Blockchain consensus method, system, electronic device and storage medium

Abstract

This application discloses a blockchain consensus method, system, electronic device, and storage medium. The method includes acquiring resource data corresponding to all nodes in the blockchain network; aggregating the nodes based on the resource data to form multiple logical groups with similar performance indicators; obtaining a comprehensive evaluation value for each logical group leader based on the current operating status data of the blockchain network and dimensions such as block construction contribution, transaction throughput saturation, and stability contribution; selecting the winning logical group leader from all logical group leaders based on the comprehensive evaluation value, and executing block construction based on the anchor node of the winning logical group leader to generate the latest block and complete the blockchain consensus. This application improves the overall throughput and consensus efficiency of the blockchain network by aggregating nodes into logical groups with similar performance indicators, so that the consensus performance of the blockchain is determined by the average capability of the logical groups, rather than the minimum capability of a single node.

Description

Technical Field

[0001] This application relates to the field of blockchain technology, and in particular to a blockchain consensus method, system, electronic device and storage medium. Background Technology

[0002] As blockchain technology extends to the Internet of Things (IoT), edge computing, and the Industrial Internet, the physical form of the underlying blockchain network is undergoing fundamental changes. Network nodes exhibit strong heterogeneity, with high-performance servers and low-power edge devices coexisting as the norm. In such heterogeneous network environments, the traditional flat architecture's Proof-of-Contribution (PoC) consensus mechanism becomes a core bottleneck restricting the efficient and fair operation of blockchain networks. Its prominent problem is a significant bottleneck effect: the flat PoC mechanism directly quantifies the contributions and competes for consensus among massive heterogeneous physical nodes. To ensure the consistency of the blockchain ledger, the overall speed of block broadcasting, verification, and consensus processes is often limited by the worst-performing node in the network. This means that the performance of the blockchain system is determined by the lowest capability of all nodes, rather than the average capability, significantly reducing the overall throughput and consensus efficiency. At the same time, the block-producing right competition based on this mechanism is strongly determined by the node's contribution value. Low-contribution nodes have almost no chance of obtaining block-producing rights, continuously reducing their enthusiasm for participating in blockchain network maintenance, further exacerbating the inefficiency of network performance, and making it difficult to adapt to the actual application needs of heterogeneous dynamic networks. Summary of the Invention

[0003] To address the shortcomings mentioned above, this application provides a blockchain consensus method, system, electronic device, and storage medium.

[0004] Firstly, to achieve the above objectives, this application provides a blockchain consensus method, comprising: Based on the perceived multi-dimensional performance indicators of nodes in the blockchain network, obtain the resource data corresponding to all nodes in the blockchain network; Based on resource data, nodes are aggregated to form multiple logical groups with similar performance metrics, and each logical group leader has an anchor node. Based on the current operating status data of the blockchain network, as well as the dimensions of block construction contribution, transaction throughput saturation, and stability contribution, a comprehensive evaluation value is obtained for each logical group owner. Based on the comprehensive evaluation value, the winning logical group leader is selected from all logical group leaders, and block construction is performed according to the anchor node of the winning logical group leader to generate the latest block and complete the blockchain consensus.

[0005] Preferably, the multidimensional performance indicators include at least one of the node's computing power indicators, bandwidth indicators, and storage indicators, and the resource data is the real-time operating data of the node under the corresponding multidimensional performance indicators.

[0006] Preferably, based on resource data, nodes are aggregated to form multiple logical groups with similar performance metrics, and each logical group leader has an anchor node, including: Clustering algorithms are used to analyze the resource data of all nodes, and nodes whose resource data matching degree reaches a preset threshold are divided into the same logical group. Based on the node's resource stability, data processing efficiency, and network connectivity, select a node from the nodes within the logical group as the anchor node.

[0007] Preferably, a comprehensive evaluation value for each logical group owner is obtained based on the current operational status data of the blockchain network, as well as dimensions such as block construction contribution, transaction throughput saturation, and stability contribution. The current operational status data of the blockchain network includes the latest block height and evaluation window for the entire network; Within the evaluation window, initial scores are obtained for anchor nodes in the dimensions of block construction contribution, transaction throughput saturation, and stability contribution. The initial scores of each dimension are weighted and aggregated to obtain the comprehensive evaluation value of the logical group.

[0008] Preferably, the initial score calculation formula for the anchor node in the block construction contribution dimension is as follows: ; in, ; ; In the formula: i is the logical group number; H is the current latest block height of the entire network; h is the historical block height; I(·) is the indicator function; λ is the recovery rate; K comp δ represents the maximum compensation limit, Δh represents the growth rate parameter, Δh represents the difference between the latest block height and the last block height of logical group i, and N represents the evaluation window size for the construction contribution dimension. The initial score calculation formula for the anchor node in the transaction throughput saturation dimension is as follows: ; ; In the formula: Let L be the actual transaction volume of group i at historical block height h; L is the evaluation window size for the stability contribution dimension. This serves as the baseline for the average load across the entire network. This is the proportionality coefficient; The initial score for anchor nodes in the stability contribution dimension is calculated using the following formula: ; In the formula: Δdh D represents the actual interval at which logical group i produces a block at historical block height h. target Let σ be the target block interval, σ be the standard deviation parameter of the Gaussian distribution, and W be the size of the evaluation window for the stability contribution dimension.

[0009] Preferably, the winning logical group is selected from all logical groups based on the comprehensive evaluation value, including: Set the comprehensive evaluation value as the consensus competition weight for the logical group; A verifiable random function (VRF) is used to perform weighted random selection based on consensus competition weights to select the winning logical group. The logical group with the higher the comprehensive evaluation value is, the greater the probability of being selected.

[0010] Secondly, this application also provides a blockchain consensus system, including: The data acquisition module is used to obtain resource data corresponding to all nodes in the blockchain network based on the perceived multi-dimensional performance indicators of the nodes in the blockchain network. The processing module is used to aggregate nodes based on resource data to form multiple logical groups with similar performance indicators, and each logical group leader has an anchor node; based on the current operating status data of the blockchain network and the dimensions of block construction contribution, transaction throughput saturation, and stability contribution, a comprehensive evaluation value of each logical group leader is obtained. The execution module is used to select the winning logical group leader from all logical group leaders based on the comprehensive evaluation value, and to perform block construction based on the anchor node of the winning logical group leader to generate the latest block and complete the blockchain consensus.

[0011] Thirdly, this application also provides an electronic device, including at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the program is executed by the processing unit, the processing unit performs the above-described method.

[0012] Fourthly, this application also provides a storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the above-described method.

[0013] Compared with the prior art, the beneficial effects of this application are as follows: This application improves the overall throughput and consensus efficiency of the blockchain network by aggregating nodes into logical groups with similar performance metrics, so that the consensus performance of the blockchain is determined by the average capability of the logical groups rather than the minimum capability of a single node. Attached Figure Description

[0014] Figure 1 This is a flowchart of the blockchain consensus method in this application; Figure 2 This is a framework diagram of the blockchain consensus mechanism in this application. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] Reference Figure 1 This application provides a blockchain consensus method, including: Based on the perceived multi-dimensional performance indicators of nodes in the blockchain network, obtain the resource data corresponding to all nodes in the blockchain network; As one implementation method, multidimensional performance indicators include at least one of the node's computing power indicators, bandwidth indicators, and storage indicators, and resource data is the real-time operating data of the node under the corresponding multidimensional performance indicators.

[0017] Based on resource data, nodes are aggregated to form multiple logical groups with similar performance metrics, and each logical group leader has an anchor node. As one implementation method, a clustering algorithm is used to analyze the resource data of all nodes, and nodes whose resource data matching degree reaches a preset threshold are divided into the same logical group; Based on the node's resource stability, data processing efficiency, and network connectivity, select a node from the nodes within the logical group as the anchor node.

[0018] Based on the current operating status data of the blockchain network, as well as the dimensions of block construction contribution, transaction throughput saturation, and stability contribution, a comprehensive evaluation value is obtained for each logical group owner. As one implementation method, the current operating status data of a blockchain network includes the latest block height and evaluation window of the entire network; Within the evaluation window, initial scores are obtained for anchor nodes in the dimensions of block construction contribution, transaction throughput saturation, and stability contribution. The initial scores of each dimension are weighted and aggregated to obtain the comprehensive evaluation value of the logical group.

[0019] Furthermore, the initial score calculation formula for the anchor node's contribution to block construction is as follows: ; in, ; ; In the formula: i is the logical group number; H is the current latest block height of the entire network; h is the historical block height; I(·) is the indicator function; λ is the recovery rate; K comp δ represents the maximum compensation limit, Δh represents the growth rate parameter, Δh represents the difference between the latest block height and the last block height of logical group i, and N represents the evaluation window size for the construction contribution dimension. Furthermore, ; The above formula iterates through all blocks in the evaluation window, automatically filtering out blocks not generated by this group (multiplied by 0), and only retaining blocks generated by this group (multiplied by 1).

[0020] This is the saturation weighting function, a non-linear coefficient that monotonically increases with distance, with a range of (0, 1). Its purpose is to suppress decentralization. This function determines the value of each historical block.

[0021] when When it is very small (just after a block is generated), The contribution value of this block is suppressed by the system to prevent nodes from continuously racking up points using the latest block.

[0022] when When it is very large (ancient block), The contribution value of this block is more advantageous compared to a newly generated block. To control the recovery rate, the saturation weighting function curve is... The hyperparameter of the ascent rate. Used to adjust the severity of anti-monopoly measures in the system. Larger value: extremely fast recovery, only suppressing the most recent one or two blocks, suitable for high-throughput networks. The smaller the value, the slower the recovery. After a node produces a block, it needs a longer "recovery period" for the new block to gain full weight, resulting in a stronger anti-monopoly effect. The latest block height H and the last block height h of group i last The difference. Specifically, for initialization and cold start scenarios: If group i has not yet built any blocks in the current evaluation period (i.e., its historical block production record is empty), then h is defined. last This is equal to the block height at which the group joined the network; this setting treats the group's joining height as its "virtual block height." If a group fails to build any blocks in the current evaluation window, its first part, "explicit cumulative score," is 0. At this point, the group's consensus weight will depend entirely on the second part, "implicit potential compensation." last It's just a table used to calculate distances; it can't tamper with the historical ledger. Unless a block is actually generated, the explicit score will always be 0. This is the maximum compensation limit. No matter how long you wait, the compensation points will not exceed this value. Growth rate parameter. How quickly the "relief" is adjusted. The larger the value, the faster the compensation points will reach the upper limit. The smaller the number, the longer it will take to receive the full compensation.

[0023] Transaction throughput is a key indicator for measuring the value a group brings to the blockchain network ecosystem. The commercial value of blockchain is realized through transaction flow; the more transactions a group packages, the higher its score, similar to compound interest, greatly motivating nodes. To enhance the commercial value of blockchain, it is essential to incentivize groups to maximize transaction packaging, avoid empty blocks, and increase the total number of transactions across the network. However, a linear, work-for-reward mechanism can easily induce nodes to generate massive amounts of low-value dust transactions through scripts for malicious order manipulation. This not only leads to computing power monopoly but also causes state explosion and network congestion. To address these contradictions, this application abandons the traditional single linear function and proposes a three-stage segmented control strategy based on the network average anchor. The core idea of ​​this strategy is to average the network load... As a dynamic water level, the load behavior of the node is divided into three physical regions: excitation region, saturation region, and circuit breaker region, and different control gains are applied to each region.

[0024] Incentive Zone ( This method employs a power function to construct a geometric progression growth curve. It rapidly increases transaction throughput when network load is low or nodes are just beginning to operate. Within this range, even a small increase in transaction volume can lead to a significant increase in weight. This highly sensitive incentive effectively breaks the "lazy equilibrium" of nodes, prompting them to quickly increase their transaction volume to above the network average.

[0025] Saturation region ( : Employing a low-slope linear function, based on linear damping with "diminishing marginal utility," when the load exceeds a certain threshold ( Excessive incentives can lead to network resource strain. A damping mechanism is introduced. The system acknowledges a node's high output contribution but significantly reduces its marginal score reward. This is a "soft rate limiting" method, informing nodes that the return on investment for continuing to increase transaction volume is extremely low, thereby suppressing their impulse to further seize block space.

[0026] Circuit breaker zone ( The system employs a negative exponential function to construct a rapid decline curve. Based on the exponential penalty of "congestion collapse," when traffic exceeds the limit, the system faces the risk of paralysis. The system determines that such excessive traffic is highly likely to be a malicious order-brushing attack. Through the exponential decay of weights, the cost of malicious behavior for attackers tends to infinity, thus mathematically guaranteeing the decentralized security boundary of the system.

[0027] Therefore, the initial score calculation formula for the anchor node in the transaction throughput saturation dimension is as follows: ; ; In the formula: Let L be the actual transaction volume of group i at historical block height h; L is the evaluation window size for the stability contribution dimension. This serves as the baseline for the average load across the entire network. q is the proportionality coefficient; q is the throughput exponent, usually... Because it is As transaction volume increases, the score increases significantly in a parabolic manner. Nodes below the average level are given extremely strong positive feedback, encouraging them to make more transactions and reach the network average level more quickly. This uses a small, fixed score to break the deadlock of "no transactions, no points." It ensures that new nodes or nodes operating during periods of low transaction activity still receive a minimum score. TPC Thus, hope is preserved. base The saturation baseline score is the score at the end of the first stage. This is to ensure that the function curve is within the specified range. The fractions are continuous (there will be no sudden jumps in the fractions). The damping coefficient is... Within this range, the score increases only slightly for each additional transaction packaged. This is to prevent nodes from maliciously hoarding resources for such a small amount of points.

[0028] Speak refers to the peak score, which is the score at the end of the second stage. ). This is the attenuation coefficient. The larger the value, the faster the score drops (e.g., taking...). =5), once it exceeds twice the average, the score drops like a waterfall.

[0029] The robustness of a blockchain network depends not only on the number of blocks produced by nodes but also on the continuity of their services. In a distributed network, the ability to participate in consensus consistently, rhythmically, and over a long period is as valuable a contribution as a mere burst of computing power. The more smoothly a group operates and the more compliant blocks it outputs, the higher its stability score, reflecting affirmation of the long-term online operation and maintenance work of the nodes. Under an ideal decentralized rotation mechanism, if there are M groups in the network, theoretically, each group should obtain the right to produce a block every M blocks. Some nodes may exploit momentary network advantages to produce blocks in a "burst," followed by long periods of silence. This increases the uncertainty of the system. The block production interval of nodes fluctuates closely around the expected value of the entire network, which is the healthiest steady state of the system. To prevent nodes from using network advantages to launch computing power attacks, a coherence detection based on the expected block production interval of the entire network is established. A Gaussian kernel function from signal processing is introduced to calculate the correlation between the "interval" of each block production by a group and the "ideal interval," and its stability contribution is quantified in an cumulative manner.

[0030] Therefore, the initial score calculation formula for the anchor node in the stability contribution dimension is as follows: ; In the formula: Δd h D represents the actual interval at which logical group i produces a block at historical block height h. target Let σ be the target block interval, σ be the standard deviation parameter of the Gaussian distribution, and W be the size of the evaluation window for the stability contribution dimension.

[0031] Furthermore, ; The current block height h is compared to the height of the last block generated in this group. The difference between them represents the "landing point interval" of the group in space. For example, if a group produces blocks at heights of 100 and 110 respectively, then the step size is 10. To determine the desired block interval, typically This value can be periodically adjusted based on the dynamic joining / leaving of groups across the network. In an ideal, fair competitive environment, this represents the block distance interval required for a group to gain the right to produce a block again after completing one block generation. This is a nonlinear mapping function based on exponential decay. When the actual spacing is close to the ideal value ( When the exponential term approaches 0 and the function value approaches 1, it represents a high-quality, stable contribution. Whether the actual interval is too short or too long compared to the expected block interval, Both will increase, leading to a decrease in score. The standard deviation parameter is a Gaussian distribution. It adjusts the system's sensitivity to step size deviation. Smaller: Requires groups to build blocks rhythmically, suitable for highly deterministic networks. Larger size: Allows the step size to fluctuate within a certain range, enhancing the robustness of the system under dynamic networks.

[0032] Since the scores for different dimensions have different ranges and physical dimensions, directly weighting them would cause the larger-valued indicators to "overwhelm" the smaller-valued ones. Therefore, the common Min-Max standardization method is used to linearly map the scores of each dimension to the interval [0, 1].

[0033] Let S be the set of original scores for all groups at the current height H, along dimension k (k∈{BG,TPC,SC}). k The standardized score N of group i k (i) is: ; and These are the lowest and highest scores for the current entire network group in the k-th dimension, respectively. For different dimensions ( The score calculation follows a window mechanism. This ensures that the consensus mechanism reflects the recent state of the group. Let the current block height be H, the evaluation window size be P, and the current list of groups in the entire network be... The stability module has the most stringent requirements for window length (requiring 3-5 M times), while the transaction and block building modules are more lenient on window length (a larger window can better reflect long-term contributions and prevent short-term cheating). Therefore, the evaluation window size P is selected based on W, which can meet the requirements of the three indicators.

[0034] Step 1: Construct the original score vector; At the current altitude H, each group g in the system's parallel computing list... i Contribution score within the window [HP, H-1].

[0035] The original score set S of the k-th dimension k Defined as a vector composed of the scores of all groups in this dimension across the entire network: The computation of any element Scorek(i) in the set is strictly limited to within the window: Score BG (i): Statistical summary of block construction within the interval [HP, H-1].

[0036] Score TPC (i): Calculate the cumulative value of the number of transactions contained in a block within the interval [HP, H-1].

[0037] ScoreSC (i): Calculate the stability integral of the block interval within the interval [HP, H-1].

[0038] Step 2: Extract dynamic boundaries; Based on the above real-time calculations, the set S k (H), dynamically extracting the current extreme value: : Represents the score of the best-performing group across the entire network during the current window period.

[0039] : Represents the score of the worst-performing (or newly joined) group across the entire network during the current window period.

[0040] To smooth out the minimum value, a very small positive number is used to prevent mathematical singularities where max = min (i.e., when all scores across the network are exactly the same), resulting in a denominator of zero. This formula measures the relative position of group i's performance within the current level of the entire network.

[0041] Formula for calculating the overall group assessment value: ; In the formula: k is the set of evaluation dimensions, w k For dynamic weighting coefficients, satisfying .

[0042] Based on the comprehensive evaluation value, a winning logical group leader is selected from all logical group leaders. Block construction is then performed based on the anchor node of the winning logical group leader to generate the latest block and complete the blockchain consensus. After the latest block is generated, the height H of the latest block in the entire network is incremented by 1, and the evaluation window is simultaneously slid to [H-P+1, H]. Based on the updated block height and evaluation window, the step of calculating the comprehensive evaluation value of the logical group is returned, and the blockchain consensus process is executed cyclically to achieve the continuous generation of the blockchain ledger.

[0043] As one implementation method, the comprehensive evaluation value is set as the consensus competition weight of the logical group; A verifiable random function (VRF) is used to perform weighted random selection based on consensus competition weights to select the winning logical group. The logical group with the higher the comprehensive evaluation value is, the greater the probability of being selected.

[0044] Reference Figure 2This application constructs a three-layer governance architecture: physical layer, logical layer, and consensus layer. This architecture is applied to consensus methods. The physical layer is the physical carrier and original data source of computing power, bandwidth, and storage resources. It does not participate in decision-making, but is only responsible for existence and execution. At the logical layer, a clustering algorithm based on multi-dimensional performance indicators is used to dynamically aggregate the underlying physical nodes into several logical groups. Simultaneously, a periodic splitting and merging mechanism adaptively responds to dynamic changes in the network, shielding against differences in underlying hardware and network jitter. Secondly, at the consensus layer, the G-PoC consensus algorithm is adopted. This algorithm no longer allows individual physical nodes to compete for the right to record transactions; instead, anchor nodes in a group represent their group in participating in the network-wide consensus. The competition for block production rights is based on multi-dimensional evaluation, covering block construction, transaction volume, and stability. Furthermore, to prevent the Matthew effect, an algorithm with anti-monopoly design is introduced.

[0045] Ultimately, through the dynamic stability of the architecture and the nonlinear dynamic evaluation of the consensus layer, a balance between high throughput, low latency, high scalability, and decentralized fairness was achieved.

[0046] Secondly, this application also provides a blockchain consensus system, including: The data acquisition module is used to obtain resource data corresponding to all nodes in the blockchain network based on the perceived multi-dimensional performance indicators of the nodes in the blockchain network. The processing module is used to aggregate nodes based on resource data to form multiple logical groups with similar performance indicators, and each logical group leader has an anchor node; based on the current operating status data of the blockchain network and the dimensions of block construction contribution, transaction throughput saturation, and stability contribution, a comprehensive evaluation value of each logical group leader is obtained. The execution module is used to select the winning logical group leader from all logical group leaders based on the comprehensive evaluation value, and to perform block construction based on the anchor node of the winning logical group leader to generate the latest block and complete the blockchain consensus.

[0047] This application also provides an electronic device, including at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the program is executed by the processing unit, the processing unit performs the above-described method.

[0048] This application also provides a storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the above-described method.

[0049] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A blockchain consensus method, characterized in that, include: Based on the perceived multi-dimensional performance indicators of nodes in the blockchain network, obtain the resource data corresponding to all nodes in the blockchain network; Based on the resource data, the nodes are aggregated to form multiple logical groups with similar performance indicators, and each logical group leader has an anchor node. Based on the current operating status data of the blockchain network and the dimensions of block construction contribution, transaction throughput saturation, and stability contribution, a comprehensive evaluation value is obtained for each logical group owner. Based on the comprehensive evaluation value, a winning logical group leader is selected from all the logical group leaders, and block construction is performed based on the anchor node of the winning logical group leader to generate the latest block and complete the blockchain consensus.

2. The blockchain consensus method according to claim 1, characterized in that, The multidimensional performance indicators include at least one of the node's computing power indicators, bandwidth indicators, and storage indicators, and the resource data is the real-time operating data of the node under the corresponding multidimensional performance indicators.

3. The blockchain consensus method according to claim 2, characterized in that, The process involves aggregating the nodes based on the resource data to form multiple logical groups with similar performance metrics, and each logical group leader has an anchor node, including: Clustering algorithms are used to analyze the resource data of all nodes, and nodes whose resource data matching degree reaches a preset threshold are divided into the same logical group. Based on the node's resource stability, data processing efficiency, and network connectivity, a node is selected from the nodes within the logical group as the anchor node.

4. The blockchain consensus method according to claim 1, characterized in that, The comprehensive evaluation value of each logical group owner is obtained based on the current operating status data of the blockchain network, as well as dimensions of block construction contribution, transaction throughput saturation, and stability contribution. The current operating status data of the blockchain network includes the latest block height and evaluation window of the entire network; Within the evaluation window, the initial scores of the anchor node in the block construction contribution dimension, transaction throughput saturation dimension, and stability contribution dimension are obtained respectively. The initial scores of each dimension are weighted and aggregated to obtain the comprehensive evaluation value of the logical group.

5. The blockchain consensus method according to claim 4, characterized in that, The initial score calculation formula for the anchor node in the block construction contribution dimension is as follows: ； in, ; ； In the formula: i is the logical group number; H is the current latest block height of the entire network; h is the historical block height; I(·) is the indicator function; λ is the recovery rate; K comp δ represents the maximum compensation limit, Δh represents the growth rate parameter, Δh represents the difference between the latest block height and the last block height of logical group i, and N represents the evaluation window size for the construction contribution dimension. The initial score calculation formula for the anchor node in the transaction throughput saturation dimension is as follows: ； ； In the formula: Let L be the actual transaction volume of group i at historical block height h; L is the evaluation window size for the stability contribution dimension. This serves as the baseline for the average load across the entire network. This is the proportionality coefficient; The initial score calculation formula for the anchor node in the stability contribution dimension is as follows: ； In the formula: Δd h D represents the actual interval at which logical group i produces a block at historical block height h. target Let σ be the target block interval, σ be the standard deviation parameter of the Gaussian distribution, and W be the size of the evaluation window for the stability contribution dimension.

6. The blockchain consensus method according to claim 1, characterized in that, The step of selecting the winning logical group from all the logical groups based on the comprehensive evaluation value includes: Set the comprehensive evaluation value as the consensus competition weight of the logical group; A verifiable random function (VRF) is used to perform weighted random selection based on the consensus competition weight, and the winning logical group is selected. The logical group with the higher the comprehensive evaluation value is, the greater the probability of being selected.

7. A blockchain consensus system, characterized in that, include: The data acquisition module is used to obtain resource data corresponding to all nodes in the blockchain network based on the perceived multi-dimensional performance indicators of the nodes in the blockchain network. The processing module is used to aggregate the nodes according to the resource data to form multiple logical groups with similar performance indicators, and each logical group leader has an anchor node; and to obtain a comprehensive evaluation value for each logical group leader according to the current operating status data of the blockchain network and the dimensions of block construction contribution, transaction throughput saturation, and stability contribution. The execution module is used to select the winning logical group leader from all the logical group leaders based on the comprehensive evaluation value, and to perform block construction based on the anchor node of the winning logical group leader to generate the latest block and complete the blockchain consensus.

8. An electronic device, characterized in that, The method includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the method described in any one of claims 1 to 6.

9. A storage medium, characterized in that, It stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the method described in any one of claims 1 to 6.

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