A reservoir group flood control risk identification and early warning system and method

Abstract

This invention provides a system and method for identifying and issuing early warnings of flood control risks in reservoir groups, belonging to the field of flood control and disaster relief technology. The method collects rainfall, reservoir water levels, discharge flow, and topographic data, processes the data, and generates a standardized dataset. Based on this standardized dataset, a hydrological model of the reservoir group is constructed using GIS data to simulate the discharge capacity under different rainfall scenarios. The model calculates discharge capacity index, flood control risk index, downstream vulnerability index, upstream flood formation index, and midstream flood propagation index, and then weights and fuses these indices to generate a comprehensive risk index. The current risk level is determined and an early warning is issued based on a comparison of the comprehensive risk index with preset low, medium, and high warning thresholds. Simultaneously, user feedback is received, and model parameters and early warning strategies are dynamically adjusted. This invention achieves accurate identification and timely early warning of flood control risks in reservoir groups through multi-source data fusion technology, deep learning algorithms, and a dynamic feedback mechanism.

Description

Technical Field

[0001] This invention relates to the field of flood control and disaster relief technology, and in particular to a system and method for identifying and warning of flood risks in a group of reservoirs. Background Technology

[0002] With the intensification of global climate change, the frequency and intensity of extreme rainfall events have increased significantly, posing a tremendous challenge to flood control efforts in reservoir systems. Traditional flood warning methods mainly rely on single-source water level monitoring and experience-based judgment, which are insufficient to meet the needs of identifying complex and ever-changing flood risks. Existing technologies have significant shortcomings in the following aspects:

[0003] (1) Insufficient response speed and accuracy of early warning mechanisms: Traditional early warning systems are mostly based on fixed thresholds and experience judgments, lacking the ability to dynamically analyze real-time data, resulting in delayed release of early warning information and inability to respond to sudden flood events in a timely manner.

[0004] (2) Lack of dynamic simulation of flood propagation path: Existing methods cannot dynamically simulate the propagation path and speed of floods under different terrain conditions, resulting in inaccurate vulnerability assessment of downstream areas and difficulty in effectively guiding flood control decisions.

[0005] (3) Lack of user feedback mechanism: The existing system cannot dynamically adjust model parameters and early warning strategies based on user feedback in actual flood events, making it difficult to guarantee the timeliness and accuracy of early warning information.

[0006] Therefore, there is an urgent need for a reservoir group flood control risk identification and early warning system and method based on multi-source data fusion, deep learning algorithms and dynamic feedback mechanisms to overcome the limitations of existing technologies and achieve accurate identification, dynamic assessment and timely early warning of reservoir group flood control risks. Summary of the Invention

[0007] The purpose of this invention is to provide a flood control risk identification and early warning system and method for reservoir groups. By collecting real-time rainfall, reservoir water levels, discharge flow, and topographic data, and combining this data with IoT, satellite remote sensing, and GIS technologies, the system utilizes deep learning algorithms to fuse and process multi-source data, constructing a hydrological model to simulate discharge capacity, calculating a comprehensive risk index, and issuing risk information through a three-level early warning mechanism. Simultaneously, the system receives user feedback, dynamically adjusts model parameters and early warning strategies, improves the accuracy and real-time nature of early warnings, and ultimately achieves intelligent management of flood control and disaster reduction.

[0008] To achieve the above objectives, the present invention provides a method for identifying and warning of flood control risks in a reservoir group, comprising the following steps:

[0009] Step S1: Collect rainfall, reservoir water level, flood discharge and topographic data in real time, clean, calibrate and fuse the collected data to generate a standardized dataset;

[0010] Step S2: Based on the standardized dataset generated in Step S1, calculate the flood control risk index of the reservoir group;

[0011] A hydrological model of the reservoir group was constructed by combining GIS data to simulate the flood discharge capacity under different rainfall scenarios and calculate the flood discharge capacity index.

[0012] Based on the population density, infrastructure distribution, and topographic features of the downstream region, a vulnerability index for the downstream region is calculated.

[0013] Calculate the flood formation index of the upstream region based on rainfall intensity, soil moisture, and topographic slope.

[0014] The flood propagation index for the middle reaches of the river is calculated based on the river slope, riverbed roughness, and river width in the middle reaches.

[0015] The flood control risk index, flood discharge capacity index, downstream vulnerability index, upstream flood formation index, and midstream flood propagation index are weighted and integrated to generate a comprehensive risk index.

[0016] Step S3: Set low, medium and high warning thresholds, compare the comprehensive risk index with the three warning thresholds to obtain the current risk level, and issue warning information;

[0017] Step S4: The system receives user feedback and makes dynamic adjustments based on the user feedback.

[0018] Preferably, in step S1, data acquisition is performed through IoT devices, satellite remote sensing and geographic information systems, and historical databases.

[0019] Preferably, in step S1, the data fusion uses a deep learning algorithm for feature extraction, including using a convolutional neural network (CNN) to extract features from rainfall, reservoir water level, flood discharge, and topographic data to obtain multi-source data features, using a recurrent neural network (RNN) to extract features from the time series of rainfall and water level reservoir data to obtain time series features, and then fusing the multi-source data features and time series features to obtain fused features.

[0020] Preferably, in step S2, the specific operation for calculating the flood control risk index of the reservoir group is as follows:

[0021] FRI t =α×LSTM'(F 融合 )+β×HIST 数据 ;

[0022] Among them, FRI t F represents the flood risk index at time step t, α and β represent weighting coefficients, and F 融合 The LSTM' represents the fused features, and HIST represents the LSTM network with a multi-source data temporal correlation modeling module. 数据 Indicates the impact of historical data;

[0023] LSTM'(F 融合 The calculation formula for ) is as follows:

[0024]

[0025] Among them, A t,i The attention weight of time step t on data source i is represented; n represents the number of data sources.

[0026] The multi-source data temporal correlation modeling module is as follows:

[0027] A t =Attention(F 融合 W 时间序列 );

[0028] Among them, A t W represents the attention weight vector at time step t; 时间序列 Representing time series characteristics;

[0029] The formula for calculating the impact of historical data is as follows:

[0030]

[0031] Where m represents the number of historical data samples, FRI ls,j DCI represents the flood risk index of the j-th historical sample. ls,j This represents the flood discharge capacity index of the j-th historical sample;

[0032] DCI ls,j =γ×f(W t ,S t )+δ×HD t ;

[0033] Where γ and δ represent weighting coefficients, f(W) t ,S t ) indicates based on the current water level W t and facility status S t The function, HD t This represents historical flood discharge data with a time step t.

[0034] f(W t ,S t )=aW t +bSt ;

[0035] Where a and b represent weighting coefficients.

[0036] Preferably, in step S2, the vulnerability index of the downstream region is calculated using the following formula:

[0037] VUL t =c×P t +d×I t +e×TF t ;

[0038] Among them, VUL t This represents the vulnerability index, where c, d, and e represent weighting coefficients, and P... t I represents the population density of the downstream area. t Indicates the distribution of infrastructure in the downstream region, TF t Indicates the topographic features of the downstream area;

[0039] The formula for calculating the flood formation index in the upstream area is as follows:

[0040] UFI t =g×R t +h×SM t +k×SG t ;

[0041] Among them, UFI t R represents the flood formation index, where g, h, and k represent weighting coefficients. t Indicates rainfall in the upstream area, SM t SG indicates soil moisture in the upstream area. t Indicates the slope of the terrain in the upstream area;

[0042] The formula for calculating the flood propagation index in the middle reaches of the river is as follows:

[0043] MCI t =l×RG t +o×RC t +p×RW t ;

[0044] Among them, MCI t RG represents the flood propagation index, where l, o, and p represent weighting coefficients. t RC represents the river channel slope in the middle reaches. t RW represents the channel roughness in the middle reaches of the river. t This indicates the width of the river channel in the middle reaches.

[0045] Preferably, in step S2, the formula for calculating the comprehensive risk index is as follows:

[0046] CRI综合 =ι×FRI t +κ×DCI t +λ×VUI t +μ×UFI t +ν×MCI t ;

[0047] Among them, CRI 综合 The DCI represents the comprehensive risk index, where ι, κ, λ, μ, and ν represent weighting coefficients. t This indicates the flood discharge capacity index.

[0048] Preferably, in step S3, when the comprehensive risk index is lower than the low-level warning threshold, it indicates that the current risk is low; when the comprehensive risk index is greater than or equal to the low-level warning threshold and less than or equal to the medium-level warning threshold, it indicates that the current risk is medium; and when the comprehensive risk index is greater than the medium-level warning threshold, it indicates that the current risk is high.

[0049] Preferably, in step S3, the warning information is sent via SMS, APP push, broadcast, and email.

[0050] Preferably, in step S4, user feedback is received, and the model parameters in step S2 and the early warning strategy in step S3 are dynamically adjusted based on the user feedback. The specific steps are as follows:

[0051] Update the weights of the LSTM':

[0052]

[0053] Where, θ τ+1 θ represents the updated weight coefficients. τ This represents the current weight coefficients, and ψ represents the learning rate. Represents the error gradient;

[0054] Adjust the warning threshold based on actual water levels and rainfall intensity information provided by users:

[0055] T 低 =ξ-ζ×σ;

[0056] T 中 =ξ + ζ × σ;

[0057] Among them, T 低 T represents the low-level warning threshold. 中 ξ represents the intermediate warning threshold, σ represents the average value of historical data, ζ represents the standard deviation of historical data, and ζ represents the adjustment factor.

[0058] This invention also provides a flood control risk identification and early warning system for reservoir groups, comprising:

[0059] The data acquisition module is used to collect real-time data on rainfall, reservoir water level, flood discharge, and topography.

[0060] The data processing module is used to clean, calibrate, and fuse the collected data to generate standardized datasets;

[0061] The risk calculation module is used to calculate the flood control risk index, flood discharge capacity index, downstream vulnerability index, upstream flood formation index, and midstream flood propagation index of a reservoir group based on a standardized dataset, and generate a comprehensive risk index through weighted fusion.

[0062] The early warning module is used to set low, medium and high warning thresholds, compare the comprehensive risk index with the thresholds, and issue corresponding early warning information.

[0063] Feedback and Adjustment Module: Used to receive user feedback and dynamically adjust the parameters and early warning strategies of the risk calculation model, including updating weight coefficients and adjusting early warning thresholds;

[0064] GIS modeling module: used to build hydrological models of reservoir groups by combining geographic information system data, and to simulate flood discharge capacity under different rainfall scenarios;

[0065] Historical data analysis module: used to analyze the impact of historical data on current flood control risks, and to optimize model parameters and early warning strategies.

[0066] Therefore, the present invention employs the above-mentioned flood control risk identification and early warning system and method for reservoir groups, and the beneficial technical effects are as follows:

[0067] (1) Application of multi-source data fusion and deep learning algorithms.

[0068] This invention utilizes IoT devices, satellite remote sensing, and Geographic Information Systems (GIS) to collect real-time data on rainfall, reservoir water levels, flood discharge, and topography. Deep learning algorithms (such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are then employed for feature extraction and data fusion. This multi-source data fusion technology overcomes the limitations of traditional methods that rely on a single data source, enabling a more comprehensive and accurate reflection of the real-time status and potential risks of a reservoir group. Through the modeling capabilities of deep learning algorithms on time-series data, this invention can dynamically capture implicit patterns in the data, significantly improving the accuracy and efficiency of data processing.

[0069] (2) Construction of dynamic risk assessment model and spatiotemporal correlation analysis.

[0070] This invention enables dynamic assessment of flood control risks in reservoir groups across time and space dimensions, overcoming the limitations of existing technologies that rely on single-dimensional analysis. Specifically, this is reflected in the following aspects:

[0071] Dynamic simulation in the time dimension:

[0072] This invention introduces a Long Short-Term Memory (LSTM) network and an attention mechanism to dynamically model the time-series characteristics of flood formation, propagation, and discharge capacity. Through the LSTM network, the system can capture the long-term dependencies of time-series data such as rainfall intensity and reservoir water level changes, accurately predicting the formation and development trends of floods. For example, the system can identify the real-time impact of sudden changes in rainfall intensity on reservoir discharge capacity, thereby providing early warnings of potential flood discharge risks.

[0073] Spatial dimension regional correlation analysis:

[0074] This invention comprehensively considers the dynamic correlation between upstream, midstream, and downstream regions, calculates the upstream flood formation index, the midstream flood propagation index, and the downstream vulnerability index respectively, and generates a comprehensive risk index through weighted fusion.

[0075] Upstream region: By analyzing rainfall intensity, soil moisture, and topographic slope, the system dynamically assesses flood formation potential. For example, when upstream rainfall intensity exceeds the soil's absorption capacity, the system can quickly identify high-risk areas for flood formation.

[0076] Midstream region: By combining river slope, riverbed roughness, and channel width, the propagation path and velocity of floods in the midstream are simulated. This dynamic simulation can help predict the time it takes for floods to reach the downstream areas, providing crucial support for flood control decisions.

[0077] Downstream areas: The vulnerability of downstream areas is assessed based on population density, infrastructure distribution, and topographic features. For example, the system can identify potential hazards that flooding may pose to downstream towns and critical infrastructure such as bridges and hospitals.

[0078] A comprehensive assessment combining time and space:

[0079] By combining dynamic analysis across time and space, this invention can comprehensively quantify the flood control risks of a reservoir group. For example, during an extreme rainfall event, the system can simultaneously identify high-risk areas for upstream flood formation, changes in the path of midstream flood propagation, and vulnerable downstream areas, thereby achieving precise location and tiered early warning of flood risks. This spatiotemporal dynamic assessment model significantly improves the comprehensiveness and timeliness of risk assessment, overcoming the shortcomings of existing technologies in single-dimensional analysis.

[0080] (3) User feedback-driven adaptive adjustment mechanism.

[0081] This invention dynamically adjusts model parameters and early warning strategies by receiving user feedback, including updating weight coefficients and adjusting early warning thresholds. This adaptive adjustment mechanism overcomes the shortcomings of existing systems that cannot dynamically optimize based on actual flood events, ensuring that early warning information can be continuously improved to adapt to complex and ever-changing flood scenarios. For example, when users report a deviation between the actual water level and the model's predicted value, the system can dynamically update the weight coefficients and optimize the model parameters through the learning rate and error gradient, thereby improving the accuracy of early warnings. Attached Figure Description

[0082] Figure 1 This is a flowchart of a method for identifying and warning of flood control risks in a reservoir group according to the present invention;

[0083] Figure 2 This is a structural diagram of a flood control risk identification and early warning system for a reservoir group according to the present invention. Detailed Implementation

[0084] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0085] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0086] Example 1

[0087] like Figure 1 As shown, a method for identifying and warning of flood control risks in a reservoir group includes the following steps:

[0088] Step S1: Collect rainfall, reservoir water level, flood discharge and topographic data in real time, clean, calibrate and fuse the collected data to generate a standardized dataset.

[0089] Data acquisition is conducted through IoT devices, satellite remote sensing and geographic information systems, as well as historical databases.

[0090] Internet of Things (IoT) devices: Sensors deployed in reservoirs, rivers, and critical infrastructure (such as bridges and sluices) collect data such as rainfall, reservoir water levels, and flood discharge in real time.

[0091] Satellite remote sensing: acquiring remote sensing images of topography, soil moisture, and flood coverage.

[0092] Geographic Information System (GIS): Provides static data such as the geographical distribution of reservoir groups, downstream population density, and infrastructure distribution.

[0093] Historical database: retrieves data on past flood events, including rainfall intensity, reservoir discharge, and damage levels.

[0094] The data processing is as follows:

[0095] Missing value handling: Missing values ​​in sensor data are filled using time series interpolation.

[0096] Noise filtering: High-frequency noise in rainfall and water level data is removed using a low-pass filter.

[0097] Data calibration: Compare IoT device data with satellite remote sensing data to calibrate sensor biases.

[0098] Data fusion employs deep learning algorithms for feature extraction, including using convolutional neural networks (CNNs) to extract features from rainfall, reservoir water levels, flood discharge, and topographic data to obtain multi-source data features. Recurrent neural networks (RNNs) are then used to extract features from the time series of rainfall and water level reservoir data to obtain time series features. Finally, the multi-source data features and time series features are fused to obtain the fused features.

[0099] Step S2: Based on the standardized dataset generated in Step S1, calculate the flood control risk index of the reservoir group;

[0100] A hydrological model of a reservoir group is constructed by combining GIS data to simulate the flood discharge capacity under different rainfall scenarios and calculate the flood discharge capacity index. This is an existing technology, which will be briefly described below.

[0101] To construct a hydrological model of the reservoir group, GIS data was first collected, including topographic elevation, land use type, soil type, river network, and watershed boundaries. Next, the collected GIS data underwent preprocessing, including data cleaning, missing value imputation, and noise filtering, to ensure data accuracy and usability. Then, GIS tools were used to divide the watershed, determine the catchment area of ​​each reservoir, and extract key watershed features such as catchment area, river length, slope, and river network density. Based on this GIS data, a distributed hydrological model (such as a SWAT model or a HEC-HMS model) was constructed. The model inputs included rainfall, evaporation, soil moisture, and topographic slope, while the outputs were reservoir discharge, water level changes, and flood propagation paths. Finally, the model was calibrated using historical flood event data, adjusting model parameters to improve simulation accuracy. The accuracy and reliability of the model were verified by comparing the simulation results with actual observation data.

[0102] Based on the population density, infrastructure distribution, and topographic features of the downstream region, a vulnerability index for the downstream region is calculated.

[0103] Calculate the flood formation index of the upstream region based on rainfall intensity, soil moisture, and topographic slope.

[0104] The flood propagation index for the middle reaches of the river is calculated based on the river slope, riverbed roughness, and river width in the middle reaches.

[0105] A comprehensive risk index is generated by weighting and integrating the flood control risk index, flood discharge capacity index, downstream vulnerability index, upstream flood formation index, and midstream flood propagation index.

[0106] The specific steps for calculating the flood control risk index of a reservoir group are as follows:

[0107] FRI t =α×LSTM'(F 融合 )+β×HIST 数据 (1);

[0108] Among them, FRI t F represents the flood risk index at time step t, α and β represent weighting coefficients, and F 融合 The LSTM' represents the fused features, and HIST represents the LSTM network with a multi-source data temporal correlation modeling module. 数据 Indicates the impact of historical data;

[0109] LSTM'(F 融合 The calculation formula for ) is as follows:

[0110]

[0111] Among them, A t,i This represents the attention weight of time step t for data source i;

[0112] The multi-source data temporal correlation modeling module is as follows:

[0113] A t =Attention(F 融合 W 时间序列 (3);

[0114] Among them, A t W represents the attention weight vector at time step t; 时间序列 Representing time series characteristics;

[0115] The formula for calculating the impact of historical data is as follows:

[0116]

[0117] Where m represents the number of historical data samples, FRI ls,j DCI represents the flood risk index of the j-th historical sample. ls,j This represents the flood discharge capacity index of the j-th historical sample;

[0118] DCI ls,j=γ×f(W t ,S t )+δ×HD t (5);

[0119] Where γ and δ represent weighting coefficients, f(W) t ,S t ) indicates based on the current water level W t and facility status S t The function, HD t This represents historical flood discharge data with a time step t.

[0120] f(W t ,S t )=aW t +bS t (6);

[0121] Where a and b represent weighting coefficients.

[0122] The formula for calculating the vulnerability index of downstream areas is as follows:

[0123] VUL t =c×P t +d×I t +e×TF t (7);

[0124] Among them, VUL t This represents the vulnerability index, where c, d, and e represent weighting coefficients, and P... t I represents the population density of the downstream area. t Indicates the distribution of infrastructure in the downstream region, TF t Indicates the topographic features of the downstream area;

[0125] The formula for calculating the flood formation index in the upstream area is as follows:

[0126] UFI t =g×R t +h×SM t +k×SG t (8);

[0127] Among them, UFI t R represents the flood formation index, where g, h, and k represent weighting coefficients. t Indicates rainfall in the upstream area, SM t SG indicates soil moisture in the upstream area. t Indicates the slope of the terrain in the upstream area;

[0128] The formula for calculating the flood propagation index in the middle reaches of the river is as follows:

[0129] MCI t=l×RG t +o×RC t +p×RW t (9);

[0130] Among them, MCI t RG represents the flood propagation index, where l, o, and p represent weighting coefficients. t RC represents the river channel slope in the middle reaches. t RW represents the channel roughness in the middle reaches of the river. t This indicates the width of the river channel in the middle reaches.

[0131] The formula for calculating the comprehensive risk index is as follows:

[0132] CRI 综合 =ι×FRI t +κ×DCI t +λ×VUI t +μ×UFI t +ν×MCI t (10);

[0133] Among them, CRI 综合 The DCI represents the comprehensive risk index, where ι, κ, λ, μ, and ν represent weighting coefficients. t The flood discharge capacity index is calculated using the same formula as formula (5).

[0134] Step S3: Set low, medium, and high warning thresholds, compare the comprehensive risk index with the three warning thresholds to obtain the current risk level, and issue warning information (warning information is sent via SMS, APP push, broadcast, and email).

[0135] When the comprehensive risk index is below the low-level warning threshold, it indicates that the current risk level is low. When the comprehensive risk index is greater than or equal to the low-level warning threshold and less than or equal to the medium-level warning threshold, it indicates that the current risk level is medium. When the comprehensive risk index is greater than the medium-level warning threshold, it indicates that the current risk level is high.

[0136] Step S4: Receive user feedback and dynamically adjust the model parameters in step S2 and the early warning strategy in step S3 based on the user feedback.

[0137] Receive user feedback and dynamically adjust the model parameters in step S2 and the early warning strategy in step S3 based on the user feedback. The specific steps are as follows:

[0138] Update the weights of the LSTM':

[0139]

[0140] Where, θ τ+1 θ represents the updated weight coefficients.τ This represents the current weight coefficients, and ψ represents the learning rate. Represents the error gradient;

[0141] Adjust the warning threshold based on actual water levels and rainfall intensity information provided by users:

[0142] T 低 =ξ-ζ×σ(12);

[0143] T 中 =ξ+ζ×σ(13);

[0144] Among them, T 低 T represents the low-level warning threshold. 中 ξ represents the intermediate warning threshold, σ represents the average value of historical data, ζ represents the standard deviation of historical data, and ζ represents the adjustment factor.

[0145] The invention will be further illustrated by simulation experiments below.

[0146] Data source:

[0147] Historical data: Data on flood events in a certain area over the past 10 years.

[0148] Simulation data: Simulated moderate rainfall (100mm / 24h), extreme rainfall (200mm / 24h), and continuous rainfall events (3-day rainfall amounts of 120mm, 150mm, and 180mm, respectively).

[0149] Data generation method:

[0150] Rainfall data were randomly generated using a normal distribution.

[0151] Based on rainfall and reservoir discharge capacity, simulate reservoir water level and discharge flow.

[0152] Extract static data such as terrain slope and river width from the GIS database.

[0153] To illustrate the effects of the present invention, the following comparative model was also provided:

[0154] Comparison with Model 1: Reduce the upstream flood formation index.

[0155] Comparison Model 2: Reduce the flood propagation index in the middle reaches.

[0156] Comparison Model 3: Reduce the downstream vulnerability index.

[0157] Comparison Model 4: Simultaneously reduce the flood formation index in both upstream and midstream.

[0158] The simulation results are shown in Table 1. In the simulation experiment, the performance of the complete model and multiple comparative models under different rainfall scenarios was compared to verify the impact of various factors on the performance of the reservoir group flood control risk identification and early warning system. The complete model showed high accuracy in calculating the comprehensive risk index and high early warning accuracy under all rainfall scenarios.

[0159] Specifically, the complete model achieves a comprehensive risk index of 0.45 under a moderate rainfall scenario, 0.85 under an extreme rainfall scenario, and gradually increases from 0.5 to 0.75 under a continuous rainfall scenario, with early warning accuracy rates of 95%, 90%, and 88%, respectively. In contrast, the comparative model, after reducing the upstream flood formation index and midstream flood propagation index, shows a decrease in both the calculated comprehensive risk index and early warning accuracy, particularly under extreme and continuous rainfall scenarios. For example, comparative model 4 achieves a comprehensive risk index of only 0.78 under an extreme rainfall scenario, with early warning accuracy dropping to 80%. This demonstrates that all factors considered in the complete model are crucial for ensuring the system's high performance and reliability.

[0160] Table 1 Simulation Results

[0161]

[0162]

[0163] It is worth noting that all contents not described in detail in this invention are existing technologies and are well known to those skilled in the art.

[0164] Therefore, this invention employs the aforementioned reservoir group flood control risk identification and early warning system and method. By real-time collection of rainfall, reservoir water level, discharge flow, and topographic data, combined with IoT, satellite remote sensing, and GIS technologies, and utilizing deep learning algorithms to fuse multi-source data, a hydrological model is constructed to simulate discharge capacity, calculate a comprehensive risk index, and release risk information through a three-level early warning mechanism. Simultaneously, the system receives user feedback, dynamically adjusts model parameters and early warning strategies, improves the accuracy and real-time nature of early warnings, and ultimately achieves intelligent management of flood control and disaster reduction.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for identifying and issuing early warning of flood control risks in a group of reservoirs, characterized in that, Includes the following steps: Step S1: Collect rainfall, reservoir water level, flood discharge and topographic data in real time, clean, calibrate and fuse the collected data to generate a standardized dataset; Step S2: Based on the standardized dataset generated in Step S1, calculate the flood control risk index of the reservoir group; A hydrological model of the reservoir group was constructed by combining GIS data to simulate the flood discharge capacity under different rainfall scenarios and calculate the flood discharge capacity index. Based on the population density, infrastructure distribution, and topographic features of the downstream region, a vulnerability index for the downstream region is calculated. Calculate the flood formation index of the upstream region based on rainfall intensity, soil moisture, and topographic slope. The flood propagation index for the middle reaches of the river is calculated based on the river slope, riverbed roughness, and river width in the middle reaches. The flood control risk index, flood discharge capacity index, downstream vulnerability index, upstream flood formation index, and midstream flood propagation index are weighted and integrated to generate a comprehensive risk index. Step S3: Set low, medium and high warning thresholds, compare the comprehensive risk index with the three warning thresholds to obtain the current risk level, and issue warning information; Step S4: The system receives user feedback and makes dynamic adjustments based on the user feedback; In step S2, the specific steps for calculating the flood control risk index of the reservoir group are as follows: ； in, Indicates time step The flood control risk index , Indicates the weighting coefficient. Indicates the characteristics after fusion. This represents an LSTM network that incorporates a multi-source data temporal correlation modeling module. Indicates the impact of historical data; The calculation formula is as follows: ； in, Indicates time step For data source Attention weights; Indicates the number of data sources; The multi-source data temporal correlation modeling module is as follows: ； in, Indicates time step Attention weight vector; Representing time series characteristics; The formula for calculating the impact of historical data is as follows: ； in, This indicates the number of samples in the historical data. Indicates the first Flood risk index of a historical sample, Indicates the first The flood discharge capacity index of a historical sample; ； in, , Indicates the weighting coefficient. Indicates based on the current water level and facility status The function, Indicates time step Historical flood discharge data; In step S2, the vulnerability index of the downstream region is calculated using the following formula: ； in, Indicates a vulnerability index, , , Indicates the weighting coefficient. Indicates the population density of the downstream area. Indicates the distribution of infrastructure in the downstream region. Indicates the topographic features of the downstream area; The formula for calculating the flood formation index in the upstream area is as follows: ； in, Indicates the flood formation index, , , Indicates the weighting coefficient. This indicates the rainfall in the upstream area. Indicates soil moisture in the upstream area. Indicates the slope of the terrain in the upstream area; The formula for calculating the flood propagation index in the middle reaches of the river is as follows: ； in, Indicates the flood propagation index, , , Indicates the weighting coefficient. Indicates the riverbed slope in the middle reaches. Indicates the riverbed roughness in the middle reaches. Indicates the width of the river channel in the middle reaches; In step S2, the formula for calculating the comprehensive risk index is as follows: ； in, This represents the overall risk index. Indicates the flood discharge capacity index. , , , , This represents the weighting coefficient.

2. The method for identifying and warning of flood control risks in a reservoir group according to claim 1, characterized in that, In step S1, data acquisition is carried out through IoT devices, satellite remote sensing and geographic information systems, and historical databases.

3. The method for identifying and warning of flood control risks in a reservoir group according to claim 1, characterized in that, In step S1, data fusion employs deep learning algorithms for feature extraction, including using a convolutional neural network (CNN) to extract features from rainfall, reservoir water level, flood discharge, and topographic data to obtain multi-source data features. A recurrent neural network (RNN) is then used to extract features from the time series of rainfall and water level reservoir data to obtain time series features. Finally, the multi-source data features and time series features are fused to obtain the fused features.

4. The method for identifying and warning of flood control risks in a reservoir group according to claim 1, characterized in that, In step S3, when the comprehensive risk index is lower than the low-level warning threshold, it indicates that the current risk is low; when the comprehensive risk index is greater than or equal to the low-level warning threshold and less than or equal to the medium-level warning threshold, it indicates that the current risk is medium; and when the comprehensive risk index is greater than the medium-level warning threshold, it indicates that the current risk is high.

5. The method for identifying and warning of flood control risks in a reservoir group according to claim 1, characterized in that, In step S3, the warning information is sent via SMS, APP push, broadcast and email.

6. The method for identifying and warning of flood control risks in a reservoir group according to claim 1, characterized in that, In step S4, user feedback is received, and the model parameters in step S2 and the early warning strategy in step S3 are dynamically adjusted based on the user feedback. The specific steps are as follows: renew Weighting coefficients: ； in, This represents the updated weight coefficients. This represents the current weighting coefficient. Indicates the learning rate. Represents the error gradient; Adjust the warning threshold based on actual water levels and rainfall intensity information provided by users: ； ； in, This indicates the low-level warning threshold. This indicates the intermediate warning threshold. This represents the average value of historical data. The standard deviation of historical data This represents the adjustment factor.

7. A flood control risk identification and early warning system for a reservoir group, characterized in that, The method for implementing the flood control risk identification and early warning method for reservoir groups as described in any one of claims 1-6 includes: The data acquisition module is used to collect real-time data on rainfall, reservoir water level, flood discharge, and topography. The data processing module is used to clean, calibrate, and fuse the collected data to generate standardized datasets; The risk calculation module is used to calculate the flood control risk index, flood discharge capacity index, downstream vulnerability index, upstream flood formation index, and midstream flood propagation index of a reservoir group based on a standardized dataset, and generate a comprehensive risk index through weighted fusion. The early warning module is used to set low, medium and high warning thresholds, compare the comprehensive risk index with the thresholds, and issue corresponding early warning information. Feedback and Adjustment Module: Used to receive user feedback and dynamically adjust the parameters and early warning strategies of the risk calculation model, including updating weight coefficients and adjusting early warning thresholds.

Images