A Method and System for Assessing Coupled Failure Risks of Urban Infrastructure

Abstract

This invention discloses a method, system, storage medium, and electronic device for assessing the risk of coupled failure of urban infrastructure. The method includes: establishing a multi-layer network model based on the internal network structure characteristics of the infrastructure and the coupling dependencies between different types of infrastructure; extracting the node centrality index of the multi-layer network model and calculating the single-layer node importance of each node in the single-layer network of the multi-layer network model; determining the comprehensive importance of each node in the multi-layer network based on the single-layer node importance; dynamically updating the network structure state of the multi-layer network model based on the multi-layer network model and the comprehensive importance of each node, and measuring the operational level of the updated multi-layer network model to obtain the operational status of the infrastructure at different times; verifying the comprehensive importance based on various simulation scenarios, and obtaining the operational status assessment results of the infrastructure under each simulation scenario to complete the coupled failure risk assessment.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and more specifically, to a method and system for assessing the risk of coupled failure of urban infrastructure. Background Technology

[0002] Urban infrastructure such as power grids, water supply, and gas supply face multi-source risks. In recent years, equipment failures and system outages caused by natural disasters, extreme weather, and cyberattacks have been on the rise. Critical infrastructure is the lifeblood of a nation and the foundation upon which its citizens depend for survival. Any anomalies or failures will have serious consequences for national security, the economy, public health and safety, and the ecological and social environment. The power grid is the central node of all types of critical infrastructure, and its coupling relationships with other critical infrastructure are the most complex. Once a disturbance or failure triggers a chain reaction, the secondary consequences will extend far beyond the power system itself. Summary of the Invention

[0003] The present invention provides a method and system for assessing the risk of coupled failure of urban infrastructure, in order to solve the problem of how to quantitatively assess the impact of coupled failure of infrastructure based on the coupling relationship of urban infrastructure.

[0004] To address the above problems, this invention provides a method for assessing the risk of coupled failure of urban infrastructure, characterized in that the method includes: Based on the internal network structure characteristics of infrastructure and the coupling dependencies between different types of infrastructure, a multi-layer network model of infrastructure is established. The node centrality index of the multi-layer network model is extracted. By normalizing and weighting the node centrality index, the single-layer node importance of each node in the single-layer network of the multi-layer network model is calculated. Based on the single-layer node importance and the total number of infrastructure nodes, the comprehensive importance of each node in the multi-layer network is determined. Based on the multi-layer network model and the comprehensive importance of each node, the network structure state of the multi-layer network model is dynamically updated, and the operational level of the updated multi-layer network model is measured to obtain the operational status of the infrastructure at different times. Multiple simulation scenarios of different types are set up, the comprehensive importance is verified based on each simulation scenario, and the operational status assessment results of the infrastructure under each simulation scenario are obtained to complete the coupling failure risk assessment.

[0005] Preferably, the step of establishing a multi-layer network model for the infrastructure based on the internal network structure characteristics of the infrastructure and the coupling dependencies between different types of infrastructure includes: Use hyperadjacency matrix To represent a containing A multi-layered network model; in, This represents the set of adjacency matrices for each layer in a multilayer network model, where each layer is an unweighted and undirected network. yes Elements in: when Layer nodes and nodes When there are connecting edges between them, ,otherwise ; Let represent the set of adjacency matrices of the inter-layer network, considering inter-layer dependencies. The inter-layer network is an unweighted directed network. yes Elements in: when Layer nodes Existence of pointers Layer nodes When connecting edges, ,otherwise ; Based on the above definition, the general form of a multilayer network model can be represented by a hyperadjacency matrix as follows: (1)

[0006] Preferably, the step of extracting the node centrality index of the multi-layer network model, and calculating the single-layer node importance of each node in the single-layer network of the multi-layer network model by normalizing the node centrality index and assigning weights to the node centrality index, includes: Extract the node centrality index of the multilayer network model, the node centrality index including: node degree. Betweenness centrality Proximity centrality ; The node centrality index is normalized: In a network containing N nodes, the maximum possible degree of a node is The node degree normalization process is as follows: (4) in, The adjacency matrix is ​​the first... i Line number j The value of the element at the corresponding position in the column; N is the number of nodes in the network; j Number the node location.

[0007] In various types of networks, the central node of a star network has the largest betweenness value. All other pairs of nodes except the central node have a unique shortest path that passes through it. The betweenness value of this node is the number of these shortest paths. Betweenness centrality normalization is as follows: (5) in, For node pairs The nodes that are passed through in all shortest paths between them i Quantity; For node pairs The number of shortest paths between them; s , t , i Number the node location.

[0008] Obtain the importance evaluation index of infrastructure network nodes to determine the importance of nodes in a single layer: (6) in, The normalized node degree weights; The normalized betweenness centrality weight; These are the normalized near-centrality weights.

[0009] Preferably, determining the overall importance of each node in the multi-layer network based on the importance of the single-layer nodes and the total number of infrastructure nodes includes: The importance of each node in the multi-layer network is determined by combining the importance of each node in the single-layer network: (7) (8) (9) (10) in, Indicates the first The overall importance of each power node; Indicates the first The importance of each power node within the power network layer; , They respectively represent the first Each power node is incorporated into the importance of the water and gas supply networks at the corresponding layer based on their interdependencies; , , These represent the importance coefficients of the power, water supply, and gas network layers, respectively. , , These represent the number of nodes for electricity, water supply, and gas, respectively.

[0010] Preferably, the step of dynamically updating the network structure state of the multi-layer network model based on the multi-layer network model and the comprehensive importance of each node, and measuring the operational level of the updated multi-layer network model to obtain the operational status of the infrastructure at different times includes: Sure The superadjacency matrix of the multilayer network model at time t is ; Update the power layer adjacency matrix based on power system node failure status. And update the dependency matrix of electricity, water and gas supply. , ; Update the adjacency matrices of the water supply layer and the gas supply layer according to the changes in dependency relationships. , ; Based on the updated power layer adjacency matrix and the updated power layer adjacency matrix , , obtain New state matrix at time 1 ; The operational level of the updated multi-layer network model is calculated to obtain the operational status of the infrastructure at different times: (11) in, for The overall operational level of the real-time infrastructure; , , These are respectively represented as electricity, water supply, and gas networks. The operational level at any given moment, Let the area of ​​the triangle representing the current operating status of the three-layer network of electricity, water supply, and gas be the area of ​​the triangle. Let be the area of ​​the triangle formed by the three-layer network of electricity, water supply, and gas at the initial moment.

[0011] Based on another aspect of the present invention, the present invention provides a method and system for assessing the coupled failure risk of urban infrastructure, the system comprising: Establishment unit, used to build a multi-layer network model of infrastructure based on the internal network structure characteristics of infrastructure and the coupling dependencies between different types of infrastructure; A determining unit is used to extract the node centrality index of the multi-layer network model, and calculate the single-layer node importance of each node in the single-layer network of the multi-layer network model by normalizing and weighting the node centrality index; based on the single-layer node importance and the total number of infrastructure nodes, the comprehensive importance of each node in the multi-layer network is determined. The acquisition unit is used to obtain the operating status of the infrastructure at different times by dynamically updating the network structure state of the multi-layer network model and measuring the operating level of the updated multi-layer network model based on the multi-layer network model and the comprehensive importance of each node. The results unit is used to set up various types of simulation scenarios, verify the comprehensive importance based on each simulation scenario, obtain the operational status assessment results of the infrastructure under each simulation scenario, and complete the coupling failure risk assessment.

[0012] Preferably, the establishing unit is used to establish a multi-layer network model of the infrastructure based on the internal network structure characteristics of the infrastructure and the coupling dependencies between different types of infrastructure, including: Use hyperadjacency matrix To represent a containing A multi-layered network model; in, This represents the set of adjacency matrices for each layer in a multilayer network model, where each layer is an unweighted and undirected network. yes Elements in: when Layer nodes and nodes When there are connecting edges between them, ,otherwise ; Let represent the set of adjacency matrices of the inter-layer network, considering inter-layer dependencies. The inter-layer network is an unweighted directed network. yes Elements in: when Layer nodes Existence of pointers Layer nodes When connecting edges, ,otherwise ; Based on the above definition, the general form of a multilayer network model can be represented by a hyperadjacency matrix as follows: (1)

[0013] Preferably, the determining unit is used to extract the node centrality index of the multi-layer network model, and calculate the single-layer node importance of each node in the single-layer network of the multi-layer network model by normalizing the node centrality index and assigning weights to the node centrality index, including: Extract the node centrality index of the multilayer network model, the node centrality index including: node degree. Betweenness centrality Proximity centrality ; The node centrality index is normalized: In a network containing N nodes, the maximum possible degree of a node is The node degree normalization process is as follows: (4) in, The adjacency matrix is ​​the first... i Line number j The value of the element at the corresponding position in the column; N is the number of nodes in the network; j Number the node location.

[0014] In various types of networks, the central node of a star network has the largest betweenness value. All other pairs of nodes except the central node have a unique shortest path that passes through it. The betweenness value of this node is the number of these shortest paths. Betweenness centrality normalization is as follows: (5) in, For node pairs The nodes that are passed through in all shortest paths between them i Quantity; For node pairs The number of shortest paths between them; s , t , i Number the node location.

[0015] Obtain the importance evaluation index of infrastructure network nodes to determine the importance of nodes in a single layer: (6) in, The normalized node degree weights; The normalized betweenness centrality weight; These are the normalized near-centrality weights.

[0016] Preferably, the determining unit is used to determine the comprehensive importance of each node in the multi-layer network based on the importance of the single-layer nodes and the total number of infrastructure nodes, including: The importance of each node in the multi-layer network is determined by combining the importance of each node in the single-layer network: (7) (8) (9) (10) in, Indicates the first The overall importance of each power node; Indicates the first The importance of each power node within the power network layer; , They respectively represent the first Each power node is incorporated into the importance of the water and gas supply networks at the corresponding layer based on their interdependencies; , , These represent the importance coefficients of the power, water supply, and gas network layers, respectively. , , These represent the number of nodes for electricity, water supply, and gas, respectively.

[0017] Preferably, the acquisition unit is used to acquire the operational status of the infrastructure at different times by dynamically updating the network structure state of the multi-layer network model based on the multi-layer network model and the comprehensive importance of each node, and by measuring the operational level of the updated multi-layer network model, including: Sure The superadjacency matrix of the multilayer network model at time t is ; Update the power layer adjacency matrix based on power system node failure status. And update the dependency matrix of electricity, water and gas supply. , ; Update the adjacency matrices of the water supply layer and the gas supply layer according to the changes in dependency relationships. , ; Based on the updated power layer adjacency matrix and the updated power layer adjacency matrix , , obtain New state matrix at time 1 ; The operational level of the updated multi-layer network model is calculated to obtain the operational status of the infrastructure at different times: (11) in, for The overall operational level of the real-time infrastructure; , , These are respectively represented as electricity, water supply, and gas networks. The operational level at any given moment, Let the area of ​​the triangle representing the current operating status of the three-layer network of electricity, water supply, and gas be the area of ​​the triangle. Let be the area of ​​the triangle formed by the three-layer network of electricity, water supply, and gas at the initial moment.

[0018] Based on another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for assessing the risk of coupled failure of urban infrastructure.

[0019] According to another aspect of the present invention, the present invention provides an electronic device, comprising: The aforementioned computer-readable storage medium; and One or more processors for executing a program in the computer-readable storage medium.

[0020] This invention provides a method and system for assessing the risk of coupled failure of urban infrastructure. The method includes: establishing a multi-layer network model of the infrastructure based on the internal network structure characteristics of the infrastructure and the coupling dependencies between different types of infrastructure; extracting the node centrality index of the multi-layer network model, and calculating the single-layer node importance of each node in the single-layer network model by normalizing and weighting the node centrality index; determining the comprehensive importance of each node in the multi-layer network based on the single-layer node importance and the total number of infrastructure nodes; dynamically updating the network structure state of the multi-layer network model based on the multi-layer network model and measuring the operational level of the updated multi-layer network model to obtain the operational status of the infrastructure at different times; setting multiple different types of simulation scenarios, verifying the comprehensive importance based on each simulation scenario, and obtaining the operational status assessment results of the infrastructure under each simulation scenario to complete the coupled failure risk assessment. The technical solution of this invention breaks through the limitations of traditional single-layer network modeling. It constructs a multi-layer coupled network model that integrates multiple infrastructures through a super-adjacency matrix, fully preserves the inter-layer dependency topology information, solves the problem of unified modeling caused by the differences in physical principles and time scales of different infrastructures, and accurately depicts the coupling relationship. Attached Figure Description

[0021] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures: Figure 1 This is a flowchart of a method for assessing the coupled failure risk of urban infrastructure according to a preferred embodiment of the present invention; Figure 2 This is a flowchart of a method for assessing the coupled failure risk of urban infrastructure according to a preferred embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the cascading failure status update of an associated infrastructure network according to a preferred embodiment of the present invention. Figure 4This is a schematic diagram of the state update of the super-adjacency matrix of the associated infrastructure network from time T0 to T1 according to a preferred embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the changes in the operating status of the three-tiered infrastructure (electricity, water supply, and gas) from time T0 to T1 according to a preferred embodiment of the present invention. Figure 6 This is a schematic diagram of an urban infrastructure network according to a preferred embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the power-water-gas dependency relationship according to a preferred embodiment of the present invention; Figure 8 This is a schematic diagram illustrating the changes in infrastructure network performance under different fault scenarios according to a preferred embodiment of the present invention; Figure 9 This is a structural diagram of a city infrastructure coupling failure risk assessment system according to a preferred embodiment of the present invention. Detailed Implementation

[0022] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0023] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0024] Figure 1 This is a flowchart of a method for assessing the coupled failure risk of urban infrastructure according to a preferred embodiment of the present invention.

[0025] Urban infrastructure is inherently vulnerable to risks. A single disturbance or failure can trigger a chain reaction within the infrastructure system, leading to widespread failures or even global collapse. Simply improving the safety and stability of the power grid and its fault tolerance capabilities is insufficient to address the increasingly severe unconventional security risks; a comprehensive study of the coupling relationships between the power grid and other infrastructure is essential. Due to the different physical principles followed by different types of urban infrastructure, the time scales for modeling and simulation vary significantly, making unified modeling and computational analysis challenging. This invention aims to propose a method for assessing the risk of coupled failures in urban infrastructure. By establishing a multi-layered infrastructure coupling relationship model, considering the interdependencies among multiple layers, a method for comprehensively assessing the importance of infrastructure nodes is proposed. Furthermore, a system-wide performance evaluation model under cascading failure conditions is constructed, supporting the quantitative assessment of the impact of coupled infrastructure failures.

[0026] like Figure 1 As shown, the present invention provides a method for assessing the risk of coupled failure of urban infrastructure, characterized in that the method includes: Step 101: Based on the internal network structure characteristics of the infrastructure and the coupling dependencies between different types of infrastructure, establish a multi-layer network model of the infrastructure; Preferably, based on the internal network structure characteristics of the infrastructure and the coupling dependencies between different types of infrastructure, a multi-layer network model of the infrastructure is established, including: Use hyperadjacency matrix To represent a containing A multi-layered network model; in, This represents the set of adjacency matrices for each layer in a multilayer network model, where each layer is an unweighted and undirected network. yes Elements in: when Layer nodes and nodes When there are connecting edges between them, ,otherwise ; Let represent the set of adjacency matrices of the inter-layer network, considering inter-layer dependencies. The inter-layer network is an unweighted directed network. yes Elements in: when Layer nodes Existence of pointers Layer nodes When connecting edges, ,otherwise ; Based on the above definition, the general form of a multilayer network model can be represented by a hyperadjacency matrix as follows: (1)

[0027] This invention provides a framework for assessing the risk of coupling failures among urban infrastructure facilities, as detailed in the appendix. Figure 2 Specifically, it includes: Considering the internal network structure characteristics of the infrastructure and the coupling relationships between different infrastructures, a multi-layer network model is constructed. This invention constructs a multi-layered infrastructure network model.

[0028] For interconnected infrastructure networks where nodes are interdependent, both the internal network structure of the infrastructure and the interdependencies between infrastructure nodes can be considered simultaneously, thus abstracting the interconnected infrastructure into a multi-layered network for analysis. Multi-layered networks can be represented using an aggregation approach, which compresses the multi-layered network into a single-layered network (this single-layered network is called an aggregation network) without considering the inter-layer interactions. Aggregate networks are a simplified form of multi-layered networks. While this modeling method reduces the difficulty of subsequent research, it loses the unique topological information (inter-layer interactions) of multi-layered networks.

[0029] A containing Multilayer networks can use hyperadjacency matrices. To represent. Among them... This represents the set of adjacency matrices for each layer in a multi-layer network, where each layer is an unweighted and undirected network. yes Elements in: when Layer nodes and nodes When there are connecting edges between them, ,otherwise . Let represent the set of adjacency matrices of the inter-layer network, considering inter-layer dependencies. The inter-layer network is an unweighted directed network. yes Elements in: when Layer nodes Existence of pointers Layer nodes When connecting edges, ,otherwise .

[0030] Based on the above definition, the general form of a multilayer network can be represented by a superadjacency matrix as follows: (1) Specifically, for critical infrastructure systems with multi-layered network structures, considering the three types of power, water, and gas, their hyperadjacency matrix can be represented as: (2) In the formula: , , These represent the intra-layer adjacency matrices of the power, water supply, and gas networks, respectively. , These represent the interdependencies between electricity and water supply, and gas supply, respectively. An element value of 1 indicates that the electricity node provides support to other associated infrastructure nodes. , These represent the interdependencies between water supply and electricity and gas, respectively. An element value of 1 indicates that the water supply node supports other related infrastructure nodes. , These represent the interdependencies between gas and electricity, and water supply, respectively. An element value of 1 indicates that the gas node provides support to other associated infrastructure nodes.

[0031] Considering only the dependence of water and gas supply on electricity, , , , All are all-zero matrices (using) (represented), the above formula can be further simplified to: (3) in for The elements in Represents the first power system The node is the first node of the water supply system. Each node provides power supply, and similarly, the dependency relationship between gas nodes and power nodes can be represented.

[0032] Step 102: Extract the node centrality index of the multi-layer network model. By normalizing and weighting the node centrality index, calculate the single-layer node importance of each node in the single-layer network of the multi-layer network model. Based on the single-layer node importance and the total number of infrastructure nodes, determine the comprehensive importance of each node in the multi-layer network. Preferably, the node centrality index of the multi-layer network model is extracted. By normalizing and weighting the node centrality index, the single-layer node importance of each node within a single layer of the multi-layer network model is calculated, including: Extract node centrality metrics from multilayer network models. These metrics include node degree. Betweenness centrality Proximity centrality ; Normalize the node centrality index: In a network containing N nodes, the maximum possible degree of a node is The node degree normalization process is as follows: (4) in, The adjacency matrix is ​​the first... i Line number j The value of the element at the corresponding position in the column; N is the number of nodes in the network; j Number the node location.

[0033] In various types of networks, the central node of a star network has the largest betweenness value. All other pairs of nodes except the central node have a unique shortest path that passes through it. The betweenness value of this node is the number of these shortest paths. Betweenness centrality normalization is as follows: (5) in, For node pairs The nodes that are passed through in all shortest paths between them i Quantity; For node pairs The number of shortest paths between them; s , t , i Number the node location.

[0034] Obtain the importance evaluation index of infrastructure network nodes to determine the importance of nodes in a single layer: (6) in, The normalized node degree weights; The normalized betweenness centrality weight; These are the normalized near-centrality weights.

[0035] Preferably, the overall importance of each node in the multi-layer network is determined based on the importance of a single-layer node and the total number of infrastructure nodes, including: The importance of each node in the multi-layer network is determined by combining the importance of nodes in each layer: (7) (8) (9) (10) in, Indicates the first The overall importance of each power node; Indicates the first The importance of each power node within the power network layer; , They respectively represent the first Each power node is incorporated into the importance of the water and gas supply networks at the corresponding layer based on their interdependencies; , , These represent the importance coefficients of the power, water supply, and gas network layers, respectively. , , These represent the number of nodes for electricity, water supply, and gas, respectively.

[0036] This invention is based on the centrality index of complex networks and takes into account the coupling relationship between multiple layers of networks to calculate the comprehensive importance of urban infrastructure nodes and evaluate the structural characteristics of infrastructure networks.

[0037] This invention determines the comprehensive importance of urban infrastructure nodes.

[0038] (1) Construct a single-layer network node importance index A single infrastructure network can use an adjacency matrix This paper describes how constructing a node importance index based on the centrality method of complex networks can identify key nodes in infrastructure networks, serving as an important basis for assessing the vulnerability of individual infrastructure. By employing different node removal strategies and analyzing changes in network performance, the paper further verifies the operational characteristics of a single infrastructure under different damage patterns, providing a reference for hardening vulnerable nodes.

[0039] Node degree Betweenness centrality Proximity centrality Each of these indicators reflects one aspect of a node's importance in the network. However, due to significant differences in characteristic indicators across different network structures, using only one indicator for evaluation may be biased, leading to discrepancies between the node importance assessment results and the actual situation. This project, based on the aforementioned three indicators, considers the role each indicator plays in the evolution and propagation of infrastructure faults, assigns different weight coefficients, and comprehensively evaluates the importance of infrastructure network nodes through multiple indicators.

[0040] Since the three indicators have different dimensions, each indicator needs to be normalized to facilitate comparison and construct a comprehensive indicator. In a network containing N nodes, the maximum possible degree of a node is The node degree normalization process is as follows: (4) In various types of networks, the central node of a star network has the largest betweenness value. All other pairs of nodes have a unique shortest path that passes through this central node. The betweenness value of this node is the number of these shortest paths. .

[0041] (5) The definition of proximity centrality already possesses normalization properties and requires no further processing.

[0042] Obtain the infrastructure network node importance evaluation index: (6) The importance of the three indicators can be assessed using the analytic hierarchy process (AHP) to obtain their weight coefficients.

[0043] In infrastructure networks, the higher the importance of a node, the greater its role in the failure evolution and propagation process. By calculating the importance of event nodes, key nodes that dominate failure propagation can be identified, providing a reference for infrastructure vulnerability assessment and hardening of critical nodes.

[0044] This invention considers the comprehensive importance index of infrastructure nodes based on coupling relationships.

[0045] For multi-layered infrastructure networks, in addition to considering intra-layer connectivity, inter-layer dependencies must also be taken into account. This project focuses on the impact of power system nodes on water and gas supply systems. For any node in the power system... The importance of a node in the power grid can be directly calculated using the node importance index in the single infrastructure vulnerability assessment. Based on the node's dependence on the water supply or gas system, the node and the water supply or gas network layer can be compressed into a new aggregated network. Thus, the importance of the node in the water supply or gas network can be calculated using the single infrastructure vulnerability assessment method. By combining the importance of the node in each layer of infrastructure, the comprehensive importance index of the node in the multi-layer infrastructure network can be obtained, as shown in equations (7) to (10). This index can be used as the basis for the vulnerability assessment of related infrastructure.

[0046] (7) (8) (9) (10) In the formula: Indicates the first The overall importance of each power node; Indicates the first The importance of each power node within the power network layer; , They respectively represent the first Each power node is incorporated into the importance of the water and gas supply networks at the corresponding layer based on their interdependencies; , , These represent the importance coefficients of the power, water supply, and gas network layers, respectively. , , These represent the number of nodes for electricity, water supply, and gas, respectively.

[0047] Step 103: Based on the multi-layer network model and the comprehensive importance of each node, the network structure state of the multi-layer network model is dynamically updated, and the operation level of the updated multi-layer network model is calculated to obtain the operation status of the infrastructure at different times. Preferably, based on the multi-layer network model and the comprehensive importance of each node, the network structure state of the multi-layer network model is dynamically updated, and the operational level of the updated multi-layer network model is measured to obtain the operational status of the infrastructure at different times, including: Sure The hyperadjacency matrix of the multilayer network model at time step 1 is: ; Update the power layer adjacency matrix based on power system node failure status. And update the dependency matrix of electricity, water and gas supply. , ; Update the adjacency matrices of the water supply layer and the gas supply layer according to the changes in dependency relationships. , ; Based on the updated power layer adjacency matrix and the updated power layer adjacency matrix , , obtain New state matrix at time 1 ; Calculate the operational level of the updated multi-layer network model and obtain the operational status of the infrastructure at different times: (11) in, for The overall operational level of the real-time infrastructure; , , These are respectively represented as electricity, water supply, and gas networks. The operational level at any given moment, Let the area of ​​the triangle representing the current operating status of the three-layer network of electricity, water supply, and gas be the area of ​​the triangle. Let be the area of ​​the triangle formed by the three-layer network of electricity, water supply, and gas at the initial moment.

[0048] This invention considers the destruction of urban infrastructure in extreme scenarios, updates the network structure state based on the failure nodes and the coupling relationship between systems, calculates the overall system operation level at different times, and evaluates the dynamic operation characteristics of urban infrastructure under fault conditions.

[0049] This invention conducts a risk assessment of infrastructure coupling failure.

[0050] (1) Update the state of the coupled failure network When a node in a certain layer of infrastructure network is attacked, damaged, or malfunctions, other related infrastructure nodes will also fail, and the network structure and operating status of the multi-layer infrastructure system will change accordingly. Figure 3 It demonstrates the cascading failure process of a three-tiered infrastructure network consisting of electricity, water supply, and gas. Prior to this time, all infrastructure systems were operating normally, and the rightmost node of the power system was... The system is prone to failure due to unforeseen events, resulting in a power outage, cessation of water and gas supply, and updates to the status of all infrastructure layers. At any given moment, the system enters a new steady state. When a new node in the power system fails, the infrastructure network at each layer will again enter a new transient process until a new steady state is reached.

[0051] The network state update process described above can be represented using a hyperadjacency matrix, such as... Figure 4 As shown, to The state change at any given time constitutes one iteration. The hyperadjacency matrix at time t is First, update the power layer adjacency matrix based on the fault status of power system nodes. Then update the dependency matrix of electricity, water, and gas. , Finally, based on the changes in dependencies, the adjacency matrices of the water supply layer and the gas supply layer are updated. , Thus we obtain New state matrix at time 1 .

[0052] This invention conducts coupled failure risk assessment, based on a method for evaluating the impact of node failure on system performance in a single infrastructure, which can be extended to the performance analysis of multi-layered infrastructure network systems. Based on the comprehensive importance assessment results of power nodes in the aforementioned interconnected infrastructure, and according to two node removal methods—random failure and deliberate sabotage—it considers... Starting from each moment, power nodes are removed sequentially, and the topology of the water and gas networks is updated according to dependencies. The global network performance of each layer of infrastructure is calculated at each moment, thereby obtaining the dynamic change law of network performance of each layer after the power nodes are lost.

[0053] Figure 5 Showing to The operational status of the three-tier infrastructure (electricity, water supply, and gas) changes over time. In the diagram, axes a, b, and c within the unit circle represent the performance levels of the electricity, water supply, and gas networks, respectively, with each pair of axes forming a 120° angle. The ratio of the current network's global efficiency to the initial network's global efficiency is used. To characterize it. Before that time, all systems were in normal operation. , At any given moment, a power system failure occurs, nodes fail and cause associated nodes to stop operating, and the operational level of each layer of the network degrades to [a certain level]. The triangle drawn from the three vertices determined by the operational level of each layer of the network at each moment reflects the overall operational level of the infrastructure system. Similarly, the area of ​​the triangle in the current running state can be used. Area of ​​the triangle at the initial time It is characterized by the ratio of .

[0054] to Changes in the operational status of the three-tiered infrastructure of electricity, water supply, and gas. For an equilateral triangle with a circumradius of 1, it is easy to find: The operational state triangle at any given time is divided into three smaller triangles with known sides and their included angle (120°) by the three axes a, b, and c. From this, the overall operational level of the infrastructure system can be obtained as shown in equation (11): (11) In the formula: express The overall operational level of the real-time infrastructure system; , , These represent the electricity, water supply, and gas networks, respectively. The operational level at any given moment.

[0055] Similarly, the above calculation method can be extended to subsequent time points to update the network structure changes at each time point, and then calculate the infrastructure system operation level at the corresponding time point, thereby obtaining the dynamic changes in system performance.

[0056] At the same time, this method can also be extended to multi-layer networks composed of more than three types of infrastructure to assess the risk of coupling failure of infrastructure at each layer.

[0057] Step 104: Set up multiple simulation scenarios of different types, verify the overall importance based on each simulation scenario, obtain the operational status assessment results of the infrastructure under each simulation scenario, and complete the coupling failure risk assessment.

[0058] This invention provides an assessment of the impact of different damage modes on system performance.

[0059] This invention considers both random sabotage and deliberate attacks, simulates infrastructure node failure modes, and analyzes the differentiated impacts of different attack modes on the dynamic operating characteristics of the system.

[0060] This invention considers two node damage scenarios: random failure and intentional sabotage. It calculates the global network performance after continuously removing nodes according to a specific sorting method, characterizing the impact of node failures on system performance. A random sequence method is used to simulate random failures such as occasional equipment malfunctions and human error. Nodes are randomly sorted and removed. The previously proposed node importance assessment method is applied, ranking nodes in descending order of importance and removing the most important node. The importance of all nodes is recalculated in each round. By damaging one power node at each moment, the multi-layered infrastructure network is updated based on the interdependencies between infrastructures. The overall system operating level at that moment is calculated, resulting in a continuous change curve of the system operating state. The differences in the impact on system operation under the two damage scenarios are compared and analyzed.

[0061] The following provides illustrative examples of embodiments of the present invention.

[0062] (1) Preparation of basic data Considering the difficulty in obtaining real infrastructure network data, this invention combines typical network structure characteristics of various infrastructure types with random factors to simulate and generate various infrastructure network models. For example... Figure 6 As shown.

[0063] The "hub-branch" structure of the simulated power system is used to generate a power network with 20 nodes. The first 5 nodes are used as central nodes and are fully connected to simulate the interconnection of hub substations in the power grid. Each central node is randomly connected to 4-6 other nodes to simulate the main line. Non-central nodes are randomly connected with a 10% probability. If an isolated node exists, it is connected to the nearest non-isolated node.

[0064] The system simulates a water supply system with a "water plant-pump station" hierarchical structure, generating a water supply network with 10 nodes. The first node is used as the regional core water plant, connecting to four main pump stations to simulate the water transmission network; the remaining nodes are connected to the main network with a 15% random probability to simulate the water distribution network; if an isolated node exists, it is connected to the nearest non-isolated node.

[0065] The "ring-radial" structure of the simulated gas system is used to generate a gas network with 10 nodes. First, a basic ring network is constructed to simulate the main urban gas pipeline ring network; the remaining nodes are randomly connected with a 10% probability to simulate regional branch gas stations; if an isolated node exists, it is connected to the nearest non-isolated node.

[0066] (2) Construction of multi-layer network model Following the principle of using single or dual power supply for water and gas supply nodes, 1-2 power nodes are randomly selected as dependent objects for each water or gas supply node to establish dependencies between infrastructure. For example... Figure 7 As shown.

[0067] (3) Calculation of the overall importance of nodes 1) Calculation of importance of a single-layer network The weights of each indicator were calculated using the analytic hierarchy process (AHP), and pairwise comparison matrices were constructed using the 9-scale method.

[0068] Table 1 Comparison Matrix

[0069] The comparison matrix is ​​eigenvalued and normalized to obtain the relative weight of each indicator. 0.1804, 0.7482, 0.0714 The results of the analytic hierarchy process (AHP) show that the largest eigenvalue is 3.029 and CI = 0.015. According to the RI table, the corresponding RI value is 0.525. Therefore, CR = CI / RI = 0.028 < 0.1, which passes the first-pass test.

[0070] Formula (6) is the formula for calculating the importance of nodes in a single-layer network.

[0071] 2) Multi-level comprehensive importance calculation Number of electricity, water and gas nodes , , The importance coefficients of the power, water supply, and gas network layers are 20, 10, and 10 respectively, and can be calculated using formulas (8)-(10). , , The values ​​are 0.5, 0.25, and 0.25, respectively.

[0072] According to formula (7), the comprehensive importance of multi-layer network nodes considering the inter-infrastructure dependencies can be calculated. This invention only considers the one-way dependence of water supply and gas networks on the power network, as well as the cascading failures caused by power node failures. Therefore, it is only necessary to calculate the comprehensive importance of each power node.

[0073] (4) Coupling failure risk assessment 1) Simulation of cascading failure scenarios Considering two types of destructive scenarios—extreme disasters and deliberate attacks—simulations are conducted using random removal of fault points (Strategy 1) and removal based on importance (Strategy 2), respectively. Assuming there are 8 faulty nodes, Strategy 1 randomly selects 8 power nodes, removing one node at a time, while also considering the dependencies of water and gas supply on power nodes, simultaneously removing faulty nodes without power supply. Strategy 2 removes 8 nodes sequentially from highest to lowest importance based on the overall ranking of power nodes, while also considering the dependencies of water and gas supply on power nodes, simultaneously removing faulty nodes without power supply.

[0074] 2) System performance evaluation In the above cascading failure scenario simulation, for each step of the infrastructure network after node removal and dependency update, the global efficiency of the power, water supply and gas networks is calculated respectively, and the overall operating efficiency of the infrastructure system is calculated according to formula (11). Figure 8 It demonstrates the continuous changes in the overall performance of each network and system under two types of failure scenarios.

[0075] (1) Breaking through the limitations of traditional single-layer network modeling, a coupled network model of multi-layer infrastructure such as power, water supply and gas is constructed using a super adjacency matrix. At the same time, the network structure within the layer and the inter-layer dependency topology information are preserved, solving the problem of unified modeling caused by the differences in physical principles and time scales of different infrastructures.

[0076] (2) A method for calculating the comprehensive importance of urban infrastructure nodes is proposed. It combines the normalization of node degree, betweenness centrality, and proximity centrality with the weight assignment of the analytic hierarchy process and integrates the multi-layer node importance calculation logic of inter-layer dependency relationship to achieve accurate identification of key nodes.

[0077] (3) A method for assessing the risk of coupled failure of urban infrastructure is proposed, and a complete process of “fault simulation-state update-performance assessment” is established. Through iterative updates of network state under extreme scenarios (dynamic adjustment of superadjacency matrix) and calculation of the overall system operation level, the overall system operation level and the impact of cascading failure are quantified. A technical solution for comparative analysis of failure impact under two types of damage scenarios is proposed.

[0078] This invention overcomes the limitations of traditional single-layer network modeling by constructing a multi-layer coupled network model integrating power, water, and gas through a hyperadjacency matrix. It fully preserves inter-layer dependency topology information, solving the challenge of unified modeling caused by differences in the physical principles and time scales of various infrastructures, and accurately characterizing coupling relationships. Combining complex network centrality indices and the analytic hierarchy process (AHP), a method for evaluating the comprehensive importance of nodes is proposed, taking into account both intra-layer structure and inter-layer coupling effects to quantitatively identify key nodes. Simultaneously, a system-wide performance evaluation model is constructed, visually presenting the dynamic impact of faults through a horizontal triangle. By simulating two scenarios—random faults and deliberate attacks—the cascading failure process under different damage methods is deduced, clearly identifying failure propagation paths. This model can be extended to more than three types of infrastructure systems, providing effective technical support for risk prevention and control of urban lifelines and reinforcement of key nodes.

[0079] Figure 9 This is a structural diagram of a city infrastructure coupling failure risk assessment system according to a preferred embodiment of the present invention.

[0080] like Figure 9 As shown, the present invention provides a method and system for assessing the coupled failure risk of urban infrastructure, the system comprising: Unit 601 is established to build a multi-layer network model of infrastructure based on the internal network structure characteristics of the infrastructure and the coupling dependencies between different types of infrastructure. Preferably, unit 601 is used to establish a multi-layer network model of the infrastructure based on the internal network structure characteristics of the infrastructure and the coupling dependencies between different types of infrastructure, including: Use hyperadjacency matrix To represent a containing A multi-layered network model; in, This represents the set of adjacency matrices for each layer in a multilayer network model, where each layer is an unweighted and undirected network. yes Elements in: when Layer nodes and nodes When there are connecting edges between them, ,otherwise ; Let represent the set of adjacency matrices of the inter-layer network, considering inter-layer dependencies. The inter-layer network is an unweighted directed network. yes Elements in: when Layer nodes Existence of pointers Layer nodes When connecting edges, ,otherwise ; Based on the above definition, the general form of a multilayer network model can be represented by a hyperadjacency matrix as follows: (1)

[0081] Unit 602 is used to extract the node centrality index of the multi-layer network model. By normalizing the node centrality index and assigning weights to the node centrality index, the single-layer node importance of each node in the single-layer network of the multi-layer network model is calculated. Based on the single-layer node importance and the total number of infrastructure nodes, the comprehensive importance of each node in the multi-layer network is determined. Preferably, the determining unit 602 is used to extract the node centrality index of the multilayer network model, and to calculate the single-layer node importance of each node in the single-layer network model by normalizing the node centrality index and assigning weights to the node centrality index, including: Extract node centrality metrics from multilayer network models. These metrics include node degree. Betweenness centrality Proximity centrality ; Normalize the node centrality index: In a network containing N nodes, the maximum possible degree of a node is The node degree normalization process is as follows: (4) in, The adjacency matrix is ​​the first... i Line number j The value of the element at the corresponding position in the column; N is the number of nodes in the network; j Number the node location.

[0082] In various types of networks, the central node of a star network has the largest betweenness value. All other pairs of nodes except the central node have a unique shortest path that passes through it. The betweenness value of this node is the number of these shortest paths. Betweenness centrality normalization is as follows: (5) in, For node pairs The nodes that are passed through in all shortest paths between them i Quantity; For node pairs The number of shortest paths between them; s , t , i Number the node location.

[0083] Obtain the importance evaluation index of infrastructure network nodes to determine the importance of nodes in a single layer: (6) in, The normalized node degree weights; The normalized betweenness centrality weight; These are the normalized near-centrality weights.

[0084] Preferably, the determining unit 602 is used to determine the comprehensive importance of each node in the multi-layer network based on the importance of a single-layer node and the total number of infrastructure nodes, including: The importance of each node in the multi-layer network is determined by combining the importance of nodes in each layer: (7) (8) (9) (10) in, Indicates the first The overall importance of each power node; Indicates the first The importance of each power node within the power network layer; , They respectively represent the first Each power node is incorporated into the importance of the water and gas supply networks at the corresponding layer based on their interdependencies; , , These represent the importance coefficients of the power, water supply, and gas network layers, respectively. , , These represent the number of nodes for electricity, water supply, and gas, respectively.

[0085] The acquisition unit 603 is used to obtain the operating status of the infrastructure at different times by dynamically updating the network structure state of the multi-layer network model and measuring the operating level of the updated multi-layer network model based on the multi-layer network model and the comprehensive importance of each node. Preferably, the acquisition unit 603 is used to acquire the operational status of the infrastructure at different times by dynamically updating the network structure state of the multi-layer network model based on the multi-layer network model and the comprehensive importance of each node, and by measuring the operational level of the updated multi-layer network model, including: Sure The hyperadjacency matrix of the multilayer network model at time step 1 is: ; Update the power layer adjacency matrix based on power system node failure status. And update the dependency matrix of electricity, water and gas supply. , ; Update the adjacency matrices of the water supply layer and the gas supply layer according to the changes in dependency relationships. , ; Based on the updated power layer adjacency matrix and the updated power layer adjacency matrix , , obtain New state matrix at time 1 ; Calculate the operational level of the updated multi-layer network model and obtain the operational status of the infrastructure at different times: (11) in, for The overall operational level of the real-time infrastructure; , , These are respectively represented as electricity, water supply, and gas networks. The operational level at any given moment, Let the area of ​​the triangle representing the current operating status of the three-layer network of electricity, water supply, and gas be the area of ​​the triangle. Let be the area of ​​the triangle formed by the three-layer network of electricity, water supply, and gas at the initial moment.

[0086] Result unit 604 is used to set up various types of simulation scenarios, verify the comprehensive importance based on each simulation scenario, obtain the operational status assessment results of the infrastructure under each simulation scenario, and complete the coupling failure risk assessment.

[0087] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for assessing the risk of coupled failure of urban infrastructure.

[0088] This invention provides an electronic device, comprising: The computer-readable storage medium mentioned above; and One or more processors for executing a program in a computer-readable storage medium.

[0089] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0090] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0091] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0093] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0094] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0095] The invention has been described with reference to a few embodiments. However, as will be known to those skilled in the art, and as defined in the appended claims, other embodiments besides those disclosed above fall equivalently within the scope of the invention.

[0096] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed unless explicitly stated otherwise.

Claims

1. A method for assessing the risk of coupled failure of urban infrastructure, characterized in that, The method includes: Based on the internal network structure characteristics of infrastructure and the coupling dependencies between different types of infrastructure, a multi-layer network model of infrastructure is established. The node centrality index of the multi-layer network model is extracted. By normalizing and weighting the node centrality index, the single-layer node importance of each node in the single-layer network of the multi-layer network model is calculated. Based on the single-layer node importance and the total number of infrastructure nodes, the comprehensive importance of each node in the multi-layer network is determined. Based on the multi-layer network model and the comprehensive importance of each node, the network structure state of the multi-layer network model is dynamically updated, and the operational level of the updated multi-layer network model is measured to obtain the operational status of the infrastructure at different times. Multiple simulation scenarios of different types are set up, the comprehensive importance is verified based on each simulation scenario, and the operational status assessment results of the infrastructure under each simulation scenario are obtained to complete the coupling failure risk assessment.

2. The method according to claim 1, characterized in that, The aforementioned multi-layer network model of infrastructure, based on the internal network structure characteristics of the infrastructure and the coupling dependencies between different types of infrastructure, includes: Use hyperadjacency matrix To represent a containing A multi-layered network model; in, This represents the set of adjacency matrices for each layer in a multilayer network model, where each layer is an unweighted and undirected network. yes Elements in: when Layer nodes and nodes When there are connecting edges between them, ,otherwise ; Let represent the set of adjacency matrices of the inter-layer network, considering inter-layer dependencies. The inter-layer network is an unweighted directed network. yes Elements in: when Layer nodes Existence of pointers Layer nodes When connecting edges, ,otherwise ; Based on the above definition, the general form of a multilayer network model can be represented by a hyperadjacency matrix as follows: （1）。 3. The method according to claim 2, characterized in that, The step of extracting the node centrality index of the multi-layer network model, and calculating the single-layer node importance of each node in the single-layer network of the multi-layer network model by normalizing and weighting the node centrality index, includes: Extract the node centrality index of the multilayer network model, the node centrality index including: node degree. Betweenness centrality Proximity centrality ; The node centrality index is normalized: In a network with N nodes, the maximum possible degree of a node is The node degree normalization process is as follows: （4） in, The first in the adjacency matrix i Line number j The value of the element at the corresponding position in the column; N is the number of nodes in the network; j Number the node location. In various types of networks, the central node of a star network has the largest betweenness value. All other pairs of nodes except the central node have a unique shortest path that passes through it. The betweenness value of this node is the number of these shortest paths. The betweenness centrality normalization process is as follows: （5） in, For node pairs The nodes that are passed through in all shortest paths between them i Quantity; For node pairs The number of shortest paths between them; s , t , i Number the node location. Obtain the importance evaluation index of infrastructure network nodes to determine the importance of nodes in a single layer: （6） in, The normalized node degree weights; The normalized betweenness centrality weight; These are the normalized near-centrality weights.

4. The method according to claim 3, characterized in that, The determination of the overall importance of each node in the multi-layer network based on the importance of the single-layer nodes and the total number of infrastructure nodes includes: The importance of each node in the multi-layer network is determined by combining the importance of each node in the single-layer network: （7） （8） （9） （10） in, Indicates the first The overall importance of each power node; Indicates the first The importance of each power node within the power network layer; , They respectively represent the first Each power node is incorporated into the importance of the water and gas supply networks at the corresponding layer based on their interdependencies; , , These represent the importance coefficients of the power, water supply, and gas network layers, respectively. , , These represent the number of nodes for electricity, water supply, and gas, respectively.

5. The method according to claim 1, characterized in that, The process involves dynamically updating the network structure state of the multi-layer network model based on the comprehensive importance of each node, calculating the operational level of the updated multi-layer network model, and obtaining the operational status of the infrastructure at different times, including: Sure The superadjacency matrix of the multilayer network model at time t is ; Update the power layer adjacency matrix based on power system node failure status. And update the dependency matrix of electricity, water and gas supply. , ; Update the adjacency matrices of the water supply layer and the gas supply layer according to the changes in dependency relationships. , ; Based on the updated power layer adjacency matrix And the updated power layer adjacency matrix , , obtain New state matrix at time 1 ; The operational level of the updated multi-layer network model is calculated to obtain the operational status of the infrastructure at different times: （11） in, for The overall operational level of the real-time infrastructure; , , These are respectively represented as electricity, water supply, and gas networks. The operational level at any given moment, Let the area of ​​the triangle representing the current operating status of the three-layer network of electricity, water supply, and gas be the area of ​​the triangle. Let be the area of ​​the triangle formed by the three-layer network of electricity, water supply, and gas at the initial moment.

6. A method system for assessing the coupled failure risk of urban infrastructure, characterized in that, The system includes: Establishment unit, used to build a multi-layer network model of infrastructure based on the internal network structure characteristics of infrastructure and the coupling dependencies between different types of infrastructure; A determining unit is used to extract the node centrality index of the multi-layer network model, and calculate the single-layer node importance of each node in the single-layer network of the multi-layer network model by normalizing and weighting the node centrality index; based on the single-layer node importance and the total number of infrastructure nodes, the comprehensive importance of each node in the multi-layer network is determined. The acquisition unit is used to obtain the operating status of the infrastructure at different times by dynamically updating the network structure state of the multi-layer network model and measuring the operating level of the updated multi-layer network model based on the multi-layer network model and the comprehensive importance of each node. The results unit is used to set up various types of simulation scenarios, verify the comprehensive importance based on each simulation scenario, obtain the operational status assessment results of the infrastructure under each simulation scenario, and complete the coupling failure risk assessment.

7. The system according to claim 6, characterized in that, The establishment unit is used to establish a multi-layer network model of the infrastructure based on the internal network structure characteristics of the infrastructure and the coupling dependencies between different types of infrastructure, including: Use hyperadjacency matrix To represent a containing A multi-layered network model; in, This represents the set of adjacency matrices for each layer in a multilayer network model, where each layer is an unweighted and undirected network. yes Elements in: when Layer nodes and nodes When there are connecting edges between them, ,otherwise ; Let represent the set of adjacency matrices of the inter-layer network, considering inter-layer dependencies. The inter-layer network is an unweighted directed network. yes Elements in: when Layer nodes Existence of pointers Layer nodes When connecting edges, ,otherwise ; Based on the above definition, the general form of a multilayer network model can be represented by a hyperadjacency matrix as follows: （1）。 8. The system according to claim 7, characterized in that, The determining unit is used to extract the node centrality index of the multilayer network model, and calculate the single-layer node importance of each node in the single-layer network of the multilayer network model by normalizing the node centrality index and assigning weights to the node centrality index, including: Extract the node centrality index of the multilayer network model, the node centrality index including: node degree. Betweenness centrality Proximity centrality ; The node centrality index is normalized: In a network with N nodes, the maximum possible degree of a node is The node degree normalization process is as follows: （4） in, The first in the adjacency matrix i Line number j The value of the element at the corresponding position in the column; N is the number of nodes in the network; j Number the node location. In various types of networks, the central node of a star network has the largest betweenness value. All other pairs of nodes except the central node have a unique shortest path that passes through it. The betweenness value of this node is the number of these shortest paths. The betweenness centrality normalization process is as follows: （5） in, For node pairs The nodes that are passed through in all shortest paths between them i Quantity; For node pairs The number of shortest paths between them; s , t , i Number the node location. Obtain the importance evaluation index of infrastructure network nodes to determine the importance of nodes in a single layer: （6） in, The normalized node degree weights; The normalized betweenness centrality weight; These are the normalized near-centrality weights.

9. The system according to claim 8, characterized in that, The determining unit is used to determine the comprehensive importance of each node in the multi-layer network based on the importance of the single-layer nodes and the total number of infrastructure nodes, including: The importance of each node in the multi-layer network is determined by combining the importance of each node in the single-layer network: （7） （8） （9） （10） in, Indicates the first The overall importance of each power node; Indicates the first The importance of each power node within the power network layer; , They respectively represent the first Each power node is incorporated into the importance of the water and gas supply networks at the corresponding layer based on their interdependencies; , , These represent the importance coefficients of the power, water supply, and gas network layers, respectively. , , These represent the number of nodes for electricity, water supply, and gas, respectively.

10. The system according to claim 6, characterized in that, The acquisition unit is used to obtain the operational status of the infrastructure at different times by dynamically updating the network structure state of the multi-layer network model based on the multi-layer network model and the comprehensive importance of each node, and by measuring the operational level of the updated multi-layer network model, including: Sure The superadjacency matrix of the multilayer network model at time t is ; Update the power layer adjacency matrix based on power system node failure status. And update the dependency matrix of electricity, water and gas supply. , ; Update the adjacency matrices of the water supply layer and the gas supply layer according to the changes in dependency relationships. , ; Based on the updated power layer adjacency matrix And the updated power layer adjacency matrix , , obtain New state matrix at time 1 ; The operational level of the updated multi-layer network model is calculated to obtain the operational status of the infrastructure at different times: （11） in, for The overall operational level of the real-time infrastructure; , , These are respectively represented as electricity, water supply, and gas networks. The operational level at any given moment, Let the area of ​​the triangle representing the current operating status of the three-layer network of electricity, water supply, and gas be the area of ​​the triangle. Let be the area of ​​the triangle formed by the three-layer network of electricity, water supply, and gas at the initial moment.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-5.

12. An electronic device, characterized in that, include: The computer-readable storage medium as described in claim 11; as well as One or more processors for executing a program in the computer-readable storage medium.

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