Urban waterlogging and stormwater data assimilation method, system and storage medium

Abstract

The application relates to the technical field of urban flood control and drainage, and discloses a city waterlogging rain flood data assimilation method, a system and a storage medium. The method comprises the following steps: obtaining rainfall, flow and surface elevation data from multi-source hydrological observation data, and obtaining a multi-source data set after preprocessing; extracting hydrological parameters based on the data set, and determining the influence weight of the hydrological parameters on the flooded area by combining parameter sensitivity analysis; updating the key hydrological parameters through a filtering algorithm to obtain a refined hydrological parameter set; combining terrain gradient analysis and a prediction algorithm to establish a prediction model of the change rate of the flooded boundary, and dynamically adjusting the inversion time interval; correcting the surface elevation in real time through a finite element method to improve the flooded prediction accuracy; further optimizing the hydrological parameters according to the real-time prediction result, and generating a drainage scheduling decision instruction. The application has high accuracy and real-time performance, can provide scientific and effective decision support for a city flood control and drainage system, and significantly improves the emergency response capability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban flood control and drainage, and particularly relates to a method and system for assimilating urban waterlogging and rainstorm data, and a storage medium. The method and system can be widely applied to the fields of urban waterlogging prediction and emergency management, urban drainage system optimization, flood warning and scheduling, and the like, and help to improve the accuracy and response speed of the urban flood control and drainage system. BACKGROUND

[0002] With the acceleration of urbanization, the problem of urban waterlogging is becoming increasingly serious, which brings many troubles to urban management and residents' life. The traditional urban drainage system often cannot respond in time under extreme weather and heavy rain conditions, resulting in a large amount of water accumulation, road traffic paralysis, infrastructure damage, and even catastrophic consequences. Therefore, accurately warning and predicting the occurrence of urban waterlogging and flood, and timely and effective drainage scheduling have become important goals to improve the urban disaster resistance.

[0003] In the past research, waterlogging prediction mainly relies on hydrological models and numerical simulation. However, the traditional hydrological model often ignores the complexity of urban terrain and the dynamic changes of water flow, resulting in low prediction accuracy. In addition, the acquisition of urban hydrological observation data depends on distributed observation systems, and various data sources have different accuracies, which makes the input hydrological parameters of the model not accurate enough. In recent years, with the development of remote sensing technology and geographic information system (GIS) technology, researchers have gradually introduced multi-source data fusion, data assimilation and real-time updating mechanism to improve the prediction accuracy.

[0004] Although the existing technology has made certain progress in waterlogging prediction, it still faces problems such as hydrological parameter sensitivity, real-time data updating and multi-source data fusion. In addition, due to the complexity of urban terrain, the influence of surface elevation and water flow gradient on water flow path and inundation boundary has not been effectively integrated, and the traditional model cannot accurately reflect the dynamic changes of urban waterlogging.

[0005] The present application aims to solve the above technical problems by introducing a prediction method based on multi-source data assimilation, using accurate hydrological parameters and real-time terrain correction to improve the accuracy and response speed of waterlogging prediction, thereby providing a scientific basis for urban drainage scheduling and helping the city to effectively cope with waterlogging and flood disasters. SUMMARY

[0006] The present application relates to a method and system for assimilating urban waterlogging and rainstorm data, and a storage medium, which aims to improve the accuracy and real-time performance of waterlogging and flood prediction by fusing multi-source hydrological data and establishing a prediction model, and to provide more scientific decision support for urban drainage management and emergency response.

[0007] In a first aspect, the present application provides a method for assimilating urban waterlogging and stormwater data, the method comprising:

[0008] Step 1: Obtain rainfall, flow, and surface elevation data from multi-source hydrological observation data, smooth the surface elevation data using interpolation methods, and standardize the multi-source data using data fusion weights to obtain a multi-source dataset in a unified format. Step 2: Extract hydrological parameters based on the multi-source dataset, and determine the weight of each parameter's impact on the inundation range using parameter sensitivity analysis to obtain a hydrological parameter set. Step 3: If the difference between a set parameter in the hydrological parameter set and the corresponding preset parameter threshold is greater than the set value, update the set parameter using a filtering algorithm to obtain a refined hydrological parameter set.

[0009] Step 4: Based on the refined hydrological parameter set, the inundation boundary change rate is predicted by combining the prediction algorithm and terrain gradient analysis to obtain the inundation boundary change rate data;

[0010] Step 5: Adjust the inversion time interval based on the inundation boundary change rate data to generate an optimized time interval sequence; Step 6: Obtain the current time interval from the optimized time interval sequence, and perform real-time correction of the surface elevation through numerical methods to obtain updated surface elevation data; Step 7: Based on the updated surface elevation data, combined with terrain gradient analysis, the hydraulic model is used to calculate the inundation range and water depth distribution, thereby obtaining a real-time inundation prediction result; Step 8: If the deviation between the real-time inundation prediction result and the observed data is greater than the preset inundation threshold, the refined hydrological parameter set is secondary optimized through the optimization algorithm to obtain the final hydrological parameter set; Step 9: Generate a drainage scheduling decision instruction based on the final hydrological parameter set and the real-time inundation prediction result, and output a scheduling control signal.

[0011] In a second aspect, the present application provides an urban flooding data assimilation system, the system comprising:

[0012] The data acquisition module is used to obtain rainfall, flow and surface elevation data from multi-source hydrological observation data, smooth the surface elevation data using interpolation methods, and standardize the multi-source data in combination with data fusion weights to obtain a multi-source data set in a unified format;

[0013] The set generation module is used to extract hydrological parameters from multi-source data sets, determine the influence weight of each parameter on the inundation range by combining parameter sensitivity analysis, and obtain the hydrological parameter set;

[0014] A parameter updating module is used to update the set parameters in the hydrological parameter set by a filtering algorithm when the difference between the set parameters and the corresponding preset parameter threshold is greater than the set value to obtain a refined hydrological parameter set;

[0015] The rate acquisition module is configured to predict the change rate of the inundation boundary according to the refined set of hydrological parameters, in combination with a prediction algorithm and a terrain gradient analysis, and obtain change rate data of the inundation boundary.

[0016] The time interval optimization module is configured to adjust the inversion time interval according to the change rate data of the inundation boundary, and generate an optimized time interval sequence. The elevation data updating module is configured to obtain a current time interval from the optimized time interval sequence, and correct the ground surface elevation in real time by a numerical method, to obtain updated ground surface elevation data. The inundation prediction module is configured to calculate the inundation range and water depth distribution by using a hydraulics model in combination with a terrain gradient analysis according to the updated ground surface elevation data, and obtain real-time inundation prediction results. The parameter optimization module is configured to perform secondary optimization on the refined set of hydrological parameters by an optimization algorithm when the deviation between the real-time inundation prediction results and the observation data is greater than a preset inundation threshold, and obtain a final set of hydrological parameters. The scheduling control module is configured to generate a drainage scheduling decision instruction according to the final set of hydrological parameters and the real-time inundation prediction results, and output a scheduling control signal.

[0017] In a third aspect, the present application provides a computer-readable storage medium, which stores instructions, when the instructions are executed on a computer, causing the computer to perform the above-mentioned urban waterlogging rain flood data assimilation method.

[0018] Compared with the prior art, the technical scheme of the present application has at least the following advantages:

[0019] 1. Through multi-source data fusion and dynamic updating of refined hydrological parameters, the actual situation of urban waterlogging can be more accurately reflected, especially in complex terrain and severe rainfall intensity changes in urban environments, which can significantly improve the accuracy of inundation prediction.

[0020] 2. Through the adaptive time step algorithm and real-time ground surface elevation data correction, the prediction model can quickly respond to sudden rainfall events and dynamic changes in urban waterlogging, timely generate drainage scheduling instructions, and improve the efficiency and real-time response capability of urban waterlogging emergency response.

[0021] 3. Based on the accurate inundation prediction results and combined with the optimized updating of hydrological parameters, scientific decision-making basis can be provided for drainage scheduling, avoiding excessive or insufficient drainage operations, improving the utilization efficiency of the drainage system, optimizing drainage scheduling decisions, and reducing the risk of urban flood disasters.

[0022] 4. The technical scheme of the present application can flexibly adapt to different urban environments and changing hydrological conditions, has strong scalability, can be applied in different regions, and meets the needs of urban waterlogging prevention and control in complex cities. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0024] Figure 1 An embodiment of a city waterlogging rain flood data assimilation method in the present application;

[0025] Figure 2 An embodiment of a city waterlogging rain flood data assimilation system in the present application. DETAILED DESCRIPTION

[0026] The terms "first", "second", "third", "fourth" and the like (if any) in the description, claims and above drawings of the present application are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "comprising" or "having" and any variation thereof is intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] For the sake of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 An embodiment of a city waterlogging rain flood data assimilation method in the present application includes:

[0028] Step 1, obtaining rainfall, flow and surface elevation data from multi-source hydrological observation data, smoothing the surface elevation data by using interpolation method, combining data fusion weight to standardize the multi-source data, and obtaining a unified format multi-source data set.

[0029] In a specific embodiment, the process of step 1 can specifically include the following steps:

[0030] (1) smoothing the surface elevation data by using Kriging interpolation method to obtain smoothed surface elevation data; (2) weighting and fusing the rainfall, flow and smoothed surface elevation data according to the preset data fusion weight to generate a multi-source data set, wherein the data fusion weight is determined according to the observation accuracy of each data source.

[0031] Specifically, we first extract rainfall time series data, river flow monitoring data, and spatial distribution data of surface elevation from rain gauges, flow monitoring sites, and topographic survey equipment. Rainfall data includes timestamps, geographic coordinates, and rainfall intensity values; flow data includes information such as flow velocity and cross-sectional water levels; and surface elevation data provides three-dimensional coordinate points and corresponding elevation values. These data may contain outliers and missing values ​​in their original state, so they require format conversion and quality inspection to eliminate non-compliant data and ensure their integrity and reliability.

[0032] To address the spatial distribution characteristics of surface elevation data, Kriging interpolation is used for smoothing. Based on the theory of spatial autocorrelation, Kriging interpolation constructs a variogram model. It estimates the elevation values ​​of unknown points based on the spatial correlation between observation points. The variogram parameters are then fitted using the least squares method to obtain accurate weight coefficients, ultimately producing smoothed surface elevation data. This interpolation method effectively eliminates noise and discontinuities in terrain data, enhancing its spatial continuity and accuracy.

[0033] Subsequently, a weighted fusion of rainfall, flow, and smoothed surface elevation data is performed using data fusion weights. This weighted fusion process assigns weights based on the observation accuracy of each data source, with higher-accuracy data sources receiving higher weights. Specifically, the measurement errors and uncertainties of rainfall, flow, and elevation data are assessed, and the fusion weights are determined using the inverse-accuracy weighting method based on the observation accuracy assessment results. For example, assuming the accuracy of rainfall data is 0.1 mm, flow data is 5%, and elevation data is 0.05 m, the weight coefficients calculated using the inverse-accuracy weighting method are 0.4, 0.3, and 0.3, respectively. This ensures that the more accurate data source has a greater impact on the final result, thereby improving the accuracy and reliability of the data fusion. Through this weighted fusion process, the resulting multi-source dataset has a unified format and contains optimized rainfall distribution, flow field information, and corrected surface elevation data. These datasets provide accurate input for hydrological simulations and inundation forecasts, supporting more precise waterlogging predictions and drainage scheduling.

[0034] For example, in an urban drainage basin, when faced with heavy rainfall of 50 mm / hour, elevation data processed through kriging interpolation guides the calculation of runoff paths, while flow data provides the necessary boundary constraints, and rainfall data drives the entire hydrological simulation. The organic integration and precise calculation of these three elements greatly improves the accuracy of inundation predictions, helping city managers make timely and effective flood prevention decisions.

[0035] By combining Kriging interpolation to smooth surface elevation data and using a weighted fusion method based on observation accuracy, the quality and accuracy of multi-source datasets were optimized. This effectively addresses the heterogeneity and precision inconsistencies of multi-source data encountered in urban waterlogging data assimilation, improving the accuracy and real-time response capabilities of waterlogging predictions and providing a scientific basis and decision-making support for urban waterlogging prevention and drainage scheduling. Step 2: Extract hydrological parameters based on the multi-source datasets. Combined with parameter sensitivity analysis, the weight of each parameter's impact on the inundation range was determined, resulting in a set of hydrological parameters.

[0036] In a specific embodiment, the process of performing step 2 may specifically include the following steps:

[0037] (1) Based on the multi-source dataset, the principal component analysis method is used to extract rainfall intensity, permeability coefficient and roughness parameters; (2) The influence weights of rainfall intensity, permeability coefficient and roughness parameters on the inundation range are determined through parameter sensitivity analysis; (3) According to the influence weights, the rainfall intensity, permeability coefficient and roughness parameters are weighted and combined to generate a hydrological parameter set.

[0038] Specifically, first, the rainfall, flow, and surface elevation data in the multi-source dataset are standardized using the principal component analysis method to construct a hydrological data matrix to ensure the dimensionality of different data types. After standardization, the covariance matrix of the hydrological data matrix is ​​calculated, and eigenvalue decomposition is performed to obtain the principal components and their corresponding eigenvalues. By analyzing the principal component loading matrix, the principal components related to rainfall intensity, permeability coefficient, and roughness are determined, and then converted from the principal component space back to the original parameter space to obtain the numerical values ​​of rainfall intensity, permeability coefficient, and roughness. This process ensures that the principal components that can characterize key hydrological parameters are extracted from a large amount of multi-source data, reducing the data dimension and improving computational efficiency.

[0039] Next, parameter sensitivity analysis was performed to determine the weight of each hydrological parameter's impact on the inundation range. This process first established a perturbation range for each parameter, and single-factor perturbation experiments were conducted for rainfall intensity, permeability coefficient, and roughness. For example, rainfall intensity was perturbed by ±10%, ±20%, and ±30% of the baseline value; permeability coefficient was varied by ±15%, ±25%, and ±35%; and roughness parameter was adjusted by ±5%, ±10%, and ±15%. Each perturbation kept other parameters constant, forming a test suite in which a single parameter was varied. By analyzing parameters with different perturbation ranges, a two-dimensional shallow water equation was run using the hydraulic calculation module to obtain inundation range predictions for each parameter variation. Based on the ratio of the inundation range change to the parameter change, the sensitivity coefficient for each parameter was calculated, reflecting the degree of impact of each parameter change on the inundation range. By normalizing each parameter's sensitivity coefficient, the relative weight of each parameter's impact on the inundation range was determined. For example, changes in rainfall intensity may have a significant impact on the flooded area, with a larger sensitivity coefficient; whereas the roughness parameter may have a smaller impact on the flooded area.

[0040] Each parameter is linearly weighted according to its influence weight to form an initial set of comprehensive hydrological parameters. In this weighted combination process, parameters with larger influence weights (such as rainfall intensity) play a more important role in the hydrological simulation, while parameters with smaller influence weights participate in the combination as auxiliary factors.

[0041] During the implementation process, these steps effectively solved the key technical problems in urban waterlogging or stormwater management by integrating numerical models with actual observation data. Principal component analysis reduced the dimensions of multi-source data and extracted parameters that are crucial for flooding prediction, thereby improving the accuracy of the simulation. Sensitivity analysis ensures the reasonable distribution of weights of various hydrological parameters in the prediction model, reflecting the actual impact of different hydrological parameters on the flooding range. Ultimately, the set of hydrological parameters generated by weighted combination can more accurately simulate the flooding process, providing a scientific basis for further urban drainage, waterlogging prediction and prevention. The technical solution of this application can accurately extract and reasonably combine various hydrological parameters in the process of multi-source data integration and hydrological simulation, thereby improving the accuracy and reliability of urban waterlogging prediction and stormwater management.

[0042] Step 3: If the difference between the set parameter in the hydrological parameter set and the corresponding preset parameter threshold is greater than the set value, the set parameter is updated through a filtering algorithm to obtain a refined hydrological parameter set.

[0043] In a specific embodiment, the process of executing step 3 may specifically include the following steps:

[0044] (1) If the difference between the permeability coefficient in the hydrological parameter set and the preset parameter threshold is greater than the set value, the permeability coefficient is dynamically updated through the Kalman filter algorithm combined with the multi-parameter coupling relationship; (2) The updated permeability coefficient is combined with other parameters in the hydrological parameter set to generate a refined hydrological parameter set, where the multi-parameter coupling relationship is determined by analyzing historical observation data.

[0045] Specifically, if the hydraulic conductivity is detected to deviate from a preset threshold by more than a set value (typically 10% to 15%), the Kalman filter algorithm is activated. The algorithm first establishes a state-space model based on the time-varying nature of the hydraulic conductivity. The state vector contains variables such as the hydraulic conductivity, its rate of change, and soil moisture content at the current moment, while the observation vector comprises real-time runoff, soil moisture, and rainfall infiltration data. Using this model, the Kalman filter algorithm predicts changes in the hydraulic conductivity and modifies the prediction based on new observations, ultimately generating an updated hydraulic conductivity.

[0046] To ensure the accuracy and consistency of hydraulic conductivity updates, the Kalman filter also incorporates a multi-parameter coupling relationship. This relationship analyzes the interactions between different hydrological parameters using historical observations, specifically the correlation between hydraulic conductivity and rainfall intensity, soil moisture content, and surface roughness. This process uses correlation analysis and regression models to determine the quantitative relationships between the various hydrological parameters. These relationships are then used to optimize the hydraulic conductivity update process, ensuring that hydraulic conductivity corrections align with actual hydrological conditions.

[0047] After updating the hydraulic conductivity, the Kalman filter algorithm combines it with other hydrological parameters to generate a refined set of hydrological parameters. These parameters are ensured to be physically consistent and numerically compatible. In particular, after adjusting the hydraulic conductivity, related parameters such as soil porosity and saturated water content are adjusted accordingly to maintain the inherent compatibility between the hydrological parameters. This process ensures that the hydrological parameters maintain physical consistency, ensuring that the generated set of hydrological parameters is highly accurate and reliable, accurately reflecting actual hydrological conditions.

[0048] For example, in a city's flood warning application scenario, assume an initial permeability coefficient of 15 mm per hour, a preset threshold of 12 mm per hour, and a 25% deviation from the set value. The Kalman filter algorithm dynamically updates the permeability coefficient to 9.5 mm per hour based on real-time rainfall infiltration data, soil moisture changes, and surface runoff data. At this point, combined with the multi-parameter coupling relationship determined by historical data, there is a significant negative correlation between the permeability coefficient and soil moisture content, with a correlation coefficient of -0.72. The updated permeability coefficient, combined with the adjusted soil porosity of 0.42 and saturated water content of 0.38, forms a consistent and coordinated set of refined hydrological parameters.

[0049] The technical solution of the present invention improves the accuracy of hydrological parameter estimation in the hydrological model, especially in the dynamically changing urban waterlogging prediction scenario. By dynamically updating the permeability coefficient and combining the coordination of other parameters, the accuracy and reliability of the inundation range prediction are significantly improved.

[0050] Step 4: Based on the refined hydrological parameter set, the prediction algorithm and terrain gradient analysis are combined to predict the inundation boundary change rate and obtain the inundation boundary change rate data.

[0051] In a specific embodiment, the process of executing step 4 may specifically include the following steps:

[0052] (1) Extract the roughness parameter and updated permeability coefficient from the refined hydrological parameter set; (2) Use the support vector machine algorithm to establish a prediction model for the change rate of the inundation boundary based on the updated permeability coefficient, roughness parameter, and terrain gradient information;

[0053] (3) Generate flood boundary change rate data based on the output results of the prediction model, where terrain gradient analysis is performed based on surface elevation data from multiple source datasets.

[0054] Specifically, the refined hydrological parameter set provides key hydrological information, including the hydraulic conductivity and roughness parameter. These parameters determine the propagation characteristics of water flow over the surface. The hydraulic conductivity reflects the permeability of the soil or surface material, while the roughness parameter represents the surface's resistance to water flow. By extracting these two key parameters from the refined hydrological parameter set and updating them based on actual conditions, they can adapt to current hydrological conditions.

[0055] Based on this, a support vector machine algorithm was used to develop a predictive model for the rate of change of the inundation boundary. By inputting updated hydraulic conductivity, roughness parameters, and terrain gradient information, the support vector machine algorithm can learn the nonlinear relationship between the input features and the rate of change of the inundation boundary in a high-dimensional feature space. Terrain gradient information is extracted from surface elevation data from a multi-source dataset and reflects topographic characteristics of the study area, such as slope and aspect. These characteristics directly influence the distribution of water flow and the pattern of inundation expansion.

[0056] Topographic gradient analysis uses a digital elevation model (DEM), which describes topographic variations by calculating the slope and aspect at each grid point. Slope information reveals how water flow accelerates or slows in different terrain areas, thereby affecting the rate of change of the inundation boundary. A support vector machine (SVM) uses this terrain information, along with hydrological parameter inputs, to optimize the inundation boundary change rate prediction model, ensuring that the model is adaptable to various terrain conditions, particularly water flow in complex urban terrain.

[0057] The trained support vector machine model can predict the rate of change of the inundation boundary at different time steps based on the real-time permeability coefficient, roughness parameter, and terrain gradient characteristics. The rate of change data output by the model is highly accurate and can adapt to complex terrain environments, such as urban waterlogging or mountain flooding. In practical applications, the rate of change data output by the model can also be combined with terrain corrections to enhance the model's adaptability to areas with different slopes. For example, in areas with larger slopes, the water flow speeds up and the inundation boundary expands faster, so the weight coefficient can be increased; in areas with smaller slopes, the water flow slows down and the inundation expands relatively slowly, so the weight coefficient can be adjusted to reflect this characteristic.

[0058] These predictions are generated in the form of time, spatial coordinates, and change rate data. These data are stored and further processed as needed, providing real-time decision support to disaster warning systems, drainage systems, and other organizations. In this way, the inundation boundary change rate prediction not only provides accurate predictions in time and space, but also achieves higher accuracy under complex terrain and hydrological conditions. Compared to traditional hydraulic equation methods, this technology can better handle areas with complex terrain, improving prediction accuracy by 15% to 25%.

[0059] For example, in urban flooding prediction scenarios, the support vector machine algorithm combined with terrain gradient analysis can better adapt to topographic variations in different urban areas, such as the impact of road slopes and building layout on water flow paths. The model can effectively predict the speed of water flow, guiding the disaster warning system to take early emergency measures and reduce the risks and losses caused by urban flooding.

[0060] Step 5: Adjust the inversion time interval based on the flooding boundary change rate data to generate an optimized time interval sequence.

[0061] In a specific embodiment, the process of executing step 5 may specifically include the following steps:

[0062] (1) Based on the flood boundary change rate data, an adaptive time step algorithm is used to dynamically adjust the inversion time interval; (2) An optimized time interval sequence is generated based on the inversion time interval, where the adaptive time step algorithm determines the adjustment amplitude of the time interval according to the fluctuation amplitude of the flood boundary change rate.

[0063] Specifically, the core of the adaptive time step algorithm lies in adjusting the time interval of inversion in real time according to the fluctuation amplitude of the submergence boundary change rate data, so as to ensure that the prediction process can not only be calculated efficiently, but also accurately track the dynamic changes of the submergence boundary. When the fluctuation amplitude is small, the system increases the time interval to improve the calculation efficiency; when the fluctuation amplitude is large, the time interval is reduced to improve the accuracy of the prediction. The algorithm first calculates the statistical characteristics of the submergence boundary change rate, including the average change rate, the maximum change rate, and the standard deviation of the change rate, to reflect the change trend, the fastest change rate, and the dispersion degree of the rate data. Then, based on the standard deviation of the change rate, a fluctuation amplitude coefficient is calculated, which reflects the intensity of the rate fluctuation. When the fluctuation amplitude coefficient is small, it indicates that the submergence boundary changes are relatively stable, and the time interval can be increased to reduce the calculation load; when the fluctuation amplitude coefficient is large, it indicates that the submergence boundary changes rapidly, and the time interval is shortened to accurately capture the rapid changes in the dynamic. According to the size of the fluctuation amplitude coefficient, a time interval adjustment factor is determined. For example, when the fluctuation amplitude coefficient is small, the adjustment factor is set to be large, thereby increasing the inversion interval; when the fluctuation amplitude coefficient is large, the adjustment factor is reduced to ensure that the rapid changes of the submergence boundary can be captured in time. The adjusted inversion time interval is multiplied by the time interval adjustment factor to obtain a new time interval, which ensures the balance between calculation accuracy and calculation efficiency. In this process, the time interval is also subject to upper and lower limits to avoid problems caused by too small or too large time intervals, and to ensure the stable operation of the system.

[0064] In terms of generating an optimized time interval sequence, based on the adjusted inversion time interval, a time node sequence is generated that covers the entire prediction time range according to the optimized time interval. In order to ensure the smoothness of the time interval sequence and avoid sharp differences between adjacent time intervals, the algorithm uses smoothing processing, such as moving average method, to smooth the continuous time intervals and ensure the stability of the time interval sequence.

[0065] For example, in a certain rainstorm event, the initial submergence boundary change rate is low, and the fluctuation amplitude coefficient is small, so the time interval is adjusted from 5 minutes to 7.5 minutes; as the rainfall intensity increases, the submergence boundary change rate rises sharply, the fluctuation amplitude coefficient increases, and the time interval is shortened to 3 minutes to better respond to the rapidly changing submergence boundary. This process significantly improves the sensitivity and accuracy of urban waterlogging prediction, especially in dealing with rainstorm and sudden flood events. Through reasonable adjustment of the time interval, not only the timeliness and accuracy of the prediction are improved, but also the calculation efficiency is optimized, and unnecessary calculation burden is reduced.

[0066] The technical scheme can automatically adjust the inversion time interval according to the real-time fluctuation of the change of the flooding boundary, thereby optimizing the calculation efficiency and accuracy of the prediction model, especially when dealing with complex precipitation patterns and sudden urban waterlogging, ensuring more accurate early warning and preventive measures, and effectively solving the problems of excessive calculation burden or inaccurate prediction caused by improper setting of the time interval.

[0067] Step 6: obtaining the current time interval from the optimized time interval sequence, and correcting the ground elevation in real time by a numerical method to obtain updated ground elevation data.

[0068] In a specific embodiment, the process of performing step 6 can specifically include the following steps:

[0069] (1) extracting the time interval corresponding to the current calculation period in time sequence and defining it as the current time interval, and taking the current time interval as the time step parameter of the finite element calculation;

[0070] (2) correcting the ground elevation data in real time by a finite element method based on the grid division accuracy and boundary condition setting, wherein the boundary condition setting is based on the flow data in the multi-source data set;

[0071] (3) obtaining the numerical error distribution in the finite element solving process, and generating updated ground elevation data in combination with error propagation control.

[0072] First, according to the optimized time interval sequence generated by the adaptive time step algorithm, the time interval value corresponding to the current calculation period is extracted in time sequence, which is used as the time step parameter of the subsequent finite element calculation. The selection of this time interval ensures that the distribution of time within the calculation period can efficiently and accurately reflect the change of the flooding boundary, avoiding the problems of low calculation efficiency or insufficient accuracy caused by excessively long or short time steps.

[0073] Second, the ground elevation data is corrected in real time by using a finite element method based on the grid division accuracy and boundary condition setting.

[0074] The spatial distribution characteristics of the ground elevation data are used to determine the grid division precision parameters, including the grid cell size and the node density distribution. The grid cell size is adaptively adjusted according to the terrain gradient change rate. In the area with large terrain changes, finer grids are used to improve the calculation precision. In the area with gentle terrain, the grid cell size is larger to improve the calculation efficiency. The node density distribution is determined by calculating the second-order derivative of the ground elevation. The node density is increased in the area with a large absolute value of the second-order derivative. In addition, the setting of the boundary condition is based on the flow data in the multi-source data set. The flow data is converted into the velocity and pressure boundary conditions suitable for finite element calculation. By converting the flow data into the flow velocity component and pressure value of the boundary node, and combining the water depth and the calculation of the pressure boundary condition of the gravitational acceleration, the dynamic boundary effect of the fluid can be accurately reflected, further ensuring the boundary accuracy when the ground elevation is corrected by the finite element method. Then, the finite element discrete equation set is constructed for the ground elevation correction calculation, and the Newton-Raphson iteration method is used to solve the equation set.

[0075] Furthermore, the numerical error distribution generated in the finite element solution process needs to be effectively controlled. In order to ensure that the error in the correction process does not have a large impact on the results, an error propagation control algorithm is used for error suppression. By calculating the distribution of numerical error and adjusting the artificial viscosity according to the error estimate, numerical oscillation is suppressed to ensure the stability and precision of the calculation. When the local error exceeds the preset threshold, the local grid refinement strategy is used to refine the area with large error and increase the number of nodes to improve the local calculation precision.

[0076] By combining the optimized time interval sequence and the finite element correction method, the ground elevation data can be corrected in real time during the simulation of complex urban waterlogging, and the dynamic changes of the terrain and fluid can be accurately captured. During the rainfall process, accurate ground elevation data is crucial for waterlogging prediction, which can timely reflect the changes of terrain and water flow distribution, thereby effectively improving the response speed and accuracy of the waterlogging disaster warning system. Through effective control of numerical error, the stability and reliability of the correction results can be ensured, and the prediction deviation caused by error accumulation can be avoided. Based on the updated ground elevation data, the hydraulic model is used to calculate the flooded area and water depth distribution based on the terrain gradient analysis, thereby obtaining real-time flooding prediction results.

[0077] Specifically, the updated surface elevation data, after the previous finite element correction, reflects more accurate terrain information, which serves as the basis for the hydraulic model to calculate the inundation range and water depth distribution. By inputting these corrected elevation data, combined with rainfall, surface runoff and flow data, the hydraulic model can simulate the propagation path, speed and depth of water accumulation on the ground after rainfall. Terrain gradient analysis further assists the hydraulic model by analyzing the slope changes in different areas and identifying the convergence areas and drainage areas of water flow, thereby optimizing the accuracy of water flow simulation. In areas with steeper terrain gradients, water flows gather faster and the inundation is more severe, while in flatter areas, water flows are more dispersed and the inundation range is relatively small.

[0078] By combining terrain gradient analysis with hydraulic models, the model's boundary and initial conditions can be dynamically adjusted to make predictions more realistic. For example, when a region experiences a steep slope, the hydraulic model adjusts the flow rate and water accumulation based on this characteristic, ensuring the accuracy of the inundation range and depth distribution. Furthermore, the model accounts for temporal and spatial variations. By using real-time updated surface elevation data, it can capture rapidly changing rainfall and water flow patterns, enabling dynamic inundation predictions.

[0079] By efficiently combining hydraulic models and terrain information, it is possible to accurately predict possible flooded areas within a short period of time after rainfall occurs, thereby providing a timely basis for disaster warning and emergency response, and solving the problems of time sensitivity and spatial complexity in urban waterlogging prediction.

[0080] Step 8: If the deviation between the real-time flooding prediction result and the observed data is greater than the preset flooding threshold, the refined hydrological parameter set is optimized twice through the optimization algorithm to obtain the final hydrological parameter set.

[0081] In a specific embodiment, the process of executing step 8 may specifically include the following steps:

[0082] (1) Using the gradient descent algorithm, the refined hydrological parameter set is optimized twice in combination with the optimization objective function and the gradient descent step size. The optimization objective function is determined based on the prediction error of the flooding range.

[0083] (2) The final hydrological parameter set is generated based on the secondary optimization results, combined with the data observation weights and convergence judgment criteria.

[0084] Specifically, the deviation value is calculated by comparing the predicted inundation boundary coordinates with the observed inundation boundary coordinates point by point. The root mean square error method is used to quantify the degree of error. When the error exceeds a preset threshold, a secondary optimization process is triggered. At this point, the gradient descent algorithm is applied to optimize the hydrological parameter set to reduce the inundation prediction error. An optimization objective function based on the inundation range prediction error is constructed. The optimization objective function quantifies the prediction deviation by integrating the inundation area error, the inundation depth distribution error, and the boundary shape deviation, thereby guiding the adjustment of hydrological parameters. Based on the optimization results of the objective function, the gradient descent algorithm iteratively adjusts the hydrological parameter values, calculates the gradient of each hydrological parameter (such as rainfall intensity, permeability coefficient, and roughness), and updates it along the negative gradient direction using the gradient descent method.

[0085] In practice, the gradient calculation during the optimization process is performed using numerical differentiation methods. By making small perturbations to each parameter and then recalculating the objective function value, the corresponding gradient components are obtained. During each iteration, the optimization algorithm adjusts the hydrological parameters along the gradient direction based on the current parameter values ​​and the partial derivatives of the objective function. The initial step size is determined through sensitivity analysis, with smaller step sizes used for parameters with higher sensitivity and larger step sizes for parameters with lower sensitivity. The step size adjustment mechanism uses an adaptive strategy, dynamically adjusting the step size during the iteration process based on the magnitude of the change in the objective function value to optimize the convergence rate. When the decrease in the objective function value is less than the set threshold, the step size is reduced to avoid over-adjustment. If the objective function value increases, the step size is appropriately reduced and reverts to the previous iteration result.

[0086] To ensure the validity of the optimization results, data observation weights are incorporated into the optimization algorithm. This allows the optimization process to be weighted and adjusted based on the reliability and timeliness of the observation data. These weights are assigned based on the reliability and timeliness of the observation data, with recent observations given a higher weight (e.g., 1) and longer-term data given a lower weight (e.g., a weight of 0.8 for observations from a week ago and a weight of 0.6 for observations from a month ago). This ensures that high-quality and timely data have the greatest impact on the optimization results. This weighting strategy enables the optimization algorithm to more precisely adjust hydrological parameters, improving the accuracy of inundation predictions. Convergence is determined through a multi-level mechanism, ensuring that the optimization process neither terminates prematurely nor falls into an infinite loop. Convergence is determined by the magnitude of the objective function change, the rate of parameter change, and the modulus of the gradient vector. The optimization process is considered converged when any of these conditions are met.

[0087] Ultimately, the hydrological parameters optimized through gradient descent form the final set, and cross-validation ensures their reliability and accuracy under different rainfall conditions. For example, the optimized rainfall intensity parameter for a specific period is 30 mm / hour, the permeability coefficient parameter is 0.05 m / hour, and the roughness parameter is 0.12. These parameters provide stable prediction results across different periods and rainfall conditions. This optimization technique not only significantly improves the accuracy of inundation predictions, but also enhances the effectiveness of emergency management decisions.

[0088] The implementation of this optimization method significantly improved the prediction accuracy of urban waterlogging, solved the prediction bias problem caused by inaccurate initial hydrological parameter estimation, and improved the model's adaptability to multi-source observation data through fine-tuning, ensuring the high reliability and timeliness of flood prediction results.

[0089] Step 9: Generate drainage scheduling decision instructions based on the final hydrological parameter set and real-time inundation prediction results, and output scheduling control signals.

[0090] Specifically, a dynamic drainage scheduling model is constructed based on optimized hydrological parameters such as rainfall intensity, permeability coefficient, and roughness, combined with real-time inundation prediction results to identify inundation range, depth, and regional distribution. This model calculates drainage demand in different regions and, based on rainfall forecasts and surface water flow conditions, assesses the load and operating status of each drainage station. Based on these, the model generates scheduling control instructions based on decision signals output by the model, guiding the start and stop times and operating intensity of drainage facilities to maximize drainage efficiency and prevent further expansion of urban waterlogging. These generated scheduling control signals are transmitted to each drainage facility through the intelligent drainage system, adjusting drainage strategies in real time and enabling the system to flexibly respond to varying rainfall intensities. For example, if the inundation depth in a particular area exceeds the specified limit, the scheduling system will instruct drainage pumps in that area to increase their operation and prioritize the removal of accumulated water.

[0091] By optimizing drainage scheduling decisions, we can achieve intelligent and real-time urban waterlogging prevention and control, significantly improving the emergency response speed of waterlogging and the resource allocation efficiency of the drainage system, increasing the reaction speed of the drainage system, and ensuring that the city has stronger waterlogging prevention capabilities during rainfall events.

[0092] The above describes a method for assimilating urban waterlogging and rainwater data in an embodiment of the present application. The following describes a system for assimilating urban waterlogging and rainwater data in an embodiment of the present application. Figure 2 In an embodiment of the present application, an urban flooding data assimilation system includes:

[0093] The data acquisition module 10 is used to obtain rainfall, flow and surface elevation data from multi-source hydrological observation data, smooth the surface elevation data using an interpolation method, and standardize the multi-source data in combination with data fusion weights to obtain a multi-source data set in a unified format.

[0094] The set generation module 20 is used to extract hydrological parameters based on multi-source data sets, determine the influence weight of each parameter on the flooding range in combination with parameter sensitivity analysis, and obtain a hydrological parameter set.

[0095] The parameter updating module 30 is configured to update the set parameters in the hydrological parameter set by a filtering algorithm when the difference between the set parameters and the corresponding preset parameter threshold is greater than a set value, so as to obtain a refined hydrological parameter set.

[0096] The rate acquisition module 40 is used to predict the rate of change of the flood boundary based on the refined hydrological parameter set in combination with the prediction algorithm and terrain gradient analysis to obtain the flood boundary change rate data.

[0097] The time interval optimization module 50 is used to adjust the inversion time interval according to the flood boundary change rate data to generate an optimized time interval sequence. The elevation data update module 60 is used to obtain the current time interval from the optimized time interval sequence, and to perform real-time correction of the surface elevation using numerical methods to obtain updated surface elevation data. The flood prediction module 70 is used to calculate the flood range and water depth distribution based on the updated surface elevation data using a hydraulic model combined with terrain gradient analysis to obtain real-time flood prediction results. The parameter optimization module 80 is used to perform secondary optimization of the refined hydrological parameter set through an optimization algorithm when the deviation between the real-time flood prediction result and the observed data is greater than a preset flood threshold to obtain the final hydrological parameter set. The scheduling control module 90 is used to generate a drainage scheduling decision instruction based on the final hydrological parameter set and the real-time flood prediction result, and output a scheduling control signal.

[0098] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, cause the computer to execute the steps of the urban waterlogging and rainwater data assimilation method.

[0099] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0100] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0101] The above-described embodiments are merely used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still make modifications to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some of the technical features; and these modifications or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for assimilation of urban flooding data, characterized in that: include: Step 1: Obtain rainfall, flow, and surface elevation data from multi-source hydrological observation data, smooth the surface elevation data using an interpolation method, and standardize the multi-source data using data fusion weights to obtain a multi-source dataset in a unified format. Step 2: extracting hydrological parameters based on the multi-source dataset, and determining the influence weight of each parameter on the flooding range by combining parameter sensitivity analysis to obtain a hydrological parameter set; Step 3: If the difference between the set parameter in the hydrological parameter set and the corresponding preset parameter threshold is greater than the set value, the set parameter is updated through a filtering algorithm to obtain a refined hydrological parameter set; Step 4: Based on the refined hydrological parameter set, the flood boundary change rate is predicted in combination with the prediction algorithm and terrain gradient analysis to obtain flood boundary change rate data; Step 5: adjusting the inversion time interval based on the flooding boundary change rate data to generate an optimized time interval sequence; Step 6: obtaining the current time interval from the optimized time interval sequence, and performing real-time correction on the surface elevation using a numerical method to obtain updated surface elevation data; Step 7: Based on the updated surface elevation data and combined with terrain gradient analysis, a hydraulic model is used to calculate the flooding range and water depth distribution, thereby obtaining a real-time flooding prediction result; Step 8: If the deviation between the real-time flooding prediction result and the observed data is greater than a preset flooding threshold, the refined hydrological parameter set is optimized twice using an optimization algorithm to obtain a final hydrological parameter set; Step 9: Generate a drainage scheduling decision instruction based on the final hydrological parameter set and the real-time flooding prediction result, and output a scheduling control signal.

2. The method according to claim 1, wherein The step 1 comprises: The surface elevation data is smoothed using a Kriging interpolation method to obtain smoothed surface elevation data; The rainfall, the flow and the smoothed surface elevation data are weightedly fused according to preset data fusion weights to generate the multi-source data set, wherein the data fusion weights are determined according to the observation accuracy of each data source.

3. The method according to claim 1, wherein The step 2 includes: According to the multi-source data set, the principal component analysis method is used to extract rainfall intensity, permeability coefficient and roughness parameters; Determining the influence weights of the rainfall intensity, the permeability coefficient, and the roughness parameter on the flooding range through parameter sensitivity analysis; The rainfall intensity, the permeability coefficient, and the roughness parameter are weightedly combined according to the influence weight to generate the hydrological parameter set.

4. The method according to claim 3, wherein The step 3 comprises: If the difference between the permeability coefficient in the hydrological parameter set and the preset parameter threshold is greater than the set value, dynamically updating the permeability coefficient by combining a multi-parameter coupling relationship with a Kalman filter algorithm; The updated permeability coefficient is combined with other parameters in the hydrological parameter set to generate the refined hydrological parameter set, wherein the multi-parameter coupling relationship is determined by analyzing historical observation data.

5. The method according to claim 4, wherein The step 4 comprises: Extracting the roughness parameter and the updated permeability coefficient from the refined hydrological parameter set; using a support vector machine algorithm to establish a prediction model for the rate of change of the flooding boundary based on the updated permeability coefficient, the roughness parameter, and terrain gradient information; The flood boundary change rate data is generated based on the output result of the prediction model, wherein terrain gradient analysis is performed based on the surface elevation data in the multi-source dataset.

6. The method according to claim 1, wherein The step 5 comprises: Based on the flood boundary change rate data, an adaptive time step algorithm is used to dynamically adjust the inversion time interval; An optimized time interval sequence is generated based on the inversion time interval, wherein the adaptive time step algorithm determines the adjustment amplitude of the time interval according to the fluctuation amplitude of the flooding boundary change rate.

7. The method according to claim 1, wherein The step 6 comprises: Extracting the time interval corresponding to the current calculation cycle in chronological order and defining it as the current time interval, and using the current time interval as the time step parameter of the finite element calculation; The surface elevation data is corrected in real time based on meshing accuracy and boundary condition settings using a finite element method, wherein the boundary condition settings are based on flow data in the multi-source dataset; The numerical error distribution in the finite element solution process is obtained, and the updated surface elevation data is generated in combination with error propagation control.

8. The method according to claim 1, wherein The step 8 comprises: Performing secondary optimization on the refined hydrological parameter set by combining an optimization objective function and a gradient descent step size using a gradient descent algorithm, wherein the optimization objective function is determined based on a prediction error of the flooding range; The final hydrological parameter set is generated according to the secondary optimization result in combination with the data observation weight and the convergence judgment criterion.

9. An urban waterlogging and stormwater data assimilation system, used to implement the method according to any one of claims 1 to 8, characterized in that: The system comprises: A data acquisition module is used to obtain rainfall, flow and surface elevation data from multi-source hydrological observation data, smooth the surface elevation data using an interpolation method, and standardize the multi-source data using data fusion weights to obtain a multi-source data set in a unified format; A set generation module is used to extract hydrological parameters based on the multi-source data set, determine the influence weight of each parameter on the flooding range in combination with parameter sensitivity analysis, and obtain a hydrological parameter set; a parameter updating module, configured to update the set parameters in the hydrological parameter set by a filtering algorithm when the difference between the set parameters and the corresponding preset parameter threshold is greater than a set value, so as to obtain a refined hydrological parameter set; A rate acquisition module is used to predict the rate of change of the inundation boundary based on the refined hydrological parameter set in combination with a prediction algorithm and terrain gradient analysis to obtain inundation boundary change rate data; a time interval optimization module, configured to adjust the inversion time interval according to the flood boundary change rate data to generate an optimized time interval sequence; an elevation data updating module, configured to obtain a current time interval from the optimized time interval sequence, and to perform real-time correction on the surface elevation using a numerical method to obtain updated surface elevation data; A flooding prediction module is used to calculate the flooding range and water depth distribution based on the updated surface elevation data using a hydraulic model combined with terrain gradient analysis to obtain real-time flooding prediction results; a parameter optimization module, configured to perform secondary optimization on the refined hydrological parameter set using an optimization algorithm to obtain a final hydrological parameter set when the deviation between the real-time flooding prediction result and the observed data is greater than a preset flooding threshold; The scheduling control module is used to generate a drainage scheduling decision instruction according to the final hydrological parameter set and the real-time flooding prediction result, and output a scheduling control signal.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, an urban flooding data assimilation method according to any one of claims 1 to 8 is implemented.

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