Emergency natural disaster fault scene generation and influence identification method and system

Abstract

The invention discloses a sudden natural disaster fault scene generation and influence identification method and system, and belongs to the technical field of power system disaster risk assessment and toughness modeling, and the method comprises the steps: building an interdependence relation matrix based on a node set of a three-network coupling network model; calculating the damage probability of each node in combination with the geographic coordinates of the nodes, and generating an initial fault set by using a Monte Carlo sampling method; defining a node state vector, and performing fault cascade propagation simulation until the node state vector reaches a stable state; respectively identifying a power failure area, a communication out-of-control area and a traffic blocking area; and outputting the initial fault set, the stable state and the three regions together as a simulated disaster scene sample to finish identification. According to the method, the problem that multi-network cascade fault propagation and system interruption identification are difficult under the influence of complex disasters is solved, automatic generation of samples and accurate identification of disaster influence areas are supported, data support is provided for follow-up, and the method can be used for evaluation and resource scheduling of multi-network integration.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system disaster risk assessment and resilience modeling, and particularly relates to a sudden natural disaster fault scenario generation and influence identification method and system. BACKGROUND

[0002] At present, with the high interconnection and fusion of urban infrastructure, the power system, communication system and transportation system have formed a highly coupled "multi-network fusion" key infrastructure system. Under the action of sudden extreme natural disasters (such as typhoon, flood, earthquake, mudslide, etc.), a single system failure can quickly cause cross-network cascading failure through coupling relationship, forming a so-called "power outage - signal outage - road blockage" "three-break" disaster composite scenario, which may threaten the load recovery and urban operation resilience to some extent.

[0003] At present, the existing technology for power disaster assessment is mostly based on modeling of a single system, and cannot fully consider the dependence and interaction relationship between multiple networks, so it is difficult to accurately capture the power dispatch failure caused by communication interruption, or the extended failure caused by the delay of repair resources due to road blockage. In addition, the disaster influence modeling often adopts a static or rule-based sample generation method, which lacks system modeling of geographical space, probabilistic uncertainty and cascading propagation mechanism, and it is difficult to form a diversified, real and effective set of expected fault scenarios. Therefore, as described above, in the current power disaster assessment, the dependence and interaction relationship between multiple networks cannot be fully considered, which leads to the inability to accurately simulate the multi-network cascading failure propagation process under complex disaster influence; and the system modeling of geographical space, probabilistic uncertainty and cascading propagation mechanism is lacking, making it difficult to form a diversified, real and effective set of expected fault scenarios. SUMMARY

[0004] The application provides a sudden natural disaster fault scenario generation and influence identification method and system, which aims to solve the problem that in the current power disaster assessment, the dependence and interaction relationship between multiple networks cannot be fully considered, which leads to the inability to accurately simulate the multi-network cascading failure propagation process under complex disaster influence; and the system modeling of geographical space, probabilistic uncertainty and cascading propagation mechanism is lacking, making it difficult to form a diversified, real and effective set of expected fault scenarios.

[0005] To achieve the above purpose, the application adopts the following technical solutions: The application provides a sudden natural disaster fault scenario generation and influence identification method, which comprises the following steps: S1, a three-network coupling network model comprising node sets and edge sets of power network, communication network and transportation network is adopted, and a mutual dependence relationship matrix is established based on the node sets, the mutual dependence relationship matrix being used to define the functional dependence relationship between the three types of network nodes; S2, based on the disaster intensity field model, the node geographic coordinates are combined to calculate the damage probability of each node, and the initial fault set is generated based on the damage probability of each node using the Monte Carlo sampling method; S3, define the node state vector, and perform fault cascade propagation simulation based on the interdependence relationship matrix. Set the fault propagation rule for iteration calculation until the node state vector reaches a stable state; S4, according to the node state in the stable state, the power failure area, the communication out-of-control area and the traffic blocking area are identified respectively; Among them, the identification of traffic blocking area is based on the path reachability analysis of traffic network nodes to the specified starting point, and the traffic blocking area is identified based on Dijkstra algorithm to calculate the remaining accessible path. S5, the initial fault set, the stable state, the power failure area, the communication out-of-control area and the traffic blocking area are collectively used as a disaster scene sample output of a simulation to complete the generation and identification of the impact of the sudden natural disaster fault scene.

[0006] In some embodiments, in S1, the three-network coupled network model includes: Power grid ; Among them represents the set of power grid nodes, including various types of power sources, substations and load nodes, represents the set of transmission lines; Communication network ; Among them, represents the communication base station, core router and relay device of the communication network, represents the communication link; Traffic network ; Among them, represents the set of traffic key nodes of the traffic network, represents the road connection.

[0007] Further, in S1, the interdependence relationship matrix includes: ; Among them, is the total number of power grid nodes, is the total number of traffic network nodes, is the total number of communication network; is dimension, is the total number of three-network nodes; .

[0008] Further, in S1, in the interdependence matrix, the dependence of dispatch nodes in the power grid on control nodes in the communication network is a one-way dependence; the dependence of communication nodes in the communication network on power supply nodes is a two-way dependence.

[0009] In some embodiments, in S2, the disaster intensity field model is represented by a two-dimensional normal distribution function as follows: ; wherein, is the disaster intensity field, is the disaster center, is a distribution function parameter for controlling the influence range; The damage probability of a node is defined as: ; wherein, is the damage probability, is the node coordinate, is an adjustment factor.

[0010] Further, in S2, an initial fault set is generated by a Monte Carlo sampling method combined with the damage probability, satisfying: ; wherein, is the initial fault set, is data obtained by Monte Carlo sampling.

[0011] In some embodiments, in S3, the fault cascade propagation simulation includes: A state vector is defined, representing the functional state of a node at time step , 1 for available and 0 for failure; A propagation rule is defined based on the interdependence matrix: ; The propagation iteration is repeated until the stable state vector satisfies: ; wherein, is the interdependence matrix, reflecting the influence degree of the vector between nodes, is the stable state vector, reflecting the stable state of a node at time , is a mathematical continuous multiplication symbol.

[0012] In some embodiments, in S4, the identification of the power failure area, the communication out-of-control area, and the traffic blockage area includes: power failure region is defined as: ; communication failure region is defined as: ; traffic disruption region is defined as: ; wherein, denotes all feasible paths from the nodes to the traffic origin.

[0013] In some embodiments, in S5, the form of the disaster scenario sample output is in the form of a quadruple: ; wherein, is a set of disaster scenario sample outputs, is a power grid-communication grid-traffic grid failure scenario.

[0014] The present application also provides a sudden natural disaster failure scenario generation and impact identification system, the system comprising a model function definition module, an initial failure sampling module, a failure cascade propagation simulation module, an impact area identification module and a sample output module, wherein: The model function definition module is used to adopt a three-network coupled network model comprising node sets and edge sets of the power grid, the communication grid and the traffic grid, and to establish a mutual dependence relationship matrix based on the node sets, the mutual dependence relationship matrix being used to define the functional dependence relationship between the three types of network nodes; The initial failure sampling module is used to calculate the damage probability of each node based on a disaster intensity field model in combination with the node geographic coordinates, and to generate an initial failure set based on the damage probability of each node using a Monte Carlo sampling method; The failure cascade propagation simulation module is used to define a node state vector, and to perform failure cascade propagation simulation based on the mutual dependence relationship matrix, set failure propagation rules for iterative calculation until the node state vector reaches a stable state; The impact area identification module is used to identify the power failure region, the communication failure region and the traffic disruption region respectively according to the node state in the stable state; Wherein, the identification of the traffic disruption region is based on the path reachability analysis of the traffic network nodes to the specified origin, and the traffic disruption region identification is based on the Dijkstra algorithm to calculate the remaining accessible paths; ​Sample output module: used for outputting the initial fault set, stable state, power failure area, communication out-of-control area and traffic blocking area as a disaster scene sample of one simulation, to complete the generation of sudden natural disaster fault scene and the identification of influence.

[0015] Compared with the prior art, the sudden natural disaster fault scene generation and influence identification method and system has the following beneficial effects: The sudden natural disaster fault scene generation and influence identification method of the present application integrates the fault scene modeling of power, information and traffic three types of infrastructure and the three-break (power interruption, communication out-of-control, traffic blocking) area identification, and the method describes the complex coupling structure and propagation mechanism between the three-network infrastructure by constructing a three-network coupling network model, comprehensively considering the spatial position, function category and mutual dependence relationship between the three networks. The mutual dependence relationship matrix is introduced in the three-network coupling network model to accurately describe the dependence logic between the dispatching nodes, communication control nodes and traffic key facilities, support the heterogeneous representation of one-way and two-way dependence, and improve the expression ability of the interconnection properties of the real system. According to the spatial distribution characteristics of natural disasters, the present application models the disaster intensity field by using a two-dimensional Gaussian distribution, and defines the node failure probability in combination with the node geographic coordinates and the terrain weighting function. The present application further generates an initial fault set by the Monte Carlo method, effectively fusing the disaster uncertainty and geographical heterogeneity, and further improving the rationality and simulation credibility of the scene generation.

[0016] In addition, the present application constructs a cascading failure evolution model by the fault propagation simulation based on the state vector, sets a failure trigger threshold to simulate the step-by-step failure phenomenon in actual disasters on the basis of considering the node function state evolution, iteratively updates the node state until stable, and completely presents the dynamic evolution process of the failure. According to the key function characteristics of the three types of networks, the discrimination logic of power interruption, communication out-of-control and traffic blocking area is designed respectively, wherein the communication connectivity is combined with the core node judgment, and the traffic blocking area is combined with the path reachability analysis and Dijkstra algorithm, which improves the fineness and accuracy of the influence identification. The present application supports multiple disaster simulation and scene batch generation, forms a structured fault scene sample set, serves as an important input for risk assessment, resilience analysis and recovery strategy optimization, and comprehensively records the initial fault, propagation final state and three-break area characteristics by introducing a scene four-tuple expression method, to provide data support for subsequent decision-making.

[0017] Further, the present application not only breaks the limitation of single network fault analysis, but also reveals the evolution law of fault propagation mechanism and influence area in multi-network coupled infrastructure; meanwhile, through the dual modeling of geography and function and multi-round propagation simulation, the breadth and depth of disaster scene generation are improved, and the generalization ability is good; the method of the present application can be widely applied to fault risk analysis and prevention and control strategy research in the city multi-network fusion scene, and has certain applicability. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings are included to provide a further understanding of the present application, and are incorporated in and constitute a part of this specification, illustrate embodiments of the present application and serve to explain the principles of the present application, and are not intended to limit the present application.

[0019] Figure 1 A flowchart of a sudden natural disaster fault scene generation and influence recognition method of the present application. DETAILED DESCRIPTION

[0020] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor are within the scope of protection of the present application.

[0022] It should be noted that in this paper, the term "include", "contain" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such process, method, article or equipment. Without more limitation, the element defined by the sentence "including a" does not exclude the presence of other identical elements in the process, method, article or equipment including the element.

[0023] 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 drawings show possible architectural, functional, and operational scenarios of apparatuses, methods and computer program products according to various embodiments herein. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment or a portion of code which comprises one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in a different order than that shown in the figures. For example, two blocks shown in succession can in fact be executed substantially concurrently or in the reverse order, depending on the functionality involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by dedicated hardware-based systems that perform specified functions or actions, or can be implemented using a combination of dedicated hardware and computer instructions.

[0024] In addition, each functional module in various embodiments herein 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.

[0025] How to build a systematic multi-layer coupling network vulnerability modeling framework, combined with disaster probability modeling and cascade propagation simulation, to systematically generate complex failure samples and influence areas in the typical "power interruption, communication out of control, traffic blockage" "three break" scenarios, and provide technical support for power distribution network disaster resistance, emergency response and resilience improvement.

[0026] As shown in Figure 1 The natural disaster fault scenario generation and influence identification method provided by the application comprises the following steps: S1, a three-network coupling network model comprising node sets and edge sets of power networks, communication networks and traffic networks is adopted, and a mutual dependence relationship matrix is established based on the node sets, the mutual dependence relationship matrix being used to define functional dependence relationships between nodes of the three types of networks; S2, based on a disaster intensity field model, the damage probability of each node is calculated in combination with node geographic coordinates, and an initial fault set is generated based on the damage probability of each node by using a Monte Carlo sampling method; S3, a node state vector is defined, and fault cascade propagation simulation is performed based on the mutual dependence relationship matrix, and a fault propagation rule is set to perform iterative calculation until the node state vector reaches a stable state; S4, identifying the power failure area, the communication out-of-control area and the traffic blockage area according to the node state in the stable state; The traffic blockage area is identified based on path reachability analysis of the traffic network nodes to a specified starting point, and the traffic blockage area is identified based on Dijkstra algorithm to calculate the remaining reachable paths. S5, the initial fault set, the stable state, the power failure area, the communication out-of-control area and the traffic blockage area are collectively output as a disaster scene sample of a simulation, and the generation and identification of the fault scene of the sudden natural disaster are completed.

[0027] The identification method of the application is aimed at the coupling and dependence characteristics of the power grid, the communication network and the traffic network under the sudden extreme natural disaster, and a multi-layer coupled network modeling method is proposed, the power grid is modeled as a topology layer, the communication network is modeled as a logic layer, and the traffic network is modeled as a support layer, and the functional coupling between the nodes of the three networks is expressed through a mutual dependence relationship matrix. The application uses disaster probability distribution modeling, Monte Carlo sampling and cascade propagation mechanism to generate a set of complex fault samples that meet the disaster intensity distribution and geographical influence law, form the fault propagation path and the affected area under the typical "three breaks" (power interruption, communication out-of-control, traffic blockage) situation; by introducing a node vulnerability evaluation model based on the centrality index, the key nodes and path bottlenecks affecting load recovery are identified, and the system evaluation and identification of the fault node set, the propagation link and the influence area under different disaster scenes are realized.

[0028] In some embodiments, the identification method of the application outputs each simulation result as a scene four-tuple when generating a disaster scene sample, which is used to support subsequent disaster resilience evaluation and strategy optimization tasks; supports batch generation of disaster scene sample sets under different parameters, and realizes diversity simulation under a high-dimensional disaster disturbance space.

[0029] The application also provides a system for generating and identifying the fault scene of a sudden natural disaster, which comprises a model function definition module, an initial fault sampling module, a fault cascade propagation simulation module, an influence area identification module and a sample output module, wherein: The model function definition module is used to adopt a three-network coupled network model comprising the node set and the edge set of the power grid, the communication network and the traffic network, and to establish a mutual dependence relationship matrix based on the node set, the mutual dependence relationship matrix being used to define the functional dependence relationship between the nodes of the three networks; The initial fault sampling module is used to calculate the damage probability of each node based on the disaster intensity field model in combination with the geographic coordinates of the nodes, and to generate an initial fault set based on the damage probability of each node by using the Monte Carlo sampling method; Fault cascade propagation simulation module: used for defining node state vector and carrying out fault cascade propagation simulation based on interdependence relation matrix, setting fault propagation rule for iteration calculation until the node state vector reaches stable state; Impact area identification module: used for identifying power failure area, communication out-of-control area and traffic blockage area respectively according to node state in stable state; Wherein, the identification of traffic blockage area is based on path reachability analysis of traffic network node to specified starting point, and the traffic blockage area identification is based on Dijkstra algorithm to calculate residual reachable path; Sample output module: used for outputting initial fault set, stable state, power failure area, communication out-of-control area and traffic blockage area as disaster scene sample of one simulation, completing generation of sudden natural disaster fault scene and identification of impact.

[0030] The method and system for generating and identifying impact of sudden natural disaster fault scene will be further described in detail through specific embodiments.

[0031] The identification method of the application comprises: S1, constructing a three-network coupled network model, including definition of node and connection relationship of power grid, communication network and traffic network, and establishing interdependence relation matrix among nodes of three networks to describe function dependence direction and strength; The three-network coupled network model is constructed by the following steps: Power grid Wherein represents power grid node set containing various power sources, transformer substations and load nodes, represents power transmission line set; Communication network Wherein represents communication base station, core router and relay device of communication network, represents communication link; Traffic network Wherein represents traffic key point node set of traffic network such as intersection and bridge, represents road connection; Establishing interdependence relation matrix ; Defining dependence relation among various nodes, wherein represents node functional dependence node ; represents no dependence relation. Wherein is total number of power grid nodes, is total number of traffic network nodes, Total number of communication networks; Total number of nodes in the communication network Dimension, Total number of nodes in the three networks .

[0032] S2, disaster impact modeling and initial fault sampling, a two-dimensional Gaussian function is constructed to build a disaster intensity field, combined with node coordinates and terrain weighting coefficients, the node damage probability is calculated, and the Monte Carlo method is used to generate an initial fault set; Disaster impact modeling and initial fault sampling, comprising the following steps: For the study area , set the disaster intensity field obeys two-dimensional normal distribution: ; Wherein, disaster intensity field, disaster center, distribution function parameters, used to control the influence range; Define the damage probability of node : ; Wherein, node coordinates, adjusting factor.

[0033] Generate an initial fault set by Monte Carlo sampling method combined with historical experience data, meet: ; S3, perform cascading propagation simulation and evolution modeling, based on the interdependence relationship matrix, define node state update rule and introduce propagation threshold, simulate the gradual diffusion process of faults among the three networks, until the system state converges to a stable state; Fault propagation simulation and cascading evolution modeling, comprising the following steps: Define state vector , indicating the functional state of the node at time step , 1 is available, 0 is fault; Based on the interdependence matrix , define the propagation rule: ; Repeat the propagation iteration until the stable state satisfies: ; Wherein, interdependence relationship matrix, reflecting the influence degree of the node vector, stable state vector, reflecting the node At time Steady state at time Is the mathematical multiplication symbol.

[0034] S4, identify three break influence areas, respectively according to the node final state and graph connectivity, extract power interruption area, communication out-of-control area and traffic block area, wherein the traffic block area identification is based on Dijkstra algorithm to calculate the remaining accessible path; "Three break" influence area identification: Power failure area Defined as: ; Communication out-of-control area Defined as: ; Traffic block area Defined as: ; Wherein, Indicates the set of all feasible paths from node To the traffic starting point .

[0035] S5, output scene four-tuple, generate scene samples for each simulation, for subsequent risk assessment, strategy optimization and the like, specifically: ; In the interdependence matrix Of the application, the dispatching node in the power grid depends on the control node in the communication network, and the dependence direction is unidirectional; the communication node has a bidirectional dependence relationship with the power supply node.

[0036] The strength field Of the application can introduce a terrain weighting coefficient According to different disaster types (such as landslides, earthquakes, floods), so that: ; Wherein: Used to describe the influence weight caused by the ability of three-network infrastructure (especially recovery traffic, operation and maintenance repair) to correct the failure probability; Indicates that this area is more prone to disaster amplification / failure propagation due to terrain; In the propagation simulation of the application, the failure node can define a delay propagation trigger threshold in the propagation chain, when the number of failed function nodes that a node depends on exceeds the threshold , the propagation is activated: ; The analysis of the traffic network blocking area of the application searches for a path after the residual graph after the failure edge set by Dijkstra algorithm to identify the traffic island area.

[0037] The identification method of the application supports batch generation of different disaster intensities, fault combinations and three-break area samples to form a scene sample set: ; for the scene sample set.

[0038] In summary, the sudden natural disaster fault scene generation and influence identification method and system of the application are suitable for vulnerability modeling, complex fault sample generation and three-break scene identification of the power grid, communication network and traffic network coupled system under the condition of sudden extreme natural disasters. The application improves the problem in the prior art that the process of multi-network cascading failure propagation under the influence of complex disasters cannot be accurately simulated and the identification of the "electricity-communication-traffic" system function interruption is difficult. By fusing complex network modeling, probabilistic disaster modeling, mutual dependence propagation mechanism and graph search algorithm, the application supports automatic generation of large-scale scene samples and accurate identification of disaster influence areas, provides high-quality data support for subsequent resilience evaluation and emergency strategy optimization, can be used for disaster impact evaluation, key node identification and emergency resource allocation optimization of multi-network integrated infrastructure, and has better practicability.

[0039] Finally, it needs to be pointed out that: the above description is only a preferred embodiment of the application, and does not limit the application in any form; any person skilled in the art can easily implement the application according to the description and the above description, make some changes, modifications and equivalent changes of the above-mentioned technical content, which are equivalent embodiments of the application; at the same time, any equivalent changes, modifications and evolution of the above-mentioned embodiments according to the essential technology of the application are still within the protection scope of the technical solutions of the application.

Claims

1. A method for burst natural disaster failure scenario generation and impact identification, characterized in that, The method comprises the following steps: S1, a three-network coupling network model comprising node sets and edge sets of power grid, communication network and traffic network is adopted, and a mutual dependence relationship matrix is established based on the node sets, the mutual dependence relationship matrix being used to define functional dependence relationships between nodes of the three types of networks; S2, based on a disaster intensity field model, the damage probability of each node is calculated in combination with the geographic coordinates of the nodes, and an initial fault set is generated by using a Monte Carlo sampling method based on the damage probability of each node; S3, a node state vector is defined, and fault cascade propagation simulation is performed based on the mutual dependence relationship matrix, and iteration calculation is performed by setting fault propagation rules until the node state vector reaches a stable state; S4, the node states in the stable state are used to identify power failure areas, communication out-of-control areas and traffic blockage areas, respectively; In the S1, the three-network coupling network model comprises: In the S1, the mutual dependence relationship matrix comprises:

2. The method of claim 1, wherein the burst natural disaster failure scenario generation and impact identification method is characterized by, In the S1, in the mutual dependence relationship matrix, the dependence of dispatching nodes in the power grid on control nodes in the communication network is a one-way dependence; the dependence of communication nodes in the communication network on power supply nodes is a two-way dependence relationship. Power grid ; wherein denotes the set of power grid nodes, containing various types of power sources, substations and load nodes, denotes the set of transmission lines; Communication network ; wherein, represents a communication network communication base station, a core router and a relay device, represents a communication link; Transport network ; wherein, denotes a set of traffic key nodes of the traffic network, denotes a road connection.

3. The method of claim 2, wherein the burst natural disaster failure scenario generation and impact identification method is characterized by, In the S2, the disaster intensity field model is represented by the following two-dimensional normal distribution function: ； wherein, is the total number of power grid nodes, is the total number of transport grid nodes, is the total number of communication grid nodes; is the total number of nodes in the three grids, is the dimension, is the total number of three-grid nodes; 。 4. The method of claim 3, wherein the burst natural disaster failure scenario generation and impact identification method is characterized by, In the S3, the fault cascade propagation simulation comprises:

5. The method of claim 1, wherein the method further comprises: The propagation rules are defined based on the mutual dependence matrix: ； wherein, is a disaster intensity field, is a disaster center, is a distribution function parameter for controlling the influence range; Definition node The probability of impairment of the node is: ； wherein, is the probability of impairment, is the node coordinate, is the adjustment factor.

6. The method of claim 5, wherein the burst natural disaster failure scenario generation and impact identification method is characterized by, In the S2, an initial fault set is generated by a Monte Carlo sampling method combined with a damage probability , satisfies: ； wherein, is the initial set of faults, is the data obtained from Monte Carlo sampling.

7. The method of claim 1, wherein the method further comprises: In the S4, the identification of the power failure areas, the communication out-of-control areas and the traffic blockage areas comprises: Definition of state vector , representing the functional state of the node at time step , 1 for available, 0 for failed; In the S5, the disaster scenario sample output is in the form of a four-tuple: ； Repeat the propagation iteration until the steady state vector satisfies: ； wherein, is a mutual dependence matrix, reflecting the influence degree of the vector between nodes, is a stable state vector, reflecting the node at time moment of stable state, is a mathematical continuous multiplication symbol.

8. The method of claim 1, wherein the method further comprises: The system is used to implement the method for generating and identifying the impact of a sudden natural disaster fault scenario according to any one of claims 1-9, and the system comprises 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: Power failure area is defined as: ； Communication out-of-control area is defined as: ； Traffic disruption area is defined as: ； wherein, represents the set of all feasible paths from the node to the origin of transportation .

9. The method of claim 1, wherein the method further comprises: The model function definition module is used to adopt a three-network coupling network model comprising node sets and edge sets of power grid, communication network and traffic network, and to establish a mutual dependence relationship matrix based on the node sets, the mutual dependence relationship matrix being used to define functional dependence relationships between nodes of the three types of networks; ； wherein, is a set of disaster scenario samples, is a power grid-transportation grid-communication grid failure scenario.

10. A burst natural disaster failure scenario generation and identification of impacts system, characterized in that, The initial fault sampling module is used to calculate the damage probability of each node based on a disaster intensity field model in combination with the geographic coordinates of the nodes, and to generate an initial fault set by using a Monte Carlo sampling method based on the damage probability of each node; The fault cascade propagation simulation module is used to define a node state vector, and to perform fault cascade propagation simulation based on the mutual dependence relationship matrix, and to perform iteration calculation by setting fault propagation rules until the node state vector reaches a stable state; The impact area identification module is used to identify power failure areas, communication out-of-control areas and traffic blockage areas, respectively, according to the node states in the stable state; and The sample output module is used to output the initial fault set, the stable state, the power failure areas, the communication out-of-control areas and the traffic blockage areas as a disaster scenario sample of one simulation, thereby completing the generation and identification of the impact of a sudden natural disaster fault scenario. ​ The traffic blocking area is identified based on path reachability analysis of the traffic network nodes to a specified starting point. The sample output module is used for outputting the initial fault set, the stable state, the power failure area, the communication out-of-control area and the traffic blocking area as a disaster scene sample of one simulation, and completes the generation of the sudden natural disaster fault scene and the identification of the influence.

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