Heterogeneous infrastructure state evolution method, system and product under flood risk

Abstract

The invention provides a heterogeneous infrastructure state evolution method, system and product under a flood risk, and relates to the technical field of disaster emergency disposal, and the method comprises the steps: constructing a corresponding different function diagram according to all infrastructure systems in a target region; pre-defining a state switching rule of the node for switching among various running states; nodes are selected from the different-function diagram to serve as flood initial failure nodes in a failure state, and a target different-function diagram is obtained; determining heterogeneity feature values of nodes in the target different function diagram; according to various state switching rules and the heterogeneity feature value of each node, determining a state switching rule met by each node in the target different function diagram in the flood scene; and according to the state switching rule satisfied by each node, determining the running state evolution condition of each node. The method aims to identify key fragile nodes in an infrastructure system so as to reveal a state conduction mechanism of facility function nodes in a city in a flood scene.

Description

Technical Field

[0001] This application relates to the technical field of disaster emergency response, and particularly to a method, system, and product for the state evolution of heterogeneous infrastructure under flood risk. Background Art

[0002] The urban heterogeneous infrastructure system is an important support for ensuring the orderly operation of the city, and its stability and service continuity have a direct impact on social and economic activities, public safety, and the quality of life of residents. However, against the background of the increasing frequency of extreme climate events such as rainstorms and floods, the disaster risks faced by urban infrastructure are constantly intensifying, and its vulnerability and cascading failure problems are gradually emerging. Once a certain type of infrastructure is damaged in a flood situation, it is very likely to trigger a chain of failures through the cross-system service dependency relationship, resulting in system-level service interruptions and functional paralysis, forming a complex cascading evolution process.

[0003] Current research on flood disaster risks mainly focuses on the simulation and analysis of natural factors such as rainfall, terrain, and drainage capacity, and often uses technical means such as two-dimensional hydrodynamic models and waterlogging depth prediction models to describe the physical diffusion process of floods. There are also some studies that use tools such as complex networks and system dynamics to explore the resilience and vulnerability of urban systems and initially identify risk propagation paths. However, these studies have a single risk propagation path evolution process under the highly dependent, structurally complex, and attribute-heterogeneous urban infrastructure system, and it is difficult to reflect the differences of nodes in the system, resulting in poor evolution effects. Summary of the Invention

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

[0005] In the first aspect of this application, a method for the state evolution of heterogeneous infrastructure under flood risk is provided. The method includes: Construct a corresponding heterogeneous function graph according to 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 heterogeneous infrastructure system, and the directed edges between the nodes point from the first node to the second node, where the operation of the second node depends on the operation of the first node; Pre-define various state transition rules for the nodes to switch between various operating states. Among them, various state transition rules are defined based on the heterogeneous characteristics of the nodes, and one state transition rule corresponds to one operating state transition relationship; Select at least one node in the heterogeneous function graph as the initial flood failure node in the failure state to obtain the target heterogeneous function graph; Determine the heterogeneity feature values of each node in the target heterogeneous function graph, where the heterogeneity features at least include: importance, vulnerability index, network resilience, overload rate, and dependence strength; According to the various state transition rules and the heterogeneity feature values of each node, determine the state transition rules satisfied by each node in the target heterogeneous function graph under the flood scenario; According to the state transition rules satisfied by each node, determine the operation state evolution of each node.

[0006] In the second aspect of the present application, there is provided a heterogeneous infrastructure state evolution system under flood risk, and the system includes: A heterogeneous function graph construction module, configured to construct a corresponding heterogeneous function graph according to 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, and the directed edges between the nodes point from the first node to the second node, where the operation of the second node depends on the operation of the first node; A rule definition module, configured to pre-define various state transition rules for nodes to switch between various operation states. Among them, the various state transition rules are related to the heterogeneity features of the nodes, and one state transition rule corresponds to one operation state transition relationship; A target heterogeneous function graph determination module, configured to select at least one node in the heterogeneous function graph as the initial flood failure node in the failure state to obtain the target heterogeneous function graph; A heterogeneity feature determination module, configured to determine the heterogeneity feature values of each node in the target heterogeneous function graph, where the heterogeneity features at least include: importance, vulnerability index, network resilience, overload rate, and dependence strength; A state transition rule determination module, configured to determine the state transition rules satisfied by each node in the target heterogeneous function graph under the flood scenario according to the various state transition rules and the heterogeneity feature values of each node; A state evolution module, configured to determine the operation state evolution of each node according to the state transition rules satisfied by each node.

[0007] In the third aspect of the present application, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the heterogeneous infrastructure state evolution method under flood risk described in the first aspect of the present application.

[0008] In the fourth aspect of the present application, there is provided a storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the heterogeneous infrastructure state evolution method under flood risk described in the first aspect of the present application is implemented.

[0009] Compared with the prior art, the present application has the following advantages: An evolution method for the state of heterogeneous infrastructure under flood risk provided by an embodiment of the present application. First, according to all infrastructure systems in the target area, a corresponding heterogeneous function graph is constructed. Nodes in the heterogeneous function graph are composed of the functions of functional components in the heterogeneous infrastructure system. The directed edges between nodes point from the first node to the second node, where the operation of the second node depends on the operation of the first node. Various state transition rules for switching between various operating states of nodes are predefined, where the various state transition rules are defined based on the heterogeneity characteristics of the nodes, and one state transition rule corresponds to one operating state transition relationship. At least one node is selected in the heterogeneous function graph as a flood initial failure node in a failure state to obtain a target heterogeneous function graph. The heterogeneity characteristic values of each node in the target heterogeneous function graph are determined. According to the various state transition rules and the heterogeneity characteristic values of each node, the state transition rules satisfied by each node in the target heterogeneous function graph under the flood scenario are determined. According to the state transition rules satisfied by each node respectively, the operation state evolution of each node is determined.

[0010] Therefore, the present application first constructs a corresponding heterogeneous function graph based on all infrastructure systems in the target area, and defines various state transition rules for nodes in the heterogeneous function graph to switch between various operating states. These state transition rules are defined based on the heterogeneity characteristics of the nodes. Accordingly, based on the specific values of the heterogeneity characteristics of the nodes in the heterogeneous function graph, it can be determined which state transition rule the node currently conforms to, so that the operating state to which the node will switch can be determined. Then, before simulation, an initial state of the heterogeneous function graph under the flood scenario is given, and this initial state refers to randomly selecting at least one node as a flood initial failure node that is already in a failure state. Finally, based on the defined various state transition rules and the heterogeneity characteristics of the nodes, the state evolution of each node in the entire target heterogeneous function graph starting from this initial state is determined. By this method, key vulnerable nodes in the urban infrastructure system can be effectively identified, the conduction mechanism of flood disasters among multiple infrastructure systems can be revealed, and it has good interpretability and generality, and can provide technical support for the scientific assessment and precise governance of urban flood disasters.

[0011] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically exemplifies the specific implementation manners of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art.

[0013] Figure 1 It is a flowchart of a method for the state evolution of heterogeneous infrastructure under flood risk provided by an embodiment of the present application; Figure 2 It is a schematic diagram for constructing a heterogeneous function graph in a method for the state evolution of heterogeneous infrastructure under flood risk provided by an embodiment of the present application; Figure 3 It is a schematic diagram for the switching of various operating states in a method for the state evolution of heterogeneous infrastructure under flood risk provided by an embodiment of the present application; Figure 4 It is a schematic diagram for the state evolution of nodes in a heterogeneous function graph in a method for the state evolution of heterogeneous infrastructure under flood risk provided by an embodiment of the present application; Figure 5 It is a schematic diagram of a system for the state evolution of heterogeneous infrastructure under flood risk provided by an embodiment of the present application. Detailed implementation manners

[0014] The following will describe the exemplary embodiments of the present application in more detail with reference to the accompanying drawings.

[0015] Figure 1 It is a flowchart of a method for the state evolution of heterogeneous infrastructure under flood risk provided by an embodiment of the present application. As Figure 1 shown, the method includes: Step S1: Construct a corresponding heterogeneous function graph according to 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, and the directed edges between the nodes point from the first node to the second node, where the operation of the second node depends on the operation of the first node.

[0016] In this embodiment, the target area is divided based on cities, and one city corresponds to one target area. The state evolution process of the infrastructure in any target area is the same. Here, an example of one target area is used for illustration. All the infrastructure in the target area refers to all infrastructure systems that can provide services for 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 make up the infrastructure system. For example, in the power system, there are physical components such as power plants, substations, and transmission lines that transmit electricity from the power plant physical components to the substation physical components. One 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 performing wireless information transmission.

[0017] In this embodiment, in order to more clearly represent the functions performed by each physical component, the present application pre-constructs a tool called "physical component - function mapping matrix". This matrix is a table composed of 0s and 1s, where the first column represents various physical components, the first row represents various functions, and the data in other cells is either 1 or 0. When the value in a cell is 1, it means that the physical component in the row where the cell is located has the function in the column where the cell is located; when the value in a cell is 0, it means that the physical component in the row where the cell is located does not have the function in the column where the cell is located. Through this matrix, the functions possessed by each physical component can be quickly determined, thereby providing a logical basis for the subsequent construction of the heterogeneous function diagram.

[0018] In this embodiment, first, all the infrastructure within the target area is obtained through public channels and / or data sources provided by official channels, and all the physical components included in all this infrastructure are determined, thereby obtaining all the physical components within the target area. For example, as Figure 2 shown, all the physical components obtained within target area 1 through public channels or data sources provided by 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, power transmission line physical component E1 between R1 and R2, power transmission line physical component E2 between R2 and R3, power transmission line component E3 between R1 and R4, power transmission line component E4 between R4 and R5, and power transmission line component E5 between R4 and R6. Based on all the physical components within the determined target area, a physical component topological relationship is created, and in this topological relationship, the physical components are connected to the physical components on which they have a dependency relationship. For example, as Figure 2 shown, the power plant physical component R1 depends on the power transmission line component E1 to transmit electricity to the substation physical component R2. Therefore, the power plant physical component R1 will be connected to the power transmission line component E1. The substation physical component R2 depends on the power transmission line component E1 to receive the electricity transmitted by the power plant physical component R1. Therefore, the substation physical component R2 will also be connected to the power transmission line component E1.

[0019] In this embodiment, after determining all the physical components within the target area, through the pre-constructed "physical component - function mapping matrix", the functions possessed by each physical component are determined, and for each determined function, a corresponding function node is created, that is, one function has a corresponding function node. For the convenience of describing the subsequent function nodes, they will all be described as nodes.

[0020] Exemplarily, as Figure 2As shown, since the physical component R1 has the function of generating electricity, a corresponding functional node φ1 is determined; since the physical component E1 has the function of transporting electricity from R1 to R2, a corresponding functional node φ2 is determined; since the physical component R2 has the function of converting high-voltage electricity into low-voltage electricity, a corresponding functional node φ3 is determined; since the physical component E2 has the function of transporting electricity from R2 to R3, a corresponding functional node φ4 is determined; since the physical component R3 has the function of realizing voice communication, a corresponding functional node φ5 is determined; since the physical component R3 has the function of wireless signal transmission, a corresponding functional node φ6 is determined; since the physical component E3 has the function of transporting electricity from R1 to R4, a corresponding functional node φ7 is determined; since the physical component R4 has the function of converting high-voltage electricity into low-voltage electricity, a corresponding functional node φ8 is determined; since the physical component E4 has the function of transporting electricity from R4 to R5, a corresponding functional node φ9 is determined; since the physical component E5 has the function of transporting electricity from R4 to R6, a corresponding functional node φ11 is determined; since the physical component R5 has the function of distributing gas, a corresponding functional node φ10 is determined; since the physical component R6 has the function of distributing gas, a corresponding functional node φ12 is determined; since the physical component E6 has the function of transporting gas from R5 to R6, a corresponding functional node φ13 is determined.

[0021] In this embodiment, after constructing all the nodes in the target area, the dependency relationships between the nodes are determined, and two nodes with a dependency relationship are connected by a directed edge. The directed edge points from the first node to the second node among the two nodes, and the operation of the second node depends on the operation of the first node, that is, the second node can operate normally only when the first node operates normally. After connecting all the nodes with dependency relationships by directed edges, the heterogeneous function graph corresponding to the target area is obtained.

[0022] Exemplarily, as Figure 2 shown, the node φ1 is the node corresponding to the power generation function of the physical component R1, and the node φ2 is the node corresponding to the function of transporting electricity from R1 to R2 of the physical component E2. The function of transporting electricity of the node φ2 depends on the normal power generation of the node φ1. Therefore, the operation of the node φ2 depends on the operation of the node φ1. Therefore, a directed edge pointing from the node φ1 to the node φ2 is created. Figure 2 N1 to N3 in it represent demand nodes, representing the final users, such as residential communities, commercial buildings, etc.

[0023] Step S2: Predetermine various state transition rules for a node to switch between various operating states, where the various state transition rules are defined based on the heterogeneous characteristics of the node, and one state transition rule corresponds to one operating state transition relationship.

[0024] In this embodiment, the present application predetermines various operating states of the nodes in the heterogeneous function graph, including at least: vulnerable state, degradation state, failure state, and recovery state. At the same time, various state transition rules for the node to switch between various operating states are predetermined, and one state transition rule corresponds to one operating state transition relationship. The various state transition rules are defined based on the heterogeneous characteristics of the node. When the value of the heterogeneous characteristics of the node is determined, the state transition rule that can be matched is determined by matching the value of the heterogeneous characteristics of the node with the various state transition rules. Based on the matched state transition rule, the operating state to which the node will switch from the current operating state can be determined.

[0025] Step S3: Select at least one node in the heterogeneous function graph as the initial flood failure node in the failure state to obtain a target heterogeneous function graph.

[0026] In this embodiment, in the heterogeneous function graph corresponding to the constructed target area, at least one node is selected and its operating state is set to the failure state. These selected nodes set to the failure state are called the initial flood failure nodes. Thus, an initial state of the heterogeneous function graph in the flood scenario is obtained, and the corresponding initial state is a target heterogeneous function graph. The operating state of the selected nodes in the target heterogeneous function is the failure state, while other nodes are in the vulnerable state threatened by floods.

[0027] Step S4: Determine the values of the heterogeneous characteristics of each node in the target heterogeneous function graph. The heterogeneous characteristics at least include: importance, vulnerability index, network resilience, overload rate, and dependence intensity.

[0028] In this embodiment, after obtaining the target heterogeneous function graph through step S3, the heterogeneous characteristics of each node in the target heterogeneous function graph are identified and quantified. This process starts from dimensions such as the service role, damage sensitivity, and bearing capacity of the node in the flood disaster scenario, and extracts the values of the heterogeneous characteristics reflecting its state evolution tendency, at least including: importance, vulnerability index, network resilience, overload rate, and dependence intensity, etc.

[0029] Step S5: According to the various state transition rules and the values of the heterogeneous characteristics of each node, determine the state transition rules satisfied by each node in the target heterogeneous function graph in the flood scenario.

[0030] In this embodiment, since the various state transition rules for the predefined nodes to switch between various operating states are defined based on the heterogeneous characteristics of the nodes. Therefore, after calculating the heterogeneous characteristic values of each node in the target heterogeneous function graph through step S4, based on the heterogeneous characteristic values of each node and the various state transition rules, it is possible to determine the state transition rules satisfied by each node in the target heterogeneous function graph.

[0031] Step S6: Determine the evolution of the operating state of each node according to the state transition rules satisfied by each node.

[0032] In this embodiment, the state transition rules correspond to the switching relationship of the operating states. After obtaining the state transition rules satisfied by each node through step S5, based on the state transition rules satisfied by itself, determine the new operating state to which each node will switch, so as to obtain the evolution of the operating state of each node in the target heterogeneous function graph.

[0033] A method for state evolution of heterogeneous infrastructure under flood risk provided by an embodiment of the present application. First, construct a corresponding heterogeneous function graph according to 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 point from the first node to the second node, where the operation of the second node depends on the operation of the first node; pre-define various state transition rules for the predefined nodes to switch between various operating states, where the various state transition rules are defined based on the heterogeneous characteristics of the nodes, and one state transition rule corresponds to one operating state switching relationship; select at least one node in the heterogeneous function graph as the initial flood failure node in the failure state to obtain the target heterogeneous function graph; determine the heterogeneous characteristic values of each node in the target heterogeneous function graph; according to the various state transition rules and the heterogeneous characteristic values of each node, determine the state transition rules satisfied by each node in the target heterogeneous function graph under the flood scenario; determine the evolution of the operating state of each node according to the state transition rules satisfied by each node.

[0034] Accordingly, this application first constructs a corresponding heterogeneous function graph based on all infrastructure systems within the target area, and defines various state transition rules for the nodes in the heterogeneous function graph to switch between various operating states. These state transition rules are defined based on the heterogeneous characteristics of the nodes. Correspondingly, based on the specific values of the heterogeneous characteristics of the nodes in the heterogeneous function graph, it can be determined which state transition rule the node currently conforms to, and thus the operating state to which the node will switch can be determined. Then, before the simulation, an initial state of the heterogeneous function graph under the flood scenario is given, and this initial state refers to randomly selecting at least one node as the initial flood failure node that is already in the failure state. Finally, based on the defined various state transition rules and the heterogeneous characteristics of the nodes, the state evolution of each node in the entire target heterogeneous function graph is determined starting from this initial state. Through this method, key vulnerable nodes in the urban infrastructure system can be effectively identified, the transmission mechanism of flood disasters among multiple infrastructure systems can be revealed, and it has good interpretability and generality, and can provide technical support for the scientific assessment and precise governance of urban flood disasters.

[0035] Combined with the above embodiments, in one implementation, the embodiments of this application also provide a method for the state evolution of heterogeneous infrastructure under flood risk. In this method for the state evolution of heterogeneous infrastructure under flood risk, the importance of a 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 services after flood disasters; the overload rate of the node is used to quantify the degree of overload of the service pressure borne by the node; the dependence strength of the node is used to quantify the dependence of the node on other nodes; the various operating states of the node at least include: the normal operating state of the node, facing flood threats, the vulnerable state with potential degradation risks; the degraded state where part of the node's functions are damaged, the operating ability decreases, and it is not completely failed; the failed state where the node's service function is interrupted and it cannot output resources or services externally; the restored state where the node regains partial or all service capabilities after repair or external adjustment.

[0036] Combined with the above embodiments, in one implementation, the embodiments of this application also provide a method for the state evolution of heterogeneous infrastructure under flood risk. In this method for the state evolution of heterogeneous infrastructure under flood risk, determining the value of the heterogeneous characteristics of a node includes: determining the importance of the node in the target heterogeneous function graph through an importance algorithm, and the expression of the importance algorithm is:

[0037]

[0038] where, 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 importance of node i after normalization; is the minimum value of the importance among all n nodes in the target heterogeneous functional graph; is the maximum value of the importance among all n nodes in the target heterogeneous functional graph; Determine the vulnerability index of nodes in the target heterogeneous functional graph through the vulnerability index algorithm. The expression of the vulnerability index algorithm is:

[0039] where, is the vulnerability index of node i; is the exposure factor of node i; is the shock resistance index of node i; Evaluate the infrastructure resilience of the target area through the PSR framework to obtain the corresponding network resilience; Determine the maximum functional load capacity of nodes in the target heterogeneous functional graph through the functional load algorithm. The expression of the functional load algorithm is:

[0040]

[0041] where, set each node initial functional load follows the truncated normal distribution within the load range ; , 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 functional nodes in the target heterogeneous functional graph, used to determine the tolerance of nodes to overload; Determine the reallocated functional load of nodes in the target heterogeneous functional graph after load reallocation through the load reallocation algorithm. The expression of the load reallocation algorithm is:

[0042] where, is the reallocated functional load of node j that belongs to the same category as node i in the failure state at time t and is connected to each other; is the node set composed of all nodes that belong to the same category as the node in the failure state at time t and are connected to each other; is the reallocated functional load of node i in the failure state at time t - 1; is the maximum capacity of the functional load of node j; Determine the overload rate of the node according to the relationship between the reallocated functional load and the maximum capacity of the functional load of the node; Determine the dependence strength between the node and other nodes in the target heterogeneous functional graph through the dependence strength algorithm, and the expression of the dependence strength algorithm is:

[0043] where, is the dependence strength of node i on node j at time t; is an adjustable parameter; , are the degrees of nodes i and j respectively with the nodes that are not in the failure state at time t; represents that there is a directed edge from node j to node i in the target heterogeneous functional graph at time t; represents that there is no directed edge from node j to node i in the target heterogeneous functional graph at time t.

[0044] In this embodiment, the implementation manner of determining the value of the heterogeneity feature of any node in the target heterogeneous functional graph is the same, and here an example of one node is used for illustration.

[0045] 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 center or a transmission channel. In this application, the importance of the node is calculated by the effective distance gravity method. First, calculate the effective distance between all nodes, then calculate the interaction score between all node pairs, and finally use the cumulative sum to obtain the EffG centrality score of each node. Specifically, determine the importance of the node in the target heterogeneous functional graph through the importance algorithm, and the expression of the importance algorithm is:

[0046]

[0047] where, is the initial importance of node i; , are the degrees of nodes i and j respectively, and the degree represents the number of edges connected to the corresponding node in the target heterogeneous functional graph; is the effective distance from node i to node j, and the effective distance is the shortest path passed by node i to node j; is the normalized importance of node i, that is, the finally solved importance of node i; is the minimum value of the importance among all n nodes in the target heterogeneous functional graph; is the maximum value of the importance among all n nodes in the target heterogeneous functional graph.

[0048] For the vulnerability index of a node, it is used to quantify the sensitivity of the node's state transition (degradation or failure) during a flood disaster. For the vulnerability index of a node, this application comprehensively considers two major categories of factors: on the one hand, the exposure of the node, mainly including the maximum inundation depth at the location of the node, the duration of the flood, and the service life of its affiliated facilities (equipment aging degree); on the other hand, the shock resistance ability of the node, which measures its structural protection level and emergency response ability when facing a flood disaster, specifically 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 control walls, waterproof encapsulation). This application determines the vulnerability index of the node by considering these two dimensions simultaneously. Specifically, the vulnerability index of the node in the target heterogeneous function graph is determined through a vulnerability index algorithm, and the expression of this vulnerability index algorithm is:

[0049] Among them, is the vulnerability index of node i; is the exposure factor of node i; is the shock resistance index of node i, and finally divided by 10 to obtain the PV value within the range of 0–1.

[0050] For the network resilience of a node, it is used to quantify the ability of the target area to maintain services after a flood disaster. This application introduces the "Pressure - State - Response (PSR)" theoretical framework to conduct a multi - dimensional comprehensive assessment of the resilience of urban infrastructure. Based on this theory, this application constructs a node resilience evaluation system composed of multiple sub - indicators, covering multiple aspects such as operation ability, maintenance mechanism, and emergency response efficiency to determine the network resilience of the entire target area. As shown in Table 1, Table 1 shows the node resilience evaluation system composed of multiple sub - indicators in this application.

[0051] Table 1

[0052] First, collect the relevant data of the multiple sub - indicators in Table 1 within the target area, calculate and determine the positive and negative ideal solution distances of the target area through the entropy weight method and the topsis method, and obtain the network resilience of the target area by further calculating the mean value.

[0053] Exemplarily, as shown in Table 2, Table 2 exemplarily shows the network resilience calculation results of the target area from 2018 to 2022.

[0054] Table 2

[0055] The real-time functional load of a node (the maximum capacity of its original functional load 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 extensive service interruptions may occur once the node fails, thus exacerbating the cascading risk of the system. To comprehensively reflect the dynamic change characteristics of the node load during the system evolution process, two parts are introduced in this application: namely, the maximum capacity of the node's original functional load and the dynamic functional load redistribution mechanism. Correspondingly, for the overload rate of a node, first determine the maximum capacity of the node's functional load. In the case where the node will be assigned a load by other nodes through load redistribution, determine the redistributed functional load of the node after redistribution through the load redistribution algorithm. Finally, determine the overload rate of the node based on the maximum capacity of the node's original functional load and the redistributed functional load after its own redistribution.

[0056] Specifically, for the overload rate of a node, first determine the maximum capacity of the node's functional load in the target heterogeneous function graph through the functional load algorithm. The expression of the functional load algorithm is:

[0057]

[0058] Among them, to truly reproduce the statistical distribution characteristics of the node's initial load level, this application draws on the idea of the CASCADE model and sets the initial functional load of each node to follow a truncated normal distribution within the load range ; , are the standard deviation and mean in the truncated normal distribution respectively; is the maximum capacity of the functional load of node i; is the tolerance parameter of the functional node in the target heterogeneous function graph, which is used to determine the node's tolerance to overload.

[0059] After calculating the maximum capacity of the node's functional load, during the flood evolution process, if a certain node enters a failure state, the service pressure it originally bears will need to be borne by other nodes with the same function and connected to each other in the target heterogeneous function graph. For this reason, this application designs a functional load redistribution mechanism in the target heterogeneous function graph to ensure that when the functional load of the node is transferred, both the idle capacity of the node and its current operating state are considered. The specific solution process is to determine the redistributed functional load of the node after load redistribution in the target heterogeneous function graph through the load redistribution algorithm. The expression of this load redistribution algorithm is:

[0060] Among them, is the reallocated functional load of node j that belongs to the same category as node i in the failed state at time t and is connected to each other; is the node set composed of all nodes that belong to the same category as node i in the failed state at time t and are connected to each other; is the reallocated functional load of node i in the failed state at time t - 1; is the maximum capacity of the functional load of node j.

[0061] This functional load reallocation mechanism follows the strategy of "the more capable, the more work". Nodes with more idle capacity can receive more loads from failed nodes, thus reducing the impact of load reallocation on the entire target heterogeneous function graph.

[0062] After calculating the reallocated functional load of a node, based on the relationship between the reallocated functional load of this node and the maximum capacity of the functional load of this node, the overload rate of this node is determined. The greater the degree to which the reallocated functional load of this node is greater than the maximum capacity of the functional load of this node, the greater the overload rate of this node.

[0063] For the dependence intensity of a node, the dependence intensity between this node and other nodes in the target heterogeneous function graph is determined through a dependence intensity algorithm. The expression of this dependence intensity algorithm is:

[0064] where is the dependence intensity of node i on node j at time t; is an adjustable parameter; , are the degrees of nodes i and j respectively with nodes that are not in the failed state at time t; indicates that there is a directed edge from node j to node i in the target heterogeneous function graph at time t; indicates that there is no directed edge from node j to node i in the target heterogeneous function graph at time t.

[0065] In this embodiment, through the above implementation manner, the values of various heterogeneity characteristics of each node in the target heterogeneous function graph will be calculated.

[0066] Combined with the above embodiments, in one implementation, the embodiments of the present application further provide a method for the state evolution of heterogeneous infrastructure under flood risk. In this method for the state evolution of heterogeneous infrastructure under flood risk, according to the relationship between the reallocated functional load of a node and the maximum functional load capacity, the overload rate of the node is determined, including: comparing the reallocated functional load of the node with the maximum functional load capacity; when the maximum functional load capacity of the node is greater than or equal to its own reallocated functional load, determining that the overload rate of the node is zero; when the maximum functional load capacity of the node is less than its own reallocated functional load, determining the overload rate of the node through an overload rate algorithm, and the expression of the overload rate algorithm is: 。

[0067] In this embodiment, the reallocated power load after reallocation of the node itself is compared with the maximum functional load capacity of the node itself. When the maximum functional load capacity of the node itself is greater than or equal to its own reallocated functional load, it is determined that the overload rate of the node is zero. While when the maximum functional load capacity of the node is less than its own reallocated functional load, the overload rate of the node is determined through an overload rate algorithm, and the expression of the overload rate algorithm is: 。

[0068] Combined with the above embodiments, in one implementation, the embodiments of the present application further provide a method for the state evolution of heterogeneous infrastructure under flood risk. In this method for the state evolution of heterogeneous infrastructure under flood risk, various state transition rules for the node to switch between various operating states are predefined, including: When the node is in a vulnerable state, the state transition rule for the node to switch from the vulnerable state to the degraded state is defined as that the risk propagation probability of the node is higher than a first threshold, and the expression of the risk propagation probability is:

[0069] Wherein, is the probability that the influence of the failed state node j in the target heterogeneous function graph propagates to node i; is the basic risk propagation probability; is the vulnerability index in the heterogeneity characteristics of node i; is the dependence intensity of node i on the failed state node j in the directed edges from the failed state node j to node i at time t in the heterogeneity characteristics of node i; is the importance degree in the heterogeneity characteristics of the failed state node j; When the node is in a degraded state, the state transition rule for the node to switch from the degraded state to the failed state is defined as that the failure probability of the node is greater than or equal to a preset recovery probability, and the expression of the failure probability is:

[0070] Among them, is the basic failure probability; is the network resilience of the entire target heterogeneous function graph; is the overload rate in the heterogeneity characteristics of node i at time t; In the case where the node is in a degraded state, the state transition rule for the node to switch from the degraded state to the restored state is defined as that the failure probability of the node is less than a preset restoration probability; In the case where the node is in a failed state, the state transition rule for the node to switch from the failed state to the restored state is defined as that the duration of the node in the failed state is greater than or equal to the target duration corresponding to itself.

[0071] In this embodiment, as Figure 3 shown, the state transition relationships of the node include: switching from the vulnerable state to the degraded state, switching from the degraded state to the failed state, switching from the degraded state to the restored state, and switching from the failed state to the restored state. For each state transition relationship, there is a corresponding state transition rule. When the node is in the vulnerable state, since the node only switches to the degraded state, it is only necessary to determine whether the value of the heterogeneity characteristic of the node satisfies the state transition rule corresponding to switching from the vulnerable state to the degraded state. When the node is in the degraded state, since the node may switch to the failed state or the restored state, it is necessary to determine whether the value of the heterogeneity characteristic of the node satisfies the state transition rule corresponding to switching from the degraded state to the failed state, and whether it satisfies the state transition rule corresponding to switching from the degraded state to the restored state. When the node is in the failed state, since the node only switches to the restored state, it is only necessary to determine whether the value of the heterogeneity characteristic of the node satisfies the state transition rule corresponding to switching from the failed state to the restored state.

[0072] In this embodiment, the state transition rule for the node to switch from the degraded state to the failed state is defined as that the risk propagation probability of the node is higher than a first threshold. This first threshold can be set according to the actual application scenario and will not be specifically limited here. The expression of this risk propagation probability is:

[0073] Among them, is the risk propagation probability that the influence of the failed node j in the target heterogeneous function graph propagates to node i. In the summation from j = 1 to N, the value of j is not equal to i, and at the same time, it only targets the nodes in the failed state in the target heterogeneous function graph at time t. For example, if the nodes in the failed state in the target heterogeneous function graph at time t are node 1 and node 3, then in the summation from j = 1 to N and i ≠ j at time t, the value of j only takes 1 and 3; is the basic risk propagation probability; is the vulnerability index in the heterogeneity characteristics of node i; is the dependence strength of node i on node j in the failure state on the directed edge from node j in the failure state to node i at time t in the heterogeneity characteristics of node i; is the importance in the heterogeneity characteristics of node j in the failure state.

[0074] In this embodiment, the state transition rule for a node to switch from the degradation state to the failure state is defined as that the failure probability of the node is greater than or equal to a preset recovery probability , and this recovery probability can be set according to the actual application scenario and is not specifically limited here. The expression of the failure probability is:

[0075] where is the basic failure probability; is the network resilience of the entire target heterogeneous function graph; is the overload rate in the heterogeneity characteristics of node i at time t.

[0076] In this embodiment, the state transition rule for a node to switch from the degradation state to the recovery state is defined as that the failure probability of the node is less than the preset recovery probability.

[0077] In this embodiment, the state transition rule for a node to switch from the failure state to the recovery state is defined as that the duration of the node in the failure state is greater than or equal to the target duration corresponding to itself, and this target duration can be set according to the actual application scenario and is not specifically limited here.

[0078] Combined with the above embodiments, in one implementation, the embodiments of the present application also provide a method for the state evolution of heterogeneous infrastructure under flood risk. In this method for the state evolution of heterogeneous infrastructure under flood risk, the method further includes: According to the various state transition rules and the heterogeneity characteristics of the nodes in the heterogeneous function graph, through the Monte Carlo simulation method and the corresponding differential equations, the operation state changes of each node in the target heterogeneous function graph are iteratively simulated for a preset number of rounds in the flood scenario; Based on the results of the iterative simulation for the preset number of rounds, determine the dynamic evolution of the nodes in the target heterogeneous function graph in various operating states over time in the flood scenario; The expression of the differential equation is: ; ; ; ; wherein, 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 a restored state at time t; represents the rate of change of the probability that node i is in a vulnerable state with respect to time when node i is in a vulnerable state; represents the rate of change of the probability that node i is in a degraded state with respect to time when node i is in a degraded state; represents the rate of change of the probability that node i is in a failed state with respect to time when node i is in a failed state; represents the rate of change of the probability that node i is in a restored state with respect to time when node i is in a restored state; represents the restoration probability; represents the restoration rate; is the risk propagation probability that the influence of the failed node j in the target heterogeneous function graph propagates to node i; is the failure probability of node i at time t.

[0079] In this embodiment, based on various predefined state transition rules and the heterogeneity characteristics of the nodes in the heterogeneous function graph, the present application introduces the Monte Carlo simulation method and the corresponding differential equations, and performs iterative simulations for a preset number of rounds on the changes in the operating states of each node in the target heterogeneous function graph in a flood scenario, obtaining the iterative simulation results for the preset number of rounds. Based on the obtained iterative simulation results for the preset number of rounds, the dynamic evolution of the nodes in the target heterogeneous function graph in various operating states with respect to time is determined in a flood scenario. In this way, the evolution path of the nodes in the system, the service chain break sequence, and the cascade propagation path can be dynamically tracked, thereby identifying the key vulnerable nodes and risk concentration areas in the city. By iteratively running the simulation process multiple times, the state changes of the nodes at each moment can be tracked, the path and speed of the risk spreading from the core nodes to the outside can be identified, and the state sequences and propagation chains of different nodes can be extracted. During the simulation process, the system records the function degradation and failure conditions at key time nodes, and outputs the global risk propagation graph, the node failure rate curve, and the path dependence graph, thereby clearly presenting the cascade effect and spatial expansion characteristics of the flood impact in the heterogeneous network.

[0080] In this embodiment, the expression of the differential equation is: ; ; ; ; Among them, 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 a recovery state at time t; represents the rate of change of the probability that node i is in a vulnerable state over time when it is in a vulnerable state; represents the rate of change of the probability that node i is in a degraded state over time when it is in a degraded state; represents the rate of change of the probability that node i is in a failed state over time when it is in a failed state; represents the rate of change of the probability that node i is in a recovery state over time when it is in a recovery state; represents the recovery probability; represents the recovery rate; is the risk propagation probability that the influence of the failed node j in the target heterogeneous functional graph propagates to node i; is the failure probability of node i at time t.

[0081] Exemplarily, the present application sets the values of the initial parameters as , , , , , . At the same time, the average network resilience of a certain target area over the past years is selected as the network resilience of the target area . At the same time, 7 nodes in the heterogeneous functional graph of the target area are selected as the initial flood failure nodes, and by solving the constructed differential equation with python, the result is as Figure 4The evolution of nodes in different operating states in the heterogeneous function graph of the target area under the shown flood scenario (i.e., the change in the proportion of nodes in different operating states). Initially, only 7 nodes were in the failed state, and the remaining nodes were in the vulnerable state. Under the risk propagation of nodes in the failed state in the heterogeneous function graph network and the continuous impact of the flood disaster, considering the heterogeneity characteristics of all nodes, the risk propagation probability matrix at different times was determined, and the change in the proportion of nodes in different operating states under the evolution over time was obtained. It can be found that the number of nodes in the state degradation state increased sharply in the initial few stages of the simulation, and the number of nodes in the vulnerable state decreased sharply. At the same time, due to the limited network resilience and node overload, the recovery rate of some nodes in the degradation state was less than their failure rate, and the operating state of the nodes changed from the degradation state to the failed state. Therefore, the number of nodes in the failed state continued to increase. Finally, the nodes in the failed state and the degradation state recovered slowly, and the number of nodes in the recovery state continued to climb. When the simulation step size was 55, all node states in the network changed to the recovery state.

[0082] Based on the same inventive concept, the present application provides a heterogeneous infrastructure state evolution system under flood risk, as Figure 5 shown. The heterogeneous infrastructure state evolution system 500 under flood risk includes: A heterogeneous function graph construction module 501, configured to construct a corresponding heterogeneous function graph according to all infrastructure systems in the target area. Nodes in the heterogeneous function graph are composed of functions possessed by functional components in the infrastructure system, and the directed edges between nodes point from the first node to the second node, where the operation of the second node depends on the operation of the first node; A rule definition module 502, configured to pre-define various state transition rules for nodes to switch between various operating states. Among them, various state transition rules are related to the heterogeneity characteristics of nodes, and one state transition rule corresponds to one operating state transition relationship; A target heterogeneous function graph determination module 503, configured to select at least one node in the heterogeneous function graph as the initial flood failure node in the failed state to obtain a target heterogeneous function graph; A heterogeneity characteristic determination module 504, configured to determine the values of the heterogeneity characteristics of each node in the target heterogeneous function graph. The heterogeneity characteristics at least include: importance, vulnerability index, network resilience, overload rate, and dependence intensity; A state transition rule determination module 505, configured to determine the state transition rules satisfied by each node in the target heterogeneous function graph under the flood scenario according to the various state transition rules and the values of the heterogeneity characteristics of each node; A state evolution module 506, configured to determine the operating state evolution of each node according to the state transition rules satisfied by each node.

[0083] 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 the target area to maintain services after flood disasters; the overload rate of nodes is used to quantify the degree of overload of the service pressure borne by nodes; the dependence intensity of nodes is used to quantify the dependence of nodes on other nodes; the various operating states of nodes at least include: the node operating state is normal, facing flood threats, and a vulnerable state with potential degradation risks; the node has partially impaired functions, reduced operating capabilities, and a degraded state that has not completely failed; the node service function is interrupted, and it is unable to output resources or services externally, which is a failure state; after the node undergoes repair or external adjustment, it resumes partial or all service capabilities, which is a recovery state.

[0084] Optionally, the heterogeneity feature determination module 504 is specifically configured to determine the importance of nodes in the target heterogeneous function graph through an importance algorithm. The expression of the importance algorithm is:

[0085]

[0086] Among them, 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 value of the importance among all n nodes in the target heterogeneous function graph; is the maximum value of the importance among all n nodes in the target heterogeneous function graph; And, it is used to determine the vulnerability index of nodes in the target heterogeneous function graph through a vulnerability index algorithm. The expression of the vulnerability index algorithm is:

[0087] Among them, is the vulnerability index of node i; is the exposure factor of node i; is the impact resistance index of node i; And, it is used to evaluate the infrastructure resilience of the target area through the PSR framework to obtain the corresponding network resilience; And, it is used to determine the maximum functional load capacity of nodes in the target heterogeneous function graph through a functional load algorithm. The expression of the functional load algorithm is:

[0088]

[0089] Among them, each node is set with an initial functional load following a truncated normal distribution within the load range ; , being the standard deviation and mean in the truncated normal distribution respectively; is the maximum capacity of the functional load of node i; is the tolerance parameter of the functional node in the target heterogeneous functional graph, used to determine the tolerance of the node to overload; And, it is used to determine the reallocated functional load of the node in the target heterogeneous functional graph through the load reallocation algorithm, and the expression of the load reallocation algorithm is:

[0090] Among them, is the reallocated functional load of node j that belongs to the same category as node i in the failed state at time t and is connected to each other; is the node set composed of all nodes that belong to the same category as the node in the failed state at time t and are connected to each other; is the reallocated functional load of node i in the failed state at time t - 1; is the maximum capacity of the functional load of node j; And, it is used to determine the overload rate of the node according to the relationship between the reallocated functional load and the maximum capacity of the functional load of the node; And, it is used to determine the dependence strength between the node and other nodes in the target heterogeneous functional graph through the dependence strength algorithm, and the expression of the dependence strength algorithm is:

[0091] Among them, is the dependence strength of node i on node j at time t; is an adjustable parameter; , are the degrees of nodes i and j respectively with the nodes that are not in the failed state at time t; indicates that there is a directed edge from node j to node i in the target heterogeneous functional graph at time t; indicates that there is no directed edge from node j to node i in the target heterogeneous functional graph at time t.

[0092] Optionally, the heterogeneity feature determination module 504 is specifically used to compare the reallocated functional load and the maximum capacity of the functional load of the node; And, when the maximum capacity of the functional load of a node is greater than or equal to its own reallocated functional load, determining that the overload rate of the node is zero; And, when the maximum capacity of the functional load of a node is less than its own reallocated functional load, determining the overload rate of the node through an overload rate algorithm, and the expression of the overload rate algorithm is: .

[0093] Optionally, the rule definition module 502 is specifically configured to, when the node is in a vulnerable state, define the state transition rule for the node to switch from the vulnerable state to the degraded state as that the risk propagation probability of the node is higher than a first threshold, and the expression of the risk propagation probability is:

[0094] Wherein, is the risk propagation probability that the influence of the failed state node j in the target heterogeneous function graph propagates to node i; is the basic risk propagation probability; is the vulnerability index in the heterogeneity characteristics of node i; is the dependence intensity of node i on the failed state node j in its own heterogeneity characteristics among the directed edges pointing from the failed state node j to node i at time t; is the importance degree in the heterogeneity characteristics of the failed state node j; And, when the node is in a degraded state, defining the state transition rule for the node to switch from the degraded state to the failed state as that the failure probability of the node is greater than or equal to a preset recovery probability, and the expression of the failure probability is:

[0095] Wherein, is the basic failure probability; is the network resilience of the entire target heterogeneous function graph; is the overload rate in the heterogeneity characteristics of node i at time t; And, when the node is in a degraded state, defining the state transition rule for the node to switch from the degraded state to the recovery state as that the failure probability of the node is less than the preset recovery probability; And, when the node is in a failed state, defining the state transition rule for the node to switch from the failed state to the recovery state as that the duration of the node in the failed state is greater than or equal to the target duration corresponding to itself.

[0096] Optionally, the heterogeneous infrastructure state evolution system 500 under the flood risk further includes: A simulation module, configured to, according to the various state transition rules and the heterogeneity characteristics of the nodes in the heterogeneous function graph, through the Monte Carlo simulation method and the corresponding differential equations, perform iterative simulations for a preset number of rounds on the changes in the operating states of the nodes in the target heterogeneous function graph in a flood scenario; A dynamic evolution module, configured to determine the dynamic evolution of the nodes in various operating states in the target heterogeneous function graph over time in a flood scenario based on the results of the iterative simulations for a preset number of rounds; The expression of the differential equation is: ; ; ; ; Wherein, 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 a restored state at time t; represents the rate of change of the probability that node i is in a vulnerable state with respect to time when it is in a vulnerable state; represents the rate of change of the probability that node i is in a degraded state with respect to time when it is in a degraded state; represents the rate of change of the probability that node i is in a failed state with respect to time when it is in a failed state; represents the rate of change of the probability that node i is in a restored state with respect to time when it is in a restored state; represents the restoration probability; represents the restoration rate; is the risk propagation probability that the influence of the failed node j in the target heterogeneous function graph propagates to node i; is the failure probability of node i at time t.

[0097] 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, and the processor executes the computer program to implement the method for evolving the states of heterogeneous infrastructure under flood risk according to the first aspect of the present application.

[0098] Based on the same inventive concept, the present application provides a storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the method for evolving the states of heterogeneous infrastructure under flood risk according to the first aspect of the present application is implemented.

[0099] For the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, please refer to the partial description of the method embodiments.

[0100] It should be noted that for the method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the embodiments of the present application are not limited by the described action sequence, because according to the embodiments of the present application, some steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential for the embodiments of the present application.

[0101] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other.

[0102] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the embodiments of the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0103] Although the preferred embodiments of the embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be interpreted to include the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present application.

[0104] The above has introduced in detail a method, a system, and a product for the state evolution of heterogeneous infrastructure under flood risk provided by the present application. Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for the state evolution of heterogeneous infrastructure under flood risk, characterized in that, The method includes: Construct a corresponding heterogeneous function graph according to 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, and the directed edges between the nodes point from the first node to the second node, where the operation of the second node depends on the operation of the first node; Pre-define various state transition rules for the nodes to switch between various operating states. Among them, various state transition rules are defined based on the heterogeneous characteristics of the nodes, and one state transition rule corresponds to one operating state transition relationship; Select at least one node in the heterogeneous function graph as the initial flood failure node in the failure state to obtain the target heterogeneous function graph; Determine the values of the heterogeneous characteristics of each node in the target heterogeneous function graph. The heterogeneous characteristics at least include: importance, vulnerability index, network resilience, overload rate, and dependence intensity; According to the various state transition rules and the values of the heterogeneous characteristics of each node, determine the state transition rules satisfied by each node in the target heterogeneous function graph under the flood scenario; According to the state transition rules satisfied by each node, determine the evolution of the operating state of each node; 2. A method for the state evolution of heterogeneous infrastructure under flood risk according to claim 1, characterized in that, The importance of a node is used to quantify the importance of the node in the infrastructure system; the vulnerability index of a node is used to quantify the sensitivity of the node to state transition in a flood disaster; Network resilience is used to quantify the ability of the target area to maintain services after a flood disaster; the overload rate of a node is used to quantify the degree of overload of the service pressure borne by the node; The dependence intensity of a node is used to quantify the dependence of the node on other nodes; The various operating states of a node at least include: the node is in a normal operating state, facing flood threats, and a vulnerable state with potential degradation risks; the node has partially damaged functions, its operating ability has declined, and it is in a degraded state that has not completely failed; the node's service function is interrupted and it cannot output resources or services externally, which is a failure state; after the node undergoes repair or external adjustment, it resumes partial or all service capabilities, which is a recovery state.

3. A method for the state evolution of heterogeneous infrastructure under flood risk according to claim 2, characterized in that Determine the values of the heterogeneous characteristics of the node, including: Determine the importance of the node in the target heterogeneous function graph through an importance algorithm. The expression of the importance algorithm is: Among them, 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 value of the importance among all n nodes in the target heterogeneous functional graph; is the maximum value of the importance among all n nodes in the target heterogeneous functional graph; Determine the vulnerability index of the node in the target heterogeneous function graph through a vulnerability index algorithm. The expression of the vulnerability index algorithm is: Among them, is the vulnerability index of node i; is the exposure factor of node i; is the impact resistance index of node i; Evaluate the infrastructure resilience of the target area through the PSR framework to obtain the corresponding network resilience; Determine the maximum functional load capacity of the node in the target heterogeneous function graph through a functional load algorithm. The expression of the functional load algorithm is: Among them, the initial functional load of each node follows a truncated normal distribution within the load range ; , are the standard deviation and mean in the truncated normal distribution respectively; is the maximum capacity of the functional load of node i; is the tolerance parameter of the functional node in the target heterogeneous functional graph, which is used to determine the tolerance of the node to overload; Determine the reallocated functional load of the node in the target heterogeneous function graph after load reallocation through a load reallocation algorithm. The expression of the load reallocation algorithm is: Among them, is the reallocated functional load of node j that belongs to the same category as node i in the failed state at time t and is connected to each other; is the node set composed of all nodes that belong to the same category as node i in the failed state at time t and are connected to each other; is the reallocated functional load of node i in the failed state at time t-1; is the maximum capacity of the functional load of node j; Determine the overload rate of the node according to the relationship between the reallocated functional load and the maximum functional load capacity of the node; Determine the dependence intensity between the node and other nodes in the target heterogeneous function graph through a dependence intensity algorithm. The expression of the dependence intensity algorithm is: Among them, is the dependence strength of node i on node j at time t; is an adjustable parameter; , are the degrees of node i and node j respectively with the nodes that are not in the failure state at time t; indicates that there is a directed edge from node j to node i in the target heterogeneous functional graph at time t; indicates that there is no directed edge from node j to node i in the target heterogeneous functional graph at time t.

4. A method for the state evolution of heterogeneous infrastructure under flood risk according to claim 3, characterized in that Determine the overload rate of the node according to the relationship between the reallocated functional load and the maximum functional load capacity of the node, including: Compare the reallocation functional load of a node with its maximum functional load capacity. When the maximum functional load capacity of a node is greater than or equal to its own reallocation functional load, determine that the overload rate of the node is zero. When the maximum capacity of the functional load of a node is less than its own reallocated functional load, the overload rate of the node is determined by an overload rate algorithm, and the expression of the overload rate algorithm is: 。 5. A method for the state evolution of heterogeneous infrastructure under flood risk according to claim 2, characterized in that Pre - define various state transition rules for a node to switch between various operating states, including: When the node is in a vulnerable state, define the state transition rule for the node to switch from the vulnerable state to the degraded state as that the risk propagation probability of the node is higher than a first threshold, and the expression of the risk propagation probability is: Among them, is the risk propagation probability that the influence of the failed state node j in the target heterogeneous function graph propagates to node i; is the basic risk propagation probability; is the vulnerability index in the heterogeneity characteristics of node i; is the dependence strength of node i on the failed state node j in its own heterogeneity characteristics among the directed edges pointing from the failed state node j to node i at time t; is the importance in the heterogeneity characteristics of the failed state node j; When the node is in a degraded state, define the state transition rule for the node to switch from the degraded state to the failure state as that the failure probability of the node is greater than or equal to a preset recovery probability, and the expression of the failure probability is: Among them, is the basic failure probability; is the network resilience of the entire target abnormal function graph; is the overload rate in the heterogeneity characteristics of node i at time t; When the node is in a degraded state, define the state transition rule for the node to switch from the degraded state to the recovery state as that the failure probability of the node is less than the preset recovery probability. When the node is in a failure state, define the state transition rule for the node to switch from the failure state to the recovery state as that the duration of the node in the failure state is greater than or equal to a target duration corresponding to itself.

6. A method for the state evolution of heterogeneous infrastructure under flood risk according to claim 1, characterized in that, The method further includes: According to the various state transition rules and the heterogeneity characteristics of the nodes in the heterogeneous functional graph, through the Monte Carlo simulation method and the corresponding differential equations, conduct iterative simulations for a preset number of rounds on the changes in the operating states of each node in the target heterogeneous functional graph in a flood scenario. Based on the results of the iterative simulations for the preset number of rounds, determine the dynamic evolution of the nodes in the target heterogeneous functional graph in various operating states over time in a flood scenario. The expression of the differential equation is: ； ； ； ； Among them, 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 a recovery state at time t; represents the rate of change of the probability that node i is in a vulnerable state with respect to time when it is in a vulnerable state; represents the rate of change of the probability that node i is in a degraded state with respect to time when it is in a degraded state; represents the rate of change of the probability that node i is in a failed state with respect to time when it is in a failed state; represents the rate of change of the probability that node i is in a recovery state with respect to time when it is in a recovery state; represents the recovery probability; represents the recovery rate; is the risk propagation probability that the influence of the failed node j in the target heterogeneous function graph propagates to node i; is the failure probability of node i at time t.

7. A heterogeneous infrastructure state evolution system under flood risk, characterized in that, The system includes: A heterogeneous functional graph construction module, used to construct a corresponding heterogeneous functional graph according to all infrastructure systems in the target area. The nodes in the heterogeneous functional graph are composed of the functions of the functional components in the infrastructure system, and the directed edges between the nodes point from the first node to the second node, where the operation of the second node depends on the operation of the first node. A rule definition module, used to pre - define various state transition rules for a node to switch between various operating states. Among them, the various state transition rules are related to the heterogeneity characteristics of the nodes, and one state transition rule corresponds to one operating state transition relationship. A target heterogeneous functional graph determination module, used to select at least one node in the heterogeneous functional graph as the initial flood failure node in the failure state to obtain the target heterogeneous functional graph. A heterogeneity characteristic determination module, used to determine the values of the heterogeneity characteristics of each node in the target heterogeneous functional graph. The heterogeneity characteristics at least include: importance, vulnerability index, network resilience, overload rate, and dependence strength. A state transition rule determination module, used to determine the state transition rules satisfied by each node in the target heterogeneous functional graph in a flood scenario according to the various state transition rules and the values of the heterogeneity characteristics of each node. A state evolution module, used to determine the operating state evolution of each node according to the state transition rules satisfied by each node.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the method for the state evolution of heterogeneous infrastructure under flood risk according to any one of claims 1 to 6.

9. A storage medium, characterized in that, A program or instruction is stored on the storage medium. When the program or instruction is executed by a processor, the method for the state evolution of heterogeneous infrastructure under flood risk according to any one of claims 1 to 6 is implemented.

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