CN115473895B

CN115473895B - Method and device for dividing digital object warehouse node consensus groups under ubiquitous environment

Info

Publication number: CN115473895B
Application number: CN202211068409.8A
Authority: CN
Inventors: 罗超然; 黄罡; 景翔; 柳熠; 姜海鸥; 马新建
Original assignee: Beijing Big Data Advanced Technology Research Institute; Peking University
Current assignee: Beijing Big Data Advanced Technology Research Institute; Peking University
Priority date: 2022-09-01
Filing date: 2022-09-01
Publication date: 2023-09-12
Anticipated expiration: 2042-09-01
Also published as: CN115473895A

Abstract

The application provides a method and a device for dividing a node consensus group of a digital object warehouse in a ubiquitous environment, which belong to the technical field of digital object architecture, and the embodiment of the application maps the node consensus group on a two-dimensional plane based on the position information of the node, divides the node by constructing a Thiessen polygon, divides the nodes with adjacent space distances into the same consensus group, effectively reduces the network delay between the nodes and improves the fragmentation performance; meanwhile, the whole Thiessen polygon is stored in the nodes in a distributed mode, and according to the characteristics of the Thiessen polygon, dynamic change of a certain node only affects adjacent constant unit cells, so that self-adaptive adjustment of dynamic nodes in a consensus group can be realized with low cost, the advantage of the piecewise blockchain technology can be exerted in a ubiquitous environment, and further high-efficiency reliability of digital object access transaction records is guaranteed.

Description

Method and device for dividing digital object warehouse node consensus groups under ubiquitous environment

Technical Field

The application relates to the technical field of digital object architecture, in particular to a method and a device for dividing a digital object warehouse node consensus group under a ubiquitous environment.

Background

The digital object architecture DOA (Digital Object Architecture) is a software architecture proposed by the parent Robert Kann professor of the graphic prize and the Internet, and aims to solve the interoperability between information system resources in an open environment by centering on the digital object, wherein the digital object is essentially the data abstraction of a resource entity, and in the Internet environment, the digital object is mainly generated in a cloud information system and is formed by converting data existing in the information system, such as a library table, a document, a picture and the like. The ubiquitous environment is a decentralised environment, which is different from the centralized management of data in the Internet environment, and the main components in the ubiquitous environment are greatly dispersed in terminals and edge devices all over the world. Digital object access transactions generated in the decentralized environment often result from interactions among a plurality of digital object bodies, involve a consensus among the plurality of digital object bodies, and have risks of losing and being tampered with when stored in terminals and edge warehouse nodes, so that the credibility of the digital object transaction records needs to be ensured.

The blockchain is used as a data credible storage and verification technology in a decentralization environment, so that the credibility of the digital object access transaction record can be ensured. However, access transactions are generated at any moment on massive digital objects in the ubiquitous environment, and the terminal equipment has insufficient stability and limited storage capacity, and the throughput bottleneck of the blockchain technology is added, so that the traditional blockchain technology is difficult to directly apply to the ubiquitous environment.

In a traditional blockchain system, all nodes of the whole network belong to the same consensus group, and the system can only store transactions serially, so that the throughput of the system cannot be effectively improved. Although the partition technology in the blockchain technology can be utilized to divide the nodes into a plurality of consensus groups, each consensus group independently and parallelly generates blocks, so that the overall throughput of the blockchain system is improved, the nodes in each consensus group are required to perform data synchronization and data consensus, and the interaction of the nodes is obviously more frequent than the interaction of the nodes between groups. The digital objects are positioned at the end of the network and are randomly distributed, so that the network delay difference between the nodes is larger; meanwhile, unlike static data such as documents and table libraries in the internet environment, digital object entities in the ubiquitous environment are generally perceived and generated in real time by intelligent equipment, the values of the digital object entities can change in real time along with the change of the real world, and how to divide the digital object warehouse nodes in the ubiquitous environment is a problem to be solved currently.

Disclosure of Invention

The application provides a method and a device for dividing a digital object warehouse node consensus group in a ubiquitous environment, which are used for solving the problem that the traditional block chain slicing technology is difficult to divide the digital object warehouse node in the ubiquitous environment.

In order to solve the problems, the application adopts the following technical scheme:

in a first aspect, an embodiment of the present application provides a method for dividing a digital object warehouse node consensus group in a ubiquitous environment, where the method for dividing a digital object warehouse node consensus group in a ubiquitous environment includes:

mapping a plurality of digital objects onto a two-dimensional plane based on position information of the plurality of digital objects to obtain a plurality of nodes; wherein different nodes correspond to different digital objects;

dividing the two-dimensional plane based on the plurality of nodes to obtain a Thiessen polygon; the Thiessen polygon includes n cells; n is an integer greater than or equal to 1;

dividing nodes belonging to the same unit cell into the same consensus group to obtain n consensus groups;

creating n blockchains based on the n consensus groups; wherein different blockchains correspond to different consensus groups; the blockchain is used to store transaction information for nodes in the corresponding consensus group.

In an embodiment of the present application, dividing the two-dimensional plane based on the plurality of nodes to obtain a Thiessen polygon includes:

determining n nodes with fixed position information in the node set as n core nodes;

Dividing the two-dimensional plane by a Delaunay triangulation algorithm based on the n nuclear nodes to generate a Delaunay triangulation graph;

obtaining the Thiessen polygon based on the Delaunay triangle section diagram;

wherein different core nodes correspond to different cells; each cell comprises a corresponding core node and a plurality of member nodes; the core node stores a neighbor node set, a triangle set and a consensus group member set; the set of neighbor nodes represents a set of all neighbor core nodes that are contiguous with the core node; the triangle set represents a triangle relation formed by the triangle set and the neighbor core nodes; the set of consensus group members represents all nodes in the cell.

In an embodiment of the present application, the method for dividing a consensus group of digital object warehouse nodes in a ubiquitous environment further includes:

under the condition that a new member node appears, taking any one core node as an initial core node, and calculating the Euclidean distance between the neighbor core node of the initial core node and the new member node;

determining a neighboring core node nearest to the new member node as a nearest neighbor node;

judging whether the Euclidean distance between the nearest neighbor node and the new member node is smaller than or equal to the Euclidean distance between the initial kernel node and the new member node;

If not, determining the initial core node as a target core node closest to the new member node, and adding the new member node into a consensus group where the target core node is located;

if yes, the nearest adjacent node is used as the initial core node, and the steps are repeated until the target core node is found.

when the number of nodes in any consensus group exceeds an upper threshold, determining new core nodes in member nodes of cells corresponding to the consensus group, so that the number of member nodes, which are closer to the new core nodes, in the cells is equivalent to the number of member nodes, which are closer to the prokaryotic nodes corresponding to the cells;

generating a new Delaunay triangulation graph by an incremental triangulation algorithm based on the new nuclear node;

updating a neighbor node set and a triangle set of neighbor core nodes of the prokaryotic node based on the new Delaunay triangle split map;

updating the consensus group member set of the affected core node based on the updated neighbor node set and the triangle set to obtain a reconstructed consensus group; the reconstructed consensus group comprises a newly added consensus group where the new core node is located.

In an embodiment of the present application, generating a new Delaunay triangulation graph by an incremental triangulation algorithm based on the new nuclear node includes:

determining a target triangle containing the new core node in the triangle set stored by the prokaryotic node; the vertexes of the target triangle are the prokaryotic node, the first neighbor core node and the second neighbor core node respectively;

the prokaryotic node, the first neighbor core node and the second neighbor core node are respectively connected with the new core node to obtain three new triangles; and legalizing the new triangle to generate the new Delaunay triangle split map.

In an embodiment of the present application, performing legal operations on the new triangle includes:

judging whether the new triangle meets the empty circle characteristic of Delaunay triangulation or not;

if yes, determining that the new triangle is legal and reserving the new triangle;

if not, determining that the new triangle is an illegal triangle, deleting the illegal triangle, and connecting the new core node and the opposite-side core node to construct two reconstructed triangles; the opposite side core nodes are opposite side vertexes of an adjacent triangle of the illegal triangle on an adjacent side, and the adjacent side is an edge formed by connecting the first adjacent core node and the second adjacent core node;

Repeating the steps, and performing legal operation on the reconstructed triangles until all the reconstructed triangles are legal triangles.

In an embodiment of the present application, updating the set of consensus group members of the affected core node based on the updated set of neighbor nodes and the set of triangles includes:

determining affected target member nodes based on the updated neighbor node set and the triangle set;

judging whether the distance from the target member node to the new core node is smaller than the distance from the target member node to the core node in the original consensus group;

if not, keeping the target member node unchanged in the original consensus group;

if yes, the target member node is added into a new added consensus group where the new core node is located.

under the condition that the number of nodes in any consensus group is lower than a lower limit threshold value or the core nodes in any consensus group exit, determining the consensus group as a to-be-combined consensus group, and determining the core nodes in the cells corresponding to the to-be-combined consensus group as core nodes to be deleted;

Determining a target neighbor core node based on the neighbor node set of the core node to be deleted;

generating a local triangular subdivision graph through a Delaunay triangulation algorithm based on the target neighbor nuclear node;

updating a neighbor node set and a triangle set of the target neighbor core node based on the local triangle split map to delete the core node to be deleted from the neighbor node set of the target neighbor core node, and deleting the triangle containing the core node to be deleted from the triangle set of the target neighbor core node;

and adding each node in the consensus group to be combined into a consensus group member set where a corresponding target neighbor core node is located based on the position information of all nodes in the consensus group to be combined so as to update the consensus group member set of the target neighbor core node and obtain the consensus group with the combined completion.

In an embodiment of the present application, based on the location information of all the nodes in the to-be-combined consensus group, adding each node in the to-be-combined consensus group to a consensus group member set where a corresponding target neighbor core node is located includes:

for each node in the consensus group to be combined, respectively acquiring the distance between the node and the target neighbor core node; and adding the node into a consensus group member set where the target neighbor core node nearest to the node is located.

In a second aspect, based on the same inventive concept, an embodiment of the present application provides a digital object warehouse node consensus group dividing device in a ubiquitous environment, where the digital object warehouse node consensus group dividing device in the ubiquitous environment includes:

the mapping module is used for mapping the plurality of digital objects onto a two-dimensional plane based on the position information of the plurality of digital objects to obtain a plurality of nodes; wherein different nodes correspond to different digital objects;

the node dividing module is used for dividing the two-dimensional plane based on the plurality of nodes to obtain a Thiessen polygon; the Thiessen polygon includes n cells; n is an integer greater than or equal to 1;

the consensus group dividing module is used for dividing nodes belonging to the same cell into the same consensus group to obtain n consensus groups;

the block chain creation module is used for creating n block chains based on the n consensus groups; wherein different blockchains correspond to different consensus groups; the blockchain is used to store transaction information for nodes in the corresponding consensus group.

In an embodiment of the present application, the node dividing module includes:

the core node determining submodule is used for determining n nodes with fixed position information in the node set as n core nodes;

The triangular dissection molecular module is used for dividing the two-dimensional plane based on the n nuclear nodes through a Delaunay triangulation algorithm to generate a Delaunay triangular dissection map;

the Thiessen polygon acquisition submodule is used for acquiring the Thiessen polygon based on the Delaunay triangular segmentation map; wherein different core nodes correspond to different cells; each cell comprises a corresponding core node and a plurality of member nodes; the core node stores a neighbor node set, a triangle set and a consensus group member set; the set of neighbor nodes represents a set of all neighbor core nodes that are contiguous with the core node; the triangle set represents a triangle relation formed by the triangle set and the neighbor core nodes; the set of consensus group members represents all nodes in the cell.

In an embodiment of the present application, the digital object warehouse node consensus group dividing device in the ubiquitous environment further includes:

the distance calculation module is used for taking any one core node as an initial core node and calculating the Euclidean distance between the neighbor core node of the initial core node and the new member node under the condition that the new member node appears;

A nearest neighbor node determining module, configured to determine a neighboring core node nearest to the new member node as a nearest neighbor node;

the distance judging module is used for judging whether the Euclidean distance between the nearest adjacent node and the new member node is smaller than or equal to the Euclidean distance between the initial core node and the new member node;

the target core node determining module is used for determining the initial core node as a target core node closest to the new member node when the Euclidean distance between the nearest adjacent node and the new member node is larger than the Euclidean distance between the initial core node and the new member node, and adding the new member node into a consensus group where the target core node is located;

and the target core node exploration module is used for repeating the steps until the target core node is found when the Euclidean distance between the nearest adjacent node and the new member node is smaller than or equal to the Euclidean distance between the initial core node and the new member node.

The new core node determining module is used for determining new core nodes in member nodes of cells corresponding to any consensus group under the condition that the number of nodes in the consensus group exceeds an upper limit threshold value, so that the number of member nodes which are closer to the new core nodes in the cells is equivalent to the number of member nodes which are closer to the prokaryotic nodes corresponding to the cells;

the new triangulation graph generation module is used for generating a new Delaunay triangulation graph through an incremental triangulation algorithm based on the new nuclear node;

the first updating module is used for updating the neighbor node set and the triangle set of the neighbor core nodes of the prokaryotic node based on the new Delaunay triangle split diagram;

the second updating module is used for updating the set of the consensus group members of the affected core nodes based on the updated neighbor node set and the triangle set to obtain a reconstructed consensus group; the reconstructed consensus group comprises a newly added consensus group where the new core node is located.

In an embodiment of the present application, the first update module includes:

a target triangle determining sub-module, configured to determine a target triangle including the new core node in the triangle set stored in the prokaryotic node; the vertexes of the target triangle are the prokaryotic node, the first neighbor core node and the second neighbor core node respectively;

The triangle legalization submodule is used for respectively connecting the prokaryotic node, the first neighbor core node and the second neighbor core node with the new core node to obtain three new triangles; and legalizing the new triangle to generate the new Delaunay triangle split map.

In an embodiment of the present application, the triangle legalization submodule includes:

the validity judging unit is used for judging whether the new triangle meets the empty circle characteristic of Delaunay triangulation or not;

the legal triangle determining unit is used for determining the new triangle as a legal triangle and reserving the legal triangle when the new triangle meets the empty circle characteristic of Delaunay triangulation;

the triangle legalization unit is used for determining that the new triangle is an illegal triangle when the new triangle does not meet the empty circle characteristic of Delaunay triangulation, deleting the illegal triangle, and connecting the new kernel node and the opposite-side kernel node to construct two reconstructed triangles; the opposite side core nodes are opposite side vertexes of an adjacent triangle of the illegal triangle on an adjacent side, and the adjacent side is an edge formed by connecting the first adjacent core node and the second adjacent core node;

The recursion unit is used for repeating the steps and carrying out legal operation on the reconstructed triangles until all the reconstructed triangles are legal triangles;

in an embodiment of the present application, the second update module includes:

a target member node determining sub-module, configured to determine an affected target member node based on the updated neighboring node set and the triangle set;

the distance judging sub-module is used for judging whether the distance from the target member node to the new core node is smaller than the distance from the target member node to the core node in the own original consensus group;

the node maintaining sub-module is used for maintaining the target member node unchanged in the original consensus group when the distance from the target member node to the new core node is greater than or equal to the distance from the target member node to the core node in the original consensus group;

and the node adding sub-module is used for adding the target member node into a new consensus group where the new core node is located when the distance from the target member node to the new core node is smaller than the distance from the target member node to the core node in the consensus group where the target member node is originally located.

the to-be-combined consensus group determining module is used for determining the consensus group as a to-be-combined consensus group and determining the core node in the cell corresponding to the to-be-combined consensus group as a to-be-deleted core node under the condition that the number of the nodes in any consensus group is lower than a lower limit threshold value or the core node in any consensus group exits;

the target neighbor core node determining module is used for determining target neighbor core nodes based on the neighbor node set of the core nodes to be deleted;

the local triangulation graph generation module is used for generating a local triangulation graph through a Delaunay triangulation algorithm based on the target neighbor nuclear node;

a third updating module, configured to update, based on the partial triangle splitting graph, a neighboring node set and a triangle set of the target neighboring core node, so as to delete the core node to be deleted from the neighboring node set of the target neighboring core node, and delete a triangle containing the core node to be deleted from the triangle set of the target neighboring core node;

and a fourth updating module, configured to add each node in the to-be-combined consensus group to a consensus group member set where a corresponding target neighbor core node is located based on the position information of all nodes in the to-be-combined consensus group, so as to update the consensus group member set of the target neighbor core node, and obtain a consensus group in which the combination is completed.

In an embodiment of the present application, the fourth update module is specifically configured to, for each node in the to-be-combined consensus group, obtain a distance between the node and the target neighboring core node; and adding the node into a consensus group member set where the target neighbor core node nearest to the node is located.

Compared with the prior art, the application has the following advantages:

according to the node consensus group dividing method for the digital object warehouse in the ubiquitous environment, which is provided by the embodiment of the application, the nodes are mapped on a two-dimensional plane based on the position information of the nodes, the nodes are divided by constructing Thiessen polygons, and the nodes with adjacent space distances are divided into the same consensus group, so that the network delay between the nodes is effectively reduced, and the fragmentation performance is improved; meanwhile, the whole Thiessen polygon is stored in the nodes in a distributed mode, and according to the characteristics of the Thiessen polygon, dynamic change of a certain node only affects adjacent constant unit cells, so that self-adaptive adjustment of dynamic nodes in a consensus group can be realized with low cost, the advantage of the piecewise blockchain technology can be exerted in a ubiquitous environment, and further high-efficiency and credibility of digital object access transaction records are guaranteed.

Drawings

In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart illustrating steps of a method for partitioning a consensus group of digital object warehouse nodes in a ubiquitous environment according to an embodiment of the present application.

FIG. 2 is a schematic illustration of a Voronoi diagram in accordance with one embodiment of the present application.

FIG. 3 is a schematic representation of Delaunay triangulation in an embodiment of the present application.

FIG. 4 is a schematic diagram of common group splitting in an embodiment of the present application.

FIG. 5 is a schematic illustration of a process for generating a new Delaunay triangle section in an embodiment of the application.

FIG. 6 is a schematic diagram of a process for legalizing a single triangle in accordance with one embodiment of the application.

FIG. 7 is a schematic diagram of a common combination in an embodiment of the present application.

Fig. 8 is a schematic functional block diagram of a digital object warehouse node consensus group partitioning apparatus in a ubiquitous environment according to an embodiment of the present application.

Reference numerals: 800-a digital object warehouse node consensus group dividing device under a ubiquitous environment; 801-a mapping module; an 802-node partition module; 803-consensus group partitioning module; 804-blockchain creation module.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

To facilitate a further understanding of the relevant terms and background of the present application, a description of a digital object in a ubiquitous environment is first provided below.

With the development of software and hardware technologies, more and more "intelligent" devices are distributed in the real world, and intelligent devices refer to terminal devices with computing processing capabilities. The popularity of smart devices also means that computing is a ubiquitous resource that is incorporated into physical systems and human society. Through computing resources, human society, physical systems and information systems can conveniently interact and cooperate, and ubiquitous computing resources also means that the combination of the human society, the physical systems and the information systems is more and more compact, and the boundaries of the combination are more and more fuzzy. The ubiquitous environment provided by the application is a novel environment different from the Internet, which is generated by ubiquitous computing resources.

In a ubiquitous environment, massive data are generated by massive ubiquitous intelligent devices at any time. In the internet environment, the digital object is mainly static data such as documents, library tables and the like, and the digital object entity is an actual byte sequence of the resources, so that frequent changes cannot be generated, for example, the digital object application DOI which is the most widespread at present is allocated with a unique identifier for the published paper and is regarded as the digital object. While digital object entities in a ubiquitous environment are typically perceived and generated by smart devices in real-time, their values change in real-time as the real world changes. Taking the example of a digital object of a purifier in a ubiquitous environment, the digital object representing a purifier entity whose identity is still a globally unique sequence of characters assigned by an identity resolution system, whose metadata is a key pair describing the purifier, comprising: the product name, date of manufacture, manufacturer, serial number, etc., the identification and metadata are not substantially different from the paper digital objects in the internet. The entity of the digital object of the purifier is a digital representation of the purifier, representing the real-time status of a plurality of sensors in the purifier, including: air quality, humidity, fan speed, etc. change in real time, and thus it is not possible to directly sequence a snapshot at a certain moment in bytes in a digital object entity like a paper digital object. The digital object entity of the purifier typically stores the data interface of the purifier sensor through which real-time data is acquired and returned when the digital object is accessed. The difference between the ubiquitous environment and the Internet environment on the digital object entity makes the terminal and the edge equipment more suitable as carriers of a digital object warehouse to provide access services for the digital object.

In the ubiquitous environment, the digital object is provided with access service by the terminal and the edge warehouse where the digital object is located, and corresponding access transaction is generated in the terminal and the edge digital object warehouse. In DOA, the access transaction is used as a unique evidence for tracing the operation behavior of the digital object afterwards, so that the historical operation record of the digital object can be restored, the audit service of the data is provided, and the related rights and interests of the data are ensured. Thus, access transactions for digital objects need to be securely and reliably recorded and authenticated.

Blockchain systems are essentially a distributed database in which a Transaction (Transaction), i.e., a Transaction, generated in the system is stored. The transaction is backed up in a plurality of nodes, so that the integrity of the transaction is ensured; through hash pointers among the transactions, the sequence among the transactions is ensured, and the tamper resistance of the transactions is enhanced; and ensuring the legitimacy of the transaction through a consensus algorithm. The digital object access transaction under the ubiquitous environment is verified based on the block chain technology, the integrity of the access transaction can be guaranteed through multiple backups among nodes, and the security of the access transaction can be guaranteed through Hash pointers among data.

However, in the traditional blockchain system, all nodes of the whole network belong to the same consensus group, the system can only store transactions serially, so that the throughput of the system can not be effectively improved, and the access transaction storage requirements of mass digital objects in a ubiquitous environment are difficult to meet. Although the partition technology in the blockchain technology can divide nodes into a plurality of consensus groups, each consensus group independently and parallelly generates blocks, so that the overall throughput of the blockchain system is improved, the partition technology needs to maintain the number of nodes in each consensus group at a constant level, and the state storage efficiency is not affected too high, and the integrity of a historical state is not affected too low. However, the terminal and edge devices as digital object warehouse in ubiquitous environment are distributed randomly and dynamically, which mainly brings the following challenges to the division of consensus groups:

(1) The terminal and the edge warehouse node are positioned at the end of the network, the network delay difference between the nodes is large, and the data synchronization and the data consensus of the nodes in the consensus group are affected.

(2) The terminal and edge warehouse nodes are distributed randomly, unevenly and unpredictably. The traditional mode of dividing the consensus group based on the space grid or the mode of dividing the consensus group based on the edge server needs to know the distribution rule of the terminal nodes in advance or assume that the terminal nodes are uniformly distributed, so that the method can not be applied to the consensus group division of the distributed digital object warehouse.

(3) The behavior of the terminal and the edge warehouse node is dynamic, and the node joining and exiting behaviors are frequent. After the consensus group is partitioned according to the initial node distribution, the consensus group also needs the ability to exit the dynamic telescoping consensus group as nodes join to maintain the number of nodes within the consensus group at a constant level.

In order to solve the problems in the prior art, the application aims to provide a method for dividing a digital object warehouse node consensus group in a ubiquitous environment, which is characterized in that digital objects are mapped into nodes on a two-dimensional plane, nodes are divided by constructing Thiessen polygons, nodes with adjacent space distances are divided into the same consensus group, and the self-adaptive adjustment of dynamic nodes in the consensus group is realized, so that the advantage of a segmented blockchain technology can be exerted in the ubiquitous environment, and the credibility of the access transaction record of the digital objects is further ensured.

Referring to fig. 1, there is shown a flow chart of steps of a method for partitioning a consensus group of digital object warehouse nodes in a ubiquitous environment according to the present application, the method comprising:

s101: based on the location information of the plurality of digital objects, the plurality of digital objects are mapped onto a two-dimensional plane, resulting in a plurality of nodes.

In this embodiment, the consensus group is constructed based on Thiessen polygons, first of all, all digital objects need to be mapped onto a two-dimensional plane where different nodes correspond to different digital objects.

In this embodiment, the location information of the plurality of digital objects may be longitude and latitude information, and in a ubiquitous environment, the digital objects mainly include terminal nodes distributed in the real world, and naturally have longitude and latitude information. In a specific implementation, longitude and latitude information of the digital object can be automatically acquired through a Global Positioning System (GPS), and longitude and latitude attributes of the warehouse node can be manually set.

In the present embodiment, each node corresponds to a two-dimensional vector V _i As shown in formula (1):

V _i ＝<lat _i ,lon _i > (1)；

wherein lat _i Is node V _i Longitude, lon of (2) _i Is node V _i Is a latitude of (c).

S102: based on a plurality of nodes, dividing the two-dimensional plane to obtain a Thiessen polygon, wherein the Thiessen polygon comprises n unit cells.

It should be noted that, the Thiessen polygon is also called Feng Luo Nori diagram (Voronoi diagram), hereinafter referred to as Voronoi diagram, the Voronoi diagram divides a two-dimensional plane into n Voronoi polygons according to a point set formed by a plurality of nodes, each Voronoi polygon is a Voronoi unit cell (hereinafter referred to as unit cell), each unit cell includes a plurality of nodes, and the nodes belonging to the same Voronoi unit cell can be divided into the same consensus group, wherein n is an integer greater than or equal to 1.

It should be noted that, common algorithms for constructing Voronoi diagrams include: scan line algorithm, divide-and-conquer algorithm, increment method, etc. But constructing Voronoi diagrams using the Delaunay triangulation algorithm is the most efficient of these. In this embodiment, a Delaunay triangulation algorithm is used to construct the Voronoi diagram, and based on the Delaunay triangulation algorithm, S102 may specifically include the following steps:

s102-1: n nodes with fixed location information in the node set are determined as n core nodes.

Referring to fig. 2, a schematic diagram of a Voronoi diagram is shown, where there are two types of nodes in the Voronoi diagram constructed by the Delaunay triangulation algorithm: one type is a core node, such as a triangle in fig. 2, and one and only one of each cell of the core node is a fixed node whose geographic position is not frequently changed, and the consensus group is mainly divided according to the geographic position of the core node so as toThe representation is made of a combination of a first and a second color,each->Representing longitude and latitude vectors corresponding to the core nodes, namely:the other type is member node, such as the circle in fig. 2, which also has geographic position information, and the member node selects corresponding core node as its guiding node according to its cell, and joins the consensus group to Indicating as well->The distance between nodes is defined in terms of Euclidean distance, i.e. any two nodes V _i And V is equal to _j The distance between them can be expressed by the formula (2):

wherein D is _i,j Represents V _i And V is equal to _j A Euclidean distance between them; lat _i And lon _i Respectively represent V _i Longitude and latitude of (a); lat _j And lon _j Respectively represent V _j Longitude and latitude of (a).

In this embodiment, before the Voronoi diagram is constructed by the Delaunay triangulation algorithm, it is necessary to first determine a core node, and a node having fixed location information, such as a base station, a camera, and a router, which does not change frequently, may be determined as a core node.

S102-2: based on n nuclear nodes, the two-dimensional plane is divided through a Delaunay triangulation algorithm, and a Delaunay triangulation graph is generated.

It should be noted that, as shown in fig. 3, a Delaunay triangulation schematic diagram is shown, the Delaunay triangulation diagram is a dual diagram of the Voronoi diagram, a Delaunay triangulation can be uniquely obtained by connecting the nuclear nodes of each adjacent cell in the Voronoi diagram, and correspondingly, a perpendicular bisector is made for each side in the Delaunay triangulation diagram, and each perpendicular bisector and the intersection point thereof are taken as the sides and points of the polygon, so that a Voronoi diagram can be obtained.

In the embodiment, the Delaunay triangulation algorithm is applied to the node consensus group division of the digital object warehouse in the ubiquitous environment, and has the following advantages:

uniqueness: the Delaunay triangulation is unique for a given point set, and therefore the Delaunay triangulation can also be performed in a distributed fashion.

Voronoi duality: each vertex of the Delaunay triangle is a nuclear node of each cell in the Voronoi graph, and the digital object can be added into the consensus group corresponding to the correct cell only by finding the vertex of the Delaunay triangle closest to the vertex.

Empty circularity: the circumscribed circle of any Delaunay triangle does not necessarily contain other triangle vertexes, and the characteristic is used as an important characteristic of consensus group allocation and can be used as a judgment basis for splitting or merging of a subsequent consensus group.

S102-3: based on Delaunay triangle sectional diagram, the Thiessen polygon is obtained.

In the present embodiment, based on the dual relationship between the Voronoi diagram and the Delaunay triangle split diagram, the storage of the Delaunay triangle split diagram is represented by storing the Voronoi diagram, and the complete Delaunay triangle split diagram is represented by G _Trianglation G represents _Trianglation Representing a set of points throughout the graphEdge Set [ E ] _i,j ]Triangle Set [ T ] _i,j,k ]As shown in formula (3):

in this embodiment, the entire Delaunay triangulation map is distributedRespectively stored in each core node. For a core nodeIf in Delaunay triangulation there is an edge +.>Then consider->Is thatAs shown in equation (4):

if and only if->

For a core nodeThe local triangulation and consensus group information is only saved, and the method mainly comprises the following three parts:

(1) Nuclear nodeNeighbor node set neighbor _i Representing inclusion and core node->The set of all neighboring core nodes that are contiguous, as shown in equation (5):

(2) Comprises a core nodeTriangle set trianges _i For recording nuclear node->Triangle relation with neighbor core nodes, as shown in formula (6) and formula (7):

Triangles _i ＝＜T _i,j,k ＞j,k∈neighbours _i (6)；

T _i,j,k ＝＜V _i ,V _j ,V _k ＞ (7)；

(3) Nuclear nodeShared group member collection card _i By core node->Itself is composed of member nodes in the same cell as shown in equation (8):

in the present embodiment, each core node stores its corresponding neighbor node set neighbor _i Triangle set trianges _i And consensus group member collection card _i Storing Delaunay triangle section map is equivalent to storing one Delaunay triangle section map, and storing Delaunay triangle section map is representative of storing the whole Voronoi map. And when the state of the subsequent digital object is changed, only the information stored by the core nodes in the consensus group is updated.

S103: nodes belonging to the same cell are divided into the same consensus group to obtain n consensus groups.

In the embodiment, the digital object is mapped on a two-dimensional plane according to the longitude and latitude information of the digital object, and the nodes are divided based on the Voronoi graph, so that the nodes with adjacent spatial distances can be divided into the same consensus group, the network delay between the nodes in the group is reduced, and the data synchronization and data consensus efficiency of the nodes in the consensus group is improved.

S104: based on the n consensus groups, n blockchains are created.

In this embodiment, different blockchains correspond to different consensus groups, and each blockchain is used to store transaction information for nodes in the corresponding consensus group.

In the embodiment, the same consensus group only carries out consensus and evidence storage on the access transaction of the digital object in the group, the groups are not affected mutually, and each consensus group independently and parallelly generates blocks, so that the throughput of the whole block chain system is improved; meanwhile, the whole Voronoi diagram is stored in the nodes in a distributed mode, according to the characteristics of Thiessen polygons, dynamic change of a certain node only affects adjacent constant cells, so that self-adaptive adjustment of dynamic nodes in a consensus group can be realized with low cost, the advantage of the piecewise blockchain technology can be exerted in a ubiquitous environment, the credibility of digital object access transaction records is further guaranteed, and the access transaction evidence storage requirement of massive digital objects is further met.

In one possible implementation manner, when a new node is generated in the Voronoi diagram, the method for dividing a digital object warehouse node consensus group in a ubiquitous environment provided by the embodiment may further include the following steps:

s105: and under the condition that a new member node appears, taking any one core node as an initial core node, and calculating the Euclidean distance between the neighbor core node of the initial core node and the new member node.

In the present embodiment, in the triangular sectional view G _Trianglation After the construction is completed, other member nodes or new member nodesThe corresponding core nodes are selected based on the coordinate information of the core nodes, and the core nodes are added into the consensus group, and the adding process of the new member nodes is described below as an example.

It should be noted that, according to VorThe characteristics of the onoi diagram, for a node belonging to a certain cell, must be the closest nuclear node to that cell, and thus for the nodeOnly the closest core node needs to be selected +.>Adding the consensus group of the user. Due to triangular sectional view G _Trianglation Only the local view is stored in the core node, so that the core node closest to the new member node is found and selected by adopting a depth-first search algorithm.

In a specific implementation, any one core node is selectedIs an initial core node, and the initial core node is used as a query starting point, and the neighbor node set neighbor is queried first _i And finding a neighbor core node of the initial core node.

S106: the neighboring core node nearest to the new member node is determined to be the nearest neighbor node.

In a specific implementation, the Euclidean distance D between the new member node and all the neighboring core nodes can be calculated respectively through a formula (2) _j,k Wherein k is epsilon neighbors _i Obtaining the distanceMinimum neighbor node->And its nearest distance D _j,k 。

S107: and judging whether the Euclidean distance between the nearest adjacent node and the new member node is smaller than or equal to the Euclidean distance between the initial kernel node and the new member node.

S108: if not, determining the initial core node as a target core node closest to the new member node, and adding the new member node into a consensus group where the target core node is located;

s109: if yes, the nearest adjacent node is used as an initial core node, and the steps are repeated until a target core node is found.

In the present embodiment, judgment D will be made _j,k ≤D _j,i If the condition of (2) is satisfied, if not, then the core node is describedNamely distance->The nearest nuclear node, at this time, will +. >Join Nuclear node->The consensus group is located; if yes, the core node is described as +.>Not distance->The nearest nuclear node, at this time, let +.>And repeating steps S106-S107 such that +.>Gradual direction->Close together and finally find distance->The nearest core node.

The characteristics according to Delaunay triangle sectionIf a core nodeNot from the new member node>The nearest node, then there must be a closer core node in its neighbors. Therefore, the core node with the nearest whole network distance can be found in a decentralized environment, and a new member node is guided +.>Adding to the correct consensus group.

In the present embodiment, a new member nodeFinding the nearest core node of the whole network +.>And add the same to the consensus group card _i Afterwards, will be from->Synchronous board _i Neighbor bours _i Information for consensus and witness of the transaction.

In this embodiment, the new node is a member node when joining the consensus network, and selects the nearest consensus group to join. The number of nodes in the consensus group increases/decreases as member nodes join/exit. In order to avoid that the number of nodes in the consensus group is too high to cause the transaction memory card to be too low in efficiency or too low to cause the transaction memory card to be unreliable and unreliable, the consensus group needs to be dynamically stretched when the number of the consensus group reaches an upper/lower threshold value.

In one possible implementation manner, to implement automatic splitting of the consensus group, the method for dividing the consensus group of the digital object warehouse nodes in the ubiquitous environment provided by the embodiment may further include the following steps:

s201: and under the condition that the number of the nodes in any consensus group exceeds an upper limit threshold, determining a new core node in the member nodes of the cells corresponding to the consensus group, so that the number of the member nodes which are closer to the new core node in the cells is equivalent to the number of the member nodes which are closer to the prokaryotic node corresponding to the cells.

S202: generating a new Delaunay triangulation graph by an incremental triangulation algorithm based on the new nuclear node;

s203: based on the new Delaunay triangle splitting diagram, the neighbor node set and the triangle set of the neighbor core nodes of the prokaryotic node are updated.

S204: updating the consensus group member set of the affected core node based on the updated neighbor node set and the triangle set to obtain a reconstructed consensus group; the reconstructed consensus group comprises a new added consensus group where the new core node is located.

Referring to fig. 4, a schematic diagram of consensus group splitting is shown. Wherein, when a consensus group is a standard _i The number of intermediate nodes exceeds an upper threshold τ _up As shown in fig. 4 (a); shelter (S) _i The "split" flow is initiated to generate a new core nodeAs shown in fig. 4 (b); a new consensus group card _new As shown in fig. 4 (c).

Note that, the new core nodeThe selection of (a) follows the rule of equipartition, i.e. new core node +.>Should be a static node with relatively stable physical location and be +.>Closer and further from the prokaryotic node->The number of nodes closer should be comparable. That is, when new core nodes are out of the consensus group to be split +.>And prokaryotic node->If the number of the other member nodes is even, the even member nodes are equally divided; if the number is odd, taking an upper integer and a lower integer after halving, and randomly distributing the two integers to a new core node +.>And prokaryotic node->

In the present embodiment, according to the newly added new core nodeRepartitioning the Voronoi diagram through an incremental triangulation algorithm, and reconstructing the adjacency relationship between the affected core nodes, as shown in FIG. 4 (c); finally, the members in the consensus group involved in the reconstruction are according to +>Physical location reselection addition of +.>Shared group board _new Or remain unchanged in the original consensus group.

In a possible embodiment, S202 may specifically include the following substeps:

S202-1: determining a target triangle containing a new core node in a triangle set stored by the original core node; the vertexes of the target triangle are respectively a prokaryotic node, a first neighbor core node and a second neighbor core node.

S202-2: the method comprises the steps of respectively connecting a prokaryotic node, a first neighbor core node and a second neighbor core node with new core nodes to obtain three new triangles; legal operation is carried out on the new triangle so as to generate a new Delaunay triangle split map.

In the present embodiment, the shared group card for splitting is divided _i The split executing node is defined by a card _i Is a nuclear node of (a)Execution (S)>Generating a local new Delaunay triangulation graph G by executing an incremental triangulation algorithm _new 。

By way of example only, and not by way of limitation,generating a new Delaunay triangle section graph G _new The process is as shown in fig. 5: firstly use->Neighbor of (C) _i And trianges _i Construction of initial partial triangular section G _new The method comprises the steps of carrying out a first treatment on the surface of the In the process of determining new core nodes according to the equipartition principleAfter that, at->Trianges of local triangle sets of (c) _i Is selected from->Target triangle T where it is located _i,j,k As shown in FIG. 5 (a), the dotted line indicates that the new core node is included +.>Is of the order of (2)Label triangle T _i,j,k The method comprises the steps of carrying out a first treatment on the surface of the From G _new Delete T in _i,j,k And respectively connecting three vertexes of the target triangle with the new core nodes to obtain three new triangles T _i,j,new ，T _i,new,k ，T _new,j,k As shown in fig. 5 (b), three new triangles are indicated by three dotted lines; followed by sequentially recursively legalizing T _i,j,new ，T _i,new,k ，T _new,j,k As shown in fig. 5 (c), each new triangle is made to conform to the empty circle characteristics of Delaunay triangulation; finally, when all triangles are legal, G _new The obtained partial triangular sectional view after splitting is obtained.

It should be noted that since Delaunay triangulation has a blank circle characteristic, namely: any triangle circumcircle does not contain any other nodes, and the legal goal of the triangle is to aim at a new nuclear nodeIt is determined whether it exists in the circumscribed circles of the other triangles. Obviously (I)>Only possibly exist in the adjacent triangles of the new triangle generated by the triangle, so that the triangle legal process is to sequentially legal the adjacent triangles of the new triangle, and judging +.>If the triangle is in the adjacent triangle, if the triangle is not legal, the triangle needs to be legal to generate a new legal triangle.

In a possible embodiment, the step of legalizing the new triangle in S202-2 may specifically include the following sub-steps:

s202-2-1: and judging whether the new triangle meets the empty circle characteristic of Delaunay triangulation.

S202-2-2: if yes, determining the new triangle as a legal triangle and reserving the legal triangle.

With continued reference to FIG. 5 (b), in whichNew triangle T above _i,j,new And a new triangle T on the right _i,new,k All conform to the empty circle characteristic, i.e. determine a new triangle T _i,j,new And T _i,new,k Is a legal triangle.

S202-2-3: if not, determining that the new triangle is an illegal triangle, deleting the illegal triangle, and connecting the new core node and the opposite-side core node to construct two reconstructed triangles. The opposite side core node is the opposite side vertex of the adjacent triangle of the illegal triangle on the adjacent side, and the adjacent side is the side formed by connecting the first adjacent core node and the second adjacent core node.

S202-2-4: repeating the steps, and legalizing the reconstructed triangles until all the reconstructed triangles are legal triangles.

In the present embodiment, with continued reference to fig. 5 (b), a new triangle T is shown _new,j,k Comprises other core nodes which are new triangles T _new,j,k Opposite side vertices of an adjacent triangle to the new triangle T _new,j,k Sharing a contiguous edge E _j,k . At this time, a new triangle T is required _new,j,k And (5) performing legal treatment.

Referring to fig. 6, a process diagram of single triangle legalization is shown. Wherein, the liquid crystal display device comprises a liquid crystal display device, In triangle T _i,k,l In the circumcircle of (2), the triangle T is deleted in the process of legalizing the triangle _i,k,l Connection->And->Triangle T structure _new,k,l And T _new,j,l Two new triangles are generated in the process of legalizing the triangle, and the triangle T _new,k,l And T _new,j,l I.e. reconstructed triangles, which need to be further validated. Therefore, legalizationThe process of triangles is a recursive process until all triangles are validated.

It should be noted that triangle legalization is terminated and the average time complexity is O1), regardless of the network size. In a decentralized environment, the main overhead of consensus group splitting comes from triangle legalization. Therefore, the present embodiment can realize capacity expansion of the consensus group without center and autonomously, while affecting only a constant number of nodes.

It should be noted that after the local Delaunay incremental triangulation is completed, the kernel node is executedLocally an updated partial triangular segmentation graph G is obtained _new And the core node Set involved in splitting _updated [V]。/>Will G _new Set of neighbor nodes in (V)]Triangle Set [ T ]]Send to Set _updated [V]All affected core nodes +.> Upon receipt of G _new After that, from Set [ V ]And Set [ T ]]Selecting core nodes adjacent to the core nodes and triangles, and updating neighbor nodes _updated Triangles _updated . Since it is already during the legal processAll neighbor of (C) _updated And trianges _updated Added to G _new Thus the neighbor node set and the triangle set can be updated in a completely alternative manner; finally, let(s)>Book co-productionGroup identifying board _updated Broadcasting new core node by all member nodes in the network>Information, member node according to self and +.>And->Selecting a smaller core node to rejoin the consensus group in which it is located.

In a possible implementation manner, the step of updating the set of common group members of the affected core nodes based on the updated set of neighboring nodes and the triangle set in S204 may specifically include the following substeps:

s204-1: and determining the affected target member node based on the updated neighbor node set and the triangle set.

In this embodiment, the affected target member nodes are all affected core nodesA member node in the consensus group; that is to say +>Is influenced, meaning +.>Some of the member nodes of (c) will be added to the newly added consensus group.

S204-2: and judging whether the distance from the target member node to the new core node is smaller than the distance from the target member node to the core node in the consensus group where the target member node is originally located.

S204-3: if not, keeping the target member node unchanged in the original consensus group.

S204-4: if yes, the target member node is added into the new public identification group where the new core node is located.

In this embodiment, by calculating the distance between the target member node and the new core node and the distance between the target member node and the core node in the consensus group where the target member node itself is originally located, it can be determined which core node the target member node is closer to. If the new core node is closer to the new core node, adding a new added consensus group where the new core node is located; if the core node is closer to the core node in the self-original consensus group, the core node is kept unchanged in the self-original consensus group. After all target member nodes are selected, a reconstructed consensus group including a newly added consensus group can be obtained, and then the whole consensus group splitting process is completed.

In a possible implementation manner, in order to implement automatic merging of consensus groups, the method for dividing the consensus groups of the digital object warehouse nodes in the ubiquitous environment provided by the embodiment may further include the following steps:

s301: and under the condition that the number of the nodes in any common identification group is lower than a lower limit threshold value or the core nodes in any common identification group exit, determining the common identification group as a to-be-combined common identification group, and determining the core nodes in the cells corresponding to the to-be-combined common identification group as core nodes to be deleted.

S302: and determining the target neighbor core node based on the neighbor node set of the core node to be deleted.

S303: based on the target neighbor nuclear node, a local triangular subdivision graph is generated through a Delaunay triangulation algorithm.

S304: based on the partial triangle segmentation graph, updating a neighbor node set and a triangle set of the target neighbor core node to delete the core node to be deleted from the neighbor node set of the target neighbor core node, and deleting the triangle containing the core node to be deleted from the triangle set of the target neighbor core node.

S305: and adding each node in the to-be-combined consensus group into the consensus group member set where the corresponding target neighbor core node is located based on the position information of all nodes in the to-be-combined consensus group so as to update the consensus group member set of the target neighbor core node and obtain the consensus group for completing combination.

In the present embodimentIn the consensus group card _i The number of intermediate nodes reaches a lower threshold τ _low Or its corresponding core nodeWhen exiting the network, merging of the consensus groups is triggered.

The merging of the consensus groups can be regarded as a reverse process of the splitting of the consensus groups, and the new core nodes and the new triangles generated in the splitting process are restored to the states before the splitting of the consensus groups.

In a specific implementation, referring to FIG. 7, a consensus combining and schematic diagram is shown. First, as shown in fig. 7 (a), the to-be-combined consensus group and its core node of which the node number is lower than the lower threshold are determinedThe core node->Namely, the core node to be deleted; then, as shown in FIG. 7 (b), based on the core node to be deleted +>Is a set of neighbor nodes trianges _i Determining surrounding affected target neighbor core nodes; based on the target neighbor core node, the local triangular subdivision diagram shown in the figure 7 (c) can be regenerated through a Delaunay triangulation algorithm; finally, originally belonging to the consensus group to be combined Shard _i Selecting a core node closest to the core node from the target neighbor core nodes, and re-adding the core node to the consensus group, namely, respectively acquiring the distance between each node in the consensus group to be combined and the target neighbor core node for each node in the consensus group to be combined; and adding the node into a consensus group member set where the target neighbor core node nearest to the node is located.

It should be noted that, since the local triangulation graph is actually stored in the target neighboring core node, the target neighboring core needs to be updated based on the local triangulation graph Neighbor node set and triangle set of nodes to neighbor node set neighbor from target neighbor core node _updated Core node to be deleted is deleted in middleAnd from the triangle set trianges of the target neighbor core node _updated The deletion of the middle includes the core node to be deleted +.>Is a triangle of (2); for the target neighbor core node->In other words, will include self->Triangle of (2) is added to trianges _updated In which a core node point having a connection relationship with itself is added to neighbors _updated Is a kind of medium.

It should be noted that the theoretical overhead of the algorithm can be estimated by the number of core nodes affected in the execution process of the algorithm, and since the number of neighbor cells of each cell is a constant level and is on average 6 in the Voronoi diagram in which the core nodes are randomly distributed. Therefore, when the consensus group splitting is carried out, the core nodes which are affected and need to be reconstructed are all new core nodes after the splittingNeighbor nodes of (a) neighbor nodes of (b) _new ，neighbours _new Is 6 on average, plus the cell itselfThe number of the core nodes affected in the splitting process is 7 on average; when the consensus combination is performed, the reconstruction nodes involved in the algorithm are only +.>Neighbor node neighbor of (a)s _i On average 6 and thus the merging process affects on average only the nearby 6 consensus groups. It can be seen that the algorithm provided in this embodiment does not generate excessive overhead for the overall operation of the system.

It should be further described that, for the massive digital object access transaction generated in the decentralized digital object, the core idea is to guarantee the high efficiency and credibility of the transaction deposit certificate through the technology of the slicing block chain, but the key challenge is the contradiction between the nodes which are randomly distributed and dynamically added and exited and the balanced dividing requirement of the slicing technology consensus group. Therefore, the application provides a digital object warehouse node consensus group dividing method under a ubiquitous environment: firstly, mapping the digital object on a two-dimensional plane according to longitude and latitude information of the digital object, slicing based on a Voronoi graph, dividing nodes (particularly including core nodes and member nodes) with adjacent space distances into the same consensus group, and storing the whole Voronoi graph in the nodes in a distributed manner, wherein the same consensus group only carries out consensus and evidence storage on access transactions of the digital object in the group, and the groups are not influenced mutually; and then, based on a distributed Delaunay triangulation algorithm, the consensus groups can dynamically stretch and contract along with the addition and the withdrawal of the nodes, so that each consensus number can dynamically expand along with the addition and the withdrawal of the nodes, the balance of the division of the consensus groups is ensured at a small cost, the advantage of the segmented blockchain technology can be exerted under the ubiquitous environment, and the high efficiency and the credibility of the digital object access transaction records are further ensured.

In a second aspect, referring to fig. 8, based on the same inventive concept, an embodiment of the present application provides a digital object warehouse node consensus group partitioning apparatus 800 in a ubiquitous environment, the digital object warehouse node consensus group partitioning apparatus 800 in the ubiquitous environment including:

a mapping module 801, configured to map a plurality of digital objects onto a two-dimensional plane based on position information of the plurality of digital objects, to obtain a plurality of nodes; wherein different nodes correspond to different digital objects;

the node dividing module 802 is configured to divide the two-dimensional plane based on a plurality of nodes to obtain a Thiessen polygon; the Thiessen polygon includes n cells; n is an integer greater than or equal to 1;

a consensus group dividing module 803, configured to divide nodes belonging to the same unit cell into the same consensus group to obtain n consensus groups;

a blockchain creation module 804 for creating n blockchains based on n consensus groups; wherein different blockchains correspond to different consensus groups; the blockchain is used to store transaction information for nodes in the corresponding consensus group.

In one possible implementation, the node partition module 802 includes:

The triangular subdivision molecular module is used for dividing a two-dimensional plane based on n nuclear nodes through a Delaunay triangulation algorithm to generate a Delaunay triangular subdivision graph;

the Thiessen polygon acquisition submodule is used for acquiring the Thiessen polygon based on the Delaunay triangular segmentation map; wherein different core nodes correspond to different cells; each cell comprises a corresponding core node and a plurality of member nodes; the core node stores a neighbor node set, a triangle set and a consensus group member set; the neighbor node set represents a set of all neighbor core nodes adjacent to the core node; the triangle set represents a triangle relation formed by the triangle set and the neighbor core nodes; the consensus group member set represents all nodes in a cell.

In one possible embodiment, the digital object warehouse node consensus group partitioning apparatus 800 in a ubiquitous environment further comprises:

the nearest neighbor node determining module is used for determining a neighbor core node nearest to the new member node as a nearest neighbor node;

The distance judging module is used for judging whether the Euclidean distance between the nearest adjacent node and the new member node is smaller than or equal to the Euclidean distance between the initial kernel node and the new member node;

the target core node determining module is used for determining the initial core node as the target core node closest to the new member node when the Euclidean distance between the nearest adjacent node and the new member node is larger than the Euclidean distance between the initial core node and the new member node, and adding the new member node into the consensus group where the target core node is located;

the new core node determining module is used for determining new core nodes in member nodes of cells corresponding to the consensus groups under the condition that the number of nodes in any consensus group exceeds an upper limit threshold value, so that the number of member nodes which are closer to the new core nodes in the cells is equivalent to the number of member nodes which are closer to the prokaryotic nodes corresponding to the cells;

the second updating module is used for updating the consensus group member set of the affected core node based on the updated neighbor node set and the triangle set to obtain a reconstructed consensus group; the reconstructed consensus group comprises a new added consensus group where the new core node is located.

In one possible embodiment, the first update module includes:

the target triangle determining submodule is used for determining a target triangle containing a new core node in a triangle set stored by the original core node; the vertexes of the target triangle are respectively a prokaryotic node, a first neighbor core node and a second neighbor core node;

the triangle legalization submodule is used for respectively connecting the prokaryotic node, the first neighbor core node and the second neighbor core node with the new core nodes to obtain three new triangles; legal operation is carried out on the new triangle so as to generate a new Delaunay triangle split map.

In one embodiment of the present application, the triangle legalization submodule includes:

the legitimacy judging unit is used for judging whether the new triangle meets the empty circle characteristic of Delaunay triangulation or not;

the triangle legalization unit is used for determining that the new triangle is an illegal triangle when the new triangle does not meet the empty circle characteristic of Delaunay triangulation, deleting the illegal triangle, and connecting the new kernel node and the opposite-side kernel node to construct two reconstructed triangles; the opposite side core node is an opposite side vertex of an adjacent triangle of the illegal triangle on the adjacent side, and the adjacent side is an edge formed by connecting the first adjacent core node and the second adjacent core node;

the recursion unit is used for repeating the steps and legally operating the reconstructed triangles until all the reconstructed triangles are legal triangles;

in one possible embodiment, the second updating module includes:

the target member node determining submodule is used for determining the affected target member node based on the updated neighbor node set and the triangle set;

The distance judging sub-module is used for judging whether the distance from the target member node to the new core node is smaller than the distance from the target member node to the core node in the natural consensus group of the target member node;

the node maintaining sub-module is used for maintaining the target member node unchanged in the original common-mode group when the distance from the target member node to the new core node is greater than or equal to the distance from the target member node to the core node in the original common-mode group;

and the node adding sub-module is used for adding the target member node into the new consensus group where the new core node is located when the distance from the target member node to the new core node is smaller than the distance from the target member node to the core node in the consensus group where the target member node is originally located.

the to-be-combined consensus group determining module is used for determining the consensus group as the to-be-combined consensus group and determining the core node in the cell corresponding to the to-be-combined consensus group as the to-be-deleted core node under the condition that the number of the nodes in any consensus group is lower than a lower limit threshold value or the core node in any consensus group exits;

the third updating module is used for updating the neighbor node set and the triangle set of the target neighbor core node based on the partial triangle splitting graph so as to delete the core node to be deleted from the neighbor node set of the target neighbor core node and delete the triangle containing the core node to be deleted from the triangle set of the target neighbor core node;

and a fourth updating module, configured to add each node in the to-be-combined consensus group into the consensus group member set where the corresponding target neighbor core node is located based on the position information of all the nodes in the to-be-combined consensus group, so as to update the consensus group member set of the target neighbor core node, and obtain the consensus group with the combined completion.

In a possible implementation manner, the fourth updating module is specifically configured to, for each node in the to-be-combined consensus group, obtain a distance between the node and the target neighboring core node; and adding the node into a consensus group member set where the target neighbor core node nearest to the node is located.

It should be noted that, referring to the foregoing specific implementation manner of the method for dividing a digital object warehouse node consensus group in a ubiquitous environment according to the first aspect of the embodiment of the present application, the description thereof is omitted herein,

It will be apparent to those skilled in the art that embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.

Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.

The above detailed description of the method and the device for dividing the node consensus group of the digital object warehouse in the ubiquitous environment provided by the invention applies specific examples to illustrate the principle and the implementation of the invention, and the description of the above examples is only used for helping to understand the method and the core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims

1. The digital object warehouse node consensus group dividing method in the ubiquitous environment is characterized by comprising the following steps of:

creating n blockchains based on the n consensus groups; wherein different blockchains correspond to different consensus groups; the block chain is used for storing transaction information of nodes in the corresponding consensus group;

dividing the two-dimensional plane based on the plurality of nodes to obtain a Thiessen polygon, including:

Obtaining the Thiessen polygon based on the Delaunay triangle section diagram;

wherein different core nodes correspond to different cells; each cell comprises a corresponding core node and a plurality of member nodes; the core node stores a neighbor node set, a triangle set and a consensus group member set; the set of neighbor nodes represents a set of all neighbor core nodes that are contiguous with the core node; the triangle set represents a triangle relation formed by the triangle set and the neighbor core nodes; the set of consensus group members represents all nodes in the cell;

the digital object warehouse node consensus group dividing method in the ubiquitous environment further comprises the following steps:

2. The method for partitioning a digital object warehouse node consensus group in a ubiquitous environment according to claim 1, further comprising:

3. The method for partitioning a digital object warehouse node consensus group in a ubiquitous environment according to claim 1, further comprising:

4. A method of partitioning a node consensus group of a digital object warehouse in a ubiquitous environment according to claim 3, wherein generating a new Delaunay triangulation graph by an incremental triangulation algorithm based on the new core node comprises:

5. The method for partitioning a node consensus group of a digital object warehouse in a ubiquitous environment according to claim 4, wherein legalizing the new triangle comprises:

6. A method of partitioning a consensus group of digital object warehouse nodes in a ubiquitous environment according to claim 3, wherein updating the set of consensus group members of the affected core nodes based on the updated set of neighbor nodes and the set of triangles comprises:

7. The method for partitioning a digital object warehouse node consensus group under a ubiquitous environment according to claim 1, wherein adding each node in the consensus group to be merged into a consensus group member set where a corresponding target neighbor core node is located based on position information of all nodes in the consensus group to be merged, comprises:

8. A digital object warehouse node consensus group partitioning device in a ubiquitous environment, wherein the digital object warehouse node consensus group partitioning device in the ubiquitous environment comprises:

the block chain creation module is used for creating n block chains based on the n consensus groups; wherein different blockchains correspond to different consensus groups; the block chain is used for storing transaction information of nodes in the corresponding consensus group;

the node dividing module includes:

The Thiessen polygon acquisition submodule is used for acquiring the Thiessen polygon based on the Delaunay triangular segmentation map; wherein different core nodes correspond to different cells; each cell comprises a corresponding core node and a plurality of member nodes; the core node stores a neighbor node set, a triangle set and a consensus group member set; the set of neighbor nodes represents a set of all neighbor core nodes that are contiguous with the core node; the triangle set represents a triangle relation formed by the triangle set and the neighbor core nodes; the set of consensus group members represents all nodes in the cell;

the digital object warehouse node consensus group dividing device in the ubiquitous environment further comprises: