Three-network fault propagation risk assessment method and system for complex network analysis

Abstract

The invention discloses a three-network fault propagation risk assessment method and system for complex network analysis, and belongs to the technical field of power system toughness assessment and disaster risk management, and the method comprises the steps: collecting node and edge data of a power network, an information network and a traffic network, and forming a heterogeneous network topology structure; constructing a node importance evaluation function; carrying out weighted correction, and outputting a three-network coupling node importance degree sequence; generating a fault event triggering list; updating the node state matrix until the node state does not change any more, and outputting a fault influence range and a fault propagation path; and calculating a three-network overall connectivity loss rate, function recovery time estimation, key node fault sensitivity and a coupling dependence vulnerability index, and outputting a three-network fault propagation risk assessment index. According to the method, the dependency relationship and the influence strength among the three networks can be truly reflected, the contribution degree of the nodes to the system toughness is quantified, and the multi-dimensional, quantifiable and explainable effective evaluation of the fault propagation risk under the complex network is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of distribution network resilience improvement effectiveness analysis, and particularly relates to a three-network fault propagation risk assessment method and system for complex network analysis. BACKGROUND

[0002] At present, the research on three-network coupling risk propagation in the industry field mainly focuses on single-network or double-network coupling analysis, and the system risk assessment method for multi-dimensional coupling network is still immature, and there is a lack of a comprehensive index system capable of quantifying node importance and fault propagation path. Traditional risk assessment relies on static indicators or experience, and it is difficult to capture the complex dynamic interaction and multi-level propagation mechanism among the three networks, resulting in one-sided understanding of disaster risks and difficulty in accurately revealing key weak links and potential risk points. In addition, the existing reinforcement schemes for power distribution networks and transportation networks usually rely on expert experience or local optimization, lack data-driven scientific decision support, and are difficult to accurately locate key nodes and paths affecting system resilience, resulting in waste of reinforcement resources and limited effect.

[0003] To solve this problem, the complex network theory provides an effective tool for analyzing the structural characteristics and fault propagation rules of the three networks. Through node importance indicators and cascading failure simulation, the diffusion mode and risk level of faults under multi-network coupling can be systematically evaluated, which helps to reveal the vulnerable nodes and their propagation paths of the three networks, and provides a scientific basis for pre-disaster protection and post-disaster recovery. However, current research on three-network coupling risk propagation has not formed a perfect evaluation framework, lacks comprehensive analysis combining actual operation data and multi-source information, and is difficult to meet the needs of rapid and accurate risk identification and early warning in complex disaster environments. SUMMARY

[0004] The application provides a three-network fault propagation risk assessment method and system for complex network analysis, aiming to solve the problem that current research on three-network coupling risk propagation has not formed a perfect evaluation framework, lacks comprehensive analysis combining actual operation data and multi-source information, and is difficult to meet the needs of rapid and accurate risk identification and early warning in complex disaster environments.

[0005] To achieve the above purpose, the technical scheme adopted by the application is as follows: The application provides a three-network fault propagation risk assessment method for complex network analysis, comprising the following steps: S1, collecting the node and edge data of the power network, information network and transportation network, and generating the node set of the power network, information network and transportation network; constructing a node mapping matrix based on geographical location and functional association, defining the coupling edges among the three networks according to the physical connection relationship, and forming a heterogeneous network topology structure; S2, based on the heterogeneous network topology, a plurality of network centrality indexes of each node are calculated, a node importance evaluation function is constructed by linear weighting, the node importance evaluation function is weighted and corrected in combination with a node dependence matrix, and a three-network coupling node importance ranking is output; S3, a fault event trigger list is generated according to an initial fault set set in the three-network cascading fault propagation model; a node state matrix is updated by traversing the trigger list until the node state no longer changes, and a fault influence range and a fault propagation path are output; The construction of the three-network cascading fault propagation model includes: setting an initial fault set based on the three-network coupling node importance ranking, iteratively calculating a secondary failure node set through a node dependence matrix, and stopping until a convergence condition is met. S4, based on the propagation path, a three-network overall connectivity loss rate, a function recovery time estimation, a key node fault sensitivity, and a coupling dependence vulnerability index are calculated, and a three-network fault propagation risk evaluation index is output.

[0006] In some embodiments, in S1, the nodes include substations, base stations, and road intersections, and the edges include power transmission lines, communication links, and traffic roads.

[0007] In some embodiments, S2 specifically includes: S21, the degree centrality, the betweenness centrality, the eigenvector centrality, and the k-core decomposition index of the three-network nodes are calculated, and a multi-index fusion node importance evaluation function is constructed by a linear weighting method; S22, considering the coupling dependence relationship, a coupling node dependence matrix is defined, and the importance of each node in the node dependence matrix is weighted and corrected to form a three-network coupling node importance ranking.

[0008] Further, in S21, the calculation formula of the node importance evaluation function is as follows: ; Wherein, is the degree centrality, is the betweenness centrality, is the eigenvector centrality, is the k-core decomposition index, is the importance evaluation function, and are weight coefficients of each index, respectively; In S22, the coupling node dependence matrix is defined in the following way: ; Wherein, is the coupling node dependence matrix, is the node dependence strength of the node .

[0009] In some embodiments, in S3, a breadth-first search algorithm is used to traverse the fault event trigger list, and a matrix representing node states is updated; the node states include normal, failure and isolation, and the matrix representing node states is iteratively updated by combining node redundancy and emergency repair probability.

[0010] In some embodiments, in S3, the convergence condition is that the set of secondary failure nodes no longer changes or the system is stable.

[0011] In some embodiments, in S4, the formula for calculating the overall connectivity loss rate of the three networks is: ; The system function recovery time estimation is calculated using a shortest path algorithm under repair capacity constraints; The coupling dependency vulnerability index calculation formula is: ; Wherein, is the coupling dependency vulnerability index, is the number of times of cascading failure of node after node fails, is the total number of nodes.

[0012] In some embodiments, in S4, the data three-network fault propagation risk assessment index is obtained by constructing a multi-dimensional risk assessment vector, normalizing and weighting fusion, and then outputting a multi-dimensional risk index fusion and score.

[0013] Further, in S4, the multi-dimensional risk index fusion and score specifically includes: A fault propagation sequence matrix is established to record the set of failed nodes and their state changes in each round of propagation; by combining the node importance index and the fault propagation path, the influence factor of node failure is calculated by the following formula: ; Wherein, is the influence factor of node failure, is the node importance index, is the indicator function; Based on the system connectivity loss, function recovery time, and coupling dependency vulnerability index, a multi-dimensional risk assessment vector is constructed; principal component analysis or entropy weight method is used to normalize and weight the multi-dimensional index, and a comprehensive risk score is formed.

[0014] The application also provides a three-network fault propagation risk assessment system for complex network analysis, which comprises a data acquisition module, a data correction module, a model construction module, and a risk assessment module, wherein: The data collection module is used for collecting node and edge data of the power grid, the information network and the traffic network, generating node sets of the power grid, the information network and the traffic network, constructing a node mapping matrix based on geographical position and function association, defining coupling edges among the three networks according to physical connection relationship, and forming a heterogeneous network topology structure. The data correction module is used for calculating various network centrality indexes of each node based on the heterogeneous network topology structure, constructing a node importance evaluation function through linear weighting, and outputting a three-network coupling node importance ranking by weighting and correcting the node importance evaluation function in combination with a node dependence matrix. The model construction module is used for generating a fault event trigger list according to an initial fault set set in a three-network cascading fault propagation model, and updating a node state matrix by traversing the trigger list until the node state no longer changes, and outputting a fault influence range and a fault propagation path. The construction of the three-network cascading fault propagation model includes setting an initial fault set based on a three-network coupling node importance ranking, and iteratively calculating a secondary failure node set through a node dependence matrix until a convergence condition is met. The risk assessment module is used for calculating a three-network overall connectivity loss rate, a function recovery time estimation, a key node fault sensitivity and a coupling dependence vulnerability index based on the propagation path, and outputting a three-network fault propagation risk assessment index.

[0015] Compared with the prior art, the three-network fault propagation risk assessment method and system for complex network analysis have the following beneficial effects: The three-network fault propagation risk assessment method for complex network analysis realizes modeling of a linkage relationship among multiple networks and visual analysis of a fault propagation path by constructing a three-network coupling complex network model, comprehensively considering geographical position mapping, physical dependence relationship and function coupling characteristics among the power grid, the information network and the traffic network, and effectively improving the accuracy of key node identification by introducing a multi-index fusion node importance evaluation method (comprehensive degree centrality, betweenness centrality, eigenvector centrality and k-core decomposition index), and overcoming the misjudgment problem caused by the limitation of a single centrality index.

[0016] The three-network cascade failure propagation model designed by the application can simulate multiple rounds of node failure and chain effect through an iterative propagation mechanism, and support triggering and propagation path deduction of multiple initial failure types (such as node failure, link damage, and road blockage). The failure propagation simulation algorithm combines breadth-first search and node state transition matrix iterative update strategy, and can efficiently and accurately calculate the failure diffusion range and propagation link, ensuring the scalability and applicability of the algorithm. Further, by comprehensively outputting multi-dimensional risk indicators such as system connectivity loss rate, function recovery time, key node failure sensitivity, and coupling dependence vulnerability index, and combining multi-attribute decision-making methods such as principal component analysis or entropy weight method, the multi-dimensional, quantifiable and interpretable scientific evaluation of failure propagation risk in complex networks is realized.

[0017] Compared with existing single network analysis methods, the application can systematically and globally reveal the coupling dependence relationship between power networks, information networks and transportation networks and the risk propagation mechanism under disaster impact, providing scientific basis and decision support for multi-network collaborative resilience improvement, key node priority reinforcement decision and emergency resource optimization allocation, and has significant theoretical innovation value and engineering application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute a part of this application. The schematic embodiments of the application and their descriptions serve to explain the application, and do not constitute an improper limitation on the application.

[0019] Figure 1 The flowchart of the three-network failure propagation risk evaluation method for complex network analysis of the application. DETAILED DESCRIPTION

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

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

[0022] It should be noted that similar reference numerals and letters refer to like items in the accompanying drawings, and once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings.

[0023] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "horizontal", "inner" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the application is usually placed, only for the convenience of describing the application and simplifying the description, and it is not intended to indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application. In addition, the terms "first", "second" and the like are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0024] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly inclined. For example, "horizontal" only means that its direction is relatively more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly inclined.

[0025] In the description of the embodiments of the present application, it should also be noted that unless otherwise explicitly specified and limited, if the terms "arrangement", "installation", "connection", "connection" appear, they should be understood in a broad sense, for example, they can be fixedly connected, or they can be detachably connected, or integrally connected; can be mechanically connected, or electrically connected; can be directly connected, or indirectly connected through an intermediate medium; can be the communication between the two elements inside. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0026] How to provide a three-network fault propagation risk assessment technology based on complex network analysis, build a comprehensive model integrating node importance, multi-dimensional propagation path and cascading failure effect, improve the accuracy of survivability assessment of multi-element coupled network facilities, and ensure the continuity of urban operation safety and public service.

[0027] As shown in Figure 1 The three-network fault propagation risk assessment method of the complex network analysis of the present application comprises the following steps: S1, collect the node and edge data of the power grid, information network and traffic network, and generate the node set of the power grid, information network and traffic network; based on the geographical location and functional association, a node mapping matrix is constructed, and the coupled edges between the three networks are defined according to the physical connection relationship, forming a heterogeneous network topology structure; S2, based on the heterogeneous network topology, a plurality of network centrality indexes of each node are calculated, a node importance evaluation function is constructed by linear weighting, and the node importance evaluation function is weighted and corrected in combination with a node dependence matrix, and a three-network coupling node importance ranking is output; S3, generating a fault event trigger list according to an initial fault set set in the three-network cascading fault propagation model; traversing the trigger list to update the node state matrix until the node state no longer changes, and outputting a fault influence range and a fault propagation path; The construction of the three-network cascading fault propagation model comprises the following steps: setting an initial fault set based on the three-network coupling node importance ranking, iteratively calculating a secondary failure node set through a node dependence matrix, and stopping until a convergence condition is met. S4, based on the propagation path, calculating a three-network overall connectivity loss rate, a function recovery time estimation, a key node fault sensitivity and a coupling dependence vulnerability index, and outputting a three-network fault propagation risk evaluation index.

[0028] The present application specifically relates to a power-information-transportation network three-network fault propagation risk evaluation method based on complex network analysis, aiming to improve the problems in the prior art, such as difficult accurate identification of key nodes in a multi-network coupling scene, unclear fault propagation mechanism, and difficult quantification of multi-dimensional risks, resulting in one-sided disaster prevention and control strategies, low resource allocation efficiency, and difficult effective improvement of the overall resilience level of urban infrastructure systems. The present application is particularly suitable for key node identification, fault propagation simulation and multi-dimensional risk quantification in a multi-network coupling scene through reasonable method design, and provides decision support for resilience improvement and emergency prevention and control of power systems under extreme natural disasters and other emergencies, and has good practical significance.

[0029] In some embodiments, the present application is a three-network fault propagation risk evaluation method based on complex network analysis, specifically comprising the following steps: S1, constructing a three-network coupling complex network model, comprising the following steps: 1) collecting and organizing basic data of nodes and edges of the power network, the information network and the transportation network, the nodes including substations, base stations and road intersections, and the edges including power transmission lines, communication links and transportation roads; generating a power network node set , an information network node set and a transportation network node set ; 2) aligning the nodes of the three networks in space according to geographical positions and functional associations to form a node mapping matrix; 3) defining coupling edges between the three networks according to physical connection relationships (such as actual dependence relationships between power equipment, communication equipment and road facilities), and forming a heterogeneous network topology.

[0030] The method of the present application collects and preprocesses the node and boundary data of the power grid, information network and traffic network. The node types include power transformation facilities in the power system, signal base stations in the communication system and intersections in the traffic system. The edge types correspond to power transmission lines, communication links and traffic arteries respectively. Based on this, a node set is generated. Then, according to the overlapping of spatial positions and the functional complementary relationship, the nodes of the three types of networks are matched in coordinates and mapped in functions, a node alignment matrix is constructed to depict the spatial consistency, and coupled edges between the three networks are defined according to the physical connection relationship (such as the actual dependence relationship between power equipment, communication equipment and road facilities), and a heterogeneous network topology structure is formed.

[0031] S2, based on the improved node importance index, the influence of the key nodes of the three networks on the fault propagation is evaluated, specifically including: 1) calculating the degree centrality of the nodes of the three networks , betweenness centrality , eigenvector centrality , k-core decomposition index , and constructing a node importance evaluation function by linear weighting method ; 2) considering the coupling dependence relationship, defining the node dependence matrix , and weighting and correcting the importance of each node to form a three-network coupled node importance ranking.

[0032] S3, constructing a three-network cascading fault propagation model, specifically including: 1) establishing an iterative propagation mechanism of node failure-dependence relationship propagation-secondary failure 2) setting an initial fault set to trigger the node failure event; 3) calculating the secondary failure node set caused by coupling dependence step by step through the node dependence matrix , until the system is stable or the fault propagation stops.

[0033] S4, designing a three-network coupled fault propagation simulation algorithm, the steps including: generating a fault event trigger list according to the set initial fault type (such as node failure, link damage, road blockage); traversing the trigger list to calculate the node state update of each round in turn, the node state including normal, failure and isolation; considering the node redundancy, emergency repair probability and other factors, updating the node state matrix; iterating until the propagation chain converges, outputting the fault influence range and propagation path.

[0034] ​S5, based on the simulation results, output three network fault propagation risk assessment index, three network fault propagation risk assessment index includes system connectivity loss rate, function recovery time estimation, key node fault sensitivity, coupling dependence vulnerability index.

[0035] As a preferred, the node importance index is constructed , each centrality index is integrated in a weighted superposition manner, and the node importance evaluation function is calculated as: ; Among them: , respectively, are the weight coefficients of each index, used to balance the contribution of each index to the node importance, and have .

[0036] The method of the application, based on the improved node importance evaluation mechanism, comprehensively determines the key nodes in the power grid, information network and transportation network and their influence in the fault propagation process. First, the degree centrality , betweenness centrality , eigenvector centrality , k-core decomposition index of the nodes in the three networks are calculated, and a multi-index fusion node importance evaluation function is constructed by linear weighting to quantify the degree of the node in the network. Subsequently, combined with the functional dependence relationship among the three networks, a node dependence matrix is constructed to describe the mutual coupling effect between the networks, and the preliminary evaluation results are weighted and corrected on this basis, and finally the node importance ranking reflecting the coupling characteristics of the three networks is formed, improving the accuracy and pertinence of key node identification.

[0037] Further, in the application, the coupling node dependence matrix is defined as follows: ; Among them, is the dependence strength of node on node , and has , the weighted correction of each node importance is performed to form the three-network coupling node importance ranking.

[0038] The fault propagation model is realized by the following iteration mechanism: An iterative propagation mechanism for node failure and dependence relationship propagation is established: set the initial fault set , calculate the iterative propagation result according to the dependence matrix , calculate the secondary failure node set by iteration until the convergence condition is met or the fault propagation stops the system stable.

[0039] In the three-network fault propagation risk assessment method of complex network analysis of the application, the fault propagation simulation algorithm adopts an iterative updating strategy combining breadth-first search (BFS) and node state transition matrix to simulate the fault propagation of each round. According to the initial fault type, a fault event trigger list is generated, the trigger list is traversed, the node state matrix is iteratively updated, and the node state matrix is updated in each iteration , to meet: ; In combination with the node redundancy and emergency repair probability, breadth-first search and state transition matrix iterative updating are adopted until: .

[0040] Further, in the three-network fault propagation risk assessment method of complex network analysis of the application, the risk index output includes: The overall connectivity loss rate of the three networks is calculated by the formula: ; System function recovery time is estimated using the shortest path algorithm under repair capacity constraints; Coupling dependency vulnerability index The calculation formula is: ; Wherein, is the number of cascading failures of node after node fails, is the total number of nodes.

[0041] Further, the application establishes a fault propagation sequence matrix to record the set of failed nodes and their state changes in each round of propagation; By combining the node importance index with the fault propagation path, the influence factor of node failure is calculated, which is defined as: ; Wherein, is an indicator function, and the value of node is 1 when it fails in , otherwise it is 0; Based on the system connectivity loss rate , the function recovery time , the coupling dependency vulnerability index and other indexes, a multi-dimensional risk assessment vector is constructed; The principal component analysis (PCA) or entropy weight method is used to normalize and weight the multi-dimensional indexes to form a comprehensive risk score .

[0042] The weight coefficient in the node importance evaluation function in the three-network fault propagation risk assessment method of complex network analysis of the application is determined by historical data fitting. The coupling node dependence matrix is determined by historical data fitting. The dependence strength in the coupling node dependence matrix is determined by historical data fitting. The coupling node dependence matrix is determined by historical data fitting. The fault propagation simulation algorithm adopts an iterative updating strategy of breadth-first search combined with a node state transition matrix to update the node state until convergence. The system function recovery time estimation adopts a shortest path algorithm based on repair capability constraints.

[0043] The application further provides a three-network fault propagation risk assessment system for complex network analysis, which comprises a data acquisition module, a data correction module, a model construction module and a risk assessment module, wherein: The data acquisition module is used to acquire the node and edge data of the power network, the information network and the traffic network, generate the node set of the power network, the information network and the traffic network, construct a node mapping matrix based on the geographical position and the functional association, define the coupling edges among the three networks according to the physical connection relationship, and form a heterogeneous network topology structure. The data correction module is used to calculate various network centrality indexes of each node based on the heterogeneous network topology structure, construct a node importance evaluation function through linear weighting, and output the three-network coupling node importance ranking by weighting and correcting the node importance evaluation function in combination with the node dependence matrix. The model construction module is used to generate a fault event trigger list according to the initial fault set in the three-network cascading fault propagation model, update the node state matrix by traversing the trigger list until the node state no longer changes, and output the fault influence range and the fault propagation path. The construction of the three-network cascading fault propagation model comprises the following steps: setting an initial fault set based on the three-network coupling node importance ranking, iteratively calculating a secondary failure node set through the node dependence matrix, and stopping until the convergence condition is met. The risk assessment module is used to calculate the three-network overall connectivity loss rate, the function recovery time estimation, the key node fault sensitivity and the coupling dependence vulnerability index based on the propagation path, and output the three-network fault propagation risk assessment indexes.

[0044] In summary, the three-network fault propagation risk assessment method and system for complex network analysis of the application comprehensively considers the topological structure and coupling relationship between the three networks, and constructs a multi-level and multi-dimensional three-network collaborative fault propagation model. First, the node importance index (such as degree centrality, betweenness centrality, closeness centrality, etc.) is used to extract the characteristics of the three-network nodes, and the key nodes and key links are identified. Second, through cascading failure simulation, the evolution process of single-point or multi-point fault propagation in the three networks is dynamically analyzed, and the fragile link and transmission path of fault propagation are revealed. Then, based on the fault propagation results, a multi-network linkage risk index system is constructed to quantitatively evaluate the fault propagation risk level of different nodes and lines. Finally, combined with various data-driven and simulation analysis methods, the precise identification and sorting of high-risk areas and key nodes in the three-network system are formed. This method can effectively reveal the complex coupling relationship between the power network, information network and transportation network and its influence on fault propagation, and improve the scientificity and accuracy of disaster warning and emergency decision-making. The system has high adaptability and expandability, and can provide certain technical support for the resilience improvement, disaster prevention and reduction and resource optimization allocation of the three-network integration, and is suitable for popularization and application.

[0045] Finally, it should be noted that: the above is only the 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 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 three-network fault propagation risk assessment method based on complex network analysis, characterized in that: The steps include: S1. Collect node and edge data of the power grid, information network, and transportation network to generate node sets for the power grid, information network, and transportation network; construct a node mapping matrix based on geographic location and functional associations; define coupling edges between the three networks based on physical connection relationships to form a heterogeneous network topology; S2. Based on the heterogeneous network topology, various network centrality indicators of each node are calculated, and a node importance evaluation function is constructed through linear weighting. The node importance evaluation function is weighted and modified based on the node dependency matrix, and the importance ranking of the three-network coupling nodes is output. S3. Generate a fault event trigger list based on the initial fault set set in the three-network cascade fault propagation model; Traverse the trigger list and update the node status matrix until the node status no longer changes, and output the fault impact range and fault propagation path; The construction of the three-network cascading fault propagation model includes: setting the initial fault set based on the importance ranking of the three-network coupled nodes, iteratively calculating the secondary failure node set through the node dependency matrix until the convergence condition is met; S4. Based on the propagation path, calculate the overall connectivity loss rate of the three networks, the estimated function recovery time, the key node failure sensitivity, and the coupling dependency vulnerability index, and output the three-network fault propagation risk assessment index.

2. The three-network fault propagation risk assessment method based on complex network analysis according to claim 1 is characterized in that: In S1, nodes include substations, base stations, and road intersections, and edges include power transmission lines, communication links, and traffic roads.

3. The three-network fault propagation risk assessment method based on complex network analysis according to claim 1 is characterized in that: The S2 specifically includes: S21. Calculate the degree centrality, betweenness centrality, eigenvector centrality, and k-core decomposition index of the three-network nodes, and construct a node importance evaluation function that integrates multiple indicators through a linear weighting method; S22. Considering the coupling dependency relationship, define a coupling node dependency matrix, and perform weighted correction on the importance of each node in the node dependency matrix to form an importance ranking of the three-network coupling nodes.

4. The three-network fault propagation risk assessment method based on complex network analysis according to claim 3 is characterized in that: In the above S21, the calculation formula of the node importance evaluation function is as follows: ； in, is the degree centrality, is the betweenness centrality, is the eigenvector centrality, is the k-core decomposition index, is the importance evaluation function, are the weight coefficients of each indicator respectively; In the S22, the coupling node dependency matrix is ​​defined as follows: ； in, is the coupling node dependency matrix, For nodes For Node The intensity of dependence.

5. The method for evaluating the risk of three-network fault propagation based on complex network analysis according to claim 1, characterized in that: In S3, a breadth-first search algorithm is used to traverse the fault event trigger list and update the matrix representing the node status; the node status includes normal, failed, and isolated, and the matrix representing the node status is iteratively updated by combining node redundancy and emergency repair probability.

6. The method for evaluating the risk of three-network fault propagation based on complex network analysis according to claim 1, characterized in that: In S3, the convergence condition is that the set of secondary failure nodes no longer changes or the system is stable.

7. The method for evaluating the risk of three-network fault propagation based on complex network analysis according to claim 1, characterized in that: In S4, the calculation formula for the overall connectivity loss rate of the three networks is: ； The system function recovery time estimate is calculated using the shortest path algorithm under the repair capability constraint; The calculation formula of coupling dependency vulnerability index is: ； in, is the coupling dependency vulnerability index, For nodes Triggering node after failure The number of chain failures, is the total number of nodes.

8. The method for evaluating the risk of three-network fault propagation based on complex network analysis according to claim 1, characterized in that: In S4, the data three-network fault propagation risk assessment index is obtained by constructing a multi-dimensional risk assessment vector, normalizing and weighted fusion, and then outputting the multi-dimensional risk index fusion and scoring.

9. The method for evaluating the risk of three-network fault propagation based on complex network analysis according to claim 8, characterized in that: In S4, the fusion and scoring of multi-dimensional risk indicators specifically include: A fault propagation sequence matrix is ​​established to record the set of failed nodes and their state changes in each round of propagation. By combining the node importance index with the fault propagation path, the impact factor of node failure is calculated using the following formula: ； in, is the impact factor of node failure, is the node importance index, For the instruction letter; Based on system connectivity loss, function recovery time, and coupling dependency vulnerability index, a multidimensional risk assessment vector is constructed; principal component analysis or entropy weight method is used to normalize and weight the multidimensional indicators to form a comprehensive risk score.

10. The system on which the three-network fault propagation risk assessment method for complex network analysis according to any one of claims 1 to 9 is based is characterized in that: The system includes a data acquisition module, a data correction module, a model building module and a risk assessment module, wherein: Data acquisition module: This module collects node and edge data from the power grid, information network, and transportation network, generating node sets for the power grid, information network, and transportation network. It also constructs a node mapping matrix based on geographic location and functional associations, and defines coupling edges between the three networks based on physical connection relationships, forming a heterogeneous network topology. Data correction module: Based on the heterogeneous network topology, it calculates various network centrality indicators of each node and constructs a node importance evaluation function through linear weighting. It also performs weighted correction on the node importance evaluation function by combining the node dependency matrix and outputs the importance ranking of the three-network coupling nodes. Model construction module: used to generate a fault event trigger list based on the initial fault set set in the three-network cascade fault propagation model; traverse the trigger list to update the node state matrix until the node state no longer changes, and output the fault impact range and fault propagation path; The construction of the three-network cascading fault propagation model includes: setting the initial fault set based on the importance ranking of the three-network coupled nodes, iteratively calculating the secondary failure node set through the node dependency matrix until the convergence condition is met; Risk Assessment Module: This module is used to calculate the overall connectivity loss rate of the three networks, function recovery time estimate, key node fault sensitivity, and coupling dependency vulnerability index based on the propagation path, and output the three-network fault propagation risk assessment indicators.

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