the edge intelligent server, Indicates that H is from y i The state evolves to y j The state transition probability matrix of the state, Pr represents the probability, H(t) and H(t+1) represent the computing resources H of the edge intelligent server at the current moment and the next moment respectively; Therefore, the state transition probability matrix is ​​expressed as: The computation offloading ratio is O∈[0,1], and the computation delays of the edge intelligent server and the cloud server are expressed as: and Among them, G(t) refers to the calculation period, F m and F c denote the computing frequencies of the edge intelligent server and the cloud server, respectively; in addition, D(t) represents the data task size, and λ(t) refers to the data transmission rate; According to the barrel effect, the total computational delay of parallel computing is given by the following formula: t1(t)=max{t1 m (t),t1 c (t)} Clearly classify transaction types based on blockchain sharding; transaction types in IIoT supported by sharded blockchain can be roughly divided into two types, namely intra-shard transactions (IST) and cross-shard transactions (CST); The consensus delay of IST is expressed as: Among them, the value of C(t) comes from the set {C P (t),C Z 1 (t),C Z 2 (t),C Q (t)}; In addition, T I represents the block interval, F d Refers to the frequency of the device, t b Indicates the broadcast delay between consensus nodes; The consensus delay of CST is expressed as: In summary, the total delay is calculated as: T(t)=t1(t)+t2(t) Among them, t1(t) represents the calculation delay, and its value is t1 m (t) or t1 c (t); In addition, t2(t) is the consensus delay, which depends on the transaction type of the blockchain and is taken as t2 d (t) or t2 m (t); The transaction throughput of the blockchain system is evaluated by calculating the number of transactions completed per unit time. In addition, since all shards verify IIoT data tasks in parallel, the throughput Ξ is positively correlated with the number of blockchain shards K. The calculation formula for transaction throughput Ξ is: Among them, S B is the block size, T I represents the block interval and A(t) is the average transaction size.

6. The method for improving industrial Internet performance based on intelligent blockchain sharding according to claim 5 is characterized in that: In step 5, we first establish the optimization problem and model the Markov decision process, setting the state space, action space and reward function as follows: The defined state space consists of five key variables, namely, data task size D(t), transaction batch size B(t), transmission rate λ(t), computing resources H of edge intelligence server and reputation level R of all device nodes; therefore, the state space is set as: S(t)={D(t),B(t),λ(t),H,R} The data task size D(t) ranges from 4MB to 8MB, and the transaction batch size B(t) ranges from 1MB to 2MB. Due to the diversity and dynamic characteristics of the wireless link conditions of computational offloading, the data transmission rate is modeled as a finite state Markov decision process. The data transmission rate assigned to each computational offloading task comes from the set {125, 100, 75, 50} Mbps. Its probability transfer matrix is ​​expressed as: In order to obtain better long-term returns, it is necessary to flexibly adjust the action strategy according to environmental changes; therefore, the action space is set as follows: A(t)={S B ,T I ,O,C(t),K} Among them, S B = {1, 2, ..., B} represents the block size, T I ={0.5,1,...,I} represents the block interval; in addition, O∈[0,1] represents the computation offloading ratio; C(t) represents the decision factor of the consensus protocol; in addition, K represents the number of shards; In order to jointly optimize the transaction throughput Ξ and the total latency T(t), the reward function is defined as the weighted sum of the inverse of the throughput and the total latency, and its formula is: s.t.C1:D(t)≤S B C2:T(t)≤T I <h2 style=";text-align:left;direction:ltr">C3:K≤N / (3N<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> +1) Among them, α1+α2=1, ν is a reward and punishment parameter.

7. The method for improving industrial Internet performance based on intelligent blockchain sharding according to claim 6 is characterized in that: In step 6, the algorithm execution strategy and neural network training parameters are set according to the PPO algorithm, as follows: Considering the dynamic characteristics of IIoT, cloud servers with super computing power are considered as intelligent agents of the PPO network; the output of the actor network is a strategy π that follows a normal distribution. θ , whose mean and variance are μ(s t ) and σ(s t );The intelligent agent follows a normal distribution strategy π θ Sample the initial action and feed it back to the environment to obtain the next state information; To ensure the consistency of strategy optimization, the clipping objective function CSOF is introduced into PPO, and then the mini-batch stochastic gradient ascent method is used to optimize it; the calculation formula of CSOF is: Among them, ε represents the shear index, r(θ) represents the ratio of the previous strategy to the new strategy, which is used to ensure the difference between the new and old strategies, as follows: also, represents the generalized advantage estimation function, where V φ (s t ) and V φ (s t+1 ) represents the approximate value function at different times; in addition, the critic network updates the strategy by gradient descent of the loss function, and the loss function is expressed as: To quantify the advantage of an action at a given time t to a certain extent, it is defined as: in and Represent the value functions of actions and states respectively; Finally, the mini-batch stochastic optimization algorithm is used to update the performer and critic parameters θ and φ respectively through gradient ascent and gradient descent as follows:

8. The method for improving industrial Internet performance based on intelligent blockchain sharding according to claim 7 is characterized in that: In step seven, the optimal action is selected according to the optimal strategy obtained in each state to obtain the maximum benefit until the execution of the instruction is completed.

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