Data transmission method, system and device of web3.0-based blockchain network and medium

Abstract

The embodiment of the present disclosure relates to a data transmission method, system, device and medium based on a web3.0-based blockchain network, which comprises the following steps: determining a first quantity corresponding to load data of the blockchain network; determining topology structure data and historical behavior data corresponding to network nodes in a network node set; selecting a first quantity of neighbor nodes from each neighbor node of the network nodes in the network node set based on the topology structure data and the historical behavior data corresponding to the network nodes, to obtain a first neighbor node subset corresponding to the network nodes; dividing the network node set into multiple groups based on the first neighbor node subsets respectively corresponding to the network nodes in the network node set, and determining an intra-group transmission path and an inter-group transmission path of target data to be transmitted; and transmitting the target data in the blockchain network according to the intra-group transmission path and the inter-group transmission path. Thus, the transmission efficiency of data in the blockchain network can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of Web3.0 technology, and in particular to a data transmission method, system, device and medium for a Web3.0-based blockchain network. Background Technology

[0002] Blockchain networks typically rely on a P2P (Peer-to-Peer) architecture for data exchange between nodes. The network layer, as the foundation for information exchange, plays a crucial role in data transmission efficiency. However, existing blockchain technologies often suffer from excessively long data synchronization times when handling large-scale node communication, making them unsuitable for scenarios in the Industrial Internet of Things (IIoT) where nodes are densely packed and data real-time requirements are high. For example, traditional gossip protocols not only generate large amounts of redundant data but also exacerbate network congestion; furthermore, the clustering results of some node clustering methods do not match actual transmission needs, further impacting transmission efficiency.

[0003] It is evident that improving the data transmission efficiency in blockchain networks is a technical issue worthy of attention. Summary of the Invention

[0004] In view of this, in order to solve some or all of the above-mentioned technical problems, this disclosure provides a data transmission method, system, device and medium for a blockchain network based on Web3.0.

[0005] In a first aspect, embodiments of this disclosure provide a data transmission method for a web3.0-based blockchain network, the method comprising:

[0006] Determine a first quantity corresponding to the load data of the blockchain network, wherein the blockchain network comprises a set of network nodes, and the first quantity represents the amount of data to be processed in a first time period;

[0007] Determine the topology data and historical behavior data of the network nodes in the set of network nodes. The topology data represents the topology of the neighboring nodes of the network node, and the historical behavior data represents the historical behavior of the neighboring nodes.

[0008] Based on the topology data and historical behavior data of the network nodes in the network node set, a first number of neighboring nodes are selected from each of the neighboring nodes of the network node to obtain the first neighboring node subset corresponding to the network node.

[0009] Based on the subset of first neighbor nodes corresponding to each network node in the network node set, the network node set is divided into multiple groups, and the intra-group transmission path and inter-group transmission path of the target data to be transmitted are determined.

[0010] The target data is transmitted in the blockchain network according to the intra-group transmission path and the inter-group transmission path.

[0011] In some possible implementations, based on the topology data and historical behavior data corresponding to the network nodes in the network node set, a first number of neighboring nodes are selected from the neighboring nodes of the network node to obtain a first subset of neighboring nodes corresponding to the network node, including:

[0012] Based on the topology data corresponding to the network node in the network node set, determine the degree of each of the network node's neighboring nodes;

[0013] Based on the historical behavior data of the network nodes in the network node set, the interaction achievement rate and target interaction frequency of each neighbor node of the network node are determined, where the target interaction frequency represents the interaction frequency between the network node and its neighbor nodes.

[0014] Based on the degree, interaction achievement rate, and target interaction frequency of each neighbor node of a network node in the network node set, a first number of neighbor nodes are selected from the neighbor nodes of the network node to obtain the first neighbor node subset corresponding to the network node.

[0015] In some possible implementations, the network node set is divided into multiple groups based on the first neighbor node subset corresponding to each network node, including:

[0016] Determine the target similarity for each network node in the set of network nodes, wherein the target similarity is determined based on the similarity between the network node and the neighboring nodes in the first subset of the network node's corresponding neighbor nodes;

[0017] Based on the target similarity corresponding to each network node in the network node set, the network node set is clustered to obtain multiple node clusters. Network nodes belonging to the same node cluster are grouped into the same group, and network nodes belonging to different node clusters are grouped into different groups.

[0018] In some possible implementations, the target similarity corresponding to each network node in the set of network nodes is determined in the following manner:

[0019] For the neighboring nodes in the first neighboring node subset corresponding to the network node, determine the data transmission duration between the neighboring node and the network node, obtain the data transmission duration corresponding to the neighboring node, and determine the similarity between the neighboring node and the network node as the reciprocal of the data transmission duration corresponding to the neighboring node.

[0020] Determine the sum of the similarities between each neighbor node in the first neighbor node subset corresponding to the network node and the network node;

[0021] The sum of all similarities is determined as the target similarity corresponding to that network node.

[0022] In some possible implementations, the network node set is clustered based on the target similarity corresponding to each network node, including:

[0023] For each network node in the set of network nodes, the importance of the network node in the blockchain network is determined based on the similarity between the network node and its neighboring nodes, the target similarity of the network node's neighboring nodes, the degree of the network node's neighboring nodes, and the degree of the network node itself.

[0024] Based on the importance of each network node in the network node set, the label update order of each network node in the network node set is determined.

[0025] Update the labels of each network node sequentially according to the label update order;

[0026] Network nodes with the same label are grouped into the same node cluster; network nodes with different labels are grouped into different node clusters.

[0027] In some possible implementations, after determining the first quantity corresponding to the load data of the blockchain network, the method further includes:

[0028] Determine a second quantity corresponding to the load data, where the second quantity represents the amount of data to be processed within a second time period; and

[0029] Update the label of each network node in the network node set as follows:

[0030] According to the order of importance of neighboring nodes from high to low, select a second number of neighboring nodes from each of the network node's neighboring nodes to obtain the second set of neighboring nodes corresponding to the network node;

[0031] Update the label of the network node based on the labels of the neighboring nodes in the subset of the second neighboring nodes corresponding to the network node.

[0032] In some possible implementations, determining the intra-group transmission path and inter-group transmission path of the target data to be transmitted includes:

[0033] Construct a spanning tree within a group based on multiple network nodes belonging to the same group;

[0034] Based on the intra-group spanning tree, determine the intra-group transmission path of the target data to be transmitted;

[0035] Each group is treated as a node, and the connections between groups are treated as edges. The target edge with the smallest weight among the multiple edges connecting two groups is selected, and a spanning tree between groups is constructed based on the target edge. The weight is determined based on the data transmission time between the two network nodes connected by the edge.

[0036] Based on the inter-group spanning tree, the inter-group transmission path of the target data is determined.

[0037] Secondly, embodiments of this disclosure provide a data transmission system for a web3.0-based blockchain network, the system comprising:

[0038] The first determining unit is configured to: determine a first quantity corresponding to the load data of the blockchain network, wherein the blockchain network includes a set of network nodes, and the first quantity represents the amount of data to be processed in a first time period;

[0039] The second determining unit is configured to: determine the topology data and historical behavior data corresponding to the network nodes in the network node set, wherein the topology data represents the topology of the neighboring nodes of the network node, and the historical behavior data represents the historical behavior of the neighboring nodes;

[0040] The selection unit is configured to: select a first number of neighboring nodes from each of the neighboring nodes of the network node based on the topology data and historical behavior data corresponding to the network node in the network node set, thereby obtaining the first subset of neighboring nodes corresponding to the network node;

[0041] The partitioning unit is configured to: divide the network node set into multiple groups based on the first neighbor node subset corresponding to each network node in the network node set, and determine the intra-group transmission path and inter-group transmission path of the target data to be transmitted;

[0042] The transmission unit is configured to transmit target data in the blockchain network according to intra-group transmission paths and inter-group transmission paths.

[0043] In some possible implementations, the selection unit includes:

[0044] The first determining subunit is configured to: determine the degree of each neighboring node of the network node based on the topology data corresponding to the network node in the network node set;

[0045] The second determining subunit is configured to: determine the interaction achievement rate and target interaction frequency of each neighbor node of the network node based on the historical behavior data corresponding to the network node in the network node set, wherein the target interaction frequency represents the interaction frequency between the network node and its neighbor nodes.

[0046] The first selection subunit is configured to: select a first number of neighboring nodes from the neighboring nodes of the network node based on the degree, interaction achievement rate and target interaction frequency of each neighboring node of the network node in the network node set, to obtain the first neighboring node subset corresponding to the network node.

[0047] In some possible implementations, the partitioning unit includes:

[0048] The third determining subunit is configured to: determine the target similarity corresponding to each network node in the network node set, wherein the target similarity is determined based on the similarity between the network node and the neighbor nodes in the first neighbor node subset corresponding to the network node;

[0049] The clustering subunit is configured to: cluster the network node set based on the target similarity corresponding to each network node in the network node set to obtain multiple node clusters; divide network nodes belonging to the same node cluster into the same group; and divide network nodes belonging to different node clusters into different groups.

[0050] In some possible implementations, the target similarity corresponding to each network node in the set of network nodes is determined in the following manner:

[0051] For the neighboring nodes in the first neighboring node subset corresponding to the network node, determine the data transmission duration between the neighboring node and the network node, obtain the data transmission duration corresponding to the neighboring node, and determine the similarity between the neighboring node and the network node as the reciprocal of the data transmission duration corresponding to the neighboring node.

[0052] Determine the sum of the similarities between each neighbor node in the first neighbor node subset corresponding to the network node and the network node;

[0053] The sum of all similarities is determined as the target similarity corresponding to that network node.

[0054] In some possible implementations, the clustering subunit includes:

[0055] The first determining module is configured to: for each network node in the network node set, determine the importance of the network node in the blockchain network based on the similarity between the network node and its neighboring nodes, the target similarity of the network node's neighboring nodes, the degree of the network node's neighboring nodes, and the degree of the network node, and obtain the importance of the network node.

[0056] The second determining module is configured to: determine the label update order of each network node in the network node set based on the importance of each network node in the network node set;

[0057] The update module is configured to update the labels of each network node sequentially according to the label update order;

[0058] The partitioning module is configured to: group network nodes with the same label into the same node cluster; and group network nodes with different labels into different node clusters.

[0059] In some possible implementations, after determining the first quantity corresponding to the load data of the blockchain network, the system further includes:

[0060] The third determining unit is configured to: determine a second quantity corresponding to the load data, wherein the second quantity represents the amount of data to be processed within a second time period; and

[0061] Update the label of each network node in the network node set as follows:

[0062] According to the order of importance of neighboring nodes from high to low, select a second number of neighboring nodes from each of the network node's neighboring nodes to obtain the second set of neighboring nodes corresponding to the network node;

[0063] Update the label of the network node based on the labels of the neighboring nodes in the subset of the second neighboring nodes corresponding to the network node.

[0064] In some possible implementations, the transmission unit includes:

[0065] The sub-unit is configured to: construct a spanning tree within a group based on multiple network nodes belonging to the same group;

[0066] The fourth determining subunit is configured to: determine the intra-group transmission path of the target data to be transmitted based on the intra-group spanning tree;

[0067] The second selection sub-unit is configured to: treat each group as a node, treat the connection relationship between each group as an edge, select the target edge with the smallest weight among the multiple edges connecting two groups, and construct an inter-group spanning tree based on the target edge, wherein the weight is determined based on the data transmission duration between the two network nodes connected by the edge.

[0068] The fifth determining subunit is configured to: determine the inter-group transmission path of the target data based on the inter-group spanning tree.

[0069] Thirdly, embodiments of this disclosure provide an electronic device, including:

[0070] Memory, used to store computer programs;

[0071] A processor is configured to execute a computer program stored in the memory, wherein, when the computer program is executed, it implements any embodiment of the data transmission method of the web3.0-based blockchain network of the first aspect of this disclosure.

[0072] Fourthly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of any embodiment of the data transmission method for a web3.0-based blockchain network as described in the first aspect above.

[0073] Fifthly, embodiments of this disclosure provide a computer program including computer-readable code, wherein when the computer program instructions are executed by a processor, they implement the method of any embodiment of the data transmission method of the web3.0-based blockchain network described in the first aspect.

[0074] Based on the embodiments of this disclosure, a first quantity corresponding to the load data of the blockchain network can be determined, wherein the blockchain network includes a set of network nodes, and the first quantity represents the amount of data to be processed in a first time period. Then, the topology data and historical behavior data corresponding to the network nodes in the set of network nodes are determined, wherein the topology data represents the topology of the neighboring nodes of the network node, and the historical behavior data represents the historical behavior of the neighboring nodes. Then, based on the topology data and historical behavior data corresponding to the network nodes in the set of network nodes, a first quantity of neighboring nodes are selected from each of the neighboring nodes of the network node to obtain a first subset of neighboring nodes corresponding to the network node. Subsequently, based on the first subset of neighboring nodes corresponding to each network node in the set of network nodes, the set of network nodes is divided into multiple groups, and the intra-group transmission path and inter-group transmission path of the target data to be transmitted are determined. Finally, the target data is transmitted in the blockchain network according to the intra-group transmission path and the inter-group transmission path. Since the load data directly reflects the real-time operating pressure of the blockchain network, determining the number of neighboring nodes accordingly can dynamically adjust the group size with changes in load, thereby improving the data transmission efficiency in scenarios with large blockchain network loads. Furthermore, by combining the actual connection relationships and historical performance between neighboring nodes based on their topological structure and historical behavior, the grouping of network node sets can effectively improve the collaboration efficiency between network nodes and the stability and security of data transmission. Attached Figure Description

[0075] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0076] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0077] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0078] Figure 1 A schematic flowchart illustrating a data transmission method for a web3.0-based blockchain network provided in this embodiment of the disclosure;

[0079] Figure 2 A schematic flowchart illustrating another data transmission method for a web3.0-based blockchain network provided in this embodiment of the present disclosure;

[0080] Figure 3 A schematic flowchart illustrating another data transmission method for a web3.0-based blockchain network provided in this embodiment of the present disclosure;

[0081] Figure 4 A schematic diagram of the structure of a data transmission system for a web3.0-based blockchain network provided in this embodiment of the disclosure;

[0082] Figure 5 A schematic diagram of the structure of a selected unit in a data transmission system of a web3.0-based blockchain network provided in this embodiment of the disclosure;

[0083] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0084] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It is obvious that the described embodiments are only a part of, and not all, of the embodiments described herein. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0085] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of this disclosure are only used to distinguish different steps, devices or modules, and do not represent any specific technical meaning, nor do they indicate the logical order between them.

[0086] It should also be understood that in this embodiment, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0087] It should also be understood that any component, data or structure mentioned in the embodiments of this disclosure can generally be understood as one or more unless expressly defined or given to the contrary in the context.

[0088] Furthermore, the term "and / or" in this disclosure is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this disclosure generally indicates that the preceding and following related objects have an "or" relationship.

[0089] It should also be understood that the description of the various embodiments in this disclosure emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0090] The following description of at least one exemplary embodiment is merely illustrative and is not intended to limit the scope of this disclosure or its application or use.

[0091] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0092] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0093] It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other. To facilitate understanding of the embodiments of this disclosure, the disclosure will be described in detail below with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0094] The acquisition and processing of data (such as historical behavioral data) in this disclosure comply with relevant laws, regulations, social ethics, and provisions concerning public interests. For example, the acquisition, storage, and processing of data in this disclosure are all carried out with the knowledge and consent of the users and have obtained the full authorization of the relevant users.

[0095] The surge in devices and data in the Industrial Internet of Things (IIoT) renders traditional centralized cloud models inadequate for the stringent requirements of large scale and low latency in these scenarios. As an emerging technology, edge computing enables real-time processing and analysis on devices closer to the data source, while reducing bandwidth demands. Blockchain, due to its decentralized nature, can enhance data security. Therefore, integrating edge computing and blockchain into IIoT aims to reduce latency and improve security. However, the low data transmission efficiency of blockchain leads to increased transmission latency in IIoT.

[0096] To address the technical challenge of improving data transmission efficiency in blockchain networks, this disclosure provides a data transmission method, system, device, and medium for a Web3.0-based blockchain network. Since load data directly reflects the real-time operational pressure of the blockchain network, determining the number of neighboring nodes based on this data allows for dynamic adjustment of the group size according to load changes. Furthermore, by considering both the topology and historical behavior of neighboring nodes, and comprehensively analyzing the actual connection relationships and historical performance between them, grouping network node sets can effectively improve the collaboration efficiency between network nodes and the stability and security of data transmission.

[0097] Web3.0 represents the third major evolution of the internet, with its core characteristic being the reconstruction of resource allocation and interaction patterns in the digital world through blockchain, decentralized technology, and smart contracts.

[0098] Blockchain is a tamper-proof distributed ledger used for storing and sharing data. With its immutability and other advantages, it is widely used in numerous industries. Leveraging its decentralized nature, it enhances data security. As a highly reliable distributed transaction ledger technology, it ensures data integrity, traceability, and immutability, significantly improving data security in the Industrial Internet. Blockchain is typically divided into five layers based on function and technology: data layer, network layer, consensus layer, incentive layer, contract layer, and application layer. Each layer plays a different role in its operation: the data layer encapsulates timestamped data blocks; the network layer broadcasts transaction data throughout the network; and the consensus layer reaches a consensus on the validity and legitimacy of transaction data through a specific mechanism. At the network layer, blockchain operates through a peer-to-peer network, where each node can independently forward and verify data, and participate in the consensus process and ledger management. In the edge cloud architecture of the Industrial Internet, edge nodes in the edge layer collectively form the blockchain network. Distributed edge nodes broadcast transactions and transmit data through this network, and their transmission efficiency largely determines the overall transmission efficiency.

[0099] Distributed architecture refers to an architecture where multiple independent nodes collaborate in data processing, storage, and transmission, rather than relying on a single central node. In this technical solution, distribution is manifested in the collaborative operation of the blockchain network and edge computing: multiple edge nodes at the edge layer jointly form the blockchain network, with each edge node acting as a peer independently in communication and data transmission, without relying on a central server. For example, in the edge cloud architecture of the Industrial Internet, blockchain, as a distributed transaction ledger technology, is used by edge nodes to distribute and manage data, ensuring data integrity and immutability; edge computing processes data closer to the device through distributed edge nodes, reducing dependence on the cloud and achieving low-latency services. This distributed model gives the system decentralized and fault-tolerant characteristics; the failure of a single node will not affect the operation of the overall network, while also improving data processing and transmission efficiency through inter-node collaboration.

[0100] Figure 1 This is a flowchart illustrating a data transmission method for a web3.0-based blockchain network, as provided in this embodiment. This method can be applied to one or more electronic devices, such as a web3.0-based blockchain network data transmission system, a smartphone, a laptop, a desktop computer, a portable computer, or a server. Furthermore, the execution entity of this method can be hardware or software. When the execution entity is hardware, it can be one or more of the aforementioned electronic devices. For example, a single electronic device can execute this method, or multiple electronic devices can cooperate with each other to execute this method. When the execution entity is software, this method can be implemented as multiple software programs or software modules, or as a single software program or software module. No specific limitations are made here.

[0101] like Figure 1 As shown, the method specifically includes:

[0102] Step 101: Determine a first quantity corresponding to the load data of the blockchain network, wherein the blockchain network includes a set of network nodes, and the first quantity represents the amount of data to be processed in a first time period.

[0103] In this embodiment, a blockchain network is a decentralized distributed ledger system, in which multiple nodes are connected through cryptographic methods to achieve distributed data storage and verification. Here, the blockchain network serves as the carrier of data transmission, responsible for the distributed transmission and processing of target data. In practice, blockchain networks can be used to achieve secure sharing of equipment data in the Industrial Internet of Things (IIoT) and synchronization of transaction information in the supply chain. For example, in a smart factory, each piece of equipment transmits production data through a blockchain network to ensure that the data is tamper-proof; upstream and downstream enterprises in the supply chain synchronize logistics information through a blockchain network.

[0104] Load data refers to the overall data processing pressure of a blockchain network within a certain period, including the amount of data to be transmitted and the busyness of nodes. Here, load data can be used to determine the initial number of nodes to select as neighbors, ensuring that node selection matches the network load. For example, when the network load is too high, the number of selected nodes can be adjusted using load data to distribute the pressure; when the load is low, the number of selected nodes can be reduced to decrease redundancy.

[0105] The first quantity represents the amount of data to be processed within the first time period, and is the number of neighbor nodes selected based on the load data. Here, the first quantity determines the number of neighbor nodes that each network node needs to select, ensuring that the selected neighbor nodes can meet the current data processing requirements.

[0106] In some cases, the larger the load data, the smaller the initial quantity can be.

[0107] A network node set refers to the collection of nodes in a blockchain network, whether partially or entirely, that participate in data transmission and verification. Here, the network node set is the object of grouping and path planning; by grouping the nodes in the set, efficient data transmission is achieved. Each network node can have one or more neighboring nodes. A network node's neighboring node is a network node that communicates directly with that network node without going through other network nodes. For example, if during communication between network node A and network node B, data can be directly transmitted from network node A to network node B without going through other network nodes, then network node A can be a neighboring node of network node B, and network node B can be a neighboring node of network node A.

[0108] Each network node can represent one or more electronic devices.

[0109] The first time period represents the time period during which a single network node selects its corresponding first subset of neighbor nodes. The first quantity represents the number of neighbor nodes selected for a single network node within the first time period.

[0110] Here, the load data of the blockchain network can be collected in real time or obtained under preset conditions, and then the first quantity can be determined through the preset correspondence between the load data and the first quantity.

[0111] Step 102: Determine the topology data and historical behavior data corresponding to the network nodes in the network node set. The topology data represents the topology of the neighboring nodes of the network node, and the historical behavior data represents the historical behavior of the neighboring nodes.

[0112] In this embodiment, the topology data reflects the connection relationship and layout (such as the number of neighboring nodes, connection strength, etc.) between the neighboring nodes of the network node and its neighboring nodes.

[0113] Historical behavior data records the past data transmission performance of neighboring nodes (such as the number of successful transactions, response time, and failure rate). Here, historical behavior data is used to evaluate the reliability and efficiency of neighboring nodes, ensuring that the selected nodes can stably participate in data transmission.

[0114] Here, the topology data can be determined through the attribute data of network nodes, and the historical behavior data can be determined through the log data of network nodes.

[0115] Step 103: Based on the topology data and historical behavior data of the network nodes in the network node set, select a first number of neighboring nodes from each of the network node's neighboring nodes to obtain the first subset of neighboring nodes corresponding to the network node.

[0116] In this embodiment, the first neighbor node subset is a set of neighbor nodes selected from all the neighbor nodes of the network node, and the set consists of a first number of neighbor nodes. Here, the first neighbor node subset is the basis for node grouping, ensuring that the nodes within the group have good connectivity and reliability.

[0117] Here, for each neighboring node of a network node, the weights of the edges connecting the neighboring node and the network node can be extracted from the topology data of the neighboring node (determined based on connection strength and / or data transmission duration). The confidence level of the neighboring node can be extracted from its historical behavior data (determined based on the number of successful and failed transactions in history). Then, the weighted sum of the weights and confidence levels is calculated, and a first number (e.g., 5) of neighboring nodes are selected from the network node's neighboring nodes in descending order of the results to obtain the first subset of neighboring nodes corresponding to the network node.

[0118] In addition, each network node in the set of network nodes can correspond to a subset of first neighbor nodes.

[0119] Step 104: Based on the first neighbor node subsets corresponding to each network node in the network node set, divide the network node set into multiple groups, and determine the intra-group transmission path and inter-group transmission path of the target data to be transmitted.

[0120] In this embodiment, the intra-group transmission path refers to the path for transmitting target data between network nodes within the same group. Here, the intra-group transmission path is used to synchronize data within the group and reduce latency in data transmission. For example, the intra-group transmission path can be a path from the root node to a leaf node in the group's spanning tree; it can also be a transmission path formed by direct links between nodes within the group.

[0121] Inter-group transmission path: The path for transmitting target data between network nodes in different groups. Here, the inter-group transmission path is used to enable data interaction between groups and ensure the consistency of data across the entire network. For example, the inter-group transmission path can be a path formed by routing nodes connecting different groups in the inter-group spanning tree; it can also be a transmission path built between groups through primary and backup routing nodes.

[0122] Step 105: Transmit the target data in the blockchain network according to the intra-group transmission path and the inter-group transmission path.

[0123] In this embodiment, the target data is the specific data (such as transaction information, block data, etc.) to be transmitted in the blockchain network. Here, the target data is the object of transmission, and its transmission process must follow intra-group transmission paths and inter-group transmission paths. For example, the target data can be transaction data generated by the execution of a smart contract; or newly generated block data that needs to be transmitted to all nodes in the network.

[0124] Here, by determining a primary number corresponding to the load data, the number of selected neighbor nodes is matched with network processing requirements, avoiding waste or insufficiency of node resources. Secondly, a subset of primary neighbor nodes is selected based on topology data and historical behavior data, ensuring stable and reliable connections and laying a solid foundation for subsequent grouping. Then, grouping is performed based on this subset, and intra-group and inter-group transmission paths are planned to optimize data transmission and reduce invalid transmissions. Finally, data can be transmitted along the planned paths, with intra-group and inter-group transmission paths running in parallel, accelerating data synchronization and preventing excessive data concentration on a few nodes, thus achieving load balancing. Furthermore, due to the high reliability of the selected nodes and the optimized transmission paths, the data transmission failure rate is reduced, and stability is enhanced.

[0125] In some optional implementations of this embodiment, the intra-group transmission path and inter-group transmission path of the target data to be transmitted can be determined in the following ways:

[0126] The first step is to construct a spanning tree within the same group based on multiple network nodes belonging to the same group.

[0127] An intra-group spanning tree is a tree-like topology constructed from network nodes within the same group. It includes all nodes in the group and has no loops. For example, after selecting a network node as the root node, the intra-group spanning tree can be a spanning tree centered on the root node, with all nodes (other network nodes besides the root node) connected through parent-child relationships.

[0128] Here, the STTC (Spanning Tree Topology Construction) method can be used. First, select a network node as the root node, and then add other network nodes according to the rules to build the spanning tree.

[0129] The second step is to determine the intra-group transmission path of the target data to be transmitted based on the intra-group generation tree.

[0130] An intra-group transmission path is a path formed by the intra-group spanning tree for transmitting target data between nodes within the same group. Here, intra-group transmission paths reduce redundancy in data transmission within the group and shorten transmission distances. For example, an intra-group transmission path can be a direct path from the root node to a leaf node in the intra-group spanning tree; it can also be a transmission path formed by intra-group nodes forwarding data in parallel.

[0131] Here, the path from the source node to the target node can be determined based on the parent-child relationship of the nodes in the spanning tree within the group, thus obtaining the intra-group transmission path; alternatively, the transmission path can be determined by calculating the shortest path between the nodes in the spanning tree within the group, thus obtaining the intra-group transmission path.

[0132] The third step is to treat each group as a node, the connection between each group as an edge, select the target edge with the smallest weight among the multiple edges connecting two groups, and construct a spanning tree between groups based on the target edge.

[0133] The weights are determined based on the data transmission time between the two network nodes connected by the edge.

[0134] The target edge can be the edge with the smallest weight among multiple edges connecting two groups. The weight of the edge can be determined based on the data transmission duration; the shorter the transmission duration, the smaller the weight.

[0135] An inter-group spanning tree (IPB) is a tree-like topology that treats each group as a node and inter-group connections as edges, used to connect different groups. Here, the IPB provides a unified path for cross-group data transmission, preventing data from being transmitted out of order between groups. For example, an IPB can be formed by connecting three groups through a target edge.

[0136] The fourth step is to determine the inter-group transmission path of the target data based on the inter-group spanning tree.

[0137] Inter-group transmission paths are paths for transmitting target data between nodes in different groups, formed by the inter-group spanning tree. Here, inter-group transmission paths ensure the efficiency and orderliness of cross-group data transmission. For example, an inter-group transmission path could be a path from a network node in group 1 to a network node in group 2 via a target edge.

[0138] Here, the path from the source group to the target group can be determined by the connection relationship between groups in the inter-group spanning tree.

[0139] It is understandable that, among the above optional implementation methods, the construction of the intra-group spanning tree ensures loop-free data transmission within the group, avoiding repeated data transmission and reducing redundancy. Simultaneously, the tree structure ensures that data can spread from the source node to all network nodes via the shortest path, shortening intra-group transmission latency. Secondly, the inter-group spanning tree is constructed based on the target edge with the minimum weight, and the weight is determined by the transmission duration. This means that inter-group connections have the shortest transmission duration, providing an efficient link for inter-group data transmission. The inter-group spanning tree also avoids loops, preventing redundancy and conflicts in inter-group data transmission. Finally, the transmission path determined by the spanning tree makes data transmission orderly and controllable. The spanning tree topology reduces data transmission time, improves transmission efficiency, and is suitable for scenarios with large-scale nodes.

[0140] Alternatively, in addition to determining the weight of the target edge based on the data transmission duration, the weight can also be determined based on the historical transmission success rate of the nodes connected by the edge (the higher the success rate, the smaller the weight), and the edge with the smallest weight can be selected to construct the inter-group spanning tree. This method can prioritize the selection of reliable edges and improve the stability of data transmission between groups.

[0141] To address the issue of path failures caused by node malfunctions after the spanning tree (including intra-group and inter-group spanning trees) is constructed, a backup path mechanism can be introduced to improve the fault tolerance and reliability of the transmission path.

[0142] Specifically, based on the intra-group spanning tree and the inter-group spanning tree, a backup transmission path can be set for each main transmission path, including: in the intra-group spanning tree, additionally associating 1-2 backup parent nodes with the parent node of each node (for example, selecting the neighbor node with the second shortest transmission time to the parent node); in the inter-group spanning tree, additionally associating 1 backup edge with each target edge (selecting the edge with the second smallest weight connecting the same pair); when a node or edge in the main transmission path fails, automatically switching to the backup transmission path; after the failure is recovered, switching back to the main transmission path.

[0143] In some optional implementations of this embodiment, to address the problem that a fixed spanning tree topology cannot adapt to dynamic changes in network load, the spanning tree structure can be dynamically adjusted to optimize data transmission paths and improve network resource utilization.

[0144] Specifically, the transmission performance (including average transmission delay and node load) of the intra-group spanning tree and inter-group spanning tree can be evaluated periodically (e.g., hourly). When the transmission delay of a path within a group exceeds a preset duration or the node load exceeds a preset load, the parent node of that path is reselected (selecting a neighboring node with low transmission delay and low load), and the intra-group spanning tree is adjusted. When the transmission delay of a target edge between groups exceeds a threshold, the edge with the smallest (or second smallest) weight among the edges connecting the group is reselected as the new target edge, and the inter-group spanning tree is adjusted. The transmission path is updated based on the adjusted spanning tree.

[0145] In some optional implementations of this embodiment, to solve the problem of the root node in the spanning tree being overloaded and becoming a transmission bottleneck, multiple root nodes can be set to share the load, thereby improving the data transmission efficiency within the group.

[0146] Specifically, when constructing the spanning tree within a group, select 2-3 nodes with high importance and low load as root nodes (primary root node and backup root node); divide the nodes within the group into multiple sub-regions according to geographical area or function, with each sub-region having a root node responsible for data transmission; the primary root node is responsible for data synchronization between sub-regions, and the backup root node takes over some sub-regions when the primary root node is overloaded; determine the transmission path within the group based on the multi-root node spanning tree, with data within a sub-region transmitted through the regional root node, and data across sub-regions forwarded through the primary root node.

[0147] It should be noted that, where there is no conflict, the technical features described in different alternative implementations can be included in the same embodiment. For the sake of brevity, they will not be elaborated here.

[0148] Based on the data transmission method of the Web3.0-based blockchain network provided in this disclosure embodiment, a first quantity corresponding to the load data of the blockchain network can be determined. The blockchain network includes a set of network nodes, and the first quantity represents the amount of data to be processed within a first time period. Then, the topology data and historical behavior data corresponding to the network nodes in the set of network nodes are determined. The topology data represents the topology of the neighboring nodes of the network node, and the historical behavior data represents the historical behavior of the neighboring nodes. Then, based on the topology data and historical behavior data corresponding to the network nodes in the set of network nodes, a first quantity of neighboring nodes are selected from each of the neighboring nodes of the network node to obtain a first subset of neighboring nodes corresponding to the network node. Subsequently, based on the first subset of neighboring nodes corresponding to each network node in the set of network nodes, the set of network nodes is divided into multiple groups, and the intra-group transmission path and inter-group transmission path of the target data to be transmitted are determined. Finally, the target data is transmitted in the blockchain network according to the intra-group transmission path and inter-group transmission path. Since the load data directly reflects the real-time operating pressure of the blockchain network, determining the number of neighboring nodes accordingly allows the group size to be dynamically adjusted according to load changes, thereby improving the data transmission efficiency in scenarios with high blockchain network load. Furthermore, by combining the actual connection relationships and historical performance between neighboring nodes based on their topological structure and historical behavior, the grouping of network node sets can effectively improve the collaboration efficiency between network nodes and the stability and security of data transmission.

[0149] Figure 2 This is a flowchart illustrating another data transmission method for a web3.0-based blockchain network provided in this embodiment of the disclosure. Figure 2 As shown, the method specifically includes:

[0150] Step 201: Determine a first quantity corresponding to the load data of the blockchain network, wherein the blockchain network includes a set of network nodes, and the first quantity represents the amount of data to be processed in a first time period.

[0151] In this embodiment, step 201 and Figure 1 Step 101 in the corresponding embodiment is basically the same, and will not be repeated here.

[0152] Step 202: Determine the topology data and historical behavior data corresponding to the network nodes in the network node set. The topology data represents the topology of the neighboring nodes of the network node, and the historical behavior data represents the historical behavior of the neighboring nodes.

[0153] In this embodiment, step 202 and Figure 1 Step 102 in the corresponding embodiment is basically the same, and will not be repeated here.

[0154] Step 203: Based on the topology data corresponding to the network node in the network node set, determine the degree of each neighbor node of the network node.

[0155] In this embodiment, degree, in network topology, refers to the number of neighboring nodes connected to a node (e.g., a network node, a neighboring node), reflecting the node's connectivity and its hub status in the network. Here, degree is used to evaluate the connectivity range of neighboring nodes; network nodes with higher degrees have stronger information dissemination capabilities.

[0156] Here, the topology data of neighboring nodes may include the degree of neighboring nodes.

[0157] Step 204: Based on the historical behavior data corresponding to the network nodes in the network node set, determine the interaction achievement rate and target interaction frequency of each neighbor node of the network node, where the target interaction frequency represents the interaction frequency between the network node and its neighbor nodes.

[0158] In this embodiment, the interaction success rate refers to the proportion of successful interactions (such as data transmission and transaction verification) between a network node and its neighboring nodes out of the total number of interactions, reflecting the reliability of the neighboring nodes. Here, the interaction success rate is used to filter reliable neighboring nodes and improve the success rate of data transmission.

[0159] Target interaction frequency represents the number of interactions between a network node and a specific neighbor node, reflecting the closeness of the interaction between them. Here, neighbor nodes with high target interaction frequency usually have more stable connections and higher interaction efficiency, and are therefore given priority.

[0160] Here, the interaction achievement rate and target interaction frequency can be determined by querying the historical interaction logs of the node and calculating the percentage of successful interactions out of the total number of interactions.

[0161] Step 205: Based on the degree, interaction achievement rate and target interaction frequency of each neighbor node of the network node in the network node set, select a first number of neighbor nodes from each neighbor node of the network node to obtain the first neighbor node subset corresponding to the network node.

[0162] In this embodiment, the degree, interaction achievement rate and target interaction frequency can be quantified, the weight of each indicator can be set, and the first number of neighboring nodes can be selected by weighted summation.

[0163] Step 206: Based on the first neighbor node subsets corresponding to each network node in the network node set, divide the network node set into multiple groups, and determine the intra-group transmission path and inter-group transmission path of the target data to be transmitted.

[0164] In this embodiment, step 206 and Figure 1 Step 104 in the corresponding embodiment is basically the same, and will not be repeated here.

[0165] Step 207: Transmit the target data in the blockchain network according to the intra-group transmission path and the inter-group transmission path.

[0166] In this embodiment, step 207 and Figure 1 Step 105 in the corresponding embodiment is basically the same, and will not be repeated here.

[0167] It should be noted that, in addition to the contents described above, this embodiment may also include... Figure 1 The corresponding technical features described in the corresponding embodiments, thereby achieving Figure 1 The technical effects of the data transmission method based on the Web3.0 blockchain network shown are detailed in the documentation. Figure 1 The relevant descriptions are presented concisely and will not be elaborated upon here.

[0168] Based on the embodiments of this disclosure, the degree of neighboring nodes is determined through topology data; network nodes with higher degrees can better distribute data. Interaction achievement rate and target interaction frequency are determined based on historical behavior data; a high interaction achievement rate indicates node reliability, and a high target interaction frequency indicates connection stability. Selecting nodes by combining these three indicators is more comprehensive than using a single indicator, avoiding the problem of considering only the number of connections while ignoring reliability or interaction efficiency. The selected nodes can participate in data transmission more efficiently, reducing transmission failures and retries, thereby improving overall transmission performance.

[0169] Alternatively, in addition to selecting nodes based on degree, interaction achievement rate, and target interaction frequency, selection can also be based on the degree of neighboring nodes, as well as response time and available resources (such as computing power and storage space) in historical behavioral data. Nodes with short response times can process and transmit data quickly, while nodes with sufficient available resources can undertake more transmission tasks. Combining these two indicators can also select a suitable subset of first neighboring nodes.

[0170] Figure 3 This is a schematic flowchart illustrating another data transmission method for a web3.0-based blockchain network provided in this disclosure. Figure 3 As shown, the method specifically includes:

[0171] Step 301: Determine a first quantity corresponding to the load data of the blockchain network, wherein the blockchain network includes a set of network nodes, and the first quantity represents the amount of data to be processed in a first time period.

[0172] In this embodiment, step 301 and Figure 1 Step 101 in the corresponding embodiment is basically the same, and will not be repeated here.

[0173] Step 302: Determine the topology data and historical behavior data corresponding to the network nodes in the network node set. The topology data represents the topology of the neighboring nodes of the network node, and the historical behavior data represents the historical behavior of the neighboring nodes.

[0174] In this embodiment, step 302 and Figure 1 Step 102 in the corresponding embodiment is basically the same, and will not be repeated here.

[0175] Step 303: Based on the topology data and historical behavior data of the network nodes in the network node set, select a first number of neighboring nodes from each of the network node's neighboring nodes to obtain the first subset of neighboring nodes corresponding to the network node.

[0176] In this embodiment, step 303 and Figure 1 Step 103 in the corresponding embodiment is basically the same, and will not be repeated here.

[0177] Step 304: Determine the target similarity for each network node in the network node set, wherein the target similarity is determined based on the similarity between the network node and the neighboring nodes in the first neighboring node subset corresponding to the network node.

[0178] In this embodiment, target similarity reflects the overall similarity between a network node and all its neighboring nodes in its first subset of neighboring nodes, and is a comprehensive indicator for measuring the closeness of the relationship between nodes.

[0179] For example, if the target similarity of network node X is 0.8 and the target similarity of network node Y is 0.75, the two nodes have similar target similarities and may be classified into the same group; nodes with large differences in target similarity may belong to different groups.

[0180] Here, the target similarity can be obtained by calculating the similarity between the network node and each neighbor in the first neighbor node subset, and then summing or averaging the results; alternatively, a weighted summation method can be used, assigning weights to the similarity of different neighbor nodes before calculation.

[0181] Step 305: Based on the target similarity corresponding to each network node in the network node set, the network node set is clustered to obtain multiple node clusters. Network nodes belonging to the same node cluster are divided into the same group, and network nodes belonging to different node clusters are divided into different groups. The intra-group transmission path and inter-group transmission path of the target data to be transmitted are also determined.

[0182] In this embodiment, a node cluster refers to a group of nodes with high internal node similarity obtained by dividing a set of network nodes using a clustering algorithm. Here, a node cluster is the basis of a group; nodes in the same node cluster are grouped into the same group, which facilitates data transmission and management within the group.

[0183] Here, clustering can be performed based on the label propagation algorithm.

[0184] Step 306: Transmit the target data in the blockchain network according to the intra-group transmission path and the inter-group transmission path.

[0185] In this embodiment, step 306 and Figure 1 Step 105 in the corresponding embodiment is basically the same, and will not be repeated here.

[0186] In some optional implementations of this embodiment, the target similarity corresponding to each network node in the network node set can be determined in the following manner:

[0187] The first step is to determine the data transmission duration between the neighboring nodes (hereinafter referred to as nodes B) in the first neighboring node subset corresponding to the network node (hereinafter referred to as node A) and the network node (node ​​A), obtain the data transmission duration corresponding to the neighboring node (node ​​B), and determine the reciprocal of the data transmission duration corresponding to the neighboring node (node ​​B) as the similarity between the neighboring node (node ​​B) and the network node (node ​​A).

[0188] Data transmission duration refers to the time required for a network node to transmit data with its neighboring nodes, reflecting the transmission efficiency between them. Here, data transmission duration is the basis for calculating similarity; the shorter the duration, the higher the similarity.

[0189] Here, the data transmission duration can be obtained by adding a timestamp during data transmission and calculating the time difference between sending and receiving.

[0190] The second step is to determine the sum of the similarities between each neighbor node in the first neighbor node subset corresponding to the network node and the network node.

[0191] The third step is to sum the similarities and determine the target similarity corresponding to the network node.

[0192] It is understandable that in the above-mentioned optional implementation methods, using the reciprocal of the data transmission duration as the similarity directly reflects the negative correlation between transmission efficiency and similarity; that is, the shorter the transmission duration, the higher the similarity, which aligns with the preference for efficient nodes in actual data transmission. Using the sum of the similarities of all neighboring nodes as the target similarity comprehensively considers the transmission efficiency of a network node with all its first neighboring nodes, fully reflecting the overall correlation between the network node and its first neighboring nodes. Clustering based on this calculated target similarity enables nodes within a group to have higher transmission efficiency and reduces intra-group transmission latency.

[0193] Alternatively, in addition to using the reciprocal of data transmission duration as the similarity criterion, the data transmission bandwidth can also be used as the basis for calculating the similarity criterion; the larger the transmission bandwidth, the higher the similarity. Then, the similarity scores of each neighboring node are summed to obtain the target similarity score, which also reflects the transmission capacity and degree of association between nodes.

[0194] In some optional implementations of this embodiment, the network node set can be clustered based on the target similarity corresponding to each network node:

[0195] The first step is to determine the importance of each network node (hereinafter referred to as node A) in the blockchain network based on the similarity between the network node (node ​​A) and its neighboring nodes (hereinafter referred to as node B), the target similarity of the network node (node ​​A) and its neighboring nodes (node ​​B), the degree of the network node (node ​​A) and the degree of the network node (node ​​A).

[0196] Importance is a metric that measures a network node's influence and role in the blockchain network by comprehensively considering the similarity between the network node and its neighbors, the similarity of the neighbors' corresponding targets, the degree of the neighbors' nodes, and the network node's own degree. Here, importance determines the order of label updates; important network nodes are updated with labels first, reducing clustering randomness.

[0197] Here, the importance level I corresponding to network node i can be determined based on the following formula (1).i :

[0198] Formula (1)

[0199] Where, d i d represents the degree of network node i. j S represents the degree of neighbor node j of network node i. j S represents the target similarity corresponding to neighbor node j. i，j Let N(i) represent the similarity between network node i and its neighbor node j, and let N(i) represent the set of all or some of the neighbor nodes of network node i.

[0200] The second step is to determine the label update order of each network node in the network node set based on the importance of each network node.

[0201] Labels are used to identify the category or cluster to which a network node belongs. Here, the label is the basis for dividing node clusters, and updating the label allows similar nodes to be grouped into the same category. For example, the initial label is the network node number. After updating, the node label of the same node cluster is unified as "Cluster 1"; the node labels of different node clusters are "Cluster 2", "Cluster 3", etc.

[0202] The label update order is determined by the importance of the nodes. Here, nodes with higher importance are updated first, as their labels have a greater impact on other nodes, thus improving clustering stability.

[0203] The third step is to update the labels of each network node in the order they are updated.

[0204] Here, the network node's own label can be updated based on the label with the highest proportion among neighboring nodes; alternatively, the labels of neighboring nodes can be weighted according to their importance, and the label with the highest weight can be selected for updating.

[0205] The fourth step is to group network nodes with the same label into the same node cluster; and to group network nodes with different labels into different node clusters.

[0206] A node cluster consists of network nodes with the same label, exhibiting high similarity and close relationships among nodes within the cluster. Here, node clusters form the basis of network grouping, facilitating efficient data transmission within the group. Application scenarios include blockchain network partitioning and local data synchronization.

[0207] It is understandable that, among the above-mentioned optional implementation methods, the determination of importance integrates multi-dimensional characteristics of the node itself and its neighbors, more comprehensively reflecting the actual influence of the node in the network and avoiding evaluation bias caused by a single factor. Secondly, determining the label update order according to importance ensures that the labels of important nodes stabilize first, reducing the randomness of traditional label propagation algorithms and improving clustering stability. Then, through ordered label updates, similar nodes are more likely to form the same label, ensuring close association between nodes within a cluster. Finally, reasonable node cluster partitioning results in shorter and more efficient data transmission paths within a group, reducing the complexity of cross-cluster transmission, thereby reducing overall data transmission latency and redundancy.

[0208] In addition to determining the label update order based on node importance, the update order can also be determined based on node response speed. Nodes with faster response speeds are prioritized for label updates because they can process and propagate information more quickly, resulting in more timely label updates and improving the effectiveness of clustering.

[0209] In some application scenarios of the above-mentioned optional implementation methods, after determining the first quantity corresponding to the load data of the blockchain network, a second quantity corresponding to the load data can also be determined.

[0210] The second quantity represents the amount of data to be processed in the second time period, determined by the load data of the blockchain network. Here, the second quantity determines the number of neighboring nodes selected when updating the label, ensuring that the label update matches the changes in network load.

[0211] In some cases, the larger the load data, the smaller the second quantity can be.

[0212] The second time period represents the time during which a single network node selects its corresponding subset of second neighbor nodes. The second quantity represents the number of neighbor nodes selected for a single network node within the second time period.

[0213] Here, the load data of the blockchain network can be collected in real time or obtained under preset conditions, and then the second quantity can be determined through the preset correspondence between the load data and the second quantity.

[0214] Based on this, the label of each network node in the network node set can be updated in the following way:

[0215] The first step is to select a second number (e.g., 3) of the neighboring nodes from the network node in descending order of their importance, thus obtaining the second set of neighboring nodes corresponding to the network node.

[0216] The second subset of neighboring nodes is a set of neighboring nodes selected by a network node from all its neighbors, ranked by importance, and numbering second. Here, the second subset of neighboring nodes serves as the basis for dynamic label updates, ensuring that the neighboring nodes participating in the update have higher importance. The second subset may be equal to or different from the first subset.

[0217] Here, the second number of neighboring nodes can be selected strictly according to their importance from high to low; alternatively, the load of neighboring nodes can be considered in addition to the importance ranking to avoid selecting overloaded nodes.

[0218] The second step is to update the label of the network node based on the labels of the neighboring nodes in the subset of the second neighboring nodes corresponding to the network node.

[0219] Here, you can either select the label that appears most frequently in the second neighbor node subset to update your own label, or you can weight the labels of each node in the second neighbor node subset according to their importance and select the label with the highest weight to update.

[0220] Understandably, in the above application scenario, the second quantity is determined based on the load data of the second time period, ensuring that the selected number of neighbor nodes matches the amount of data to be processed during that time period, thus avoiding the problem of unreasonable label update criteria when the load changes. Secondly, selecting a subset of second neighbor nodes based on their importance ensures that nodes participating in label updates have high influence, improving the reliability of label updates. Then, updating node labels based on the labels of this second subset of neighbor nodes allows node clusters to dynamically adjust with network load changes, maintaining efficient collaboration among nodes within the cluster. Finally, dynamically adjusted node clusters enable data transmission paths to adapt to load changes, avoiding transmission bottlenecks that occur with fixed cluster partitioning during load fluctuations, thereby improving the overall adaptability and stability of the network.

[0221] Alternatively, in addition to selecting a subset of second neighbor nodes based on their importance, the subset can also be selected based on the real-time data transmission rate between the neighbor nodes and the current node. Neighbor nodes with higher transmission rates are better able to reflect the current network connectivity status, and updating node labels based on their labels also allows the node cluster to adapt to dynamic network changes.

[0222] It should be noted that, in addition to the contents described above, this embodiment may also include... Figure 1 The corresponding technical features described in the corresponding embodiments, thereby achieving Figure 1 The technical effects of the data transmission method based on the Web3.0 blockchain network shown are detailed in the documentation. Figure 1 The relevant descriptions are presented concisely and will not be elaborated upon here.

[0223] Based on the embodiments of this disclosure, the target similarity is determined based on the similarity between a network node and a subset of its first neighbor nodes, ensuring the targeted nature of the similarity calculation. Clustering based on target similarity groups similar nodes into the same group, resulting in close connections and efficient interactions among nodes within the same group. This leads to shorter and more efficient data transmission paths within the group. Simultaneously, reasonable grouping reduces the number of nodes within a group, lowers the complexity of data transmission within the group, and facilitates management and optimization of transmission paths within the group.

[0224] Alternatively, in addition to clustering based on target similarity, nodes can also be grouped based on their functional type and data processing needs. For example, nodes responsible for data storage can be grouped into one group, and nodes responsible for data verification into another. Nodes within the same functional group have similar data transmission needs, which can also achieve efficient intra-group transmission.

[0225] Figure 4 This is a schematic diagram illustrating the structure of a data transmission system for a web3.0-based blockchain network, as provided in this embodiment of the disclosure. Specifically, it includes:

[0226] The first determining unit 401 is configured to: determine a first quantity corresponding to the load data of the blockchain network, wherein the blockchain network includes a set of network nodes, and the first quantity represents the amount of data to be processed in a first time period;

[0227] The second determining unit 402 is configured to: determine the topology data and historical behavior data corresponding to the network nodes in the network node set, wherein the topology data represents the topology of the neighboring nodes of the network node, and the historical behavior data represents the historical behavior of the neighboring nodes.

[0228] The selection unit 403 is configured to: select a first number of neighboring nodes from each of the neighboring nodes of the network node based on the topology data and historical behavior data corresponding to the network node in the network node set, thereby obtaining the first subset of neighboring nodes corresponding to the network node;

[0229] The partitioning unit 404 is configured to: divide the network node set into multiple groups based on the first neighbor node subset corresponding to each network node in the network node set, and determine the intra-group transmission path and inter-group transmission path of the target data to be transmitted.

[0230] Transmission unit 405 is configured to transmit target data in the blockchain network according to intra-group transmission paths and inter-group transmission paths.

[0231] Figure 5 This is a schematic diagram of the structure of a selected unit in a data transmission system of a web3.0-based blockchain network provided in an embodiment of this disclosure.

[0232] like Figure 5As shown, in some possible implementations, the selection unit 403 includes:

[0233] The first determining subunit 4031 is configured to: determine the degree of each neighboring node of the network node based on the topology data corresponding to the network node in the network node set;

[0234] The second determining subunit 4032 is configured to: determine the interaction achievement rate and target interaction frequency of each neighbor node of the network node based on the historical behavior data corresponding to the network node in the network node set, wherein the target interaction frequency represents the interaction frequency between the network node and its neighbor nodes.

[0235] The first selection subunit 4033 is configured to: select a first number of neighboring nodes from the neighboring nodes of the network node based on the degree, interaction achievement rate and target interaction frequency of each neighboring node of the network node in the network node set, to obtain the first neighboring node subset corresponding to the network node.

[0236] In some possible implementations, the partitioning unit 404 includes:

[0237] The third determining subunit (not shown in the figure) is configured to: determine the target similarity corresponding to each network node in the network node set, wherein the target similarity is determined based on the similarity between the network node and the neighboring nodes in the first neighboring node subset corresponding to the network node.

[0238] The clustering subunit (not shown in the figure) is configured to: cluster the network node set based on the target similarity corresponding to each network node in the network node set to obtain multiple node clusters, divide network nodes belonging to the same node cluster into the same group, and divide network nodes belonging to different node clusters into different groups.

[0239] In some possible implementations, the target similarity corresponding to each network node in the set of network nodes is determined in the following manner:

[0240] For the neighboring nodes in the first neighboring node subset corresponding to the network node, determine the data transmission duration between the neighboring node and the network node, obtain the data transmission duration corresponding to the neighboring node, and determine the similarity between the neighboring node and the network node as the reciprocal of the data transmission duration corresponding to the neighboring node.

[0241] Determine the sum of the similarities between each neighbor node in the first neighbor node subset corresponding to the network node and the network node;

[0242] The sum of all similarities is determined as the target similarity corresponding to that network node.

[0243] In some possible implementations, the clustering subunit includes:

[0244] The first determining module (not shown in the figure) is configured to: for each network node in the network node set, determine the importance of the network node in the blockchain network based on the similarity between the network node and its neighboring nodes, the target similarity corresponding to the neighboring nodes, the degree of the neighboring nodes, and the degree of the network node, and obtain the importance of the network node.

[0245] The second determining module (not shown in the figure) is configured to: determine the label update order of each network node in the network node set based on the importance of each network node in the network node set;

[0246] The update module (not shown in the figure) is configured to update the labels of each network node sequentially according to the label update order;

[0247] The partitioning module (not shown in the figure) is configured to: partition network nodes with the same label into the same node cluster; and partition network nodes with different labels into different node clusters.

[0248] In some possible implementations, after determining the first quantity corresponding to the load data of the blockchain network, the system further includes:

[0249] The third determining unit (not shown in the figure) is configured to: determine a second quantity corresponding to the load data, wherein the second quantity represents the amount of data to be processed within a second time period; and

[0250] Update the label of each network node in the network node set as follows:

[0251] According to the order of importance of neighboring nodes from high to low, select a second number of neighboring nodes from each of the network node's neighboring nodes to obtain the second set of neighboring nodes corresponding to the network node;

[0252] Update the label of the network node based on the labels of the neighboring nodes in the subset of the second neighboring nodes corresponding to the network node.

[0253] In some possible implementations, the transmission unit 405 includes:

[0254] The sub-unit (not shown in the figure) is configured to: construct a spanning tree within a group based on multiple network nodes belonging to the same group;

[0255] The fourth determining subunit (not shown in the figure) is configured to: determine the intra-group transmission path of the target data to be transmitted based on the intra-group spanning tree;

[0256] The second selection sub-unit (not shown in the figure) is configured to: treat each group as a node, treat the connection relationship between each group as an edge, select the target edge with the smallest weight among the multiple edges connecting two groups, and construct an inter-group spanning tree based on the target edge, wherein the weight is determined based on the data transmission duration between the two network nodes connected by the edge.

[0257] The fifth determining subunit (not shown in the figure) is configured to: determine the inter-group transmission path of the target data based on the inter-group spanning tree.

[0258] The data transmission system based on a web3.0 blockchain network provided in this embodiment can execute the corresponding steps of the data transmission methods based on web3.0 blockchain networks described above, thereby achieving the technical effects of the data transmission methods based on web3.0 blockchain networks described above. The data transmission system and the data transmission methods based on web3.0 blockchain networks can refer to and cite each other in terms of specific implementation and technical effects. For the sake of brevity, they will not be elaborated here.

[0259] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. Figure 6 The illustrated electronic device 500 includes at least one processor 501, a memory 502, at least one network interface 504, and other user interfaces 503. The various components in the electronic device 500 are coupled together via a bus system 505. It is understood that the bus system 505 is used to implement communication between these components. In addition to a data bus, the bus system 505 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 6 The general designated all buses as Bus System 505.

[0260] The user interface 503 may include a display, keyboard, or clicking device (e.g., mouse, trackball, touchpad, or touchscreen).

[0261] It is understood that the memory 502 in this embodiment of the present disclosure may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 502 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0262] In some implementations, memory 502 stores elements, executable units or data structures, or subsets thereof, or extended sets thereof: operating system 5021 and application program 5022.

[0263] The operating system 5021 includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application program 5022 includes various applications, such as a media player and a browser, used to implement various application functions. The program implementing the method of this embodiment can be included in the application program 5022.

[0264] In this embodiment, by calling the program or instructions stored in memory 502, specifically the program or instructions stored in application program 5022, processor 501 executes the method steps provided in each method embodiment, including, for example:

[0265] Determine a first quantity corresponding to the load data of the blockchain network, wherein the blockchain network comprises a set of network nodes, and the first quantity represents the amount of data to be processed in a first time period;

[0266] Determine the topology data and historical behavior data of the network nodes in the set of network nodes. The topology data represents the topology of the neighboring nodes of the network node, and the historical behavior data represents the historical behavior of the neighboring nodes.

[0267] Based on the topology data and historical behavior data of the network nodes in the network node set, a first number of neighboring nodes are selected from each of the neighboring nodes of the network node to obtain the first neighboring node subset corresponding to the network node.

[0268] Based on the subset of first neighbor nodes corresponding to each network node in the network node set, the network node set is divided into multiple groups, and the intra-group transmission path and inter-group transmission path of the target data to be transmitted are determined.

[0269] The target data is transmitted in the blockchain network according to the intra-group transmission path and the inter-group transmission path.

[0270] The methods disclosed in the above embodiments of this disclosure can be applied to or implemented by processor 501. Processor 501 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 501 or by instructions in the form of software. The processor 501 may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software units in the decoding processor. The software units may be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 502. Processor 501 reads the information in memory 502 and, in conjunction with its hardware, completes the steps of the above method.

[0271] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described above, or combinations thereof.

[0272] For software implementation, the techniques described herein can be implemented by units that perform the functions described above. The software code can be stored in memory and executed by a processor. The memory can be implemented within the processor or external to the processor.

[0273] The electronic device provided in this embodiment may be as follows: Figure 6 The electronic device shown can execute all the steps of the data transmission methods of the various web3.0-based blockchain networks described above, thereby achieving the technical effects of the data transmission methods of the various web3.0-based blockchain networks described above. For details, please refer to the relevant descriptions above. For the sake of brevity, further details are not provided here.

[0274] This disclosure also provides a storage medium (computer-readable storage medium). This storage medium stores one or more programs. The storage medium may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive; the memory may also include combinations of the above types of memory.

[0275] When one or more programs in the storage medium can be executed by one or more processors to implement the above-mentioned data transmission method of the web3.0-based blockchain network executed on the electronic device side.

[0276] The aforementioned processor is used to execute a data transmission program for a web3.0-based blockchain network stored in memory, to implement the following steps of a data transmission method for a web3.0-based blockchain network executed on the electronic device side:

[0277] Determine a first quantity corresponding to the load data of the blockchain network, wherein the blockchain network comprises a set of network nodes, and the first quantity represents the amount of data to be processed in a first time period;

[0278] Determine the topology data and historical behavior data of the network nodes in the set of network nodes. The topology data represents the topology of the neighboring nodes of the network node, and the historical behavior data represents the historical behavior of the neighboring nodes.

[0279] Based on the topology data and historical behavior data of the network nodes in the network node set, a first number of neighboring nodes are selected from each of the neighboring nodes of the network node to obtain the first neighboring node subset corresponding to the network node.

[0280] Based on the subset of first neighbor nodes corresponding to each network node in the network node set, the network node set is divided into multiple groups, and the intra-group transmission path and inter-group transmission path of the target data to be transmitted are determined.

[0281] The target data is transmitted in the blockchain network according to the intra-group transmission path and the inter-group transmission path.

[0282] Furthermore, the computer program product provided in this disclosure embodiment may include computer-readable code that, when executed on a device, causes a processor in the device to implement the following steps of a data transmission method for a web3.0-based blockchain network, executed on the electronic device side:

[0283] Determine a first quantity corresponding to the load data of the blockchain network, wherein the blockchain network comprises a set of network nodes, and the first quantity represents the amount of data to be processed in a first time period;

[0284] Determine the topology data and historical behavior data of the network nodes in the set of network nodes. The topology data represents the topology of the neighboring nodes of the network node, and the historical behavior data represents the historical behavior of the neighboring nodes.

[0285] Based on the topology data and historical behavior data of the network nodes in the network node set, a first number of neighboring nodes are selected from each of the neighboring nodes of the network node to obtain the first neighboring node subset corresponding to the network node.

[0286] Based on the subset of first neighbor nodes corresponding to each network node in the network node set, the network node set is divided into multiple groups, and the intra-group transmission path and inter-group transmission path of the target data to be transmitted are determined.

[0287] The target data is transmitted in the blockchain network according to the intra-group transmission path and the inter-group transmission path.

[0288] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0289] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0290] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0291] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A data transmission method for a blockchain network based on Web3.0, characterized in that, The method includes: Based on the load data of the blockchain network, a first quantity corresponding to the load data is determined, wherein the blockchain network includes a set of network nodes; Determine the topology data and historical behavior data corresponding to the network nodes in the network node set, wherein the topology data represents the topology of the neighboring nodes of the network node, and the historical behavior data represents the historical behavior of the neighboring nodes; Based on the topology data and historical behavior data corresponding to the network nodes in the network node set, a first subset of neighboring nodes is obtained by selecting a first number of neighboring nodes from each of the network node's neighboring nodes. This includes: determining the degree of each neighboring node of the network node based on the topology data corresponding to the network node in the network node set, where the degree of a neighboring node represents the number of neighboring nodes connected to that neighboring node; determining the interaction achievement rate and target interaction frequency of each neighboring node of the network node based on the historical behavior data corresponding to the network node in the network node set, where the target interaction frequency represents the interaction frequency between the network node and its neighboring nodes; and selecting a first number of neighboring nodes from each of the network node's neighboring nodes based on their respective degree, interaction achievement rate, and target interaction frequency to obtain the first subset of neighboring nodes corresponding to that network node. Based on the first subset of neighboring nodes corresponding to each network node in the network node set, the network node set is divided into multiple groups, including: determining the target similarity corresponding to each network node in the network node set, wherein the target similarity is determined based on the similarity between the network node and the neighboring nodes in the first subset of neighboring nodes corresponding to the network node; and clustering the network node set based on the target similarity corresponding to each network node in the network node set to obtain multiple node clusters, grouping network nodes belonging to the same node cluster into the same group, and grouping network nodes belonging to different node clusters into different groups; And determine the intra-group and inter-group transmission paths for the target data to be transmitted; The target data is transmitted in the blockchain network according to the intra-group transmission path and the inter-group transmission path; The target similarity corresponding to each network node in the network node set is determined using the following method: For the neighboring nodes in the first neighboring node subset corresponding to the network node, determine the data transmission duration between the neighboring node and the network node, obtain the data transmission duration corresponding to the neighboring node, and determine the similarity between the neighboring node and the network node as the reciprocal of the data transmission duration corresponding to the neighboring node. Determine the sum of the similarities between each neighbor node in the first neighbor node subset corresponding to the network node and the network node; The sum of the aforementioned similarities is determined as the target similarity corresponding to the network node.

2. The method according to claim 1, characterized in that, The step of clustering the network node set based on the target similarity corresponding to each network node in the network node set includes: For each network node in the set of network nodes, the importance of the network node in the blockchain network is determined based on the similarity between the network node and its neighboring nodes, the target similarity of the network node's neighboring nodes, the degree of the network node's neighboring nodes, and the degree of the network node itself. Based on the importance of each network node in the network node set, the label update order of each network node in the network node set is determined; Update the labels of each network node sequentially according to the label update order. Network nodes with the same label are grouped into the same node cluster; network nodes with different labels are grouped into different node clusters.

3. The method according to claim 2, characterized in that, After determining the first quantity corresponding to the load data based on the load data of the blockchain network, the method further includes: Based on the load data, determine a second quantity corresponding to the load data; and The label of each network node in the network node set is updated in the following manner: According to the order of importance of neighboring nodes from high to low, select the second number of neighboring nodes from each of the network node's neighboring nodes to obtain the second neighboring node subset corresponding to the network node; The label of the network node is updated based on the labels of the neighbor nodes in the second subset of neighbor nodes corresponding to the network node.

4. The method according to any one of claims 1-3, characterized in that, Determining the intra-group transmission path and inter-group transmission path of the target data to be transmitted includes: Construct a spanning tree within a group based on multiple network nodes belonging to the same group; Based on the intra-group generation tree, the intra-group transmission path of the target data to be transmitted is determined. Each group is treated as a node, and the connections between groups are treated as edges. The target edge with the smallest weight among the multiple edges connecting two groups is selected, and a spanning tree between groups is constructed based on the target edge. The weight is determined based on the data transmission time between the two network nodes connected by the edge. Based on the inter-group spanning tree, the inter-group transmission path of the target data is determined.

5. A data transmission system for a blockchain network based on Web3.0, characterized in that, The system includes: The first determining unit is configured to: determine a first quantity corresponding to the load data based on the load data of the blockchain network, wherein the blockchain network includes a set of network nodes; The second determining unit is configured to: determine the topology data and historical behavior data corresponding to the network nodes in the network node set, wherein the topology data represents the topology of the neighboring nodes of the network node, and the historical behavior data represents the historical behavior of the neighboring nodes; The selection unit is configured to: select a first number of neighboring nodes from each of the neighboring nodes of the network node based on the topology data and the historical behavior data corresponding to the network node in the network node set, thereby obtaining a first subset of neighboring nodes corresponding to the network node; The partitioning unit is configured to: divide the network node set into multiple groups based on the first neighbor node subset corresponding to each network node in the network node set, and determine the intra-group transmission path and inter-group transmission path of the target data to be transmitted; The transmission unit is configured to transmit the target data in the blockchain network according to the intra-group transmission path and the inter-group transmission path; The selection unit includes: The first determining subunit is configured to: determine the degree of each neighboring node of the network node based on the topology data corresponding to the network node in the network node set, wherein the degree of a neighboring node represents the number of neighboring nodes connected to the neighboring node; The second determining subunit is configured to: determine the interaction achievement rate and target interaction frequency of each neighbor node of the network node based on the historical behavior data corresponding to the network node in the network node set, wherein the target interaction frequency represents the interaction frequency between the network node and its neighbor nodes. The first selection subunit is configured to: select a first number of neighboring nodes from the neighboring nodes of the network node based on the degree, interaction achievement rate and target interaction frequency of each neighboring node of the network node in the network node set, to obtain the first neighboring node subset corresponding to the network node. The partitioning unit includes: The third determining subunit is configured to: determine the target similarity corresponding to each network node in the network node set, wherein the target similarity is determined based on the similarity between the network node and the neighbor nodes in the first neighbor node subset corresponding to the network node; The clustering subunit is configured to: cluster the network node set based on the target similarity corresponding to each network node in the network node set to obtain multiple node clusters, divide network nodes belonging to the same node cluster into the same group, and divide network nodes belonging to different node clusters into different groups; The target similarity corresponding to each network node in the network node set is determined using the following method: For the neighboring nodes in the first neighboring node subset corresponding to the network node, determine the data transmission duration between the neighboring node and the network node, obtain the data transmission duration corresponding to the neighboring node, and determine the similarity between the neighboring node and the network node as the reciprocal of the data transmission duration corresponding to the neighboring node. Determine the sum of the similarities between each neighbor node in the first neighbor node subset corresponding to the network node and the network node; The sum of the aforementioned similarities is determined as the target similarity corresponding to the network node.

6. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing a computer program stored in the memory, wherein when the computer program is executed, it implements the method described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-4.

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