Urban water and electricity infrastructure failure analysis method, device, equipment and storage medium

Abstract

The application discloses a kind of urban water and electricity infrastructure failure analysis method, device, equipment and storage medium, it is related to urban water and electricity failure analysis field, comprising: the failure proportion of upstream node is determined, whether it is failure state based on failure proportion, wherein, to be analyzed node is corresponding with urban water and electricity infrastructure;In the case of being not failure state, the water depth of to-be-analyzed node is determined according to water depth variation model;When water depth is greater than water depth threshold value, the target failure probability of to-be-analyzed node is determined based on water depth and target failure probability model;The random value of to-be-analyzed node is generated by Monte Carlo simulation method, and whether to-be-analyzed node is failure state is determined again based on target failure probability and random value;If still not failure state, whether it is failure state is further determined based on random failure probability;Failure analysis result is generated based on the state of to-be-analyzed node, and the scientific analysis of water and electricity infrastructure failure is realized.

Description

Technical Field

[0001] This invention relates to the field of failure analysis technology for urban hydropower infrastructure, and more particularly to methods, apparatus, equipment and storage media for failure analysis of urban hydropower infrastructure. Background Technology

[0002] Extreme weather disasters, especially rainstorm-type disasters such as heavy rainfall and urban flooding, have occurred frequently in recent years. These disasters exacerbate the safety problems of critical urban infrastructure and pose a serious threat to the operation of urban functions. There is a highly coupled structure between urban water and electricity facilities, and their functions are interdependent and mutually influential. When the function of a single node fails, this failure can quickly spread to the entire system, triggering network cascading failures, which can have a chain reaction impacting the operation of urban functions and affecting the normal functioning of the city.

[0003] Existing technologies have analyzed the characteristics, parameters, and failure probabilities of critical nodes in infrastructure topologies based on functional relationships. However, these technologies have not effectively taken into account factors such as rainstorm disaster scenarios, geographical relationships (e.g., the geographical location of facilities, topography, etc.), and functional dependencies (e.g., the functional connections between water and electricity facilities).

[0004] Therefore, the reliability and scientific rigor of existing models are insufficient, and they cannot accurately assess and predict the safety status of urban water-electricity related critical infrastructure systems under extreme climate disasters. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for analyzing the failure of urban hydropower infrastructure, so as to achieve a scientific and effective analysis of whether urban hydropower infrastructure has failed.

[0006] According to one aspect of the present invention, a method for failure analysis of urban hydropower infrastructure is provided, comprising:

[0007] Determine the failure rate of the upstream nodes of the node to be analyzed, and determine whether the node to be analyzed is in a failed state based on the failure rate. The node to be analyzed corresponds to the urban hydropower infrastructure.

[0008] When the node to be analyzed is in a non-failure state, 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] The random value corresponding to the node to be analyzed is generated by the Monte Carlo simulation method. Based on the target failure probability and the random value, it is determined again whether the state of the node to be analyzed is a failure state.

[0010] If the state of the node to be analyzed is determined to be non-failed again, the state of the node to be analyzed is determined to be failed based on the random failure probability.

[0011] Failure analysis results for urban hydropower infrastructure are generated based on the state of the nodes to be analyzed.

[0012] According to another aspect of the present invention, a failure analysis device for urban hydropower infrastructure is provided, comprising:

[0013] The failure ratio determination module is used to determine the failure ratio of the upstream nodes of the node to be analyzed, and to determine whether the node to be analyzed is in a failed state based on the failure ratio. The node to be analyzed corresponds to the urban hydropower infrastructure.

[0014] The water depth determination module is used to determine the water depth of the node to be analyzed based on the water depth change model when the node to be analyzed is in a non-failure state.

[0015] The target failure probability calculation module is used to determine the target failure probability of 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] The first state determination module is used to generate random values ​​corresponding to the node to be analyzed through the Monte Carlo simulation method, and then determine whether the state of the node to be analyzed is a failure state based on the target failure probability and the random values.

[0017] The second state determination module is used to determine whether the state of the node to be analyzed is a failure state based on the random failure probability, after determining that the state of the node to be analyzed is a non-failure state again.

[0018] An analysis result determination module is used to generate failure analysis results for urban hydropower infrastructure based on the state of the node to be analyzed. According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0019] At least one processor;

[0020] and a memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the urban hydropower infrastructure failure analysis method according to any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the urban hydropower infrastructure failure analysis method according to any embodiment of the present invention.

[0023] The technical solution of this invention involves determining the failure ratio of the upstream nodes of the node to be analyzed, and then determining whether the node to be analyzed is in a failed state based on the failure ratio. The node to be analyzed corresponds to urban hydropower infrastructure. If the node to be analyzed is in a non-failed state, the water depth corresponding to the node to be analyzed is determined according to a water depth variation model. If the water depth is greater than a water depth threshold, the target failure probability corresponding to the node to be analyzed is determined based on a water depth and target failure probability model. A random value corresponding to the node to be analyzed is generated using a Monte Carlo simulation method. Based on the target failure probability and the random value, the state of the node to be analyzed is again determined to be in a failed state. If the state of the node to be analyzed is again determined to be non-failed, the state of the node to be analyzed is determined based on the random failure probability. Finally, failure analysis results corresponding to the urban hydropower infrastructure are generated based on the state of the node to be analyzed. The technical solution of this invention solves the technical problem that the reliability and scientific nature of existing models are insufficient, and they cannot accurately assess and predict the safety status of urban water-electricity related critical infrastructure systems under extreme weather disasters. By using geographical, functional and stochastic association types for failure determination, a node can be considered a failed node if it meets any one of these types, thus comprehensively assessing the dynamic reliability evolution process of urban critical infrastructure nodes under rainstorm conditions.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart of a method for failure analysis of urban hydropower infrastructure provided in an embodiment of the present invention;

[0027] Figure 2 A node topology type diagram provided for embodiments of the present invention;

[0028] Figure 3A flowchart of another method for failure analysis of urban hydropower infrastructure provided in an embodiment of the present invention;

[0029] Figure 4 The flowchart for constructing the cascading failure model under rainstorm disaster, i.e., the target failure probability model, is provided in the embodiments of the present invention.

[0030] Figure 5 A flowchart of another method for failure analysis of urban hydropower infrastructure provided in an embodiment of the present invention;

[0031] Figure 6 A schematic diagram of the structure of a failure analysis device for urban hydropower infrastructure provided in an embodiment of the present invention;

[0032] Figure 7 A schematic diagram of the electronic device used to implement the urban hydropower infrastructure failure analysis method of this invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0035] Figure 1 This is a flowchart illustrating a method for failure analysis of urban hydropower infrastructure provided in an embodiment of the present invention. This embodiment is applicable to situations involving rainfall scenarios for effectiveness analysis of urban hydropower infrastructure. The method can be executed by an urban hydropower infrastructure failure analysis device, which can be implemented in hardware and / or software and can be configured in a computer device. Figure 1 As shown, the method specifically includes the following steps:

[0036] S110. Determine the failure ratio of the upstream nodes 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.

[0037] Among them, the nodes to be analyzed correspond to the urban water and electricity infrastructure. Each infrastructure can be regarded as a node to be analyzed, and all the nodes to be analyzed in a specified geographical area together form the node network topology.

[0038] Understandably, for a node to be analyzed, it's advisable to first determine whether it will experience cascading failures due to the failure of upstream nodes. That is, if the failure rate of upstream nodes is too high, the node to be analyzed may also become unusable.

[0039] The failure rate refers to the proportion of upstream nodes that are in a failed state out of the total number of upstream nodes.

[0040] It should be noted that in order to determine the failure rate of the upstream nodes of the node to be analyzed, it is necessary to first establish the node network topology, determine the upstream nodes of the node to be analyzed based on the node network topology, and then analyze the status of the upstream nodes.

[0041] Considering the importance of the components and the ease of data access, this embodiment mainly studies important functional sites such as power plants, 220kV substations, 110kV substations, water plants, water distribution plants, and pumping stations. In numbering, g, t, s, r, f, and p represent the above-mentioned types of nodes, respectively. For example, power plant No. 1 is defined as node g1. An edge is defined as a connection between two nodes. For example, if g1 supplies power resources to node t10, then the two are connected, and a directed arrow points to t10 to indicate the resource flow direction of g1 supplying power resources to node t10.

[0042] The physical operation relationship between water supply and power supply was analyzed, key system nodes were defined and numbered, and three basic topology types—starting node, intermediate node, and terminal node—were identified and proposed based on node type and their functional relationship.

[0043] Construct a critical infrastructure network topology model, v i Represents an infrastructure node, e ij This represents the connection relationship between nodes; if there is a connection between two nodes, then e represents the connection between them. ij If 1 is true, then 0 is true; otherwise, 0 is true. V is the total set of nodes in the city's critical infrastructure, E is the set of all connecting 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 the city's critical infrastructure is obtained, and its mathematical expression is as follows:

[0044] V = {v} i|i=1,2,…,N} (1)

[0045] E={e ij |i=1,2,…,N; j=1,2,…,N; i≠j} (2)

[0046]

[0047] like Figure 2 The diagram shown is a node topology structure type diagram provided in an embodiment of the present invention.

[0048] S120. When the node to be analyzed is in a non-failure state, determine the water depth corresponding to the node to be analyzed based on the water depth change model.

[0049] Among them, the water depth change model refers to a mathematical model that reflects the dynamic changes of water depth over time; urban hydropower infrastructure can be equipment related to water supply and power supply within the city, including but not limited to power plants, substations, water plants, water distribution plants, pumping stations, etc.

[0050] Specifically, in the aforementioned steps, the failure rate of the upstream nodes can be used to determine whether the node to be analyzed is in a failed state. If the node to be analyzed is determined to be in a non-failed state, meaning the failure severity of the upstream nodes is insufficient to affect the state of the node to be analyzed, then the water depth of the node to be analyzed can be determined based on the water depth variation model, and subsequent analysis can be performed based on this water depth. In some embodiments, the water depth variation model is constructed based on at least one of the historical water depth, rainfall intensity, evaporation rate, infiltration rate, inflow water depth, and outflow water depth corresponding to the node to be analyzed.

[0051] Among them, the historical period can be the previous period corresponding to the current period, the inflow 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 depth refers to the depth of water flowing from the study area corresponding to the node to be analyzed to other areas.

[0052] Specifically, the influence of geographical features on water flow and the impact of hydrological factors such as evaporation and infiltration on water depth can be considered to establish a dynamic change equation for the water depth of the node to be analyzed in each time period as a model for water depth change:

[0053] h(t)=h(t-1)+I(t)-E(t)-Q(t)+Δh in (t)-Δh out (t) (4)

[0054] Where h(t) is the water depth at time t, h(t-1) is the water depth in the previous time period, I(t) is the rainfall intensity at time t, E(t) is the evaporation rate at time t, Q(t) is the infiltration rate at time t, and Δh in (t) represents the depth of water flowing in from the nearby area during time period t, Δh out (t) The depth of water flowing from the study area to other areas during the time period t.

[0055] Furthermore, when it is necessary to determine the water depth of the node to be analyzed within time period t, the water depth of the node to be analyzed can be calculated using the above-mentioned water depth change model.

[0056] S130. When the water depth is greater than the water depth threshold, determine the target failure probability of the node to be analyzed based on the water depth and the target failure probability model.

[0057] The water depth threshold can be based on experience or experiments. For example, before the water depth reaches the threshold, the failure probability of various infrastructure components is about 5%; when the water depth exceeds the threshold, the failure probability of the components gradually increases.

[0058] Therefore, the water depth can be compared with a preset water depth threshold. If the water depth is greater than the water depth threshold, it means that the failure probability of the node to be analyzed will gradually increase. It is necessary to further determine the target failure probability of the node to be analyzed based on the water depth and the target failure probability model, and then determine whether it has failed.

[0059] The target failure probability model can reflect the functional relationship between the water depth and the failure probability of the node to be analyzed. The target failure probability can be the failure probability calculated based on this target failure probability model.

[0060] In some embodiments, determining the target failure probability of 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.

[0061] S140. Generate random values ​​corresponding to the nodes to be analyzed using the Monte Carlo simulation method. Based on the target failure probability and the random values, determine again whether the state of the nodes to be analyzed is a failure state.

[0062] The random value can be a random number obtained based on a Monte Carlo simulation that conforms to a uniform distribution, and this random value can be represented by p. r This indicates that the random value is uniformly distributed between 0 and 1; the failure state refers to the infrastructure corresponding to the node to be analyzed being faulty or damaged, while the non-failure state refers to the infrastructure corresponding to the node to be analyzed being fully functional.

[0063] In some embodiments, determining whether the state of the node to be analyzed is in a failed state again based on the target failure probability and the random value 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, then determining that the state of the node to be analyzed is in a failed state; if the target failure probability is not greater than the random value, then determining that the state of the node to be analyzed is in a non-failed state.

[0064] Specifically, the failure state of the node to be analyzed is randomly determined by comparing the target failure probability with a random value. The target failure probability is compared to a random value; if the target failure probability is greater than the random value, the node is considered to have a sufficient probability of failure under the current circumstances, and its state is determined to be failed. If the target failure probability is not greater than the random value, the node is considered to have a low probability of failure under the current circumstances, and its state is temporarily determined to be non-failed.

[0065] S150. If the state of the node to be analyzed is determined to be non-failure state again, determine whether the state of the node to be analyzed is failure state based on the random failure probability.

[0066] The random failure probability refers to the probability of a Monte Carlo simulation that conforms to the (1,10) Beta distribution. The (1,10) Beta distribution is an assumed distribution designed to simulate random failure events with low probability.

[0067] Specifically, if the state of the node to be analyzed is determined to be non-failed again, it can be further determined whether the node to be analyzed is in a failed state based on the random failure probability.

[0068] S160. Generate failure analysis results for urban hydropower infrastructure based on the state of the node to be analyzed.

[0069] In some embodiments, there are multiple nodes to be analyzed. Based on the state of the nodes to be analyzed, failure analysis results corresponding to urban hydropower infrastructure are generated, including: 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 the reliability results; and using the reliability results as the failure analysis results.

[0070] It is understandable that when conducting failure analysis of urban hydropower infrastructure, a city or a designated area may include multiple hydropower infrastructures, with each node to be analyzed corresponding to one hydropower infrastructure, thus the number of nodes to be analyzed is multiple.

[0071] To assess the system reliability of urban hydropower infrastructure, the number of nodes in a failed state among all nodes to be analyzed can be counted as the failure count. Simultaneously, the total number of all nodes to be analyzed is counted as the total number of nodes. Further, the counted failure count and the total number of nodes are substituted into the reliability assessment model to obtain the reliability result, which represents the reliability level of the urban hydropower infrastructure. Finally, the obtained reliability result is used as the failure analysis result. The reliability assessment model is shown below:

[0072]

[0073] R(t) represents the system reliability, S t S(t) represents the number of nodes that fail in the system at time t, and 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.

[0074] The technical solution of this invention can cover failure determination based on geographical, functional, and stochastic correlation types, enabling a scientific and effective analysis of whether urban hydropower infrastructure has failed. Geographical correlation refers to multiple infrastructures located close to each other, but an earthquake or flood could cause simultaneous damage to multiple facilities within a small area. Functional correlation involves resource transfer between different types of infrastructure to ensure normal operation; for example, damage to power facilities may affect the operation of water supply, transportation, and communication facilities. Stochastic correlation refers to unplanned dependencies caused by unpredictable disturbances such as random failures or sudden accidents. Examples include equipment damage due to human error or ground subsidence during rainstorms.

[0075] The technical solution of this invention involves determining the failure ratio of the upstream nodes of the node to be analyzed, and then determining whether the node to be analyzed is in a failed state based on the failure ratio. The node to be analyzed corresponds to urban hydropower infrastructure. If the node to be analyzed is in a non-failed state, the water depth corresponding to the node to be analyzed is determined according to a water depth variation model. If the water depth is greater than a water depth threshold, the target failure probability corresponding to the node to be analyzed is determined based on a water depth and target failure probability model. A random value corresponding to the node to be analyzed is generated using a Monte Carlo simulation method. Based on the target failure probability and the random value, the state of the node to be analyzed is again determined to be in a failed state. If the state of the node to be analyzed is again determined to be non-failed, the state of the node to be analyzed is determined based on the random failure probability. Finally, failure analysis results corresponding to the urban hydropower infrastructure are generated based on the state of the node to be analyzed. The technical solution of this invention solves the technical problem that the reliability and scientific nature of existing models are insufficient, and they cannot accurately assess and predict the safety status of urban water-electricity related critical infrastructure systems under extreme weather disasters. By using geographical, functional and stochastic association types for failure determination, a node can be considered a failed node if it meets any one of these types, thus comprehensively assessing the dynamic reliability evolution process of urban critical infrastructure nodes under rainstorm conditions.

[0076] Figure 3 This is a flowchart illustrating another method for failure analysis of urban hydropower infrastructure provided by an embodiment of the present invention. Based on the above embodiments, this embodiment further explains the construction process of the target failure probability model. Specific implementation details can be found in the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here. Figure 3 As shown, the method specifically includes the following steps:

[0077] S210. Determine the failure ratio of the upstream nodes 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.

[0078] In some embodiments, determining the failure ratio of upstream nodes of the node to be analyzed includes: determining the available water depth corresponding to the upstream node based on the water depth variation model; when the available water depth is greater than the water depth threshold, determining the available failure probability corresponding to the upstream node based on the available water depth and the target failure probability model; judging the state of the upstream node based on the available failure probability, and determining the failure ratio based on the total number of upstream nodes and the number of failed upstream nodes.

[0079] It should be noted that the state determination method for the upstream nodes of the node to be analyzed can be the same as that for the node to be analyzed. Specifically, for each upstream node, a water depth variation model can be used to calculate the water depth of the upstream node, i.e., the water depth to be used. If the water depth to be used of an upstream node exceeds the water depth threshold, the failure probability of that upstream node, i.e., the failure probability to be used, is calculated using the target failure probability model.

[0080] Based on the calculated failure probability of the upstream node to be used, it is determined whether the upstream node has failed, for example, by comparing the failure probability to be used with a random number. Then, the number of failed nodes among all upstream nodes is counted, and the proportion of failed nodes to the total number of upstream nodes is calculated to obtain the failure ratio.

[0081] In other embodiments, determining whether the state of the node to be analyzed is in a failed 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, then determining that the state of the node to be analyzed is in a failed state; if the failure ratio is not greater than the functional association strength threshold, then determining that the state of the node to be analyzed is in a non-failed state.

[0082] 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.

[0083] Specifically, the previously calculated failure rate of upstream nodes is compared with a preset functional association strength threshold. If the failure rate is greater than the functional association strength threshold, it indicates that the failure of upstream nodes is severe enough to affect the function of the node under analysis, and therefore the node under analysis is determined to be in a failed state. If the failure rate is not greater than the functional association strength threshold, it indicates that the failure of upstream nodes has not yet reached the level of affecting the function of the node under analysis, and therefore the node under analysis is determined to be in a non-failed state.

[0084] 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 upstream nodes, the node to be analyzed is considered to be ineffective.

[0085] S220. When the node to be analyzed is in a non-failure state, determine the water depth corresponding to the node to be analyzed based on the water depth change model.

[0086] S230. Obtain the sample water depth and node status data corresponding to the sample nodes, and fit the basic failure probability model based on the sample water depth and node status data.

[0087] Among them, sample nodes can be some sample hydropower infrastructure; sample water depth refers to the historical water depth of sample nodes within a historical time period; node status data refers to some data reflecting whether the sample node is in failure at the historical water depth.

[0088] Since the obtained sample water depth and node status data are relatively discrete, the Slogistic3 model in the Sigmoid function is selected to fit the discrete data, establishing a functional correspondence between water depth and the failure probability of various infrastructure nodes, and constructing a node basic failure probability model, as follows:

[0089]

[0090] Among them, P i o (t) represents the node failure probability of node i at water depth h(t) at time t. This expression will be used as one of the basic input conditions for the node failure probability in subsequent network cascade failure simulation.

[0091] S240. Based on network node topology indicators and basic failure probability models, determine the target failure probability model.

[0092] Among them, the network node topology index includes at least one of the following: degree centrality, betweenness centrality, compact centrality, and eigenvector centrality of the network node.

[0093] Specifically, network node topology indicators such as degree centrality (DC), betweenness centrality (BC), compact centrality (CC), and eigenvector centrality (EC) are selected as parameters for correcting node failure probability. A weighted comprehensive evaluation correction method is proposed, as follows:

[0094] P i a (t)=P i o (t)·(1+ω1DC i +ω2BC i +ω3CC i +ω4EC i -ω5C i (7)

[0095] Among them, P i a (t) represents the failure probability of node i of each type, ω i Let be the weight of the i-th indicator.

[0096] By modifying the basic failure probability model, the target failure probability model can be obtained. The specific values ​​of the aforementioned indicators can be determined based on the previously constructed node network topology.

[0097] like Figure 4 The diagram shows the construction flowchart of the cascading failure model under rainstorm disasters, also known as the target failure probability model, provided in this embodiment of the invention. Based on the fundamental fact that extreme rainfall leads to surface runoff and convergence, resulting in urban flooding, and severely impacting substations, power distribution facilities, pumping stations, water plants, etc., located in low-lying areas, causing short circuits, equipment failures, or damage, especially transformers and distribution boxes in medium and low voltage power grids are more susceptible to flooding, leading to power supply anomalies, this paper proposes a construction flowchart for the cascading failure model under rainstorm disasters.

[0098] S250. When the water depth is greater than the water depth threshold, determine the target failure probability of the node to be analyzed based on the water depth and the target failure probability model.

[0099] S260. Generate random values ​​corresponding to the nodes to be analyzed using the Monte Carlo simulation method. Based on the target failure probability and the random values, determine again whether the state of the nodes to be analyzed is a failure state.

[0100] S270. If the state of the node to be analyzed is determined to be non-failure state again, determine whether the state of the node to be analyzed is failure state based on the random failure probability.

[0101] In some embodiments, determining whether the state of the node to be analyzed is a failed 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, then determining that the state of the node to be analyzed is a failed state; if the random value is not less than the random failure probability, then determining that the state of the node to be analyzed is a non-failed state.

[0102] Specifically, the random value is compared with the random failure probability. If the random value is less than the random failure probability, the node is considered to have a sufficient probability of failure under the current circumstances, and thus the node is determined to be in a failed state.

[0103] If the random value is not less than the random failure probability, it is considered that the node is not likely to fail under the current circumstances, and therefore the node is determined to be in a non-failure state.

[0104] S280. Generate failure analysis results for urban hydropower infrastructure based on the state of the node to be analyzed.

[0105] like Figure 5 The diagram shown is a flowchart of another method for analyzing the failure of urban hydropower infrastructure provided in an embodiment of the present invention.

[0106] Figure 6 This is a schematic diagram of a failure analysis device for urban hydropower infrastructure provided in an embodiment of the present invention. Figure 6As shown, the device includes:

[0107] The failure ratio determination module 310 is used to determine the failure ratio of the upstream nodes of the node to be analyzed, and to determine whether the state of the node to be analyzed is in a failed state based on the failure ratio. The node to be analyzed corresponds to the urban hydropower infrastructure.

[0108] The water depth determination module 320 is used to determine the water depth corresponding to the node to be analyzed based on the water depth change model when the node to be analyzed is in a non-failure state.

[0109] The target failure probability calculation module 330 is used to determine the target failure probability of 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.

[0110] The first state determination module 340 is used to generate random values ​​corresponding to the node to be analyzed through the Monte Carlo simulation method, and to determine whether the state of the node to be analyzed is a failure state based on the target failure probability and the random values.

[0111] The second state determination module 350 is used to determine whether the state of the node to be analyzed is a failure state based on the random failure probability when the state of the node to be analyzed is determined to be a non-failure state again.

[0112] The analysis result determination module 360 ​​is used to generate failure analysis results for urban hydropower infrastructure based on the state of the node to be analyzed. In some embodiments, the water depth variation model is constructed based on at least one of the historical water depth, rainfall intensity, evaporation rate, infiltration rate, inflow water depth, and outflow water depth for the node to be analyzed.

[0113] In some embodiments, the urban hydropower infrastructure failure analysis device further includes a target failure probability model construction module, specifically used for:

[0114] Obtain the sample water depth and node status data corresponding to the sample nodes, and fit the basic failure probability model based on the sample water depth and node status data;

[0115] Based on network node topology indicators and basic failure probability models, determine the target failure probability model;

[0116] Among them, the network node topology index includes at least one of the following: degree centrality, betweenness centrality, compact centrality, and eigenvector centrality of the network node.

[0117] In some embodiments, the target failure probability calculation module 330 is specifically used for:

[0118] Substituting the water depth into the target failure probability model yields the target failure probability.

[0119] In some embodiments, the first state determination module 340 is specifically used for:

[0120] Determine whether the probability of target failure is greater than a random value;

[0121] If the probability of target failure is greater than the random value, then the state of the node to be analyzed is determined to be a failed state.

[0122] If the probability of target failure is not greater than a random value, then the state of the node to be analyzed is determined to be a non-failure state.

[0123] In some embodiments, the failure ratio determination module 310 includes:

[0124] The failure ratio determination submodule is used to determine the usable water depth corresponding to the upstream node based on the water depth change model.

[0125] When the water depth to be used is greater than the water depth threshold, the failure probability of the upstream node is determined based on the water depth to be used and the target failure probability model.

[0126] The status of upstream nodes is determined based on the probability of failure, and the failure ratio is determined based on the total number of upstream nodes and the number of failed upstream nodes.

[0127] In some embodiments, the failure ratio determination module 310 includes:

[0128] The failure ratio comparison submodule is used to determine whether the failure ratio is greater than the functional association strength threshold.

[0129] If the failure rate is greater than the functional association strength threshold, then the node to be analyzed is determined to be in a failed state.

[0130] If the failure rate is not greater than the functional association strength threshold, then the state of the node to be analyzed is determined to be non-failure state.

[0131] In some embodiments, the second state determination module 350 is specifically used for:

[0132] Determine if the random value is less than the random failure probability;

[0133] If the random value is less than the random failure probability, then the state of the node to be analyzed is determined to be a failure state.

[0134] If the random value is not less than the random failure probability, then the state of the node to be analyzed is determined to be a non-failure state.

[0135] In some embodiments, the number of nodes to be analyzed is multiple, and the analysis result determination module 360 ​​is specifically used for:

[0136] The number of nodes in the failure state to be analyzed is counted, as well as the total number of nodes to be analyzed.

[0137] Substitute the number of failures and the total amount of data at each node into the reliability assessment model to obtain the reliability results.

[0138] Reliability results are used as failure analysis results.

[0139] The urban hydropower infrastructure failure analysis device provided in this embodiment of the invention can execute the urban hydropower infrastructure failure analysis method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0140] Figure 7 This is a schematic diagram of the structure of an electronic device for implementing the urban hydropower infrastructure failure analysis method according to embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, 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 processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0141] like Figure 7 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0142] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0143] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for failure analysis of urban hydropower infrastructure.

[0144] In some embodiments, the urban hydropower infrastructure failure analysis method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the urban hydropower infrastructure failure analysis method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the urban hydropower infrastructure failure analysis method by any other suitable means (e.g., by means of firmware).

[0145] Various embodiments of the systems and techniques described above 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), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0146] Computer programs used to implement 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 executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0147] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0148] 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 provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0149] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0150] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0151] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0152] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for failure analysis of urban hydropower infrastructure, characterized in that, include: Determine the failure ratio of the upstream nodes of the node to be analyzed, and determine whether the state of the node to be analyzed is in a failed state based on the failure ratio, wherein the node to be analyzed corresponds to the urban hydropower infrastructure; When the node to be analyzed is in a non-failure state, 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 the target failure probability model. The random value corresponding to the node to be analyzed is generated by the Monte Carlo simulation method. Based on the target failure probability and the random value, it is determined again whether the state of the node to be analyzed is a failure state. If the state of the node to be analyzed is determined to be non-failed again, the state of the node to be analyzed is determined to be failed based on the random failure probability. Based on the state of the node to be analyzed, generate the failure analysis results corresponding to the urban hydropower infrastructure; The process of constructing the target failure probability model includes: 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; Based on the network node topology indicators and the basic failure probability model, the target failure probability model is determined; The network node topology metrics include the degree centrality, betweenness centrality, compactness centrality, and eigenvector centrality of the network nodes.

2. The method according to claim 1, characterized in that, The water depth variation model is constructed based on the historical water depth, rainfall intensity, evaporation rate, infiltration rate, inflow water depth, and outflow water depth corresponding to the node to be analyzed.

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

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

5. The method according to claim 1, characterized in that, Determine the failure rate of the upstream nodes of the node to be analyzed, including: The usable water depth corresponding to the upstream node is determined based on the water depth variation model. If the depth of water to be used is greater than the water depth threshold, the failure probability of the upstream node is determined based on the depth of water to be used and the target failure probability model. The status of the upstream node is determined based on the failure probability to be used, and the failure ratio is determined based on the total number of upstream nodes and the number of failed upstream nodes.

6. The method according to claim 1, characterized in that, The step of determining whether the state of the node to be analyzed is in a failed state again based on the failure ratio includes: Determine whether the failure rate is greater than the functional association strength threshold; wherein, the functional association strength threshold is a preset value that reflects the sensitivity of the upstream node failure to the function of the node to be analyzed; If the failure rate is greater than the functional association strength threshold, then the state of the node to be analyzed is determined to be a failure state. If the failure rate is not greater than the functional association strength threshold, then the state of the node to be analyzed is determined to be a non-failure state.

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

8. The method according to claim 1, characterized in that, The number of nodes to be analyzed is multiple, and the generation of failure analysis results for the urban hydropower infrastructure based on the status of the nodes to be analyzed includes: The number of failures corresponding to the nodes to be analyzed in the failure state is counted, as well as the total number of nodes to be analyzed; Substitute the number of failures and the total number of nodes into the reliability assessment model to obtain the reliability results; The reliability results are used as the failure analysis results.

9. A failure analysis device for urban hydropower infrastructure, characterized in that, include: The failure ratio determination module is used to determine the failure ratio of the upstream nodes of the node to be analyzed, and to determine whether the state of the node to be analyzed is in a failed state based on the failure ratio, wherein the node to be analyzed corresponds to the urban hydropower infrastructure. The water depth determination module is used to determine the water depth corresponding to the node to be analyzed based on the water depth change model when the node to be analyzed is in a non-failure state. The target failure probability calculation module is used to determine the target failure probability of 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. The first state determination module is used to generate a random value corresponding to the node to be analyzed by Monte Carlo simulation method, and to determine whether the state of the node to be analyzed is a failure state based on the target failure probability and the random value. The second state determination module is used to determine whether the state of the node to be analyzed is a failure state based on the random failure probability when the state of the node to be analyzed is determined to be a non-failure state again. The analysis result determination module is used to generate failure analysis results for the urban hydropower infrastructure based on the state of the node to be analyzed. The target failure probability model construction module is specifically used for: Obtain the sample water depth and node status data corresponding to the sample nodes, and fit the basic failure probability model based on the sample water depth and node status data; Based on network node topology indicators and basic failure probability models, determine the target failure probability model; Among them, the network node topology indicators include the degree centrality, betweenness centrality, compact centrality, and eigenvector centrality of the network nodes.

10. An electronic device, characterized in that, The electronic device includes: 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, the computer program being executed by the at least one processor to enable the at least one processor to perform the urban hydropower infrastructure failure analysis method according to any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the urban hydropower infrastructure failure analysis method according to any one of claims 1-8.

Images