A method, system and product for heterogeneous infrastructure state evolution under flood risk

Abstract

The present application provides a method, system, and product for analyzing the state evolution of heterogeneous infrastructure under flood risk, which relates to the field of disaster emergency response technology. The method includes: constructing a corresponding heterogeneous functional diagram based on all infrastructure systems in a target area; predefining state switching rules for nodes to switch between various operating states; selecting a node in the heterogeneous functional diagram as an initial flood failure node in a failed state to obtain a target heterogeneous functional diagram; determining the heterogeneous feature values ​​of the nodes in the target heterogeneous functional diagram; determining the state switching rules satisfied by each node in the target heterogeneous functional diagram under flood scenarios based on various state switching rules and the heterogeneous feature values ​​of each node; and determining the operational state evolution of each node based on the state switching rules satisfied by each node. The method aims to identify key vulnerable nodes in infrastructure systems and reveal the state transmission mechanism of facility function nodes in cities under flood scenarios.

Description

Technical Field

[0001] The present application relates to the field of disaster emergency response technology, and in particular to a method, system and product for the state evolution of heterogeneous infrastructure under flood risk. Background Art

[0002] Heterogeneous urban infrastructure systems are crucial for ensuring the orderly operation of cities. Their stability and service continuity have a direct impact on socioeconomic activities, public safety, and the quality of life of residents. However, with the increasing frequency of extreme climate events such as rainstorms and floods, the disaster risks faced by urban infrastructure are increasing, and its vulnerability and interconnected failures are becoming increasingly prominent. Once a certain type of infrastructure suffers damage in a flooding scenario, it is very likely to trigger a chain reaction of failures through cross-system service dependencies, resulting in system-level service interruptions and functional paralysis, forming a complex cascading evolutionary process.

[0003] Current research on flood risk primarily focuses on the simulation and analysis of natural factors such as rainfall, topography, and drainage capacity. Technical tools such as two-dimensional hydrodynamic models and water depth prediction models are often used to describe the physical spread of floodwaters. Some studies have also explored the resilience and vulnerability of urban systems using tools such as complex networks and system dynamics, preliminarily identifying risk transmission pathways. However, these studies, in the context of highly dependent, complex, and heterogeneous urban infrastructure systems, have a limited evolutionary process for risk transmission pathways, failing to reflect the diversity of nodes within the system, resulting in poor results. Summary of the Invention

[0004] In view of this, the present application provides a method, system and product for heterogeneous infrastructure state evolution under flood risk, aiming to solve or partially solve the problems existing in the background technology.

[0005] In a first aspect of the present application, a method for state evolution of heterogeneous infrastructure under flood risk is provided, the method comprising:

[0006] Constructing a corresponding heterogeneous functional graph based on all infrastructure systems in the target area, wherein the nodes in the heterogeneous functional graph are composed of functions possessed by functional components in the heterogeneous infrastructure systems, and directed edges between the nodes are directed from a first node to a second node, wherein the operation of the second node depends on the operation of the first node;

[0007] Predefine various state switching rules for nodes to switch between various operating states, wherein the various state switching rules are defined based on the heterogeneous characteristics of the nodes, and one state switching rule corresponds to one operating state switching relationship;

[0008] Selecting at least one node in the heterogeneous functional graph as an initial flood failure node in a failure state to obtain a target heterogeneous functional graph;

[0009] Determine the heterogeneity feature values ​​of each node in the target heterogeneous function graph. The heterogeneity features include at least: importance, vulnerability index, network resilience, overload rate, and dependency strength.

[0010] Determining, according to the various state switching rules and the heterogeneous feature values ​​of the nodes, the state switching rules satisfied by each node in the target heterogeneous function graph under the flood scenario;

[0011] According to the state switching rules satisfied by each of the nodes, the evolution of the running state of each of the nodes is determined.

[0012] In a second aspect of the present application, a system for heterogeneous infrastructure state evolution under flood risk is provided, the system comprising:

[0013] A hetero-functional graph construction module is configured to construct a corresponding hetero-functional graph based on all infrastructure systems in the target area, wherein the nodes in the hetero-functional graph are composed of functions possessed by functional components in the infrastructure system, and directed edges between the nodes are directed from a first node to a second node, wherein the operation of the second node depends on the operation of the first node;

[0014] The rule definition module is used to predefine various state switching rules for nodes to switch between various operating states. The various state switching rules are related to the heterogeneous characteristics of the nodes, and one state switching rule corresponds to one operating state switching relationship;

[0015] a target heterogeneous functional graph determining module, configured to select at least one node in the heterogeneous functional graph as an initial flood failure node in a failed state to obtain a target heterogeneous functional graph;

[0016] A heterogeneity feature determination module is used to determine the heterogeneity feature value of each node in the target heterogeneous function graph. The heterogeneity features include at least: importance, vulnerability index, network resilience, overload rate and dependency strength;

[0017] A state switching rule determination module is used to determine the state switching rule satisfied by each node in the target heterogeneous function graph under the flood scenario according to the various state switching rules and the heterogeneous feature values ​​of each node;

[0018] The state evolution module is used to determine the evolution of the operating state of each node according to the state switching rules satisfied by each node.

[0019] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement the method for state evolution of heterogeneous infrastructure under flood risk described in the first aspect of the present application.

[0020] In a fourth aspect of the present application, a storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the method for state evolution of heterogeneous infrastructure under flood risk described in the first aspect of the present application is implemented.

[0021] Compared with the prior art, this application has the following advantages:

[0022] The embodiment of the present application provides a method for the state evolution of heterogeneous infrastructure under flood risk. First, a corresponding heterogeneous function graph is constructed based on all infrastructure systems in the target area. The nodes in the heterogeneous function graph are composed of the functions of functional components in the heterogeneous infrastructure system. The directed edges between the nodes are from the first node to the second node, wherein the operation of the second node depends on the operation of the first node; various state switching rules for switching nodes between various operating states are predefined, wherein the various state switching rules are defined based on the heterogeneous characteristics of the nodes, and one state switching rule corresponds to one operating state switching relationship; at least one node is selected in the heterogeneous function graph as the initial failure node of the flood in the failure state to obtain the target heterogeneous function graph; the heterogeneous characteristic values ​​of each node in the target heterogeneous function graph are determined; according to the various state switching rules and the heterogeneous characteristic values ​​of each node, the state switching rules satisfied by each node in the target heterogeneous function graph under the flood scenario are determined; according to the state switching rules satisfied by each node, the evolution of the operating state of each node is determined.

[0023] Therefore, this application first constructs a corresponding heterogeneous function diagram based on all infrastructure systems in the target area, and defines various state switching rules for nodes in the heterogeneous function diagram to switch between various operating states. These state switching rules are defined based on the heterogeneous characteristics of the nodes. The specific values ​​of the corresponding heterogeneous characteristics of the nodes in the heterogeneous function diagram can determine which state switching rule the node currently complies with, thereby determining the operating state to which the node will switch. Then, before the simulation, an initial state of the heterogeneous function diagram is given in the flood scenario. The initial state refers to randomly selecting at least one node as the flood initial failure node that is already in a failed state. Finally, based on the defined various state switching rules and the heterogeneous characteristics of the nodes, the state evolution of each node in the entire target heterogeneous function diagram is determined starting from the initial state. This method can effectively identify key vulnerable nodes in urban infrastructure systems and reveal the transmission mechanism of flood disasters between multiple infrastructure systems. It has good interpretability and versatility and can provide technical support for the scientific assessment and precise governance of urban flood disasters.

[0024] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art.

[0026] Figure 1 A flowchart of a method for heterogeneous infrastructure state evolution under flood risk provided in an embodiment of the present application;

[0027] Figure 2 A schematic diagram of constructing a heterogeneous function graph in a method for state evolution of heterogeneous infrastructure under flood risk provided in an embodiment of the present application;

[0028] Figure 3 A schematic diagram of switching between various operating states in a method for evolving the state of heterogeneous infrastructure under flood risk provided in an embodiment of the present application;

[0029] Figure 4 A schematic diagram of node state evolution in a heterogeneous function graph in a method for heterogeneous infrastructure state evolution under flood risk provided in an embodiment of the present application;

[0030] Figure 5 A schematic diagram of a heterogeneous infrastructure state evolution system under flood risk provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings.

[0032] Figure 1 A flowchart of a method for evolving the state of heterogeneous infrastructure under flood risk provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0033] Step S1: Based on all infrastructure systems in the target area, a corresponding hetero-functional graph is constructed, where the nodes in the hetero-functional graph are composed of the functions of the functional components in the infrastructure system, and the directed edges between the nodes are directed from the first node to the second node, where the operation of the second node depends on the operation of the first node.

[0034] In this embodiment, the target area is divided based on cities, and one city corresponds to one target area. The infrastructure state evolution process of any target area is the same. Taking one target area as an example, all infrastructure in the target area refers to all infrastructure systems that can provide services to residents in the target area. The types of these infrastructure systems include at least power systems, water systems, gas supply systems, transportation systems, etc. The physical components in the infrastructure system refer to the various physical engineering facilities that constitute the infrastructure system, such as the physical components of the power plant in the power system, the physical components of the substation, and the physical components of the transmission line that transmits electricity from the physical components of the power plant to the physical components of the substation. A physical component has at least one function, and some physical components have multiple functions. For example, a communication base station has the function of realizing voice communication and the function of wireless information transmission.

[0035] In this embodiment, in order to more clearly represent the functions of each physical component, the present application pre-constructs a "physical component-function mapping matrix" tool. This matrix is ​​a table composed of 0s and 1s, in which the first column represents various physical components, the first row represents various functions, and the data in other cells are 1 or 0. When the value in the cell is 1, it means that the physical component in the row where the cell is located has the function of the column where the cell is located; when the value in the cell is 0, it means that the physical component in the row where the cell is located does not have the function of the column where the cell is located. Through this matrix, the functions of each physical component can be quickly determined, thereby providing a logical basis for the construction of subsequent heterofunctional diagrams.

[0036] In this embodiment, first, all infrastructure in the target area is obtained through data sources provided by public channels and / or official channels, and all physical components included in all infrastructure are determined, thereby obtaining all physical components in the target area. Figure 2 As shown, all physical components in the target area 1 obtained through data sources provided by public channels or official channels include: power plant physical component R1, substation physical component R2, communication base station R3, substation physical component R4, gas station physical components R5 and R6, transmission line physical component E1 between R1 and R2, transmission line physical component E2 between R2 and R3, transmission line component E3 between R1 and R4, transmission line component E4 between R4 and R5, and transmission line component E5 between R4 and R6. Based on all physical components in the determined target area, a physical component topology relationship is created, in which the physical components are connected to the physical components with which they have dependencies. For example, Figure 2As shown, the power plant physical component R1 relies on the transmission line component E1 to transmit electricity to the substation physical component R2, so the power plant physical component R1 will be connected to the transmission line component E1, and the substation physical component R2 relies on the transmission line component E1 to receive electricity transmitted by the power plant physical component R1, so the substation physical component R2 will also be connected to the transmission line component E1.

[0037] In this embodiment, after determining all physical components within the target area, the functions of each physical component are determined through a pre-built "physical component-function mapping matrix", and a corresponding function node is created for each determined function, that is, one function has a corresponding function node. For the convenience of description, subsequent function nodes are described as nodes.

[0038] For example, Figure 2 As shown, for the physical component R1, it has the function of generating electricity, so a corresponding functional node φ1 is determined; for the physical component E1, it has the function of transmitting electricity from R1 to R2, so a corresponding functional node φ2 is determined; for the physical component R2, it has the function of converting high voltage electricity into low voltage electricity, so a corresponding functional node φ3 is determined; for the physical component E2, it has the function of transmitting electricity from R2 to R3, so a corresponding functional node φ4 is determined; for the physical component R3, it has the function of realizing voice communication, so a corresponding functional node φ5 is determined; for the physical component R3, it has the function of wireless signal transmission, so a corresponding functional node φ6 is determined; for the physical component E3, it has the function of transmitting electricity from R1 to R4 , therefore a corresponding functional node φ7 is determined; for the physical component R4, it has the function of converting high voltage electricity into low voltage electricity, therefore a corresponding functional node φ8 is determined; for the physical component E4, it has the function of transmitting electricity from R4 to R5, therefore a corresponding functional node φ9 is determined; for the physical component E5, it has the function of transmitting electricity from R4 to R6, therefore a corresponding functional node φ11 is determined; for the physical component R5, it has the function of distributing gas, therefore a corresponding functional node φ10 is determined; for the physical component R6, it has the function of distributing gas, therefore a corresponding functional node φ12 is determined; for the physical component E6, it has the function of transmitting gas from R5 to R6, therefore a corresponding functional node φ13 is determined.

[0039] In this embodiment, after constructing all nodes within the target area, the dependencies between the nodes are determined. Two nodes with a dependency are connected via a directed edge, with the directed edge pointing from the first of the two nodes to the second. The operation of the second node depends on the operation of the first node, that is, the second node can only operate normally if the first node operates normally. By connecting all nodes with dependencies via directed edges, a heterogeneous functional graph corresponding to the target area is obtained.

[0040] For example, Figure 2 As shown, node φ1 is the node corresponding to the power generation function of physical component R1, and node φ2 is the node corresponding to the function of physical component E2 to transmit electricity from R1 to R2. The power transmission function of node φ2 depends on the normal power generation of node φ1, so the operation of node φ2 depends on the operation of node φ1, so a directed edge is created from node φ1 to node φ2. Figure 2 N1 to N3 in the figure represent demand nodes, which represent end users, such as residential areas and commercial buildings.

[0041] Step S2: pre-define various state switching rules for nodes to switch between various operating states, wherein the various state switching rules are defined based on the heterogeneous characteristics of the nodes, and one state switching rule corresponds to one operating state switching relationship.

[0042] In this embodiment, the present application predefines various operating states of nodes in the heterogeneous function diagram, including at least: fragile state, degraded state, failed state and recovery state. At the same time, various state switching rules for nodes to switch between various operating states are predefined, and one state switching rule corresponds to one operating state switching relationship. Various state switching rules are defined based on the heterogeneous characteristics of the node. When the heterogeneous characteristic value of the node is determined, the state switching rule that can be matched is determined by matching the heterogeneous characteristic value of the node with various state switching rules. Based on the matched state switching rule, it can be determined that the node will switch from the current operating state to the operating state.

[0043] Step S3: selecting at least one node in the heterofunctional graph as an initial flood failure node in a failure state to obtain a target heterofunctional graph.

[0044] In this embodiment, at least one node is selected from the heterofunctional graph corresponding to the constructed target area and its operating state is set to a failure state. These nodes selected and set to a failure state are called flood initial failure nodes, thereby obtaining an initial state of the heterofunctional graph in a flood scenario. The initial state corresponds to a target heterofunctional graph, and the operating state of the selected node in the target heterofunctional graph is a failure state, while other nodes are in a vulnerable state threatened by floods.

[0045] Step S4: Determine the heterogeneity feature values ​​of each node in the target heterogeneous function graph. The heterogeneity features include at least: importance, vulnerability index, network resilience, overload rate and dependency strength.

[0046] In this embodiment, after obtaining the target heterogeneous functional graph through step S3, the heterogeneous characteristics of each node in the target heterogeneous functional graph are identified and quantified. This process starts from the dimensions of the node's service role, damage sensitivity and carrying capacity in flood disaster scenarios, and extracts heterogeneous characteristic values ​​that reflect its state evolution tendency, including at least: importance, vulnerability index, network resilience, overload rate and dependence intensity, etc.

[0047] Step S5: determining the state switching rules satisfied by each node in the target heterogeneous function graph under the flood scenario according to the various state switching rules and the heterogeneous feature values ​​of each node.

[0048] In this embodiment, the various state switching rules for pre-defined node switching between various operating states are defined based on the heterogeneous characteristics of the nodes. Therefore, after calculating and obtaining the heterogeneous characteristic values ​​of each node in the target heterogeneous function graph in step S4, the state switching rules satisfied by each node in the target heterogeneous function graph can be determined based on the heterogeneous characteristic values ​​of each node and the various state switching rules.

[0049] Step S6: Determine the evolution of the running state of each node according to the state switching rules satisfied by each node.

[0050] In this embodiment, the state switching rules correspond to the switching relationship of the operating state. After obtaining the state switching rules satisfied by each node through step S5, the new operating state to which each node will switch is determined based on the state switching rules satisfied by itself, thereby obtaining the evolution of the operating state of each node in the target heterogeneous functional diagram.

[0051] The embodiment of the present application provides a method for the state evolution of heterogeneous infrastructure under flood risk. First, a corresponding heterogeneous function graph is constructed based on all infrastructure systems in the target area. The nodes in the heterogeneous function graph are composed of the functions of the functional components in the infrastructure system. The directed edges between the nodes are from the first node to the second node, wherein the operation of the second node depends on the operation of the first node; various state switching rules for switching nodes between various operating states are predefined, wherein the various state switching rules are defined based on the heterogeneous characteristics of the nodes, and one state switching rule corresponds to one operating state switching relationship; at least one node is selected in the heterogeneous function graph as the flood initial failure node in the failure state to obtain the target heterogeneous function graph; the heterogeneous characteristic values ​​of each node in the target heterogeneous function graph are determined; according to the various state switching rules and the heterogeneous characteristic values ​​of each node, the state switching rules satisfied by each node in the target heterogeneous function graph under the flood scenario are determined; according to the state switching rules satisfied by each node, the evolution of the operating state of each node is determined.

[0052] Therefore, this application first constructs a corresponding heterogeneous function diagram based on all infrastructure systems in the target area, and defines various state switching rules for nodes in the heterogeneous function diagram to switch between various operating states. These state switching rules are defined based on the heterogeneous characteristics of the nodes. The specific values ​​of the corresponding heterogeneous characteristics of the nodes in the heterogeneous function diagram can determine which state switching rule the node currently complies with, thereby determining the operating state to which the node will switch. Then, before the simulation, an initial state of the heterogeneous function diagram is given in the flood scenario. The initial state refers to randomly selecting at least one node as the flood initial failure node that is already in a failed state. Finally, based on the defined various state switching rules and the heterogeneous characteristics of the nodes, the state evolution of each node in the entire target heterogeneous function diagram is determined starting from the initial state. This method can effectively identify key vulnerable nodes in urban infrastructure systems and reveal the transmission mechanism of flood disasters between multiple infrastructure systems. It has good interpretability and versatility and can provide technical support for the scientific assessment and precise governance of urban flood disasters.

[0053] In combination with the above embodiments, in one embodiment, the embodiment of the present application also provides a method for the state evolution of heterogeneous infrastructure under flood risk. In the method for the state evolution of heterogeneous infrastructure under flood risk, the importance of the node is used to quantify the importance of the node in the infrastructure system; the vulnerability index of the node is used to quantify the sensitivity of the node to state transition during flood disasters; the network resilience is used to quantify the ability of the target area to maintain service after the flood disaster; the overload rate of the node is used to quantify the degree of service pressure overload borne by the node; the dependency strength of the node is used to quantify the dependency of the node on other nodes; the various operating states of the node include at least: the node is in normal operating state, facing the threat of floods, and has a potential degradation risk; the node is in a degraded state where some functions are damaged, the operating capacity is reduced, and it is not completely ineffective; the node service function is interrupted and it is unable to output resources or services to the outside world; the node has undergone repair or external adjustment and has regained some or all service capabilities.

[0054] In conjunction with the above embodiments, in one implementation, the present application also provides a method for heterogeneous infrastructure state evolution under flood risk. In this method for heterogeneous infrastructure state evolution under flood risk, determining the heterogeneous feature value of a node includes: determining the importance of the node in the target heterogeneous function graph using an importance algorithm, wherein the importance algorithm expression is:

[0055]

[0056]

[0057] in, is the initial importance of node i; , are the degrees of nodes i and j respectively; is the effective distance from node i to node j; is the normalized importance of node i; is the minimum importance value of all n nodes in the target heterogeneous functional graph; is the maximum importance value among all n nodes in the target heterogeneous functional graph;

[0058] The vulnerability index of the node in the target heterogeneous function graph is determined by the vulnerability index algorithm, and the vulnerability index algorithm expression is:

[0059]

[0060] in, is the vulnerability index of node i; is the exposure factor of node i; is the impact resistance index of node i;

[0061] Assess the infrastructure resilience of the target area through the PSR framework and obtain the corresponding network resilience;

[0062] The maximum functional load capacity of the node in the target heterogeneous functional graph is determined by the functional load algorithm, and the functional load algorithm expression is:

[0063]

[0064]

[0065] Among them, set each node Initial functional load Follow the load range The truncated normal distribution of , are the standard deviation and mean in the truncated normal distribution, respectively; is the maximum functional load capacity of node i; is the tolerance parameter of the functional node in the target heterogeneous functional graph, which is used to determine the node's tolerance to overload;

[0066] The redistributed functional load of the node after load redistribution in the target heterogeneous functional graph is determined by a load redistribution algorithm. The load redistribution algorithm expression is:

[0067]

[0068] in, is the redistributed functional load of node j that belongs to the same category and is connected to node i at time t; is the node in the failed state at time t A node set consisting of all nodes belonging to the same category and connected to each other; is the redistribution function load of node i that is in a failed state at time t-1; is the maximum functional load capacity of node j;

[0069] determining an overload rate of the node based on a relationship between a redistributed functional load of the node and a maximum functional load capacity;

[0070] The dependency strength between the node and other nodes in the target heterogeneous function graph is determined by a dependency strength algorithm, and the dependency strength algorithm expression is:

[0071]

[0072] in, is the dependence strength of node i on node j at time t; is an adjustable parameter; , are the degrees between nodes i and j and the nodes that are not in a failed state at time t; Indicates that there is a directed edge from node j to node i in the target heterogeneous graph at time t; Indicates that there is no directed edge from node j to node i in the target heterogeneous graph at time t.

[0073] In this embodiment, the method for determining the heterogeneous feature value of any node in the target heterogeneous function graph is the same, and one node is taken as an example for description.

[0074] The importance of a node reflects the importance of the node in the urban infrastructure system, mainly considering its topological position in the network as a functional hub or transmission channel. In this application, the calculation of node importance is calculated using the effective distance gravity method. First, the effective distance between all nodes is calculated, then the interaction score between all node pairs is calculated, and finally the cumulative sum is used to obtain the EffG centrality score of each node. Specifically, the importance of the node in the target heterogeneous functional graph is determined by the importance algorithm, and the expression of the importance algorithm is:

[0075]

[0076]

[0077] in, is the initial importance of node i; , are the degrees of nodes i and j respectively, which represent the number of edges connected to the corresponding nodes in the target heterogeneous graph; is the effective distance from node i to node j, which is the shortest path from node i to node j; is the normalized importance of node i, that is, the importance of node i finally solved; is the minimum importance value of all n nodes in the target heterogeneous functional graph; is the maximum importance value among all n nodes in the target heterogeneous functional graph.

[0078] The vulnerability index of a node is used to quantify the sensitivity of the node to state transition (degradation or failure) during flood disasters. For the vulnerability index of a node, this application comprehensively considers two major factors: on the one hand, the exposure of the node, which mainly includes the maximum flooding depth of the node's location, the duration of the flood, and the service life of its facilities (equipment aging); on the other hand, the impact resistance of the node, which measures its structural protection level and emergency response capabilities in the face of flood disasters, including whether the facility has a complete emergency response plan, the relative elevation (elevation) of the node's location, and its own protection level (such as flood walls, waterproof packaging). This application considers these two dimensions simultaneously to determine the vulnerability index of the node. Specifically, the vulnerability index of the node in the target heterogeneous function graph is determined by the vulnerability index algorithm, and the vulnerability index algorithm expression is:

[0079]

[0080] in, is the vulnerability index of node i; is the exposure factor of node i; is the impact resistance index of node i, and finally divided by 10 to obtain the PV value in the interval 0–1.

[0081] Node network resilience is used to quantify the target area's ability to maintain services after a flood disaster. This application introduces the "Pressure-State-Response (PSR)" theoretical framework to conduct a comprehensive, multi-dimensional assessment of urban infrastructure resilience. Based on this theory, this application constructs a node resilience evaluation system composed of multiple sub-indicators, covering operational capabilities, maintenance mechanisms, emergency response efficiency, and other aspects to determine the network resilience of the entire target area. Table 1 shows this application's node resilience evaluation system, which is composed of multiple sub-indicators.

[0082] Table 1

[0083]

[0084] First, relevant data of multiple sub-indicators in Table 1 are collected in the target area. The positive and negative ideal solution distances of the target area are calculated and determined by the entropy weight method and the topsis method. The network resilience of the target area is obtained by further calculating the mean.

[0085] For example, as shown in Table 2, Table 2 exemplarily shows the network resilience calculation results of the target area in 2018-2022.

[0086] Table 2

[0087]

[0088] The real-time functional load of a node (its original maximum functional load capacity and the redistributed functional load after redistribution) is used to measure the service pressure borne by the node before and during a flood. The higher the load, the more likely it is that a more extensive service interruption will occur once the node fails, thereby exacerbating the cascading risk of the system. In order to fully reflect the dynamic change characteristics of the node load during the system evolution process, this application introduces two parts: the original maximum functional load capacity of the node and the dynamic functional load redistribution mechanism. Accordingly, for the overload rate of a node, the maximum functional load capacity of the node is first determined. In the case that the node will be allocated a load by other nodes through load redistribution, the redistributed functional load of the node after redistribution is determined by the load redistribution algorithm. Finally, the overload rate of the node is determined based on the original maximum functional load capacity of the node and the redistributed functional load after its own redistribution.

[0089] Specifically, for the overload rate of a node, the maximum functional load capacity of the node in the target heterogeneous functional graph is first determined by the functional load algorithm. The functional load algorithm expression is:

[0090]

[0091]

[0092] In order to truly reproduce the statistical distribution characteristics of the initial load level of the node, this application draws on the idea of ​​the CASCADE model and sets the Initial functional load Follow the load range The truncated normal distribution of , are the standard deviation and mean in the truncated normal distribution, respectively; is the maximum functional load capacity of node i; It is the tolerance parameter of the functional node in the target heterogeneous functional graph, which is used to determine the node's tolerance to overload.

[0093] After calculating the maximum functional load capacity of the node, during the evolution of floods, if a node enters a failure state, the service pressure it originally borne will need to be borne by other nodes in the target heterogeneous functional graph that have the same functions and are interconnected. To this end, this application designs a functional load redistribution mechanism in the target heterogeneous functional graph to ensure that when the functional load of the node is transferred, both the idle capacity of the node and its current operating status are taken into account. The specific solution process is to determine the redistributed functional load of the node after load redistribution in the target heterogeneous functional graph through a load redistribution algorithm. The expression of the load redistribution algorithm is:

[0094]

[0095] in, is the redistributed functional load of node j that belongs to the same category and is connected to node i at time t; is the node set consisting of all nodes that belong to the same category as the node i in the failed state at time t and are interconnected; is the redistribution function load of node i that is in a failed state at time t-1; is the maximum functional load capacity of node j.

[0096] The functional load redistribution mechanism follows the strategy of "the more capable, the more work". Nodes with more idle capacity can receive more load from failed nodes, thereby reducing the impact of load redistribution on the entire target heterogeneous functional graph.

[0097] After calculating the reallocated functional load of the node, the overload rate of the node is determined based on the relationship between the reallocated functional load of the node and the maximum functional load capacity of the node. The greater the extent to which the reallocated functional load of the node exceeds the maximum functional load capacity of the node, the greater the overload rate of the node.

[0098] For the dependency strength of a node, the dependency strength between the node and other nodes in the target heterogeneous function graph is determined by the dependency strength algorithm. The dependency strength algorithm expression is:

[0099]

[0100] in, is the dependence strength of node i on node j at time t; is an adjustable parameter; , are the degrees between nodes i and j and the nodes that are not in a failed state at time t; Indicates that there is a directed edge from node j to node i in the target heterogeneous graph at time t; Indicates that there is no directed edge from node j to node i in the target heterogeneous graph at time t.

[0101] In this embodiment, through the above implementation, the values ​​of various heterogeneous features of each node in the target heterogeneous function graph are calculated.

[0102] In combination with the above embodiments, in one implementation, the embodiments of the present application further provide a method for heterogeneous infrastructure state evolution under flood risk. In this method for heterogeneous infrastructure state evolution under flood risk, the overload rate of the node is determined based on the relationship between the node's redistributed functional load and the maximum functional load capacity, including: comparing the node's redistributed functional load and the maximum functional load capacity; when the node's maximum functional load capacity is greater than or equal to its own redistributed functional load, determining the node's overload rate to be zero; when the node's maximum functional load capacity is less than its own redistributed functional load, determining the node's overload rate using an overload rate algorithm, wherein the overload rate algorithm expression is: .

[0103] In this embodiment, the node's redistributed power load after redistribution is compared with its maximum functional load capacity. If the node's maximum functional load capacity is greater than or equal to its redistributed functional load, the node's overload rate is determined to be zero. If the node's maximum functional load capacity is less than its redistributed functional load, the node's overload rate is determined using an overload rate algorithm, which is expressed as: .

[0104] In conjunction with the above embodiments, in one implementation, the present application also provides a method for state evolution of heterogeneous infrastructure under flood risk. In this method for state evolution of heterogeneous infrastructure under flood risk, various state switching rules for switching nodes between various operating states are predefined, including:

[0105] When a node is in a vulnerable state, a state switching rule for the node to switch from a vulnerable state to a degraded state is defined as the risk propagation probability of the node being higher than a first threshold. The expression of the risk propagation probability is:

[0106]

[0107] in, is the probability that the influence of node j in the target heterogeneous functional graph is propagated to node i; is the probability of basic risk transmission; is the vulnerability index of the heterogeneous characteristics of node i; is the intensity of the dependence of node i on the failed node j in the directed edge from failed node j to node i at time t; is the importance of the heterogeneous features of node j in the failed state;

[0108] When a node is in a degraded state, the state switching rule for the node to switch from the degraded state to the failed state is defined as the failure probability of the node is greater than or equal to the preset recovery probability. The expression of the failure probability is:

[0109]

[0110] in, is the basic failure probability; The network resilience of the entire target heterogeneous functional graph; is the overload rate of the heterogeneity characteristics of node i at time t;

[0111] When a node is in a degraded state, a state switching rule for switching the node from the degraded state to the recovery state is defined as the failure probability of the node is less than a preset recovery probability;

[0112] When a node is in a failed state, a state switching rule for switching the node from the failed state to the recovery state is defined as the duration of the node being in the failed state being greater than or equal to the target duration corresponding to the node.

[0113] In this embodiment, if Figure 3 As shown, the state switching relationship of a node includes: switching from a fragile state to a degraded state, switching from a degraded state to a failed state, switching from a degraded state to a restored state, and switching from a failed state to a restored state. Each state switching relationship has a corresponding state switching rule. When a node is in a fragile state, since the node will only switch to a degraded state, it is only necessary to determine whether the node's heterogeneous feature values ​​satisfy the state switching rule corresponding to switching from a fragile state to a degraded state. When a node is in a degraded state, since the node may switch to a failed state or a restored state, it is necessary to determine whether the node's heterogeneous feature values ​​satisfy the state switching rule corresponding to switching from a degraded state to a failed state, and whether the node's heterogeneous feature values ​​satisfy the state switching rule corresponding to switching from a degraded state to a restored state. When a node is in a failed state, since the node will only switch to a restored state, it is only necessary to determine whether the node's heterogeneous feature values ​​satisfy the state switching rule corresponding to switching from a failed state to a restored state.

[0114] In this embodiment, the state transition rule corresponding to the node switching from the degraded state to the failed state is defined as the node's risk propagation probability being higher than a first threshold. The first threshold can be set according to the actual application scenario and is not specifically limited here. The expression of the risk propagation probability is:

[0115]

[0116] in, is the risk propagation probability of the influence of node j in the failed state in the target heterogeneous functional graph propagating to node i. In the summation of j = 1 to N, the value of j is not equal to i. At the same time, it only applies to the nodes in the failed state at time t in the target heterogeneous functional graph. For example, if the nodes in the failed state at time t in the target heterogeneous functional graph are nodes 1 and 3, then in the summation of j = 1 to N and i ≠ j at time t, the value of j can only be 1 and 3; is the probability of basic risk transmission; is the vulnerability index of the heterogeneous characteristics of node i; is the intensity of the dependence of node i on the failed node j in the directed edge from failed node j to node i at time t; is the importance of the heterogeneous features of node j in the failed state.

[0117] In this embodiment, the state switching rule for a node to switch from a degraded state to a failed state is defined as the failure probability of the node is greater than or equal to the preset recovery probability. , the recovery probability It can be set according to the actual application scenario and is not specifically limited here. The expression of the failure probability is:

[0118]

[0119] in, is the basic failure probability; The network resilience of the entire target heterogeneous functional graph; is the overload rate of the heterogeneous characteristics of node i at time t.

[0120] In this embodiment, the state switching rule for a node to switch from a degraded state to a restored state is defined as the failure probability of the node being less than a preset restoration probability.

[0121] In this embodiment, the state switching rule for a node to switch from a failure state to a recovery state is defined as the time the node is in the failure state is greater than or equal to the target time corresponding to itself. The target time can be set according to the actual application scenario and is not specifically limited here.

[0122] In combination with the above embodiments, in one implementation, the present application also provides a method for heterogeneous infrastructure state evolution under flood risk. In the method for heterogeneous infrastructure state evolution under flood risk, the method further includes:

[0123] According to the various state switching rules and the heterogeneous characteristics of the nodes in the heterogeneous function diagram, a preset round of iterative simulation is performed on the operating state changes of each node in the target heterogeneous function diagram under a flood scenario using a Monte Carlo simulation method and corresponding differential equations;

[0124] Based on the results of the iterative simulation of the preset rounds, determining the dynamic evolution of the nodes in various operating states in the target heterogeneous functional graph over time under the flood scenario;

[0125] The differential equation is expressed as:

[0126] ;

[0127] ;

[0128] ;

[0129] ;

[0130] in, represents the probability that node i is in a vulnerable state at time t; represents the probability that node i is in a degraded state at time t; represents the probability that node i is in a failed state at time t; represents the probability that node i is in the recovery state at time t; It represents the rate of change of the probability of node i being in a vulnerable state over time. It represents the rate of change of the probability of node i being in a degraded state over time; It represents the rate of change of the probability of node i being in a failure state over time. It represents the rate of change of the probability of node i being in the recovery state over time. represents the probability of recovery; Indicates the recovery rate; is the risk propagation probability of the influence of node j in the failure state in the target heterogeneous functional graph being propagated to node i; is the failure probability of node i at time t.

[0131] In this embodiment, based on predefined state transition rules and the heterogeneous characteristics of nodes in the heterogeneous functional graph, the present application introduces a Monte Carlo simulation method and corresponding differential equations to perform a preset number of iterative simulations on the operational state changes of each node in the target heterogeneous functional graph under a flood scenario, obtaining the preset iterative simulation results. Based on the obtained preset iterative simulation results, the dynamic evolution of nodes in various operational states in the target heterogeneous functional graph over time under the flood scenario is determined. This approach can dynamically track the evolutionary paths of nodes in the system, the service chain break sequences, and the cascading propagation paths, thereby identifying key vulnerable nodes and risk concentration areas in the city. By running the simulation process multiple times, the state changes of nodes at each moment can be tracked, the paths and speeds of risk diffusion from core nodes can be identified, and the state sequences and propagation chains of different nodes can be extracted. During the simulation process, the system records the functional degradation and failure status at key time nodes and outputs a global risk propagation map, a node failure rate curve, and a path dependency diagram, thus clearly demonstrating the cascading effects and spatial expansion characteristics of flood impacts in heterogeneous networks.

[0132] In this embodiment, the differential equation is expressed as:

[0133] ;

[0134] ;

[0135] ;

[0136] ;

[0137] in, represents the probability that node i is in a vulnerable state at time t; represents the probability that node i is in a degraded state at time t; represents the probability that node i is in a failed state at time t; represents the probability that node i is in the recovery state at time t; It represents the rate of change of the probability of node i being in a vulnerable state over time. It represents the rate of change of the probability of node i being in a degraded state over time; It represents the rate of change of the probability of node i being in a failure state over time. It represents the rate of change of the probability of node i being in the recovery state over time. represents the probability of recovery; Indicates the recovery rate; is the risk propagation probability of the influence of node j in the failure state in the target heterogeneous functional graph being propagated to node i; is the failure probability of node i at time t.

[0138] For example, the application sets the values ​​of the initial parameters to be , , , , , At the same time, the average network resilience of a target area in the past few years is selected as the network resilience of the target area. At the same time, 7 nodes in the heterogeneous functional diagram of the target area are selected as the initial failure nodes of flooding, and the constructed differential equation is solved by Python to obtain the following: Figure 4 The following figure shows the evolution of nodes in different operating states (i.e., the change in the proportion of nodes in different operating states) in the heterogeneous functional graph of the target area under a flood scenario. Initially, only seven nodes were in a failed state, while the remaining nodes were in a vulnerable state. Considering the risk propagation of failed nodes in this heterogeneous functional graph network and the ongoing impact of flood disasters, the heterogeneous characteristics of all nodes were considered, and the risk propagation probability matrix at different moments was determined. The change in the proportion of nodes in different operating states over time was obtained. It can be seen that in the initial stages of the simulation, the number of nodes in a degraded state increased dramatically, while the number of nodes in a vulnerable state decreased sharply. At the same time, due to limited network resilience and node overload, the recovery rate of some degraded nodes was lower than their failure rate, causing the nodes' operating state to transition from a degraded state to a failed state, resulting in a continuous increase in the number of failed nodes. Finally, the failed and degraded nodes gradually recovered, and the number of recovered nodes continued to rise. When the simulation step size reached 55, all nodes in the network transitioned to a recovered state.

[0139] Based on the same inventive concept, this application provides a heterogeneous infrastructure state evolution system under flood risk, such as Figure 5 As shown, the heterogeneous infrastructure state evolution system 500 under flood risk includes:

[0140] A hetero-functional graph construction module 501 is configured to construct a corresponding hetero-functional graph based on all infrastructure systems in the target area, wherein the nodes in the hetero-functional graph are composed of functions of functional components in the infrastructure system, and directed edges between the nodes are directed from a first node to a second node, wherein the operation of the second node depends on the operation of the first node;

[0141] A rule definition module 502 is used to predefine various state switching rules for switching nodes between various operating states, wherein the various state switching rules are related to the heterogeneous characteristics of the nodes, and each state switching rule corresponds to a corresponding operating state switching relationship;

[0142] A target heterogeneous functional graph determining module 503 is configured to select at least one node in the heterogeneous functional graph as an initial flood failure node in a failed state to obtain a target heterogeneous functional graph;

[0143] The heterogeneity feature determination module 504 is used to determine the heterogeneity feature value of each node in the target heterogeneous function graph, where the heterogeneity features include at least importance, vulnerability index, network resilience, overload rate, and dependency strength.

[0144] A state switching rule determination module 505 is configured to determine the state switching rule satisfied by each node in the target heterogeneous function graph under a flood scenario based on the various state switching rules and the heterogeneous feature values ​​of each node;

[0145] The state evolution module 506 is configured to determine the evolution of the running state of each node according to the state switching rules satisfied by each node.

[0146] Optionally, the importance of nodes in the heterogeneous infrastructure state evolution system 500 under flood risk is used to quantify the importance of nodes in the infrastructure system; the vulnerability index of nodes is used to quantify the sensitivity of nodes to state transitions during flood disasters; network resilience is used to quantify the ability of target areas to maintain services after flood disasters; the overload rate of nodes is used to quantify the degree of service pressure overload borne by nodes; the dependency strength of nodes is used to quantify the dependence of nodes on other nodes; various operating states of nodes include at least: the node is in normal operating state, facing flood threats, and has a potential degradation risk; the node is in a degraded state where some functions are damaged, the operating capacity is reduced, and the failure state is not completely failed; the node service function is interrupted and resources or services cannot be exported externally; the node is in a recovery state where it has regained some or all service capabilities after repair or external adjustment.

[0147] Optionally, the heterogeneous feature determination module 504 is specifically configured to determine the importance of a node in the target heterogeneous function graph by using an importance algorithm. The importance algorithm expression is:

[0148]

[0149]

[0150] in, is the initial importance of node i; , are the degrees of nodes i and j respectively; is the effective distance from node i to node j; is the normalized importance of node i; is the minimum importance value of all n nodes in the target heterogeneous functional graph; is the maximum importance value among all n nodes in the target heterogeneous functional graph;

[0151] And, it is used to determine the vulnerability index of the node in the target heterogeneous function graph through a vulnerability index algorithm, and the vulnerability index algorithm expression is:

[0152]

[0153] in, is the vulnerability index of node i; is the exposure factor of node i; is the impact resistance index of node i;

[0154] Also, it is used to assess the infrastructure resilience of the target area through the PSR framework and obtain the corresponding network resilience;

[0155] And, it is used to determine the maximum functional load capacity of the node in the target heterogeneous functional graph through a functional load algorithm, wherein the functional load algorithm expression is:

[0156]

[0157]

[0158] Among them, set each node Initial functional load Follow the load range The truncated normal distribution of , are the standard deviation and mean in the truncated normal distribution, respectively; is the maximum functional load capacity of node i; is the tolerance parameter of the functional node in the target heterogeneous functional graph, which is used to determine the node's tolerance to overload;

[0159] And, for determining the redistributed functional load of the node after load redistribution in the target heterogeneous functional graph through a load redistribution algorithm, the load redistribution algorithm expression is:

[0160]

[0161] in, is the redistributed functional load of node j that belongs to the same category and is connected to node i at time t; is the node in the failed state at time t A node set consisting of all nodes belonging to the same category and connected to each other; is the redistribution function load of node i that is in a failed state at time t-1; is the maximum functional load capacity of node j;

[0162] and, for determining an overload rate of the node based on a relationship between a redistributed functional load and a maximum functional load capacity of the node;

[0163] And, it is used to determine the dependency strength between the node and other nodes in the target heterogeneous function graph through a dependency strength algorithm, wherein the dependency strength algorithm expression is:

[0164]

[0165] in, is the dependence strength of node i on node j at time t; is an adjustable parameter; , are the degrees between nodes i and j and the nodes that are not in a failed state at time t; Indicates that there is a directed edge from node j to node i in the target heterogeneous graph at time t; Indicates that there is no directed edge from node j to node i in the target heterogeneous graph at time t.

[0166] Optionally, the heterogeneity feature determination module 504 is specifically configured to compare the redistributed functional load of the node with the maximum functional load capacity;

[0167] and, for determining that the overload rate of the node is zero when the maximum functional load capacity of the node is greater than or equal to the reallocated functional load of the node;

[0168] And, when the maximum functional load capacity of the node is less than the redistributed functional load of the node, the overload rate of the node is determined by an overload rate algorithm, wherein the expression of the overload rate algorithm is: .

[0169] Optionally, the rule definition module 502 is specifically configured to define, when a node is in a vulnerable state, a state switching rule for the node to switch from the vulnerable state to the degraded state as follows: the risk propagation probability of the node is higher than a first threshold, and the expression of the risk propagation probability is:

[0170]

[0171] in, is the risk propagation probability of the influence of node j in the failure state in the target heterogeneous functional graph being propagated to node i; is the probability of basic risk transmission; is the vulnerability index of the heterogeneous characteristics of node i; is the intensity of the dependence of node i on the failed node j in the directed edge from failed node j to node i at time t; is the importance of the heterogeneous features of node j in the failed state;

[0172] And, when a node is in a degraded state, a state switching rule for the node to switch from a degraded state to a failed state is defined as the failure probability of the node is greater than or equal to a preset recovery probability, and the expression of the failure probability is:

[0173]

[0174] in, is the basic failure probability; The network resilience of the entire target heterogeneous functional graph; is the overload rate of the heterogeneity characteristics of node i at time t;

[0175] and, for, when a node is in a degraded state, defining a state switching rule for switching the node from the degraded state to the recovered state as the failure probability of the node being less than a preset recovery probability;

[0176] Also, when a node is in a failed state, a state switching rule for defining a node to switch from a failed state to a recovery state is defined as the duration of the node being in the failed state being greater than or equal to the target duration corresponding to itself.

[0177] Optionally, the heterogeneous infrastructure state evolution system 500 under flood risk further includes:

[0178] a simulation module, configured to perform a preset number of iterative simulations on the operating state changes of each node in the target heterogeneous function diagram under a flood scenario by using a Monte Carlo simulation method and corresponding differential equations based on the various state switching rules and the heterogeneous characteristics of the nodes in the heterogeneous function diagram;

[0179] A dynamic evolution module is used to determine the dynamic evolution of nodes in various operating states in the target heterogeneous functional diagram over time under a flood scenario based on the results of a preset round of iterative simulations;

[0180] The differential equation is expressed as:

[0181] ;

[0182] ;

[0183] ;

[0184] ;

[0185] in, represents the probability that node i is in a vulnerable state at time t; represents the probability that node i is in a degraded state at time t; represents the probability that node i is in a failed state at time t; represents the probability that node i is in the recovery state at time t; It represents the rate of change of the probability of node i being in a vulnerable state over time. It represents the rate of change of the probability of node i being in a degraded state over time; It represents the rate of change of the probability of node i being in a failure state over time. It represents the rate of change of the probability of node i being in the recovery state over time. represents the probability of recovery; Indicates the recovery rate; is the risk propagation probability of the influence of node j in the failure state in the target heterogeneous functional graph being propagated to node i; is the failure probability of node i at time t.

[0186] Based on the same inventive concept, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement the method for state evolution of heterogeneous infrastructure under flood risk described in the first aspect of the present application.

[0187] Based on the same inventive concept, the present application provides a storage medium storing a program or instruction. When the program or instruction is executed by a processor, the method for state evolution of heterogeneous infrastructure under flood risk described in the first aspect of the present application is implemented.

[0188] As for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0189] It should be noted that for the method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present application are not limited by the order of the actions described, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0190] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0191] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the embodiments of the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0192] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0193] The above is a detailed introduction to the method, system and product for the state evolution of heterogeneous infrastructure under flood risk provided by this application. Specific examples are used in this article to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method of this application and its core idea; at the same time, for general technical personnel in this field, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on this application.

Claims

1. A method for state evolution of heterogeneous infrastructure under flood risk, characterized by: The method comprises: Constructing a corresponding hetero-functional graph based on all infrastructure systems in the target area, wherein the nodes in the hetero-functional graph are composed of functions of functional components in the infrastructure system, and directed edges between the nodes are directed from a first node to a second node, wherein the operation of the second node depends on the operation of the first node; Predefine various state switching rules for nodes to switch between various operating states, wherein the various state switching rules are defined based on the heterogeneous characteristics of the nodes, and one state switching rule corresponds to one operating state switching relationship; Selecting at least one node in the heterogeneous functional graph as an initial flood failure node in a failure state to obtain a target heterogeneous functional graph; Determine the heterogeneity characteristic values ​​of each node in the target heterogeneous function graph. The heterogeneity characteristics include at least: importance, vulnerability index, network resilience, overload rate, and dependency strength. Among them, the importance of the node is used to quantify the importance of the node in the infrastructure system; the vulnerability index of the node is used to quantify the sensitivity of the node to state transition during flood disasters; network resilience is used to quantify the ability of the target area to maintain service after the flood disaster; the overload rate of the node is used to quantify the degree of service pressure overload borne by the node; and the dependency strength of the node is used to quantify the dependence of the node on other nodes. Determining, according to the various state switching rules and the heterogeneous feature values ​​of the nodes, the state switching rules satisfied by each node in the target heterogeneous function graph under the flood scenario; Determining the evolution of the operating state of each node according to the state switching rules satisfied by each node; Among them, various state switching rules for nodes to switch between various operating states are predefined, including: When a node is in a vulnerable state, a state switching rule for the node to switch from a vulnerable state to a degraded state is defined as the risk propagation probability of the node being higher than a first threshold. The expression of the risk propagation probability is: in, is the risk propagation probability of the influence of node j in the failure state in the target heterogeneous functional graph propagating to node i; is the probability of basic risk transmission; is the vulnerability index of the heterogeneous characteristics of node i; is the dependence strength of node i on the failed node j in the directed edge from failed node j to node i at time t; is the importance of the heterogeneous features of node j in the failed state; When a node is in a degraded state, the state switching rule for the node to switch from the degraded state to the failed state is defined as the failure probability of the node is greater than or equal to the preset recovery probability. The expression of the failure probability is: in, is the basic failure probability; The network resilience of the entire target heterogeneous functional graph; is the overload rate of the heterogeneity characteristics of node i at time t; When a node is in a degraded state, a state switching rule for switching the node from the degraded state to the recovery state is defined as the failure probability of the node is less than a preset recovery probability; When a node is in a failed state, a state switching rule for switching the node from the failed state to the recovery state is defined as the duration of the node being in the failed state being greater than or equal to the target duration corresponding to the node.

2. A method for state evolution of heterogeneous infrastructure under flood risk according to claim 1, characterized in that: The various operating states of nodes include at least: the node is in normal operating state, facing the threat of flooding and has a vulnerable state with potential degradation risk; the node is in a degraded state where some functions are damaged, the operating capacity is reduced, but not completely failed; the node service function is interrupted and it is unable to export resources or services to the outside world; the node is in a recovered state where it has regained some or all service capabilities after repair or external adjustment.

3. A method for state evolution of heterogeneous infrastructure under flood risk according to claim 2, characterized in that: Determine the heterogeneous feature values ​​of the node, including: The importance of the node in the target heterogeneous function graph is determined by the importance algorithm, and the expression of the importance algorithm is: in, is the initial importance of node i; , are the degrees of nodes i and j respectively; is the effective distance from node i to node j; is the normalized importance of node i; is the minimum importance value of all n nodes in the target heterogeneous functional graph; is the maximum importance value among all n nodes in the target heterogeneous functional graph; The vulnerability index of the node in the target heterogeneous function graph is determined by the vulnerability index algorithm, and the vulnerability index algorithm expression is: in, is the vulnerability index of node i; is the exposure factor of node i; is the impact resistance index of node i; Assess the infrastructure resilience of the target area through the PSR framework and obtain the corresponding network resilience; The maximum functional load capacity of the node in the target heterogeneous functional graph is determined by the functional load algorithm, and the functional load algorithm expression is: Among them, set each node Initial functional load Follow the load range The truncated normal distribution of ; , are the standard deviation and mean in the truncated normal distribution, respectively; is the maximum functional load capacity of node i; is the tolerance parameter of the functional node in the target heterogeneous functional graph, which is used to determine the node's tolerance to overload; The redistributed functional load of the node after load redistribution in the target heterogeneous functional graph is determined by a load redistribution algorithm. The load redistribution algorithm expression is: in, is the redistributed functional load of node j that belongs to the same category and is connected to node i at time t; is the node set consisting of all nodes that belong to the same category as the node i in the failed state at time t and are interconnected; is the redistribution function load of node i that is in a failed state at time t-1; is the maximum functional load capacity of node j; determining an overload rate of the node based on a relationship between a redistributed functional load of the node and a maximum functional load capacity; The dependency strength between the node and other nodes in the target heterogeneous function graph is determined by a dependency strength algorithm, and the dependency strength algorithm expression is: in, is the dependence strength of node i on node j at time t; is an adjustable parameter; , are the degrees between nodes i and j and the nodes that are not in a failed state at time t; Indicates that there is a directed edge from node j to node i in the target heterogeneous graph at time t; Indicates that there is no directed edge from node j to node i in the target heterogeneous graph at time t.

4. A method for state evolution of heterogeneous infrastructure under flood risk according to claim 3, characterized in that: Determining an overload rate of a node according to a relationship between a redistributed functional load of the node and a maximum functional load capacity includes: Compare the node's redistributed functional load with its maximum functional load capacity; When the maximum functional load capacity of the node is greater than or equal to the redistributed functional load of the node, determining that the overload rate of the node is zero; When the maximum functional load capacity of a node is less than its own redistributed functional load, the overload rate of the node is determined by an overload rate algorithm. The expression of the overload rate algorithm is: .

5. The method for state evolution of heterogeneous infrastructure under flood risk according to claim 1, characterized in that: The method further comprises: According to the various state switching rules and the heterogeneous characteristics of the nodes in the heterogeneous function diagram, a preset round of iterative simulation is performed on the operating state changes of each node in the target heterogeneous function diagram under a flood scenario using a Monte Carlo simulation method and corresponding differential equations; Based on the results of the iterative simulation of the preset rounds, determining the dynamic evolution of the nodes in various operating states in the target heterogeneous functional graph over time under the flood scenario; The differential equation is expressed as: ； ； ； ； in, represents the probability that node i is in a vulnerable state at time t; represents the probability that node i is in a degraded state at time t; represents the probability that node i is in a failed state at time t; represents the probability that node i is in the recovery state at time t; It represents the rate of change of the probability of node i being in a vulnerable state over time. It represents the rate of change of the probability of node i being in a degraded state over time; It represents the rate of change of the probability of node i being in a failure state over time. It represents the rate of change of the probability of node i being in the recovery state over time. represents the probability of recovery; Indicates the recovery rate; is the risk propagation probability of the influence of node j in the failure state in the target heterogeneous functional graph propagating to node i; is the failure probability of node i at time t.

6. A heterogeneous infrastructure state evolution system under flood risk, characterized by: The system comprises: A hetero-functional graph construction module is configured to construct a corresponding hetero-functional graph based on all infrastructure systems in the target area, wherein the nodes in the hetero-functional graph are composed of functions possessed by functional components in the infrastructure system, and directed edges between the nodes are directed from a first node to a second node, wherein the operation of the second node depends on the operation of the first node; The rule definition module is used to predefine various state switching rules for nodes to switch between various operating states. The various state switching rules are related to the heterogeneous characteristics of the nodes, and one state switching rule corresponds to one operating state switching relationship; a target heterogeneous functional graph determining module, configured to select at least one node in the heterogeneous functional graph as an initial flood failure node in a failed state to obtain a target heterogeneous functional graph; The heterogeneity feature determination module is used to determine the heterogeneity feature values ​​of each node in the target heterogeneous function graph. The heterogeneity features include at least: importance, vulnerability index, network resilience, overload rate and dependency strength. Among them, the importance of the node is used to quantify the importance of the node in the infrastructure system; the vulnerability index of the node is used to quantify the sensitivity of the node to state transition during flood disasters; the network resilience is used to quantify the ability of the target area to maintain service after the flood disaster; the overload rate of the node is used to quantify the degree of service pressure overload borne by the node; the dependency strength of the node is used to quantify the dependence of the node on other nodes. A state switching rule determination module is used to determine the state switching rule satisfied by each node in the target heterogeneous function graph under the flood scenario according to the various state switching rules and the heterogeneous feature values ​​of each node; A state evolution module, configured to determine the evolution of the operating state of each node according to the state switching rules satisfied by each node; The rule definition module is specifically configured to define, when a node is in a vulnerable state, a state switching rule for a node to switch from a vulnerable state to a degraded state as follows: the risk propagation probability of the node is higher than a first threshold. The expression for the risk propagation probability is: in, is the risk propagation probability of the influence of node j in the failure state in the target heterogeneous functional graph propagating to node i; is the probability of basic risk transmission; is the vulnerability index of the heterogeneous characteristics of node i; is the dependence strength of node i on the failed node j in the directed edge from failed node j to node i at time t; is the importance of the heterogeneous features of node j in the failed state; And, when a node is in a degraded state, a state switching rule for the node to switch from a degraded state to a failed state is defined as the failure probability of the node is greater than or equal to a preset recovery probability, and the expression of the failure probability is: in, is the basic failure probability; The network resilience of the entire target heterogeneous functional graph; is the overload rate of the heterogeneity characteristics of node i at time t; and, for, when a node is in a degraded state, defining a state switching rule for switching the node from the degraded state to the recovered state as the failure probability of the node being less than a preset recovery probability; Also, when a node is in a failed state, a state switching rule for defining a node to switch from a failed state to a recovery state is defined as the duration of the node being in the failed state being greater than or equal to the target duration corresponding to itself.

7. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the method for state evolution of heterogeneous infrastructure under flood risk according to any one of claims 1 to 5.

8. A storage medium, characterized in that: The storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the method for heterogeneous infrastructure state evolution under flood risk described in any one of claims 1 to 5 is implemented.

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