Block chain network-based power grid asset data management method and device, computer equipment and readable storage medium

Abstract

The invention relates to a power grid asset data management method and device based on a block chain network, computer equipment and a readable storage medium. Any node in the block chain network sends a request message to other nodes, receives a response message fed back by other nodes, and calculates the distance between any node and other nodes; based on a fuzzy C-means clustering algorithm and the distance between any node and other nodes, grouping the plurality of nodes of the block chain network to obtain a plurality of grouping results; and according to a grouping result, splitting the block chain network into a plurality of independent block chain sub-networks corresponding to the grouping result, and assisting storage of a data account book in the block chain sub-networks based on an erasure code technology. The communication delay between power grid nodes in different regions is reduced, the node storage burden is reduced, the cost of power grid mass business data storage is reduced, and the mass data processing efficiency during multi-party cooperation in the power grid asset data management process is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of block volume data management, and in particular to a power grid asset data management method, device, computer equipment and readable storage medium based on a blockchain network. Background Art

[0002] In the process of power grid asset data management, data management and storage based on the blockchain platform can prevent data from being tampered with. However, due to the characteristics of the power grid, there are many participating roles and a large amount of data, so it is necessary to ensure the security of data management under the collaboration of multiple roles. Summary of the invention

[0003] Based on this, it is necessary to provide a power grid asset data management method, device, computer equipment and readable storage medium based on a blockchain network that can improve the efficiency of power grid asset data management in response to the above technical problems.

[0004] In a first aspect, the present application provides a method for managing power grid asset data based on a blockchain network, the method comprising:

[0005] Any node in the blockchain network sends a request message to other nodes, receives a response message based on feedback from other nodes, and calculates the distance between any node and other nodes;

[0006] Based on the fuzzy C-means clustering algorithm and the distance between any node and other nodes, multiple nodes of the blockchain network are grouped to obtain multiple grouping results;

[0007] According to the grouping results, the blockchain network is split into multiple independent blockchain sub-networks corresponding to the grouping results. The erasure code technology is used within the blockchain sub-network to assist in the storage of data ledgers.

[0008] In one embodiment, based on the fuzzy C-means clustering algorithm and the distance between any node and other nodes, multiple nodes of the blockchain network are grouped to obtain multiple grouping results, including:

[0009] Define a coordinate system, and determine the coordinates of each node in the coordinate system based on the distance between any node and other nodes;

[0010] Randomly select C cluster centers cj (j=1,2,...,c) and configure an N*C membership matrix U=[u ij ], where u ij Indicates that the i-th node belongs to the j-th cluster and satisfies u ij ∈[0,1] and The initial value of , N is the number of nodes, and C is the number of clusters;

[0011] Iterative execution to calculate cluster centers , update membership u ij steps until the change of the membership matrix or cluster center is less than a predetermined threshold, or the maximum number of iterations is reached; where m is the fuzzy coefficient and xi is the coordinate of the i-th node;

[0012] The new cluster centers are used to update the membership matrix to determine the main cluster to which each node belongs and obtain the grouping result.

[0013] In one embodiment, the membership matrix is ​​updated using the new cluster centers, including:

[0014] Update the membership matrix according to the following formula and the new cluster center:

[0015]

[0016] Here, ||·|| represents the Euclidean distance.

[0017] In one embodiment, the step of determining the initial value includes:

[0018] Determine the initial value based on a uniform distribution or a distance-based heuristic.

[0019] In one embodiment, the method further includes:

[0020] If a node in the blockchain subnetwork initiates a new transaction, the summary of the transaction is calculated, and the summary of the transaction and the summary of the previous block are packaged to generate a new block and uploaded to the chain;

[0021] Generate a new block summary;

[0022] If it is verified that the summary of the new block matches the summary of the previous block, it is determined that the power grid asset data carried by the transaction information has not been tampered with.

[0023] In a second aspect, a power grid asset data management device based on a blockchain network is provided, the device comprising:

[0024] A distance determination module, used to calculate the distance between any node and other nodes when any node in the blockchain network sends a request message to other nodes and receives a response message based on feedback from other nodes;

[0025] A grouping module is used to group multiple nodes of the blockchain network based on the fuzzy C-means clustering algorithm and the distance between any node and other nodes to obtain multiple grouping results;

[0026] The sub-blockchain splitting module is used to split the blockchain network into multiple independent blockchain sub-networks corresponding to the grouping results according to the grouping results. The blockchain sub-network assists in the storage of data ledgers based on the erasure code technology.

[0027] In one embodiment, the grouping module includes:

[0028] A coordinate determination unit, used to define a coordinate system and determine the coordinates of each node in the coordinate system according to the distance between any node and other nodes;

[0029] The membership matrix configuration unit is used to randomly select C cluster centers cj (j=1,2,...,c) and configure an N*C membership matrix U=[u ij ], where u ij Indicates that the i-th node belongs to the j-th cluster and satisfies u ij ∈[0,1] and The initial value of , N is the number of nodes, and C is the number of clusters;

[0030] Loop iteration unit, used to iteratively execute the calculation of cluster centers , update membership u ij steps until the change of the membership matrix or cluster center is less than a predetermined threshold, or the maximum number of iterations is reached; where m is the fuzzy coefficient and xi is the coordinate of the i-th node;

[0031] The grouping determination unit is used to update the membership matrix using the new cluster center to determine the main cluster to which each node belongs and obtain the grouping result.

[0032] In one embodiment, the grouping determination unit includes:

[0033] The membership matrix update unit is used to update the membership matrix according to the following formula and the new cluster center:

[0034]

[0035] Here, ||·|| represents the Euclidean distance.

[0036] In a third aspect, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0037] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0038] The above-mentioned power grid asset data management method, device, computer equipment and readable storage medium based on blockchain network. Based on blockchain, using the blockchain ledger structure, in the process of multi-party collaborative operation, clustering is performed based on the geographical location of different nodes in the power grid blockchain network, and blockchain nodes are grouped based on the classification results to reduce the communication delay between the corresponding nodes of power grid units / departments in different regions. In addition, during the ledger storage process, the erasure code technology is used within the group to assist in the storage of data ledgers, reduce the node storage burden, reduce the cost of massive business data storage in the power grid, and achieve the efficiency of massive data processing during multi-party collaboration in the power grid asset data management process. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A flowchart of a method for managing power grid asset data based on a blockchain network in one embodiment;

[0041] Figure 2 A schematic flow chart of the steps of grouping multiple nodes of a blockchain network to obtain multiple grouping results based on a fuzzy C-means clustering algorithm and the distance between any node and other nodes in one embodiment;

[0042] Figure 3 A partial flow chart of a method for managing power grid asset data based on a blockchain network in one embodiment;

[0043] Figure 4 It is a structural block diagram of a power grid asset data management device based on a blockchain network in one embodiment;

[0044] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0046] In an exemplary embodiment, Figure 1 As shown, a method for managing power grid asset data based on a blockchain network is provided, the method comprising:

[0047] S101, any node in the blockchain network sends a request message to other nodes, receives response messages based on feedback from other nodes, and calculates the distance between any node and other nodes. In a blockchain network, the "distance" between nodes is usually not a physical distance (such as meters or kilometers), but a more abstract concept, which may be based on network latency, hop count (i.e., the number of intermediate nodes that a data packet needs to pass through from one node to another), or the relative position or trust between nodes in a certain consensus algorithm.

[0048] For example, network delay (Round-Trip Time, RTT) is used as a measure of the distance between nodes. The specific process is as follows:

[0049] The request message sent by the node and the response message received can contain a timestamp for calculating the RTT. The format of the request message can be as follows:

[0050] Message Type: Request

[0051] Send Node ID

[0052] Receive Node ID

[0053] Timestamp (time when the request was sent)

[0054] The response message format can be as follows:

[0055] Message Type: Response

[0056] Receive Node ID

[0057] Send Node ID

[0058] Timestamp (response sent time)

[0059] Taking the communication between node A and node B as an example, node A sends a request message (including the request sending time) to node B. After receiving the request message, node B records the time when the message is received, and then generates a response message. The response message contains the ID of node B, the ID of node A, and the current timestamp (that is, the response sending time). Node B then sends this response message back to node A.

[0060] After receiving the response message from node B, node A first records the response reception time, and then uses this response reception time and the request sending time in the request message to calculate the RTT. RTT = response reception time - request sending time.

[0061] Since RTT is a time measurement (such as milliseconds), it can be considered that each millisecond of RTT corresponds to a virtual unit distance in the network. Based on this relationship, RTT can be converted into distance.

[0062] S102, based on the fuzzy C-means clustering algorithm and the distance between any node and other nodes, multiple nodes of the blockchain network are grouped to obtain multiple grouping results.

[0063] S103, according to the grouping results, split the blockchain network into multiple independent blockchain sub-networks corresponding to the grouping results, and assist in the storage of data ledgers based on the erasure code technology within the blockchain sub-network.

[0064] Specifically, based on blockchain and utilizing the blockchain ledger structure, in the process of multi-party collaborative operations, clustering is performed based on the geographical locations of different nodes in the power grid blockchain network, and blockchain nodes are grouped based on the classification results to reduce the communication delay between corresponding nodes of power grid units / departments in different regions. In addition, during the ledger storage process, erasure coding technology is used within the group to assist in the storage of data ledgers, reduce the node storage burden, reduce the cost of storing massive business data in the power grid, and achieve efficient massive data processing during multi-party collaboration in the process of power grid asset data management.

[0065] In one embodiment, based on the fuzzy C-means clustering algorithm and the distance between any node and other nodes, multiple nodes of the blockchain network are grouped to obtain multiple grouping results, such as Figure 2 As shown, including:

[0066] S201, define a coordinate system, and determine the coordinates of each node in the coordinate system based on the distance between any node and other nodes. For example, an XYZ coordinate system can be established with the position of the node as the origin, two mutually perpendicular axes on a plane passing through the origin and parallel to the ground as the X-axis and Y-axis, and an axis perpendicular to the ground as the Z-axis. Based on the distance between the nodes determined in step S101, the coordinates of each node can be determined. It should be understood that if step S101 determines that the "distance" that characterizes communication efficiency is RTT, etc., then the coordinates of each node determined here are also virtual coordinates, which are "virtual coordinates" used to characterize the timeliness of communication when the node communicates with other nodes.

[0067] S202, randomly select C cluster centers cj (j=1,2,...,c), and configure an N*C membership matrix U=[u ij ], where u ij Indicates that the i-th node belongs to the j-th cluster and satisfies u ij ∈[0,1] and is the initial value of , N is the number of nodes, and C is the number of clusters.

[0068] By randomly selecting C cluster centers, this step provides a starting point for the subsequent iterative process. Randomness helps the algorithm avoid falling into the local optimal solution and increases the possibility of finding the global optimal solution. In addition, the N*C membership matrix is ​​configured based on the number of nodes, and the membership value is initialized, which provides a basis for the membership update in the subsequent iterative process. This initialization method ensures that each node has the potential to belong to all clusters, reflecting the characteristics of fuzzy clustering. The constraint of uij∈[0,1] ensures the rationality of the membership, that is, the membership of a node to a cluster is between 0 (not belonging at all) and 1 (completely belonging). At the same time, the constraint that the sum of the membership of all clusters is 1 reflects the fuzziness of the node in the clustering process, that is, a node can belong to multiple clusters at the same time, but the total membership must be 1.

[0069] S203, iterative execution to calculate cluster center , update membership u ij steps until the change of the membership matrix or cluster center is less than a predetermined threshold, or the maximum number of iterations is reached; where m is the fuzzy coefficient and xi is the coordinate of the i-th node.

[0070] By iteratively calculating new cluster centers, the algorithm can gradually optimize the location of the cluster, making the cluster center closer to the center of its internal nodes and improving the accuracy of clustering. In each iteration, the membership matrix is ​​updated according to the new cluster center, reflecting the dynamic changes of the node's cluster affiliation, which helps the algorithm gradually clarify the cluster affiliation of each node during the iteration process. When the change of the membership matrix or cluster center is less than a predetermined threshold, or the maximum number of iterations is reached, the algorithm stops iterating, ensuring the stability and efficiency of the algorithm and avoiding unnecessary computational overhead.

[0071] S204, using the new cluster center to update the membership matrix to determine the main cluster to which each node belongs, and obtain a grouping result.

[0072] The final cluster center is used to update the membership matrix to obtain the final membership of each node to each cluster. Through the membership matrix, the main cluster affiliation of each node (i.e., the cluster with the largest membership) can be clearly identified. This step converts the fuzzy clustering results into clear grouping results, which is convenient for subsequent analysis and application. Since the algorithm continuously optimizes the cluster center and membership during the iteration process, the final grouping result has a high accuracy. At the same time, the characteristics of fuzzy clustering enable the algorithm to process data sets with overlapping or fuzzy boundaries, improving the flexibility and applicability of clustering. The solution provided in the embodiment of the present application is based on "distance" as the basis for clustering grouping, and the above-mentioned specific classification steps are used to realize the grouping of blockchain network nodes, which can ensure the grouping accuracy and flexibility of the blockchain sub-network, so as to ensure the communication efficiency when managing power grid asset data within the sub-network.

[0073] In one embodiment, the membership matrix is ​​updated using the new cluster centers, including:

[0074] Update the membership matrix according to the following formula and the new cluster centers:

[0075]

[0076] Here, ||·|| represents the Euclidean distance.

[0077] By calculating the Euclidean distance from each node to the center of each cluster and updating the membership based on these distances, the similarity or proximity between nodes and clusters can be more accurately reflected. As the iteration proceeds, the membership matrix gradually converges to a stable state, at which point each node has a higher membership to its main cluster and a lower membership to other clusters, thereby improving the accuracy of clustering, and in turn improving the accuracy of blockchain subnetwork generation, adapting to the actual communication capabilities of nodes, and improving the efficiency of blockchain management of massive data.

[0078] In one embodiment, the step of determining the initial value includes:

[0079] Determine the initial value based on a uniform distribution or a distance-based heuristic.

[0080] Specifically, according to the uniform distribution, the process of determining the initial value is as follows:

[0081] For each element uij in the matrix U, it is initialized to 1 / C, that is: uij=1 / C. In this way, the sum of all membership degrees of each node is 1, which satisfies the constraints of fuzzy clustering.

[0082] The distance-based heuristic method takes into account the actual distance between nodes to more reasonably assign the initial membership. First, C initial cluster centers can be selected randomly or according to K-means++ initialization.

[0083] For each node i, calculate its distance dij to all cluster centers j.

[0084] The initial membership can be calculated using the following formula and distance:

[0085]

[0086] All memberships of each node are normalized to ensure that the sum is 1 and that the membership of each node is in the range [0,1].

[0087] In one embodiment, each block in the blockchain contains a header and a data part. The header contains metadata information such as the summary of the previous block (such as hash value), timestamp, difficulty target value, etc., and the data part contains the actual transaction information. In order to ensure the security of multi-party collaborative operations, a summary verification mechanism can be introduced. Therefore, in one embodiment, if Figure 3 As shown, the above method also includes:

[0088] S301, if a node in the blockchain sub-network initiates new transaction information, the summary of the transaction information is calculated, and the summary of the transaction information and the summary of the previous block are packaged to generate a new block and uploaded to the chain;

[0089] S302, generating a summary of a new block;

[0090] S303: If it is verified that the summary of the new block matches the summary of the previous block, it is determined that the power grid asset data carried by the transaction information has not been tampered with.

[0091] Take the hash value as an example:

[0092] When new transaction information is generated (new power grid asset data is generated), the hash of the transaction information is first calculated, which can be calculated using SHA-256 or other hash algorithms. Then, the hash value of the new transaction information is uploaded to the chain together with the hash value of the previous block. When a blockchain node wants to access power grid asset data, it can verify the integrity and authenticity of the data through the hash value stored on the blockchain. For example:

[0093] When new transaction information needs to be added to the blockchain, the hash value of the previous block, the hash value of the new transaction information, and other metadata information are packaged into a new block.

[0094] In the new block, the hash value of the previous block, the hash value of the new transaction information, and other metadata information are hashed to generate the hash value of the new block.

[0095] After a new block is generated, it is added to the blockchain. Other users can verify the integrity of the block by verifying whether the hash value of the new block matches the hash value of the previous block to prevent the power grid asset data from being tampered with.

[0096] Through the above steps, the summary verification mechanism can be implemented in the blockchain to ensure the secure access and collaborative operation of power grid asset data by multiple parties.

[0097] In addition, during the data access process, the security of asset data access can be guaranteed by combining role permission management and smart contract authorization:

[0098] Different roles can be set in the blockchain, and different permissions can be defined for each role. In this way, access restrictions and management of different roles can be achieved. For example, different roles such as administrators, ordinary users, auditors, etc. can be divided and given different permissions, such as reading data, writing data, etc. This ensures that different roles can collaborate according to their permissions during the access process.

[0099] A single power grid user can be assigned multiple roles. For example, a department leader can be both an administrator and an auditor. In the implementation process, the corresponding relationship table of "user account - job role - rank - department" can be obtained to determine the corresponding role according to the department, job role and rank, so as to realize the intelligent management of the rights of each power grid user with unified standards. If you want to apply for special rights, for example, ordinary employees want to apply for audit rights, they need to go through the approval process to open it.

[0100] Access rights and rules can also be defined in smart contracts to implement access control over data. For example, a user's identity information and permissions can be verified through smart contracts, and only users who meet specific conditions can access or modify data. This ensures that transactions and operations in the blockchain are secure and trustworthy.

[0101] Through the above two methods, the collaborative management of multiple roles in the blockchain data access process can be achieved to ensure the security and integrity of the data. At the same time, the appropriate permission management method can also be selected and implemented according to specific needs and scenarios.

[0102] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0103] Based on the same inventive concept, the embodiment of the present application also provides a power grid asset data management device on a blockchain network for implementing the power grid asset data management method on a blockchain network involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the power grid asset data management device based on a blockchain network provided below can refer to the limitations of the power grid asset data management method on a blockchain network above, and will not be repeated here.

[0104] In an exemplary embodiment, Figure 4 As shown, a power grid asset data management device based on a blockchain network is provided, the device comprising: a distance determination module 401, a grouping module 402, and a sub-blockchain splitting module 403. Among them:

[0105] A distance determination module, used to calculate the distance between any node and other nodes when any node in the blockchain network sends a request message to other nodes and receives a response message based on feedback from other nodes;

[0106] A grouping module is used to group multiple nodes of the blockchain network based on the fuzzy C-means clustering algorithm and the distance between any node and other nodes to obtain multiple grouping results;

[0107] The sub-blockchain splitting module is used to split the blockchain network into multiple independent blockchain sub-networks corresponding to the grouping results according to the grouping results. The blockchain sub-network assists in the storage of data ledgers based on the erasure code technology.

[0108] In one embodiment, the grouping module 402 includes:

[0109] A coordinate determination unit, used to define a coordinate system and determine the coordinates of each node in the coordinate system according to the distance between any node and other nodes;

[0110] The membership matrix configuration unit is used to randomly select C cluster centers cj (j=1,2,...,c) and configure an N*C membership matrix U=[u ij ], where u ij Indicates that the i-th node belongs to the j-th cluster and satisfies u ij ∈[0,1] and The initial value of , N is the number of nodes, and C is the number of clusters;

[0111] Loop iteration unit, used to iteratively execute the calculation of cluster centers , update membership u ijsteps until the change of the membership matrix or cluster center is less than a predetermined threshold, or the maximum number of iterations is reached; where m is the fuzzy coefficient and xi is the coordinate of the i-th node;

[0112] The grouping determination unit is used to update the membership matrix using the new cluster center to determine the main cluster to which each node belongs and obtain the grouping result.

[0113] In one embodiment, the grouping determination unit includes:

[0114] The membership matrix update unit is used to update the membership matrix according to the following formula and the new cluster center:

[0115]

[0116] Here, ||·|| represents the Euclidean distance.

[0117] In one embodiment, the above-mentioned power grid asset data management device based on blockchain network further includes:

[0118] The initial value determination module is used to determine the initial value according to a uniform distribution or a distance-based heuristic method.

[0119] In one embodiment, the above-mentioned power grid asset data management device based on blockchain network further includes:

[0120] A module for calculating the summary of a transaction when a node in a blockchain subnetwork initiates a new transaction, and packaging the summary of the transaction and the summary of the previous block to generate a new block and upload it to the chain;

[0121] New block summary generation module, used to generate the summary of a new block;

[0122] The verification module is used to verify that the summary of the new block matches the summary of the previous block, thereby determining that the power grid asset data carried by the transaction information has not been tampered with.

[0123] Each module in the above-mentioned blockchain network-based power grid asset data management device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0124] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store XX data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a power grid asset data management method based on a blockchain network is implemented.

[0125] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0126] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the above-mentioned blockchain network-based power grid asset data management method are implemented and corresponding beneficial effects are achieved, which will not be elaborated here.

[0127] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of a power grid asset data management method based on a blockchain network are implemented and corresponding beneficial effects are achieved, which will not be elaborated here.

[0128] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of a power grid asset data management method based on a blockchain network and achieves corresponding beneficial effects, which will not be elaborated herein.

[0129] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0130] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0131] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for managing power grid asset data based on a blockchain network, characterized in that: The method comprises: Any node in the blockchain network sends a request message to other nodes, receives a response message fed back by the other nodes, and calculates the distance between the node and the other nodes; Based on the fuzzy C-means clustering algorithm and the distance between any one of the nodes and the other nodes, the multiple nodes of the blockchain network are grouped to obtain multiple grouping results; According to the grouping result, the blockchain network is split into multiple independent blockchain sub-networks corresponding to the grouping result, and the storage of data ledgers is assisted based on the erasure code technology in the blockchain sub-network.

2. The method according to claim 1, characterized in that: The multiple nodes of the blockchain network are grouped based on the fuzzy C-means clustering algorithm and the distance between any node and the other nodes to obtain multiple grouping results, including: Define a coordinate system, and determine the coordinates of each of the nodes in the coordinate system according to the distance between any of the nodes and the other nodes; Randomly select C cluster centers cj (j=1,2,...,c) and configure an N*C membership matrix U=[u ij ], where u ij Indicates that the i-th node belongs to the j-th cluster and satisfies u ij ∈[0,1] and The initial value of , N is the number of nodes, and C is the number of clusters; Iterative execution to calculate cluster center , update membership u ij steps until the change of the membership matrix or cluster center is less than a predetermined threshold, or the maximum number of iterations is reached; where m is the fuzzy coefficient and xi is the coordinate of the i-th node; The new cluster center is used to update the membership matrix to determine the main cluster to which each node belongs, thereby obtaining the grouping result.

3. The method according to claim 2, characterized in that The method of using the new cluster center to update the membership matrix includes: Update the membership matrix according to the following formula and the new cluster center: Here, ||·|| represents the Euclidean distance.

4. The method according to claim 2, characterized in that: The step of determining the initial value comprises: The initial value is determined based on a uniform distribution or a distance-based heuristic method.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: If a node in the blockchain subnetwork initiates new transaction information, the summary of the transaction information is calculated, and the summary of the transaction information and the summary of the previous block are packaged to generate a new block and uploaded to the chain; generating a digest of the new block; If it is verified that the summary of the new block matches the summary of the previous block, it is determined that the power grid asset data carried by the transaction information has not been tampered with.

6. A power grid asset data management device based on a blockchain network, characterized in that: The device comprises: A distance determination module, used to calculate the distance between any node and the other nodes when any node in the blockchain network sends a request message to other nodes and receives a response message fed back by the other nodes; A grouping module, used for grouping the multiple nodes of the blockchain network based on a fuzzy C-means clustering algorithm and the distance between any node and the other nodes to obtain multiple grouping results; The sub-blockchain splitting module is used to split the blockchain network into multiple independent blockchain sub-networks corresponding to the grouping results according to the grouping results, and the storage of data ledgers is assisted based on the erasure code technology in the blockchain sub-network.

7. The device according to claim 6, characterized in that The grouping module comprises: A coordinate determination unit, used for defining a coordinate system and determining the coordinates of each of the nodes in the coordinate system according to the distance between any node and the other nodes; The membership matrix configuration unit is used to randomly select C cluster centers cj (j=1,2,...,c) and configure an N*C membership matrix U=[u ij ], where u ij Indicates that the i-th node belongs to the j-th cluster and satisfies u ij ∈[0,1] and The initial value of , N is the number of nodes, and C is the number of clusters; Loop iteration unit, used to iteratively execute the calculation of cluster centers , update membership u ij steps until the change of the membership matrix or cluster center is less than a predetermined threshold, or the maximum number of iterations is reached; where m is the fuzzy coefficient and xi is the coordinate of the i-th node; The grouping determination unit is used to update the membership matrix using the new cluster center to determine the main cluster to which each node belongs, thereby obtaining the grouping result.

8. The device according to claim 7, characterized in that The grouping determination unit includes: The membership matrix update unit is used to update the membership matrix according to the following formula and the new cluster center: Here, ||·|| represents the Euclidean distance.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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