CN114793200B - Important internet of things node identification method based on electric power internet of things network structure - Google Patents
Important internet of things node identification method based on electric power internet of things network structure Download PDFInfo
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- CN114793200B CN114793200B CN202110095341.1A CN202110095341A CN114793200B CN 114793200 B CN114793200 B CN 114793200B CN 202110095341 A CN202110095341 A CN 202110095341A CN 114793200 B CN114793200 B CN 114793200B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Abstract
The application relates to an important Internet of things node identification method based on an electric power Internet of things network structure. The method specifically comprises the following steps: establishing an electric power Internet of things network by utilizing a complex network theory; analyzing a network structure based on a modified breadth-first search algorithm, and extracting a tangent point set in the network; important thing networking nodes in the network are identified by comparing the impact of the set of tangent points on the robustness of the network. The method can be different from the traditional node importance judging method, and the importance of the analyte coupling node is analyzed on the theoretical level by utilizing a non-ordering algorithm.
Description
Technical Field
The application relates to an important Internet of things node identification method based on a network structure of an electric power Internet of things, which can be used for identifying important Internet of things nodes in the ubiquitous electric power Internet of things more accurately and promoting robustness analysis of the important Internet of things nodes, and belongs to the field of the ubiquitous electric power Internet of things.
Background
Under the background of the ubiquitous electric power internet of things, the data volume and the data type of the ubiquitous electric power internet of things are rapidly increased due to the access of massive electric power internet of things nodes (such as electric power terminals, intelligent home of users, other energy system devices and the like). The method is used for serving advanced application scenes such as intelligent operation and maintenance, real-time situation awareness, personalized energy consumption service recommendation, optimal operation scheduling of a power system and the like in the ubiquitous power internet of things, and is key in collecting, storing and processing mass data. Therefore, the improvement of the robustness of the electric power Internet of things to emergency situations is one of important works of the electric power network in China, and the accurate identification of the important Internet of things nodes is beneficial to the analysis of the robustness of the electric power Internet of things.
At present, many students focus on research on a network important node identification method at home and abroad, but most of the methods are based on ordering nodes in a network according to a certain measurement index, the importance identification effect is poor, and the evaluation of different indexes is not uniform. Therefore, research on a more accurate important internet-of-things node recognition algorithm is urgent in the ubiquitous electric power internet of things field. How to distinguish from the traditional node importance evaluation method is not limited to importance judgment under a certain index, and the important Internet of things node is identified from multiple angles, so that the method has great strategic significance in evaluating the robustness of the electric power Internet of things and further maintaining the stable operation of the national power grid and the electric power system.
The electric power internet of things and the electric power system are very fragile under some external interference, and the structure and the function of the whole network can be seriously influenced by a few important node failures, for example, the large-scale power failure accident occurs only because of the failure of one node in the electric power network in the north part of the United states of 14 days in 8 months in 2003 and Canada, and the normal operation of a plurality of other real systems is influenced. Most of the existing important node identification methods are based on ordering a certain metric, however, any metric cannot fully describe all the characteristics of the network.
Disclosure of Invention
Aiming at the technical defects, the application aims to provide an important Internet of things node identification method based on the network structure of the electric power Internet of things, which is used for modeling the electric power Internet of things in a networking way, analyzing the network topology structure, evaluating the importance degree of the Internet of things node and promoting the maintenance and the guarantee of the electric power Internet of things.
The technical scheme adopted by the application for achieving the purpose is as follows:
an important internet of things node identification method based on an electric power internet of things network structure comprises the following steps:
establishing an electric power Internet of things model;
defining network characteristics of the electric power Internet of things according to the complex network;
according to network characteristic definition of the electric power Internet of things, an important Internet of things node identification method based on structure mining is established, and all candidate important Internet of things node sets in the electric power Internet of things are obtained;
screening all candidate important thing joint sets;
combining the screened multiple candidate important union node sets into a fixed number of target important union node sets, and selecting the optimal important union node in the multiple combinations to complete identification.
The electric power Internet of things model is established specifically as follows:
and taking the communication infrastructure resource units and the power system infrastructure resource units as the Internet of things nodes, and if the mutual power transportation or information exchange exists among any 2 resource units, 1 connecting edge exists among the 2 Internet of things nodes.
The communication infrastructure resource unit comprises: at least one of a communication machine room, a communication pipeline, a communication optical cable, a mobile communication base station and a communication iron tower; the power system infrastructure resource unit comprises: at least one of a power plant, a power supply and distribution station, a transformation station and a load center.
The important Internet of things node represents the importance degree of the influence of the Internet of things node on the connectivity of the electric power Internet of things network, the GCsize of the Internet of things node contained in the largest connected piece in the electric power Internet of things network is used for measuring, and when the selected Internet of things node is removed, the smaller the GCsize of the electric power Internet of things network is, the more important the Internet of things node is indicated.
The method for establishing the important thing joint point identification based on the structure mining specifically comprises the following steps:
step one: randomly selecting an Internet of things node in an electric power Internet of things network as a start, recording n neighbor Internet of things node sets of the Internet of things node, if at least n Internet of things nodes are removed, obtaining a communication piece with the size of 1 by removing the n Internet of things nodes, wherein the communication piece only comprises the Internet of things node, recording the set only comprising the Internet of things node as an available communication piece set, and recording the set of the n Internet of things node as an alternative important Internet of things node set;
selecting an Internet of things node with the minimum neighbor degree value of the communication sheet, adding the Internet of things node into the available communication sheet set to obtain a new available communication sheet set, and recording the set of neighbor Internet of things nodes of the new available communication sheet set as another alternative important Internet of things node set;
and repeating the second step until the obtained available connected sheet set contains all the Internet of things nodes in the electric power Internet of things network.
The screening of all the candidate important thing joint sets is specifically as follows: and respectively selecting the candidate important Internet of things node sets with the maximum average degree value, the maximum average medium value and the maximum influence from all the candidate important Internet of things node sets.
The maximum influence indicates that the worst network connectivity is obtained after the candidate important Internet of things node set fails in the initial power Internet of things network.
The number of the selected candidate important union node sets is less than or equal to three.
The fixed number of target significant union sets is comprised of two alternative significant union sets.
And the optimal important Internet of things node is an Internet of things node set of the largest connected piece with the smallest number of Internet of things nodes after failure.
An important thing allies oneself with node identification system based on electric power thing networking network structure includes:
the model construction module is used for establishing an electric power Internet of things model and defining network characteristics of the electric power Internet of things according to a complex network;
the important Internet of things node construction module is used for establishing an important Internet of things node identification method based on structure mining according to network characteristic definition of the electric power Internet of things to obtain all candidate important Internet of things node sets in the electric power Internet of things;
and the important thing joint point identification module is used for screening all candidate important thing joint point sets, combining a plurality of screened candidate important thing joint point sets into a target important thing joint point set with a fixed number, and selecting the optimal important thing joint point in a plurality of combinations to finish identification.
The application has the following beneficial effects and advantages:
1. the application applies the complex network theory to the identification problem of important Internet of things nodes of the electric power Internet of things, and provides a new view angle for the field of the electric power Internet of things.
2. The method is different from the traditional important node identification method limited to measurement index sequencing, and the important internet of things node is identified through analysis of the network structure, so that the robustness design and analysis of the electric power internet of things are facilitated.
3. The application introduces various heuristic improvements for improving the calculation efficiency of the method.
Drawings
FIG. 1 is an exemplary diagram of a method for identifying importance union nodes;
FIG. 2 is an exemplary diagram of an importance union node method search process;
FIG. 3 is a flow chart of the method for identifying important things nodes;
fig. 4 is a schematic diagram of a model network.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings and examples.
The application provides an important Internet of things node identification method based on an electric power Internet of things network structure.
The application provides an effective non-ordered important thing node identification method which promotes the robustness assessment and guarantee of the electric power Internet of things.
As shown in fig. 3, the method specifically comprises the following steps:
step one, carrying out networking modeling on the electric power Internet of things, namely setting communication infrastructure resources and electric power system infrastructure resource units as nodes, and if mutual electric power transportation or information exchange exists between 2 resource units, 1 connecting edge exists between the 2 nodes. Based on the above process, an electric power internet of things network can be constructed, as shown in fig. 4.
According to the actual electric power internet of things network structure, a complex network can be constructed, and a model network is shown in fig. 4.
And step two, defining the network characteristics of the electric power Internet of things according to the definition in the complex network. The electric power internet of things network is abstracted into a complex network G= (V, E), wherein V is a set of internet of things nodes in the electric power internet of things, E is a set of connecting edges between the internet of things nodes, and then the number N= |V| and the number M= |E| of connecting edges are set. The important nodes in the application refer to the importance degree of the influence of the nodes on the connectivity of the network, so that the importance degree of the nodes on the network can be measured by the number of nodes (GCsize) contained in the maximum connected piece in the network, and when the selected nodes are removed, the smaller the GCsize of the network indicates that the nodes are more important. Let the failed node set in the network be Q, and the connectivity corresponding to the node set Q may be denoted as GCsize (Q). Identifying the most important commodity node in the network aims at obtaining an optimal node set Q such that GCsize (Q) is minimized.
For example, for a power network, a substation, a converter station, etc. in the network may be considered as nodes, and the presence of a power line between the nodes indicates that there is a connection edge between the pair of nodes. It is noted that in the network of the present application, the distance of the power lines does not affect the characteristics of the edges in the network.
And thirdly, establishing an important node identification method based on structure mining according to the setting in the second step. As shown in fig. 1, first, a modified breadth-first search algorithm is performed from each node in the network to identify some less functional connected patches in the network. In the breadth-first search algorithm of each step, we select the node with the smallest neighbor middle value as the next search target, and the search process of the algorithm is shown in fig. 2. In the searching process, firstly, randomly selecting a node in a network as a start, and recording n neighbor node sets of the node, if at least n nodes can be removed, a connected patch with the size of 1 can be obtained by removing the n nodes, and the connected patch only comprises the node. The record only contains the set of the node as the set of the available connected slices, and the set of the n nodes is recorded as the set of the candidate important nodes. And then, selecting a node with the minimum neighbor medium value of the communication sheet, adding the node into the available communication sheet set to obtain a new available communication sheet set, and recording the neighbor node set of the new available communication sheet set as another alternative important node set. And repeatedly executing the searching process of the connected patch-neighbor node set until the obtained available connected patch set contains all nodes in the network.
The initial node selected by the search process in step three will traverse all nodes in the network.
In the context of this problem, the node degree value in the network indicates the number of power lines to which the node is connected, and the likelihood of importance of the node can be determined somewhat quickly and roughly.
After the fourth step and the third step, a series of alternative important nodes can be obtained, but the number of the alternative important node sets is too large, so that the calculation complexity of the subsequent combination into a fixed number of important nodes is too high. Therefore, the step filters the candidate important node set, and limits the number of the candidate important node set. In the screening process, for each number of candidate important node sets, screening is performed according to three aspects: maximum average value; maximum average median value; maximum influence. Where the greatest impact means that the worst network connectivity is obtained after the failure of the alternative set of important nodes in the initial network. Notably, in this screening process, some network node metrics are used as screening criteria. Although the network metric ranking does not yield an optimal set of important nodes, these metrics still measure some degree of importance of certain aspects of the nodes.
Through this step, the number of candidate important node sets can be greatly reduced, thereby improving the efficiency of the method.
Step five, after a plurality of candidate important node sets are obtained, combining the plurality of candidate important node sets into a fixed number of target important node sets. For a fixed target significant node number k, there is a decomposition as follows:
k＝k d1 +k d2 +…+k dr ,r∈1,2,…,k
for each decomposition of k, the summand in the decomposition formula (k di ) The candidate important node sets of the individual nodes are combined into a final important node set. In the process, the algorithm always records the most important node set up to the present, namely the node set which can obtain the smallest maximum connected piece after the group of nodes fail. Worth of itNote that this approach does not guarantee an optimal set of important nodes for all networks and all important node numbers, since the algorithm only focuses on a portion of the available connectivity patches (extended by minimum values) that are more likely to appear after the most important node fails.
However, the number of k decomposition increases exponentially with the k value, and as the k value increases, the number of k decomposition approaches:therefore, not limiting the number of k-th decompositions is not feasible for larger scale network significant node identification problems. In the present application, in order to reduce the excessive calculation time due to decomposition of the excessive target significant node number k, only the decomposition of k into two parts at most is considered. Of course, more careful division of the k value may possibly result in a better set of important nodes, but in order to enable the algorithm to be applied to the problem of large-scale important union node identification of the electric power Internet of things, the application selects the most efficient decomposition of the k value decomposition, namely the decomposition of k into two parts.
Through this step we will get the final identified important thing node.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application is described with reference to a method flow diagram in accordance with an embodiment of the application. It will be understood that each of the flows in the flowchart may be implemented by computer program instructions. 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows.
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.
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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.
Claims (9)
1. The important internet of things node identification method based on the electric power internet of things network structure is characterized by comprising the following steps of:
establishing an electric power Internet of things model;
defining network characteristics of the electric power Internet of things according to the complex network;
according to network characteristic definition of the electric power Internet of things, an important Internet of things node identification method based on structure mining is established, and all candidate important Internet of things node sets in the electric power Internet of things are obtained;
screening all candidate important thing joint sets;
combining the screened multiple candidate important thing node sets into a fixed number of target important thing node sets, and selecting an optimal important thing node in the multiple combinations to finish identification;
the method for establishing the important thing joint point identification based on the structure mining specifically comprises the following steps:
step one: randomly selecting an Internet of things node in an electric power Internet of things network as a start, recording n neighbor Internet of things node sets of the Internet of things node, if at least n Internet of things nodes are removed, obtaining a communication piece with the size of 1 by removing the n Internet of things nodes, wherein the communication piece only comprises the Internet of things node, recording the set only comprising the Internet of things node as an available communication piece set, and recording the set of the n Internet of things node as an alternative important Internet of things node set;
selecting an Internet of things node with the minimum neighbor degree value of the communication sheet, adding the Internet of things node into the available communication sheet set to obtain a new available communication sheet set, and recording the set of neighbor Internet of things nodes of the new available communication sheet set as another alternative important Internet of things node set;
and repeating the second step until the obtained available connected sheet set contains all the Internet of things nodes in the electric power Internet of things network.
2. The method for identifying important internet of things nodes based on the network structure of the electric power internet of things according to claim 1, wherein the establishing of the model of the electric power internet of things is specifically as follows:
and taking the communication infrastructure resource units and the power system infrastructure resource units as the Internet of things nodes, and if the mutual power transportation or information exchange exists among any 2 resource units, 1 connecting edge exists among the 2 Internet of things nodes.
3. The method for identifying the important internet of things node based on the electric power internet of things network structure according to claim 1, wherein the important internet of things node represents the importance degree of the influence of the internet of things node on the connectivity of the electric power internet of things network, and the GCsize of the number of the internet of things nodes contained in the maximum connected piece in the electric power internet of things network is used for measuring, when the selected internet of things node is removed, the smaller the GCsize of the electric power internet of things network is, the more important the internet of things node is indicated.
4. The method for identifying important internet of things nodes based on the network structure of the electric power internet of things according to claim 1, wherein the screening of all candidate important internet of things node sets is specifically as follows: and respectively selecting the candidate important Internet of things node sets with the maximum average degree value, the maximum average medium value and the maximum influence from all the candidate important Internet of things node sets.
5. The method for identifying important internet of things nodes based on the network structure of the electric power internet of things according to claim 4, wherein the maximum influence indicates that the worst network connectivity is obtained after the candidate important internet of things node set fails in the initial electric power internet of things network.
6. The method for identifying important internet of things nodes based on the network structure of the electric power internet of things according to claim 1 or 4, wherein the number of the screened candidate important internet of things nodes is less than or equal to three.
7. The method for identifying the important thing node based on the network structure of the electric power internet of things according to claim 1, wherein the fixed number of target important thing node sets are composed of two alternative important thing node sets.
8. The method for identifying the important internet of things nodes based on the electric power internet of things network structure according to claim 1, wherein the optimal important internet of things nodes are an internet of things node set of a maximum connected piece with the minimum number of internet of things nodes obtained after failure.
9. Important thing allies oneself with node identification system based on electric power thing networking network structure, characterized by comprising:
the model construction module is used for establishing an electric power Internet of things model and defining network characteristics of the electric power Internet of things according to a complex network;
the important Internet of things node construction module is used for establishing an important Internet of things node identification method based on structure mining according to network characteristic definition of the electric power Internet of things to obtain all candidate important Internet of things node sets in the electric power Internet of things;
the important thing joint point identification module is used for screening all candidate important thing joint point sets, combining a plurality of screened candidate important thing joint point sets into a fixed number of target important thing joint point sets, and selecting the optimal important thing joint point in a plurality of combinations to finish identification;
the important internet of things node identification system based on the electric power internet of things network structure is used for executing the important internet of things node identification method based on the electric power internet of things network structure of claim 1.
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