Urban hydropower infrastructure protection resource allocation method and device and storage medium

Abstract

The invention discloses an urban hydropower infrastructure protection resource configuration method and device and a storage medium, and relates to the field of urban hydropower failure analysis and resource configuration.The method comprises the steps that the failure proportion of an upstream node is determined, and whether the upstream node is in a failure state or not is determined based on the failure proportion; under the condition of a non-failure state, determining the ponding depth of the node to be analyzed according to a ponding depth change model; when the ponding depth is greater than the ponding depth threshold, determining a target failure probability of the node based on the ponding depth and a target failure probability model; a random value of the node is generated through a Monte Carlo simulation method, and whether the node is in a failure state or not is determined again based on the target failure probability and the random value; if the state is still the non-failure state, further determining whether the state is the failure state based on the random failure probability; and determining a system reliability result based on the state of the node, and performing protection resource configuration on the node based on a protection resource configuration strategy, thereby realizing failure analysis and protection resource configuration on the hydropower infrastructure.

Description

Technical Field

[0001] The present invention relates to the technical field of failure analysis and protection resource configuration of urban water and power infrastructure, and in particular to a method, device and storage medium for configuring protection resources of urban water and power infrastructure. Background Art

[0002] Extreme climate disasters, particularly rainstorms such as heavy rainfall and urban flooding, have become frequent in recent years. These disasters exacerbate the security risks of critical urban infrastructure and pose a serious threat to urban functioning. Urban water and power facilities are highly coupled, with their functions interdependent and mutually impacting. Failure of a single node can rapidly spread throughout the system, triggering cascading network failures that can have a knock-on impact on urban functioning and disrupt its normal operation.

[0003] Existing technologies have analyzed the topological network characteristics of infrastructure based on functional associations, network parameters, and the failure probability of key nodes. However, these technologies have not effectively taken into account factors such as rainstorm disaster scenarios, geographical associations (such as the geographical location of facilities, topography, etc.), and functional dependencies (such as the functional connection between water and power facilities). In addition, it is very necessary to allocate protection resources to nodes, strengthen the protection of key nodes, reduce the vulnerability of the system, reduce the impact of external shocks, and thus enhance the resilience of urban infrastructure. However, traditional resource allocation methods rely on static historical data and manual experience, lack real-time disaster perception capabilities, and lead to delayed or wasted resource allocation.

[0004] In summary, existing models lack reliability and scientific validity, failing to accurately assess and predict the safety of critical urban water and power infrastructure systems under extreme climate disasters. Furthermore, traditional resource allocation methods lack real-time disaster awareness, leading to delayed or wasted resource allocation. Therefore, accurately analyzing node effectiveness and allocating protective resources are pressing technical challenges. Summary of the Invention

[0005] The present invention provides a method, device and storage medium for allocating protective resources for urban water and power infrastructure, so as to achieve scientific and effective analysis of whether urban water and power infrastructure has failed, and to accurately allocate protective resources for water and power infrastructure.

[0006] According to one aspect of the present invention, a method for allocating protection resources for urban water and power infrastructure is provided, comprising:

[0007] Determining a failure ratio of upstream nodes of the node to be analyzed, and determining whether the state of the node to be analyzed is a failure state based on the failure ratio, wherein the node to be analyzed corresponds to urban water and power infrastructure;

[0008] When the state of the node to be analyzed is non-failure, the water depth corresponding to the node to be analyzed is determined according to the water depth change model; when the water depth is greater than the water depth threshold, the target failure probability corresponding to the node to be analyzed is determined based on the water depth and target failure probability model;

[0009] Generate random values ​​corresponding to the nodes to be analyzed using the Monte Carlo simulation method, and determine again whether the state of the nodes to be analyzed is a failure state based on the target failure probability and the random values;

[0010] In the case where it is determined again that the state of the node to be analyzed is a non-failure state, determining whether the state of the node to be analyzed is a failure state based on the random failure probability;

[0011] The system reliability result corresponding to the urban water and power infrastructure is determined based on the status of the node to be analyzed, and when the system reliability result is unreliable, protection resources are configured for the node to be analyzed based on the protection resource configuration strategy.

[0012] According to another aspect of the present invention, there is provided a device for allocating resources for protecting urban water and power infrastructure, comprising:

[0013] a failure ratio determination module, configured to determine a failure ratio of an upstream node of a node to be analyzed, and determine whether the state of the node to be analyzed is a failure state based on the failure ratio, wherein the node to be analyzed corresponds to urban water and power infrastructure;

[0014] A water depth determination module is used to determine the water depth corresponding to the node to be analyzed according to the water depth change model when the state of the node to be analyzed is a non-failure state;

[0015] A target failure probability calculation module is used to determine the target failure probability corresponding to the node to be analyzed based on the water depth and the target failure probability model when the water depth is greater than the water depth threshold;

[0016] A first state determination module is configured to generate a random value corresponding to the node to be analyzed by a Monte Carlo simulation method, and to determine again whether the state of the node to be analyzed is a failure state based on the target failure probability and the random value;

[0017] a second state determination module, configured to determine whether the state of the node to be analyzed is a failure state based on a random failure probability when the state of the node to be analyzed is determined to be a non-failure state again;

[0018] The protection resource configuration module is used to determine the system reliability result corresponding to the urban water and power infrastructure based on the status of the node to be analyzed, and when the system reliability result is unreliable, configure protection resources for the node to be analyzed based on the protection resource configuration strategy.

[0019] According to another aspect of the present invention, an electronic device is provided, comprising:

[0020] at least one processor;

[0021] and a memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the urban water and power infrastructure protection resource configuration method described in any embodiment of the present invention.

[0023] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the urban water and power infrastructure protection resource configuration method described in any embodiment of the present invention when executed.

[0024] The technical solution of an embodiment of the present invention includes: determining the failure ratio of the upstream node of the node to be analyzed, and determining whether the state of the node to be analyzed is a failure state based on the failure ratio, wherein the node to be analyzed corresponds to the urban water and power infrastructure; when the state of the node to be analyzed is a non-failure state, determining the water depth corresponding to the node to be analyzed according to a water depth change model; when the water depth is greater than the water depth threshold, determining the target failure probability corresponding to the node to be analyzed based on the water depth and the target failure probability model; generating a random value corresponding to the node to be analyzed by a Monte Carlo simulation method, and determining again whether the state of the node to be analyzed is a failure state based on the target failure probability and the random value; when it is again determined that the state of the node to be analyzed is a non-failure state, determining whether the state of the node to be analyzed is a failure state based on the random failure probability; determining the system reliability result corresponding to the urban water and power infrastructure based on the state of the node to be analyzed, and when the system reliability result is unreliable, configuring protection resources for the node to be analyzed based on the protection resource configuration strategy.

[0025] The technical solution of the present invention solves the technical problem that the existing model is insufficient in reliability and scientificity, and cannot accurately evaluate and predict the safety status of urban water-power-related key infrastructure systems under extreme climate disasters. Through failure judgment of geographical, functional, and random association types, a node can be considered a failed node if it meets one of the types, and the dynamic reliability evolution process of urban key infrastructure nodes under heavy rain conditions can be more comprehensively evaluated.

[0026] In addition, it also solves the technical problem that traditional resource allocation methods rely on static historical data and manual experience, lack real-time disaster perception capabilities, and lead to delayed or wasted resource allocation. Through resource allocation strategies such as uniform distribution, node load demand distribution, and node importance-oriented distribution, the system's protection and resistance capabilities are maximized, thereby improving the reliability of urban infrastructure when disasters occur.

[0027] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 This is a flow chart of a method for allocating protective resources for urban water and power infrastructure provided in Example 1 of the present invention;

[0030] Figure 2 A diagram of a node topology structure type provided in the first embodiment of the present invention;

[0031] Figure 3 A flowchart of a protection resource configuration for urban water and power infrastructure provided by the second embodiment of the present invention;

[0032] Figure 4 A flowchart for constructing a cascading failure model (target failure probability model) under a rainstorm disaster provided in the second embodiment of the present invention;

[0033] Figure 5 A flowchart of a method for analyzing failure of urban water and power infrastructure provided in the second embodiment of the present invention;

[0034] Figure 6 A flowchart of another configuration of urban water and power infrastructure protection resources provided in the third embodiment of the present invention;

[0035] Figure 7 A flowchart of a configuration of protection resources for urban water and power infrastructure provided by the fourth embodiment of the present invention;

[0036] Figure 8 A flowchart of a preferred configuration of urban water and power infrastructure protection resources provided by the fourth embodiment of the present invention;

[0037] Figure 9 This is a schematic diagram of the structure of a device for allocating protective resources for urban water and electricity infrastructure provided by the fifth embodiment of the present invention;

[0038] Figure 10 A schematic structural diagram of an electronic device for implementing the method for allocating urban water and electricity infrastructure protection resources according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0040] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0041] Example 1

[0042] Figure 1 This is a flow chart of a method for configuring urban water and power infrastructure protection resources provided in the first embodiment of the present invention. This embodiment is applicable to the situation of analyzing the effectiveness and configuring protection resources for urban water and power infrastructure in rainfall scenarios. This method can be executed by an urban water and power infrastructure protection resource configuration device, which can be implemented in the form of hardware and / or software and can be configured in a computer device. Figure 1 As shown, the method specifically includes the following steps:

[0043] S110 : Determine the failure ratio of the upstream node of the node to be analyzed, and determine whether the state of the node to be analyzed is a failure state based on the failure ratio.

[0044] The nodes to be analyzed correspond to the urban water and electricity infrastructure. Each infrastructure can be regarded as a node to be analyzed. All nodes to be analyzed in a specified geographical area together constitute the node network topology.

[0045] It is understandable that for a node to be analyzed, it is possible to first determine whether it will cause a cascading failure due to the failure of an upstream node. That is, if the failure rate of an upstream node is too high, the node to be analyzed will also become a failure state.

[0046] The failure ratio refers to the ratio of upstream nodes in a failed state to the total upstream nodes.

[0047] It should be noted that in order to determine the failure ratio of the upstream node of the node to be analyzed, it is necessary to first establish a node network topology, determine the upstream node of the node to be analyzed based on the node network topology, and analyze the status of the upstream node accordingly.

[0048] Considering the importance of components and the accessibility of data, this example focuses on key functional sites such as power plants, 220kV substations, 110kV substations, water plants, water distribution plants, and pumping stations. For numbering, g, t, s, r, f, and p represent each of these node types. For example, power plant 1 is defined as node g1. An edge is defined as a connection between two nodes. For example, if g1 is supplying power to node t10, the two nodes are connected, with a directed arrow pointing to node t10, indicating the flow of power from g1 to node t10.

[0049] The physical operation correlation relationship of water supply and power supply is analyzed, and the key nodes of the system are defined and numbered. According to the node type and its functional correlation relationship, three basic topological structure types of starting node, intermediate node and end node are identified and proposed.

[0050] Constructing a topology model of critical infrastructure networks, v i represents the infrastructure node, e ij Indicates the connection relationship between nodes. If there is a connection relationship between two nodes, then e ij is 1, otherwise 0, V is the total set of urban key infrastructure nodes, E is the set of all connected edges, E(G) refers to the set of edges in graph G, and (vi,vj) refers to the directed edge from node i to node j. The complex network model of urban key infrastructure is obtained, and its mathematical expression is as follows:

[0051] V={v i|i=1,2,…,N} (1)

[0052] E={e ij |i=1,2,…,N; j=1,2,…,N; i≠j} (2)

[0053]

[0054] like Figure 2 , which is a diagram of a node topology structure type provided in the first embodiment of the present invention.

[0055] S120 : When the state of the node to be analyzed is a non-failure state, determine the water depth corresponding to the node to be analyzed according to the water depth variation model.

[0056] Among them, the water accumulation depth change model refers to a mathematical model that reflects the dynamic changes of water accumulation over time; urban water and power infrastructure can be equipment related to water supply and power supply in the city. Urban water and power infrastructure includes but is not limited to power plants, substations, water plants, water distribution plants, pumping stations, etc.

[0057] Specifically, in the aforementioned steps, the failure rate of the upstream nodes can be used to determine whether the node under analysis is in a failed state. If the node under analysis is determined to be in a non-failed state, meaning the severity of the upstream node failure is insufficient to affect the state of the node under analysis, the water depth at the node under analysis can be determined using the water depth variation model, allowing subsequent analysis to be based on this water depth.

[0058] In some embodiments, the water depth change model is constructed based on at least one of the water depth, rainfall intensity, evaporation rate, infiltration rate, inflow water depth, and outflow water depth in the historical period corresponding to the node to be analyzed.

[0059] Among them, the historical period can be the previous time period corresponding to the current period, the inflow water depth refers to the depth of water flowing from the nearby area into the area where the node to be analyzed is located, and the outflow water depth refers to the depth of water flowing out of the study area corresponding to the node to be analyzed to other areas.

[0060] Specifically, we can consider the impact of geographical characteristics on water flow and the impact of hydrological factors such as evaporation and infiltration on the depth of ponding, and establish a dynamic change equation of the ponding depth at the node to be analyzed in each time period as the ponding depth change model:

[0061] h(t)=h(t-1)+I(t)-E(t)-Q(t)+Δh in (t)-Δh out (t) (4)

[0062] Where h(t) is the depth of ponding in period t, h(t-1) is the depth of ponding in the previous period, I(t) is the rainfall intensity in period t, E(t) is the evaporation rate in period t, Q(t) is the infiltration rate in period t, and Δh in (t) is the depth of water flowing into the nearby area during time period t, Δh out (t) The depth of water flowing out of the study area to other areas during period t.

[0063] Furthermore, when it is necessary to determine the water depth of the node to be analyzed within a period of time t, the water depth of the node to be analyzed can be calculated using the above water depth variation model.

[0064] S130 : When the water depth is greater than the water depth threshold, determine the target failure probability corresponding to the node to be analyzed based on the water depth and the target failure probability model.

[0065] The water depth threshold can be a threshold value obtained based on experience or experimentation. For example, before the water depth reaches the water depth threshold, the failure probability of various infrastructure components is approximately 5%. When the water depth exceeds the water depth threshold, the failure probability of the components gradually increases.

[0066] Therefore, the water depth can be compared with the preset water depth threshold. When the water depth is greater than the water depth threshold, the failure probability of the node to be analyzed will gradually increase. It is necessary to further determine the target failure probability corresponding to the node to be analyzed based on the water depth and the target failure probability model, and judge whether it has failed.

[0067] The target failure probability model may reflect the functional correspondence between the water depth and the failure probability of the node to be analyzed, and the target failure probability may be a failure probability calculated based on the target failure probability model.

[0068] In some embodiments, determining the target failure probability corresponding to the node to be analyzed based on the water depth and the target failure probability model may include: substituting the water depth into the target failure probability model to obtain the target failure probability.

[0069] S140 , generating a random value corresponding to the node to be analyzed by a Monte Carlo simulation method, and determining again whether the state of the node to be analyzed is a failure state based on the target failure probability and the random value.

[0070] The random value can be a random number obtained based on Monte Carlo simulation that conforms to the uniform distribution. The random value can be expressed as p r Indicates that the random value is uniformly distributed between 0 and 1; the failure state refers to the failure or damage of the infrastructure corresponding to the node to be analyzed, and the non-failure state refers to the intact function of the infrastructure corresponding to the node to be analyzed.

[0071] In some embodiments, based on the target failure probability and the random value, determining again whether the state of the node to be analyzed is a failure state may include: determining whether the target failure probability is greater than the random value; if the target failure probability is greater than the random value, determining that the state of the node to be analyzed is a failure state; if the target failure probability is not greater than the random value, determining that the state of the node to be analyzed is a non-failure state.

[0072] Specifically, the failure state of the node being analyzed is randomly determined by comparing the target failure probability with a random value. If the target failure probability is greater than the random value, the node is considered to have a high probability of failure under the current circumstances, and the node's state is determined to be failed. If the target failure probability is not greater than the random value, the node's failure probability is not high enough under the current circumstances, and the node's state is temporarily determined to be non-failed.

[0073] S150 : When it is determined again that the state of the node to be analyzed is a non-failed state, determine whether the state of the node to be analyzed is a failed state based on the random failure probability.

[0074] The random failure probability refers to the probability of Monte Carlo simulation that conforms to the (1,10)Beta distribution. The (1,10)Beta distribution is an assumed distribution that aims to simulate random failure events with low probability.

[0075] Specifically, when it is determined again that the state of the node to be analyzed is a non-failure state, it may be further determined whether the node to be analyzed is in a failure state based on the random failure probability.

[0076] S160: Determine the system reliability result corresponding to the urban water and power infrastructure based on the status of the node to be analyzed, and if the system reliability result is unreliable, configure protection resources for the node to be analyzed based on the protection resource configuration strategy.

[0077] In some embodiments, there are multiple nodes to be analyzed, and determining the system reliability results corresponding to the urban water and power infrastructure based on the status of the nodes to be analyzed can include: counting the number of failures corresponding to the nodes to be analyzed in the failure state, and the total number of nodes to be analyzed; substituting the number of failures and the total amount of node data into the reliability assessment model to obtain reliability results; and using the reliability results as failure analysis results.

[0078] It is understandable that when performing failure analysis on urban hydropower infrastructure, a city or a designated area includes multiple hydropower infrastructures, and each node to be analyzed corresponds to one hydropower infrastructure, that is, there are multiple nodes to be analyzed.

[0079] To assess the system reliability of urban hydropower infrastructure, we can count the number of failed nodes among all nodes to be analyzed, which we use as the failure count. We also count the total number of nodes to be analyzed, which we use as the total node count. Furthermore, we substitute the failure count and total node count into the reliability assessment model to obtain a reliability result, which represents the reliability level of the urban hydropower infrastructure. Finally, we calculate the reliability result. The reliability assessment model is shown below:

[0080]

[0081] R(t) is the system reliability, S t is the number of nodes that fail in the system at time t, N is the total number of nodes in the system, when S(t) = 0, the system reliability R(t) is 1, when S(t) = N, the system fails completely.

[0082] The technical solution of the present invention can cover geographical, functional, and random correlation types of failure determination, achieving scientific and effective analysis of whether urban water and power infrastructure has failed. Among them, geographical correlation refers to the situation where multiple infrastructures are geographically close, but if an earthquake or flood occurs, multiple facilities in a small area will be damaged simultaneously; functional correlation: there is resource transfer between different types of infrastructure to ensure the normal operation of the facilities. For example, damage to power facilities may affect the operation of water supply facilities, transportation facilities, communication facilities, etc.; random correlation: unplanned dependencies caused by unpredictable disturbances such as random failures and sudden accidents. For example, during a rainstorm disaster, equipment damage caused by human error or ground collapse may occur.

[0083] Among them, the protection resource allocation strategy can be understood as a strategy for allocating protection resources to each node. Effective protection resource allocation reduces the vulnerability of the system and the impact of external shocks, thereby significantly enhancing the resilience of urban infrastructure.

[0084] Therefore, when the system reliability result is unreliable, protection resources can be configured for the analyzed nodes based on the protection resource configuration strategy to ensure the system's protection resistance and improve the reliability of urban infrastructure when disasters occur.

[0085] The technical solution of the embodiment of the present invention is to determine the failure ratio of the upstream node of the node to be analyzed, and determine whether the state of the node to be analyzed is a failure state based on the failure ratio, wherein the node to be analyzed corresponds to the urban water and power infrastructure; when the state of the node to be analyzed is a non-failure state, determine the water depth corresponding to the node to be analyzed according to the water depth change model; when the water depth is greater than the water depth threshold, determine the target failure probability corresponding to the node to be analyzed based on the water depth and the target failure probability model; generate a random value corresponding to the node to be analyzed by a Monte Carlo simulation method, and determine again whether the state of the node to be analyzed is a failure state based on the target failure probability and the random value; when it is again determined that the state of the node to be analyzed is a non-failure state, determine whether the state of the node to be analyzed is a failure state based on the random failure probability; and generate a failure analysis result corresponding to the urban water and power infrastructure based on the state of the node to be analyzed. The technical solution of the present invention solves the technical problem that the existing model is insufficient in reliability and scientificity, and cannot accurately evaluate and predict the safety status of urban water-power-related key infrastructure systems under extreme climate disasters. Through failure judgment of geographical, functional, and random association types, a node can be considered a failed node if it meets one of the types. The dynamic reliability evolution process of urban key infrastructure nodes under heavy rain conditions is evaluated more comprehensively, providing a basis for the configuration of protective resources.

[0086] Example 2

[0087] Figure 3 This is a flowchart of the configuration of urban water and power infrastructure protection resources provided by the second embodiment of the present invention. Based on the above embodiment, this embodiment further illustrates the construction process of the target failure probability model. Its specific implementation method can refer to the technical solution of this embodiment. Among them, the technical terms that are the same or corresponding to the above embodiment are not repeated here. Figure 3 As shown, the method specifically includes the following steps:

[0088] S210: Determine the failure ratio of the upstream node of the node to be analyzed, and determine whether the state of the node to be analyzed is a failure state based on the failure ratio.

[0089] In some embodiments, determining the failure ratio of the upstream node of the node to be analyzed includes: determining the to-be-used water depth corresponding to the upstream node based on a water depth change model; when the to-be-used water depth is greater than a water depth threshold, determining the to-be-used failure probability corresponding to the upstream node based on the to-be-used water depth and a target failure probability model; judging the state of the upstream node based on the to-be-used failure probability, and determining the failure ratio based on the total number of upstream nodes and the number of failed upstream nodes.

[0090] It should be noted that the state of the upstream node of the node to be analyzed may be determined in the same manner as that of the node to be analyzed.

[0091] Specifically, for each upstream node, the water depth variation model can be used to calculate the upstream node's water depth, also known as the available water depth. If the available water depth of an upstream node exceeds the water depth threshold, the target failure probability model is used to calculate the failure probability of that upstream node, also known as the available failure probability.

[0092] Based on the calculated failure probability to be used, it is determined whether the upstream node is failed, for example, by comparing the failure probability to be used with a random number. Furthermore, the number of failed nodes among all upstream nodes is counted, and the ratio of the number of failed nodes to the total number of upstream nodes is calculated to obtain the failure ratio.

[0093] In other embodiments, determining whether the state of the node to be analyzed is a failure state based on the failure ratio includes: determining whether the failure ratio is greater than the functional association strength threshold; if the failure ratio is greater than the functional association strength threshold, determining that the state of the node to be analyzed is a failure state; if the failure ratio is not greater than the functional association strength threshold, determining that the state of the node to be analyzed is a non-failure state.

[0094] Among them, the preset functional association strength threshold reflects the sensitivity of the impact of upstream node failure on the function of the node to be analyzed.

[0095] Specifically, the previously calculated failure ratio of the upstream node is compared with a preset functional correlation strength threshold. If the failure ratio is greater than the functional correlation strength threshold, it indicates that the failure of the upstream node is severe enough to affect the function of the node to be analyzed, and the node to be analyzed is therefore determined to be in a failed state. If the failure ratio is not greater than the functional correlation strength threshold, it indicates that the failure of the upstream node has not yet reached a level that affects the function of the node to be analyzed, and the node to be analyzed is therefore determined to be in a non-failed state.

[0096] For example, if the functional association strength is 0.6 and the total number of upstream nodes of a node to be analyzed is 10, then if there are more than 6 nodes, the node to be analyzed is considered invalid.

[0097] S220 : When the state of the node to be analyzed is a non-failure state, determine the water depth corresponding to the node to be analyzed according to the water depth variation model.

[0098] S230 , obtaining sample water accumulation depth and node status data corresponding to the sample node, and fitting a basic failure probability model according to the sample water accumulation depth and node status data.

[0099] Among them, the sample nodes can be some sample hydropower infrastructure; the sample water accumulation depth refers to the historical water accumulation depth of the sample node in the historical time period, and the node status data refers to some data reflecting whether the sample node is invalid under the historical water accumulation depth.

[0100] Since the sample water depth and node status data obtained are relatively discrete, the Slogistic3 model in the Sigmoid function is selected to fit the discrete data. The functional correspondence between the water depth and the failure probability of various infrastructure nodes is established, and the node basic failure probability model is constructed as follows:

[0101]

[0102] Among them, P i o (t) represents the node failure probability of each node numbered i under the water depth h(t) at time t. This expression will be used as one of the basic input conditions for the node failure probability for subsequent network cascade failure simulation.

[0103] S240: Determine a target failure probability model based on the network node topology index and the basic failure probability model.

[0104] The network node topology index includes at least one of degree centrality, betweenness centrality, closeness centrality and eigenvector centrality corresponding to the network node.

[0105] Specifically, we select network node topology indicators such as degree centrality DC, betweenness centrality BC, closeness centrality CC, and eigenvector centrality EC as parameters for node failure probability correction, and propose a comprehensive evaluation correction method based on empowerment, as follows:

[0106] P i a (t) = P i o (t)·(1+ω1DC i +ω2BC i +ω3CC i +ω4EC i -ω5C i ) (7)

[0107] Among them, P i a (t) represents the modified failure probability of each type of node numbered i (), ω i is the weight of the i-th indicator.

[0108] That is, by modifying the basic failure probability model, the target failure probability model can be obtained. The specific values ​​of the above indicators can be determined based on the previously constructed node network topology.

[0109] like Figure 4 The figure shows a flowchart for constructing a cascading failure model (target failure probability model) for rainstorm disasters, as provided in Example 2 of the present invention. Based on the fundamental fact that extreme rainfall can cause surface runoff and convergence, leading to urban waterlogging, severely impacting substations, distribution facilities, pumping stations, water plants, and other facilities in low-lying areas, causing short circuits, equipment failures, or damage. In particular, transformers and distribution boxes in medium and low voltage power grids are more susceptible to flooding, resulting in power supply anomalies, a process for constructing a cascading failure model for rainstorm disasters is proposed.

[0110] S250 : When the water depth is greater than the water depth threshold, determine the target failure probability corresponding to the node to be analyzed based on the water depth and the target failure probability model.

[0111] Specifically, the target failure probability is calculated based on formula (7).

[0112] S260 , generating a random value corresponding to the node to be analyzed by a Monte Carlo simulation method, and determining again whether the state of the node to be analyzed is a failure state based on the target failure probability and the random value.

[0113] S270 : When it is determined again that the state of the node to be analyzed is a non-failed state, determine whether the state of the node to be analyzed is a failed state based on the random failure probability.

[0114] In some embodiments, determining whether the state of the node to be analyzed is a failure state based on the random failure probability includes: determining whether the random value is less than the random failure probability; if the random value is less than the random failure probability, determining that the state of the node to be analyzed is a failure state; if the random value is not less than the random failure probability, determining that the state of the node to be analyzed is a non-failure state.

[0115] Specifically, the random value is compared with the random failure probability. If the random value is less than the random failure probability, it is considered that the node has a sufficient probability of failure under the current situation, and thus the state of the node is determined to be a failed state.

[0116] If the random value is not less than the random failure probability, it is considered that the probability of failure of the node is not high enough under the current situation, and the state of the node is determined to be a non-failed state.

[0117] S280: Determine the system reliability result corresponding to the urban water and power infrastructure based on the status of the node to be analyzed, and if the system reliability result is unreliable, configure protection resources for the node to be analyzed based on the protection resource configuration strategy.

[0118] like Figure 5 FIG2 is a flowchart of a method for analyzing failure of urban hydropower infrastructure according to a second embodiment of the present invention. Based on the analysis of this process, the node status can be obtained, and then the reliability of the system can be determined. Based on the reliability, it is determined whether to configure protection resources for each node.

[0119] The technical solution of the present invention solves the technical problem that the reliability and scientificity of existing models are insufficient and cannot accurately evaluate and predict the safety status of urban water-power related key infrastructure systems under extreme climate disasters. Through failure judgment of geographical, functional and random association types, a node can be considered as a failed node if it meets one of the types. The dynamic reliability evolution process of urban key infrastructure nodes under heavy rain conditions can be evaluated more comprehensively, and the status of nodes and systems can be accurately determined, providing a basis for the configuration of protective resources.

[0120] Example 3

[0121] Figure 6 This is another flowchart of the configuration of protection resources for urban water and electricity infrastructure provided by the third embodiment of the present invention. Based on the above embodiment, this embodiment further illustrates the process of configuring protection resources for each node to be analyzed based on the protection resource configuration strategy. The specific implementation method can be found in the technical solution of this embodiment. Among them, the technical terms that are the same or corresponding to the above embodiment are not repeated here. Figure 6 As shown, the method specifically includes the following steps:

[0122] S310: Determine the system reliability result corresponding to the urban water and power infrastructure based on the status of the node to be analyzed.

[0123] It should be noted that before executing step S310, steps S110-S150 may be executed first to obtain the failure status of each node to be analyzed. The specific implementation method may refer to the above embodiment and will not be repeated here.

[0124] S320: When the system reliability result is unreliable, determine the resources to be allocated, and determine the protection capability improvement level of the node to be analyzed based on the resources to be allocated and the protection resource configuration strategy.

[0125] Among them, the resources to be allocated refer to the total protection resources that the system can obtain; the protection resource configuration strategy includes uniform allocation strategy, node load demand allocation strategy, node importance-oriented allocation strategy, etc. The protection resource configuration strategy is used to allocate resources to be allocated.

[0126] Specifically, when the system reliability result is unreliable, various protection resource configuration strategies can be calculated to improve the protection capability of the node to be analyzed.

[0127] S330: Determine the adjusted failure probability of the node to be analyzed based on the target failure probability and the protection capability improvement level of the node to be analyzed.

[0128] Specifically, the difference between 1 and the level of protection capability improvement can be calculated, and the difference can be multiplied by the target failure probability to obtain the adjusted failure probability.

[0129] S340: Based on the adjusted failure probability, select a target protection configuration strategy from at least two protection resource configuration strategies to configure protection resources for each node to be analyzed.

[0130] Specifically, by comparing the target failure probability with the adjusted failure probability, we can determine the degree to which various protection resource allocation strategies reduce the failure probability, and accordingly select the protection resource allocation strategy with the best effect as the target protection configuration strategy.

[0131] In this embodiment, the uniform distribution strategy, the node load demand distribution strategy, and the node importance-oriented distribution strategy are described as follows:

[0132] 1. Even Distribution Strategy

[0133] Assuming that all regions can obtain protection resources equally before the disaster, each node or region will receive the same amount of resources. The basic idea is to evenly distribute resources among all nodes without considering the differences in demand between nodes. The amount of resources allocated to each node is fixed and has nothing to do with the load demand of the node. This ensures that each node in the system can obtain protection resources, thereby improving the resistance performance of the node to a certain extent and contributing to the overall resilience of the system. Specifically, assuming the total amount of protection resources is R t , there are N nodes in the system, then the amount of resources each node can get That is:

[0134]

[0135] To demonstrate the role of protection resources in strengthening the protection of each node, this paper associates protection resources with the minimum and maximum protection resource thresholds required by each node, thereby reflecting the protection effect on the node's failure probability, which facilitates subsequent simulations. After each node is allocated protection resources, its adjusted failure probability is as follows:

[0136]

[0137] in, It represents the level of improvement of node protection capability by protection resources. The maximum protection resource threshold designed for node i, The minimum protection resource threshold designed for node i, is the failure probability of node i after uniform distribution, is the cascading failure probability of node i calculated in the above embodiment.

[0138] 2. Node Load Demand Distribution Strategy

[0139] The node load demand allocation strategy allocates protection resources based on each node's load level and actual demand, and is a relatively traditional resource allocation strategy. To enable the load levels of different nodes to be compared under the same standards, the load levels need to be normalized to convert the load values ​​of all nodes into relative values, ensuring that the load level of each node is within a uniform scale. This allows for more reasonable allocation of protection resources by ranking them. Nodes with higher load levels will receive more resources, thereby improving their reliability. The normalized load level calculation formula is as follows:

[0140]

[0141] is the normalized load value of node i, L i is the load level of node i, L m With L n are the maximum and minimum load levels among all nodes.

[0142] After normalization, the load values ​​of all nodes are compressed into the range of [0,1], thereby eliminating the absolute differences in load levels and making subsequent resource allocation more fair. This paper then sorts the total protection resources according to the normalized load value of each node and allocates protection resources to each node based on priority. Specifically, nodes with higher loads will require more resources, while nodes with lower loads will require fewer. To ensure that the reliability of nodes with higher loads is improved while not wasting excessive resources, this paper adopts a linear method to reasonably allocate resources according to load levels and feeds back the specific resource reflection effect into the relevant failure probability, as follows:

[0143]

[0144] in is the amount of resources allocated to each node according to load, It represents the level of improvement of node protection capability by protection resources. l i is the failure probability of node i after the node load demand is allocated.

[0145] 3. Node Importance-Guided Allocation Strategy

[0146] In disaster scenarios, the importance of nodes in a network is determined not only by their functional attributes but also by their network characteristics within the system structure. When a node is coupled with multiple other nodes, its failure can disrupt the functions of surrounding nodes, significantly increasing the overall failure risk of the system. Therefore, this paper proposes a node importance-based resource allocation strategy. By identifying the more important nodes in the network structure, and given limited resources, prioritizing the allocation of protection resources to these nodes, this strategy enhances the overall resilience of the system.

[0147] For the evaluation criteria of node importance in the network, the node importance is evaluated by introducing indicators such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality and clustering coefficient. The weight of each indicator is the same as that when constructing the node cascade failure model under heavy rain disasters, so as to obtain the importance of each node q i , sort the nodes according to their importance, normalize them, and then, similar to the node load demand allocation strategy, calculate the adjusted failure probability based on the improvement level of the node protection capability.

[0148] q i =ω1DC i +ω2BC i +ω3CC i +ω4EC i -ω5C i (15)

[0149]

[0150] in is the amount of resources allocated to each node according to load, It represents the level of improvement of node protection capability by protection resources. is the failure probability of node i after the node load demand is allocated.

[0151] The technical solution of the embodiment of the present invention solves the technical problem that traditional resource allocation methods rely on static historical data and manual experience, lack real-time disaster perception capabilities, and lead to delayed or wasted resource allocation. Through resource allocation strategies such as uniform distribution, node load demand distribution, and node importance-oriented distribution, the system's protection and resistance capabilities are maximized, thereby improving the reliability of urban infrastructure when disasters occur.

[0152] Example 4

[0153] Figure 7The fourth embodiment of the present invention provides a flowchart of the configuration of protection resources for urban water and electricity infrastructure. Based on the above embodiment, this embodiment can also evaluate the reliability of the system after the protection resources are configured. If it is unreliable, the configuration of protection resources can be continued until the system is reliable. The specific implementation method can refer to the technical solution of this embodiment. Among them, the technical terms that are the same as or corresponding to the above embodiment are not repeated here. Figure 7 As shown, the method specifically includes the following steps:

[0154] S410: Determine the system reliability result corresponding to the urban water and power infrastructure based on the status of the node to be analyzed.

[0155] It should be noted that before executing step S410, steps S110-S150 may be executed first to obtain the failure status of each node to be analyzed. The specific implementation method may refer to the above embodiment and will not be repeated here.

[0156] S420: When the system reliability result is unreliable, determine the resources to be allocated, and determine the protection capability improvement level of the node to be analyzed based on the resources to be allocated and the protection resource configuration strategy.

[0157] S430: Determine the adjusted failure probability of the node to be analyzed based on the target failure probability and the protection capability improvement level of the node to be analyzed.

[0158] S440: Based on the adjusted failure probability, select a target protection configuration strategy from at least two protection resource configuration strategies to configure protection resources for each node to be analyzed.

[0159] S450: Determine the current water depth, and modify the target failure probability model based on the level of protection capability improvement to obtain a failure probability modification model.

[0160] It should be noted that the current flooding depth refers to the flooding depth within the current cycle or period. For example, if the previous cycle is t and the current cycle is t+1, if the system reliability result for cycle t is unreliable, then after configuring protection resources, it is necessary to further evaluate the system reliability for cycle t+1 to determine whether to continue configuring protection resources. At this time, it is necessary to obtain the flooding depth for cycle t+1, that is, the current flooding depth, so that the failure probability can be calculated based on the latest current flooding depth.

[0161] As you can understand, since protection resources have already been configured in period t, the previous target failure probability model and the current flooding depth are no longer used to calculate the target failure probability for period t+1. However, the target failure probability model can be modified based on the protection improvement level to obtain a new modified failure probability model, which is used to calculate the target failure probability for period t+1.

[0162] For example, the numerical value corresponding to the protection capability improvement level may be multiplied by the target failure probability model to obtain a failure probability correction model.

[0163] S460: Based on the current water depth and the failure probability correction model, the current failure probability of the node to be analyzed is calculated and used as the target failure probability.

[0164] Specifically, the current water depth can be substituted into the failure probability correction model to obtain the current failure probability as the target failure probability.

[0165] S470, repeatedly executing the process of determining the current water depth, calculating the target failure probability, Monte Carlo simulation, random failure, system reliability judgment, and protection resource allocation until the system reliability result is reliable.

[0166] like Figure 8 As shown, it is a flowchart of a preferred configuration of urban water and electricity infrastructure protection resources provided by the fourth embodiment of the present invention.

[0167] The technical solution of the embodiment of the present invention solves the technical problem that traditional resource allocation methods rely on static historical data and manual experience, lack real-time disaster perception capabilities, and lead to delayed or wasted resource allocation. Through resource allocation strategies such as uniform distribution, node load demand distribution, and node importance-oriented distribution, the system's protection and resistance capabilities are maximized, thereby improving the reliability of urban infrastructure when disasters occur.

[0168] Example 5

[0169] Figure 9 This is a schematic diagram of a structure of a device for allocating resources for protecting urban water and electricity infrastructure provided by the fifth embodiment of the present invention. Figure 9 As shown, the device includes:

[0170] a failure ratio determination module 510 for determining a failure ratio of an upstream node of a node to be analyzed, and determining whether the node to be analyzed is in a failure state based on the failure ratio, wherein the node to be analyzed corresponds to urban water and power infrastructure;

[0171] The water depth determination module 520 is used to determine the water depth corresponding to the node to be analyzed according to the water depth variation model when the state of the node to be analyzed is not a failure state;

[0172] A target failure probability calculation module 530 is configured to determine a target failure probability corresponding to the node to be analyzed based on the water depth and the target failure probability model when the water depth is greater than the water depth threshold;

[0173] A first state determination module 540 is configured to generate a random value corresponding to the node to be analyzed by using a Monte Carlo simulation method, and to determine again whether the state of the node to be analyzed is a failure state based on the target failure probability and the random value;

[0174] A second state determination module 550 is configured to determine whether the state of the node to be analyzed is a failure state based on a random failure probability when the state of the node to be analyzed is determined to be a non-failure state again;

[0175] The protection resource configuration module 560 is used to determine the system reliability result corresponding to the urban water and power infrastructure based on the status of the node to be analyzed, and when the system reliability result is unreliable, configure protection resources for the node to be analyzed based on the protection resource configuration strategy.

[0176] In some embodiments, the water depth change model is constructed based on at least one of the water depth, rainfall intensity, evaporation rate, infiltration rate, inflow water depth, and outflow water depth in the historical period corresponding to the node to be analyzed.

[0177] In some embodiments, the urban water and power infrastructure failure analysis device further includes a target failure probability model construction module, specifically configured to:

[0178] Obtain the sample water depth and node status data corresponding to the sample node, and fit the basic failure probability model based on the sample water depth and node status data;

[0179] Determine the target failure probability model based on the network node topology indicators and the basic failure probability model;

[0180] The network node topology index includes at least one of degree centrality, betweenness centrality, closeness centrality and eigenvector centrality corresponding to the network node.

[0181] In some embodiments, the target failure probability calculation module 530 is specifically configured to:

[0182] Substitute the water depth into the target failure probability model to obtain the target failure probability.

[0183] In some embodiments, the first state determination module 540 is specifically configured to:

[0184] Determine whether the target failure probability is greater than the random value;

[0185] If the target failure probability is greater than the random value, the state of the node to be analyzed is determined to be a failure state;

[0186] If the target failure probability is not greater than the random value, the state of the node to be analyzed is determined to be a non-failure state.

[0187] In some embodiments, the failure ratio determination module 510 includes:

[0188] The failure ratio determination submodule is used to determine the water depth to be used corresponding to the upstream node according to the water depth change model;

[0189] When the water depth to be used is greater than the water depth threshold, determining the failure probability to be used corresponding to the upstream node based on the water depth to be used and the target failure probability model;

[0190] The status of the upstream nodes is determined according to the failure probability to be used, and the failure ratio is determined according to the total number of upstream nodes and the number of failed upstream nodes.

[0191] In some embodiments, the failure ratio determination module 510 includes:

[0192] a failure ratio comparison submodule, used to determine whether the failure ratio is greater than a functional association strength threshold;

[0193] If the failure ratio is greater than the functional association strength threshold, the state of the node to be analyzed is determined to be a failure state;

[0194] If the failure ratio is not greater than the functional association strength threshold, the state of the node to be analyzed is determined to be a non-failure state.

[0195] In some embodiments, the second state determination module 550 is specifically configured to:

[0196] Determine whether the random value is less than the random failure probability;

[0197] If the random value is less than the random failure probability, the state of the node to be analyzed is determined to be a failure state;

[0198] If the random value is not less than the random failure probability, the state of the node to be analyzed is determined to be a non-failure state.

[0199] In some embodiments, there are at least two types of protection resource configuration policies, and the protection resource configuration module 560 is specifically configured to:

[0200] If the system reliability result is unreliable, protection resources are configured for the nodes to be analyzed based on the protection resource configuration strategy, including:

[0201] Determine the resources to be allocated, and based on the resources to be allocated and the protection resource configuration strategy, determine the level of protection capability improvement of the node to be analyzed;

[0202] Determine the adjusted failure probability of the node to be analyzed based on the target failure probability and the level of protection capability improvement of the node to be analyzed;

[0203] Based on the adjusted failure probability, a target protection configuration strategy is selected from at least two protection resource configuration strategies to configure protection resources for each node to be analyzed.

[0204] In some embodiments, the device further comprises a circulation module, specifically configured to:

[0205] After configuring protection resources for each node to be analyzed, the current water depth is determined, and the target failure probability model is corrected based on the level of protection capability improvement to obtain a failure probability correction model;

[0206] Based on the current water depth and the failure probability correction model, the current failure probability of the node to be analyzed is calculated as the target failure probability;

[0207] The process of determining the current water depth, calculating the target failure probability, Monte Carlo simulation, random failure, system reliability judgment, and protection resource allocation is repeated until the system reliability result is reliable.

[0208] The urban water and electricity infrastructure protection resource configuration device provided in the embodiment of the present invention can execute the urban water and electricity infrastructure protection resource configuration method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0209] Example 6

[0210] Figure 10 A schematic diagram of the structure of an electronic device for implementing the urban water and electricity infrastructure protection resource configuration method of an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0211] like Figure 10As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0212] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0213] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for allocating resources for urban water and electricity infrastructure protection.

[0214] In some embodiments, the urban water and electricity infrastructure protection resource configuration method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the urban water and electricity infrastructure protection resource configuration method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the urban water and electricity infrastructure protection resource configuration method in any other appropriate manner (for example, by means of firmware).

[0215] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0216] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0217] In the context of the present invention, computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage medium can include but is not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage medium can be a machine-readable signal medium. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0218] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0219] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0220] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0221] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0222] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for allocating urban water and power infrastructure protection resources, characterized in that: include: Determining a failure ratio of an upstream node of a node to be analyzed, and determining whether a state of the node to be analyzed is a failure state based on the failure ratio, wherein the node to be analyzed corresponds to urban water and electricity infrastructure; When the state of the node to be analyzed is a non-failure state, determining the water depth corresponding to the node to be analyzed according to the water depth change model; When the water depth is greater than a water depth threshold, determining a target failure probability corresponding to the node to be analyzed based on the water depth and a target failure probability model; generating a random value corresponding to the node to be analyzed by a Monte Carlo simulation method, and determining again whether the state of the node to be analyzed is a failure state based on the target failure probability and the random value; In the case where it is determined again that the state of the node to be analyzed is a non-failure state, determining whether the state of the node to be analyzed is a failure state based on a random failure probability; The system reliability result corresponding to the urban water and electricity infrastructure is determined based on the status of the node to be analyzed, and when the system reliability result is unreliable, protection resources are configured for the node to be analyzed based on a protection resource configuration strategy.

2. The method according to claim 1, characterized in that The process of constructing the target failure probability model includes: Obtaining sample water accumulation depth and node status data corresponding to the sample node, and fitting a basic failure probability model based on the sample water accumulation depth and the node status data; Determining the target failure probability model based on the network node topology index and the basic failure probability model; The network node topology index includes at least one of degree centrality, betweenness centrality, closeness centrality and eigenvector centrality corresponding to the network node.

3. The method according to claim 1, characterized in that The determining the target failure probability corresponding to the node to be analyzed based on the water depth and the target failure probability model includes: The water depth is substituted into the target failure probability model to obtain the target failure probability.

4. The method according to claim 1, wherein The step of determining again whether the state of the node to be analyzed is a failure state based on the target failure probability and the random value includes: determining whether the target failure probability is greater than the random value; If the target failure probability is greater than the random value, determining that the state of the node to be analyzed is a failure state; If the target failure probability is not greater than the random value, the state of the node to be analyzed is determined to be a non-failure state.

5. The method according to claim 1, wherein Determine the failure rate of upstream nodes of the node to be analyzed, including: Determining the to-be-used accumulated water depth corresponding to the upstream node according to the accumulated water depth variation model; When the water depth to be used is greater than a water depth threshold, determining a failure probability to be used corresponding to the upstream node based on the water depth to be used and the target failure probability model; The status of the upstream node is judged according to the failure probability to be used, and the failure ratio is determined according to the total number of the upstream nodes and the number of failed upstream nodes.

6. The method according to claim 1, characterized in that The re-determining whether the state of the node to be analyzed is a failure state based on the failure ratio includes: determining whether the failure ratio is greater than a functional association strength threshold; If the failure ratio is greater than the functional association strength threshold, determining that the state of the node to be analyzed is a failure state; If the failure ratio is not greater than the functional association strength threshold, it is determined that the state of the node to be analyzed is a non-failure state.

7. The method according to claim 1, characterized in that The determining, based on the random failure probability, whether the state of the node to be analyzed is a failure state includes: determining whether the random value is less than the random failure probability; If the random value is less than the random failure probability, determining that the state of the node to be analyzed is a failure state; If the random value is not less than the random failure probability, it is determined that the state of the node to be analyzed is a non-failure state.

8. The method according to claim 1, characterized in that There are multiple nodes to be analyzed, and determining the system reliability result corresponding to the urban water and power infrastructure based on the status of the nodes to be analyzed includes: Counting the number of failures corresponding to the nodes to be analyzed in a failure state and the total number of nodes to be analyzed; Substituting the number of failures and the total amount of node data into a reliability evaluation model to obtain a system reliability result; The system reliability results include reliable and unreliable.

9. The method according to claim 1, characterized in that There are at least two types of protection resource configuration strategies. When the system reliability result is unreliable, performing protection resource configuration on the node to be analyzed based on the protection resource configuration strategy includes: Determining resources to be allocated, and determining a level of improvement in the protection capability of the node to be analyzed based on the resources to be allocated and the protection resource configuration policy; Determining an adjusted failure probability of the node to be analyzed based on the target failure probability of the node to be analyzed and the protection capability improvement level; Based on the adjusted failure probability, a target protection configuration strategy is selected from at least two protection resource configuration strategies to perform protection resource configuration on each of the nodes to be analyzed.

10. The method according to claim 9, characterized in that After configuring protection resources for each of the nodes to be analyzed, the method further includes: determining a current water depth, and modifying the target failure probability model based on the protection capability improvement level to obtain the failure probability modification model; Based on the current water depth and the failure probability correction model, calculate the current failure probability of the node to be analyzed as the target failure probability; The process of determining the current water depth, calculating the target failure probability, Monte Carlo simulation, random failure, system reliability judgment, and protection resource allocation is repeated until the system reliability result is reliable.

11. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the urban water and electricity infrastructure protection resource configuration method according to any one of claims 1 to 10.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the urban water and power infrastructure protection resource configuration method according to any one of claims 1 to 10 when executed.

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