Intellectual property node fragment storage method and system based on alliance chain

Abstract

The invention relates to the technical field of electric data processing, in particular to an intellectual property node fragmentation storage method and system based on an alliance chain, and the method comprises the steps: S10, optimizing and fragmenting nodes of a network layer; and S20, storing intellectual property transaction data generated by a transaction layer based on the optimized and fragmented node group. According to the method, the network nodes are optimized and fragmented, so that the data storage efficiency can be improved, and the storage capacity can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical data processing, and particularly to a method and system for sharding and storing intellectual property nodes based on a consortium blockchain. Background Art

[0002] At the beginning of the 21st century, with the rapid development of Internet technology, intellectual property issues have become the focus of global attention. The development of the Internet has made the dissemination and replication of information, works, and technologies extremely convenient, but it has also brought about large-scale copyright infringement and piracy problems. Blockchain technology has the characteristics of decentralization and immutability, providing an effective solution for intellectual property protection. It can better ensure the authenticity and reliability of transaction data, prevent transaction fraud, achieve traceability, and define rights and responsibilities, effectively solving problems such as vague property rights confirmation, long transaction cycles, and difficult rights protection existing in traditional protection technologies. In the blockchain architecture, the consortium blockchain, as a type of blockchain that has both the characteristics of decentralization and high efficiency and permission control, has demonstrated its unique value in multiple industries. It can provide efficient transaction processing, strong data privacy protection, and compliance requirement guarantees, and is particularly suitable for application in intellectual property transaction storage.

[0003] Although blockchain technology currently demonstrates the advantages of decentralization and security in the storage of intellectual property transaction data, there are still deficiencies in many aspects. For example, since network nodes need to store the entire blockchain, the storage volume is large, and as the data volume increases, the storage requirements and costs of network nodes continue to rise, making it difficult for the blockchain network to expand its storage and processing capabilities. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for sharding and storing intellectual property nodes based on a consortium blockchain to improve the storage efficiency and capacity of intellectual property transaction data.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] In the first aspect, the present invention provides a method for sharding and storing intellectual property nodes based on a consortium blockchain, including the steps of:

[0007] S10, optimizing and sharding the nodes in the network layer;

[0008] S20, storing the intellectual property transaction data generated in the transaction layer based on the optimized and sharded node group.

[0009] As long as the nodes in the network apply for work and can start working after simple verification, there will be nodes with low work efficiency, or nodes with low transmission efficiency, or malicious nodes that do not work. In the traditional solution, data is transmitted by any node that applies to participate in work in the network layer. Selecting a node with low transmission efficiency will result in low data transmission efficiency. In the above solution, by optimizing the network nodes, malicious nodes and nodes with low work efficiency are screened out. In the optimized network node group, the transmission efficiency between nodes is improved, and the transmission delay between nodes is also improved. Therefore, the data transmission efficiency can be improved, and then the amount of data transmitted within the same time can be increased.

[0010] The S10 includes the following steps:

[0011] S101, Initialize parameters;

[0012] S102, Search for paths between nodes across the network, record the pheromone concentration σ im , and calculate the selection probability of each path;

[0013] S103, Determine whether the optimal node network distribution is obtained. If so, go to step S105; if not, go to step S104;

[0014] S104, Update the pheromone concentration, and return to step S102;

[0015] S105, Extract the optimal node network distribution, calculate the service type evaluation value and modularity between nodes, and slice the nodes in the optimal node network distribution according to the modularity;

[0016] S106, For each sharded network, perform sharding again inside it to divide it into multiple sub-shards;

[0017] S107, Cluster nodes based on the corrected sharding results, merge all nodes within the same sub-shard, regard them as a large node, and form a new network structure on this basis to update the node network structure;

[0018] S108, Determine whether the change amount of modularity has changed. If not, obtain the optimal node sharding. If so, return to step S106.

[0019] In a second aspect, the present invention provides an intellectual property node sharding storage system based on a consortium chain, including:

[0020] A network optimization module for optimizing and sharding the nodes in the network layer;

[0021] A data storage module for storing the intellectual property transaction data generated by the transaction layer based on the optimized and sharded node group.

[0022] In a third aspect, the present invention provides a computer program product comprising computer-readable instructions, characterized in that the computer-readable instructions, when executed by a processor, implement the steps in the method for sharding and storing intellectual property nodes based on a consortium blockchain of the present invention.

[0023] In a fourth aspect, the present invention provides a computer-readable storage medium comprising computer-readable instructions, characterized in that the computer-readable instructions, when executed by a processor, implement the steps in the method for sharding and storing intellectual property nodes based on a consortium blockchain of the present invention.

[0024] In a fifth aspect, the present invention provides an electronic device comprising: a memory for storing program instructions; a processor connected to the memory for executing the program instructions in the memory to implement the steps in the method for sharding and storing intellectual property nodes based on a consortium blockchain of the present invention.

[0025] Compared with the prior art, the present invention has the following technical advantages:

[0026] By optimizing network nodes, malicious nodes and nodes with low work efficiency are screened out. For the optimized network node group, the transmission efficiency between nodes is improved, and the transmission delay between nodes is also improved. Therefore, the data transmission efficiency can be enhanced, and then the amount of data transmitted within the same time can be increased.

[0027] According to the service type evaluation values between nodes in the optimal distribution network, strongly correlated node partitions are constructed, and then the optimal node distribution network of the blockchain is divided into multiple communities with service type characteristics, thereby improving the stability of the node network, enhancing both the data storage efficiency and achieving the scalability of the blockchain.

[0028] Other advantages of the present invention can be found in the relevant descriptions in the embodiment part. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0030] Figure 1 It is an architecture diagram of an intellectual property transaction data storage model based on a consortium blockchain.

[0031] Figure 2 It is a flowchart of the method for sharding and storing intellectual property nodes based on a consortium blockchain in the embodiment.

[0032] Figure 3 It is a block diagram of the composition of the intellectual property node sharding storage system based on the consortium blockchain in the embodiment.

[0033] Figure 4 It is a block diagram of the composition of the electronic device. Specific implementation manners

[0034] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0035] The storage model architecture of the intellectual property transaction data based on the consortium blockchain is as Figure 1 shown. Please refer to Figure 1 , this storage model is divided into three parts: the intellectual property transaction layer, the blockchain network layer and the data storage layer. Each layer constructs a collaborative working cycle system centered on data storage.

[0036] The transaction layer is the core part responsible for processing transactions in the blockchain system, ensuring that the creation, verification, dissemination and recording of transactions can be carried out safely and efficiently. The work content of the transaction layer covers the entire process from the user initiating a transaction to the transaction being finally written into the blockchain. For the intellectual property transactions carried out on the consortium blockchain, there will be a distributed ledger responsible for accounting storage and broadcasting to all working nodes in the network. After being successfully verified by the consensus of all network nodes, the transaction will be incorporated into the block, and the blocks will form a consortium blockchain in a chain structure.

[0037] The blockchain network layer realizes the mechanism of the distributed network through the peer-to-peer (P2P) technology, and is responsible for the information exchange and transmission between nodes. In the blockchain system, each transaction or block data needs to be broadcast to other nodes through the network layer for verification and accounting. This dissemination mechanism ensures the decentralization and distributed storage of blockchain data, and improves the reliability and security of the data.

[0038] The data layer is the core foundation of blockchain technology, responsible for the storage, organization and management of data, ensuring the immutability, transparency and security of the blockchain. When the consensus on a transaction is successfully reached by all network nodes, a new block is constructed and connected to the consortium blockchain, completing a complete storage of the intellectual property transaction. The data layer uses asymmetric encryption technology, and the Merkle tree ensures the security of the transaction data storage.

[0039] The present invention mainly optimizes and shards the nodes of the network layer, which not only optimizes the distributed network structure of the nodes, but also manages the nodes by sharding. By optimizing the node sharding of the network layer, the storage efficiency of the intellectual property transaction data is improved, the waste of network resources is reduced, and the storage expansion of the blockchain is realized.

[0040] The method for sharding and storing intellectual property nodes based on the consortium blockchain provided in this embodiment includes the steps:

[0041] S10. Optimize and shard the nodes in the network layer.

[0042] S20. Store the intellectual property transaction data generated by the transaction layer based on the optimized and sharded node group. The storage of the intellectual property transaction data in this step can be understood as including both the storage and preservation of the transaction content and the whole-network verification and consensus.

[0043] In the traditional solution, data is transmitted by any node that applies to participate in the work in the network layer. Selecting a node with low transmission efficiency will result in low data transmission efficiency. In the above solution, by optimizing the network nodes, malicious nodes and nodes with low work efficiency are screened out. For the optimized network node group, the transmission efficiency between nodes is improved, and the transmission delay between nodes is also improved. Therefore, the data transmission efficiency can be improved, and then the amount of data transmitted within the same time can be increased.

[0044] Please refer to Figure 2 , in the above step S10, optimizing and sharding the nodes in the network layer includes the following steps:

[0045] S101. Initialize the parameters and construct the pheromone matrix.

[0046] In this embodiment, the parameters that need to be initialized include the pheromone factor, the heuristic function factor, and the maximum number of iterations. In the experimental example, the initial configuration of each parameter is the pheromone factor α = 1 and the heuristic function factor β = 5.

[0047] The pheromone matrix is a multi-dimensional matrix composed of the pheromone concentrations between nodes. For example, the two-dimensional matrix formed by node i and node j indicates that the pheromone concentration between node i and node j is 0.6. Pheromones play a role in memory and guidance. Pheromones will accumulate as the node path selection is different, and pheromones will decrease over time. The difference generated is the pheromone concentration. The pheromone concentration is the accumulation of data transmission between nodes during the code operation process. Example: For nodes 1, 2, and 3, when data is transmitted from node 1 to node 2 and node 3 respectively, there will be differences in data processing results due to communication delays and the work efficiency of the nodes. For example, the effect from node 1 to node 2 is better than that from node 1 to node 3. At this time, the pheromone concentration value on the 1-2 path will be larger than the pheromone concentration value on the 1-3 path.

[0048] Constructing the pheromone matrix is to place the information flow at a starting node (usually randomly selected) in the solution space. Each unit of information flow will sequentially select the next node to be visited and assign an initial value σ0 to the pheromone concentration on all paths in the network node group to ensure that all paths have the opportunity to be explored.

[0049] S102, Search for paths between nodes across the network, record the pheromone concentration σ on each path im , and calculate the selection probability of each path.

[0050] Transmit a certain amount of data among the network nodes in the sample group, and use the data transmission rate V between nodes eL , network resource consumption, communication delay, etc. as evaluation indicators. The higher the evaluation indicator value, the better the working state between network nodes. More data will choose this node network path for data transmission. As data is continuously transmitted between nodes, the evaluation indicator value of the excellent transmission path is higher, thus guiding more data to the optimal transmission path. In the network node group, the selection probability of paths is as shown in formula (1):

[0051]

[0052] where D ij (t) is the heuristic information from node i to node j, Introduce D ij (t) as a new reference quantity and add it to the network optimization of nodes, making it easier to obtain the global optimal solution during the iteration process. reflects the resource consumption characteristics, reflects the communication delay characteristics.

[0053] B is the data transmission symbol rate (Baud), N is the number of discrete values taken by each symbol, L ij is the channel length from node i to node j, V eL is the propagation rate of data between node i and node j, t on is the time for data to pass between node i and node j, T all is the total communication time between node i and node j, that is, the time of the entire algorithm cycle, including the time when there is data passing between the two nodes and the time when there is no data passing; is the probability that the network path from node i to node j is selected, σ ij (t) is the pheromone concentration of the path from node i to node j, η ij (t) is the expected degree of data transmission from node i to node j, σ is is the pheromone concentration of the path from node i to node s, η is(t) is the expected degree of data transmission from node i to node s, r is the constraint factor, allow k represents the set of currently selectable nodes (unvisited nodes).

[0054] During the node network optimization process, the pheromone concentration τ ij (t) and the heuristic function η ij (t) values mainly affect the probability of the transmission data selecting a path between nodes. However, to avoid local optimal solutions, D ij (t) this evaluation criterion is introduced into the data flow path selection probability formula.

[0055] Through node network optimization, malicious nodes and lazy nodes can be screened out. The remaining nodes have low latency and high efficiency. Therefore, when the nodes after network optimization process intellectual property transaction data, compared with traditional methods, it can improve data storage efficiency and storage capacity.

[0056] S103, determine whether the global optimal solution is obtained. If so, enter step S105; if not, enter step S104. That is to say, if the result is the optimal network node distribution structure, enter step S105; if it falls into a local optimal solution, enter step S104.

[0057] After n iterations, if all running results converge to the same solution, that is, all solutions still converge to the same result after n iterations of the whole network search process, it proves that this solution is the optimal solution, and it is determined to be the global optimal (or strong local optimal); if the results are scattered, it is determined to be a local optimal.

[0058] The global optimal node path usually has a significantly higher pheromone concentration than other paths. The local optimal may be manifested as an overly high pheromone concentration on the paths of a few nodes, but other areas are not fully explored.

[0059] S104, update the pheromone concentration by referring to the pheromone concentration update formula, and return to step S102 to re-calculate the whole network search until the global optimal solution of the network node distribution is obtained.

[0060] To avoid the situation of local optimal solutions in the iteration process and being unable to obtain the optimal network optimization distribution result, a heuristic function strategy is introduced into the algorithm, and a new convergence strategy is designed to better achieve the global optimal solution. In this paper, by improving the pheromone concentration update formula, the model better avoids local optimality. The pheromone concentration update steps are as follows:

[0061] Assume that when the algorithm starts running initially, the pheromone concentration on each path is σ0. When the algorithm obtains the optimal solution and the results remain unchanged after n iterations, count the pheromone concentration on each path and arrange them in descending order as σ i1 ,σi2 ....σ im σ im Represents the pheromone concentration from node i to node m.

[0062] σ i1 ,σ i2 ....σ im and To compare the size, The big ones are divided into one category: σ i1 ,σ i2 ....σ ik ; will be The small ones are divided into one category: σ i(k+1) ,σ i(k+2). ... im υ represents the influence of time on workload and is a constant.

[0063] The pheromone concentration on the path is updated using the improved pheromone concentration update formula. The updated pheromone concentration is shown in formula (2):

[0064]

[0065] Among them, d ij Represents the shortest distance from node i to node j. q1 and q2 are settable constants. Generally, q1 is greater than q2.

[0066] S105, extracting the optimal node network distribution, calculating the service type evaluation value and modularity between the nodes, and sharding the nodes in the optimal node network distribution according to the modularity.

[0067] The business type of a node indicates the specific content of the node's work on the blockchain, including storage and processing of intellectual property transaction content and network-wide verification and consensus on intellectual property transaction content. First, the business relationship between nodes is determined based on the transaction operation content between nodes; second, weights are assigned based on the two reference quantities of storage and processing volume and verification and consensus volume of intellectual property transactions for calculation. Finally, the business type evaluation value between nodes within an iteration cycle is calculated, as shown in formula (3):

[0068]

[0069] E ij is the service type evaluation value of the node, λ1 and λ2 are weighted coefficients, W ij is the workload of storing data between nodes i and j, V ij is the workload of verifying consensus between nodes i and j, ε=ε0*e -λt, is an attenuation function, where ε0 and λ are both constants, t represents time, and the convergence of the function is determined by the value. sgn(x) is the sign function, and T represents a working cycle, that is, the time for the algorithm to run one cycle. When W ij > V ij , sgn(W ij , V ij ) = 1. When W ij < V ij , sgn(W ij , V ij ) = -1. The business type is directly judged through the sign function. The node with a positive business type evaluation value is defined as the working node for data storage, and the node with a negative business type evaluation value is defined as the verification node for verification consensus.

[0070] During the process of sharding the nodes in the optimal node network distribution, in the initialization stage, each node takes itself as a shard, and each node traverses all its neighbor nodes; then, through the modularity formula calculation, it tries to divide the nodes with strong business association with itself into its own shard until the sharding result no longer changes during the iteration of traversing all nodes, that is, the modularity gain ΔQ no longer changes; finally, each new shard is merged into a new node group, and the new node group constitutes a new sharded network. The modularity calculation is shown in formula (4):

[0071]

[0072] Q is the modularity, M is the sum of the weights of all edges in the network, pi represents the sum of the weights of all edges pointing to node i, p j represents the sum of the weights of all edges pointing to node j. The weight between nodes reflects the strength relationship between nodes. Here, the weight of the edge is affected by the data transmission rate between nodes. δ(c i , c j ) represents judging whether node i and node j are in the same shard. If so, the value is 1, otherwise the value is 0. represents the average communication delay between node i and other nodes in the optimal network distribution. represents the average communication delay between node j and other nodes in the optimal network distribution. W represents the number of nodes.

[0073] When there are more operations of a certain business type between two nodes, the modularity will change accordingly. The change in modularity is ΔQ, as shown in formula (5):

[0074]

[0075] k i,i crepresents the sum of the weights of all edges pointing from node i to area c, ∑ tot represents the sum of the weights of all edges of the nodes pointing to area c, k i represents the sum of the weights of all edges pointing to node i.

[0076] S106, internal refinement and correction of the shards. That is, for each shard network, sharding is performed again within it, separating nodes with a lower connection rate and dividing them into two or more sub-shards. The subdivision of the sub-shards is based on the modularity Q. If the change in modularity ΔQ is positive, it means that there can be a better-sharded network with a higher modularity, so continue to shard to ensure that the overall modularity of the network increases after each subdivision. The subdivision is to obtain a better modularity. The higher the modularity, the better the sharding effect and the better the collaborative work effect of the nodes within the shard.

[0077] S107, cluster the nodes based on the corrected sharding results, merge all the nodes within the same sub-shard, and regard them as one large node. On this basis, form a new network structure and update the node network structure.

[0078] S108, determine whether there is a change in the modularity change amount ΔQ. If not, obtain the optimal node sharding. If so, return to step S106 and continue with the internal refinement and correction of the sharding.

[0079] According to the service type evaluation values between nodes in the optimal distribution network, construct strongly correlated node partitions, and then divide the optimal node distribution network of the blockchain into multiple communities with service type characteristics, so as to improve the stability of the node network, improve the data storage efficiency, and achieve the scalability of the blockchain.

[0080] The blockchain scalability problem refers to that as the transaction volume of the blockchain increases, the processing speed and efficiency of the blockchain decrease, resulting in limitations on the performance and application of the blockchain. The scalability of the blockchain is reflected in that after sharding, the node groups have better storage capabilities and improved work efficiency, so the scalability of the blockchain can be achieved.

[0081] The nodes within the partition have the same service type, which can ensure that the nodes synchronously process transactions at the same time. Moreover, after node optimization screening and service type screening, the nodes within the partition have similar activity levels, low latency, and high efficiency, and malicious nodes and lazy nodes are also screened out. Therefore, processing services through the node groups after partitioning can maximize work efficiency and then improve data storage efficiency.

[0082] During the storage process of intellectual property transactions, the blockchain network nodes used are sharded. Nodes within the same shard have strong correlations, which can improve the efficiency of nodes storing data. Through maximizing modularity node sharding, it can ensure that the nodes within each shard are more closely connected, enhancing the security and stability of the blockchain network. This similarity sharding algorithm improves the precise processing ability and storage capacity of sharding by introducing the business types of nodes. Because before sharding, even if a single node has high working efficiency, there will still be a situation of insufficient storage capacity. Compared with a single node, the storage capacity of the sharded node group has been significantly improved.

[0083] Aiming at the problems existing in the storage process of intellectual property, such as insufficient storage scalability, large storage overhead, and low storage efficiency, the present invention proposes an intellectual property node sharding storage model based on a consortium blockchain and designs a Node Optimised Partitioned Storage Algorithm. First, the overall architecture of the storage model is constructed and the storage process is formulated. Secondly, the workload relationship between nodes is established, and a pheromone concentration update strategy is designed. The global optimal solution of the node group is found through iteration, realizing the network optimization of the nodes. Thirdly, according to the business categories, storage efficiency, communication delay, and activity of the nodes, a similarity partitioning algorithm is constructed, improving the precise processing ability and storage capacity of the partitioning and realizing the efficient data storage of the model. Finally, the performance of the model is analyzed and compared with the existing models. The research and analysis show that the model effectively solves the problems of low storage efficiency, large node communication delay, and insufficient blockchain scalability in the storage process of intellectual property.

[0084] Please refer to Figure 3 , based on the same inventive concept, in this embodiment, an intellectual property node sharding storage system based on a consortium blockchain is also provided, including a network optimization module and a data storage module. The network optimization module is used to optimize and shard the nodes at the network layer; the data storage module is used to store the intellectual property transaction data generated at the transaction layer based on the optimized and sharded node group.

[0085] The network optimization module may include a parameter configuration sub-module, a network optimization sub-module, and a network sharding sub-module; the parameter configuration sub-module is used to initialize and configure relevant parameters; the network optimization sub-module is used to search for paths between all network nodes, and according to the pheromone concentration σ im on each path and the selection probability of each path, obtain the optimal node network distribution; the network sharding sub-module is used to extract the optimal node network distribution and shard the nodes in the optimal node network distribution according to the business type evaluation value and modularity between nodes to obtain the optimal sharding.

[0086] For more detailed execution operations of each sub-module in the network optimization module, reference can be made to the relevant descriptions in the foregoing method, which will not be elaborated herein.

[0087] As Figure 4 shown, this embodiment also provides an electronic device, which may include a processor 41 and a memory 42, where the memory 42 is coupled to the processor 41. It should be noted that this figure is exemplary, and other types of structures can also be used to supplement or replace this structure to implement functions such as data extraction, report generation, communication, or other functions.

[0088] As Figure 4 shown, the electronic device may further include: an input unit 43, a display unit 44, and a power supply 45. It should be noted that the electronic device does not necessarily have to include Figure 4 all the components shown. In addition, the electronic device may further include Figure 4 components not shown in, and reference can be made to the prior art.

[0089] The processor 41 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The processor 41 receives inputs and controls the operations of the various components of the electronic device.

[0090] Among them, the memory 42 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices, and can store information such as the configuration information of the above-mentioned processor 41 and the instructions executed by the processor 41. The processor 41 can execute the programs stored in the memory 42 to implement information storage or processing, etc. In one embodiment, the memory 42 further includes a buffer memory, that is, a buffer, to store intermediate information.

[0091] This embodiment of the present invention also provides a computer program product, including computer-readable instructions. When the computer-readable instructions are executed in an electronic device, the program product causes the electronic device to execute the operation steps included in the method of the present invention.

[0092] This embodiment of the present invention also provides a storage medium storing computer-readable instructions, and the computer-readable instructions cause the electronic device to execute the operation steps included in the method of the present invention.

[0093] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0094] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0095] The above-described embodiments are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications, substitutions, and improvements, etc. These modifications, substitutions, and improvements should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for storing intellectual property node fragments based on alliance chain, characterized in that: Includes steps: S10, optimize and shard the nodes of the network layer; S20, storing the intellectual property transaction data generated by the transaction layer based on the optimized and sharded node group.

2. The intellectual property node sharding storage method based on alliance chain according to claim 1 is characterized in that: The S10 comprises the following steps: S101, initialization parameters; S102, search the entire network for paths between nodes and record the pheromone concentration σ on each path im , and calculate the selection probability of each path; S103, determine whether the optimal node network distribution is obtained, if yes, proceed to step S105, if not, proceed to step S104; S104, updating the pheromone concentration and returning to step S102; S105, extracting the optimal node network distribution, calculating the service type evaluation value and modularity between the nodes, and sharding the nodes in the optimal node network distribution according to the modularity; S106, for each shard network, sharding is performed again inside the shard network into multiple sub-shards; S107, clustering nodes based on the modified sharding results, merging all nodes in the same sub-shard as a large node, forming a new network structure on this basis, and updating the node network structure; S108, determine whether the modularity variation has changed, if not, obtain the optimal node sharding, if so, return to step S106.

3. The intellectual property node sharding storage method based on alliance chain according to claim 2 is characterized in that: In S102, the selection probability of the path is calculated using the following formula: Where D ij (t) is the heuristic information from node i to node j, B is the data transmission symbol rate, N is the number of discrete values ​​taken by the unit symbol, L ij is the channel length from node i to node j, V eL is the data propagation rate between node i and node j, t on is the time it takes for data to pass between nodes i and j, T all is the total communication time between node i and node j, is the probability of the network path from node i to node j being selected, α is the pheromone factor, β is the heuristic function factor, σ ij (t) is the pheromone concentration of the path from node i to node j, η ij (t) is the expected degree of data transmission from node i to node j, σ is is the pheromone concentration of the path from node i to node s, η is (t) is the expected degree of data transmission from node i to node s, r is the constraint factor, allow k Represents the set of nodes that are currently not visited.

4. The intellectual property node sharding storage method based on alliance chain according to claim 2 is characterized in that: In S104, the pheromone concentration updating step is as follows: (1) Arrange the pheromone concentrations on each path in descending order as σ i1 ,σ i2 ....σ im , σ im represents the pheromone concentration from node i to node m; (2) i1 ,σ i2 ....σ im and To compare the size, The larger ones are divided into one category, The smaller ones are divided into one category, υ indicates the degree of influence of time on workload; (3) Update the pheromone concentration on the path. The updated pheromone concentration is: Among them, d ij Represents the shortest distance from node i to node j. q1 and q2 are set constants, and q1 is greater than q2.

5. The intellectual property node sharding storage method based on alliance chain according to claim 2 is characterized in that: In S105, the following formula is used to calculate the service type evaluation value between nodes: E ij is the service type evaluation value of the node, λ1 and λ2 are weighted coefficients, W ij is the workload of storing data between nodes i and j, V ij is the workload of verifying consensus between nodes i and j, ε=ε0*e -λt , ε0 and λ are constants, t represents time, sgn(x) is a sign function, T represents a working cycle, when W ij >V ij , sgn(W ij ,V ij )=1, when W ij <V ij , sgn(W ij ,V ij )=-1.

6. The intellectual property node sharding storage method based on alliance chain according to claim 2 is characterized in that: In the above S105, in the process of slicing the nodes in the optimal node network distribution, in the initialization stage, each node regards itself as a slice, and each node traverses all its neighboring nodes; then the modularity is calculated, and the nodes with strong business relevance to the node are divided into their own slices according to the modularity, until the modularity gain ΔQ no longer changes during the iterative process of traversing all nodes; finally, each new slice is merged into a new node group, and the new node group constitutes a new slicing network; The modularity calculation formula is: The calculation formula of modularity change is: Q is the modularity, M is the sum of the weights of all edges of nodes in the network, and p i represents the sum of the weights of all edges pointing to node i, p j represents the sum of the weights of all edges pointing to node j, δ(c i ,c j ) indicates whether nodes i and j are in the same shard. represents the mean communication delay between node i and other nodes in the optimal network distribution, represents the mean communication delay between node j and other nodes in the optimal network distribution, W represents the number of nodes, k i,ic represents the sum of the weights of all edges from node i to patch c, ∑ tot represents the sum of the weights of all edges pointing to nodes in patch c, k i represents the sum of the weights of all edges pointing to node i.

7. An intellectual property node sharding storage system based on alliance chain, characterized in that: include: Network optimization module, used to optimize and shard nodes at the network layer; The data storage module is used to store the intellectual property transaction data generated by the transaction layer based on the optimized and sharded node group.

8. A computer program product comprising computer readable instructions, characterized in that: When executed by a processor, the computer-readable instructions implement the steps in the intellectual property node sharding storage method based on the alliance chain as described in any one of claims 1 to 6.

9. A computer-readable storage medium comprising computer-readable instructions, characterized in that: When executed by a processor, the computer-readable instructions implement the steps in the intellectual property node sharding storage method based on the alliance chain as described in any one of claims 1 to 6.

10. An electronic device, characterized in that: include: Memory, which stores program instructions; A processor is connected to the memory, executes program instructions in the memory, and implements the steps in the intellectual property node sharding storage method based on the alliance chain as described in any one of claims 1-6.

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