Flood risk assessment method and system based on cavernous body benefit
By integrating multi-source data in real-time closed-loop mode and coordinating with dynamic storage functions, the problem of dynamic adjustment and data fusion in existing urban flood risk assessment methods has been solved, achieving closed-loop control of risk assessment and scheduling, and improving the city's flood control and disaster reduction capabilities.
Patent Information
- Application Number
- CN202511686639.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-18
- Publication Date
- 2026-02-10
AI Technical Summary
Existing urban flood risk assessment methods cannot be dynamically adjusted in real time, making it difficult to adapt to rapid changes in meteorology and the urban environment. Data fusion is difficult, dispatching is not intelligent enough, and system integration is low, resulting in biased assessment results and simplistic dispatching strategies, which cannot effectively cope with extreme weather.
By introducing real-time closed-loop fusion of multi-source data and the synergy of dynamic regulation and storage functions, a unified data platform is constructed by acquiring real-time meteorological, hydrological and sponge body data, calculating the regulation and storage benefits of sponge bodies, and generating the optimal drainage scheduling scheme by combining a multi-factor risk assessment model and a regional and hierarchical scheduling method, thus realizing closed-loop control of risk perception and intelligent scheduling.
It achieves closed-loop control of risk assessment and scheduling, improves the accuracy of assessment results and the intelligence of scheduling, and can effectively reduce flood peaks, reduce inundated areas, shorten water accumulation time, and enhance urban flood control and disaster reduction capabilities under extreme rainfall conditions.
Smart Images
Figure CN121504050A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of flood risk assessment technology, and in particular to a flood risk assessment method and system based on sponge city benefits. Background Technology
[0002] With the intensification of global climate change and the acceleration of urbanization, urban flooding has become a key issue restricting socio-economic development and threatening the safety of residents' lives. The frequency and intensity of extreme rainfall events have increased significantly, leading to frequent rainstorms and flooding in coastal cities, low-lying areas, and river network plains. These disasters not only paralyze road traffic, flood underground spaces, and damage houses and infrastructure, causing huge economic losses and social disorder, but may also trigger secondary public safety incidents, posing a serious threat to the normal operation of cities.
[0003] Traditional urban flood control and drainage systems are centered on gray infrastructure such as drainage pipe networks, pumping stations, and sluice gates. Their design and construction are largely based on historical meteorological data, using fixed design storm intensity and recurrence intervals as standards. However, with extreme rainfall events caused by climate change far exceeding the original design standards, the defense capabilities of these systems are clearly insufficient. To alleviate these problems, the concept of "sponge cities" was proposed and promoted starting in 2014. The core of this concept is to achieve comprehensive regulation of rainwater infiltration, retention, storage, purification, utilization, and drainage through "sponge" facilities such as permeable pavements, sunken green spaces, rain gardens, wetland parks, green roofs, and stormwater storage tanks. This aims to reduce runoff at the source, regulate flood peaks during the process, and ensure reasonable discharge at the end of the flow, thereby restoring and simulating the natural hydrological cycle and enhancing the city's resilience to extreme rainfall. However, the effectiveness of sponge city facilities is significantly affected by the scale of deployment, spatial layout, and underlying surface conditions. Furthermore, long-term operation is prone to clogging and improper maintenance, leading to functional degradation. A single type of sponge or a static design is insufficient to meet the needs of complex flood environments.
[0004] Current urban flood risk assessments primarily rely on hydrological and hydrodynamic models and statistical analysis methods such as SWMM, MIKE, and HEC-RAS. While these methods can simulate storm and runoff processes to some extent, they have significant limitations: First, they neglect the role of green infrastructure, with most models focusing on hydraulic calculations of gray infrastructure and failing to fully couple the sponge city's storage benefits, leading to biased assessment results. Second, the assessment models are mainly static, relying on historical rainfall data and failing to effectively reflect the uncertainties brought about by climate change and urban development. Third, the utilization of multi-source data is insufficient; the rich real-time data provided by meteorological radar, remote sensing monitoring, and IoT sensors are difficult to integrate efficiently using traditional methods, resulting in insufficient model input accuracy. Finally, the scheduling strategies are simplistic; even when a few systems consider real-time scheduling, they are mostly based on fixed plans and cannot dynamically adjust drainage and storage strategies according to real-time risks. Looking at the current research status, while some cities in Europe and America have conducted collaborative modeling of "blue-green infrastructure" and gray pipe networks, these efforts are mostly at the pilot demonstration stage, and the assessment indicators are mainly based on static storm scenarios. Domestic research, on the other hand, focuses on simulating the benefits of single sponge city measures, lacking a comprehensive risk assessment framework at the city-wide system level. Existing patents mostly focus on optimizing the design of a certain type of facility or predicting risks using a single algorithm, and have not yet formed an integrated and complete solution.
[0005] In summary, against the backdrop of frequent extreme weather events and continuous urban expansion, urban flood control faces multiple challenges: First, dynamic assessment is urgently needed, as static assessment cannot adapt to the rapidly changing meteorological and urban environment, necessitating a real-time dynamic risk prediction mechanism. Second, data fusion is challenging, as data types such as meteorology, hydrology, pipe networks, and sponge city infrastructure are diverse and exhibit significant differences in spatiotemporal scales, making efficient integration difficult with traditional methods. Third, intelligent scheduling is insufficient, with existing scheduling relying heavily on manual or pre-planned approaches, lacking real-time optimization and cross-regional collaboration supported by intelligent algorithms. Fourth, system integration is low, with "information silos" existing between data collection, risk assessment, and scheduling execution, making it difficult to form a closed loop. Therefore, existing technologies are insufficient to meet the actual needs of current urban flood control, necessitating the development of a novel assessment method. Summary of the Invention
[0006] Therefore, the technical problem to be solved by this invention is to overcome the technical defects existing in the prior art, and to propose a flood risk assessment method and system based on sponge city benefits. It innovatively introduces real-time closed-loop fusion of multi-source data and dynamic storage function coordination, which not only ensures the spatiotemporal integrity and continuity of data, but also quantifies the actual benefits of sponge cities and forms a whole-chain management and control. For the first time, it realizes an integrated closed-loop framework of risk perception, risk assessment and intelligent scheduling, risk index and scheduling optimization.
[0007] To address the aforementioned technical problems, this invention provides a flood risk assessment method based on sponge city benefits, comprising the following steps: S1. Acquire real-time data and preprocess the real-time data, wherein the real-time data includes meteorological data, hydrological data, sponge city construction data, and real-time drainage data; S2. Perform multiple fusions on the preprocessed data to build a unified data platform and form standardized rainfall, drainage system status, sponge body basic parameters and topographic exposure. S3. Based on the basic parameters of the sponge body, combined with the sponge body area or volume, type coefficient, time-varying state and storage / infiltration response function, calculate the storage benefits of the sponge body; S4. Substitute rainfall, drainage system status, topographic exposure, and sponge body storage benefits into the multi-factor risk assessment model to obtain a comprehensive risk index; S5. Based on the comprehensive risk index, with the goal of minimizing the comprehensive cost, the optimal scheduling scheme for the drainage system is generated by combining the partitioned and hierarchical scheduling method with linear programming and heuristic optimization algorithms. S6. Based on the comprehensive risk index and the optimal scheduling scheme of the drainage system, output the risk forecast, scheduling suggestions and emergency response plan for a specific time period in the future, and display them visually.
[0008] In one embodiment of the present invention, in S1, the preprocessing of the real-time data includes denoising, imputing missing values, and normalizing the data.
[0009] In one embodiment of the present invention, in S2, the multi-fusion of the preprocessed data includes spatiotemporal fusion, multidimensional fusion and real-time fusion of the data.
[0010] In one embodiment of the present invention, in S3, the formula for calculating the storage efficiency of the sponge body is: ; In the formula, W(t) represents the sponge body's storage efficiency at the current time t, Ai represents the area or volume of the i-th sponge body, ki represents the type coefficient, θi(t) represents the time-varying state, fi(⋅) represents the storage / infiltration response function, and H(t) represents the rainfall at time t.
[0011] In one embodiment of the present invention, in S4, the method of substituting rainfall, drainage system status, topographic exposure, and sponge city water storage benefits into a multi-factor risk assessment model to obtain a comprehensive risk index includes: The formula for calculating the comprehensive risk index R(t) is as follows: ; In the formula, H(t) is the rainfall at time t, C(t) is the state of the drainage system, W(t) is the sponge body's storage effect, S(t) is the topographic exposure, and α1, α2, α3, α4 are weighting coefficients.
[0012] In one embodiment of the invention, in S5, the objective is to minimize the overall cost J: ; In the formula, Cj is the operating energy consumption of the j-th facility, xj is the status of whether the facility is in operation, R(t) is the comprehensive risk index, and λ is the risk penalty coefficient. The constraints include drainage volume constraints, energy constraints, and the storage capacity constraints of the sponge city.
[0013] In one embodiment of the present invention, in S5, the partitioned and hierarchical scheduling method includes first-level scheduling, second-level scheduling and third-level scheduling. First-level scheduling is to locally reduce peak flow in the pipeline network and pumping stations within the area. Second-level scheduling is to coordinate and optimize across areas. Third-level scheduling is to dynamically select self-drainage or mechanical drainage.
[0014] Furthermore, this invention also provides a flood risk assessment system based on sponge city benefits, comprising: A data processing module is used to acquire real-time data and preprocess the real-time data, wherein the real-time data includes meteorological data, hydrological data, sponge city construction data, and real-time drainage data. The data fusion module is used to perform multiple fusions on the preprocessed data to build a unified data platform and form standardized rainfall, drainage system status, sponge body basic parameters and topographic exposure. The calculation module is used to calculate the storage benefits of the sponge body based on the basic parameters of the sponge body, combined with the sponge body area or volume, type coefficient, time-varying state and storage / infiltration response function. It also substitutes rainfall, drainage system status, topographic exposure and sponge body storage benefits into the multi-factor risk assessment model to obtain a comprehensive risk index. The scheme optimization module is used to generate the optimal scheduling scheme for the drainage system based on the comprehensive risk index and with the goal of minimizing the comprehensive cost, by combining the partitioned and hierarchical scheduling method with linear programming and heuristic optimization algorithms. The visualization module is used to output risk forecasts, scheduling suggestions, and emergency response plans for a specific future time period based on the comprehensive risk index and the optimal scheduling plan of the drainage system, and to display them visually.
[0015] Furthermore, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described above.
[0016] Furthermore, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.
[0017] The technical solution of the present invention has the following advantages compared with the prior art: This invention innovatively introduces real-time closed-loop fusion of multi-source data and synergy with dynamic regulation functions, which not only ensures the spatiotemporal integrity and continuity of data, but also quantifies the actual benefits of the sponge city and forms a full-chain management and control system. Moreover, through a unified framework of risk index + scheduling optimization, it realizes for the first time a closed-loop control of risk perception, risk assessment and intelligent scheduling. The integrated closed-loop framework of risk index and scheduling optimization enables the assessment results to directly drive scheduling and the scheduling feedback to correct the risk index, forming a "rolling prediction + real-time adjustment" mode. Attached Figure Description
[0018] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.
[0019] Figure 1 This is a flowchart illustrating a flood risk assessment method based on sponge city benefits proposed in this invention.
[0020] Figure 2 This is a schematic diagram of the construction of a one-dimensional river network pipeline model in the southwest area of City B.
[0021] Figure 3 This is a simulated flood risk map of City B with 300mm of rainfall in 24 hours.
[0022] Figure 4 It is a map showing the current situation strategies and the distribution of inundation risks in sponge city construction under different return periods. Detailed Implementation
[0023] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.
[0024] Reference Figure 1 As shown, this embodiment of the invention provides a flood risk assessment method based on sponge city benefits, including the following steps: S1. Acquire real-time data and preprocess the real-time data, including meteorological data, hydrological data, sponge city construction data, and real-time drainage data. S2. Perform multiple fusions on the preprocessed data to build a unified data platform and form standardized rainfall, drainage system status, sponge body basic parameters and topographic exposure. S3. Based on the basic parameters of the sponge body, combined with the sponge body area or volume, type coefficient, time-varying state and storage / infiltration response function, calculate the storage benefits of the sponge body; S4. Substitute rainfall, drainage system status, topographic exposure, and sponge body storage benefits into the multi-factor risk assessment model to obtain a comprehensive risk index; S5. Based on the comprehensive risk index, with the goal of minimizing the comprehensive cost, the optimal scheduling scheme for the drainage system is generated by combining the partitioned and hierarchical scheduling method with linear programming and heuristic optimization algorithms. S6. Based on the comprehensive risk index and the optimal scheduling scheme of the drainage system, output the risk forecast, scheduling suggestions and emergency response plan for a specific time period in the future, and display them visually.
[0025] This invention innovatively introduces real-time closed-loop fusion of multi-source data and synergy with dynamic regulation functions, which not only ensures the spatiotemporal integrity and continuity of data, but also quantifies the actual benefits of the sponge city and forms a full-chain management and control system. Moreover, through a unified framework of risk index + scheduling optimization, it realizes for the first time a closed-loop control of risk perception, risk assessment and intelligent scheduling. The integrated closed-loop framework of risk index and scheduling optimization enables the assessment results to directly drive scheduling and the scheduling feedback to correct the risk index, forming a "rolling prediction + real-time adjustment" mode.
[0026] Step S1 aims to acquire real-time data from multiple data sources, including meteorological data, hydrological data, sponge city construction data, real-time drainage data, and benefit data. Meteorological data comes from meteorological bureaus, weather satellites, and radar; hydrological data comes from watershed and river monitoring stations and stormwater drainage networks; sponge city construction data includes information on sponge city layout, types, and construction effects provided by urban planning departments; and real-time drainage data comes from the operational data of urban drainage networks and pumping stations. After acquiring the data, it undergoes denoising, missing value imputation, and normalization to ensure the accuracy and consistency of the input data.
[0027] The purpose of step S2 is to fuse various types of data to build a unified data platform that supports the integration of cross-domain and multi-source data. Utilizing data fusion technology, the system can process various data formats and transform them into formats suitable for analysis and prediction. Data fusion includes spatiotemporal data fusion, multidimensional data fusion, and real-time data fusion. Spatiotemporal data fusion refers to fusing data from different time periods and sources; multidimensional data fusion refers to comprehensively considering multidimensional data such as meteorological, geographical, and urban infrastructure data; and real-time data fusion refers to collecting data from various urban sensors and monitoring equipment in real time to ensure the system responds quickly to real-time changes.
[0028] In step S3, a multi-factor risk index is constructed, integrating rainfall H(t), drainage system status C(t), sponge body storage benefit W(t), and topographic exposure S(t) to obtain a comprehensive risk index R(t). W(t) uses a dynamic storage function instead of a fixed parameter, reflecting the sponge body's ability to change with time and rainfall intensity. Its calculation formula is as follows: ; In the formula, W(t) represents the sponge body's storage efficiency at the current time t, Ai represents the area or volume of the i-th sponge body, ki represents the type coefficient, θi(t) represents the time-varying state, H(t) represents the rainfall at time t, and fi(⋅) represents the storage / infiltration response function, which can be in the form of piecewise linear or saturated infiltration (such as the Green-Ampt approximation or empirical piecewise linear).
[0029] In step S4, the innovation of this embodiment lies in the flood risk assessment model based on sponge city benefits. This model integrates multiple factors such as meteorological data, hydrological data, sponge city benefits, and urban infrastructure, aiming to comprehensively assess the flood risk of urban areas and quantify it through a mathematical model. The model can be described by the following formula: The formula for calculating the comprehensive risk index R(t) is as follows: ; In the formula, H(t) is the rainfall at time t, C(t) is the state of the drainage system, W(t) is the sponge body's storage effect, S(t) is the topographic exposure, such as elevation and drainage capacity; α1, α2, α3, α4 are weighting coefficients, which are dynamically adjusted according to different regions and situations.
[0030] In step S5, to improve the scheduling efficiency of the urban drainage system, this embodiment also designs an intelligent scheduling algorithm based on dynamic risk assessment. The core of this algorithm is to adjust the working status of the urban drainage network and sponge city structure according to the real-time risk assessment results to ensure the efficient operation of the urban drainage system. The above-mentioned intelligent scheduling algorithm based on dynamic risk assessment aims to minimize the overall cost J. ; In the formula, Cj represents the operating energy consumption of the j-th facility, xj represents the facility's operational status, R(t) is the comprehensive risk index, and λ is the risk penalty coefficient. The constraints include drainage volume constraints, energy constraints, and the sponge city's storage capacity constraints. The drainage volume constraint ensures that the operating status of each drainage facility meets the flow demand; the energy constraint ensures that the total energy consumption of the drainage facility cannot exceed the maximum load; and the sponge city's storage capacity constraint ensures that the water storage of the sponge city does not exceed its maximum storage capacity. By solving this optimization problem, the optimal scheduling scheme for the drainage system can be obtained. The zoned and graded scheduling includes primary, secondary, and tertiary scheduling. Primary scheduling refers to local peak shaving within a zone's pipe network and pumping stations; secondary scheduling refers to cross-zone collaborative optimization to ensure that the main river channel does not exceed limits; and tertiary scheduling refers to dynamically selecting between gravity drainage and mechanical drainage based on the backwater conditions of the external river.
[0031] In step S6, the risk forecast, scheduling suggestions and emergency response plan for the next 1 to 24 hours are output and visualized. The results are visualized as a risk map, a flooding depth distribution map and a drainage system operation condition curve.
[0032] To verify the effectiveness and applicability of this invention, this embodiment takes the southwestern part of the central urban area of City B in Province A as the research object, verifying the applicability and practical effect of the flood risk assessment method based on sponge city benefits proposed in this invention. This area is low-lying, with a dense river network, and is frequently affected by floods due to the combined effects of upstream water and regional torrential rains, making it a typical flood-prone area. Through the deployment and testing of this invention, the entire process from data collection, model building, risk index calculation to intelligent scheduling is demonstrated, ultimately achieving effective control of flood risk.
[0033] Scenario and data preparation: such as Figure 2 As shown, the southwestern region of Suqian covers approximately 116.8 km², and its river network includes the Ximinbian River, the Dongsha River, and the Xiang Yu's Hometown area. The data preparation phase mainly includes the following steps: (1) Basic topography and underlying surface data: The topography of the study area was finely depicted using a 5 m resolution DEM; combined with the data from the Third National Land Survey, the original 31 types of land use were uniformly classified into 6 categories (construction land, green space, water bodies, roads, etc.) to provide boundary conditions for two-dimensional flood calculation.
[0034] (2) Pipeline and river network data: Collect the results of the drainage pipeline survey (approximately 19,000 manholes, with a total pipeline length of over 560 km) and couple them with the river network cross-sectional data (262 km) to ensure a unified simulation of the internal and external drainage systems.
[0035] (3) Sponge body configuration data: The existing and planned permeable pavement, sunken green space, rain garden, storage tank and other sponge bodies are listed and statistically analyzed, and converted into equivalent parameters (storage capacity, infiltration rate, storage function, etc.) to calculate the sponge body benefit function W(t).
[0036] (4) Meteorological and hydrological boundaries: The 24-hour and 10-year return period rainstorm calendar is selected as the design rainfall pattern. The water level of the outer boundary adopts the typical process of Hongze Lake and Xinyi River. The initial water level is set according to the flood season control water level.
[0037] Model Construction and Coupling Mechanism: To accurately reflect the flood evolution process, this embodiment adopts a "1D river network + 1D pipe network + 2D surface" coupling mode: (1) Two-dimensional flood calculation grid: approximately 54,000 two-dimensional units are divided, and local densification is carried out in low-lying areas and densely networked areas to improve the accuracy of water accumulation simulation.
[0038] (2) Coupling method: The 1D pipeline network is coupled with the 2D surface through the inspection well node to form a two-way exchange between surface runoff and underground pipeline network; the river network water level is connected to the external river through the hydraulic boundary to realize the interaction with external floods.
[0039] (3) Integration of sponge body benefits: Transform various sponge bodies into dynamic reservoir capacity and infiltration units, and correct runoff and inflow processes in real time, so that W(t) directly affects flood evolution in the system.
[0040] Risk Index Calculation and Scheduling Rules: During system operation, the flood risk index is calculated comprehensively. ; In the formula, H(t) is the rainfall at time t, C(t) is the state of the drainage system, W(t) is the sponge body's storage effect, S(t) is the terrain exposure, such as elevation and drainage capacity; α1, α2, α3, and α4 are weighting coefficients, which are dynamically adjusted according to different regions and situations. By dynamically adjusting the weighting coefficients, the risk levels of different time and space units are obtained.
[0041] Based on this, the following tiered scheduling rules are preset: Huancheng West Road Rubber Dam: When the water level is ≥19.3 m, the dam will be lowered to release floodwater, and after it drops to 18.8 m, the dam will be erected to store water; Liuqiao Reservoir: When an early warning is issued, the water level will be pre-dropped from 17.6 m to 16.5 m, and then stored again after the peak; Xiaokouzi Sluice, Ximinbianhe Sluice, and Hongwei Sluice: The opening and closing thresholds are set according to the flood season control water level to trigger automatic scheduling.
[0042] System operation process: (1) Data fusion and forecasting: The system collects meteorological radar, ground rainfall, water level and pipeline flow data in real time, and dynamically updates the risk index in combination with the sponge city benefit database.
[0043] (2) Risk triggering and scheduling execution: When the risk index R(t) of a certain area exceeds the threshold, a scheduling command is automatically issued to adjust the start and stop of the pumping station, the opening of the gate, and the working condition of the rubber dam, so as to achieve "upstream peak shifting, midstream pressure reduction, and downstream emergency discharge".
[0044] (3) Feedback control and closed-loop optimization: The sensor provides real-time feedback on water level and flow rate, and the system automatically corrects the scheduling strategy to form a closed loop of risk perception, scheduling execution and feedback correction.
[0045] Application Results: Through simulation and comparative analysis, the following conclusions were drawn: (1) Significant peak reduction and peak extension effects: Under the condition of a 10-year return period rainstorm, the peak flow of the main river channel is reduced and the peak time is delayed by about 1.5 to 2 hours, which effectively reduces the pressure on the downstream.
[0046] (2) Reduced flooding depth and faster water receding: The maximum water depth in low-lying road sections decreased by 20-30 cm, the duration of water accumulation was shortened by about 1.5 hours, and the impact on urban traffic was significantly reduced.
[0047] (3) Improved overflow and discharge: The number of overflows in the pipeline network is reduced by 15%, and the total overflow volume is reduced by about 12%; during the period when the water level of the outer river is permissible, the system automatically seizes the opportunity to discharge by itself, reducing the energy consumption of mechanical discharge.
[0048] (4) Prominent synergistic effect: Although relying solely on the construction of sponge bodies can reduce some runoff, the peak reduction delay is limited; after the introduction of intelligent scheduling, sponge bodies and gray infrastructure complement each other, realizing a complete chain of source interception, process regulation and storage and end control.
[0049] In the actual simulation of the case in City B, the system of the present invention showed significant disaster reduction effects: the peak flow was reduced by 15-20%, alleviating the pressure on downstream drainage; the maximum water depth was reduced by 20-30 cm, reducing the risk of residents' travel and traffic disruption; the duration of water accumulation was shortened by about 1.5 hours, improving the speed of urban function recovery; the total overflow of the pipeline network was reduced by 12%, improving the quality of the urban water environment; and the energy consumption of the pumping unit was reduced by about 10%, saving operating costs.
[0050] Therefore, this embodiment verifies the applicability of the system of the present invention in the southwestern region of Suqian. The results show that the system can realize the entire process application from multi-source data fusion, risk index calculation, intelligent scheduling to closed-loop feedback, effectively reducing flood peaks, decreasing inundated areas, and accelerating water receding speed under extreme rainfall conditions, significantly improving the city's flood control and disaster reduction capabilities and resilience. This embodiment also demonstrates that the present invention has good promotional value and can be replicated and applied in other flood-prone cities.
[0051] Corresponding to the above method embodiments, the present invention also provides a flood risk assessment system based on sponge city benefits, comprising: A data processing module is used to acquire real-time data and preprocess the real-time data, wherein the real-time data includes meteorological data, hydrological data, sponge city construction data, and real-time drainage data. The data fusion module is used to perform multiple fusions on the preprocessed data to build a unified data platform and form standardized rainfall, drainage system status, sponge body basic parameters and topographic exposure. The calculation module is used to calculate the storage benefits of the sponge body based on the basic parameters of the sponge body, combined with the sponge body area or volume, type coefficient, time-varying state and storage / infiltration response function. It also substitutes rainfall, drainage system status, topographic exposure and sponge body storage benefits into the multi-factor risk assessment model to obtain a comprehensive risk index. The scheme optimization module is used to generate the optimal scheduling scheme for the drainage system based on the comprehensive risk index and with the goal of minimizing the comprehensive cost, by combining the partitioned and hierarchical scheduling method with linear programming and heuristic optimization algorithms. The visualization module is used to output risk forecasts, scheduling suggestions, and emergency response plans for a specific future time period based on the comprehensive risk index and the optimal scheduling plan of the drainage system, and to display them visually.
[0052] The flood risk assessment system based on sponge city benefits in this embodiment is used to implement the aforementioned flood risk assessment method based on sponge city benefits. Therefore, its specific implementation can be referred to the description of the corresponding embodiments, and will not be elaborated here.
[0053] Furthermore, since the flood risk assessment system based on sponge city benefits in this embodiment is used to implement the aforementioned flood risk assessment method based on sponge city benefits, its function corresponds to the function of the above method, and will not be repeated here.
[0054] Corresponding to the above method embodiments, this embodiment of the invention also provides a computer device, including: Memory, which is used to store computer programs; A processor, used to execute computer programs, implements the steps of the above-described flood risk assessment method based on sponge body benefits.
[0055] In this embodiment of the invention, the processor may be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field-programmable gate array, or other programmable logic devices.
[0056] The processor can call programs stored in the memory; specifically, the processor can execute the operations described in the above embodiments of the flood risk assessment method based on sponge city benefits.
[0057] The memory is used to store one or more programs, which may include program code, including computer operation instructions.
[0058] In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.
[0059] Corresponding to the above method embodiments, this embodiment of the invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described flood risk assessment method based on sponge city benefits.
[0060] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0061] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0062] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0063] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0064] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.
Claims
1. A flood risk assessment method based on sponge city benefits, characterized in that: Includes the following steps: S1. Acquire real-time data and preprocess the real-time data, wherein the real-time data includes meteorological data, hydrological data, sponge city construction data, and real-time drainage data; S2. Perform multiple fusions on the preprocessed data to build a unified data platform and form standardized rainfall, drainage system status, sponge body basic parameters and topographic exposure. S3. Based on the basic parameters of the sponge body, combined with the sponge body area or volume, type coefficient, time-varying state and storage / infiltration response function, calculate the storage benefits of the sponge body; S4. Substitute rainfall, drainage system status, topographic exposure, and sponge body storage benefits into the multi-factor risk assessment model to obtain a comprehensive risk index; S5. Based on the comprehensive risk index, with the goal of minimizing the comprehensive cost, the optimal scheduling scheme for the drainage system is generated by combining the partitioned and hierarchical scheduling method with linear programming and heuristic optimization algorithms. S6. Based on the comprehensive risk index and the optimal scheduling scheme of the drainage system, output the risk forecast, scheduling suggestions and emergency response plan for a specific time period in the future, and display them visually.
2. The flood risk assessment method based on sponge city benefits according to claim 1, characterized in that: In S1, the preprocessing of the real-time data includes denoising, filling in missing values, and normalizing the data.
3. A flood risk assessment method based on sponge city benefits according to claim 1 or 2, characterized in that: In S2, multiple fusions are performed on the preprocessed data, including spatiotemporal fusion, multidimensional fusion, and real-time fusion.
4. The flood risk assessment method based on sponge city benefits according to claim 3, characterized in that: In S3, the formula for calculating the storage and regulation benefits of the sponge body is: ； In the formula, W(t) represents the sponge body's storage efficiency at the current time t, Ai represents the area or volume of the i-th sponge body, ki represents the type coefficient, θi(t) represents the time-varying state, fi(⋅) represents the storage / infiltration response function, and H(t) represents the rainfall at time t.
5. The flood risk assessment method based on sponge city benefits according to claim 4, characterized in that: In S4, the method of incorporating rainfall, drainage system status, topographic exposure, and sponge city water storage benefits into a multi-factor risk assessment model to obtain a comprehensive risk index includes: The formula for calculating the comprehensive risk index R(t) is as follows: ； In the formula, H(t) is the rainfall at time t, C(t) is the state of the drainage system, W(t) is the sponge body's storage effect, S(t) is the topographic exposure, and α1, α2, α3, α4 are weighting coefficients.
6. The flood risk assessment method based on sponge city benefits according to claim 5, characterized in that: In S5, the objective is to minimize the overall cost J: ； In the formula, Cj is the operating energy consumption of the j-th facility, xj is the status of whether the facility is in operation, R(t) is the comprehensive risk index, and λ is the risk penalty coefficient. The constraints include drainage volume constraints, energy constraints, and the storage capacity constraints of the sponge city.
7. The flood risk assessment method based on sponge city benefits according to claim 6, characterized in that: In S5, the zoned and hierarchical scheduling method includes first-level scheduling, second-level scheduling and third-level scheduling. First-level scheduling is to locally reduce peak flow in the pipeline network and pumping stations within the zone. Second-level scheduling is to coordinate and optimize across zones. Third-level scheduling is to dynamically select between automatic drainage and mechanical drainage.
8. A flood risk assessment system based on sponge city benefits, characterized in that: include: A data processing module is used to acquire real-time data and preprocess the real-time data, wherein the real-time data includes meteorological data, hydrological data, sponge city construction data, and real-time drainage data. The data fusion module is used to perform multiple fusions on the preprocessed data to build a unified data platform and form standardized rainfall, drainage system status, sponge body basic parameters and topographic exposure. The calculation module is used to calculate the storage benefits of the sponge body based on the basic parameters of the sponge body, combined with the sponge body area or volume, type coefficient, time-varying state and storage / infiltration response function. It also substitutes rainfall, drainage system status, topographic exposure and sponge body storage benefits into the multi-factor risk assessment model to obtain a comprehensive risk index. The scheme optimization module is used to generate the optimal scheduling scheme for the drainage system based on the comprehensive risk index and with the goal of minimizing the comprehensive cost, by combining the partitioned and hierarchical scheduling method with linear programming and heuristic optimization algorithms. The visualization module is used to output risk forecasts, scheduling suggestions, and emergency response plans for a specific future time period based on the comprehensive risk index and the optimal scheduling plan of the drainage system, and to display them visually.
9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 7.