Parameter acquisition and analysis method for toughness state of infrastructure

Abstract

The invention relates to the technical field of parameter acquisition and analysis, in particular to a parameter acquisition and analysis method for a toughness state of an infrastructure, which comprises the steps of function modeling of an associated infrastructure network structure, fault simulation of a key infrastructure system, analysis of a cascade fault process and analysis of the influence of a community structure on the associated infrastructure network. The function modeling of the associated infrastructure network structure comprises the following steps: analyzing the influence of a community structure on the structure vulnerability and the function vulnerability of an associated infrastructure network; robustness evaluation indexes are provided from the aspects of the structure and the function by simulating the structure vulnerability and the function vulnerability of the associated infrastructure network, the change of the structure vulnerability and the function vulnerability of the power network in different attack modes is observed, and the topological characteristics and the function characteristics of the network are analyzed in combination with related theories. And proposing a association infrastructure network cascade fault vulnerability analysis framework based on a community structure.

Description

Technical Field

[0001] The present invention relates to the technical field of parameter collection and analysis, and in particular to a parameter collection and analysis method for infrastructure resilience status. Background Art

[0002] With the continuous development of technology and social economy, the scale of individual infrastructure networks is expanding, and the connections between different infrastructure networks are becoming increasingly close and complex in space and function. Most infrastructure networks are interactive and interdependent, and interdependent infrastructure systems are typical multi-layer networks. Although the interconnection between systems can allocate resources more reasonably, they are also more vulnerable to many types of threats.

[0003] When a single system is attacked, the infrastructure network nodes are closely connected and have a complex structure. The interconnectedness causes the initial collapse to spread throughout the related infrastructure systems because critical systems are exposed to cascading failures. Over time, small failures may develop into serious, large-scale failures due to cascading failures, causing incalculable losses.

[0004] In recent years, people have paid more and more attention to the security and reliability of infrastructure networks, which makes the research on infrastructure network vulnerability a key point. Infrastructure network vulnerability refers to the ability to resist external attacks or internal damage to the power network and the ability to recover from multiple failures. Fragile infrastructure networks cannot meet the needs of society, so it is necessary to conduct vulnerability assessments on infrastructure networks, and it is of great significance to study the resilience assessment of related infrastructure networks.

[0005] Currently, most scholars analyze the community structure of a single network, but lack the analysis of interconnected infrastructure networks that are interdependent or multiply coupled. To enhance the resilience of interdependent infrastructure network systems, an optimization model using mixed integer programming is proposed to restore the community structure in each infrastructure network. In addition, multiple community structure importance measures are proposed to prioritize their recovery process. By using community combinations to find the best recovery sequence and solve the community structure recovery model, the network robustness is improved from the recovery perspective; a centrality measure based on community structure is proposed to identify the key components of the system to help control faults within the community and prevent faults from propagating to large parts of the power grid.

[0006] However, most of the above methods analyze the impact of community structure on network robustness from the structural level, which is one-sided and limited, and there is little analysis on the functional level. They consider the importance of nodes in the community, but ignore the community edges.

[0007] Therefore, we propose a parameter collection and analysis method for infrastructure resilience status. Summary of the invention

[0008] The purpose of the present invention is to provide a parameter collection and analysis method for infrastructure resilience status to solve the problems raised in the above background technology.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A parameter collection and analysis method for infrastructure resilience status includes structural function modeling of associated infrastructure networks, simulation of key infrastructure system failures and analysis of cascading failure processes, and analysis of the impact of community structures on associated infrastructure networks. The structural function modeling of associated infrastructure networks includes: analyzing the impact of community structures on structural and functional vulnerabilities of associated infrastructure networks;

[0011] The critical infrastructure system failure simulation and cascading failure process analysis include:

[0012] Combine the dynamic model with the network topology of the system to establish an analysis framework when the associated infrastructure system fails;

[0013] The impact of the analyzed community structure on the associated infrastructure network includes:

[0014] Taking the power-communication network as the research object of the associated infrastructure network, this paper proposes the power network vulnerability assessment index and the community intentional attack strategy for the power network.

[0015] As a preferred solution of the present invention, the steps of modeling the network structure function of the associated infrastructure are:

[0016] S1. Establish the topological structure, dynamic model and coupling mode of the associated infrastructure system;

[0017] S2. Select typical related infrastructure systems, power-communication networks;

[0018] S3. First, based on the operation information of the actual power system and combined with complex network theory and methods, a power network topology model is established. The power system can be regarded as a series of subsystems connected to each other. These subsystems are abstracted as nodes, and the connections between subsystems are abstracted as edges.

[0019] Building a power grid model in, For power station nodes The collection of N p represents the total number of nodes in the power grid, Indicates power transmission line Set, where Mp represents the total number of edges in the power grid;

[0020] Secondly, for communication network modeling, the topological structure of the communication network is given based on network theory. The communication network is abstracted as in, represents the set of nodes in the communication network, Represents a collection of edges in a communication network.

[0021] As a preferred solution of the present invention, the steps of critical infrastructure system fault simulation and cascading failure process analysis are:

[0022] S4. In the first phase, random attacks are used to attack the associated network to evaluate the resilience of the associated system; in the second phase, community attacks are used to study the propagation of cascading failures in the associated infrastructure system; in the third phase, non-community attacks are used to study the propagation of cascading failures in the associated infrastructure system;

[0023] S5. At the structural level, based on the modeling of structural robustness, the changes in the maximum group size of the power network of the IEEE 118 network under three coupling modes and four attack models are simulated, the effects of community attacks and non-community attacks under the same coupling mode are compared, the key links in the network are analyzed, and the cascading failure process between different networks and under different attack models is analyzed from a rigorous theoretical analysis perspective through the percolation theory, combined with the simulation of structural vulnerability, to evaluate the resilience of the network;

[0024] S6. Based on the modeling of functional robustness, the changes in power supply of the associated infrastructure network under three coupling modes and four attack strategies are simulated, the impact of community attacks and non-community attacks on the electrical performance of the network is analyzed, and the impact of community attack strategies of different degrees on the electrical performance of the network is compared.

[0025] The analysis of the impact of community structure on the associated infrastructure network includes the following steps:

[0026] S7. Community detection divides the network into clusters composed of nodes, and makes the edges between communities less. Modularity is a commonly used metric in social network analysis to measure the structural characteristics of a network. It represents the degree of aggregation between different communities in the network. When the number of edges between similar nodes in the network is greater than the number of edges between nodes in the case of random connection, the value of this measure is relatively high. The modularity of a network is defined as the ratio of the difference between the number of edges within the community of the network and the number of edges within the community of the corresponding null model to the total number of edges M in the entire network, that is:

[0027]

[0028] In theory, for a common null model with the same degree sequence as the original network but without degree correlation, the expected value of the number of edges between node i and node j is where k i and k j are the degrees of nodes i and j in the original network respectively. The degree of a node in the graph refers to the number of edges directly connected to it. The degree of node i is represented by k i For an undirected graph consisting of n nodes, the degree can be expressed using the adjacency matrix as The coefficient 1 / 2 is to avoid repeated counting of edges between two nodes. The commonly used modularity is defined as:

[0029]

[0030] in

[0031] The Q value is strictly less than 1. If the actual value of the number of edges between similar nodes is greater than the expected value under random conditions, the value is positive, otherwise it is negative. The larger the Q value, the more accurate the community structure of the network division. In actual network analysis, the highest point of the Q value generally appears between 0.3 and 0.7.

[0032] The basic steps of the S8.GN algorithm are as follows:

[0033] Step (1) Calculate the edge betweenness of all edges in the network;

[0034] Step (2) find the edge with the largest betweenness and remove it from the network;

[0035] Step (3) For the remaining edges in the network, recalculate the edge betweenness of each edge;

[0036] Step (4) Repeat steps (2) and (3) until all edges are removed.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. In the present invention, by selecting a typical associated infrastructure network, the power-communication network, considering the power flow in the power grid and the information flow in the information network, the power network and the communication network are modeled from the structural and functional aspects. Then, the GN algorithm is used to divide the IEEE 118 network into communities according to its community topology, and identify the intra-community edges and inter-community edges in the network. Considering the community structure of the network, the vulnerability process of the interconnected infrastructure systems in different failure scenarios and coupling modes is analyzed from the structural and functional levels. Considering the propagation of faults between systems, the importance of edges is analyzed at the same time, the fault type is determined, the propagation process of cascading faults in the associated infrastructure network is analyzed, and the impact of community structure on the vulnerability of the associated infrastructure system is analyzed.

[0039] 2. In the present invention, by simulating the structural vulnerability and functional vulnerability of the associated infrastructure network, robustness evaluation indicators are proposed from the structural and functional perspectives respectively, and the changes in the structural vulnerability and functional vulnerability of the power network under different attack modes are observed. The topological characteristics and functional characteristics of the network are analyzed in combination with relevant theories, and a cascading failure vulnerability analysis framework for the associated infrastructure network based on the community structure is proposed. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Asynchronously updating the model graph for the present invention;

[0041] Figure 2 This is a diagram of the cascading failure process of the present invention;

[0042] Figure 3 It is a schematic diagram of the technical circuit of the present invention. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. It is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0044] See also Figure 1-Figure 3 , the present invention provides a technical solution:

[0045] A parameter collection and analysis method for infrastructure resilience status, including structural function modeling of associated infrastructure networks, simulation of key infrastructure system failures and analysis of cascading failure processes, and analysis of the impact of community structures on associated infrastructure networks. The structural function modeling of associated infrastructure networks includes: analyzing the impact of community structures on the structural and functional fragility of associated infrastructure networks;

[0046] Critical infrastructure system failure simulation and cascading failure process analysis include:

[0047] Combine the dynamic model with the network topology of the system to establish an analysis framework when the associated infrastructure system fails;

[0048] Analyzing the impact of community structure on the associated infrastructure network includes:

[0049] Taking the power-communication network as the research object of the associated infrastructure network, this paper proposes the power network vulnerability assessment index and the community intentional attack strategy for the power network.

[0050] The steps for modeling the structure and function of the associated infrastructure network are:

[0051] S1. Establish the topological structure, dynamic model and coupling mode of the associated infrastructure system;

[0052] S2. Select typical related infrastructure systems, power-communication networks;

[0053] S3. First, based on the operation information of the actual power system and combined with complex network theory and methods, a power network topology model is established. The power system can be regarded as a series of subsystems connected to each other. These subsystems are abstracted as nodes, and the connections between subsystems are abstracted as edges.

[0054] Building a power grid model in, For power station nodes The collection of N p represents the total number of nodes in the power grid, Indicates power transmission line Set, where M p represents the total number of edges in the power grid;

[0055] Secondly, for communication network modeling, the topological structure of the communication network is given based on network theory. The communication network is abstracted as in, represents the set of nodes in the communication network, Represents a collection of edges in a communication network.

[0056] The steps for critical infrastructure system failure simulation and cascading failure process analysis are:

[0057] S4. In the first phase, random attacks are used to attack the associated network to evaluate the resilience of the associated system; in the second phase, community attacks are used to study the propagation of cascading failures in the associated infrastructure system; in the third phase, non-community attacks are used to study the propagation of cascading failures in the associated infrastructure system;

[0058] S5. At the structural level, based on the modeling of structural robustness, the changes in the maximum group size of the power network of the IEEE 118 network under three coupling modes and four attack models are simulated, the effects of community attacks and non-community attacks under the same coupling mode are compared, the key links in the network are analyzed, and the cascading failure process between different networks and under different attack models is analyzed from a rigorous theoretical analysis perspective through the percolation theory, combined with the simulation of structural vulnerability, to evaluate the resilience of the network;

[0059] S6. Based on the modeling of functional robustness, the changes in power supply of the associated infrastructure network under three coupling modes and four attack strategies are simulated, the impact of community attacks and non-community attacks on the electrical performance of the network is analyzed, and the impact of community attack strategies of different degrees on the electrical performance of the network is compared.

[0060] Analyzing the impact of community structure on the associated infrastructure network involves the following steps:

[0061] S7. Community detection divides the network into clusters composed of nodes, and makes the edges between communities less. Modularity is a commonly used metric in social network analysis to measure the structural characteristics of a network. It represents the degree of aggregation between different communities in the network. When the number of edges between similar nodes in the network is greater than the number of edges between nodes in the case of random connection, the value of this measure is relatively high. The modularity of a network is defined as the ratio of the difference between the number of edges within the community of the network and the number of edges within the community of the corresponding null model to the total number of edges M in the entire network, that is:

[0062]

[0063] In theory, for a common null model with the same degree sequence as the original network but without degree correlation, the expected value of the number of edges between node i and node j is where k i and k j are the degrees of nodes i and j in the original network respectively. The degree of a node in the graph refers to the number of edges directly connected to it. The degree of node i is represented by k i For an undirected graph consisting of n nodes, the degree can be expressed using the adjacency matrix as The coefficient 1 / 2 is to avoid repeated counting of edges between two nodes. The commonly used modularity is defined as:

[0064]

[0065] in

[0066] The Q value is strictly less than 1. If the actual value of the number of edges between similar nodes is greater than the expected value under random conditions, the value is positive, otherwise it is negative. The larger the Q value, the more accurate the community structure of the network division. In actual network analysis, the highest point of the Q value generally appears between 0.3 and 0.7.

[0067] The basic steps of the S8.GN algorithm are as follows:

[0068] Step (1) Calculate the edge betweenness of all edges in the network;

[0069] Step (2) find the edge with the largest betweenness and remove it from the network;

[0070] Step (3) For the remaining edges in the network, recalculate the edge betweenness of each edge;

[0071] Step (4) Repeat steps (2) and (3) until all edges are removed.

[0072] Workflow of the present invention: When the parameter collection and analysis method for infrastructure resilience status designed by this solution is used, firstly, based on the actual operation information of the power system, combined with complex network theory and methods, a power network topology model is established. The power system can be regarded as a series of subsystems connected to each other. These subsystems are abstracted as nodes, and the connections between subsystems are abstracted as edges. Constructing the power grid model in, For power station nodes The collection of N p Represents the total number of nodes in the power grid. Indicates power transmission line Set, where M p Represents the total number of edges in the power grid. Secondly, for communication network modeling, the topological structure of the communication network is given based on network theory. The communication network is abstracted as in, represents the set of nodes in the communication network, Represents the set of edges in the communication network. Considering the limited transmission capacity of the communication node, data transmission will consume the transmission resources of the routing node. On this basis, the power network and the information network are strongly coupled according to different coupling modes, and the associated infrastructure system model is constructed to analyze the cascading failure process from the structural and functional levels. At the structural level, according to the modeling of structural robustness, the changes in the maximum group size of the power network of the IEEE 118 network under three coupling modes and four attack models are simulated, the effects of community attacks and non-community attacks under the same coupling mode are compared, and the key edges in the network are analyzed. Through the percolation theory, from the perspective of rigorous theoretical analysis, combined with the simulation of structural vulnerability, the cascading failure process between different networks and under different attack models is analyzed, and the resilience of the network is evaluated. According to the modeling of functional robustness, the changes in power supply of the associated infrastructure network under three coupling modes and four attack strategies are simulated, the impact of community attacks and non-community attacks on the electrical performance of the network is analyzed, and the impact of community attack strategies of different degrees on the electrical performance of the network is compared.

[0073] in The Q value is strictly less than 1. If the actual value of the number of edges between similar nodes is greater than the expected value under random conditions, the value is positive, otherwise it is negative. The larger the Q value, the more accurate the community structure of the network division. In actual network analysis, the highest point of the Q value generally appears between 0.3 and 0.7. Through the basic community division algorithm, further elastic modeling is carried out and elastic evaluation work is carried out.

[0074] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A parameter collection and analysis method for infrastructure resilience status, including structural function modeling of associated infrastructure network, simulation of critical infrastructure system failures and analysis of cascading failure processes, and analysis of the impact of community structure on associated infrastructure network, characterized in that: The structural function modeling of the associated infrastructure network includes: analyzing the impact of community structure on the structural vulnerability and functional vulnerability of the associated infrastructure network; The critical infrastructure system failure simulation and cascading failure process analysis include: Combine the dynamic model with the network topology of the system to establish an analysis framework when the associated infrastructure system fails; The impact of the analyzed community structure on the associated infrastructure network includes: Taking the power-communication network as the research object of the associated infrastructure network, this paper proposes the power network vulnerability assessment index and the community intentional attack strategy for the power network.

2. The parameter collection and analysis method for infrastructure resilience status according to claim 1, characterized in that: The steps of modeling the associated infrastructure network structure function are: S1. Establish the topological structure, dynamic model and coupling mode of the associated infrastructure system; S2. Select typical related infrastructure systems, power-communication networks; S3. First, based on the operation information of the actual power system and combined with complex network theory and methods, a power network topology model is established. The power system can be regarded as a series of subsystems connected to each other. These subsystems are abstracted as nodes, and the connections between subsystems are abstracted as edges. Building a power grid model in, For power station nodes The collection of N p represents the total number of nodes in the power grid, Indicates power transmission line Set, where M p represents the total number of edges in the power grid; Secondly, for communication network modeling, the topological structure of the communication network is given based on network theory. The communication network is abstracted as in, represents the set of nodes in the communication network, Represents a collection of edges in a communication network.

3. The parameter collection and analysis method for infrastructure resilience status according to claim 1 is characterized by: The steps of critical infrastructure system failure simulation and cascading failure process analysis are: S4. In the first phase, random attacks are used to attack the associated network to evaluate the resilience of the associated system; in the second phase, community attacks are used to study the propagation of cascading failures in the associated infrastructure system; in the third phase, non-community attacks are used to study the propagation of cascading failures in the associated infrastructure system; S5. At the structural level, based on the modeling of structural robustness, the changes in the maximum group size of the IEEE118 network under three coupling modes and four attack models are simulated, the effects of community attacks and non-community attacks under the same coupling mode are compared, and the key links in the network are analyzed. Through the percolation theory, from a rigorous theoretical analysis perspective, combined with the simulation of structural vulnerability, the cascading failure process between different networks and under different attack models is analyzed to evaluate the resilience of the network; S6. Based on the modeling of functional robustness, the changes in power supply of the associated infrastructure network under three coupling modes and four attack strategies are simulated, the impact of community attacks and non-community attacks on the electrical performance of the network is analyzed, and the impact of community attack strategies of different degrees on the electrical performance of the network is compared.

4. The parameter collection and analysis method for infrastructure resilience status according to claim 1 is characterized by: The analysis of the impact of community structure on the associated infrastructure network includes the following steps: S7. Community detection divides the network into clusters composed of nodes, and makes the edges between communities less. Modularity is a commonly used metric in social network analysis to measure the structural characteristics of a network. It represents the degree of aggregation between different communities in the network. When the number of edges between similar nodes in the network is greater than the number of edges between nodes in the case of random connection, the value of this measure is relatively high. The modularity of a network is defined as the ratio of the difference between the number of edges within the community of the network and the number of edges within the community of the corresponding null model to the total number of edges M in the entire network, that is: In theory, for a common null model with the same degree sequence as the original network but without degree correlation, the expected value of the number of edges between node i and node j is where k i and k j are the degrees of nodes i and j in the original network respectively. The degree of a node in the graph refers to the number of edges directly connected to it. The degree of node i is represented by k i For an undirected graph consisting of n nodes, the degree can be expressed using the adjacency matrix as The coefficient 12 is to avoid repeated counting of edges between two nodes. The commonly used modularity is defined as: in The Q value is strictly less than 1. If the actual value of the number of edges between similar nodes is greater than the expected value under random conditions, the value is positive, otherwise it is negative. The larger the Q value, the more accurate the community structure of the network division. In actual network analysis, the highest point of the Q value generally appears between 0.3 and 0.

7. The basic steps of the S8.GN algorithm are as follows: Step (1) Calculate the edge betweenness of all edges in the network; Step (2) find the edge with the largest betweenness and remove it from the network; Step (3) For the remaining edges in the network, recalculate the edge betweenness of each edge; Step (4) Repeat steps (2) and (3) until all edges are removed.

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