Method for evaluating regional storage potential combining sensitivity and toughness and related equipment

Abstract

Embodiments of the present application provide a regional storage potential evaluation method combining sensitivity and resilience and related equipment. The method comprises obtaining geographical information corresponding to multiple data dimensions of a target region, and rasterizing the geographical information corresponding to the multiple data dimensions to obtain grid data; performing weighted summation on actual factors of the grid data to obtain a regional sensitivity level of the target region; identifying a depression range of the target region based on the grid data, and performing runoff simulation on the depression range to obtain hydrological characteristics; determining a resilience adjustment coefficient of the target region based on the hydrological characteristics; and determining an evaluation result of the target region based on the regional sensitivity level, the hydrological characteristics, and the resilience adjustment coefficient. The method can improve the accuracy of regional evaluation.

Description

Technical Field

[0001] This application relates to the field of urban planning technology, and in particular to a method and related equipment for evaluating the regional water storage potential that combines sensitivity and resilience. Background Technology

[0002] Sponge city planning refers to the process of urban planning and construction management that enables buildings, roads, green spaces, water systems, etc., to act like sponges, absorbing, storing, and slowly releasing rainwater, effectively controlling runoff, and achieving natural water circulation.

[0003] Sponge city regional evaluation is a systematic assessment that uses natural catchment areas as units to evaluate the effectiveness of rainwater management, flood control, and ecological restoration based on standardized criteria. It determines the actual level of sponge city construction in a region, accurately assesses the current water cycle status and existing risks, verifies the implementation of planning goals, and objectively measures the actual effectiveness of engineering construction. However, current regional evaluations lack sufficient accuracy. Summary of the Invention

[0004] The main purpose of this application is to propose a regional water storage potential assessment method and related equipment that combines sensitivity and resilience, in order to improve the accuracy of regional assessment.

[0005] To achieve the above objectives, this application proposes a method for evaluating regional water storage potential that combines sensitivity and resilience, including: Obtain geographic information corresponding to multiple data dimensions of the target area, and rasterize the geographic information corresponding to multiple data dimensions to obtain raster data; The actual factors of the raster data are weighted and summed to obtain the regional sensitivity level of the target area. Based on raster data, the depression range of the target area is identified, and runoff simulation is performed on the depression range to obtain hydrological characteristics; Determine the resilience adjustment coefficient of the target area based on hydrological characteristics; The evaluation results for the target area are determined based on the regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient.

[0006] Optionally, in one embodiment, determining the resilience adjustment coefficient of the target area based on hydrological characteristics includes: Based on preset parameter weights, multiple hydrological parameters in the hydrological features are weighted and fused to obtain the resilience adjustment coefficient of the target area.

[0007] Optionally, in one implementation, the actual factors of the raster data are weighted to obtain the regional sensitivity level of the target area, including: The actual factors in the raster data are obtained, and values ​​are assigned to the actual factors based on ecological sensitivity to obtain the factor score of each actual factor. Perform hierarchical analysis on the actual factors of the raster data to obtain the weights corresponding to the actual factors; The regional sensitivity level is obtained by weighting the factor scores based on the weights.

[0008] Optionally, in one implementation, identifying the depression range of the target area based on raster data includes: Traverse the indentation points in the raster data; Depressions are identified based on the depression points to determine their extent.

[0009] Optionally, in one embodiment, traversing the indentations in the raster data includes: Iterate through the elevation values ​​of the adjacent rasters of each raster to be analyzed in the raster data; Based on the elevation values ​​of each raster to be analyzed and the corresponding elevation values ​​of adjacent raster cells, depression points are determined from the raster data.

[0010] Optionally, in one embodiment, depression identification is performed based on the depression points to obtain the depression range, including: Perform topological connectivity analysis on the depression points in the raster data, and aggregate continuously connected depression point raster into depression cells; Edge correction is performed on the depression units to obtain the depression range.

[0011] Optionally, in one implementation, determining the evaluation results of the target area based on regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient includes: Obtain the influence weights corresponding to the regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient; Based on the influence weights, the regional sensitivity level, hydrological characteristics and resilience adjustment coefficient are weighted and fused to obtain the fusion score; The evaluation results for the target region are determined based on the fusion score and the preset evaluation threshold.

[0012] Optionally, in one embodiment, runoff simulation is performed on the depression area to obtain hydrological characteristics, including: Obtain time-series rainfall data of different rainfall levels in the target area; Runoff simulation was performed on the depression area based on rainfall time series data to obtain hydrological characteristics under different rainfall levels; Determining the resilience adjustment coefficient of the target area based on hydrological characteristics includes: Based on the hydrological characteristics of different rainfall levels, the resilience adjustment coefficients corresponding to different rainfall levels in the target area are determined.

[0013] Optionally, in one implementation, determining the evaluation results of the target area based on regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient includes: The basic score is determined based on the regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient. When the regional sensitivity level indicates high sensitivity risk and the hydrological characteristics indicate strong runoff risk, a risk amplification correction term is determined; The baseline score is nonlinearly corrected based on the resilience adjustment coefficient and risk amplification correction term corresponding to different rainfall levels to obtain the comprehensive score corresponding to different rainfall levels. The comprehensive scores corresponding to different rainfall levels are then integrated to obtain the evaluation results of the target area.

[0014] Another aspect of this application provides a regional water storage potential evaluation device that combines sensitivity and resilience, comprising: The acquisition unit is used to acquire geographic information corresponding to multiple data dimensions of the target area, and to rasterize the geographic information corresponding to multiple data dimensions to obtain raster data. The weighted unit is used to sum the actual factors of the raster data in a weighted manner to obtain the regional sensitivity level of the target area; The simulation unit is used to identify the depression range of the target area based on raster data, and to simulate the runoff of the depression range to obtain hydrological characteristics; The first determining unit is used to determine the resilience adjustment coefficient of the target area based on hydrological characteristics; The second determining unit is used to determine the evaluation results of the target area based on the regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient.

[0015] Another aspect of this application provides an electronic device, comprising: Memory, transceiver, processor, and bus system; The memory is used to store programs; The processor is used to execute programs in memory, including methods for performing the aspects mentioned above; Bus systems are used to connect memory and processor to enable communication between them.

[0016] Another aspect of this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the methods described above.

[0017] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: This method rasterizes multi-dimensional geographic information of the target area into raster data, assigns corresponding weights to various environmental impact factors to determine the sensitivity level of the region, uses the spatial characteristics of the raster data to determine the distribution range of depressions, conducts runoff process simulation for the depressions, obtains various hydrological characteristics of the region, and adapts a resilience adjustment coefficient based on the hydrological characteristics to the actual conditions of the region. Finally, it integrates the regional sensitivity level, hydrological characteristic values, and resilience adjustment coefficient to calculate a complete regional evaluation result. Through weighted analysis, it objectively reflects the regional ecological carrying capacity; the depression runoff simulation can realistically reproduce the rainwater runoff movement pattern; the resilience coefficient can reflect the region's water storage and disaster resistance capabilities; and the combination of multiple indicators makes the final evaluation result more consistent with the actual situation, improving the accuracy of the regional evaluation. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the architecture of the regional water storage potential evaluation method that combines sensitivity and resilience provided in the embodiments of this application; Figure 2 This is a flowchart illustrating the regional water storage potential evaluation method that combines sensitivity and resilience, as provided in the embodiments of this application. Figure 3 This is a schematic diagram of the low-lying area water storage potential evaluation matrix provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the regional storage potential evaluation device combining sensitivity and resilience provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0020] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0021] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0022] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0023] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can be implemented without certain specific details. In some instances, methods, means, components, and circuits well-known to those skilled in the art have not been described in detail in order to highlight the main points of this application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0025] Sponge city regional assessment is a systematic evaluation process that uses natural catchment areas as units to calculate the effectiveness of rainwater management, flood control, and ecological restoration based on standardized criteria. It determines the actual level of sponge city construction in a region, accurately grasping the current water cycle status and existing risks, verifying the implementation of planning goals, and objectively measuring the actual effectiveness of engineering construction. However, current traditional evaluation methods rely solely on topographic analysis or focus on static identification of physical space, failing to effectively integrate multi-source geospatial data. This results in low accuracy of regional assessments and limited precision in identifying water storage and regulation spaces in complex urban environments.

[0026] Based on this, embodiments of this application provide a method for evaluating regional storage potential that combines sensitivity and resilience, which can solve the above-mentioned technical problems.

[0027] System architecture and scenario description used in the embodiments of this application: Figure 1 This is a system architecture diagram of a regional water storage potential evaluation method combining sensitivity and resilience, according to an embodiment of this application. It includes a terminal 140, an Internet 130, a gateway 120, a server 110, etc.

[0028] Terminal 140 includes various forms of devices with display screens, such as desktop computers, laptops, PDAs (personal digital assistants), mobile phones, in-vehicle terminals, home theater terminals, dedicated terminals, intelligent voice interaction devices, smart home appliances, or aircraft. Furthermore, it can be a single device or a collection of multiple devices. Terminal 140 can communicate with the Internet 130 via wired or wireless means to exchange data.

[0029] Server 110 refers to a computer system that can provide certain services to terminal 140. Compared to ordinary terminal 140, server 110 has higher requirements in terms of stability, security, and performance. Server 110 can be a single high-performance computer in a network platform, a cluster of multiple high-performance computers, a portion of a single high-performance computer (e.g., a virtual machine), or a combination of portions of multiple high-performance computers (e.g., virtual machines).

[0030] Gateway 120, also known as an internetwork connector or protocol converter, is a computer system or device that acts as a translator, enabling network interconnection at the transport layer. It bridges the gap between two systems using different communication protocols, data formats, languages, or even completely different architectures. Gateways can also provide filtering and security functions. Messages sent from terminal 140 to server 110 are forwarded to the corresponding server 110 via gateway 120. Messages sent from server 110 to terminal 140 are also forwarded to the corresponding terminal 140 via gateway 120.

[0031] The regional storage potential evaluation method combining sensitivity and resilience provided in this application embodiment can be implemented alone in terminal 140, alone in server 110, or partially in terminal 140 and partially in server 110.

[0032] When the regional water storage potential evaluation method combining sensitivity and resilience provided in this application embodiment is implemented alone in terminal 140, terminal 140 acquires geographic information corresponding to multiple data dimensions of the target area, and rasterizes the geographic information corresponding to multiple data dimensions to obtain raster data; terminal 140 performs weighted summation of the actual factors of the raster data to obtain the regional sensitivity level of the target area; terminal 140 identifies the depression range of the target area based on the raster data, and performs runoff simulation on the depression range to obtain hydrological characteristics; terminal 140 determines the resilience adjustment coefficient of the target area based on the hydrological characteristics; terminal 140 determines the evaluation result of the target area based on the regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient.

[0033] The method for evaluating regional water storage potential combining sensitivity and resilience, provided in the embodiments of this application, will now be described in conjunction with the accompanying drawings. The execution subject of the method for evaluating regional water storage potential combining sensitivity and resilience is a terminal device, which can be implemented by the terminal device running the various computer programs described above. Of course, based on the understanding of the following text, it is not difficult to see that the method for evaluating regional water storage potential combining sensitivity and resilience, provided in the embodiments of this application, can also be implemented by the terminal device and the server working together.

[0034] Please see Figure 2 ,like Figure 2 The diagram shown is a flowchart illustrating a regional water storage potential evaluation method combining sensitivity and resilience provided in this embodiment. The method includes: Step 201: Obtain geographic information corresponding to multiple data dimensions of the target area, and rasterize the geographic information corresponding to multiple data dimensions to obtain raster data.

[0035] Geographic information can be spatial information that represents the spatial location of the Earth's surface, topography, land use attributes, water system ecology, etc. Here, it specifically refers to various types of spatial data such as topography, land use, soil, water system, and rainfall that support sponge city assessment.

[0036] Rasterization is a process of converting vector-based spatial information into regular grid cell data. Here, it can unify various types of scattered geographic information into a uniform grid data format. Different categories of spatial data from the target area to be evaluated are uniformly converted into standardized raster data.

[0037] Step 202: Perform a weighted summation of the actual factors of the raster data to obtain the regional sensitivity level of the target area.

[0038] Actual factors can be various objective elements that can influence the spatial state and environmental conditions of a region, such as topographic relief, land use type, ecological cover, and hydrological conditions, which affect regional development and rainwater regulation. Different elements have different degrees of influence. By assigning corresponding weight values ​​to each factor according to the strength of its influence on the regional state, a fusion calculation can be carried out on each element based on the weights to quantify the regional sensitivity, reflecting the degree of regional ecological pressure and development constraints. This regional sensitivity can be expressed in the form of a regional sensitivity level.

[0039] Step 203: Identify the depression range of the target area based on raster data, and perform runoff simulation on the depression range to obtain hydrological characteristics.

[0040] A depression can be a low-lying area where water easily collects and accumulates; that is, a spatial range within a region where rainwater tends to gather and stagnate. Topographical comparisons can be performed using raster data to identify and define the boundaries of this area.

[0041] Runoff simulation can replicate the dynamic process of rainwater collection, flow, and discharge. It can deduce the changes in rainwater movement in depressions based on hydrological calculation logic and calculate parameters such as runoff velocity, water volume, and drainage time, which can be called hydrological characteristics.

[0042] Step 204: Determine the resilience adjustment coefficient of the target area based on hydrological characteristics.

[0043] The resilience adjustment coefficient can be used to measure the adaptive adjustment capacity of a region to cope with hydrological fluctuations and flood impacts. It is used to characterize the comprehensive ability of a site to store, divert, and resist risks. The corresponding coefficient value can be obtained by using the simulated hydrological characteristic parameters as the calculation basis and converting them with the established calculation formula.

[0044] Step 205: Determine the evaluation results of the target area based on the regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient.

[0045] The three criteria of sensitivity level, hydrological characteristics and resilience adjustment coefficient are integrated. Each condition is quantitatively defined according to a unified standard, the hierarchical range of each attribute is clarified, and a comprehensive judgment is made in combination with the actual characteristics of the three types of elements to determine the evaluation conclusion of the target area, such as dividing the evaluation results into four levels: excellent, good, medium and poor.

[0046] In one example, the method for determining the evaluation results of a target area based on regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient can be based on... Figure 3 The diagram shows a low-lying area storage capacity evaluation matrix. The horizontal axis represents the physical storage capacity evaluation axis, including depression volume and runoff characteristics, which can be indicated by hydrological characteristics to measure the site's natural potential to receive and retain rainwater. The vertical axis represents the suitability for construction and urban resilience constraints axis, indicated by ecological sensitivity level and resilience adjustment coefficient, reflecting the site's development restrictions and ecological security constraints. Through cross-analysis of these two dimensions, the matrix classifies sites into different control types. The upper right quadrant, where both the physical storage capacity axis and the suitability for construction and resilience constraints axis values ​​are high, indicates a higher development priority and can be termed a high-potential priority development zone, referring to areas with strong physical storage capacity and low environmental constraints. Conversely, the lower left quadrant, where both axes values ​​are low, can be considered areas constrained by ecological red lines, with weak physical storage capacity and limited construction conditions, resulting in a low development priority. This can be termed an ecological protection prohibited development zone, requiring strict protection of native water systems and ecological green spaces to avoid disrupting the natural hydrological cycle.

[0047] Effective influencing factors are extracted from raster data to complete weighted calculations, objectively classifying the ecological sensitivity of the region. Based on the grid topographic features, water accumulation risk areas are identified and the real hydrological movement is restored. The region's disaster resistance and regulation capacity is derived from hydrological parameters according to calculation rules. Multi-dimensional indicators corroborate and complement each other, avoiding the result bias caused by a single evaluation method, and comprehensively, objectively and accurately reflecting the natural conditions of the region.

[0048] In one implementation, determining the resilience adjustment coefficient of a target area based on hydrological characteristics includes: Based on preset parameter weights, multiple hydrological parameters in the hydrological features are weighted and fused to obtain the resilience adjustment coefficient of the target area.

[0049] In this implementation, the parameter weights can be proportional values ​​set according to the magnitude of the influence of the elements. That is, corresponding values ​​are determined by combining the strength of the influence of hydrological parameters on the regional flood control and storage capacity, and the values ​​are pre-assigned and stored with reference to industry evaluation standards. The simulated hydrological parameters are retrieved, and the pre-set parameter weights are called. The corresponding weight values ​​are matched to each parameter in turn to perform weighted calculations. The scattered individual hydrological indicators are integrated and summarized, and a resilience adjustment coefficient that can comprehensively reflect the hydrological carrying capacity of the site is obtained through unified calculation.

[0050] The calculation method based on multi-parameter weighted fusion can take into account the different effects of various hydrological indicators, avoid the one-sidedness caused by single parameter judgment, and accurately quantify the level of regional hydrological self-regulation and risk response.

[0051] In one implementation, the actual factors of the raster data are weighted to obtain the regional sensitivity level of the target area, including: The actual factors in the raster data are obtained, and values ​​are assigned to the actual factors based on ecological sensitivity to obtain the factor score of each actual factor. Perform hierarchical analysis on the actual factors of the raster data to obtain the weights corresponding to the actual factors; The regional sensitivity level is obtained by weighting the factor scores based on the weights.

[0052] In this implementation, ecological sensitivity can be defined as the degree to which the site's ecosystem is susceptible to changes in its state after being disturbed by external factors.

[0053] Factor scores can be numerical values ​​that quantify the sensitivity of a single element, calculated item by item according to a predetermined grading standard and by comparing factor attributes.

[0054] Hierarchical analysis (AHP) is an analytical method that breaks down the relationships between elements layer by layer and determines the hierarchy of influence. It is used to analyze the proportion of each actual factor's impact on the overall state of a region. After statistical analysis using AHP, corresponding weight values ​​are determined. Finally, weighted calculations are performed on the factor scores based on these weight values, and the summation of the results determines the regional sensitivity level of the target area.

[0055] Hierarchical assignment can accurately reflect the sensitive differences of single elements, hierarchical analysis can reasonably distinguish the primary and secondary relationships of factor influence, and the weighted integration of the comprehensive effects of multiple elements can objectively reflect the degree of regional ecological vulnerability.

[0056] In one implementation, identifying the depression range of a target area based on raster data includes: Traverse the indentation points in the raster data; Depressions are identified based on the depression points to determine their extent.

[0057] In this implementation, depressions can be spatial locations where the elevation of the terrain layer is lower than that of adjacent points, and can be obtained by searching and filtering based on the grid elevation attribute. Then, based on the distribution pattern and connectivity of depressions, contiguous areas can be identified, and the complete depression area can be delineated.

[0058] Point-by-point screening can comprehensively capture low-lying areas and accurately delineate catchment areas based on the distribution characteristics of the points, ensuring that the identification of depressions matches the actual terrain.

[0059] In one implementation, traversing the indentations in the raster data includes: Iterate through the elevation values ​​of the adjacent rasters of each raster to be analyzed in the raster data; Based on the elevation values ​​of each raster to be analyzed and the corresponding elevation values ​​of adjacent raster cells, depression points are determined from the raster data.

[0060] In this implementation, adjacent grids can be grid cells that are spatially connected to each other, that is, grid objects that are directly adjacent to the periphery of a single analysis grid.

[0061] Elevation values ​​can be the height of a surface point relative to a reference plane, representing the terrain height attribute parameter inherent in each grid cell. A depression point is a terrain point where the terrain is lower than the surrounding area. The process involves retrieving the elevation data of each grid cell to be analyzed, comparing the elevation value of a single grid cell with the elevation values ​​of its surrounding grid cells, and selecting grid cells that meet the characteristics of terrain depression according to the height-lowering criteria. This completes the determination of depression points.

[0062] By comparing and screening points across the entire neighborhood elevation, we can accurately identify depression locations without missing any, ensuring the accuracy of determining the basic points in depressions, in accordance with the actual terrain undulations.

[0063] In one implementation, depression identification is performed based on depression points to obtain the depression range, including: Perform topological connectivity analysis on the depression points in the raster data, and aggregate continuously connected depression point raster into depression cells; Edge correction is performed on the depression units to obtain the depression range.

[0064] In this embodiment, topological connectivity can be the positional correlation characteristics between spatial units that are interconnected and connected. It is used to determine whether the recessed point grids can be connected to each other, and can be determined by the adjacency relationship of the grid space.

[0065] A depression unit can be an independent low-lying area composed of a series of sinking points, which can be understood as a water catchment base block formed in the initial integration.

[0066] Edge correction can be a method of adjusting regional boundaries based on terrain trends, supplementing the boundary outline by incorporating surrounding elevation changes, and optimizing the delineation of land parcels. In other words, it involves fine-tuning the unit boundary outline based on the overall terrain undulations to eliminate judgment biases and ultimately obtain a complete and standardized depression area.

[0067] Connectivity aggregation can efficiently group similar low-lying areas and divide them into independent catchment blocks. Edge correction can conform to the actual terrain to correct boundary errors, so that the delineated depression range conforms to the natural surface morphology and improves the accuracy of subsequent runoff simulation.

[0068] In one implementation, determining the evaluation results of the target area based on regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient includes: Obtain the influence weights corresponding to the regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient; Based on the influence weights, the regional sensitivity level, hydrological characteristics and resilience adjustment coefficient are weighted and fused to obtain the fusion score; The evaluation results for the target region are determined based on the fusion score and the preset evaluation threshold.

[0069] In this implementation, the influence weight can be a proportional value that measures the magnitude of the effects of different evaluation elements, and the corresponding proportion parameter can be set according to the degree of influence of the three types of indicators on the level of sponge area construction.

[0070] First, the influence weights corresponding to the regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient are retrieved. The three indicators are then weighted and integrated according to the weight ratio to calculate the overall integrated score. The calculation results are compared with the predetermined evaluation thresholds one by one, and the level is determined according to the interval of the score. Finally, the comprehensive evaluation result corresponding to the target area is determined.

[0071] The weighted integration of multi-dimensional indicators can balance the evaluation value of various elements, avoid the limitations of single indicator judgment, and form a standardized judgment scale based on threshold grading. The resulting evaluation conclusions are comprehensive and objective, and can accurately reflect the regional sponge substrate conditions and hydrological carrying capacity.

[0072] In one implementation, runoff simulation is performed on the depression area to obtain hydrological characteristics, including: Obtain time-series rainfall data of different rainfall levels in the target area; Runoff simulation was performed on the depression area based on rainfall time series data to obtain hydrological characteristics under different rainfall levels; Determining the resilience adjustment coefficient of the target area based on hydrological characteristics includes: Based on the hydrological characteristics of different rainfall levels, the resilience adjustment coefficients corresponding to different rainfall levels in the target area are determined.

[0073] In this implementation, different rainfall levels can be classified by rainfall amount and rainfall intensity gradient. In the engineering field, the return period of rainstorms is often used to distinguish the size of rainfall, covering multiple gradient rainfall conditions from light rain, moderate rain, heavy rain to torrential rain. Multiple return period rainstorms with a return period of 1 year, 3 years, 5 years, 10 years and 20 years are used as classification standards to fully cover the empty, semi-saturated and fully saturated water storage states of depressions.

[0074] Rainfall time-series data can be a set of rainfall data recorded at continuous time steps. Each data point corresponds to the real-time rainfall intensity and cumulative rainfall at a specific moment, reflecting the change of rainfall over time. It includes continuous rainfall intensity time series recorded hourly / minutely for each rainfall intensity level. Specifically, using a defined target area as the analysis boundary, and employing storm intensity formulas or meteorological data sources, it generates and collects batches of continuously changing rainfall intensity sequences at multiple gradient rainfall conditions, comprehensively gathering time-series rainfall information for the entire process of rainfall of varying magnitudes.

[0075] Using the time-series rainfall data obtained in the previous step as the input boundary of the hydrological model, continuous time-series runoff simulation calculations are performed only for the pre-identified depression grid range under different working conditions. The dynamically changing depression water volume indicators for each rainfall level throughout the entire process are output, resulting in a time-series hydrological feature set corresponding to each rainfall level. This set includes instantaneous effective storage capacity (depression water saturation), peak runoff for each time period, cumulative runoff, and runoff reduction rate. Each rainfall condition corresponds to a set of independent and complete time-series hydrological indicators.

[0076] The resilience adjustment coefficients corresponding to different rainfall intensities can be matched one-to-one with the dynamic coefficient sequences of each rainfall gradient condition. Heavy rain and light rain conditions each have independent resilience change curves. The greater the rainfall intensity, the easier it is for the depression to saturate, and the lower the overall value of the resilience adjustment coefficient corresponding to the same grid. The hydrological feature datasets stored in the previous step can be grouped and processed. Each group of hydrological data under the rainfall condition is independently substituted into a nonlinear function to generate a time-series resilience coefficient specific to that rainfall intensity, thus achieving a one-to-one binding between rainfall conditions and resilience coefficients.

[0077] By using time-series hydrological time-series iterative calculations to generate dynamically changing resilience adjustment coefficients, the corresponding coefficients can be updated synchronously when the water saturation state of the depression changes, accurately matching the actual rainwater and flood buffering capacity of the depression at the moment, and avoiding evaluation biases caused by static coefficients that overestimate or underestimate regional resilience.

[0078] In one implementation, determining the evaluation results of the target area based on regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient includes: The basic score is determined based on the regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient. When the regional sensitivity level indicates high sensitivity risk and the hydrological characteristics indicate strong runoff risk, a risk amplification correction term is determined; The baseline score is nonlinearly corrected based on the resilience adjustment coefficient and risk amplification correction term corresponding to different rainfall levels to obtain the comprehensive score corresponding to different rainfall levels. The comprehensive scores corresponding to different rainfall levels are then integrated to obtain the evaluation results of the target area.

[0079] In this implementation, the regional sensitivity level corresponding to each grid cell, the time-series hydrological characteristics of different rainfall conditions, and the dynamically updated resilience adjustment coefficient under the corresponding conditions are used as fusion input parameters. The multi-index coupled constraint fusion calculation logic is used to complete the joint calculation of the three types of parameters and solve for the basic score of each grid cell under each rainfall condition.

[0080] High sensitivity risk can be defined as a site's ecological vulnerability reaching a critical threshold, where even minor hydrological disturbances can cause severe ecological damage. Specifically, if the sensitivity level of a grid cell exceeds a preset ecological sensitivity classification threshold, it indicates that the site has a fragile ecological foundation and extremely poor resistance to flooding disturbances.

[0081] Severe runoff risk can be a hydrological risk level where the runoff volume and peak runoff intensity of a plot exceed the drainage and conventional carrying capacity standards of the depression, easily leading to severe waterlogging and flooding. Specifically, it is a state indicator where the peak runoff and cumulative runoff volume in the grid time-series hydrological characteristics simultaneously exceed the preset runoff classification threshold under the corresponding rainfall conditions, representing a high level of runoff impact intensity and high flood input load in the depression grid.

[0082] Two types of risk judgment conditions are simultaneously verified on a grid-by-grid basis and under different rainfall conditions. When the regional sensitivity level of a grid cell reaches the high sensitivity critical threshold and the runoff index in the same time series hydrological characteristics meets the strong runoff judgment criteria, the grid is judged to have a double risk of ecological and rainstorm. Based on the ecological and hydrological double critical thresholds, a collaborative amplification function is constructed, and a risk amplification correction term adapted to the current rainfall scenario is generated.

[0083] Nonlinear correction can be a score correction calculation method that does not follow a fixed proportional linear increase or decrease, but adjusts the correction magnitude according to changes in the independent variable, where there is no constant direct proportional relationship between the variables. That is, the correction magnitude of the risk amplification correction term is constrained by the dynamic resilience adjustment coefficient. The resilience coefficient value is different depending on the water saturation state of the depression, and the corresponding risk amplification correction intensity changes synchronously and adaptively. The correction magnitude fluctuates nonlinearly with the resilience coefficient.

[0084] For each rainfall condition, the dynamic resilience adjustment coefficient and the corresponding risk amplification correction term of the raster are retrieved. These two parameters are linked to construct a nonlinear correction function to apply differentiated amplitude correction to the raster base score, thus obtaining a raster comprehensive score specific to each rainfall level. The comprehensive score datasets corresponding to all rainfall conditions are collected, and after eliminating differences in score intervals across multiple conditions, unified classification and spatial statistical integration are performed. Finally, a complete and unified global comprehensive evaluation result for the target area is output.

[0085] By leveraging the dynamic resilience adjustment coefficient specific to different rainfall conditions to constrain the correction range of the risk amplification correction term, nonlinear differentiated score correction is achieved. When the depression has sufficient water storage and the resilience coefficient is high, the risk amplification intensity is automatically reduced. When the depression is completely saturated and loses its buffering capacity and the resilience coefficient is low, the risk amplification intensity is simultaneously increased. This precisely matches the real-time water storage carrying capacity of the depression to adjust the composite risk assessment range, objectively and comprehensively reflecting the overall spatial distribution characteristics of the ecological and stormwater composite resilience risk of the target area, and improving the assessment effect.

[0086] The above describes a method for evaluating regional water storage potential that combines sensitivity and resilience. The following describes the apparatus for implementing this method.

[0087] Please see Figure 4 ,like Figure 4 The image shows a regional water storage potential evaluation device combining sensitivity and resilience, according to an embodiment of this application. The device 400 includes: The acquisition unit 401 is used to acquire geographic information corresponding to multiple data dimensions of the target area, and to rasterize the geographic information corresponding to multiple data dimensions to obtain raster data. Weighting unit 402 is used to sum the actual factors of the raster data to obtain the regional sensitivity level of the target area; The simulation unit 403 is used to identify the depression range of the target area based on raster data, and to simulate the runoff of the depression range to obtain hydrological characteristics. The first determining unit 404 is used to determine the resilience adjustment coefficient of the target area based on hydrological characteristics; The second determining unit 405 is used to determine the evaluation results of the target area based on the regional sensitivity level, hydrological characteristics and resilience adjustment coefficient.

[0088] Optionally, in one embodiment, the first determining unit 404 is specifically used for: Based on preset parameter weights, multiple hydrological parameters in the hydrological features are weighted and fused to obtain the resilience adjustment coefficient of the target area.

[0089] Optionally, in one embodiment, the weighting unit 402 is specifically used for: The actual factors in the raster data are obtained, and values ​​are assigned to the actual factors based on ecological sensitivity to obtain the factor score of each actual factor. Perform hierarchical analysis on the actual factors of the raster data to obtain the weights corresponding to the actual factors; The regional sensitivity level is obtained by weighting the factor scores based on the weights.

[0090] Optionally, in one embodiment, the simulation unit 403 is specifically used for: Traverse the indentation points in the raster data; Depressions are identified based on the depression points to determine their extent.

[0091] Optionally, in one embodiment, the simulation unit 403 is specifically used for: Iterate through the elevation values ​​of the adjacent rasters of each raster to be analyzed in the raster data; Based on the elevation values ​​of each raster to be analyzed and the corresponding elevation values ​​of adjacent raster cells, depression points are determined from the raster data.

[0092] Optionally, in one embodiment, the simulation unit 403 is specifically used for: Perform topological connectivity analysis on the depression points in the raster data, and aggregate continuously connected depression point raster into depression cells; Edge correction is performed on the depression units to obtain the depression range.

[0093] Optionally, in one embodiment, the second determining unit 405 is specifically used for: Obtain the influence weights corresponding to the regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient; Based on the influence weights, the regional sensitivity level, hydrological characteristics and resilience adjustment coefficient are weighted and fused to obtain the fusion score; The evaluation results for the target region are determined based on the fusion score and the preset evaluation threshold.

[0094] Optionally, in one embodiment, the simulation unit 403 is specifically used for: Obtain time-series rainfall data of different rainfall levels in the target area; Runoff simulation was performed on the depression area based on rainfall time series data to obtain hydrological characteristics under different rainfall levels; The first determining unit 404 is specifically used for: Based on the hydrological characteristics of different rainfall levels, the resilience adjustment coefficients corresponding to different rainfall levels in the target area are determined.

[0095] Optionally, in one embodiment, the second determining unit 405 is specifically used for: The basic score is determined based on the regional sensitivity level, hydrological characteristics, and resilience adjustment coefficient. When the regional sensitivity level indicates high sensitivity risk and the hydrological characteristics indicate strong runoff risk, a risk amplification correction term is determined; The baseline score is nonlinearly corrected based on the resilience adjustment coefficient and risk amplification correction term corresponding to different rainfall levels to obtain the comprehensive score corresponding to different rainfall levels. The comprehensive scores corresponding to different rainfall levels are then integrated to obtain the evaluation results of the target area.

[0096] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method for evaluating regional water storage potential that combines sensitivity and resilience. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0097] Please see Figure 5 , Figure 5 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 501 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 502 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 502 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 502 and is called and executed by the processor 501 using the regional water storage potential evaluation method combining sensitivity and resilience of the embodiments of this application. The input / output interface 503 is used to implement information input and output; The communication interface 504 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 505 transmits information between various components of the device (e.g., processor 501, memory 502, input / output interface 503, and communication interface 504); The processor 501, memory 502, input / output interface 503, and communication interface 504 are connected to each other within the device via bus 505.

[0098] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for evaluating regional water storage potential that combines sensitivity and resilience.

[0099] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0100] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0101] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0102] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0103] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0104] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification 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.

[0105] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0107] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0109] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0110] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for evaluating regional water storage potential that combines sensitivity and resilience, characterized in that, include: Geographic information corresponding to multiple data dimensions of the target area is obtained, and the geographic information corresponding to the multiple data dimensions is rasterized to obtain raster data; The actual factors of the raster data are weighted and summed to obtain the regional sensitivity level of the target area. Based on the grid data, the depression range of the target area is identified, and runoff simulation is performed on the depression range to obtain hydrological characteristics; The resilience adjustment coefficient of the target area is determined based on the hydrological characteristics. The evaluation results of the target area are determined based on the regional sensitivity level, the hydrological characteristics, and the resilience adjustment coefficient.

2. The method according to claim 1, characterized in that, Determining the resilience adjustment coefficient of the target area based on the hydrological characteristics includes: Based on preset parameter weights, multiple hydrological parameters in the hydrological features are weighted and fused to obtain the resilience adjustment coefficient of the target area.

3. The method according to claim 1, characterized in that, The step of weighting the actual factors of the raster data to obtain the regional sensitivity level of the target region includes: The actual factors in the raster data are obtained, and values ​​are assigned to the actual factors based on ecological sensitivity to obtain the factor score of each actual factor; Perform hierarchical analysis on the actual factors of the raster data to obtain the weights corresponding to the actual factors; The factor scores are weighted based on the weights to obtain the regional sensitivity level.

4. The method according to claim 1, characterized in that, The process of identifying the depression range of the target area based on the raster data includes: Traverse the indentation points in the raster data; Depressions are identified based on the depression points to determine the extent of the depressions.

5. The method according to claim 1, characterized in that, The evaluation result for determining the target area based on the regional sensitivity level, the hydrological characteristics, and the resilience adjustment coefficient includes: Obtain the influence weights corresponding to the regional sensitivity level, the hydrological characteristics, and the resilience adjustment coefficient; Based on the influence weights, the regional sensitivity level, the hydrological characteristics, and the resilience adjustment coefficient are weighted and fused to obtain a fusion score; The evaluation result of the target region is determined based on the fusion score and the preset evaluation threshold.

6. The method according to claim 1, characterized in that, The runoff simulation of the depression area yields hydrological characteristics, including: Acquire time-series rainfall data of different rainfall levels in the target area; Based on the rainfall time series data, runoff simulation was performed on the depression area to obtain hydrological characteristics at different rainfall levels; Determining the resilience adjustment coefficient of the target area based on the hydrological characteristics includes: Based on the hydrological characteristics of different rainfall levels, the resilience adjustment coefficients corresponding to different rainfall levels in the target area are determined.

7. The method according to claim 6, characterized in that, The evaluation result for determining the target area based on the regional sensitivity level, the hydrological characteristics, and the resilience adjustment coefficient includes: A basic score is determined based on the regional sensitivity level, the hydrological characteristics, and the resilience adjustment coefficient. When the regional sensitivity level indicates a high sensitivity risk and the hydrological characteristics indicate a strong runoff risk, a risk amplification correction term is determined; The baseline score is nonlinearly corrected based on the resilience adjustment coefficient corresponding to different rainfall levels and the risk amplification correction term to obtain a comprehensive score corresponding to different rainfall levels. The comprehensive scores corresponding to different rainfall levels are then integrated to obtain the evaluation result of the target area.

8. A regional water storage potential evaluation device combining sensitivity and resilience, characterized in that, include: The acquisition unit is used to acquire geographic information corresponding to multiple data dimensions of the target area, and to rasterize the geographic information corresponding to the multiple data dimensions to obtain raster data. A weighting unit is used to sum the actual factors of the raster data to obtain the regional sensitivity level of the target region. The simulation unit is used to identify the depression range of the target area based on the grid data, and to perform runoff simulation on the depression range to obtain hydrological characteristics; The first determining unit is used to determine the resilience adjustment coefficient of the target area based on the hydrological characteristics; The second determining unit is used to determine the evaluation result of the target area based on the area sensitivity level, the hydrological characteristics, and the resilience adjustment coefficient.

9. An electronic device, characterized in that, include: Memory, transceiver, processor, and bus system; The memory is used to store programs; The processor is configured to execute a program in the memory, including performing the method as described in any one of claims 1 to 7; The bus system is used to connect the memory and the processor to enable communication between the memory and the processor.

10. A computer-readable storage medium, characterized in that, Includes instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

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