CN114547915B - Power grid target node identification method, device, equipment and storage medium - Google Patents

Power grid target node identification method, device, equipment and storage medium Download PDF

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CN114547915B
CN114547915B CN202210433069.8A CN202210433069A CN114547915B CN 114547915 B CN114547915 B CN 114547915B CN 202210433069 A CN202210433069 A CN 202210433069A CN 114547915 B CN114547915 B CN 114547915B
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不公告发明人
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Abstract

The application discloses a method, a device, equipment and a storage medium for identifying a power grid target node, wherein the method comprises the following steps: acquiring a network simulation model of a power grid to be identified; obtaining a recovery efficiency value of a node in the network simulation model of the power grid to be identified based on the network simulation model of the power grid to be identified; based on the recovery efficiency value of the node in the network simulation model of the power grid to be identified, performing simulated removal on the node in the network simulation model of the power grid to be identified so as to obtain the removal cost value of the node in the network simulation model of the power grid to be identified; the removal cost value is used for representing the removal cost which needs to be considered when the nodes in the network simulation model of the power grid to be identified are removed; and obtaining a target node in the network simulation model of the power grid to be identified based on the removal cost value of the node in the network simulation model of the power grid to be identified. The technical problem that the most important target removal node of a power grid cannot be accurately identified by the existing identification method is solved.

Description

Power grid target node identification method, device, equipment and storage medium
Technical Field
The present application relates to the field of complex network analysis, and in particular, to a method, an apparatus, a device, and a storage medium for identifying a power grid target node.
Background
The complex network anti-removal research method is mainly based on an important node identification method of a complex network, but the existing identification method for the power grid target removal node generally focuses on concept theory and qualitative analysis, so that the most important target removal node of the power grid cannot be accurately identified.
Disclosure of Invention
The main purpose of the present application is to provide a method, an apparatus, a device and a storage medium for analyzing vulnerability of a power grid, and aim to solve the technical problem that the most important target removal node of the power grid cannot be accurately identified by the existing identification method.
In order to achieve the above object, an embodiment of the present application provides a method for identifying a target node of a power grid, including the following steps:
acquiring a network simulation model of a power grid to be identified;
obtaining a recovery efficiency value of a node in the network simulation model of the power grid to be identified based on the network simulation model of the power grid to be identified;
based on the recovery efficiency value of the node in the network simulation model of the power grid to be identified, performing simulated removal on the node in the network simulation model of the power grid to be identified so as to obtain the removal cost value of the node in the network simulation model of the power grid to be identified; the removal cost value is used for representing the removal cost which needs to be considered when the nodes in the network simulation model of the power grid to be identified are removed;
and obtaining a target node in the network simulation model of the power grid to be identified based on the removal cost value of the node in the network simulation model of the power grid to be identified.
Optionally, the step of obtaining, based on the network simulation model of the power grid to be identified, a recovery efficiency value of a node in the network simulation model of the power grid to be identified includes:
obtaining the recovery amount of the nodes in the network simulation model of the power grid to be identified based on the network simulation model of the power grid to be identified;
and obtaining the recovery efficiency value of the node in the network simulation model of the power grid to be identified based on the recovery quantity of the node in the network simulation model of the power grid to be identified.
In the technical scheme, the recovery quantity and the recovery efficiency value of the node in the network simulation model of the power grid to be identified are obtained based on the network simulation model of the power grid to be identified, so that the repair strategy of the power grid to be identified is obtained, and the constructed network simulation model of the power grid to be identified is more attached to the real elastic power grid.
Optionally, the recovery amount of the node in the network simulation model of the power grid to be identified is obtained through the following relational expression:
Figure 997715DEST_PATH_IMAGE001
wherein r: (v i )For the nodes in the network simulation model of the power grid to be identifiedv i The amount of recovery after removal; l: (L:)v i )For the nodes in the network simulation model of the power grid to be identifiedv i Current load value of;MLthe power supply quantity of the maximum scale node in the network simulation model of the power grid to be identified is obtained; eta (v i )Network simulation for the grid to be identifiedNode in modelv i The work efficiency value of; xi is a constant and takes a value of 0,0.5]。
Through the formula, the recovery quantity of the nodes in the network simulation model of the power grid to be identified after the nodes are removed can be obtained based on the current load value and the working efficiency value of the nodes in the network simulation model of the power grid to be identified and the power supply quantity of the maximum stage in the model, and the recovery quantity is used for calculating the recovery efficiency value of the nodes in the network simulation model of the power grid to be identified subsequently.
Optionally, the recovery efficiency value of a node in the network simulation model of the power grid to be identified is obtained by the following relation:
Figure 505051DEST_PATH_IMAGE002
wherein:
η t+1 (v max )after the t +1 round of removal, the nodes in the network simulation model of the power grid to be identifiedv max The recovery efficiency value of (a);
η t (v max )after the t round is removed, the nodes in the network simulation model of the power grid to be identifiedv max The recovery efficiency value of (a);
r(v max )for the nodes in the network simulation model of the power grid to be identifiedv max The amount of recovery after removal.
Performing simulation attack on a certain node through the formula, recording and calculating to obtain the recovery efficiency value of the node in the network simulation model of the power grid to be identified based on the efficiency of the node after t-round attack and the recovery amount of the node after the node is removed; and between the time t and the time t +1, the nodes in the power grid network are continuously recovered, and the cascade failure can be relieved. Therefore, the identification of the target node for the recovered power grid network is more reasonable and more practical.
Optionally, the step of removing, by simulation, the node in the network simulation model of the power grid to be identified based on the recovery efficiency value of the node in the network simulation model of the power grid to be identified to obtain a removal cost value of the node in the network simulation model of the power grid to be identified includes:
obtaining a removal strategy of the nodes in the network simulation model of the power grid to be identified based on the recovery efficiency values of the nodes in the network simulation model of the power grid to be identified;
based on the removal strategy of the nodes in the network simulation model of the power grid to be identified, carrying out simulated removal on the nodes in the network simulation model of the power grid to be identified;
and obtaining the removal cost value of the nodes in the network simulation model of the power grid to be identified based on the simulation removal of the nodes in the network simulation model of the power grid to be identified.
In the technical scheme, based on the recovery efficiency value of the node in the network simulation model of the power grid to be identified, the removal strategy of the node in the network simulation model of the power grid to be identified is obtained and simulated removal is carried out; the method is mainly used for simulating the real elastic power grid and searching the identification method for removing the target node in the elastic power grid with higher accuracy. The removal cost value of the node after the simulation removal is calculated, so that a better target node identification basis is provided.
Optionally, the step of obtaining a removal strategy of a node in the network simulation model of the power grid to be identified based on the recovery efficiency value of the node in the network simulation model of the power grid to be identified includes:
obtaining the defense deployment condition of the network simulation model of the power grid to be identified based on the recovery efficiency value of the node in the network simulation model of the power grid to be identified;
and obtaining a removal strategy of the network simulation model of the power grid to be identified according to the defense deployment condition of the network simulation model of the power grid to be identified.
Generally, the more important a node is, the higher the defense deployment of the node is, and if the flow of the power line in the network is larger, the more important the power line is, the higher the defense strength of the power line deployment is, and as an attacker, the cost for destroying the node is increased successively. Therefore, the defense deployment condition of the network simulation model of the power grid to be identified is obtained based on the recovery efficiency value of the node in the network simulation model of the power grid to be identified; and obtaining a removal strategy of the network simulation model of the power grid to be identified according to the defense deployment condition of the network simulation model of the power grid to be identified.
Optionally, the removal cost value of the node in the network simulation model of the power grid to be identified is obtained through the following relational expression:
Figure 218929DEST_PATH_IMAGE003
wherein the content of the first and second substances,δ i is a nodev i The cost value of (a) to be removed,I i is a nodev i The value of the importance of the service of (c),C B (v)is the importance value of the node structure.
Through the formula, the removal cost value of the node is obtained through calculation based on the service importance value of the node and the importance value of the node structure, and the node removal cost value obtained through calculation is used for predicting the defense deployment condition of the node subsequently, so that the removal strategy can be designed better.
Optionally, the nodev i The service importance of (2) is obtained by the following relation:
Figure 264245DEST_PATH_IMAGE004
wherein the content of the first and second substances,I i is a nodev i The service importance value of (2);S i is a nodev i And W is the importance value of the service matrix set.
Through the formula, the service importance of the node is limited, and the obtained service importance is used for calculating the removal cost value of the node in the network simulation model of the power grid to be identified.
Optionally, the importance of the node structure is obtained by the following relation:
Figure 472504DEST_PATH_IMAGE005
wherein the content of the first and second substances,C B (v)is an importance value of the node structure,
Figure 83614DEST_PATH_IMAGE006
to pass through the nodevS → the shortest path number of member t,
Figure 171656DEST_PATH_IMAGE007
the number of the shortest paths of the members s → t, V is all the nodes in the network simulation model of the power grid to be identifiedv i S is a member in the network simulation model of the power grid to be identified, and t is other members except the member s in the network simulation model of the power grid to be identified.
The above formula characterizes the magnitude of the dependency of member v for member s to reach all other members t in the network. If all shortest paths pass through the node v, the above formula characterizes the magnitude of the dependency of the member v that the member s wants to reach all other members t in the network. If all the shortest paths pass through the node v, the betweenness centrality of the node v is 1 at most. The nature of the betweenness centrality is: the percentage of all the shortest-circuited bars in the net that contain member v is the shortest-circuited bar. The betweenness centrality of the node v is at most 1. The nature of the betweenness centrality is: the percentage of all the shortest-circuited bars in the net that contain member v is the shortest-circuited bar. Through the formula, the importance of the node structure is limited, and the obtained importance of the node structure is used for calculating the removal cost value of the node in the network simulation model of the power grid to be identified. The applicant researches and discovers that the performance of the betweenness center can reflect the importance degree of the node as a bridge most compared with the performance of the degree center and the performance of the approach center, namely the larger the frequency of occurrence of a certain node on the shortest path in the relational grid is, the larger the influence range is, the closer the communication channels of other nodes are to the node, and the more important the node is.
Optionally, the step of obtaining a target node in the network simulation model of the power grid to be identified based on the removed cost value of the node in the network simulation model of the power grid to be identified includes:
obtaining the value of the node in the network simulation model of the power grid to be identified based on the removal cost value of the node in the network simulation model of the power grid to be identified;
and obtaining a target node in the network simulation model of the power grid to be identified based on the value of the node in the network simulation model of the power grid to be identified.
In the technical scheme, the application finds that the attack target is measuredaNeed to calculate the target based on the attack costaThe attack score value of (1). Therefore, the comprehensive score function is designed, and the calculation method of the function is positively correlated with the attack benefit and negatively correlated with the attack cost. When an object isaIs attacked, the higher the score function value, the target is indicatedaOnce destroyed, the stability and power supply capability of the current power grid are compromised to the greatest extent. If the attacked power grid is the power grid of the same party, the party calculates the optimal attack targetaAnd the defense strength of the power grid is strengthened so as to enhance the damage resistance of the power grid. And the target node in the network simulation model of the power grid to be identified, which is obtained through calculation, needs to be attacked or strengthened for protection.
Optionally, the score of the node in the network simulation model of the power grid to be identified is obtained through the following relational expression:
Figure 251738DEST_PATH_IMAGE008
wherein the content of the first and second substances,
Figure 512955DEST_PATH_IMAGE009
for the nodes in the network simulation model of the power grid to be identifiedaThe score of (a) is calculated,δafor the nodes in the network simulation model of the power grid to be identifiedaThe cost value of (a) to be removed,
Figure 916255DEST_PATH_IMAGE010
in order to remove the total energy consumption value of the power supply area,
Figure 991177DEST_PATH_IMAGE011
the total energy consumption value of the power supply area after removal.
And calculating the score of the node in the network simulation model of the power grid to be identified through the formula, and acquiring a target node which needs to attack or strengthen protection in the network simulation model of the power grid to be identified.
Optionally, the step of obtaining a network simulation model of the power grid to be identified includes:
acquiring initial load information, a maximum load value and an initial working efficiency value of a node in a power grid to be identified;
and obtaining a network simulation model of the power grid to be identified based on the initialized load information, the maximum load value and the initial working efficiency value of the node in the power grid to be identified.
In a real power grid, a defender usually can mainly defend the power station, so that the cost for damaging the power station is extremely high. Therefore, although it is feasible for an attacker to directly attack the power utilization area in the power grid, the attack strategy is relatively rough, and the cascade effect of 'striking points and destroying chips' cannot be realized. Therefore, a removal strategy for directly destroying a power utilization area is out of consideration, and researches show that the defense strength of the deployment of the defenders is higher for the transformer substation and the power transmission line with higher topological position in the network of the power grid. Based on the assumption, aiming at the limitation of the complex network theory on the vulnerability analysis of the power communication network, the self-recovery elastic power grid network simulation model is provided, and the target node in the elastic network can be better identified based on the self-recovery elastic power grid network simulation model.
Optionally, before the step of obtaining the initialization load information, the maximum load value, and the initial operating efficiency value of the node in the power grid to be identified, the method further includes:
and randomly initializing the load information of the nodes in the power grid to be identified to obtain the initialized load information of the nodes in the power grid to be identified.
In the technical scheme, based on the obtained initialization load information of the nodes in the power grid to be identified, the network simulation model of the power grid to be identified can be better constructed, so that the identification process is more accurate.
Optionally, before the step of obtaining the initialization load information, the maximum load value, and the initial operating efficiency value of the node in the power grid to be identified, the method further includes:
acquiring the current load value of the node in the power grid to be identified based on the initialized load information of the node in the power grid to be identified;
and obtaining the maximum load value of the node in the power grid to be identified based on the current load value of the node in the power grid to be identified.
In the technical scheme, based on the initialized load information of the node in the power grid to be identified, the current load value of the node in the power grid to be identified and the maximum load value of the node in the power grid to be identified are obtained, and the maximum load value of the node in the power grid to be identified is used for constructing a network simulation model of the power grid to be identified, so that the recovery strategy of the power grid can be simulated better, and a removal strategy for the power grid can be designed better.
Optionally, the current load value of the node in the power grid to be identified is obtained through the following relational expression:
Figure 745506DEST_PATH_IMAGE012
wherein L: (v j )For the nodes in the network simulation model of the power grid to be identifiedv j The current value of the load,
Figure 544835DEST_PATH_IMAGE013
for the set of all nodes in the grid to be identified,
Figure 881269DEST_PATH_IMAGE014
for the nodes in the network simulation model of the power grid to be identifiedv i The current value of the load,
Figure 311114DEST_PATH_IMAGE015
for the node in the power grid to be identifiedv i The number of neighboring nodes.
Through the formula, the accurate current load value of the node in the power grid can be obtained, so that a network simulation model of the power grid to be identified can be better constructed, and the accuracy of identifying the target node in the power grid is improved.
Optionally, the maximum load value of the node in the power grid to be identified is obtained through the following relational expression:
Figure 349477DEST_PATH_IMAGE016
wherein, ML: (v j ) For the node in the power grid to be identifiedv j The maximum load value of (a) is,Kfor the maximum tolerance of the network to be identified, L: (v j ) For the nodes in the network simulation model of the power grid to be identifiedv j The current load value of.
Through the formula, the maximum load value of the node in the power grid can be accurately identified, so that a network simulation model of the power grid to be identified can be better constructed, and the accuracy of identifying the target node in the power grid is improved.
In addition, to achieve the above object, an embodiment of the present application further provides an identification apparatus for a target node of a power grid, including:
the first acquisition module is used for acquiring a network simulation model of the power grid to be identified;
the second acquisition module is used for acquiring the recovery efficiency value of the node in the network simulation model of the power grid to be identified based on the network simulation model of the power grid to be identified;
the third acquisition module is used for carrying out simulated removal on the nodes in the network simulation model of the power grid to be identified based on the recovery efficiency values of the nodes in the network simulation model of the power grid to be identified so as to obtain the removal cost values of the nodes in the network simulation model of the power grid to be identified; the removal cost value is used for representing the removal cost which needs to be considered when the nodes in the network simulation model of the power grid to be identified are removed;
and the identification module is used for obtaining a target node in the network simulation model of the power grid to be identified based on the removal cost value of the node in the network simulation model of the power grid to be identified.
In addition, in order to achieve the above object, an embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and the processor executes the computer program to implement the method described above.
In addition, to achieve the above object, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored, and a processor executes the computer program to implement the method described above.
In addition, to achieve the above object, embodiments of the present application also provide a computer program product, which when being processed by a processor, implements the method as described above.
Compared with the prior art, the beneficial effect of this application lies in:
the embodiment of the application provides a method, a device, equipment and a medium for identifying a target node of a power grid, wherein the method comprises the following steps: acquiring a network simulation model of a power grid to be identified; obtaining a recovery efficiency value of a node in the network simulation model of the power grid to be identified based on the network simulation model of the power grid to be identified; based on the recovery efficiency value of the node in the network simulation model of the power grid to be identified, performing simulated removal on the node in the network simulation model of the power grid to be identified so as to obtain the removal cost value of the node in the network simulation model of the power grid to be identified; the removal cost value is used for representing the removal cost which needs to be considered when the nodes in the network simulation model of the power grid to be identified are removed; and obtaining a target node in the network simulation model of the power grid to be identified based on the removal cost value of the node in the network simulation model of the power grid to be identified. Namely, after the network simulation model of the power grid to be identified is subjected to recovery efficiency simulation, based on the recovery efficiency value of the network simulation model of the power grid to be identified, simulation removal is carried out on the recovery efficiency value, and the node with the lowest removal cost value is screened out as the target node in the network simulation model of the power grid to be identified. It can be seen that the power grid simulated by the method has an automatic recovery function, and when the power grid is removed in a simulated mode, the working efficiency, the load condition and the like of the power grid are also considered, and a node self-recovery mode is designed to be more fit with the power grid in real life, so that the target node identified by the identification method based on the method is more accurate.
Drawings
Fig. 1 is a schematic structural diagram of an electronic device in a hardware operating environment according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a power grid target node identification method according to an embodiment of the present application;
fig. 3 is a specific method for obtaining a network simulation model of a power grid to be identified according to an embodiment of the present application;
fig. 4 is a specific method for obtaining recovery efficiency values of nodes in a network simulation model of the power grid to be identified according to an embodiment of the present application;
fig. 5 is a specific method for obtaining the removal cost values of the nodes in the network simulation model of the power grid to be identified according to the embodiment of the present application;
fig. 6 is a specific method for obtaining a target node in a network simulation model of the power grid to be identified according to an embodiment of the present application;
FIG. 7 is a graph of a non-linear node automatic recovery function provided by an embodiment of the present application;
fig. 8 is a functional module schematic diagram of a power grid target node identification device according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Since many infrastructure systems require power networks to ensure their operation and management, in modern information-based warfare, power networks have become one of the important targets of removal of each combat unit, so that the importance and the defense capability of deployment of each power supply unit in the power network system are predicted first, and an important basis can be provided for the identification of subsequent target nodes.
At present, the research on the removal resistance of the complex network is mainly based on the identification of important nodes of the complex network, but the current identification method is mainly based on the premise of 'no cost', that is, the removal cost is not considered when the nodes in the complex network are removed. However, such networks are very vulnerable in the face of selective removal, such as scaleless networks. However, it is contradictory that real world networks generally do not collapse rapidly in the face of selective removal by hackers, i.e., real world networks are generally "at the cost". In reality, power stations and power lines in a power system are often elastic, and network elasticity is also called operation and maintenance elasticity, namely the ability of the network to quickly recover and continue to operate when a disaster event occurs; the disaster time refers to equipment power transmission failure caused by overlong service life, malicious removal and damage of a substation power transmission line, equipment failure caused by misoperation of technicians and the like. Under the background of the current informatization war, when a certain substation or a power transmission line in a power network is hit to generate a fault, cascade failure occurs, so that the load of the substation which is not damaged exceeds the maximum bearing range of the substation. Damaged substations or transmission lines often need to be repaired for a long time, so that the damaged substations or transmission lines cannot be utilized in the recovery execution process, and the system function cannot be recovered to a normal level; with the repair of the infrastructure, the relevant services can be restored to normal operation, and the system can be gradually restored to normal operation.
Therefore, the research based on the 'cost' network and the elastic power network is more practical and has more practical guiding significance.
However, no existing technology is researched for the 'costly' network and the elastic power network, most of the existing research mainly focuses on conceptual theory and qualitative analysis, and corresponding process simulation and quantitative analysis are inconsistent with reality, so that support is lacked. Therefore, in the actual attack and defense game process, how to dynamically mine the weak points in the power grid under the conditions that the removal resources are limited and the network has certain elasticity needs to be considered, and the current optimal removal target is found and identified, so that a decision maker can be effectively assisted to make a rapid and efficient battle plan.
The application provides a solution, wherein after a network simulation model of a power grid to be identified is established and recovery efficiency values of nodes in the power grid are obtained based on the simulation model, the nodes are removed and removal cost values of the nodes are obtained through calculation, and the node with the minimum removal cost value is taken as a target node to be removed. Compared with the existing method for identifying the target node in the power grid, the power grid simulated by the method has an automatic recovery function, when the power grid is removed in a simulation mode, the working efficiency, the load condition and the like of the power grid are also considered, and one node self-recovery is designed to be more fit with the power grid in real life, so that the target node identified by the identification method based on the method is more accurate.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an electronic device in a hardware operating environment according to an embodiment of the present application.
As shown in fig. 1, the electronic device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the electronic device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a data storage module, a network communication module, a user interface module, and an electronic program.
In the electronic apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the electronic device may be disposed in the electronic device, and the electronic device calls the power grid target node identification apparatus stored in the memory 1005 through the processor 1001 and executes the power grid target node identification method provided by the embodiment of the present application.
As shown in fig. 2, an embodiment of the present application provides a method for identifying a target node of a power grid, including the following steps:
and S10, acquiring a network simulation model of the power grid to be identified.
In a real power grid, a defender usually can mainly defend the power station, so that the cost for damaging the power station is extremely high. Therefore, although it is feasible for an attacker to directly attack the power utilization area in the power grid, the attack strategy is relatively rough, and the cascade effect of 'striking points and destroying chips' cannot be realized. Therefore, a removal strategy for directly destroying a power utilization area is out of consideration, and researches show that the defense strength of the deployment of the defenders is higher for the transformer substation and the power transmission line with higher topological position in the network of the power grid. Based on the assumption, aiming at the limitation of the complex network theory on the vulnerability analysis of the power communication network, the self-recovery elastic power grid network simulation model is provided.
In one embodiment, referring to fig. 3, fig. 3 is a specific method for obtaining a network simulation model of a power grid to be identified in this embodiment, that is, a specific implementation method of S10. Therefore, the step of obtaining the network simulation model of the power grid to be identified includes:
s101, acquiring initial load information, a maximum load value and an initial working efficiency value of a node in a power grid to be identified;
the initialization load information of the nodes in the power grid is obtained by randomly initializing the load information of the nodes in the power grid to be identified.
The maximum load value of the node in the power grid obtains the current load value of the node in the power grid to be identified through the initialized load information of the node in the power grid to be identified, and the maximum load value is obtained based on the current load value of the node in the power grid to be identified; namely:
load using electricity utilization area
Figure 890180DEST_PATH_IMAGE017
To initialize the substation
Figure 346700DEST_PATH_IMAGE018
Current load of (c):
Figure 744183DEST_PATH_IMAGE019
wherein L: (v j )For the current load value of a node in the network simulation model of the power grid to be identified,
Figure 473105DEST_PATH_IMAGE013
for the set of all nodes in the grid to be identified,
Figure 364969DEST_PATH_IMAGE014
for the nodes in the network simulation model of the power grid to be identifiedv i The current value of the load,
Figure 925263DEST_PATH_IMAGE020
for the node in the power grid to be identifiedv i The number of neighboring nodes.
Based on the current load value of the initialized transformer substation
Figure 431331DEST_PATH_IMAGE021
Initializing the maximum tolerance K of the transformer substation, and calculating the maximum load value of each node in the power grid:
Figure 457668DEST_PATH_IMAGE022
wherein, ML: (v j ) For the node in the power grid to be identifiedv j The maximum load value of (a) is,Kfor the maximum tolerance of the network to be identified, L: (v j ) For the nodes in the network simulation model of the power grid to be identifiedv j The current load value of.
S102, obtaining a network simulation model of the power grid to be identified based on the initialized load information, the maximum load value and the initial working efficiency value of the nodes in the power grid to be identified.
In the embodiment, in order to mine important nodes in the elastic network, a heterogeneous power grid is firstly built under the condition of no additional information
Figure 340173DEST_PATH_IMAGE023
The simulation model of (2); wherein each node in the heterogeneous power grid
Figure 754974DEST_PATH_IMAGE024
Has a type
Figure 697522DEST_PATH_IMAGE025
The node types are collected as
Figure 682927DEST_PATH_IMAGE026
. Edges between nodes
Figure 431440DEST_PATH_IMAGE027
And the node is the set of all nodes in the power grid to be identified. And constructing and obtaining a 'costly' and elastic network simulation model based on the initialized load information, the maximum load value and the initial working efficiency value of the node in the power grid to be identified so as to facilitate the progress of the subsequent identification work.
S20, obtaining the recovery efficiency value of the node in the network simulation model of the power grid to be identified based on the network simulation model of the power grid to be identified.
In reality, in the process of a multi-round game, a party often has difficulty in knowing a repair strategy of an opposite party, and if the opposite party determines to repair a certain power line in the process of the game, the power line can be quickly restored and cannot be easily removed. Thus, in the identification method described herein, the restoration of the default power line is random, and thus the probability of a damaged power line restoring power isp r ，Generating a random value in the gap between two rounds of removal
Figure 904010DEST_PATH_IMAGE028
When is coming into contact withp＞p r And, restoring the transmission line.
In an embodiment, referring to fig. 4, fig. 4 is a specific method for obtaining a recovery efficiency value of a node in a network simulation model of the power grid to be identified in the present embodiment, that is, a specific implementation method of S20. Therefore, the step of obtaining the recovery efficiency value of the node in the network simulation model of the power grid to be identified based on the network simulation model of the power grid to be identified includes:
s201, obtaining a recovery quantity of a node in the network simulation model of the power grid to be identified based on the network simulation model of the power grid to be identified;
because the power grid is usually maintained and repaired after being attacked, and the nodes are recovered slowly, the importance degree and the load condition of the network nodes need to be considered in the gap between t and t +1 of two attacks. Therefore, the network simulation model of the power grid to be identified has self-recovery capability in network attack and defense, and an automatic recovery function of the network simulation model is obtained through research and used for simulating the elasticity of the power grid in actual network attack and defense. Generally, in the process of node recovery, a node often has the following two characteristics:
1) the more important the node is, the more the load is, the more difficult the recovery is;
2) in the node repairing process, the repairing speed is not simple and linear, but is slow in the early stage of repairing, and the repairing speed is gradually increased along with the repairing.
And 1) and 2) are integrated, in the simulation node recovery process, a nonlinear node automatic recovery function is designed, and a curve of the nonlinear node automatic recovery function is shown in fig. 7:
f（x）=x k
in the formula (I), the compound is shown in the specification,kis a constant greater than 1.
As can be seen from fig. 7, for the nodes in the network, the recovery amount of the node after each simulation attack is negatively correlated with the working efficiency of the node, that is, the more seriously the node is damaged, the greater the repair difficulty is, the longer the repair period is, and the slower the progress is. On the other hand, the larger the initial load of the node, the larger the substation size. Generally, the repair difficulty of a large-scale substation is greater, so that the initial load of the substation is in inverse proportion to the recovery amount of the node.
S202, obtaining a recovery efficiency value of the node in the network simulation model of the power grid to be identified based on the recovery amount of the node in the network simulation model of the power grid to be identified.
The recovery amount of the node after each simulation attack is negatively related to the working efficiency of the node, namely, the more seriously the node is damaged, the larger the repair difficulty is, the longer the repair period is, and the slower the progress is; on the other hand, the larger the initial load value of the node is, the larger the scale of the node is. Generally, the larger the scale of the node is, the greater the difficulty of repairing the node is, and therefore the initial load value of the node is also inversely proportional to the recovery quantity of the node, and in summary, the recovery quantity of the node in the network simulation model of the power grid to be identified is obtained through the following relation:
Figure 564929DEST_PATH_IMAGE029
wherein r: (v i )For the nodes in the network simulation model of the power grid to be identifiedv i The amount of recovery after removal; l: (L:)v i )For the nodes in the network simulation model of the power grid to be identifiedv i Current load value of;MLthe power supply quantity of the maximum scale node in the network simulation model of the power grid to be identified is obtained; eta (v i )For the nodes in the network simulation model of the power grid to be identifiedv i The operating efficiency value of (a); xi is a constant and takes a value of 0,0.5]。
In the present embodiment, theMLThe amount of power supply defined as the maximum-sized node in the network simulation model of the network to be identified, i.e. the
Figure 490160DEST_PATH_IMAGE030
。
E.g. in maximum size nodesv max For example, after t rounds of attacks, the nodev max Has an efficiency ofη t (v max )And in the stage from the time t to the time t +1 of the next attack, the recovery efficiency value of the node in the network simulation model of the power grid to be identified is obtained through the following relational expression:
Figure 776785DEST_PATH_IMAGE002
wherein:
η t+1 (v max )after the t +1 round of removal, the nodes in the network simulation model of the power grid to be identifiedv max The recovery efficiency value of (a);
η t (v max )after the t round is removed, the nodes in the network simulation model of the power grid to be identifiedv max The recovery efficiency value of (a);
r(v max )for the nodes in the network simulation model of the power grid to be identifiedv max The amount of recovery after removal. It can be seen that, between the time t and the time t +1, the nodes in the power grid network are continuously recovered, and the cascade failure can be relieved. Therefore, the identification of the target node for the recovered power grid network is more reasonable and more practical.
S30, performing simulated removal on the nodes in the network simulation model of the power grid to be identified based on the recovery efficiency values of the nodes in the network simulation model of the power grid to be identified so as to obtain removal cost values of the nodes in the network simulation model of the power grid to be identified; wherein the removal cost value is used for representing the removal cost which needs to be considered when removing the nodes in the network simulation model of the power grid to be identified.
In one embodiment, referring to fig. 5, fig. 5 is a specific method for obtaining the removed cost value of the node in the network simulation model of the power grid to be identified, that is, a specific implementation method of S30. Therefore, the step of removing the nodes in the network simulation model of the power grid to be identified in a simulation manner based on the recovery efficiency values of the nodes in the network simulation model of the power grid to be identified to obtain the removal cost values of the nodes in the network simulation model of the power grid to be identified includes:
s301, obtaining a removal strategy of the nodes in the network simulation model of the power grid to be identified based on the recovery efficiency values of the nodes in the network simulation model of the power grid to be identified;
generally, the more important a node is, the higher the defense deployment of the node is, and if the flow of the power line in the network is larger, the more important the power line is, the higher the defense strength of the power line deployment is, and as an attacker, the cost for destroying the node is increased successively. Therefore, the defense deployment condition of the network simulation model of the power grid to be identified is obtained based on the recovery efficiency value of the node in the network simulation model of the power grid to be identified; and obtaining a removal strategy of the network simulation model of the power grid to be identified according to the defense deployment condition of the network simulation model of the power grid to be identified.
In this embodiment, in order to predict the defense condition of each node in the power grid in advance, the removal cost values of the nodes in the network simulation model of the power grid to be identified are obtained through the following relational expression:
Figure 916910DEST_PATH_IMAGE031
wherein the content of the first and second substances,δ i is a nodev i The cost value of (a) to be removed,I i is a nodev i The value of the importance of the service of (c),C B (v)is the importance value of the node structure.
The nodev i The business importance of (2) is obtained by the following relation:
Figure 201261DEST_PATH_IMAGE032
wherein the content of the first and second substances,I i is a nodev i The service importance value of (2);S i is a nodev i And W is the importance value of the service matrix set.
The importance of the node structure is obtained by the following relation:
Figure 480365DEST_PATH_IMAGE033
wherein, the first and the second end of the pipe are connected with each other,C B (v)is an importance value of the node structure,
Figure 305102DEST_PATH_IMAGE006
is passing through the nodevS → the shortest path number of member t,
Figure 486684DEST_PATH_IMAGE007
the number of the shortest paths of the members s → t, V is all the nodes in the network simulation model of the power grid to be identifiedv i S is a member in the network simulation model of the power grid to be identified, and t is other members except the member s in the network simulation model of the power grid to be identified.
The above formula characterizes the magnitude of the dependency of member v for member s to reach all other members t in the network. If all shortest paths pass through the node v, the above formula characterizes the magnitude of the dependency of the member v that the member s wants to reach all other members t in the network. If all the shortest paths pass through the node v, the betweenness centrality of the node v is at most 1. The nature of the betweenness centrality is: the percentage of all the shortest-circuited bars in the net that contain member v is the shortest-circuited bar. The betweenness centrality of the node v is at most 1. The nature of the betweenness centrality is: the percentage of all the shortest-circuited bars in the net that contain member v is the shortest-circuited bar. The applicant researches and discovers that the performance of the betweenness center can reflect the importance degree of the node as a bridge most compared with the performance of the degree center and the performance of the approach center, namely, the larger the frequency of the node appearing on the shortest path in the relational grid is, the larger the influence range is, the closer the communication channels of other nodes are to the node, and the more important the node is.
In some embodiments, if the node is a power line, then the importance of the power line in the power grid is measured by using edge betweenness center performance, with reference to the above formula, that is:
Figure 489406DEST_PATH_IMAGE034
in the formula (I), the compound is shown in the specification,
Figure 717125DEST_PATH_IMAGE035
in order to be of importance for the construction of the transmission line,
Figure 283236DEST_PATH_IMAGE036
the number of shortest paths s → t through edge e,
Figure 866795DEST_PATH_IMAGE037
value of [0,1]。
In reality, the attack cost of the edge is certainly far less than that of the substation, so the attack cost of the edge e can be calculated by the following formula:
Figure 820845DEST_PATH_IMAGE038
in the formula (I), the compound is shown in the specification,
Figure 207964DEST_PATH_IMAGE039
value of [0,1]，
Figure 125235DEST_PATH_IMAGE040
Value of [0,1]。
S302, based on the removal strategy of the nodes in the network simulation model of the power grid to be identified, simulation removal is carried out on the nodes in the network simulation model of the power grid to be identified.
In the present application, once a node is completely destroyed, the node is no longer repaired by selecting reconstruction. Therefore, in order to perform a simulation attack on the power grid, all the power grids need to be extracted first
Figure 78148DEST_PATH_IMAGE041
Transformer station
Figure 875203DEST_PATH_IMAGE042
And all except the edges having connections to the power station
Figure 559737DEST_PATH_IMAGE043
. The transformer substation and the power transmission line are combined to obtain all the current attack alternatives
Figure 733230DEST_PATH_IMAGE044
。
Selecting an attack object a epsilon A in sequence, and carrying out simulated attack:
1) if the object is attackedaThe transformer substation is usually not completely destroyed by one-time attack, so the working efficiency of destroying the transformer substation is as followsη t (a)Amount of destruction per time
Figure 540649DEST_PATH_IMAGE045
The working efficiency of the transformer substation after the attack is updated to
Figure 321654DEST_PATH_IMAGE046
。
2) If the object is attackedaIs a power line, the line is directly damaged and removed.
The transformer substation and the power transmission line are all the nodes. After the simulation attack is carried out, the load of each transformer substation is recalculated, and it can be seen that due to the damage of the previous round of attack, the working efficiency of each node is reduced or the related edge is damaged, and the corresponding node cannot meet the required power of the power supply area, so that the power supply area needs to coordinate electric energy from other transformer substations, and the overload of other transformer substations can be caused to generate cascade damage. In this case as
Figure 417786DEST_PATH_IMAGE047
If the current substation is overloaded, the substation fails temporarily; with the gradual recovery of the substation in the power grid, when other substations share the load for the overload substation, the overload substation returns to the normal load
Figure 191707DEST_PATH_IMAGE048
And if so, indicating that the substation has recovered to normal efficiency.
In the process of simulating the attack, when the transformer substation fails and other transformer substations are subjected to cascade overload, the simulation attack is repeated until no new transformer substation is subjected to cascade failure, and the simulation attack is completed.
In the process of simulating the attack, in order to consider the influence of node recovery on the identification of the target node, the nodes in the power grid are recovered according to the recovery efficiency values of the nodes in the network simulation model of the power grid to be identified in the step S20.
S303, based on the simulation removal of the nodes in the network simulation model of the power grid to be identified, the removal cost value of the nodes in the network simulation model of the power grid to be identified is obtained.
In this embodiment, in order to predict the defense condition of each node in the power grid in advance, the removal cost values of the nodes in the network simulation model of the power grid to be identified are obtained through the following relational expression:
Figure 322474DEST_PATH_IMAGE049
wherein, the first and the second end of the pipe are connected with each other,δ i is a nodev i The cost value of (a) to be removed,I i is a nodev i The value of the importance of the service of (c),C B (v)is the importance value of the node structure.
S40, obtaining a target node in the network simulation model of the power grid to be identified based on the removal cost value of the node in the network simulation model of the power grid to be identified.
In one embodiment, referring to fig. 6, fig. 6 is a specific implementation of a specific method, i.e., S40, for obtaining a target node in a network simulation model of the power grid to be identified. Therefore, the step of obtaining the target node in the network simulation model of the power grid to be identified based on the removed cost value of the node in the network simulation model of the power grid to be identified includes:
s401, obtaining the score of the node in the network simulation model of the power grid to be identified based on the removal cost value of the node in the network simulation model of the power grid to be identified.
In one embodiment, the present application considers objects in generalaThe method comprises the steps of designing a new attack income scoring function for identifying an important target in the current power grid according to the damaged power loss and attack cost value. In the field of complex networks, the importance of nodes is evaluated by using a maximum connected subgraph method. In the application, the power grid attack aims to reduce the power efficiency of the network to the maximum extent by using the minimum attack cost. Therefore, the scores of the nodes in the network simulation model of the power grid to be identified are obtained through the following relational expression:
Figure 8801DEST_PATH_IMAGE050
wherein, the first and the second end of the pipe are connected with each other,
Figure 592229DEST_PATH_IMAGE009
for the nodes in the network simulation model of the power grid to be identifiedaThe score of (a) is calculated,δafor the nodes in the network simulation model of the power grid to be identifiedaThe cost value of (a) to be removed,
Figure 169841DEST_PATH_IMAGE010
in order to remove the total energy consumption value of the power supply area,
Figure 702585DEST_PATH_IMAGE051
the total energy consumption value of the power supply area after removal.
S402, obtaining a target node in the network simulation model of the power grid to be identified based on the value of the node in the network simulation model of the power grid to be identified.
In one embodiment, optimal attack targetsaTwo characteristics are required:
1) targetaAfter being destroyed, it is nowThe normal power supply capacity of the power grid is relatively poor;
2) targetaThe cost of the attack is low.
Thus, the attack targets are measuredaNeed to calculate the target in consideration of the attack costaThe attack score value of (1). Therefore, the application designs a comprehensive score function
Figure 12344DEST_PATH_IMAGE052
The function calculation method is positively correlated with the attack benefit and negatively correlated with the attack cost. When an object isaThe attack on the object is carried out,
Figure 145385DEST_PATH_IMAGE053
the higher the score, the higher the target isaOnce destroyed, the stability and power supply capability of the current power grid are compromised to the greatest extent. If the attacked power grid is the power grid of the same party, the party calculates the optimal attack targetaThe defense strength of the power grid is strengthened to enhance the damage resistance of the power grid. And the target node in the network simulation model of the power grid to be identified, which is obtained through calculation, needs to be attacked or strengthened for protection.
By means of the technical scheme, after the network simulation model of the power grid to be identified is subjected to recovery efficiency simulation, the recovery efficiency value is simulated and removed based on the network simulation model of the power grid to be identified, and the node with the lowest removal cost value is selected as the target node in the network simulation model of the power grid to be identified. The power grid simulated by the method has an automatic recovery function, and when the power grid is removed in a simulated mode, the working efficiency, the load condition and the like of the power grid are also considered, and a node self-recovery is designed to be more attached to the power grid in real life, so that a target node identified by the identification method based on the method is more accurate; therefore, in the process of attacking and defending a target power grid and playing games, the vulnerability of the target power grid can be quickly attacked, and the power grid is paralyzed; or based on the identification result, more powerful defense is carried out on the vulnerability of the target power grid so as to enhance the damage resistance of the power grid.
Referring to fig. 8, based on the same inventive principle, an embodiment of the present application further provides an apparatus for identifying a target node of a power grid, including:
the first acquisition module is used for acquiring a network simulation model of the power grid to be identified;
the second acquisition module is used for acquiring the recovery efficiency value of the node in the network simulation model of the power grid to be identified based on the network simulation model of the power grid to be identified;
the third acquisition module is used for carrying out simulated removal on the nodes in the network simulation model of the power grid to be identified based on the recovery efficiency values of the nodes in the network simulation model of the power grid to be identified so as to obtain the removal cost values of the nodes in the network simulation model of the power grid to be identified; the removal cost value is used for representing the removal cost which needs to be considered when the nodes in the network simulation model of the power grid to be identified are removed;
and the identification module is used for obtaining a target node in the network simulation model of the power grid to be identified based on the removal cost value of the node in the network simulation model of the power grid to be identified.
It should be noted that, each module in the identification apparatus for the power grid target node in this embodiment corresponds to each step in the identification method for the power grid target node in the foregoing embodiment one by one, and therefore, the specific implementation and the achieved technical effect of this embodiment may refer to the implementation of the identification method for the power grid target node, which is not described herein again.
Furthermore, in one embodiment, the present application further provides a computer program product, which when executed by a processor, implements the foregoing method.
In addition, in an embodiment, the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and a processor executes the computer program to implement the foregoing method.
In some embodiments, the computer-readable storage medium may be memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash, magnetic surface memory, optical disk, or CD-ROM; or may be various devices including one or any combination of the above memories. The computer may be a variety of computing devices including intelligent terminals and servers.
In some embodiments, the executable instructions may be in the form of a program, software module, script, or code written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and it may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
By way of example, executable instructions may correspond, but do not necessarily have to correspond, to files in a file system, and may be stored in a portion of a file that holds other programs or data, such as in one or more scripts in a hypertext Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code).
By way of example, executable instructions may be deployed to be executed on one computing device or on multiple computing devices at one site or distributed across multiple sites and interconnected by a communication network.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., a rom/ram, a magnetic disk, an optical disk) and includes instructions for enabling a multimedia terminal (e.g., a mobile phone, a computer, a television receiver, or a network device) to execute the method according to the embodiments of the present application.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all the equivalent structures or equivalent processes that can be directly or indirectly applied to other related technical fields by using the contents of the specification and the drawings of the present application are also included in the scope of the present application.

Claims (17)

1. A method for identifying a target node of a power grid is characterized by comprising the following steps:
acquiring a network simulation model of a power grid to be identified;
obtaining the recovery amount of the nodes in the network simulation model of the power grid to be identified based on the network simulation model of the power grid to be identified; obtaining a recovery efficiency value of a node in the network simulation model of the power grid to be identified based on the recovery quantity of the node in the network simulation model of the power grid to be identified; the recovery efficiency value of the node in the network simulation model of the power grid to be identified is obtained through the following relational expression:
Figure 452617DEST_PATH_IMAGE001
wherein:η t+1 (v max )after the t +1 round of removal, the nodes in the network simulation model of the power grid to be identifiedv max The recovery efficiency value of (a);η t (v max )after the t round is removed, the nodes in the network simulation model of the power grid to be identifiedv max The recovery efficiency value of (a);r(v max )for the nodes in the network simulation model of the power grid to be identifiedv max The amount of recovery after removal;
based on the recovery efficiency value of the node in the network simulation model of the power grid to be identified, performing simulated removal on the node in the network simulation model of the power grid to be identified so as to obtain the removal cost value of the node in the network simulation model of the power grid to be identified; the removal cost value is used for representing the removal cost which needs to be considered when the nodes in the network simulation model of the power grid to be identified are removed;
and obtaining a target node in the network simulation model of the power grid to be identified based on the removal cost value of the node in the network simulation model of the power grid to be identified.
2. The method for identifying the target node of the power grid according to claim 1, wherein the recovery amount of the node in the network simulation model of the power grid to be identified is obtained by the following relation:
Figure 626109DEST_PATH_IMAGE002
wherein r: (v i )For the nodes in the network simulation model of the power grid to be identifiedv i The amount of recovery after removal; l: (L:)v i )For the nodes in the network simulation model of the power grid to be identifiedv i Current load value of;MLthe power supply quantity of the maximum scale node in the network simulation model of the power grid to be identified is obtained; eta (v i )In a network simulation model for the network to be identifiedNode pointv i The work efficiency value of; xi is a constant and takes a value of 0,0.5]；
Function described in the above formulaf（x）An automatic recovery function for a non-linear node, said x being η: (v i )+ξ or η [ (]) or [ (])v i )。
3. The method for identifying the target node of the power grid according to claim 1, wherein the step of performing simulated removal on the node in the network simulation model of the power grid to be identified based on the recovery efficiency value of the node in the network simulation model of the power grid to be identified so as to obtain the removal cost value of the node in the network simulation model of the power grid to be identified comprises:
obtaining a removal strategy of the nodes in the network simulation model of the power grid to be identified based on the recovery efficiency values of the nodes in the network simulation model of the power grid to be identified;
based on the removal strategy of the nodes in the network simulation model of the power grid to be identified, carrying out simulated removal on the nodes in the network simulation model of the power grid to be identified;
and obtaining the removal cost value of the nodes in the network simulation model of the power grid to be identified based on the simulation removal of the nodes in the network simulation model of the power grid to be identified.
4. The method for identifying the target node of the power grid according to claim 3, wherein the step of obtaining the removal strategy of the node in the network simulation model of the power grid to be identified based on the recovery efficiency value of the node in the network simulation model of the power grid to be identified comprises:
obtaining the defense deployment condition of the network simulation model of the power grid to be identified based on the recovery efficiency value of the node in the network simulation model of the power grid to be identified;
and obtaining a removal strategy of the network simulation model of the power grid to be identified according to the defense deployment condition of the network simulation model of the power grid to be identified.
5. The method for identifying the target node of the power grid according to claim 3, wherein the removal cost value of the node in the network simulation model of the power grid to be identified is obtained by the following relation:
Figure 964687DEST_PATH_IMAGE003
wherein the content of the first and second substances,δ i is a nodev i The cost value of (a) to be removed,I i is a nodev i The value of the importance of the service of (c),C B (v)is the importance value of the node structure.
6. Method for identifying a target node of an electrical network according to claim 4, characterized in that said node is a node of said networkv i The business importance of (2) is obtained by the following relation:
Figure 932643DEST_PATH_IMAGE004
wherein the content of the first and second substances,I i is a nodev i The service importance value of (2);S i is a nodev i And W is the importance value of the service matrix set.
7. The method for identifying the target node of the power grid according to claim 4, wherein the importance of the node structure is obtained by the following relation:
Figure 576245DEST_PATH_IMAGE005
wherein the content of the first and second substances,C B (v)is an importance value of the node structure,
Figure 350166DEST_PATH_IMAGE006
to pass through the nodevS → the shortest path number of member t,
Figure 25473DEST_PATH_IMAGE007
the number of the shortest paths of the members s → t, V is all the nodes in the network simulation model of the power grid to be identifiedv i S is a member in the network simulation model of the power grid to be identified, and t is other members except the member s in the network simulation model of the power grid to be identified.
8. The method for identifying the target node of the power grid according to claim 1, wherein the step of obtaining the target node in the network simulation model of the power grid to be identified based on the removed cost value of the node in the network simulation model of the power grid to be identified comprises:
obtaining the value of the node in the network simulation model of the power grid to be identified based on the removal cost value of the node in the network simulation model of the power grid to be identified;
and obtaining a target node in the network simulation model of the power grid to be identified based on the value of the node in the network simulation model of the power grid to be identified.
9. The method for identifying the target node of the power grid according to claim 8, wherein the score of the node in the network simulation model of the power grid to be identified is obtained through the following relation:
Figure 164330DEST_PATH_IMAGE008
wherein, the first and the second end of the pipe are connected with each other,
Figure 810075DEST_PATH_IMAGE009
for the nodes in the network simulation model of the power grid to be identifiedaThe score of (a) is calculated,δais that it isNode in network simulation model of power grid to be identifiedaThe cost value of (2) is removed,
Figure 59791DEST_PATH_IMAGE010
in order to remove the total energy consumption value of the power supply area,
Figure 858114DEST_PATH_IMAGE011
the total energy consumption value of the power supply area after removal.
10. The method for identifying a target node of a power grid according to claim 1, wherein the step of obtaining the network simulation model of the power grid to be identified comprises:
acquiring initial load information, a maximum load value and an initial working efficiency value of a node in a power grid to be identified;
and obtaining a network simulation model of the power grid to be identified based on the initialized load information, the maximum load value and the initial working efficiency value of the node in the power grid to be identified.
11. The method according to claim 10, wherein before the step of obtaining the initial load information, the maximum load value and the initial operating efficiency value of the node in the power grid to be identified, the method further comprises:
and randomly initializing the load information of the nodes in the power grid to be identified to obtain the initialized load information of the nodes in the power grid to be identified.
12. The method according to claim 11, wherein before the step of obtaining the initial load information, the maximum load value and the initial operating efficiency value of the node in the power grid to be identified, the method further comprises:
acquiring the current load value of the node in the power grid to be identified based on the initialized load information of the node in the power grid to be identified;
and obtaining the maximum load value of the node in the power grid to be identified based on the current load value of the node in the power grid to be identified.
13. The method for identifying a target node of a power grid according to claim 12, wherein the current load value of the node in the power grid to be identified is obtained by the following relation:
Figure 167873DEST_PATH_IMAGE012
wherein L: (v j )For the nodes in the network simulation model of the power grid to be identifiedv j The current value of the load,
Figure 300914DEST_PATH_IMAGE013
for the set of all nodes in the grid to be identified,
Figure 167370DEST_PATH_IMAGE014
for the nodes in the network simulation model of the power grid to be identifiedv i The current value of the load,
Figure 7150DEST_PATH_IMAGE015
for the node in the power grid to be identifiedv i The number of neighboring nodes.
14. The method for identifying a target node of a power grid according to claim 12, wherein the maximum load value of the node in the power grid to be identified is obtained by the following relation:
Figure 550126DEST_PATH_IMAGE016
wherein, ML: (v j ) For the node in the power grid to be identifiedv j The maximum load value of (a) is,Kfor the maximum tolerance of the network to be identified, L: (v j ) Is said to beIdentifying nodes in network simulation model of power gridv j The current load value of.
15. An identification device for a target node of a power grid, comprising:
the first acquisition module is used for acquiring a network simulation model of the power grid to be identified;
the second acquisition module is used for acquiring the recovery quantity of the nodes in the network simulation model of the power grid to be identified based on the network simulation model of the power grid to be identified; obtaining a recovery efficiency value of a node in the network simulation model of the power grid to be identified based on the recovery quantity of the node in the network simulation model of the power grid to be identified; the recovery efficiency value of the node in the network simulation model of the power grid to be identified is obtained through the following relational expression:
Figure 842568DEST_PATH_IMAGE001
wherein:η t+1 (v max )after the t +1 round of removal, the nodes in the network simulation model of the power grid to be identifiedv max The recovery efficiency value of (a);η t (v max )after the t round is removed, the nodes in the network simulation model of the power grid to be identifiedv max The recovery efficiency value of (a);r(v max )for the nodes in the network simulation model of the power grid to be identifiedv max The amount of recovery after removal;
the third acquisition module is used for carrying out simulated removal on the nodes in the network simulation model of the power grid to be identified based on the recovery efficiency values of the nodes in the network simulation model of the power grid to be identified so as to obtain the removal cost values of the nodes in the network simulation model of the power grid to be identified; the removal cost value is used for representing the removal cost which needs to be considered when the nodes in the network simulation model of the power grid to be identified are removed;
and the identification module is used for obtaining a target node in the network simulation model of the power grid to be identified based on the removal cost value of the node in the network simulation model of the power grid to be identified.
16. An electronic device, characterized in that the electronic device comprises a memory in which a computer program is stored and a processor, which executes the computer program to implement the method according to any of claims 1-14.
17. A computer-readable storage medium, having a computer program stored thereon, which, when executed by a processor, performs the method of any one of claims 1-14.
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