Digital twinborn basin flood disaster early warning plan generation system based on deep learning

Abstract

The invention relates to the technical field of computer science and artificial intelligence, and discloses a digital twin basin flood disaster early warning plan generation system based on deep learning, and the system comprises a data collection and processing module which obtains and standardizes multi-source data of basin meteorology, hydrology, terrain, remote sensing and the like in real time, and provides real-time data input; the digital twinborn modeling module is used for constructing a digital twinborn model of a watershed hydrological dynamic process according to the real-time data and outputting a simulation result; the deep learning prediction module is used for receiving a simulation result, training a prediction model and outputting space and time distribution prediction of flood disasters; the emergency response generation module is used for generating an emergency plan of the flood disaster according to the prediction result; and the system feedback module is used for evaluating an emergency plan and a prediction effect and providing an adjustment basis for future disaster early warning. According to the invention, accurate flood disaster prediction based on real-time data can be realized, the accuracy and response efficiency of an emergency plan are improved, and disaster loss is reduced.

Description

Technical Field

[0001] The present invention relates to the field of computer science and artificial intelligence technology, and specifically to a digital twin watershed flood disaster early warning plan generation system based on deep learning. Background Art

[0002] With the intensification of global climate change, the frequency and intensity of extreme climate events such as flood disasters are gradually increasing, bringing great challenges to watershed areas. Timely early warning and efficient emergency response to flood disasters have become the key to coping with this challenge. However, the existing flood disaster early warning and emergency response technologies have deficiencies in many aspects and need to be improved.

[0003] Traditional flood disaster early warning methods mostly rely on historical meteorological and hydrological data, and simulate the watershed hydrological process through physical models. These methods often focus on specific preset conditions and ignore the complexity and real-time changes of the watershed environment. Hydrological simulation models usually predict future disaster situations based on long-term historical data, but the accuracy of these models is limited by the timeliness and integrity of the data. Especially in the face of climate change and sudden weather events, traditional models often have difficulty reflecting actual hydrological changes in a timely manner, resulting in a significant reduction in the timeliness and accuracy of early warnings.

[0004] In addition, traditional flood emergency plans mostly rely on artificial experience and fixed response strategies, lacking flexibility. Emergency resource scheduling and evacuation route planning are usually adjusted according to experience after a disaster occurs, lacking systematic real-time data support. This experience-based approach is difficult to cope with the increasingly complex disaster environment and often has problems such as response lags and unreasonable resource allocation, seriously affecting the efficiency of emergency response and the speed of post-disaster recovery.

[0005] After a disaster occurs, the existing technology lacks an effective feedback mechanism to evaluate the deviation between the early warning results and the actual disaster situation, resulting in a slow optimization process for early warning models and emergency plans. The existing technology usually lacks real-time feedback evaluation and adaptive learning capabilities, making it impossible to continuously improve the accuracy of emergency response strategies and early warning models. Therefore, traditional flood disaster early warning and emergency response systems show great limitations in the face of complex geographical conditions and sudden disasters and are difficult to meet the needs of modern disaster management.

[0006] Therefore, the present invention proposes a digital twin watershed flood disaster early warning plan generation system based on deep learning to solve the deficiencies of the existing technology. Summary of the Invention

[0007] In view of the deficiencies of the prior art, the present invention provides a digital twin basin flood disaster early warning plan generation system based on deep learning, which solves the problems of inaccurate prediction, untimely response, inflexible plan, and difficult system self-adaptive optimization in existing flood disaster early warning and emergency response.

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions: A digital twin basin flood disaster early warning plan generation system based on deep learning, comprising:

[0009] A data acquisition and processing module, used to obtain and standardize the meteorological, hydrological, topographic, and remote sensing multi-source heterogeneous data of the basin in real time, and provide real-time multi-source heterogeneous data input;

[0010] A digital twin modeling module, used to construct a digital twin model for simulating the hydrological dynamic process of the basin according to the real-time multi-source heterogeneous data, and output the simulation results;

[0011] A deep learning prediction module, used to receive the simulation results of the digital twin model, train the prediction model and output the prediction results of the spatial and temporal distribution of flood disasters;

[0012] An emergency response generation module, used to receive the prediction results output by the deep learning prediction module, and generate a flood emergency plan based on the prediction results;

[0013] A system feedback module, used to evaluate the effects of the prediction and response plans according to the flood emergency plan and prediction results, and provide a basis for adjustment for future disaster early warnings.

[0014] Preferably, the data acquisition and processing module includes:

[0015] A meteorological data acquisition sub-module, used to collect the meteorological data of the basin;

[0016] A hydrological data acquisition sub-module, used to collect the hydrological data of the basin;

[0017] A topographic data acquisition sub-module, used to acquire the topographic data of the basin;

[0018] A remote sensing data processing sub-module, used to acquire and process the remote sensing image data of the basin;

[0019] A data standardization sub-module, used to uniformly format and standardize the collected data.

[0020] Preferably, the steps of uniformly formatting and standardizing the collected data are:

[0021] Missing value processing, interpolating and complementing the missing values in the collected data;

[0022] Outlier detection and correction, identifying outliers in the collected data based on the set threshold rules and performing smoothing correction;

[0023] Data alignment, unifying the time scales from different data sources to ensure that multi-source data matches by time step;

[0024] Normalization processing, used to normalize data with different dimensions to the interval [0, 1], and the normalization is carried out using the following formula:

[0025]

[0026] where x is the original data; x min and x max are the minimum and maximum values of this type of data respectively; x' is the result after normalization.

[0027] Preferably, the digital twin modeling module includes:

[0028] A watershed hydrological dynamic modeling sub-module, used to construct a digital twin model of the watershed based on the real-time multi-source heterogeneous data collected by the data collection and processing module;

[0029] A water flow simulation sub-module, used to simulate and predict the water flow in the watershed according to the digital twin model;

[0030] A result output sub-module, used to output the water flow simulation result as the input of the deep learning prediction module.

[0031] Preferably, the deep learning prediction module includes:

[0032] A data reception module, used to receive the simulation result of the digital twin model;

[0033] A deep learning model training sub-module, used to train a deep learning prediction model based on the simulation result;

[0034] A flood disaster prediction sub-module, used to output the prediction results of the spatial and temporal distribution of flood disasters according to the deep learning model;

[0035] A result output sub-module, used to output the prediction result as the input of the emergency response generation module.

[0036] Preferably, the deep learning model includes a convolutional neural network, a recurrent neural network or a long short-term memory network, and the deep learning model is used to train and predict the spatial distribution and temporal variation of flood disasters.

[0037] Preferably, the emergency response generation module includes:

[0038] A risk area identification sub-module, which is used to identify risk areas within the basin according to the prediction results of the spatial and temporal distribution of flood disasters;

[0039] An emergency resource scheduling sub-module, which is used to generate emergency material and manpower scheduling plans based on the risk area distribution;

[0040] A response strategy formulation sub-module, which is used to formulate response strategies corresponding to different risk levels, and the response strategies include evacuation route planning and warning level setting;

[0041] A pre-plan output sub-module, which is used to output the generated emergency response pre-plan for the use of relevant departments.

[0042] Preferably, the system feedback module includes:

[0043] A pre-plan execution monitoring sub-module, which is used to monitor the execution process and actual response of the flood emergency pre-plan;

[0044] An effect evaluation sub-module, which is used to compare the actual impact of flood disasters with the prediction results, evaluate the prediction accuracy and the effectiveness of response strategies, and generate evaluation results;

[0045] A model update suggestion sub-module, which is used to generate model adjustment suggestions according to the evaluation results, and provide an optimization basis for the deep learning model and the emergency pre-plan generation mechanism.

[0046] The present invention also provides a method for generating a digital twin basin flood disaster early warning pre-plan based on deep learning, including the following steps:

[0047] Obtain and standardize the meteorological data, hydrological data, topographic data and remote sensing data of the basin to form a multi-source heterogeneous data input in a unified format;

[0048] Build a digital twin model for simulating the hydrological dynamic process of the basin based on the multi-source data, and simulate the water flow within the basin;

[0049] Input the simulation results and the real-time dynamic data after the standardization process into the deep learning model, train and output the prediction results of the spatial and temporal distribution of flood disasters;

[0050] Identify risk areas, generate emergency resource scheduling plans and response strategies based on the prediction results to form a flood emergency pre-plan;

[0051] Conduct feedback evaluation on the implementation effects of the prediction results and the emergency pre-plan, and generate model adjustment suggestions accordingly for optimizing the subsequent disaster early warning process

[0052] The present invention provides a digital twin basin flood disaster early warning pre-plan generation system based on deep learning.

[0053] Has the following beneficial effects:

[0054] 1. By adopting the digital twin watershed flood disaster warning method based on deep learning, through the integration of multi-source data and real-time dynamic simulation, the present invention achieves a more accurate flood disaster prediction effect. Compared with the traditional hydrological prediction methods in the prior art, which mostly rely on static historical data and lack real-time performance and adaptability, the present invention can adjust the prediction results in real time, respond to the changes in the disaster situation in a timely manner, and significantly improve the warning accuracy.

[0055] 2. By combining the deep learning model with the digital twin model, the present invention forms a highly dynamic disaster response mechanism, realizing accurate risk area identification and emergency resource scheduling. Compared with the single model prediction method in the prior art, the present invention can not only identify high-risk areas, but also dynamically generate emergency resource scheduling plans to ensure that resources are timely allocated to the places where they are most needed, effectively avoiding resource waste.

[0056] 3. Through the feedback evaluation mechanism, the present invention can evaluate the prediction results and the implementation effect of the emergency plan according to the actual disaster situation, and automatically generate optimization suggestions. This mechanism continuously improves the model prediction ability during the disaster response process and provides a basis for subsequent plan adjustment. Compared with the traditional mode that only relies on manual experience correction, the present invention has stronger self-optimization ability, improving the adaptability and accuracy of the system.

[0057] 4. By adopting the method of combining digital twin and deep learning technologies, the present invention can more comprehensively simulate the hydrological dynamic process in the watershed, improving the prediction ability for complex geographical environments and disaster types. Compared with the prior art, traditional methods usually ignore the influence of terrain changes and real-time hydrological data. The present invention overcomes this deficiency and more accurately simulates the evolution of flood disasters in different environments, ensuring that the warning plan is more in line with the actual situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is the system architecture diagram of the present invention;

[0059] Figure 2 is the schematic structural diagram of the data acquisition and processing module of the present invention;

[0060] Figure 3 is the schematic structural diagram of the digital twin modeling module of the present invention;

[0061] Figure 4 is the schematic structural diagram of the deep learning prediction module of the present invention;

[0062] Figure 5 is the schematic structural diagram of the emergency response generation module of the present invention;

[0063] Figure 6 Schematic diagram of the system feedback structure of the present invention;

[0064] Figure 7 Flowchart of the method of the present invention. Detailed implementation manners

[0065] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the attached drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0066] Please refer to the attached Figure 1 - attached Figure 6 , the embodiment of the present invention provides a digital twin basin flood disaster warning plan generation system based on deep learning, including:

[0067] A data acquisition and processing module, configured to acquire and standardize multi-source heterogeneous data of meteorology, hydrology, terrain, and remote sensing of the basin in real time, and provide real-time multi-source heterogeneous data input;

[0068] The data acquisition and processing module not only provides the required real-time data basis for the digital twin modeling module, but also provides a prerequisite guarantee for the unity, timeliness, and accuracy of the data input into the deep learning prediction module. Since multi-source data is heterogeneous in terms of data structure, time granularity, spatial resolution, etc., this module also needs to implement data standardization processing to meet the requirements of subsequent functional modules for unified format and high-quality data.

[0069] From the perspective of data sources, the data types collected by this module cover multiple dimensions such as meteorology, hydrology, terrain, and remote sensing, with high diversity. In terms of the system logic structure, the data acquisition and processing module is at the head end of the system process, and its operating state and data quality directly affect the simulation accuracy and prediction reliability of the entire system.

[0070] In this embodiment, the data acquisition and processing module includes a meteorological data acquisition sub-module, a hydrological data acquisition sub-module, a terrain data acquisition sub-module, a remote sensing data processing sub-module, and a data standardization sub-module. Each sub-module communicates through a standard data interface to support data synchronization and asynchronous calls.

[0071] In a possible implementation manner, the meteorological data acquisition sub-module is configured to access the national meteorological information platform in real time to obtain element data such as rainfall, temperature, relative humidity, and wind speed. Generally, the data granularity is hourly, and it can also be set to minute-level resolution according to specific requirements.

[0072] Specifically, the hydrological data acquisition sub-module realizes continuous acquisition of river flow, water level, and evaporation by connecting various hydrological monitoring devices, including river water level stations, flow velocity monitoring sections, reservoir telemetry instruments, etc. To ensure data continuity and accuracy, this sub-module supports an automatic re-sampling mechanism and has a data quality identification function.

[0073] In some embodiments, the terrain data acquisition sub-module extracts parameters such as terrain slope, surface elevation, and flow direction within the basin by accessing DEM (Digital Elevation Model) or DSM (Digital Surface Model) data sources. As an option, this sub-module can perform high-precision terrain modeling based on lidar (LiDAR) or high-resolution aerial photography data.

[0074] The remote sensing data processing sub-module is used to process multi-temporal and multi-spectral remote sensing image data. This module can extract indicators such as land use type, vegetation coverage index (NDVI), and surface temperature from remote sensing images, providing spatial basic information for digital twin modeling. In one embodiment, remote sensing data uses data sources such as Sentinel-2 and Landsat 8, with a resolution up to the 10-meter level.

[0075] After data acquisition is completed, all raw data will enter the data standardization sub-module for unified preprocessing.

[0076] In this embodiment, the data standardization sub-module includes the following processing procedures:

[0077] In a possible implementation, the missing value processing step is first executed. If there are missing measurement points in a certain data sequence, interpolation is used to complete the filling. Common methods include linear interpolation, Lagrange interpolation, and moving average filling. The linear interpolation expression is as follows:

[0078]

[0079] Among them, x t represents the value at the interpolation moment t; x i and x j are the data values at adjacent known moments t i and t j ; t i is the timestamp of the known data point i; t j is the timestamp of the known data point j; t is the moment to be interpolated.

[0080] Subsequently, outlier detection and correction are carried out. Generally, this step is based on a set statistical threshold range. For example, the mean ± 3 times the standard deviation is used as the detection criterion, and the data beyond the range is marked as an outlier. In some embodiments, a sliding window mean repair technique is adopted to smoothly replace the outliers to prevent the introduction of mutation errors.

[0081] Immediately afterwards, data alignment processing is performed. Considering that meteorological and hydrological data often have different time steps, in one implementation, the system aligns all data to the minimum time interval uniformly, ensuring that the subsequent model input data corresponds one by one on the time axis and avoiding information misalignment.

[0082] Finally, normalization processing is executed to eliminate the influence brought by different dimensions. Generally, the min-max normalization method is adopted, and its expression is as follows:

[0083]

[0084] Among them, x is the original data; x min and x max are the minimum and maximum values of this type of data respectively; x' is the result after normalization.

[0085] This normalization operation maps all input data to the [0, 1] interval, which is beneficial to improving the training stability and convergence speed of the deep learning model.

[0086] In some extended implementation methods, to improve the efficiency and adaptability of data processing, the data standardization sub-module can also introduce a batch processing mechanism and a sliding update mechanism to cope with the scenario of a large amount of data stream input.

[0087] Through the above processing process, the data acquisition and processing module can convert the original information from heterogeneous sensor networks, remote sensing platforms, and geographical databases into unified, continuous, and standardized input data, meeting the input requirements for digital twin model construction and deep learning prediction.

[0088] The digital twin modeling module is used to construct a digital twin model for simulating the dynamic process of basin hydrology based on the real-time multi-source heterogeneous data and output the simulation results;

[0089] The digital twin modeling module effectively fuses the multi-source heterogeneous data provided by the data acquisition and processing module and constructs a digital twin model highly consistent with the actual situation of the basin, providing a basic support for water flow simulation, flood disaster prediction, and emergency response.

[0090] The function of this module is to digitize the complex situations in the real world to form a virtual model that can be interacted and calculated in real time, enabling accurate simulation of various dynamic processes in the basin and realizing more efficient disaster warning and emergency decision-making through the linkage with the deep learning prediction module.

[0091] In this embodiment, the digital twin modeling module mainly includes a basin hydrological dynamic modeling sub-module, a water flow simulation sub-module, and a result output sub-module. The sub-modules are effectively connected through standardized data interfaces to ensure the smooth transfer of data and the consistency of the models.

[0092] The basin hydrological dynamic modeling sub-module is used to construct a digital twin model based on the real-time multi-source heterogeneous data collected by the data acquisition and processing module. The digital twin model creates a real-time updated virtual basin model by integrating different data sources (such as meteorological, hydrological, topographic, and remote sensing data) and geographical information.

[0093] Specifically, the basin hydrological dynamic modeling sub-module constructs a digital representation of the basin hydrology based on a distributed hydrological model or a mechanism-driven model (such as SWMM, MIKE SHE, etc.). According to the real-time data provided by the data acquisition and processing module, this sub-module can simulate the hydrological processes of the basin under different meteorological conditions, such as changes in precipitation, evaporation, runoff, etc.

[0094] As an option, in this embodiment, the basin hydrological dynamic modeling sub-module can use the finite difference method or the finite element method to discretely calculate the spatial distribution and temporal evolution of the water flow to ensure the accuracy of the simulation results.

[0095] In a possible implementation, the water flow simulation sub-module simulates and predicts the water flow based on the constructed digital twin model. By numerically solving the flow of water in the basin, this module can predict the water level changes, flow distribution, and possible flood risk areas within several hours to several days in the future.

[0096] For example, the water flow simulation sub-module can deduce the changes in water flow under different scenarios through numerical simulation methods based on real-time meteorological data (such as precipitation, wind speed, etc.) and hydrological data (such as water level, flow rate, etc.). During the simulation process, the dynamic evolution of the water flow is controlled by the following formula:

[0097]

[0098] where the term describes the change in water depth or water level over time, usually driven by factors such as rainfall and evaporation within the basin; h is the water depth, representing the depth of the water body within the basin; q is the water flux, representing the speed and direction of the water flow; t is the time, representing the evolution process of the water flow; represents the divergence operator, used to represent the spatial distribution of the water flow; S is the source term, representing other factors affecting the water flow, such as rainfall, evaporation, runoff, etc.

[0099] This simulation equation can effectively describe the dynamic changes of water flow in the basin, advance in time based on the current hydrological state of the basin, and predict the development trend of water flow under different scenarios.

[0100] The result output sub-module generates relevant hydrological data outputs based on the results of the water flow simulation and uses them as input data for the deep learning prediction module. Specifically, the output water flow simulation results include information such as predicted water level changes, flow velocities, and basin inundation areas. These data will be provided as input to the subsequent deep learning model for more refined flood disaster prediction and risk assessment.

[0101] In some embodiments, the result output sub-module can display the simulation results in a graphical manner, such as generating flood inundation area maps, risk level maps, etc., to facilitate decision-makers to quickly identify dangerous areas and formulate emergency response measures.

[0102] Generally speaking, the digital twin modeling module enables the entire system to respond quickly in a dynamic environment by converting the physical and hydrological characteristics of the basin into a digital model, providing accurate water flow predictions and flood disaster warnings. This module not only improves the timeliness of the system but also provides reliable data support for the training of subsequent deep learning models through high-precision simulations and predictions.

[0103] The deep learning prediction module is used to receive the simulation results of the digital twin model, train the prediction model, and output the prediction results of the spatial and temporal distribution of flood disasters.

[0104] The deep learning prediction module is responsible for accurately predicting flood disasters based on the results of the basin hydrological simulation. By working in collaboration with the digital twin modeling module, the deep learning prediction module can dynamically predict the spatial distribution and temporal changes of flood disasters according to real-time data, providing accurate early warning information for decision-makers. This module receives the simulation results from the digital twin model and uses advanced deep learning techniques for training and prediction to achieve early warning of disasters.

[0105] In this embodiment, the deep learning prediction module includes a data reception module, a deep learning model training sub-module, a flood disaster prediction sub-module, and a result output sub-module. Each sub-module is connected through a standardized data interface to ensure smooth data transfer and processing efficiency.

[0106] Data reception module:

[0107] The data receiving module, as the starting part of the deep learning prediction module, is responsible for receiving the water flow simulation results from the digital twin model. According to the foregoing description, the main data output by the digital twin model includes information such as the spatio-temporal distribution of water levels in the basin, flow velocity distribution, inundated areas, and the dynamics of flood propagation. The data receiving module needs to format these input data to ensure that they meet the requirements of deep learning model training and prediction.

[0108] In some embodiments, the data receiving module may include data cleaning and preliminary processing functions. Especially when facing multi-source data, steps such as timestamp alignment, data denoising, and missing value processing are required. These preprocessing steps ensure the quality and consistency of the input data and provide reliable training data for the subsequent deep learning model.

[0109] Deep learning model training sub-module:

[0110] The deep learning model training sub-module is responsible for training a deep learning model based on the received simulation data to achieve accurate prediction of flood disasters. Generally speaking, deep learning models include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs), etc.

[0111] Convolutional neural network (CNN): CNN is good at processing data with spatial structures, so it is mainly used to extract spatial features from water flow simulation data. Specifically, CNN automatically learns local patterns in spatial data, such as the spatial distribution of water flow and inundated areas, through convolutional layers, pooling layers, and fully connected layers. The core operation of the convolutional layer is to convolve the input data through a convolutional kernel to extract local features and generate a feature map.

[0112] Recurrent neural network (RNN) and long short-term memory network (LSTM): RNN and LSTM are used to model the time series features in water flow data. The occurrence and evolution of flood disasters have significant time dependence, and RNN and LSTM can process long time series data through their recurrent structures, capture the temporal patterns of water flow changes, and predict future flood trends.

[0113] The LSTM model avoids the problem of gradient vanishing in traditional RNNs in long time series by designing a specific gating structure. Specifically, LSTM controls the flow of information through input gates, forget gates, and output gates, and can better retain long-term memories and adapt to the long-term evolution of flood disasters.

[0114] The goal during the training process is to make the output of the model as close as possible to the actual observed data by optimizing the loss function. In some embodiments, the loss function can use mean squared error (MSE) or cross-entropy, etc., and the formula is as follows:

[0115]

[0116] Among them, L is the loss function value (mean square error, MSE), which represents the difference between the model prediction value and the true value; N is the number of samples, that is, the total number of samples in the training data; y i is the true value of the i-th sample, representing the actual observation result (such as the true water level, height, or flow velocity, etc.); is the predicted value of the i-th sample, representing the predicted result output by the model; represents the summation over all training samples, calculating the prediction error of each sample and accumulating them.

[0117] Through the gradient descent algorithm or other optimization algorithms, the model will update its weights and biases to minimize the prediction error, thereby improving the prediction accuracy.

[0118] Flood disaster prediction sub-module:

[0119] The flood disaster prediction sub-module is based on the trained deep learning model to perform specific predictions on the spatial distribution and temporal changes of flood disasters. This process includes the following key links:

[0120] Temporal distribution prediction: Through the LSTM model, time series modeling is performed on the input water flow data to predict the water level, flow rate, inundated area, etc. in the next few hours or days. Specifically, the LSTM model gradually inputs the current hydrological data (such as water level, flow rate, etc.) into the network to predict the water level change at the next time step. The prediction results can be the occurrence time, high tide level, and duration of future flood disasters, etc.

[0121] Spatial distribution prediction: The CNN model is used to analyze the spatial characteristics of the water flow and predict the spatial distribution of the inundated area. Under the action of the convolutional layer, the CNN can automatically learn the spatial distribution law of the water flow in the basin from the input water flow simulation data and predict which areas will be affected by the flood. The prediction results usually include map-type outputs, indicating the possible inundated areas and their levels.

[0122] Joint spatio-temporal prediction: By combining the CNN and LSTM models, the deep learning prediction module can provide flood disaster predictions that simultaneously consider temporal changes and spatial distributions. This prediction result can provide accurate input for the emergency response generation module to help formulate more effective emergency plans.

[0123] In this embodiment, the prediction of spatio-temporal data can adopt the following model output expression:

[0124] h(t,x) = f(X(t), Y(x));

[0125] Among them, h(t, x) represents the spatio-temporal prediction result, where t is time and x is the spatial location; X(t) represents the characteristics of water flow data in the time dimension, such as the sequences of water level and flow rate changing with time; Y(x) represents the characteristics of water flow data in the spatial dimension, such as water depth and flow velocity in each region within the basin.

[0126] The deep learning model in this formula takes the input time series data X(t) and spatial data Y(x), combines the CNN and LSTM networks, and outputs the predicted flood disaster results. The output results include not only the water level, flow rate, etc. that change over time, but also the flooded areas and their changes in space.

[0127] Result output sub-module:

[0128] The result output sub-module outputs the results predicted by the deep learning model as flood disaster warning information and transmits them to the emergency response generation module. The output results include:

[0129] Spatial prediction: including spatial distribution information such as possible flooded areas and flood risk levels.

[0130] Temporal prediction: including information such as water level, flow rate, and risk occurrence time periods within several hours or days in the future.

[0131] Risk assessment: Calculate the occurrence probability of flood disasters based on the predictions of the deep learning model and generate corresponding risk level assessments.

[0132] These output results will be used as the input to the emergency response generation module to support subsequent emergency decision-making and resource scheduling.

[0133] Through the implementation of this module, the deep learning prediction module can predict complex water flow changes and flood disasters in a short time and provide timely and accurate data support for emergency responses.

[0134] In some extended implementation manners, the deep learning prediction module can also combine multi-modal data sources (such as remote sensing data, sensor data, etc.) to further improve the prediction accuracy. At the same time, this module can also be continuously iteratively updated through an adaptive optimization mechanism to adapt to the flood disaster warning requirements under different basin conditions.

[0135] Emergency response generation module, which is used to receive the prediction results output by the deep learning prediction module and generate a flood emergency plan based on the prediction results;

[0136] The emergency response generation module is a key component for implementing a rapid and effective emergency response based on the flood disaster prediction results. Through seamless connection with the deep learning prediction module and the digital twin modeling module, this module can identify the risk areas of flood disasters according to the real-time prediction results and formulate corresponding emergency response plans. This process covers the identification of risk areas, the scheduling of emergency resources, the formulation of response strategies, and the output of plans, ultimately ensuring the timely and effective response to potential disasters and reducing casualties and property losses.

[0137] In this embodiment, the emergency response generation module mainly consists of a risk area identification sub-module, an emergency resource scheduling sub-module, a response strategy formulation sub-module, and a plan output sub-module. Each sub-module is effectively connected through a standardized data interface to ensure the smooth transmission of information and the efficient execution of response decisions.

[0138] Risk area identification sub-module:

[0139] The risk area identification sub-module is the first step of the entire emergency response generation module. Its task is to quickly identify the risk areas in the basin according to the prediction results of the spatial and temporal distribution of flood disasters received from the deep learning prediction module.

[0140] Specifically, in this embodiment, this sub-module further processes and analyzes the information such as water level changes, inundation areas, and risk levels output by the deep learning prediction module. In a possible implementation, this sub-module conducts risk assessment based on the predicted flood inundation areas, temporal evolution trends, and topographic data of the basin to determine which areas will be threatened during the predicted flood disaster period.

[0141] In some embodiments, the risk area identification sub-module can also combine the historical disaster data of the basin with the prediction results, and identify and mark high-risk areas by setting different risk assessment thresholds. This process helps to accurately demarcate the priority allocation areas of emergency resources.

[0142] Emergency resource scheduling sub-module:

[0143] The emergency resource scheduling sub-module generates an emergency resource and manpower scheduling plan according to the identified risk areas. By comprehensively analyzing the distribution of risk areas, the temporal evolution of flood disasters, and the existing emergency resource situation, this sub-module automatically generates a scheduling plan to ensure that resources are timely allocated to the most urgent and needed places.

[0144] Specifically, in some embodiments, the emergency resource scheduling sub-module calculates the required materials (such as rescue equipment, emergency medicines, drinking water, etc.) and human resources (such as rescue personnel, emergency medical personnel, etc.) according to the size and severity of the risk area. The scheduling plan not only considers the allocation of resources, but also factors such as traffic conditions and the transportation efficiency of resources to ensure the efficiency and operability of resource allocation.

[0145] This sub-module may generate a resource scheduling plan based on optimization algorithms (such as linear programming or heuristic search algorithms) to minimize resource waste and maximize resource utilization efficiency. The output of the scheduling plan includes a resource allocation table and a personnel mobilization plan, and is dynamically adjusted according to different situations.

[0146] Response strategy formulation sub-module:

[0147] The response strategy formulation sub-module is responsible for formulating specific emergency response strategies according to the identified risk areas and the risk levels of flood disasters. The emergency response strategies mainly include evacuation route planning, warning level setting, and the predetermination of other post-disaster recovery measures.

[0148] Evacuation route planning: Specifically, the evacuation route planning sub-module dynamically generates safe evacuation routes according to the predicted time and spatial scope of the flood disaster, combined with factors such as traffic flow and road conditions. This route planning not only needs to ensure the evacuation efficiency, but also take into account the evacuation needs of residents in different regions.

[0149] Warning level setting: According to the risk level in the prediction results, the response strategy formulation sub-module will automatically set the corresponding warning level. Usually, the warning level can be divided into multiple levels, such as blue, yellow, orange, red, etc. Each warning level corresponds to different emergency response levels and emergency measures.

[0150] As an option, the setting of the warning level can be dynamically adjusted according to the water level changes in the basin, the scale of the flooded area, and its potential impact on residents' lives. In actual operation, the warning level is closely related to the response strategy to ensure the timely initiation of emergency responses in high-risk areas before the disaster occurs.

[0151] Emergency plan output sub-module:

[0152] The emergency plan output sub-module is responsible for outputting the generated emergency response plan in a structured form for use by relevant departments. The output content includes risk area identification, emergency resource scheduling plan, evacuation route planning, warning level setting, and other relevant emergency measures.

[0153] In some embodiments, the emergency plan output sub-module supports the automated generation of emergency response reports and presents them in a graphical and visual manner. For example, it can output a map showing areas with different flood disaster risk levels, indicating evacuation routes and the locations of key facilities. In addition, the emergency plan output sub-module can also transfer the emergency response plan to the emergency management platform or relevant decision-making systems through an interface to initiate an emergency response in a timely manner.

[0154] In this embodiment, the emergency response generation module works closely with the aforementioned deep learning prediction module, digital twin modeling module, and data collection and processing module. After obtaining the prediction results, the emergency response generation module can efficiently identify the risk areas of flood disasters and generate emergency response strategies for different risk levels. The formulation of response strategies and the generation of resource scheduling plans not only rely on real-time prediction data but also comprehensively consider historical data and disaster types to ensure a rapid and accurate response when a disaster occurs.

[0155] With the acquisition of real-time data and the support of deep learning, this module can formulate personalized emergency response plans according to the different characteristics of flood disasters, thereby improving the efficiency and response speed of disaster management.

[0156] Through the collaborative work of the above sub-modules, the emergency response generation module can quickly identify risk areas, dispatch emergency resources, plan evacuation routes, and formulate corresponding strategies in the initial stage of a flood disaster, ensuring the timeliness, accuracy, and efficiency of disaster response. Through the combination of digital twin models and deep learning technologies, the present invention significantly improves the intelligent level and operational feasibility of emergency response, providing strong technical support for the effective prevention and control of flood disasters.

[0157] The system feedback module is used to evaluate the effectiveness of the prediction and response plan based on the flood emergency plan and prediction results, and provide a basis for adjustment for future disaster warnings;

[0158] The system feedback module is an important part of ensuring the continuous optimization of the system, improving the prediction accuracy, and the efficiency of emergency response. By monitoring the execution of the emergency plan in real time, comparing and evaluating the prediction results with the actual disaster impacts, and proposing model optimization suggestions based on the evaluation results, this module can continuously improve the prediction ability of the system and the effectiveness of emergency response strategies. Through this feedback mechanism, the system can achieve adaptive adjustment and optimization, ensuring that it can provide more accurate early warnings and more efficient emergency responses when facing different types and scales of flood disasters.

[0159] In this embodiment, the system feedback module includes a pre - plan execution monitoring sub - module, an effect evaluation sub - module, and a model update suggestion sub - module. These sub - modules are seamlessly connected through standardized data interfaces to ensure that the system can provide effective feedback based on real - time data and strongly support the optimization of the system.

[0160] Pre - plan execution monitoring sub - module:

[0161] The pre - plan execution monitoring sub - module is responsible for real - time monitoring of the execution process of the flood emergency plan and the actual response situation. By collecting on - site data and real - time feedback information, it monitors the execution effect of the plan to ensure the synchronous implementation of the emergency response and the predetermined strategy. Specifically, this sub - module closely collaborates with the emergency response generation module to obtain data such as emergency resource scheduling and evacuation route execution, and monitors the execution of various emergency response measures.

[0162] In some embodiments, the pre - plan execution monitoring sub - module can integrate IoT devices (such as sensors, cameras, drones, etc.) for real - time data collection. For example, it can monitor the water level change, flow rate, and the expansion of the flooded area in real - time through sensors, and monitor the evacuation progress and the distribution efficiency of emergency resources. This module can quickly identify lags or deviations in the emergency response process and provide data support for subsequent optimization.

[0163] Effect evaluation sub - module:

[0164] The effect evaluation sub - module is used to compare the impact of the actual flood disaster with the prediction results, evaluate the prediction accuracy and the effectiveness of the emergency response strategy, and generate evaluation results. The evaluation process usually involves two main aspects: prediction accuracy evaluation and effectiveness evaluation of the response strategy.

[0165] Prediction accuracy evaluation: Specifically, the effect evaluation sub - module evaluates the accuracy of the deep - learning model by comparing the predicted water level, flooded area with the actual situation. For example, if the prediction system expects a flood disaster in a certain area within the next few hours, the effect evaluation sub - module will compare the predicted water level with the actually monitored water level to determine the prediction error. The prediction error is usually quantified by calculating the following error metrics:

[0166]

[0167] where y pred is the water level, flow rate, or flooded area predicted by the model; y true is the actually measured water level, flow rate, or flooded area.

[0168] Evaluation of the effectiveness of response strategies: After a flood disaster occurs, the effect evaluation sub-module also needs to compare the differences between the actually implemented emergency response strategies (such as evacuation routes, material dispatching, personnel allocation, etc.) and the pre-determined strategies. For example, by monitoring the real-time evacuation progress and the actual disaster impact scope, the effectiveness and flexibility of the response strategies are evaluated. For strategies with deviations, the evaluation module will generate an evaluation report to provide a basis for subsequent adjustments.

[0169] Sub-module for model update suggestions:

[0170] The sub-module for model update suggestions is responsible for generating optimization suggestions for the deep learning model and the emergency plan generation mechanism according to the evaluation results provided by the effect evaluation sub-module. Specifically, this sub-module analyzes the deviations and problems in the evaluation results and proposes adjustment suggestions for the parameters of the deep learning model, improvement directions for the input data, and optimization measures for the emergency response strategies.

[0171] In a possible implementation, the sub-module for model update suggestions can automatically adjust the learning rate, network structure, or loss function of the deep learning model according to historical prediction errors and actual response deviations. Through the feedback mechanism, the system can achieve online learning and self-optimization. During the optimization process, the sub-module for model update suggestions may propose the following improvement measures based on historical data or simulation results:

[0172] Retrain the deep learning model to enhance the model's recognition ability for specific types of disasters;

[0173] Adjust the weight distribution of the input data or add new influencing factors (such as terrain features, rainfall intensity, etc.) to improve the model's adaptability to complex environments;

[0174] Optimize the emergency plan generation mechanism, adjust the resource dispatching and evacuation route planning strategies to make them more adaptable to the actual situation.

[0175] In addition, the sub-module for model update suggestions can also use the ensemble learning method to combine the output results of multiple deep learning models to further improve the prediction accuracy and the reliability of the emergency response strategies. Through this mechanism, the system can be continuously iteratively updated to cope with different scales and types of flood disasters that may occur in the future.

[0176] As the "self-regulation" mechanism of the overall system, the system feedback module closely cooperates with other modules such as the deep learning prediction module and the emergency response generation module. The role of the feedback module is to feedback the real-time disaster situation data and the emergency response execution situation to the system, so as to provide a basis for improving the deep learning model and enhancing the accuracy of the model and the flexibility of the emergency response. Through this closed-loop feedback process, the present invention can improve the intelligent level of the system in continuous practice and ensure that the flood disaster warning and emergency response are always in the best state.

[0177] The system feedback module in this embodiment can continuously optimize the system by monitoring the execution process of the flood emergency plan, evaluating the difference between the predicted results and the actual disaster impact, and proposing model adjustment suggestions based on the evaluation results. The closed-loop feedback mechanism of this module provides strong support for the continuous improvement of deep learning models and emergency response strategies, thereby enhancing the system's ability to cope with complex disaster scenarios.

[0178] Please see attached Figure 7 The present invention also provides a method for generating a digital twin river basin flood disaster warning plan based on deep learning, comprising the following steps:

[0179] S1. Acquire and standardize the basin's meteorological data, hydrological data, topographic data and remote sensing data to form multi-source heterogeneous data input in a unified format;

[0180] S2. constructing a digital twin model for simulating the hydrological dynamic process of the basin based on the multi-source data, and simulating the water flow in the basin;

[0181] S3, inputting the simulation results and the real-time dynamic data after the standardization processing into a deep learning model, training and outputting the spatial and temporal distribution prediction results of flood disasters;

[0182] S4. Based on the prediction results, identify risk areas, generate emergency resource scheduling plans and response strategies, and form flood emergency plans;

[0183] S5. Provide feedback and evaluation on the prediction results and the implementation effect of the emergency plan, and generate model adjustment suggestions based on them to optimize the subsequent disaster warning process.

[0184] For S1, meteorological data (such as precipitation, temperature, humidity, etc.), hydrological data (such as river flow, water level, etc.), terrain data (such as surface elevation, slope, etc.) and remote sensing data (such as satellite images, land cover types, etc.) in the basin are obtained through various data collection methods. Since these data usually come from different sensors and data sources with different formats, all data need to be standardized to ensure the uniformity and integration of various types of data. This step ensures that the subsequent digital twin model can use these multi-source data for effective modeling and analysis.

[0185] For S2, by processing and integrating the standardized multi-source data, a digital twin model is constructed. This model simulates the hydrological dynamic process within the basin, accurately reflecting the flow law of water under different terrain conditions. Specifically, the digital twin model can simulate the flow path, flow velocity, change trend of water bodies, and their interaction with the surrounding environment, etc. This model provides a real and dynamic hydrological data basis for the training of subsequent deep learning models, ensuring the scientificity and accuracy of the prediction results.

[0186] For S3, the water flow simulation results output by the digital twin model and real-time dynamic data (such as meteorological changes, river water levels, etc.) are jointly input into the deep learning model for training. The deep learning model learns the laws of flood disasters based on these data, combines historical data with real-time data, and conducts spatial and temporal distribution predictions. Through continuous iterative optimization, this model can accurately predict the occurrence time, location, and possible impact range of flood disasters within the basin according to the input data.

[0187] For S4, based on the flood disaster prediction results output by the deep learning model, the system can identify high-risk areas, that is, areas with a relatively high probability of flood disasters occurring and likely to have a greater impact. Subsequently, based on these risk areas, the system generates an emergency resource scheduling plan, including the required materials, personnel, and transportation routes, etc. At the same time, according to the spatio-temporal characteristics of the disaster occurrence, the system formulates response strategies, including evacuation route planning, warning level setting, emergency resource allocation, etc., and finally forms a complete flood emergency plan. The formulation of this emergency plan aims to ensure the timely and efficient mobilization of resources and effective response in the event of a flood disaster.

[0188] For S5, during the occurrence of a disaster or the execution of the emergency plan, through real-time monitoring and evaluation of the actual response situation, a feedback evaluation is carried out on the prediction results of the deep learning model and the execution effect of the emergency plan. Specifically, by comparing the impact of the actual flood disaster with the prediction results, the accuracy of the model prediction and the timeliness and effectiveness of the emergency response are evaluated. According to the evaluation results, suggestions for model adjustment are generated, including the optimization strategy of the deep learning model, the adjustment of the emergency resource scheduling plan, etc. These feedback information is used to continuously optimize and adjust the disaster warning system to make it more accurate and efficient in future disaster predictions.

[0189] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital twin basin flood disaster warning plan generation system based on deep learning, characterized in that, It includes: A data acquisition and processing module, which is used to obtain and standardize the meteorological, hydrological, topographic, and remote sensing multi-source heterogeneous data of the basin in real time, and provide real-time multi-source heterogeneous data input; A digital twin modeling module, which is used to build a digital twin model for simulating the hydrological dynamic process of the basin according to the real-time multi-source heterogeneous data, and output the simulation results; A deep learning prediction module, which is used to receive the simulation results of the digital twin model, train a prediction model, and output the prediction results of the spatial and temporal distribution of flood disasters; An emergency response generation module, which is used to receive the prediction results output by the deep learning prediction module, and generate a flood emergency plan based on the prediction results; A system feedback module, which is used to evaluate the effects of the prediction and response plans according to the flood emergency plan and prediction results, and provide a basis for adjustment for future disaster warnings.

2. The digital twin watershed flood disaster early warning plan generation system based on deep learning according to claim 1, characterized in that, The data acquisition and processing module includes: A meteorological data acquisition sub-module, which is used to acquire the meteorological data of the basin; A hydrological data acquisition sub-module, which is used to acquire the hydrological data of the basin; A topographic data acquisition sub-module, which is used to acquire the topographic data of the basin; A remote sensing data processing sub-module, which is used to acquire and process the remote sensing image data of the basin; A data standardization sub-module, which is used to uniformly format and standardize the acquired data.

3. The digital twin watershed flood disaster early warning plan generation system based on deep learning according to claim 2, wherein, The steps of uniformly formatting and standardizing the acquired data are as follows: Missing value processing, which interpolates and completes the missing values in the acquired data; Outlier detection and correction, which identifies and smoothly corrects the outliers in the acquired data based on the set threshold rules; Data alignment, which unifies the time scales from different data sources to ensure that multi-source data matches by time step; Normalization processing, which is used to normalize data with different dimensions to the interval [0,1], and the normalization is carried out using the following formula: where x is the original data; x min and x max are the minimum and maximum values of this type of data respectively; x' is the result after normalization.

4. The digital twin basin flood disaster early warning plan generation system based on deep learning according to claim 1, wherein, The digital twin modeling module includes: A basin hydrological dynamic modeling sub-module, which is used to build a digital twin model of the basin based on the real-time multi-source heterogeneous data acquired by the data acquisition and processing module; A water flow simulation sub-module, which is used to simulate and predict the water flow in the basin according to the digital twin model; A result output sub-module, which is used to output the water flow simulation results as the input of the deep learning prediction module.

5. The digital twin basin flood disaster early warning plan generation system based on deep learning according to claim 1, wherein The deep learning prediction module includes: A data receiving module, which is used to receive the simulation results of the digital twin model; A deep learning model training sub-module, which is used to train a deep learning prediction model based on the simulation results; A flood disaster prediction sub-module, which is used to output the prediction results of the spatial and temporal distribution of flood disasters according to the deep learning model; A result output sub-module, which is used to output the prediction results as the input of the emergency response generation module.

6. The digital twin basin flood disaster early warning plan generation system based on deep learning according to claim 5, characterized in that, The deep learning model includes a convolutional neural network, a recurrent neural network, or a long short-term memory network, and the deep learning model is used to train and predict the spatial distribution and temporal variation of flood disasters.

7. The digital twin watershed flood disaster early warning plan generation system based on deep learning according to claim 1, characterized in that, The emergency response generation module includes: A risk area identification sub-module, which is used to identify the risk areas in the basin according to the prediction results of the spatial and temporal distribution of flood disasters; An emergency resource scheduling sub-module, which is used to generate emergency material and manpower scheduling plans based on the risk area distribution; A response strategy formulation sub-module, which is used to formulate response strategies corresponding to different risk levels, and the response strategies include evacuation route planning and warning level setting; A pre-plan output sub-module, which is used to output the generated emergency response pre-plan for the use of relevant departments.

8. The digital twin basin flood disaster early warning plan generation system based on deep learning according to claim 1, characterized in that, The system feedback module includes: A pre-plan execution monitoring sub-module, which is used to monitor the execution process and actual response of the flood emergency pre-plan; An effect evaluation sub-module, which is used to compare the actual flood disaster impact with the prediction results, evaluate the prediction accuracy and the effectiveness of the response strategy, and generate evaluation results; A model update suggestion sub-module, which is used to generate model adjustment suggestions based on the evaluation results, and provide an optimization basis for the deep learning model and the emergency pre-plan generation mechanism.

9. A method for generating a flood disaster warning plan for a digital twin basin based on deep learning, which is applied to the system for generating a flood disaster warning plan for a digital twin basin based on deep learning according to any one of claims 1-8, characterized in that, It includes the following steps: Obtain and standardize the meteorological data, hydrological data, topographic data and remote sensing data of the basin, and form a multi-source heterogeneous data input in a unified format; Build a digital twin model for simulating the hydrological dynamic process of the basin based on the multi-source data, and simulate the water flow in the basin; Input the simulation results and the real-time dynamic data after the standardization process into the deep learning model, train and output the prediction results of the spatial and temporal distribution of flood disasters; Identify risk areas based on the prediction results, generate emergency resource scheduling plans and response strategies, and form a flood emergency pre-plan; Conduct feedback evaluation on the implementation effects of the prediction results and the emergency pre-plan, and generate model adjustment suggestions accordingly, which are used to optimize the subsequent disaster warning process.

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