A method and system for generating a sudden natural disaster failure scenario and identifying an influence thereof

Abstract

The application discloses a burst natural disaster fault scene generation and influence identification method and system, belongs to the technical field of power system disaster risk assessment and resilience modeling, and the method is characterized in that: an interdependence relation matrix is established based on a node set of a three-network coupling network model; a node damage probability is calculated in combination with node geographic coordinates, and an initial fault set is generated by using a Monte Carlo sampling method; a node state vector is defined, fault cascade propagation simulation is carried out, and a stable state of the node state vector is reached; power failure areas, communication out-of-control areas and traffic blockage areas are identified respectively; the initial fault set, the stable state and the three areas are collectively taken as simulated disaster scene sample outputs, and the identification is completed. The application improves the problem of difficult identification of multi-network cascade fault propagation and system interruption under complex disaster influence, supports automatic generation of samples and accurate identification of disaster influence areas, provides data support for subsequent work, and can be used for multi-network integrated evaluation and resource scheduling.

Description

Technical Field

[0001] This invention belongs to the field of power system disaster risk assessment and resilience modeling technology, specifically involving a method and system for generating and identifying the impact of sudden natural disaster failure scenarios. Background Technology

[0002] Currently, with the high degree of interconnection and integration of urban infrastructure, power systems, communication systems, and transportation systems have formed a highly coupled "multi-network convergence" critical infrastructure system. Under the influence of sudden extreme natural disasters (such as typhoons, floods, earthquakes, and mudslides), a single system failure may quickly trigger cross-network cascading failures through coupling relationships, forming a so-called "three-disruption" disaster scenario of "power outage-communication outage-circuit outage," which may threaten load recovery and urban operational resilience to a certain extent.

[0003] Currently, existing methods for assessing power disasters largely rely on modeling single systems, failing to adequately consider the dependencies and interactions between multiple networks. This makes it difficult to accurately capture power dispatch failures caused by communication outages or extended faults resulting from delays in repair resources due to road blockages. Furthermore, disaster impact modeling often employs static or rule-based sample generation methods, lacking systematic modeling of geographic space, probabilistic uncertainty, and cascading propagation mechanisms, thus hindering the formation of diverse and realistic sets of anticipated fault scenarios. In conclusion, current power disaster assessments fail to adequately consider the dependencies and interactions between multiple networks, resulting in an inability to accurately simulate the multi-network cascading fault propagation process under complex disaster impacts; and the lack of systematic modeling of geographic space, probabilistic uncertainty, and cascading propagation mechanisms makes it difficult to form diverse and realistic sets of anticipated fault scenarios. Summary of the Invention

[0004] This invention provides a method and system for identifying the generation and impact of sudden natural disaster fault scenarios. The purpose is to solve the problems in the current assessment of power disasters, which cannot fully consider the dependencies and interactions between multiple networks, resulting in the inability to accurately simulate the multi-network cascading fault propagation process under the influence of complex disasters; and the lack of systematic modeling of geographic space, probabilistic uncertainty and cascading propagation mechanisms, making it difficult to form a diverse and effective set of anticipated fault scenarios.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] This invention provides a method for generating and identifying the impact of sudden natural disaster failure scenarios, comprising the following steps:

[0007] S1. A three-network coupled network model is adopted, which includes the node sets and edge sets of the power grid, communication network and transportation network respectively. A mutual dependency matrix is ​​established based on the node sets. The mutual dependency matrix is ​​used to define the functional dependency relationship between the three types of network nodes.

[0008] S2. Based on the disaster intensity field model, the probability of damage to each node is calculated by combining the geographic coordinates of the nodes, and an initial fault set is generated by using the Monte Carlo sampling method based on the probability of damage to each node.

[0009] S3. Define the node state vector and simulate the cascading propagation of faults based on the interdependence matrix. Set the fault propagation rules and perform iterative calculations until the node state vector reaches a stable state.

[0010] S4. Based on the node status in the steady state, identify the power failure area, communication failure area, and traffic blockage area respectively;

[0011] Among them, the identification of traffic blockage areas is based on the path reachability analysis from traffic network nodes to a specified starting point, and the identification of traffic blockage areas is based on the Dijkstra algorithm to calculate the remaining accessible paths;

[0012] S5. The initial fault set, stable state, power failure area, communication failure area, and traffic blockage area are combined as a sample of a simulated disaster scenario to complete the generation and identification of sudden natural disaster fault scenarios.

[0013] In some implementations, the three-network coupled network model in S1 includes:

[0014] Power grid ;

[0015] in This represents the set of power grid nodes, including various power sources, substations, and load nodes. Indicates a collection of transmission lines;

[0016] Telecommunications Network ;

[0017] in, This refers to communication network base stations, core routers, and relay equipment. Indicates a communication link;

[0018] Transportation Network ;

[0019] in, This represents the set of key nodes in the transportation network. Indicates a road connection.

[0020] Furthermore, in S1, the dependency matrix includes:

[0021] ;

[0022] in, The total number of power grid nodes. This represents the total number of nodes in the transportation network. Total number of communication networks; for Dimension This represents the total number of nodes across the three networks.

[0023] .

[0024] Furthermore, in S1, in the dependency matrix, the dependency of the scheduling node in the power grid on the control node in the communication network is a one-way dependency; the dependency of the communication node in the communication network on the power supply node is a two-way dependency.

[0025] In some implementations, in S2, the disaster intensity field model is represented by the following two-dimensional normal distribution function:

[0026] ;

[0027] in, For disaster intensity field, As the disaster center, These are parameters of the distribution function, used to control the range of influence.

[0028] Define nodes The probability of damage is:

[0029] ;

[0030] in, This represents the probability of damage. For node coordinates, It is a regulating factor.

[0031] Furthermore, in S2, an initial fault set is generated by combining the damage probability with the Monte Carlo sampling method. ,satisfy:

[0032] ;

[0033] in, For the initial fault set, Data obtained from sampling in Monte Carlo.

[0034] In some implementations, the fault cascading propagation simulation in S3 includes:

[0035] Define state vector This indicates that the node is at time step The functional status is 1 for available and 0 for fault.

[0036] Propagation rules are defined based on the interdependence matrix:

[0037] ;

[0038] Repeat the propagation iteration until a stable state vector is reached. satisfy:

[0039] ;

[0040] in, This is a dependency matrix, reflecting the degree of influence of vectors between nodes. The stable state vector reflects the nodes In time The stable state at any given moment This is the symbol for mathematical multiplication.

[0041] In some implementations, the identification of power failure areas, communication failure areas, and traffic disruption areas in S4 includes:

[0042] Power failure area Defined as:

[0043] ;

[0044] Communication outage area Defined as:

[0045] ;

[0046] Traffic disruption area Defined as:

[0047] ;

[0048] in, Represents all slave nodes To the starting point of transportation The set of feasible paths.

[0049] In some implementations, in S5, the disaster scenario sample output is in the form of a quadruple:

[0050] ;

[0051] in, This is a set of disaster scenario sample outputs. This is a fault scenario involving the power grid, transportation network, and communication network.

[0052] This invention also provides a system for generating and identifying the impact of sudden natural disaster fault scenarios. The system includes a model function definition module, an initial fault sampling module, a fault cascade propagation simulation module, an impact area identification module, and a sample output module, wherein:

[0053] Model Function Definition Module: This module is used to adopt a three-network coupled network model that includes the node sets and edge sets of the power grid, communication network, and transportation network, and to establish a dependency matrix based on the node sets. The dependency matrix is ​​used to define the functional dependencies between the three types of network nodes.

[0054] Initial fault sampling module: It is used to calculate the damage probability of each node based on the disaster intensity field model and the geographic coordinates of the nodes, and to generate an initial fault set based on the damage probability of each node using the Monte Carlo sampling method.

[0055] Fault cascading propagation simulation module: used to define node state vectors and simulate fault cascading propagation based on the interdependence matrix, set fault propagation rules and perform iterative calculations until the node state vectors reach a stable state;

[0056] Impact Area Identification Module: Used to identify power failure areas, communication failure areas, and traffic disruption areas based on the node status in a stable state;

[0057] Among them, the identification of traffic blockage areas is based on the path reachability analysis from traffic network nodes to a specified starting point, and the identification of traffic blockage areas is based on the Dijkstra algorithm to calculate the remaining accessible paths;

[0058] Sample output module: Used to output the initial fault set, stable state, power failure area, communication failure area and traffic blockage area as a simulated disaster scenario sample, and complete the generation and identification of sudden natural disaster fault scenarios.

[0059] Compared with existing technologies, the present invention provides a method and system for identifying the generation and impact of sudden natural disaster failure scenarios, which has the following advantages:

[0060] This invention presents a method for generating and identifying the impact of sudden natural disaster failure scenarios. It integrates failure scenario modeling and three-disruption (power outage, communication failure, and traffic disruption) area identification for three types of infrastructure: power, information, and transportation. The method constructs a three-network coupled network model, comprehensively considering the spatial location, functional categories, and interdependencies among the three networks to characterize the complex coupling structure and propagation mechanism among the infrastructure. An interdependency matrix is ​​introduced into the three-network coupled network model to accurately describe the dependency logic between scheduling nodes, communication control nodes, and key transportation facilities, supporting heterogeneous representations of unidirectional and bidirectional dependencies, thus improving the ability to express the interconnected attributes of real systems. Addressing the spatial distribution characteristics of natural disasters, this invention uses a two-dimensional Gaussian distribution to model the disaster intensity field and combines node geographic coordinates and terrain weighting functions to define node failure probabilities. Furthermore, this invention uses a Monte Carlo method to sample and generate an initial failure set, effectively integrating disaster uncertainty and geographical heterogeneity, thereby improving the rationality of scenario generation and the credibility of simulation.

[0061] Furthermore, this invention constructs a cascaded fault evolution model through state vector-based fault propagation simulation. Considering the evolution of node functional states, it sets fault trigger thresholds to simulate the cascading failure phenomenon in actual disasters, iteratively updating node states until stability is achieved, thus fully presenting the dynamic evolution process of the fault. For the key functional characteristics of three types of networks, discrimination logic is designed for power outages, communication failures, and traffic disruptions. Communication connectivity is determined in conjunction with core node judgment, while traffic disruptions are determined using path reachability analysis and Dijkstra's algorithm, improving the granularity and accuracy of impact identification. This invention supports multiple disaster simulations and batch scenario generation, forming a structured fault scenario sample set as an important input for risk assessment, resilience analysis, and recovery strategy optimization. By introducing a scenario quadruple representation, it comprehensively records the initial fault, propagation final state, and characteristics of the three-disruption regions, providing data support for subsequent decision-making.

[0062] Furthermore, this invention not only breaks through the limitations of single-network fault analysis, but also systematically reveals the evolutionary laws of fault propagation mechanisms and affected areas in multi-network coupled infrastructure; at the same time, through dual modeling of geography and function and multi-round propagation simulation, it enhances the breadth and depth of disaster scenario generation and has good generalization ability; the method of this invention can be widely applied to the research on fault risk analysis and prevention and control strategies in urban multi-network fusion scenarios, and has certain applicability. Attached Figure Description

[0063] The accompanying drawings are provided to further understand the invention and constitute a part of this invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0064] Figure 1This is a flowchart illustrating a method for identifying the generation and impact of sudden natural disaster failure scenarios according to the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0066] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0067] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0068] It should be noted that the apparatus and methods disclosed in the embodiments herein can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments herein. In this regard, each block in a flowchart or block diagram may represent a module, program, or part of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system to perform the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0069] In addition, the functional modules in the various embodiments of this article can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0070] How to construct a systematic multi-layer coupled network vulnerability modeling framework, and combine disaster probability modeling and cascading propagation simulation, to systematically generate complex fault samples and affected areas under the typical "three disruptions" scenario of "power outage, communication failure, and traffic disruption", so as to provide technical support for disaster prediction, emergency response and resilience improvement of distribution network.

[0071] like Figure 1 As shown, the present invention provides a method for generating and identifying the impact of sudden natural disaster failure scenarios, comprising the following steps:

[0072] S1. A three-network coupled network model is adopted, which includes the node sets and edge sets of the power grid, communication network and transportation network respectively. A mutual dependency matrix is ​​established based on the node sets. The mutual dependency matrix is ​​used to define the functional dependency relationship between the three types of network nodes.

[0073] S2. Based on the disaster intensity field model, the probability of damage to each node is calculated by combining the geographic coordinates of the nodes, and an initial fault set is generated by using the Monte Carlo sampling method based on the probability of damage to each node.

[0074] S3. Define the node state vector and simulate the cascading propagation of faults based on the interdependence matrix. Set the fault propagation rules and perform iterative calculations until the node state vector reaches a stable state.

[0075] S4. Based on the node status in the steady state, identify the power failure area, communication failure area, and traffic blockage area respectively;

[0076] Among them, the identification of traffic blockage areas is based on the path reachability analysis from traffic network nodes to a specified starting point, and the identification of traffic blockage areas is based on the Dijkstra algorithm to calculate the remaining accessible paths;

[0077] S5. The initial fault set, stable state, power failure area, communication failure area, and traffic blockage area are combined as a sample of a simulated disaster scenario to complete the generation and identification of sudden natural disaster fault scenarios.

[0078] This invention addresses the coupling dependency characteristics of power grids, communication networks, and transportation networks under sudden extreme natural disasters. It proposes a multi-layered coupled network modeling method, modeling the power grid as a topology layer, the communication network as a logic layer, and the transportation network as a support layer. The functional coupling between nodes in the three networks is expressed through a dependency matrix. This invention utilizes disaster probability distribution modeling, Monte Carlo sampling, and cascading propagation mechanisms to generate a complex fault sample set that conforms to the distribution of disaster intensity and geographical impact, forming fault propagation paths and affected areas under typical "three disruptions" (power outage, communication failure, and traffic disruption). By introducing a node vulnerability evaluation model based on centrality indicators, it identifies key nodes and path bottlenecks affecting load recovery, achieving a systematic assessment and identification of fault node sets, propagation links, and affected areas under different disaster scenarios.

[0079] In some embodiments, the identification method of the present invention outputs each simulation result as a scenario quadruple when generating disaster scenario samples, which is used to support subsequent disaster resilience assessment and strategy optimization tasks; it supports batch generation of disaster scenario sample sets under different parameters, realizing diverse simulation under high-dimensional disaster disturbance space.

[0080] This invention also provides a system for generating and identifying the impact of sudden natural disaster fault scenarios. The system includes a model function definition module, an initial fault sampling module, a fault cascade propagation simulation module, an impact area identification module, and a sample output module, wherein:

[0081] Model Function Definition Module: This module is used to adopt a three-network coupled network model that includes the node sets and edge sets of the power grid, communication network, and transportation network, and to establish a dependency matrix based on the node sets. The dependency matrix is ​​used to define the functional dependencies between the three types of network nodes.

[0082] Initial fault sampling module: It is used to calculate the damage probability of each node based on the disaster intensity field model and the geographic coordinates of the nodes, and to generate an initial fault set based on the damage probability of each node using the Monte Carlo sampling method.

[0083] Fault cascading propagation simulation module: used to define node state vectors and simulate fault cascading propagation based on the interdependence matrix, set fault propagation rules and perform iterative calculations until the node state vectors reach a stable state;

[0084] Impact Area Identification Module: Used to identify power failure areas, communication failure areas, and traffic disruption areas based on the node status in a stable state;

[0085] Among them, the identification of traffic blockage areas is based on the path reachability analysis from traffic network nodes to a specified starting point, and the identification of traffic blockage areas is based on the Dijkstra algorithm to calculate the remaining accessible paths;

[0086] Sample output module: Used to output the initial fault set, stable state, power failure area, communication failure area and traffic blockage area as a simulated disaster scenario sample, and complete the generation and identification of sudden natural disaster fault scenarios.

[0087] The following detailed description of the present invention, a method and system for identifying the generation and impact of sudden natural disaster failure scenarios, is provided through specific embodiments.

[0088] The identification method of the present invention includes:

[0089] S1. Construct a three-network coupled model, including the definition of nodes and connections in the power grid, communication network and transportation network, and establish a mutual dependence matrix among the nodes of the three networks to describe the direction and intensity of functional dependencies.

[0090] Constructing a three-network coupled model includes the following steps:

[0091] Power grid ,in This represents the set of power grid nodes, including various power sources, substations, and load nodes. Indicates a collection of transmission lines;

[0092] Telecommunications Network ,in This refers to communication network base stations, core routers, and relay equipment. Indicates a communication link;

[0093] Transportation Network ,in This represents the set of key traffic points in a transportation network, such as intersections and bridge nodes. Indicates road connections;

[0094] Establish a dependency matrix ;

[0095] Define the dependencies between various types of nodes, where Represents a node Functional dependency nodes ;

[0096] This indicates no dependency relationship. The total number of power grid nodes. This represents the total number of nodes in the transportation network. Total number of communication networks; for Dimension This represents the total number of nodes across the three networks. .

[0097] S2. Perform disaster impact modeling and initial fault sampling. Construct a disaster intensity field using a two-dimensional Gaussian function. Combine node coordinates and terrain weighting coefficients to calculate the probability of node damage. Use the Monte Carlo method to generate an initial fault set.

[0098] Disaster impact modeling and initial fault sampling include the following steps:

[0099] For the study area Set disaster intensity field Follows a two-dimensional normal distribution:

[0100] ;

[0101] in, For disaster intensity field, As the disaster center, These are parameters of the distribution function, used to control the range of influence.

[0102] Define nodes The probability of damage is:

[0103] ;

[0104] in, For node coordinates, It is a regulating factor.

[0105] An initial set of faults is generated by combining Monte Carlo sampling methods with historical experience data. ,satisfy:

[0106] ;

[0107] S3. Perform cascade propagation simulation and evolution modeling. Based on the interdependence matrix, define the node state update rules and introduce the propagation threshold to simulate the gradual spread of the fault among the three networks until the system state converges to a stable state.

[0108] Fault propagation simulation and cascade evolution modeling include the following steps:

[0109] Define state vector This indicates that the node is at time step The functional status is 1 for available and 0 for fault.

[0110] Based on interdependence matrix Define the propagation rules:

[0111] ;

[0112] Repeat the propagation iteration until a stable state is reached. satisfy:

[0113] ;

[0114] in, This is a dependency matrix, reflecting the degree of influence of vectors between nodes. The stable state vector reflects the nodes In time The stable state at any given moment This is the symbol for mathematical multiplication.

[0115] S4. Identify the three affected areas: power outage area, communication outage area, and traffic blockage area, respectively, based on the final state of the nodes and graph connectivity. The identification of the traffic blockage area is based on Dijkstra's algorithm to calculate the remaining access paths.

[0116] Identification of the areas affected by the "three disruptions":

[0117] Power failure area Defined as:

[0118] ;

[0119] Communication outage area Defined as:

[0120] ;

[0121] Traffic disruption area Defined as:

[0122] ;

[0123] in, Represents all slave nodes To the starting point of transportation The set of feasible paths.

[0124] S5. Output scenario quadruples, generating scenario samples for each simulation, for subsequent risk assessment, strategy optimization, etc., specifically:

[0125] ;

[0126] In the interdependence matrix of the present invention In the power grid, the dispatching nodes depend on the control nodes in the communication network, and the dependency is unidirectional; the communication nodes have a bidirectional dependency on the power supply nodes.

[0127] The intensity field of the present invention Topographic weighting coefficients can be introduced based on different disaster types (such as landslides, earthquakes, and floods). , so that:

[0128] ;

[0129] in: Weights are used to describe the impact of the three-network infrastructure (especially the ability to restore traffic and perform maintenance and repair) to correct the probability of failure. This indicates that the terrain in this area makes it more prone to disaster amplification / fault propagation;

[0130] In the propagation simulation of this invention, a delayed propagation trigger threshold can be defined in the propagation chain for faulty nodes. When the number of functional nodes that a node depends on fails exceeds the threshold, a delayed propagation trigger threshold is set. At that time, propagation is activated:

[0131] ;

[0132] The analysis of traffic network blockage areas in this invention utilizes Dijkstra's algorithm to analyze the set of failed edges. The remaining map is then used for path search to identify traffic island areas.

[0133] The identification method of the present invention supports batch generation. A scenario sample set is composed of samples with different disaster intensities, fault combinations, and three-fault areas:

[0134] ;

[0135] This is a set of scene samples.

[0136] In summary, this invention provides a method and system for generating and identifying the impact of sudden natural disaster failure scenarios. It is applicable to vulnerability modeling, complex fault sample generation, and identification of three-stage failure scenarios in coupled power grid, communication network, and transportation network systems under sudden extreme natural disasters. This invention improves upon existing technologies that cannot accurately simulate the propagation process of multi-network cascaded faults under complex disaster impacts and address the difficulty in identifying functional interruptions in the "electricity-communication-transportation" system. By integrating complex network modeling, probabilistic disaster modeling, interdependent propagation mechanisms, and graph search algorithms, it supports the automatic generation of large-scale scenario samples and accurate identification of disaster-affected areas. This provides high-quality data support for subsequent resilience assessment and emergency strategy optimization. It can be used for disaster impact assessment of multi-network integrated infrastructure, key node identification, and emergency resource allocation optimization, demonstrating significant practicality.

[0137] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Anyone skilled in the art can readily implement the present invention according to the description and above. Any modifications, alterations, or equivalent variations made using the technical content disclosed above are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A method for identifying the generation and impact of sudden natural disaster failure scenarios, characterized in that, Includes the following steps: S1. A three-network coupled network model is adopted, which includes the node sets and edge sets of the power grid, communication network and transportation network respectively. A mutual dependency matrix is ​​established based on the node sets. The mutual dependency matrix is ​​used to define the functional dependency relationship between the three types of network nodes. S2. Based on the disaster intensity field model, the probability of damage to each node is calculated by combining the geographic coordinates of the nodes, and an initial fault set is generated by using the Monte Carlo sampling method based on the probability of damage to each node. The disaster intensity field model is represented by the following two-dimensional normal distribution function: ； in, For disaster intensity field, As the disaster center, These are parameters of the distribution function, used to control the range of influence. Define nodes The probability of damage is: ； in, This represents the probability of damage. For node coordinates, As a regulating factor; S3. Define the node state vector and simulate the cascading propagation of faults based on the interdependence matrix. Set the fault propagation rules and perform iterative calculations until the node state vector reaches a stable state. The fault cascading propagation simulation includes: Define state vector This indicates that the node is at time step The functional status is 1 for available and 0 for fault. Propagation rules are defined based on the interdependence matrix: ； Repeat the propagation iteration until a stable state vector is reached. satisfy: ； in, This is a dependency matrix, reflecting the degree of influence of vectors between nodes. The stable state vector reflects the nodes In time The stable state at any given moment It is the symbol for mathematical multiplication; S4. Based on the node status in the steady state, identify the power failure area, communication failure area, and traffic blockage area respectively; Among them, the identification of traffic blockage areas is based on the path reachability analysis from traffic network nodes to a specified starting point, and the identification of traffic blockage areas is based on the Dijkstra algorithm to calculate the remaining accessible paths; S5. The initial fault set, stable state, power failure area, communication failure area, and traffic blockage area are combined as a sample of a simulated disaster scenario to complete the generation and identification of sudden natural disaster fault scenarios.

2. The method for identifying the generation and impact of sudden natural disaster failure scenarios according to claim 1, characterized in that, In S1, the three-network coupled network model includes: Power grid ; in This represents the set of power grid nodes, including various power sources, substations, and load nodes. Indicates a collection of transmission lines; Telecommunications Network ; in, This refers to communication network base stations, core routers, and relay equipment. Indicates a communication link; Transportation Network ; in, This represents the set of key nodes in the transportation network. Indicates a road connection.

3. The method for identifying the generation and impact of sudden natural disaster failure scenarios according to claim 2, characterized in that, In S1, the dependency matrix includes: ； in, The total number of power grid nodes. This represents the total number of nodes in the transportation network. Total number of communication networks; for Dimension This represents the total number of nodes across the three networks. 。 4. The method for identifying the generation and impact of sudden natural disaster failure scenarios according to claim 3, characterized in that, In S1, in the interdependence matrix, the dependence of the scheduling node in the power grid on the control node in the communication network is a one-way dependency; the dependence of the communication node in the communication network on the power supply node is a two-way dependency.

5. The method for identifying the generation and impact of sudden natural disaster failure scenarios according to claim 1, characterized in that, In S2, an initial fault set is generated by combining the damage probability with the Monte Carlo sampling method. ,satisfy: ； in, For the initial fault set, Data obtained from sampling in Monte Carlo.

6. The method for identifying the generation and impact of sudden natural disaster failure scenarios according to claim 1, characterized in that, In S4, the identification of power failure areas, communication outage areas, and traffic disruption areas includes: Power failure area Defined as: ； Communication outage area Defined as: ； Traffic disruption area Defined as: ； in, Represents all slave nodes To the starting point of transportation The set of feasible paths.

7. The method for identifying the generation and impact of sudden natural disaster failure scenarios according to claim 1, characterized in that, In S5, the disaster scenario sample output is in the form of a quadruple: ； in, This is a set of disaster scenario sample outputs. This is a fault scenario involving the power grid, transportation network, and communication network.

8. A system for identifying the generation and impact of sudden natural disaster failure scenarios, characterized in that, The system is used to implement the method for generating and identifying the impact of sudden natural disaster fault scenarios as described in any one of claims 1-7. The system includes a model function definition module, an initial fault sampling module, a fault cascade propagation simulation module, an impact area identification module, and a sample output module, wherein: Model Function Definition Module: This module is used to adopt a three-network coupled network model that includes the node sets and edge sets of the power grid, communication network, and transportation network, and to establish a dependency matrix based on the node sets. The dependency matrix is ​​used to define the functional dependencies between the three types of network nodes. Initial fault sampling module: It is used to calculate the damage probability of each node based on the disaster intensity field model and the geographic coordinates of the nodes, and to generate an initial fault set based on the damage probability of each node using the Monte Carlo sampling method. Fault cascading propagation simulation module: used to define node state vectors and simulate fault cascading propagation based on the interdependence matrix, set fault propagation rules and perform iterative calculations until the node state vectors reach a stable state; Impact Area Identification Module: Used to identify power failure areas, communication failure areas, and traffic disruption areas based on the node status in a stable state; Among them, the identification of traffic disruption areas is based on path reachability analysis from traffic network nodes to a designated starting point; Sample output module: Used to output the initial fault set, stable state, power failure area, communication failure area and traffic blockage area as a simulated disaster scenario sample, and complete the generation and identification of sudden natural disaster fault scenarios.

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