Risk assessment method and system based on topology analysis

Abstract

The invention discloses a risk assessment method and system based on topology analysis, and relates to the technical field of risk assessment, and the method comprises the following steps: analyzing a communication relation between nodes, extracting a plurality of propagation paths between any two nodes, and forming a path set; mapping the path set based on the risk transmission direction and the path attribute, and constructing a multi-path propagation relation graph; based on the multi-path propagation relation graph, identifying a risk aggregation node with multi-path risk input, and fusing multi-path attributes to calculate a comprehensive risk value of the node; and generating network risk distribution based on the comprehensive risk value in combination with the operation state data. According to the method, the multi-path propagation topology model is constructed, and the risks on multiple paths are subjected to convergence analysis and comprehensive quantification in combination with the node operation state and the path interaction characteristics, so that the problem that the overall risk of the network is underestimated only by depending on a single dominant path in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of risk assessment technology, and more specifically, to a risk assessment method and system based on topology analysis. Background Technology

[0002] In the operation and management of power, communication, and complex networks, risk assessment is a crucial step in ensuring system security and stability. Current network risk analysis methods often rely on shortest paths or single dominant paths for propagation calculations, using single-path propagation models to assess the risk of nodes or the network as a whole. However, in real-world networks, multiple parallel propagation paths may exist between nodes, and risks can accumulate or interact across different paths. Single-path analysis cannot reflect these multi-path effects, easily leading to an underestimation of the risk at critical nodes. Furthermore, traditional methods typically ignore the dynamic changes in node states and the interaction logic between paths, resulting in risk assessment results lacking real-time performance and accuracy, making it difficult to support risk warning, control, and optimized scheduling in high-reliability systems.

[0003] The above-disclosed technical solutions have at least the following technical problems: existing methods are mostly based on shortest path or single dominant path analysis, but in actual networks, risks may propagate in parallel through multiple paths and their intensity may be superimposed. Ignoring the multi-path effect will underestimate the global risk.

[0004] To address the above problems, this invention proposes a solution. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a risk assessment method and system based on topology analysis. By constructing a multi-path propagation topology model and combining node operating status and path interaction characteristics, the risks on multiple paths are converged, analyzed, and comprehensively quantified to solve the problem of underestimating the overall network risk by relying only on a single dominant path in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: On the one hand, a risk assessment method based on topology analysis includes the following steps: analyzing the connectivity between nodes, extracting multiple propagation paths between any two nodes to form a path set; mapping the path set based on the risk transmission direction and path attributes to construct a multi-path propagation relationship graph; based on the multi-path propagation relationship graph, identifying risk convergence nodes with multi-path risk inputs, and calculating the comprehensive risk value of the node by integrating multi-path attributes; and generating a network risk distribution based on the comprehensive risk value and combined with operational status data.

[0007] In a preferred embodiment, the analysis of connectivity between nodes is performed by establishing a topology description model. The specific steps for constructing the topology description model are as follows: collecting topology data and operational status data of the target network, and establishing an initial adjacency matrix; performing multi-path compression encoding on the initial adjacency matrix to generate a path distribution vector between any two nodes, wherein the path distribution vector includes path probability weights and path entropy values; and establishing a topology description model based on the path distribution vector and applying risk energy conservation constraints.

[0008] In a preferred embodiment, the step of performing multi-path compression encoding on the initial adjacency matrix to generate a path distribution vector between any two nodes specifically involves: enumerating all feasible propagation paths between the source node and the target node using a breadth-first search method; extracting path feature vectors for each path, the feature vectors including path length, number of nodes traversed, edge weights, and node running status labels; calculating the propagation probability weights of the paths based on the path feature vectors, normalizing all weights, and calculating the entropy value of the path distribution; integrating the normalized probability weights and entropy value into a path distribution vector, and using this vector to compress and store the multi-path information.

[0009] In a preferred embodiment, the step of analyzing the connectivity between nodes and extracting multiple propagation paths between any two nodes to form a path set specifically involves: generating an initial path list by reverse decoding based on pre-stored path distribution vectors and their associated node and edge identifiers; acquiring real-time operating status data of the target network and dynamically filtering the initial path list based on the operating status data to obtain currently valid paths with propagation conditions; supplementing the filtered valid paths with real-time attribute information to form structured path data; and classifying the structured path data according to the start-end node to form a set of valid propagation paths for multiple node pairs.

[0010] In a preferred embodiment, the step of mapping the path set based on risk transmission direction and path attributes to construct a multi-path propagation relationship graph specifically involves: mapping the effective propagation paths of multiple node pairs to the graph, constructing an initial multi-path propagation relationship graph with nodes as vertices and paths as edges; labeling the interaction relationships between paths based on shared nodes or shared edges in the relationship graph, and determining the priority transmission path and potential suppression path of the risk flow at the node; dividing the paths according to the propagation probability weights of the priority transmission path and potential suppression path and the multi-path interaction logic; and dynamically adjusting the multi-path propagation relationship graph based on the division results and the real-time operating status of the target network.

[0011] In a preferred embodiment, the step of identifying risk convergence nodes with multi-path risk inputs based on a multi-path propagation relationship graph specifically involves: identifying candidate nodes located at the intersection of multiple high-weight paths in the multi-path propagation relationship graph, and performing directional superposition of the input risk flows based on path interaction logic; constructing a risk convergence gain factor characterizing the multi-path risk amplification effect by combining the propagation probability weights and distribution entropies of each path; and jointly calculating the inherent risk response values ​​of the candidate nodes with the risk convergence gain factor to determine the risk convergence nodes and their comprehensive risk levels.

[0012] In a preferred embodiment, identifying candidate nodes located at the intersection of multiple high-weight paths in the multi-path propagation relationship graph specifically involves: sorting the propagation probability weights in the multi-path propagation relationship graph and setting weight thresholds to filter out high-weight paths; counting the frequency of each node's occurrence in the high-weight paths to obtain the path convergence index of the node; and comparing the path convergence index with a preset threshold to determine risk convergence candidate nodes.

[0013] In a preferred embodiment, the directional superposition of the input risk flow based on path interaction logic specifically involves: extracting the directional relationship between all inbound and outbound paths of the candidate node, and constructing a risk flow superposition operator based on the path interaction logic.

[0014] In a preferred embodiment, generating network risk distribution based on comprehensive risk values ​​and operational status data includes: acquiring real-time status information of risk convergence nodes and generating a set of dynamic attributes for the nodes; calculating the actual risk energy output value of each risk convergence node under the current network state based on the comprehensive risk value and dynamic attribute set of the nodes; mapping the actual risk energy output value of each node to the network topology to form a node-risk matrix; filtering the set of dominant paths based on the node-risk matrix and eliminating infeasible paths caused by node anomalies or path failures; and outputting the risk convergence nodes, actual risk energy output values, and the filtered set of dominant paths in a structured form.

[0015] On the other hand, a risk assessment system based on topology analysis includes the following modules: a path extraction module, used to analyze the connectivity between nodes, extract multiple propagation paths between any two nodes, and form a path set; a multi-path graph construction module, used to map the path set based on the risk transmission direction and path attributes, and construct a multi-path propagation relationship graph; a risk convergence identification module, used to identify risk convergence nodes with multi-path risk input based on the multi-path propagation relationship graph, and calculate the comprehensive risk value of the node by integrating multi-path attributes; and a risk distribution output module, used to generate network risk distribution based on the comprehensive risk value and combined with operational status data.

[0016] The technical effects and advantages of the risk assessment method and system based on topology analysis of this invention are as follows: 1. This invention constructs a multi-path propagation relationship graph and combines path propagation probability weights, path entropy values, and directional superposition operators to achieve quantitative analysis of the parallel propagation of risks in a network through multiple paths. This enables accurate identification of risk convergence nodes and their comprehensive risk levels, effectively avoiding the risk underestimation problem caused by traditional single-path calculation.

[0017] 2. This invention couples the comprehensive risk level of risk convergence nodes with the real-time operating status of the network to generate the actual risk energy output of the nodes, and combines the multi-path propagation relationship graph to determine the dominant risk diffusion path, thereby realizing the dynamic updating and structured output of network risk distribution, providing a reliable basis for risk warning, control and network vulnerability analysis. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a risk assessment method based on topology analysis according to the present invention. Figure 2 This is a schematic diagram of the structure of a risk assessment system based on topology analysis according to the present invention. Detailed Implementation

[0019] 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1, Figure 1 This invention presents a risk assessment method based on topology analysis, comprising the following steps: S1, Analyze the connectivity between nodes, extract multiple propagation paths between any two nodes, and form a path set; The connectivity relationships between nodes are analyzed by establishing a topology description model. The specific steps for constructing the topology description model are as follows: The topology and operational status data of the target network are collected to establish an initial adjacency matrix. In this embodiment, the topology data includes node information and connection information; the operational status data includes node status.

[0021] The initial adjacency matrix is ​​subjected to multi-path compression encoding, and the multiple propagation paths between any two nodes are stored in the form of path distribution vectors. The path distribution vectors include path probability weights and path entropy values, which are used to characterize the strength and uncertainty of multi-path parallel propagation. Based on the path distribution vector, a risk energy conservation constraint rule is established so that the risk of the source node satisfies the total amount conservation when it spreads to multiple paths, resulting in a topological description model that takes into account both the characteristics of multiple paths and the rationality of risk energy.

[0022] The process of performing multi-path compression encoding on the initial adjacency matrix, storing multiple propagation paths between any two nodes as path distribution vectors, specifically involves: The feasible propagation paths between any two nodes are enumerated using the breadth-first search method, and the path length, number of nodes traversed, edge weights, and node running status labels of each path are extracted to form a path feature vector. The propagation probability weight of a path is calculated based on its feature vectors. The shorter the path and the more fragile the node state, the greater the propagation probability. The propagation probability weights of all paths are normalized, and the path distribution entropy is calculated to characterize the dispersion and uncertainty of risk among multiple paths. The probability weights and entropy values ​​of the path set are integrated to form a path distribution vector, which is stored in the topology description model in a compressed form, replacing the traditional method of storing redundant paths one by one, thereby reducing storage complexity and improving the computability of risk propagation.

[0023] The specific formula for calculating the propagation probability weight is as follows:

[0024]

[0025]

[0026] The path distribution entropy value is calculated using the following formula:

[0027] in, For propagation probability weights, This represents the vulnerability coefficient of node v on the path. A larger value indicates that the node is more likely to transmit risk. This is the path length decay function. This represents the total number of paths in the set. The actual capacity of node v. The maximum capacity in the network. For path Length, The preset attenuation factor, The entropy value represents the path distribution. The larger the entropy value, the more evenly the risk is distributed among multiple paths and the higher the uncertainty. The smaller the entropy value, the more concentrated the risk is along a few paths, which can help identify the dominant path set.

[0028] In this embodiment, the risk energy conservation constraint rule is specifically as follows:

[0029] in, For inflow node The risk energy is obtained by superimposing the risk quantities of all incoming paths. For the node The transmitted risk energy is distributed to different downstream paths. For the node Internally consumed risk energy.

[0030] By applying the risk energy conservation constraint rule: This avoids the risk of distortion caused by multi-path convergence; This ensures that the calculation of risk values ​​is closed across the entire network, thus making the output risk distribution results more consistent with the physical and logical laws of the network. To improve the stability and interpretability of risk propagation analysis, providing a reliable basis for subsequent risk control and dominant path identification.

[0031] In this embodiment, the step of extracting multiple propagation paths between any two nodes to form a path set specifically involves: From the multipath compression coding results of the topology description model, the original feasible propagation paths are decoded in reverse according to the path distribution vector and its associated node or edge identifiers to form an initial path list. Based on the real-time operating status data of the target network, the initial path list is filtered to remove paths containing offline nodes, faulty edges, or load rates exceeding the threshold, while retaining the effective paths that currently have the conditions for propagation. Real-time attribute information is added to the selected effective paths, including the actual number of propagation hops, the real-time risk carrying capacity of the path, and the real-time risk coupling coefficient of the path-related nodes, forming structured path data; The structured path data is categorized according to the origin-end node pairs. Each node pair corresponds to an independent path subset. All subsets are integrated to form the final set of effective propagation paths for multiple node pairs, providing standardized input for the subsequent construction of a multi-path propagation relationship graph.

[0032] S2, based on the risk transmission direction and path attributes, the path set is mapped to construct a multi-path propagation relationship graph; The mapping of path sets based on risk transmission direction and path attributes to construct a multi-path propagation relationship graph is as follows: Map each path in the set of effective propagation paths for multi-node pairs to the graph, with nodes as vertices and paths as edges, to form an initial multi-path propagation relationship graph; Based on the directional information of each path in the graph, potential propagation center nodes and diffusion order are identified (the "in-degree", "out-degree" and propagation probability of nodes in all paths are calculated, and nodes with out-degree weight greater than in-degree weight are identified as potential diffusion centers). For paths with shared nodes or shared edges, mark the path interaction relationships in the graph to determine the priority transmission path and potential suppression path of risk flow at the node; Based on the propagation probability weights of priority transmission paths and potential suppression paths, as well as the multi-path interaction logic, the paths are divided into a dominant layer, an auxiliary layer, and a low-risk layer to identify core diffusion paths and alternative paths. By combining the real-time operating status of the target network, the impact of path failures or node anomalies on the graph is analyzed, and the multipath propagation relationship graph structure is dynamically adjusted. The final multipath propagation relationship graph will be saved in a structured form for subsequent risk convergence node identification and dominant path decision analysis.

[0033] S3, based on the multi-path propagation relationship graph, identifies risk convergence nodes with multi-path risk inputs, and calculates the comprehensive risk value of the node by integrating multi-path attributes; In this embodiment, identifying risk convergence nodes with multi-path risk inputs based on the multi-path propagation relationship graph specifically involves: In the multi-path propagation relationship graph, candidate nodes that are simultaneously located at the intersection of multiple high-weight paths are identified, and risk flows are directionally superimposed based on the path interaction logic recorded in the graph. By combining the propagation probability weights and distribution entropy of each path, a risk convergence gain factor is constructed to characterize the risk amplification effect of nodes under the parallel action of multiple paths. By jointly calculating the risk response value of candidate nodes with the risk convergence gain factor, the risk convergence nodes and their overall risk levels are determined, thereby avoiding the risk underestimation problem caused by traditional single-path calculation.

[0034] The process of identifying candidate nodes that are simultaneously located at the intersection of multiple high-weight paths in the multi-path propagation relationship graph specifically involves: In the multipath propagation relationship graph, the propagation probability weights of all paths are sorted and a weight threshold is set, and only high-weight paths with propagation probability weights greater than the threshold are retained. The path intersection index of a node is obtained by counting the number of times each node appears in high-weight paths. If the path convergence index of a node is greater than a preset threshold, the node is identified as a candidate node for risk convergence.

[0035] The path interaction logic recorded in the graph is used to directionally superimpose risk flows, specifically as follows: Extract the directional relationships between all inbound and outbound paths of candidate nodes, and construct a risk flow overlay operator based on path interaction logic, wherein the path interaction logic includes: When multiple inbound paths have the same risk direction, the risk amount is superimposed using a linear accumulation method; When the risks along the inbound path are opposite in direction or have a mutual offsetting relationship, differential superposition is performed according to the path probability weights to reflect the risk mitigation effect. When multiple paths compete for the same outgoing path at a candidate node, a path priority factor is introduced to determine the main propagation direction based on the propagation probability weight and path entropy value, and an attenuation coefficient is applied to the risk of secondary paths. By superimposing the above-mentioned directions, the risk response value of the candidate node not only reflects the numerical accumulation of risk, but also reflects the amplification, cancellation and priority transmission effects under the interaction of multiple paths, thus obtaining a risk convergence characterization that is more in line with the actual propagation mechanism.

[0036] The risk flow superposition operator is specifically:

[0037]

[0038] The risk convergence gain factor is specifically:

[0039] The overall risk level is as follows:

[0040] in, For risk flow superposition operators, This is the path directionality coefficient; if the risk direction is consistent with the main transmission direction, then... If the opposite is true, then , For path propagation probability weights, For the set of high-weight paths, As a path priority factor, This is a preset proportional coefficient. Let i be the length of path i. represents the vulnerability coefficient of nodes along the path. As a risk convergence gain factor, The preset gain adjustment coefficient, The path distribution entropy value. To assess the overall risk level.

[0041] S4 generates a network risk distribution based on the comprehensive risk value and combined with operational status data.

[0042] In this embodiment, generating the network risk distribution based on the comprehensive risk value and combined with operational status data specifically involves: Obtain real-time load, voltage, availability, and edge status information associated with risk aggregation nodes, and generate a dynamic attribute set for the nodes; Based on the comprehensive risk level and combined with the dynamic attributes of the nodes, the actual risk energy output of each risk aggregation node under the current network state is calculated; The actual risk energy output of all risk convergence nodes is mapped onto the network topology to form a node-risk matrix, and then weighted and superimposed with a multi-path propagation relationship graph to reflect the diffusion distribution of risk along different paths. Based on the node-risk matrix, paths with high risk propagation probability weights and low path entropy are selected to form a set of dominant paths, and infeasible paths affected by node anomalies or path failures are removed. The risk convergence nodes, their actual risk energy output, and the set of dominant paths are output in a structured form for subsequent risk warning, control, and network vulnerability analysis.

[0043] The actual risk energy output is specifically as follows:

[0044]

[0045]

[0046] in, For actual risk energy output, To assess the overall risk level, The coupling coefficient represents the real-time operating status of the node. This represents the node availability coefficient, with 1 for online and (0,1) for offline or faulty nodes. This is the load adjustment factor. The current load of the node. This represents the maximum tolerable load for the node.

[0047] Example 2, Figure 2 This invention presents a risk assessment system based on topology analysis, comprising the following modules: Path extraction module: used to analyze the connectivity between nodes, extract multiple propagation paths between any two nodes, and form a path set; Multi-path graph construction module: used to map path sets based on risk transmission direction and path attributes to construct a multi-path propagation relationship graph; Risk convergence identification module: Based on the multi-path propagation relationship graph, it identifies risk convergence nodes with multi-path risk inputs and calculates the comprehensive risk value of the node by integrating multi-path attributes; Risk distribution output module: Used to generate network risk distribution based on comprehensive risk value and combined with operational status data.

[0048] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0049] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0050] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0051] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0052] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0053] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A risk assessment method based on topology analysis, characterized in that, Includes the following steps: Analyze the connectivity between nodes, extract multiple propagation paths between any two nodes, and form a path set; Mapping the path set based on the risk transmission direction and path attributes, and constructing a multi-path propagation relationship graph; Based on the multipath propagation relationship graph, risk convergence nodes with multipath risk inputs are identified, and the comprehensive risk value of the node is calculated by integrating multipath attributes. A network risk distribution is generated based on the comprehensive risk value and combined with operational status data.

2. The risk assessment method based on topology analysis according to claim 1, characterized in that, The analysis of connectivity relationships between nodes is performed by establishing a topology description model. The specific steps for constructing the topology description model are as follows: Collect the topology and operational status data of the target network and establish an initial adjacency matrix; The initial adjacency matrix is ​​subjected to multipath compression encoding to generate a path distribution vector between any two nodes. The path distribution vector includes path probability weights and path entropy values. A topological description model is established based on the path distribution vector and the risk energy conservation constraint rule.

3. The risk assessment method based on topology analysis according to claim 2, characterized in that, The process of performing multi-path compression encoding on the initial adjacency matrix to generate a path distribution vector between any two nodes is as follows: Enumerate all feasible propagation paths between the source node and the target node using a breadth-first search method; Extract the path feature vector for each path, the feature vector including path length, number of nodes passed through, edge weight and node running status label; The propagation probability weights of the path are calculated based on the path feature vectors, all weights are normalized, and the entropy value of the path distribution is calculated. The normalized probability weights and entropy values ​​are integrated into a path distribution vector, and multi-path information is compressed and stored using this vector.

4. The risk assessment method based on topology analysis according to claim 3, characterized in that, The analysis of connectivity between nodes involves extracting multiple propagation paths between any two nodes to form a path set, specifically: Based on the pre-stored path distribution vector and its associated node and edge identifiers, the initial path list is generated by reverse decoding. Obtain real-time operational status data of the target network, and dynamically filter the initial path list based on the operational status data to obtain the effective paths that currently have the conditions for propagation; Real-time attribute information is added to the filtered valid paths to form structured path data; The structured path data is categorized according to the origin-end node to form a set of effective propagation paths with multiple node pairs.

5. The risk assessment method based on topology analysis according to claim 4, characterized in that, The mapping of path sets based on risk transmission direction and path attributes to construct a multi-path propagation relationship graph is as follows: The effective propagation paths of multiple node pairs are mapped to the graph, with nodes as vertices and paths as edges, to construct an initial multi-path propagation relationship graph; Based on the paths of shared nodes or shared edges in the relationship graph, the interaction relationships between paths are marked, and the priority transmission paths and potential suppression paths of risk flows at the nodes are determined. The paths are divided according to the propagation probability weights of the preferred transmission paths and potential suppression paths, as well as the multi-path interaction logic; Based on the partitioning results and the real-time operating status of the target network, the multipath propagation relationship graph is dynamically adjusted.

6. The risk assessment method based on topology analysis according to claim 5, characterized in that, The identification of risk convergence nodes with multi-path risk inputs based on the multi-path propagation relationship graph is specifically as follows: In the multi-path propagation relationship graph, candidate nodes located at the intersection of multiple high-weight paths are identified, and the input risk flow is directionally superimposed based on the path interaction logic; By combining the propagation probability weights and distribution entropy of each path, a risk convergence gain factor is constructed to characterize the risk amplification effect of multiple paths; The inherent risk response value of the candidate node is calculated together with the risk convergence gain factor to determine the risk convergence node and its overall risk level.

7. The risk assessment method based on topology analysis according to claim 6, characterized in that, The process of identifying candidate nodes located at the intersection of multiple high-weight paths in the multi-path propagation graph specifically involves: In the multipath propagation relationship graph, the propagation probability weights are sorted and weight thresholds are set to filter out high-weight paths. The frequency of each node's occurrence in high-weight paths is counted to obtain the path intersection index of the nodes; The path convergence index is compared with a preset threshold to determine candidate nodes for risk convergence.

8. The risk assessment method based on topology analysis according to claim 7, characterized in that, The directional superposition of input risk flows based on path interaction logic is specifically as follows: Extract the directional relationships between all inbound and outbound paths of candidate nodes, and construct a risk flow superposition operator based on path interaction logic.

9. The risk assessment method based on topology analysis according to claim 8, characterized in that, The generation of network risk distribution based on comprehensive risk value and combined with operational status data includes: Obtain real-time status information of risk aggregation nodes and generate a set of dynamic node attributes; Based on the comprehensive risk value and dynamic attribute set of the nodes, calculate the actual risk energy output value of each risk convergence node under the current network state; The actual risk energy output value of each node is mapped to the network topology to form a node-risk matrix; The dominant path set is selected based on the node-risk matrix, and infeasible paths caused by node anomalies or path failures are removed. Output risk convergence nodes, actual risk energy output values, and the set of selected dominant paths in a structured form.

10. A system using a risk assessment method based on topology analysis as described in any one of claims 1-9, characterized in that, Includes the following modules: Path extraction module: used to analyze the connectivity between nodes, extract multiple propagation paths between any two nodes, and form a path set; Multi-path graph construction module: used to map path sets based on risk transmission direction and path attributes to construct a multi-path propagation relationship graph; Risk convergence identification module: Based on the multi-path propagation relationship graph, it identifies risk convergence nodes with multi-path risk inputs and calculates the comprehensive risk value of the node by integrating multi-path attributes; Risk distribution output module: Used to generate network risk distribution based on comprehensive risk value and combined with operational status data.

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