A method and device for reverse construction of a limit scenario of a lifeline project in a flood detention area

Abstract

The application discloses a kind of flood storage area life line engineering limit scene reverse construction method and device.The method comprises the following steps of: determining the life line engineering service ability retention rate of each service object domain under various flood conditions and the engineering vulnerability of various flood conditions respectively;Determine the limit scene key information under various flood conditions;According to each engineering vulnerability, determine the limit scene of the target flood storage area life line engineering;According to the life line engineering service ability retention rate, determine the global short board service object domain;Combined with limit scene key information, reverse construction limit scene candidate scheme set for global short board service object domain;Determine the limit scene construction result of the target flood storage area life line engineering.The above scheme, by reverse construction limit scene candidate scheme set for global short board service object domain, and then determine the limit scene construction result of life line engineering, provide technical support for flood storage area life line engineering risk prevention and control, emergency dispatch, engineering reinforcement and the like.

Description

Technical Field

[0001] This application relates to the field of flood control and disaster reduction technology, specifically to a method and apparatus for reverse construction of extreme scenarios for lifeline engineering in flood storage and detention areas. Background Technology

[0002] Flood storage and detention areas are a crucial component of my country's flood control system, playing a vital role in diverting and storing excess floodwater during major floods and ensuring the safety of downstream important cities and flood-prone areas. Against the backdrop of global climate change, extreme rainstorms and floods are becoming more frequent. As critical disaster-bearing structures, the lifeline engineering of flood storage and detention areas exhibits a chain-like transmission and amplification characteristic in disasters. If a lifeline engineering system fails during flood diversion and storage operations, it not only directly impacts the basic survival of residents within the flood storage and detention area but may also spread the disaster's impact outwards through the disruption of transit facilities, triggering regional secondary disasters. Therefore, scientifically identifying the weaknesses of flood storage and detention area lifeline engineering under flood threats and constructing disaster scenarios that reflect the most unfavorable conditions is of great significance for accurately formulating risk prevention and control strategies and enhancing the resilience of flood storage and detention areas.

[0003] Current technologies for risk analysis and scenario construction for lifeline projects in flood storage and detention areas mainly rely on experience-based settings, resulting in the following technical shortcomings: First, traditional scenario construction often depends on manually setting single operating conditions such as flood level, inundation range, and facility damage degree, without basing them on vulnerability assessment results, leading to insufficient objectivity and specificity of the scenarios; Second, vulnerability assessment and scenario construction are disconnected, making it impossible to deduce disaster operating conditions from vulnerability indicators and accurately identify the actual weak links in the system; Third, existing methods only consider the failure of a single facility or a single dimension, failing to conduct systemic vulnerability analysis from three dimensions: local engineering within the flood storage and detention area, safety zone protection, and transit facilities, resulting in scenarios lacking global representativeness; Fourth, they fail to conduct combined analysis by incorporating key elements such as flood magnitude, flood inflow location, flood inflow method, flood inflow timing, and human intervention, making it difficult to construct the most unfavorable scenarios with realistic probability of occurrence. Summary of the Invention

[0004] This application provides a method and apparatus for reverse construction of extreme scenarios for lifeline engineering in flood storage and detention areas, providing technical support for risk prevention and control, emergency dispatch, and engineering reinforcement measures for lifeline engineering in flood storage and detention areas.

[0005] According to one aspect of this application, a method for reverse construction of extreme scenarios for lifeline engineering in flood storage and detention areas is provided, the method comprising: Based on the multidimensional basic information data of the target flood storage and detention area, the lifeline engineering service capacity retention rate of each service object domain under various flood diversion conditions is determined, and the engineering vulnerability of each flood diversion condition is determined based on the lifeline engineering service capacity retention rate; wherein, the service object domain includes local service objects, safe zone service objects, and transit external service objects; Based on the lifeline engineering service capacity retention rate, the short-board service object domain under various flood conditions is determined, and based on the failure facility dataset and service capacity loss distribution dataset of the short-board service object domain, the key information of the extreme scenarios under various flood conditions is determined. Based on preset scenario constraints and according to the engineering vulnerability, the extreme scenarios of the lifeline engineering of the target flood storage and detention area are determined from various flood diversion conditions. Based on the lifeline engineering service capability retention rate of each service object domain corresponding to the lifeline engineering extreme scenario, the global bottleneck service object domain under the lifeline engineering extreme scenario is determined, and based on the key information of the extreme scenario corresponding to the lifeline engineering extreme scenario, the extreme scenario candidate solution set for the global bottleneck service object domain is constructed in reverse. Using a preset scenario selection strategy, the construction result of the extreme scenario for the lifeline project of the target flood storage and detention area is determined based on the set of extreme scenario candidate schemes.

[0006] According to another aspect of this application, a reverse construction device for extreme scenarios of lifeline engineering in flood storage and detention areas is provided, the device comprising: The engineering vulnerability determination module is used to determine the lifeline engineering service capacity retention rate of each service object domain under various flood diversion conditions based on multi-dimensional basic information data of the target flood storage and detention area, and to determine the engineering vulnerability of each flood diversion condition based on the lifeline engineering service capacity retention rate; wherein, the service object domain includes local service objects, safe zone service objects, and transit external service objects; The extreme scenario key information determination module is used to determine the short-board service object domain under various flood conditions based on the lifeline engineering service capacity retention rate, and to determine the extreme scenario key information under various flood conditions based on the failure facility dataset and service capacity loss distribution dataset of the short-board service object domain. The lifeline engineering extreme scenario determination module is used to determine the extreme scenarios of the lifeline engineering of the target flood storage and detention area from various flood diversion conditions based on preset scenario constraints and the vulnerability of the engineering. The extreme scenario candidate solution set determination module is used to determine the global bottleneck service object domain under the extreme scenario of lifeline engineering based on the lifeline engineering service capability retention rate of each service object domain corresponding to the extreme scenario of lifeline engineering, and to reverse construct the extreme scenario candidate solution set for the global bottleneck service object domain based on the extreme scenario key information corresponding to the extreme scenario of lifeline engineering. The module for determining the construction results of extreme scenarios for lifeline engineering projects is used to determine the construction results of extreme scenarios for lifeline engineering projects in the target flood storage and detention area based on the set of extreme scenario candidate schemes, using a preset scenario selection strategy.

[0007] According to another aspect of this application, an electronic device is provided, the electronic device comprising: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by one or more processors, the one or more processors implement any of the extreme scenario reverse construction methods for lifeline engineering in flood storage and detention areas provided in the embodiments of this application.

[0008] According to another aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements any of the extreme scenario reverse construction methods for lifeline engineering in flood storage and detention areas provided in the embodiments of this application.

[0009] According to another aspect of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements any of the extreme scenario reverse construction methods for lifeline engineering in flood storage and detention areas provided in the embodiments of this application.

[0010] This application determines the lifeline engineering service capacity retention rate of each service object domain under various flood diversion conditions by using multi-dimensional basic information data of the target flood storage and detention area. Based on the lifeline engineering service capacity retention rate, it also determines the engineering vulnerability under various flood diversion conditions. The service object domains include local service objects, safe zone service objects, and transit service objects. Based on the lifeline engineering service capacity retention rate, it identifies the weakest service object domains under various flood diversion conditions. Furthermore, based on the failed facility dataset and service capacity loss distribution dataset of the weakest service object domains, it determines the key information of extreme scenarios under various flood diversion conditions. Based on preset scenario constraints and considering engineering vulnerability, the system identifies the extreme scenarios for lifeline engineering in target flood storage and detention areas from various flood conditions. Based on the lifeline engineering service capacity retention rate of each service object domain corresponding to the extreme scenarios, it determines the global weak link service object domain under the extreme scenarios. Furthermore, based on the key information of the extreme scenarios, it constructs a set of candidate extreme scenarios for the global weak link service object domain. Using a preset scenario optimization strategy, it determines the final construction result of the extreme scenarios for lifeline engineering in the target flood storage and detention area based on the set of candidate extreme scenarios. This approach, by constructing a set of candidate extreme scenarios for the global weak link service object domain and then determining the final construction result of the extreme scenarios for lifeline engineering, can accurately identify the most unfavorable flood scenarios and their key triggering conditions that lead to the greatest vulnerability of lifeline engineering in flood storage and detention areas. This significantly improves the accuracy and effectiveness of risk prevention and control for lifeline engineering in flood storage and detention areas, providing technical support for risk prevention and control, emergency dispatch, and engineering reinforcement measures for lifeline engineering in flood storage and detention areas. Attached Figure Description

[0011] Figure 1 This is a flowchart of a method for reverse engineering of a lifeline project in a flood storage and detention area under extreme scenarios, according to Embodiment 1 of this application; Figure 2 This is a flowchart of a method for reverse construction of extreme scenarios for lifeline engineering in flood storage and detention areas, according to Embodiment 2 of this application; Figure 3 This is a structural schematic diagram of a reverse construction device for extreme scenarios of lifeline engineering in flood storage and detention areas, provided in Embodiment 3 of this application; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the reverse construction method for extreme scenarios of flood storage and detention area lifeline engineering in Embodiment 4 of this application. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0013] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 this application 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 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.

[0014] Furthermore, it should be noted that the information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation portals are provided for users to choose to authorize or refuse.

[0015] Example 1 Figure 1 This is a flowchart illustrating a method for reverse engineering of the most unfavorable scenario for lifeline engineering in flood storage and detention areas, according to Embodiment 1 of this application. This embodiment is applicable to the reverse engineering of the most unfavorable scenario for lifeline engineering in flood storage and detention areas based on vulnerability assessment. It can be executed by a reverse engineering device for the most unfavorable scenario of lifeline engineering in flood storage and detention areas. This device can be implemented in hardware and / or software and can be configured in a computer device, such as a server. Figure 1 As shown, the method includes: S110. Based on the multi-dimensional basic information data of the target flood storage and detention area, determine the lifeline engineering service capacity retention rate of each service object domain under various flood diversion conditions, and determine the engineering vulnerability of each flood diversion condition based on the lifeline engineering service capacity retention rate; wherein, the service object domain includes local service objects, safe zone service objects, and transit external service objects.

[0016] Among them, the target flood storage and detention area is a low-lying area within the watershed flood control system used for temporary diversion and storage of excess floodwater to ensure the safety of important downstream cities or flood protection areas. Various flood diversion conditions refer to multiple flood scenarios with different diversion methods and magnitudes that the flood storage and detention area may face, including normal flood diversion and storage conditions, passive flood diversion conditions, and maximum possible flood (excessive storage and detention) conditions. Normal flood diversion and storage conditions involve proactive, orderly, and controllable flood diversion according to the dispatch plan, with the flood flow, duration, and inundation area all within design standards. Passive flood diversion conditions are unplanned and undisciplined flood diversions induced by factors such as weak dikes, local breaches, piping, and overflows. The maximum possible flood (excessive storage and detention) condition is an extreme flood exceeding the standard, with the inflow volume far exceeding the design storage and detention capacity, the water level continuously exceeding limits, and large-scale flooding across the entire area. The service target domain refers to the classification dimension of different spatial ranges and functional objects served by the lifeline engineering of the flood storage and detention area, including local service targets, safe zone service targets, and transit external service targets. Local service recipients are residents and functional areas within the flood storage and detention area that rely on local lifeline projects to maintain basic production and life. Safe zone service recipients are permanent residents and temporarily relocated populations located within safe zones or flood shelters who rely on emergency lifeline projects for survival. Transit service recipients are areas and users located outside the flood storage and detention area that rely on transit lifeline facilities (such as inter-regional water pipelines, power transmission lines, and main transportation lines) that traverse the flood storage and detention area for operation. The lifeline project service capacity retention rate refers to the ratio of the actual service capacity still available in a service area after a flood impact under specific flood conditions to the service capacity available in that service area under normal conditions. This ratio is dimensionless and ranges from 0 to 1, where 1 indicates no loss of service capacity and 0 indicates complete loss of service capacity. Project vulnerability refers to the overall risk level faced by lifeline projects in the flood storage and detention area under specific flood conditions, and can be determined in the following ways: V k =1-min(R 本地，k R 安全区，k R 过境对外，k ); Among them, V k R represents the engineering vulnerability of the k-th flood diversion condition. 本地，k R represents the lifeline engineering service capacity retention rate of local service recipients under the k-th flood diversion condition. 安全区，k R represents the lifeline engineering service capacity retention rate of the safe zone under the k-th flood diversion condition. 过境对外，k The service capacity retention rate of the lifeline project serving external service objects under the k-th flood diversion condition is denoted by k, where k represents various flood diversion conditions. If k is 1, it indicates a normal flood diversion and storage condition; if k is 2, it indicates a passive flood diversion condition; and if k is 3, it indicates a maximum possible flood (over-storage and over-detention) condition.

[0017] Optionally, the multidimensional basic information data includes basic geospatial data, hydrological and hydrodynamic disaster-causing data, lifeline engineering entity attribute data, and operational correlation data. Correspondingly, based on the multidimensional basic information data of the target flood storage and detention area, the lifeline engineering service capacity retention rate for each service target domain under various flood conditions is determined, including: determining the physical working status of each lifeline engineering facility under various flood conditions based on the hydrological and hydrodynamic disaster-causing data and the lifeline engineering entity attribute data; determining the post-disaster remaining service supply capacity value of each lifeline engineering facility under various flood conditions based on the physical working status of the facilities and the operational correlation data; spatially superimposing the post-disaster remaining service supply capacity values ​​of each lifeline engineering facility within each service target domain based on the basic geospatial data to obtain the total post-disaster remaining service supply capacity value for each service target domain; and determining the lifeline engineering service capacity retention rate for each service target domain under various flood conditions based on the ratio of the total post-disaster remaining service supply capacity value to the normal service capacity benchmark value for each service target domain.

[0018] The basic geospatial data may include the boundaries of flood storage and detention areas, dike coordinates, and the spatial locations of roads and facilities; hydrological and hydrodynamic disaster-causing data may include the maximum inundation depth, flow velocity vector, flood arrival time, and inundation duration under different operating conditions; lifeline engineering entity attribute data may include substation ground elevation, transformer foundation height, tower burial depth, roadbed elevation, bridge deck elevation, water plant intake elevation, and pump station motor floor elevation; operational correlation data may include power grid wiring diagrams, water supply network connectivity, distribution of service recipients, and service capacity benchmark values. The physical operating status of facilities refers to the state of each lifeline engineering facility due to flood intrusion under specific flood conditions. The physical operating status of facilities may include normal operation, partial failure, and complete failure. The post-disaster remaining service supply capacity value refers to the actual amount of service that a lifeline engineering facility can still provide to its service recipients after undergoing failure screening under specific flood conditions. The determination method can be as follows: screen the failed nodes according to the physical working status of the facilities, remove the failed nodes from the functional network described by the operation-related data, and obtain the actual output capacity of each facility under the post-disaster network reconstruction conditions through network topology analysis (such as water supply pressure drop calculation and traffic accessibility calculation).

[0019] Specifically, the working status of each facility under different operating conditions can be determined first by considering the relationship between flood depth, flow velocity, elevation of key parts of the facility, and structural resistance. Second, the ratio of post-disaster remaining service capacity to normal service capacity can be calculated separately for each service area to obtain the lifeline engineering service capacity retention rate of each service area under various flood conditions.

[0020] S120. Based on the lifeline engineering service capacity retention rate, determine the short-board service object domain under various flood diversion conditions, and based on the failure facility dataset and service capacity loss distribution dataset of the short-board service object domain, determine the key information of the extreme scenarios under various flood diversion conditions.

[0021] The "short-board service object domain" refers to the domain with the lowest lifeline engineering service capacity retention rate among all service object domains under a specific flood diversion condition. The failed facility dataset may include the spatial location, facility type, failure mode (e.g., flooding failure, scour damage, foundation instability), failure time, failure threshold (critical water depth, critical flow velocity, etc.), service capacity, and impact range of the failed facilities. The service capacity loss distribution dataset may include the spatial distribution of service capacity loss (the degree of decline in water / power supply capacity in each region) and the loss distribution along the service object dimension (the degree of impact on various users / regions). The "critical information for extreme scenarios" is a set of core information representing the most unfavorable state of a specific flood diversion condition, extracted from the short-board service object domain through multi-dimensional element combination and scenario parameter inversion of the set of critical vulnerable facilities and the set of critical failure paths. Key information in extreme scenarios can include two types of information: first, reverse identification results, which may include key vulnerabilities, key nodes, critical paths, weak service object domains, and disaster chain paths; second, scenario control elements, which may include flood magnitude, flood entry location, flood entry method, flood entry timing, flood entry process, and human intervention conditions.

[0022] Specifically, first, the service domain with the lowest lifeline engineering service capacity retention rate under each operating condition is identified; second, the failed facilities, lines, or nodes within the service domain that cause service capacity degradation are tracked; and third, the key control factors leading to service capacity degradation are identified by combining facility spatial location, flood depth, flow velocity, inundation duration, and network topology.

[0023] Optionally, based on the failed facility dataset and service capacity loss distribution dataset of the short-board service object domain, key information of extreme scenarios under various flood diversion conditions is determined, including: ranking the service capacity loss of each failed lifeline engineering facility under various flood diversion conditions according to the failed facility dataset, obtaining the ranking result of the service capacity loss of each failed lifeline engineering facility; identifying critical vulnerable facilities and critical failure paths for each failed lifeline engineering facility under various flood diversion conditions according to the ranking result of the service capacity loss and the service capacity loss distribution dataset, obtaining the set of critical vulnerable facilities and the set of critical failure paths for each failed lifeline engineering facility; and determining the key information of extreme scenarios under various flood diversion conditions based on the set of critical vulnerable facilities and the set of critical failure paths.

[0024] The set of critical vulnerable facilities is a collection of lifeline engineering facilities that play a decisive role in maintaining overall functionality, selected from the failed facility dataset based on the ranking of service capacity losses. The set of critical failure paths is a collection of disaster propagation paths caused by the failure of critical vulnerable facilities, which propagate along the lifeline engineering network topology.

[0025] S130. Based on preset scenario constraints, and according to the engineering vulnerability, determine the extreme scenarios of the lifeline engineering of the target flood storage and detention area from various flood diversion conditions.

[0026] The preset scenario constraints are artificially set based on actual conditions or empirical values. This application embodiment does not specifically limit these constraints. For example, these constraints may include scenario constraints related to the probability of occurrence and scenario constraints related to physical feasibility. Scenario constraints related to the probability of occurrence mean that the combination of flood elements corresponding to the determined lifeline engineering extreme scenario must conform to the flood evolution law and historical hydrological statistical characteristics of the watershed where the target flood storage area is located, and must not exceed the physically achievable extreme range. Scenario constraints related to physical feasibility mean that the breach formation mechanism, inundation evolution process, and facility failure mode corresponding to the scenario must conform to the basic principles of engineering mechanics and hydraulics, excluding hypothetical scenarios that are theoretically extremely vulnerable but cannot occur under actual engineering conditions.

[0027] Specifically, the vulnerability of various flood diversion conditions can be compared horizontally. The primary criterion is the maximum vulnerability, and the constraint is a certain probability of occurrence. This allows us to determine the extreme scenarios for the lifeline engineering of the target flood storage area, which is also the most unfavorable flood scenario globally. The extreme scenario for lifeline engineering is the extreme scenario corresponding to a complete flood event that maximizes the overall service capacity of the lifeline engineering of the target flood storage area and has a realistic probability of occurrence.

[0028] S140. Based on the lifeline engineering service capability retention rate of each service object domain corresponding to the lifeline engineering extreme scenario, determine the global bottleneck service object domain under the lifeline engineering extreme scenario, and construct a set of extreme scenario candidate solutions for the global bottleneck service object domain in reverse according to the key information of the extreme scenario corresponding to the lifeline engineering extreme scenario.

[0029] Specifically, in extreme scenarios for lifeline engineering projects, the service domain with the lowest lifeline engineering service capacity retention rate can be identified as the global bottleneck service domain under extreme scenarios. Then, key information related to extreme scenarios, such as flood magnitude, flood diversion location, flood diversion method, flood diversion timing, flood diversion process, and human intervention conditions, is combined in reverse to form different candidate scheme sets for extreme scenarios. Flood magnitude can include common floods, design floods, check floods, and maximum probable floods; flood diversion location can include vulnerable sections of dikes, historical breach sections, areas near floodgates, and sections experiencing surging at bends; flood diversion method can include orderly flood diversion, overflow diversion, local breach diversion, and gradual diversion through piping; flood diversion timing can include the initial stage of rising water, peak high water level period, receding water period, and sustained high water level period; flood diversion process can include rapid diversion, slow overflow, intermittent diversion, and continuous inundation; human intervention can include timely and effective dispatching, delayed dispatching response, failed rescue operations, and interruption of emergency support. For example, if the global bottleneck service object domain is a local service object, the extreme scenario candidate solution could be: flooding occurs during a high water level period → the plant area is rapidly submerged → power distribution / pump station fails → water purification system collapses → local water supply is completely interrupted; if the global bottleneck service object domain is a safe zone service object, the extreme scenario candidate solution could be: flooding leads to a surge in the number of relocated people → resettlement sites are overloaded → emergency water / power supply is insufficient → livelihood security fails completely; if the global bottleneck service object domain is a transit external service object, the extreme scenario candidate solution could be: flooding destroys pipe corridors, roadbeds, and bridges → cross-regional supply is interrupted → the disaster impact spreads outward, creating regional risks.

[0030] S150. Using a preset scenario selection strategy, determine the construction result of the extreme scenario for the lifeline project of the target flood storage and detention area based on the set of extreme scenario candidate schemes.

[0031] The preset scenario selection strategy is artificially set based on actual conditions or experience values. This application embodiment does not specifically limit this. For example, the preset scenario selection strategy can cover the most critical lifeline engineering vulnerability points, trigger key nodes and evolution paths of the lifeline engineering disaster chain, reduce the lifeline engineering function to the maximum extent, and have a certain probability of occurrence, which is consistent with the flood evolution and engineering failure mechanism. The lifeline engineering extreme scenario construction result is a globally complete extreme flood event description that meets the principles of "widest impact, strongest chain reaction, greatest loss, and reasonable probability".

[0032] In one optional implementation, a preset scenario validity verification rule can be used to verify the scenario validity of the lifeline engineering extreme scenario construction results, thereby obtaining the scenario validity verification results of the lifeline engineering extreme scenario construction results; based on the scenario validity verification results, the final lifeline engineering extreme scenario construction results of the target flood storage and detention area are determined.

[0033] Specifically, based on the lifeline engineering service capability retention rate of each service object domain corresponding to the lifeline engineering extreme scenario construction results, the corresponding global engineering vulnerability can be determined, and it can be confirmed whether the global engineering vulnerability is the maximum value among the engineering vulnerabilities of various flood conditions.

[0034] This application determines the lifeline engineering service capacity retention rate of each service object domain under various flood diversion conditions by using multi-dimensional basic information data of the target flood storage and detention area. Based on the lifeline engineering service capacity retention rate, it also determines the engineering vulnerability under various flood diversion conditions. The service object domains include local service objects, safe zone service objects, and transit service objects. Based on the lifeline engineering service capacity retention rate, it identifies the weakest service object domains under various flood diversion conditions. Furthermore, based on the failed facility dataset and service capacity loss distribution dataset of the weakest service object domains, it determines the key information of extreme scenarios under various flood diversion conditions. Based on preset scenario constraints and considering engineering vulnerability, the system identifies the extreme scenarios for lifeline engineering in target flood storage and detention areas from various flood conditions. Based on the lifeline engineering service capacity retention rate of each service object domain corresponding to the extreme scenarios, it determines the global weak link service object domain under the extreme scenarios. Furthermore, based on the key information of the extreme scenarios, it constructs a set of candidate extreme scenarios for the global weak link service object domain. Using a preset scenario optimization strategy, it determines the final construction result of the extreme scenarios for lifeline engineering in the target flood storage and detention area based on the set of candidate extreme scenarios. This approach, by constructing a set of candidate extreme scenarios for the global weak link service object domain and then determining the final construction result of the extreme scenarios for lifeline engineering, can accurately identify the most unfavorable flood scenarios and their key triggering conditions that lead to the greatest vulnerability of lifeline engineering in flood storage and detention areas. This significantly improves the accuracy and effectiveness of risk prevention and control for lifeline engineering in flood storage and detention areas, providing technical support for risk prevention and control, emergency dispatch, and engineering reinforcement measures for lifeline engineering in flood storage and detention areas.

[0035] Example 2 Figure 2This is a flowchart of a method for reverse-engineering extreme scenarios of lifeline engineering projects in flood storage and detention areas, according to Embodiment 2 of this application. Based on the technical solutions of the above embodiments, this embodiment refines the process of "reverse-engineering a set of candidate extreme scenarios for the global short-board service object domain based on the key information of the extreme scenarios corresponding to the extreme scenarios of lifeline engineering projects" into "determining a set of critical hydraulic conditions for failure corresponding to the global short-board service object domain based on the key information of the extreme scenarios corresponding to the extreme scenarios of lifeline engineering projects; wherein the set of critical hydraulic conditions for failure includes at least the critical water depth, critical flow velocity, and critical inundation duration corresponding to the failure of each lifeline engineering facility; based on the set of critical hydraulic conditions for failure, reverse-engineering is performed on each lifeline engineering facility to obtain a combination of extreme scenario elements corresponding to each lifeline engineering facility; wherein the combination of extreme scenario elements includes at least the flood magnitude, flood inflow location, flood inflow method, and flood inflow process; based on the combination of extreme scenario elements, a set of candidate extreme scenarios for the global short-board service object domain is determined." It should be noted that for parts not detailed in this embodiment, please refer to the relevant descriptions in other embodiments. Figure 2 As shown, the method includes: S210. Based on the multi-dimensional basic information data of the target flood storage and detention area, determine the lifeline engineering service capacity retention rate of each service object domain under various flood diversion conditions, and determine the engineering vulnerability of each flood diversion condition based on the lifeline engineering service capacity retention rate; wherein, the service object domain includes local service objects, safe zone service objects, and transit external service objects.

[0036] S220. Based on the lifeline engineering service capacity retention rate, determine the short-board service object domain under various flood conditions, and based on the failure facility dataset and service capacity loss distribution dataset of the short-board service object domain, determine the key information of the extreme scenarios under various flood conditions.

[0037] S230. Based on preset scenario constraints, and according to the engineering vulnerability, determine the extreme scenarios of the lifeline engineering of the target flood storage and detention area from various flood diversion conditions.

[0038] S240. Based on the lifeline engineering service capability retention rate of each service object domain corresponding to the lifeline engineering extreme scenario, determine the global bottleneck service object domain under the lifeline engineering extreme scenario.

[0039] S250. Based on the key information of the extreme scenarios corresponding to the extreme scenarios of lifeline engineering, determine the set of critical hydraulic conditions for failure corresponding to the global short-board service object domain; wherein, the set of critical hydraulic conditions for failure includes at least the critical water depth, critical flow velocity and critical inundation duration corresponding to the failure of each lifeline engineering facility.

[0040] Among them, the set of critical hydraulic conditions for failure refers to the set of critical hydraulic element values ​​that must be reached to cause a predetermined failure level for each lifeline engineering facility within the global short-board service object domain, under the element space determined by the key information of a given extreme scenario.

[0041] S260. Based on the set of critical hydraulic conditions for failure, reverse simulations are performed on each lifeline engineering facility to obtain the combination of extreme scenario elements corresponding to each lifeline engineering facility; wherein, the combination of extreme scenario elements includes at least the flood magnitude, flood inflow location, flood inflow mode, and flood inflow process.

[0042] Among them, the flood magnitude can include common flood, design flood, check flood, maximum possible flood, etc.; the flood diversion location can include dangerous sections of dikes, historical breach sections, near floodgates, and sections of bends; the flood diversion method can include orderly flood diversion, overflow flood diversion, local breach flood diversion, and gradual flood diversion through piping, etc.; the flood diversion process can include rapid flood diversion, slow overflow, intermittent flood diversion, and continuous inundation, etc.

[0043] S270. Based on the combination of extreme scenario elements, determine the set of extreme scenario candidate solutions for the global bottleneck service object domain.

[0044] Among them, the extreme scenario candidate solution set is a collection of multiple candidate extreme scenario solutions that are reverse-engineered by different failure paths or element combinations for the global bottleneck service object domain.

[0045] S280. Using a preset scenario selection strategy, determine the construction result of the extreme scenario for the lifeline project of the target flood storage and detention area based on the set of extreme scenario candidate schemes.

[0046] Optionally, the step of using a preset scenario selection strategy to determine the construction result of the lifeline engineering extreme scenarios of the target flood storage and detention area based on the extreme scenario candidate scheme set includes: performing extra-service object domain deductions for each extreme scenario candidate scheme other than the global short-board service object domain based on the extreme scenario candidate scheme set to obtain the scenario impact extension scheme set for the target flood storage and detention area; and using a preset scenario selection strategy to determine the construction result of the lifeline engineering extreme scenarios of the target flood storage and detention area based on the scenario impact extension scheme set.

[0047] Among them, the scenario impact extension scheme set is a complete set of schemes that includes all service object domains (local service objects, security zone service objects, and transit external service objects) under each candidate scheme after performing non-shortcoming service object domain extrapolation on each extreme scenario candidate scheme.

[0048] This application determines the lifeline engineering service capacity retention rate of each service object domain under various flood diversion conditions by using multi-dimensional basic information data of the target flood storage and detention area. Based on the lifeline engineering service capacity retention rate, it also determines the engineering vulnerability under various flood diversion conditions. The service object domains include local service objects, safe zone service objects, and transit service objects. Based on the lifeline engineering service capacity retention rate, it identifies the weakest service object domains under various flood diversion conditions. Furthermore, based on the failed facility dataset and service capacity loss distribution dataset of the weakest service object domains, it determines the key information of extreme scenarios under various flood diversion conditions. Based on preset scenario constraints and engineering vulnerability, the lifeline engineering extreme scenarios of the target flood storage and detention area are determined from various flood diversion conditions. Based on the lifeline engineering service capacity retention rate of each service object domain corresponding to the lifeline engineering extreme scenarios, the global weak link service object domain under the lifeline engineering extreme scenarios is determined. Based on the key information of the extreme scenarios corresponding to the lifeline engineering extreme scenarios, a set of extreme scenario candidate solutions for the global weak link service object domain is constructed in reverse. Using a preset scenario selection strategy, the construction result of the lifeline engineering extreme scenarios of the target flood storage and detention area is determined based on the set of extreme scenario candidate solutions. The aforementioned solution, by reverse-engineering a set of candidate extreme scenarios for the global bottleneck service domain, and then determining the construction results of extreme scenarios for lifeline engineering, can accurately identify the most unfavorable flood scenarios that lead to the greatest vulnerability of lifeline engineering in flood storage and detention areas, as well as their key triggering conditions. It achieves the reverse construction of the most unfavorable scenarios based on vulnerability assessment and from key nodes and disaster chains, accurately identifies the most vulnerable points of lifelines, and the key nodes and critical paths that trigger lifeline disaster chains. It realizes the quantitative construction of scenarios that maximize functional loss, significantly improving the accuracy and effectiveness of risk prevention and control for lifeline engineering in flood storage and detention areas. Moreover, it has strong versatility and is applicable to risk analysis and scenario construction for various lifeline engineering in flood storage and detention areas, such as water supply, power supply, transportation, and communication, providing technical support for risk prevention and control, emergency dispatch, and engineering reinforcement measures for lifeline engineering in flood storage and detention areas.

[0049] Example 3 Figure 3 This is a structural schematic diagram of a reverse reconstruction device for the most unfavorable scenario of a flood storage and detention area lifeline project, according to Embodiment 3 of this application. This embodiment is applicable to the reverse reconstruction of the most unfavorable scenario of a flood storage and detention area lifeline project based on vulnerability assessment. This reverse reconstruction device for the most unfavorable scenario of a flood storage and detention area lifeline project can be implemented in hardware and / or software, and can be configured in a computer device, such as a server. Figure 3 As shown, the device includes: The engineering vulnerability determination module 310 is used to determine the lifeline engineering service capacity retention rate of each service object domain under various flood diversion conditions based on the multi-dimensional basic information data of the target flood storage and detention area, and to determine the engineering vulnerability of each flood diversion condition based on the lifeline engineering service capacity retention rate; wherein, the service object domain includes local service objects, safe zone service objects, and transit external service objects; The extreme scenario key information determination module 320 is used to determine the short-board service object domain under various flood conditions based on the lifeline engineering service capacity retention rate, and to determine the extreme scenario key information under various flood conditions based on the failure facility dataset and service capacity loss distribution dataset of the short-board service object domain. The lifeline engineering extreme scenario determination module 330 is used to determine the extreme scenario of the lifeline engineering of the target flood storage and detention area from various flood diversion conditions based on preset scenario constraints and the vulnerability of the engineering. The extreme scenario candidate solution set determination module 340 is used to determine the global bottleneck service object domain under the extreme scenario of lifeline engineering based on the lifeline engineering service capability retention rate of each service object domain corresponding to the extreme scenario of lifeline engineering, and to reverse construct the extreme scenario candidate solution set for the global bottleneck service object domain based on the extreme scenario key information corresponding to the extreme scenario of lifeline engineering. The lifeline engineering extreme scenario construction result determination module 350 is used to determine the lifeline engineering extreme scenario construction result of the target flood storage and detention area by adopting a preset scenario selection strategy and based on the extreme scenario candidate scheme set.

[0050] Optionally, the extreme scenario candidate solution set determination module 340 is specifically used for: Based on the key information of the extreme scenarios corresponding to the extreme scenarios of lifeline engineering, the set of critical hydraulic conditions for failure corresponding to the global short-board service object domain is determined; wherein, the set of critical hydraulic conditions for failure includes at least the critical water depth, critical flow velocity and critical inundation duration corresponding to the failure of each lifeline engineering facility. Based on the set of critical hydraulic conditions for failure, reverse simulations are performed on each lifeline engineering facility to obtain the combination of extreme scenario elements corresponding to each lifeline engineering facility; wherein, the combination of extreme scenario elements includes at least the flood magnitude, flood inflow location, flood inflow mode, and flood inflow process; Based on the combination of extreme scenario elements, a set of candidate extreme scenario solutions is determined for the global bottleneck service object domain.

[0051] Optional, the lifeline engineering extreme scenario construction result determination module 350 is specifically used for: Based on the extreme scenario candidate scheme set, the service object domains other than the global short-board service object domain are deduced for each extreme scenario candidate scheme to obtain the scenario impact extension scheme set of the target flood storage and detention area; By adopting a preset scenario selection strategy and based on the scenario impact expansion scheme set, the extreme scenario construction result of the lifeline engineering in the target flood storage and detention area is determined.

[0052] Optionally, the extreme scenario key information determination module 320 is specifically used for: Based on the dataset of failed facilities, the service capacity loss of each failed lifeline engineering facility under various flood conditions is ranked, and the ranking results of the service capacity loss of each failed lifeline engineering facility are obtained. Based on the service capacity loss ranking results and the service capacity loss distribution dataset, the critical vulnerable facilities and critical failure paths of each failed lifeline engineering facility under various flood conditions are identified, and the set of critical vulnerable facilities and the set of critical failure paths of each failed lifeline engineering facility are obtained. Based on the set of critical vulnerable facilities and the set of critical failure paths, key information on extreme scenarios under various flood diversion conditions is determined.

[0053] Optionally, the multidimensional basic information data includes basic geospatial data, hydrological and hydrodynamic disaster data, lifeline engineering ontological attribute data, and operational correlation data. Correspondingly, the engineering vulnerability determination module 310 is specifically used for: Based on the hydrological and hydrodynamic disaster data and the lifeline engineering property data, determine the physical working status of each lifeline engineering facility under various flood conditions. Based on the physical working status of the facilities and the operational correlation data, determine the post-disaster remaining service supply capacity value of each lifeline engineering facility under various flood conditions; Based on the aforementioned basic geospatial data, the post-disaster remaining service supply capacity values ​​of each lifeline engineering facility within each service object domain are spatially superimposed to obtain the total post-disaster remaining service supply capacity value for each service object domain. The lifeline engineering service capacity retention rate of each service target domain under various flood conditions is determined by the ratio of the total post-disaster remaining service supply capacity value to the normal service capacity baseline value of each service target domain.

[0054] Optionally, the device further includes: The final lifeline engineering extreme scenario construction result determination module is used for: Using preset scenario validity verification rules, the scenario validity verification results of the lifeline engineering extreme scenario construction results are obtained. Based on the effectiveness verification results of the scenario, the final lifeline engineering extreme scenario construction results of the target flood storage and detention area are determined.

[0055] This application determines the lifeline engineering service capacity retention rate of each service object domain under various flood diversion conditions by using multi-dimensional basic information data of the target flood storage and detention area. Based on the lifeline engineering service capacity retention rate, it also determines the engineering vulnerability under various flood diversion conditions. The service object domains include local service objects, safe zone service objects, and transit service objects. Based on the lifeline engineering service capacity retention rate, it identifies the weakest service object domains under various flood diversion conditions. Furthermore, based on the failed facility dataset and service capacity loss distribution dataset of the weakest service object domains, it determines the key information of extreme scenarios under various flood diversion conditions. Based on preset scenario constraints and considering engineering vulnerability, the system identifies the extreme scenarios for lifeline engineering in target flood storage and detention areas from various flood conditions. Based on the lifeline engineering service capacity retention rate of each service object domain corresponding to the extreme scenarios, it determines the global weak link service object domain under the extreme scenarios. Furthermore, based on the key information of the extreme scenarios, it constructs a set of candidate extreme scenarios for the global weak link service object domain. Using a preset scenario optimization strategy, it determines the final construction result of the extreme scenarios for lifeline engineering in the target flood storage and detention area based on the set of candidate extreme scenarios. This approach, by constructing a set of candidate extreme scenarios for the global weak link service object domain and then determining the final construction result of the extreme scenarios for lifeline engineering, can accurately identify the most unfavorable flood scenarios and their key triggering conditions that lead to the greatest vulnerability of lifeline engineering in flood storage and detention areas. This significantly improves the accuracy and effectiveness of risk prevention and control for lifeline engineering in flood storage and detention areas, providing technical support for risk prevention and control, emergency dispatch, and engineering reinforcement measures for lifeline engineering in flood storage and detention areas.

[0056] The reverse construction device for extreme scenarios of flood storage and detention area lifeline engineering provided in this application embodiment can execute the reverse construction method for extreme scenarios of flood storage and detention area lifeline engineering provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing the reverse construction method for extreme scenarios of lifeline engineering in each flood storage and detention area.

[0057] According to embodiments of this application, this application also provides an electronic device, a readable storage medium, and a computer program product.

[0058] Example 4 Figure 4This is a schematic diagram of the electronic device 410 implementing the reverse engineering method for extreme scenarios of flood storage and detention area lifeline engineering according to embodiments of this application. 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 (such as 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 present application described and / or claimed herein.

[0059] like Figure 4 As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 may also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0060] Multiple components in electronic device 410 are connected to I / O interface 415, including: input unit 416, such as keyboard, mouse, etc.; output unit 417, such as various types of displays, speakers, etc.; storage unit 418, such as disk, optical disk, etc.; and communication unit 419, such as network card, modem, wireless transceiver, etc. Communication unit 419 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0061] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 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 411 performs the various methods and processes described above, such as the reverse engineering method for extreme scenarios in flood storage and detention area lifeline engineering.

[0062] In some embodiments, the method for reverse engineering of lifeline engineering in flood storage and detention areas under extreme scenarios can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the method for reverse engineering of lifeline engineering in flood storage and detention areas under extreme scenarios described above can be performed. Alternatively, in other embodiments, processor 411 can be configured as the method for reverse engineering of lifeline engineering in flood storage and detention areas under extreme scenarios by any other suitable means (e.g., by means of firmware).

[0063] 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.

[0064] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable reverse engineering device for extreme scenarios of flood storage and detention area lifeline engineering, such that when executed by the processor, the computer programs enable the functions / operations specified in the flowcharts and / or block diagrams to be implemented. 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.

[0065] In the context of this application, 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 of the foregoing. 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 fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0066] 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).

[0067] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or 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.

[0068] 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.

[0069] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0070] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. 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 application should be included within the scope of protection of this application.

Claims

1. A method for reverse-engineering extreme scenarios of lifeline engineering in flood storage and detention areas, characterized in that, include: Based on the multidimensional basic information data of the target flood storage and detention area, the lifeline engineering service capacity retention rate of each service object domain under various flood diversion conditions is determined, and the engineering vulnerability of each flood diversion condition is determined based on the lifeline engineering service capacity retention rate; wherein, the service object domain includes local service objects, safe zone service objects, and transit external service objects; Based on the lifeline engineering service capacity retention rate, the short-board service object domain under various flood conditions is determined, and based on the failure facility dataset and service capacity loss distribution dataset of the short-board service object domain, the key information of the extreme scenarios under various flood conditions is determined. Based on preset scenario constraints and according to the engineering vulnerability, the extreme scenarios of the lifeline engineering of the target flood storage and detention area are determined from various flood diversion conditions. Based on the lifeline engineering service capability retention rate of each service object domain corresponding to the lifeline engineering extreme scenario, the global bottleneck service object domain under the lifeline engineering extreme scenario is determined, and based on the key information of the extreme scenario corresponding to the lifeline engineering extreme scenario, the extreme scenario candidate solution set for the global bottleneck service object domain is constructed in reverse. Using a preset scenario selection strategy, the construction result of the extreme scenario for the lifeline project of the target flood storage and detention area is determined based on the set of extreme scenario candidate schemes.

2. The method according to claim 1, characterized in that, Based on the key information of the extreme scenarios corresponding to the extreme scenarios of the lifeline project, a set of candidate solutions for extreme scenarios targeting the global bottleneck service object domain is constructed in reverse, including: Based on the key information of the extreme scenarios corresponding to the extreme scenarios of lifeline engineering, the set of critical hydraulic conditions for failure corresponding to the global short-board service object domain is determined; wherein, the set of critical hydraulic conditions for failure includes at least the critical water depth, critical flow velocity and critical inundation duration corresponding to the failure of each lifeline engineering facility. Based on the set of critical hydraulic conditions for failure, reverse simulations are performed on each lifeline engineering facility to obtain the combination of extreme scenario elements corresponding to each lifeline engineering facility; wherein, the combination of extreme scenario elements includes at least the flood magnitude, flood inflow location, flood inflow mode, and flood inflow process; Based on the combination of extreme scenario elements, a set of candidate extreme scenario solutions is determined for the global bottleneck service object domain.

3. The method according to claim 1 or 2, characterized in that, The step of employing a preset scenario selection strategy, based on the set of extreme scenario candidate solutions, to determine the construction results of extreme scenarios for the lifeline engineering of the target flood storage and detention area, includes: Based on the extreme scenario candidate scheme set, the service object domains other than the global short-board service object domain are deduced for each extreme scenario candidate scheme to obtain the scenario impact extension scheme set of the target flood storage and detention area; Using a pre-defined scenario selection strategy, and based on the scenario impact expansion scheme set, the ultimate scenario construction result of the lifeline engineering in the target flood storage and detention area is determined.

4. The method according to claim 1, characterized in that, Based on the failure facility dataset and service capacity loss distribution dataset of the short-board service object domain, key information of extreme scenarios under various flood conditions is determined, including: Based on the dataset of failed facilities, the service capacity loss of each failed lifeline engineering facility under various flood conditions is ranked, and the ranking results of the service capacity loss of each failed lifeline engineering facility are obtained. Based on the service capacity loss ranking results and the service capacity loss distribution dataset, the critical vulnerable facilities and critical failure paths of each failed lifeline engineering facility under various flood conditions are identified, and the set of critical vulnerable facilities and the set of critical failure paths of each failed lifeline engineering facility are obtained. Based on the set of critical vulnerable facilities and the set of critical failure paths, key information of extreme scenarios under various flood diversion conditions is determined.

5. The method according to claim 1, characterized in that, The multidimensional basic information data includes basic geospatial data, hydrological and hydrodynamic disaster-causing data, lifeline engineering ontological attribute data, and operational correlation data. Accordingly, based on the multidimensional basic information data of the target flood storage and detention area, the lifeline engineering service capacity retention rate of each service domain under various flood conditions is determined, including: Based on the hydrological and hydrodynamic disaster data and the lifeline engineering property data, determine the physical working status of each lifeline engineering facility under various flood conditions. Based on the physical working status of the facilities and the operational correlation data, determine the post-disaster remaining service supply capacity value of each lifeline engineering facility under various flood conditions; Based on the aforementioned basic geospatial data, the post-disaster remaining service supply capacity values ​​of each lifeline engineering facility within each service object domain are spatially superimposed to obtain the total post-disaster remaining service supply capacity value for each service object domain. The lifeline engineering service capacity retention rate of each service target domain under various flood conditions is determined by the ratio of the total post-disaster remaining service supply capacity value to the normal service capacity baseline value of each service target domain.

6. The method according to claim 1, characterized in that, The method further includes: Using preset scenario validity verification rules, the scenario validity verification results of the lifeline engineering extreme scenario construction results are obtained. Based on the effectiveness verification results of the scenario, the final lifeline engineering extreme scenario construction results of the target flood storage and detention area are determined.

7. A reverse construction device for extreme scenarios of lifeline engineering in flood storage and detention areas, characterized in that, include: The engineering vulnerability determination module is used to determine the lifeline engineering service capacity retention rate of each service object domain under various flood diversion conditions based on multi-dimensional basic information data of the target flood storage and detention area, and to determine the engineering vulnerability of each flood diversion condition based on the lifeline engineering service capacity retention rate; wherein, the service object domain includes local service objects, safe zone service objects, and transit external service objects; The extreme scenario key information determination module is used to determine the short-board service object domain under various flood conditions based on the lifeline engineering service capacity retention rate, and to determine the extreme scenario key information under various flood conditions based on the failure facility dataset and service capacity loss distribution dataset of the short-board service object domain. The lifeline engineering extreme scenario determination module is used to determine the extreme scenarios of the lifeline engineering of the target flood storage and detention area from various flood diversion conditions based on preset scenario constraints and the vulnerability of the engineering. The extreme scenario candidate solution set determination module is used to determine the global bottleneck service object domain under the extreme scenario of lifeline engineering based on the lifeline engineering service capability retention rate of each service object domain corresponding to the extreme scenario of lifeline engineering, and to reverse construct the extreme scenario candidate solution set for the global bottleneck service object domain based on the extreme scenario key information corresponding to the extreme scenario of lifeline engineering. The module for determining the construction results of extreme scenarios for lifeline engineering projects is used to determine the construction results of extreme scenarios for lifeline engineering projects in the target flood storage and detention area based on the set of extreme scenario candidate schemes, using a preset scenario selection strategy.

8. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the reverse construction method for extreme scenarios of flood storage and detention area lifeline engineering as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the reverse construction method for extreme scenarios of lifeline engineering in flood storage and detention areas as described in any one of claims 1-6.

10. A computer program product comprising a computer program that, when executed by a processor, implements the method for reverse construction of extreme scenarios for lifeline engineering in flood storage and detention areas according to any one of claims 1-6.

Images