A blockchain structure storage architecture optimization method and device

Abstract

The application provides a blockchain structure storage architecture optimization method, comprising: mapping information communication nodes in the blockchain structure to a two-dimensional space; generating a high-order Voronoi diagram of nodes in the two-dimensional space; generating an alternative subset based on a control region of the high-order Voronoi diagram and a control point; merging alternative subsets in each control region into an alternative merged node subset by traversing all control regions of the high-order Voronoi diagram; and optimizing the blockchain structure storage architecture by integrating an alternative subset with the smallest sum of communication delays in the alternative merged node subset. The influence of the merged distributed storage engine on information communication efficiency can be quickly determined, thereby helping to optimize the blockchain distributed storage architecture.

Description

TECHNICAL FIELD

[0001] The present application relates to the blockchain technology, and in particular to a blockchain structure storage architecture optimization method and device. BACKGROUND

[0002] In the blockchain architecture, due to the limited amount of information that can be transmitted by transactions, a large amount of information still needs to be stored in the distributed storage engine of each node, and the index corresponding to the information is circulated in the blockchain, and then the data is obtained from the distributed engine by different nodes according to the index. However, a large amount of data interaction still hopes to improve the efficiency of information communication, so it is necessary to determine which nodes to merge the distributed storage engine in order to achieve faster information communication.

[0003] The traditional network communication method is often centralized, and all nodes of the distributed storage engine can be merged by the centralized management node of the network. However, the blockchain itself is a decentralized way, and there is no centralized management node. If all subsets containing k nodes are searched in order to find the nearest node set for merging the distributed storage engine to improve the communication efficiency, the computational overhead is huge. Therefore, there is a lack of a better method to quickly determine the influence of merging the distributed storage engine on the efficiency of information communication.

[0004] The above problems are urgent to be solved. SUMMARY

[0005] The present application overcomes the above-mentioned shortcomings of the prior art and provides a blockchain structure storage architecture optimization method and device.

[0006] The technical scheme adopted by the present application to solve its technical problems is: a blockchain structure storage architecture optimization method, comprising: mapping information communication nodes in the blockchain structure to a two-dimensional space; generating a high-order Voronoi diagram of nodes in the two-dimensional space; generating an alternative subset based on a control area of the high-order Voronoi diagram; merging the alternative subset set in each control area of the high-order Voronoi diagram into an alternative merging node subset; and optimizing the blockchain structure storage architecture by integrating the alternative subset with the smallest sum of communication delay in the alternative merging node subset.

[0007] Further, the mapping of the information communication nodes in the blockchain structure to the two-dimensional space comprises: sequentially numbering each node of the blockchain network; constructing an adjacency matrix based on the communication relationship between the nodes; calculating the first eigenvalue, the second eigenvalue and the corresponding eigenvectors of the adjacency matrix; and mapping the information communication nodes to the two-dimensional space based on the first eigenvalue, the second eigenvalue and the corresponding eigenvectors.

[0008] Further, the generating the high-order Voronoi diagram of the two-dimensional space node comprises: calculating the perpendicular bisectors of all point pairs in the two-dimensional space node; determining a control area of each point according to a minimum convex hull surrounded by the perpendicular bisectors of all point pairs of the each point, to obtain a first-order Voronoi diagram; splitting each control area into a high first-order control area by removing the perpendicular bisectors of all remaining point pairs of the control point based on the first-order Voronoi diagram, and merging the control areas of the same control point, to obtain a high first-order Voronoi diagram; splitting each control area into a higher first-order control area by removing the perpendicular bisectors of all remaining point pairs of the control point based on the high first-order Voronoi diagram, and merging the control areas of the same control point, to obtain a K-order Voronoi diagram.

[0009] Further, the merging the candidate sub-sets in each control area of the high-order Voronoi diagram into a candidate merging node sub-set comprises: initializing a candidate merging node sub-set set as an empty set; searching for K control points in a control area of the K-order Voronoi diagram; generating a candidate sub-set based on the control points and saving the candidate sub-set in the candidate merging node sub-set set; generating the candidate sub-sets by traversing the control area of the K-order Voronoi diagram and updating the candidate merging node sub-set.

[0010] Further, the searching for K control points in a control area of the K-order Voronoi diagram comprises: the distance from a point in the control area of the K-order Voronoi diagram to a control point is less than the distance from the point to a non-control point.

[0011] Further, the generating the candidate sub-sets by traversing the control area of the K-order Voronoi diagram and updating the candidate merging node sub-set comprises: merging the candidate sub-sets generated by traversing all control areas of the K-order Voronoi diagram and saving the candidate sub-sets in the candidate merging node sub-set; wherein the number of elements in the candidate merging node sub-set is the number of convex hulls in the K-order Voronoi diagram.

[0012] Further, the optimizing the blockchain structure storage architecture by integrating the candidate sub-set with the minimum sum of communication delays in the candidate merging node sub-set comprises: calculating the sum of communication delays of K control point information in each candidate sub-set based on the candidate sub-sets contained in the candidate merging node sub-set; selecting the candidate sub-set with the minimum sum of communication delays, and integrating a distributed storage engine of the candidate sub-set to optimize the blockchain structure storage architecture.

[0013] The application further provides a blockchain structure storage architecture optimization device, comprising: a mapping module, which is suitable for mapping information communication nodes in the blockchain structure to a two-dimensional space; a high-order Voronoi diagram generation module, which is suitable for generating a high-order Voronoi diagram of nodes in the two-dimensional space; an alternative subset generation module, which is suitable for generating an alternative subset based on a control region of the high-order Voronoi diagram to find a control point; an alternative merged node subset generation module, which is suitable for merging alternative subsets in each control region into an alternative merged node subset by traversing all control regions of the high-order Voronoi diagram; and an optimization module, which is suitable for optimizing the blockchain structure storage architecture by integrating an alternative subset with the smallest sum of communication delays in the alternative merged node subset.

[0014] The application further provides a computer readable storage medium, wherein one or more instructions are stored in the computer readable storage medium, and a processor in the one or more instructions is executed to implement the blockchain structure storage architecture optimization method.

[0015] The application further provides an electronic device, comprising a memory and a processor, wherein at least one program instruction is stored in the memory, and the processor is loaded and executed to implement the blockchain structure storage architecture optimization method.

[0016] The application has the following beneficial effects: the blockchain structure storage architecture optimization method provided by the application comprises the following steps: mapping information communication nodes in the blockchain structure to a two-dimensional space; generating a high-order Voronoi diagram of nodes in the two-dimensional space; generating an alternative subset based on a control region of the high-order Voronoi diagram to find a control point; merging alternative subsets in each control region into an alternative merged node subset by traversing all control regions of the high-order Voronoi diagram; and optimizing the blockchain structure storage architecture by integrating an alternative subset with the smallest sum of communication delays in the alternative merged node subset. The high-order Voronoi diagram is used to determine the alternative merged node subset, the optimal network optimization scheme is determined according to the merged node subset, the problem of searching for O(n^k) subsets in the prior art is optimized to a search problem of O(n*k), the influence of the merged distributed storage engine on information communication efficiency can be quickly determined, and thus the blockchain distributed storage architecture is optimized. BRIEF DESCRIPTION OF DRAWINGS

[0017] The application will be further described below in combination with the drawings and examples.

[0018] Figure 1 FIG. 1 is a flowchart of the blockchain structure storage architecture optimization method provided by the embodiment of the application.

[0019] Figure 2This is a schematic diagram of a first-order Voronoi diagram provided in an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of a third-order Voronoi diagram provided in an embodiment of the present invention.

[0021] Figure 4 This is a structural diagram of the blockchain structure storage architecture optimization device provided in the embodiments of the present invention.

[0022] Figure 5 This is a partial block diagram of the electronic device provided in the embodiments of the present invention. Detailed Implementation

[0023] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0024] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0025] To facilitate subsequent understanding, the following terms will be explained:

[0026] Voronoi diagrams, also known as Thiessen polygons, are a type of spatial partitioning of a plane. Their characteristics include that any location within a polygon is closest to a sample point (such as a settlement) within that polygon and furthest from samples within adjacent polygons, and each polygon contains exactly one sample point. Due to the equal division characteristic of Thiessen polygons in spatial partitioning, they can be used to solve problems related to nearest points, minimum closed circles, and many spatial analysis problems, such as adjacency, proximity, and reachability analysis.

[0027] The present invention will now be described in detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0028] Example 1

[0029] Please see Figure 1 The sampling method for a hierarchical storage architecture of blockchain proposed in this invention based on incremental computing ensures the coverage of the upper-level blockchain data to the lower-level blockchain as a whole, and at the same time, selects appropriate samples to replace the original sampling results in a timely manner when the lower-level data is updated, thereby helping to optimize the hierarchical multimodal data storage architecture.

[0030] As an example, the method for optimizing the storage architecture of a blockchain structure includes the following steps:

[0031] S110: Map the information communication nodes in the blockchain structure to a two-dimensional space.

[0032] As an example, mapping the information communication nodes in the blockchain structure to a two-dimensional space includes sequentially numbering each node of the blockchain network; constructing an adjacency matrix based on the communication relationships between the nodes; calculating the first eigenvalue, the second eigenvalue, and their corresponding eigenvectors of the adjacency matrix; and mapping the information communication nodes to a two-dimensional space based on the first eigenvalue, the second eigenvalue, and their corresponding eigenvectors.

[0033] Specifically, all n nodes in the blockchain network are numbered sequentially from 1 to n, and an adjacency matrix W is constructed based on all communication relationships between these n nodes. If the communication delay between node i and node j is d milliseconds (d < 10000), W... ij =d, otherwise W ij =10000, in addition, the diagonal element W ii =0. Where i, j∈(1, n), the communication delay between node i and node j is the difference between the time it takes for node i to send data to node j and the time it takes for node j to receive data sent by node i.

[0034] Calculate the eigenvalues ​​and eigenvectors of the W matrix, and take the two largest eigenvalues ​​λ1 and λ2, and their corresponding eigenvectors V1 and V2. Map the i-th point to a point in two-dimensional space, whose corresponding coordinates are (λ1*V2). 1i ,λ2*V 2i ), where V 1i This represents the value corresponding to the i-th element of the feature vector V1. Similarly, V 2i Let be the value corresponding to the i-th element of the eigenvector V2. It should be noted that the methods for obtaining eigenvalues ​​and eigenvectors in a matrix are already quite mature in existing technologies and will not be elaborated upon here.

[0035] S120: Generate a higher-order Voronoi diagram of the two-dimensional spatial nodes.

[0036] As an example, generating a higher-order Voronoi diagram of the two-dimensional spatial nodes includes calculating the perpendicular bisectors of all point pairs in the two-dimensional spatial nodes; determining the control region of each point based on the minimum convex hull formed by the perpendicular bisectors of each point pair to obtain a first-order Voronoi diagram; dividing each control region into a higher-order control region based on the first-order Voronoi diagram using the perpendicular bisectors of all remaining point pairs excluding the control point, and merging the control regions with the same control point to obtain a higher-order Voronoi diagram; dividing each control region into an even higher-order control region based on the higher-order Voronoi diagram using the perpendicular bisectors of all remaining point pairs excluding the control point, and merging the control regions with the same control point to obtain a K-order Voronoi diagram.

[0037] Specifically, such as Figure 2 The diagram shown is a first-order Voronoi diagram with 8 nodes. The method for generating this first-order Voronoi diagram is as follows: calculate the perpendicular bisectors of all pairs of points for the two-dimensional space points. The intersection of each perpendicular bisector will enclose multiple regions. Calculate the minimum convex hull of all perpendicular bisectors of each point to obtain the control region of each point. Each control region contains a control point, which is used to control the region.

[0038] Each control region is divided into higher-order control intervals by perpendicular bisectors of all remaining points excluding the control points. Control regions with the same control points are merged to obtain a higher-order Voronoi diagram. This process is repeated until a k-order Voronoi diagram is obtained. It should be noted that algorithms for generating k-order Voronoi diagrams are already very mature in current technology and will not be elaborated upon here.

[0039] S130: Based on a control region of the higher-order Voronoi diagram, find control points to generate a candidate subset.

[0040] S140: Traverse all control regions of the higher-order Voronoi diagram, and form a candidate subset within each control region as a candidate merge node subset.

[0041] As an example, the step of traversing all control regions of the higher-order Voronoi diagram and forming a candidate subset of each control region into a candidate merge node subset includes: initializing the candidate merge node subset set to an empty set; finding k control points for a control region of the k-order Voronoi diagram; generating a candidate subset based on the control points and storing it in the candidate merge node subset set; traversing the control region of the k-order Voronoi diagram to generate the candidate subset, and updating the candidate merge node subset.

[0042] Specifically, the candidate subset of merged nodes U is initialized as an empty set. For a control region of a k-order Voronoi diagram, K control points are found to form a candidate subset {q1, q2, ..., q}. K Then, iterate through all control regions of the k-th order Voronoi diagram. For each control region, find L control points, where L is the product of K and the number of convex hulls in each order Voronoi diagram, i.e., L = K * m, where m is the number of control regions (the number of convex hulls) in that order Voronoi diagram. Then, form a candidate subset {q1, q2, ..., q} within each control region. K} are stored as elements in set U.

[0043] More specifically, such as Figure 3 The diagram shows a 3rd-order Voronoi diagram generated from 8 nodes. The numbers 1-8 correspond to the 8 nodes, and the diagram contains 18 convex hulls. Each convex hull contains 3 control points. For example, the convex hull in the upper left corner contains control points (1, 3, 4), and the convex hull in the upper right corner contains control points (1, 2, 6)... Similarly, each convex hull of a K-order Voronoi diagram contains K control points. The top-left convex hull (control region) is shown. The elements q1, q2, and q3 in its candidate subset correspond to nodes 1, 3, and 4, respectively. And so on. The candidate subsets corresponding to each control region in this 3rd order Voronoi diagram are (1,3,4), (1,2,3), (1,2,6)......(6,7,8), with a total of 18 candidate subsets. These candidate subsets are then merged into a candidate merge node subset set U{(1,2,3), (1,2,6)......(6,7,8)}.

[0044] As can be seen, when finding the k nearest points from n nodes, based on the above example, when the number of nodes is 8 and K is 3, the number of candidate node subsets to be searched for these 8 nodes is 18. Compared to the existing search quantity n! / (k! * (nk)!), where n is the number of nodes and k is the number of control points, this application significantly reduces the number of searches required to find the k nearest points from n nodes by automatically ignoring unsuitable sets, thereby reducing the computational load. For example, the sets of nodes in the above example do not contain (1,2,8), (2,6,8), etc., because three points that are far apart are automatically filtered out to reduce the subsequent computational load. Thus, the effect of the above scheme will be more obvious when the number of nodes is huge or the order of the Voronoi diagram is too high.

[0045] S150: Optimize the blockchain structure storage architecture by integrating the candidate subset with the smallest sum of communication delays from the candidate merge node subset.

[0046] As an example, the optimization of the blockchain structure storage architecture by integrating the candidate subset with the smallest sum of communication latency in the candidate merge node subset includes calculating the sum of communication latency of K control point information in each candidate subset based on the candidate subsets contained in the candidate merge node subset; selecting the candidate subset with the smallest sum of communication latency, and integrating their distributed storage engines to optimize the blockchain structure storage architecture.

[0047] Specifically, still based on Figure 3 For example, if the candidate subset with the smallest sum of calculated communication delays is (1,3,4), then the distributed storage engine of nodes 1, 3, and 4 will be integrated to achieve faster information communication.

[0048] This application determines the optimal network optimization scheme based on the merged node subset, optimizing the existing technology's problem of searching from O(n^k) subsets to an O(n*k) search problem. It can quickly determine the impact of merging distributed storage engines on information communication efficiency, thereby helping to optimize the blockchain distributed storage architecture.

[0049] Example 2

[0050] Please see Figure 4 This embodiment provides a blockchain structure storage architecture optimization device, including:

[0051] The mapping module 410 is suitable for mapping information communication nodes in the blockchain structure to a two-dimensional space.

[0052] As an example, the mapping module 410 includes: a numbering unit for sequentially numbering each node of the blockchain network; an adjacency matrix construction unit for constructing an adjacency matrix based on the communication relationships between the nodes; a first calculation unit for calculating the first eigenvalue, the second eigenvalue, and their corresponding eigenvectors of the adjacency matrix; and a mapping unit for mapping the information communication nodes to a two-dimensional space based on the first eigenvalue, the second eigenvalue, and their corresponding eigenvectors.

[0053] The module 420 for generating higher-order Voronoi diagrams is suitable for generating higher-order Voronoi diagrams of the two-dimensional spatial nodes.

[0054] As an example, the higher-order Voronoi diagram generation module 420 includes: a second calculation unit for calculating the perpendicular bisectors of all point pairs in the two-dimensional space node; a first-order Voronoi diagram generation unit for determining the control region of each point based on the minimum convex hull formed by the perpendicular bisectors of each point pair, thus obtaining a first-order Voronoi diagram; a next-higher-order Voronoi diagram generation unit for dividing each control region into a next-higher-order control region based on the first-order Voronoi diagram using the perpendicular bisectors of all remaining point pairs excluding the control point, and merging the control regions with the same control point, thus obtaining a next-higher-order Voronoi diagram; and a K-order Voronoi diagram generation unit for dividing each control region into an even higher-order control region based on the next-higher-order Voronoi diagram using the perpendicular bisectors of all remaining point pairs excluding the control point, and merging the control regions with the same control point, thus obtaining a K-order Voronoi diagram.

[0055] The candidate subset generation module 430 is suitable for generating a candidate subset by finding control points in a control region based on the higher-order Voronoi diagram.

[0056] The module 440 for generating candidate merged node subsets is suitable for traversing all control regions of the higher-order Voronoi diagram, forming candidate subsets within each control region, and merging them into candidate merged node subsets.

[0057] As an example, the module 440 for generating candidate merged node subsets includes: an initialization unit for initializing the candidate merged node subset set to an empty set; a search unit for searching k control points for a control region of a k-order Voronoi diagram; a candidate merged node subset generation unit for generating candidate subsets based on the control points and storing them in the candidate merged node subset set; and an update unit for traversing the control region of the k-order Voronoi diagram to generate the candidate subsets and updating the candidate merged node subsets.

[0058] The optimization module 450 is suitable for optimizing the storage architecture of the blockchain structure by integrating the candidate subset with the smallest sum of communication delays among the candidate merged node subsets.

[0059] As an example, the optimization module 450 includes: a third calculation unit, used to calculate the sum of communication delays of K control point information in each candidate subset based on the candidate subsets included in the candidate merge node subset; and an integration unit, used to select the candidate subset with the smallest sum of communication delays and integrate their distributed storage engines to optimize the storage architecture of the blockchain structure.

[0060] Example 3

[0061] This invention also proposes a storage medium storing a blockchain structure storage architecture optimization method. When the blockchain structure optimization program is executed by a processor, it implements the steps of the blockchain structure storage architecture optimization method described above. Since this storage medium adopts all the technical solutions of all the above embodiments, it possesses at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be elaborated further here.

[0062] Example 4

[0063] Please see Figure 5 The present invention also provides an electronic device, including: a memory and a processor; the memory stores at least one program instruction; the processor loads and executes the at least one program instruction to implement the blockchain structure storage architecture optimization method provided in Embodiment 1.

[0064] The memory 502 and processor 501 are connected via a bus, which may include any number of interconnecting buses and bridges. The bus connects various circuits of one or more processors 501 and memory 502 together. The bus may also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 501 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 501.

[0065] Processor 501 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 502 can be used to store data used by processor 501 during operation.

[0066] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the scope of the present invention. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for optimizing the storage architecture of a blockchain structure, characterized in that, The method includes: Map the information communication nodes in the blockchain structure to a two-dimensional space; Generate a high-order Voronoi diagram of two-dimensional spatial nodes; Based on a control region of the higher-order Voronoi diagram, control points are searched to generate a candidate subset; Traverse all control regions of the higher-order Voronoi diagram and form a candidate subset of each control region into a candidate merge node subset; The storage architecture of the blockchain structure is optimized by integrating the candidate subset with the smallest sum of communication delays from the candidate merge node subsets; The generation of the higher-order Voronoi diagram of the two-dimensional spatial nodes includes: Calculate the perpendicular bisector of all point pairs in the two-dimensional space node; The control region of each point is determined based on the minimum convex hull formed by the perpendicular bisectors of each pair of points, thus obtaining a first-order Voronoi diagram. Based on the first-order Voronoi diagram, each control region is divided into a higher-order control region by the perpendicular bisector of all remaining points excluding the control points, and control regions with the same control points are merged to obtain a higher-order Voronoi diagram. Based on the higher-order Voronoi diagram, each control region is divided into a higher-order control region by the perpendicular bisector of all remaining points excluding the control points, and control regions with the same control points are merged to obtain a K-order Voronoi diagram.

2. The blockchain structure storage architecture optimization method as described in claim 1, characterized in that, The step of mapping the information communication nodes in the blockchain structure to a two-dimensional space includes: Each node in the blockchain network is numbered sequentially; An adjacency matrix is ​​constructed based on the communication relationships between the nodes. Calculate the first eigenvalue, the second eigenvalue, and the corresponding eigenvector of the adjacency matrix; The information communication node is mapped to a two-dimensional space based on the first feature value, the second feature value and their corresponding feature vector.

3. The blockchain structure storage architecture optimization method as described in claim 1, characterized in that, The process of traversing all control regions of the higher-order Voronoi diagram, and forming a candidate subset of nodes within each control region, includes: Initialize the subset of candidate merge nodes to an empty set; Find k control points for a control region in a k-order Voronoi diagram; Based on the control points, a candidate subset is generated and stored in the candidate merge node subset set; The candidate subset is generated by traversing the control region of the k-order Voronoi diagram, and the candidate merged node subset is updated.

4. The blockchain structure storage architecture optimization method as described in claim 3, characterized in that, The process of finding k control points for a control region of a k-order Voronoi diagram includes: In the k-th order Voronoi diagram, the distance from a point within the control region to a control point is less than the distance to a non-control point.

5. The blockchain structure storage architecture optimization method as described in claim 3, characterized in that, The process of traversing the control region of the k-order Voronoi diagram to generate the candidate subset and updating the candidate merged node subset includes: The candidate subset generated by traversing all control regions of the k-order Voronoi diagram is saved to the candidate merge node subset; The number of elements in the candidate merge node subset is equal to the number of convex hulls in the k-order Voronoi graph.

6. The blockchain structure storage architecture optimization method as described in claim 3, characterized in that, The optimization of the blockchain structure storage architecture by integrating the candidate subset with the smallest sum of communication delays from the candidate merge node subsets includes: Calculate the sum of communication delays for the K control points in each candidate subset based on the candidate subsets included in the candidate merge node subset; Select the candidate subset with the smallest sum of communication latency and integrate its distributed storage engine to optimize the storage architecture of the blockchain structure.

7. A blockchain structure storage architecture optimization device, characterized in that, The device includes: The mapping module is suitable for mapping information communication nodes in the blockchain structure to a two-dimensional space. A module for generating higher-order Voronoi diagrams is applicable to generating higher-order Voronoi diagrams for two-dimensional spatial nodes. The module includes: calculating the perpendicular bisectors of all point pairs in the two-dimensional spatial node; determining the control region of each point based on the minimum convex hull formed by the perpendicular bisectors of each point pair, thus obtaining a first-order Voronoi diagram; dividing each control region into higher-order control regions based on the first-order Voronoi diagram using the perpendicular bisectors of all remaining point pairs excluding the control point, and merging control regions with the same control point, thus obtaining a higher-order Voronoi diagram; and dividing each control region into even higher-order control regions based on the higher-order Voronoi diagram using the perpendicular bisectors of all remaining point pairs excluding the control point, and merging control regions with the same control point, thus obtaining a K-order Voronoi diagram. The module for generating candidate subsets is applicable to finding control points and generating candidate subsets based on a control region of the higher-order Voronoi diagram. The module for generating a subset of candidate merged nodes is applicable to traversing all control regions of the higher-order Voronoi diagram, forming a subset of candidate nodes within each control region, and then combining the subsets of candidate merged nodes. The optimization module is suitable for optimizing the storage architecture of the blockchain structure by integrating the candidate subset with the smallest sum of communication delays from the candidate merge node subset.

8. A computer-readable storage medium storing one or more instructions, characterized in that, The computer instructions are used to cause the computer to execute the blockchain structure storage architecture optimization method according to any one of claims 1 to 6.

9. An electronic device, characterized in that, include: Memory and processor; The memory stores at least one program instruction; The processor loads and executes the at least one program instruction to implement the blockchain structure storage architecture optimization method as described in any one of claims 1-6.

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