Flood prevention large model early warning method and system based on multi-source data fusion and dynamic optimization

Abstract

The application belongs to the technical field of flood warning, and in particular to a flood control large model warning method and system based on multi-source data fusion and dynamic optimization, which comprises a data input layer, a core model processing layer and a decision output layer; the data input layer is composed of a data acquisition module and is used for building a multi-dimensional space-time data system and acquiring multi-source heterogeneous data; the core model processing layer is composed of a data preprocessing and fusion module, a prediction model module and a dynamic optimization module and is used for processing, fusing, predicting and online optimizing the multi-source heterogeneous data; the application can quickly adapt to unique geographical and climatic characteristics by using only a small amount of local sample data, can automatically generate and accurately release differentiated action guidelines for different regions and different objects, can directly convert warning information into executable emergency instructions, and can significantly improve the intelligent level and decision efficiency of emergency response.

Description

Technical Field

[0001] This invention relates to the field of flood early warning technology, and in particular to a flood control large-scale model early warning method and system based on multi-source data fusion and dynamic optimization. Background Technology

[0002] Against the backdrop of global climate change, the suddenness, extreme nature, and destructiveness of floods are increasing, posing unprecedented challenges to traditional flood warning systems. Existing technologies have revealed fundamental limitations in their technical architecture and core methodologies in practice. These limitations are not isolated but interconnected and progressive, collectively forming technical barriers that restrict the accuracy, timeliness, and universality of early warnings. The purpose of this invention is to systematically overcome the following five interconnected core technical bottlenecks: 1. Lack of multi-source heterogeneous data fusion capability: The data input of existing early warning systems heavily relies on hydrological monitoring stations with fixed geographical locations. The water level, flow rate, and other data they acquire are typically point-like and sparse. Such solutions lack the ability to comprehensively perceive the disaster-generating environment across the entire region and in multiple dimensions. They cannot effectively integrate heterogeneous information from multiple sources, such as satellite remote sensing, weather radar, IoT sensors, and social source data. This results in an incomplete grasp of the overall situation of the flood process, an inherent deficiency in the information foundation, and consequently affects the accuracy of early warnings.

[0003] 2. The predictive model lacks both static fixation and online adaptive capabilities: Currently, most flood forecasting models are static models trained offline once based on historical data. Once the model parameters are determined, they are not adjusted during operational use. However, real flood events are affected by a variety of dynamic factors, such as real-time changes in rainfall, upstream water conservancy project scheduling, and changes in underlying surface conditions. Static models cannot learn and self-correct online based on real-time data streams, resulting in a delayed response to dynamic changes. When dealing with sudden and extreme flood events, their forecasting accuracy and timeliness are insufficient to meet the actual needs of emergency response.

[0004] 3. The model exhibits significant region dependence and insufficient cross-scene generalization ability: The evolution of floods is highly coupled with the geographical features of a specific region, such as topography, river network, and urban layout. Therefore, when an early warning model developed for a specific watershed or city is transferred to other regions with significantly different geographical environments, its predictive performance usually declines significantly. It lacks good generalization ability and portability. In order to ensure accuracy, the deployment of the model in a new region requires repeated costly and time-consuming data collection, parameter calibration, and model verification, which greatly restricts the large-scale application and promotion of advanced early warning technologies.

[0005] 4. Lack of refined decision support capabilities and insufficient information operability: The output of existing early warning systems is relatively simple, usually only providing a macro-level risk level, such as "blue" or "yellow" warning signals. This type of information cannot provide a detailed spatiotemporal risk distribution. For example, it cannot clearly indicate high-risk areas or the estimated depth and extent of flooding at a specific future time. This makes the early warning information less actionable and difficult to directly support emergency management departments in making precise decisions such as resource allocation and personnel evacuation route planning.

[0006] 5. The early warning information conversion mechanism is rudimentary and the intelligent emergency response capability is lacking: In existing technologies, there are barriers in the intermediate links between model-generated prediction results and the issuance of executable emergency instructions. Systems generally lack intelligent decision engines and cannot deeply integrate quantitative prediction results with emergency plans and expert knowledge bases to automatically generate differentiated action guidelines for different objects. Emergency response processes rely heavily on manual judgment, which affects the efficiency of emergency response and fails to maximize the value of the valuable time window brought by early warning information. Summary of the Invention

[0007] Based on the technical problems existing in the background technology, this invention proposes a flood control large model early warning method and system based on multi-source data fusion and dynamic optimization.

[0008] The flood control large-scale model early warning system proposed in this invention includes a data input layer, a core model processing layer, and a decision output layer. The data input layer consists of a data acquisition module, which is used to construct a multi-dimensional spatiotemporal data system and collect multi-source heterogeneous data, including long-term meteorological forecast data, wide-area real-time meteorological observation data, high-resolution short-term meteorological data, underlying surface and hydrological status data, and socio-economic data. The core model processing layer consists of a data preprocessing and fusion module, a prediction model module, and a dynamic optimization module, which are used to process, fuse, predict, and optimize the multi-source heterogeneous data online. The decision output layer consists of an early warning decision and release module, which is used to convert the prediction results into executable early warning commands and release them through multiple channels.

[0009] Furthermore, the data preprocessing and fusion module includes: a feature extraction unit, used to clean, interpolate, and spatiotemporally align the source data, and to extract spatial features of the gridded data using a convolutional neural network, extract temporal features of the time series data using a long short-term memory network or its variants, and process high-frequency noisy sensor data using an adaptive unscented Kalman filter algorithm; and a fusion unit, which employs a scaled dot product attention mechanism, takes the feature vector generated by the feature extraction unit as input, adaptively calculates the correlation between features from different data sources and generates dynamic attention weights, and generates a high-dimensional fusion feature vector that integrates spatiotemporal information and real-time status through weighted summation.

[0010] Furthermore, the prediction model module includes: a deep reinforcement learning model based on the Actor-Critic architecture; a scene-adaptive backbone network selection unit, used to select either a graph neural network or a convolutional long short-term memory network as the backbone network according to the warning scenario; for river basin warning scenarios, a graph neural network is selected to simulate the river network topology; for urban flooding warning scenarios, a convolutional long short-term memory network is selected to extract spatiotemporal features; and a model-independent meta-learning framework integration unit, used to find an optimal meta-initial parameters during the model training phase through an internal and external dual-loop optimization mechanism, enabling the model to quickly adapt using a small amount of local data when deployed to new areas, and possessing few-sample learning capabilities.

[0011] Furthermore, the dynamic optimization module abstracts and models the early warning process as a Markov decision process, employing a dual-delay deep deterministic policy gradient algorithm as the online optimization engine. This module constructs a reinforcement learning closed loop for agent-environment interaction, where the agent includes a policy network and a dual-critic network. The environmental state is represented by the fused feature vector output by the data fusion module, and the reward is calculated based on the deviation between the predicted results and the actual observations, with a high-weight penalty term applied to situations leading to missed reports in critical infrastructure areas. Through this closed loop, the system can learn and self-correct online based on real-time data streams, achieving dynamic optimization of the model.

[0012] Furthermore, the early warning decision-making and dissemination module includes: a hybrid decision engine, used to integrate quantitative prediction results from the prediction model module, dynamic thresholds, and a built-in expert rule base, which contains rules for raising early warning levels and triggering emergency plans based on socio-economic data; a geographic information system deep integration unit, used to couple the determined early warning area with the geographic information system to generate visualization products such as dynamic risk heat maps and flooding area simulation maps; and a multi-channel dissemination gateway, used to prioritize and precisely push early warning information based on socio-economic data, and disseminate it through channels such as SMS, application push, emergency broadcasts, and application programming interfaces.

[0013] The flood control large-scale model early warning method proposed in this invention includes the following steps: Step 1: Construct a multi-dimensional spatiotemporal data system, collecting multi-source heterogeneous data including long-term meteorological forecast data, wide-area real-time meteorological observation data, high-resolution short-term meteorological data, underlying surface and hydrological status data, and socio-economic data.

[0014] Step 2: Clean, interpolate, align and standardize the multi-source heterogeneous data, and use a scaling dot product attention mechanism to dynamically fuse the processed data to generate a high-dimensional fused feature vector.

[0015] Step 3: Based on high-dimensional fused feature vectors, a deep reinforcement learning model based on the Actor-Critic architecture is used for risk prediction, and a graph neural network or a convolutional long short-term memory network is selected as the backbone network according to the warning scenario. During the model training phase, a model-independent meta-learning framework is adopted, and the optimal meta-initial parameters are found through an internal and external double-loop optimization mechanism, so that the prediction model can gain rapid cross-regional adaptability. When the prediction model is deployed to a new region, it is quickly fine-tuned using the meta-initial parameters and a small amount of local sample data to achieve few-sample learning adaptation.

[0016] Step 4: Model the early warning process as a Markov decision process, and use a dual-delay deep deterministic policy gradient algorithm to perform online adaptive optimization of risk prediction; construct an interactive closed loop between the agent and the environment, where the environmental state is represented by a high-dimensional fused feature vector; calculate immediate rewards based on the deviation between the prediction results and the actual observations, and impose penalties on missed reports in areas where critical infrastructure is located; use the dual-delay deep deterministic policy gradient algorithm to update the parameters of the prediction model based on immediate rewards and conservative estimates of future value, achieving real-time learning and self-correction.

[0017] Step 5: Transform the optimized forecast results into early warning commands and generate visualized early warning products based on the Geographic Information System (GIS). Disseminate these commands precisely through multiple channels based on socio-economic data. Utilize a hybrid decision engine to integrate the forecast results with an expert rule base, which includes rules that automatically raise the early warning level and trigger emergency plans when the predicted inundation area covers critical infrastructure or high-density population areas. Deeply integrate the determined early warning area with the GIS to generate dynamic risk heat maps and inundation area simulation maps. Based on regional priorities in the socio-economic data, disseminate differentiated information via SMS, application push notifications, emergency broadcasts, and application programming interfaces (APIs).

[0018] The beneficial effects of this invention are as follows: 1. This invention breaks through the limitations of existing systems that rely on single, sparse hydrological station data. By constructing a multi-dimensional spatiotemporal data system and adopting a deep fusion network based on a scaling dot product attention mechanism, the system can effectively integrate heterogeneous information from multiple sources such as meteorology, remote sensing, the Internet of Things, and social sources. This provides a more comprehensive and richer information foundation for disaster-causing factors for early warning models, thereby enabling a more comprehensive grasp of the overall situation of flood processes and significantly improving the accuracy of predictions.

[0019] 2. This invention overcomes the shortcomings of traditional static models that cannot adapt to dynamic changes. By introducing a dynamic optimization module based on a dual-delay deep deterministic policy gradient algorithm, the early warning process is modeled as a Markov decision process, realizing online real-time learning and self-correction of the model. This enables the system to respond quickly to changes in real-time data streams, greatly shortening the early warning calculation cycle and thus advancing the effective early warning time window, buying valuable time for emergency decision-making and action.

[0020] 3. This invention completely solves the problems of strong regional dependence and poor generalization ability of traditional early warning models. By deeply integrating the model-independent meta-learning framework into the training process of the prediction model module, the model acquires the meta-ability of "learning to learn". This enables the model to start from an optimal meta-initial state and quickly adapt to the unique geographical and climatic characteristics of the newly deployed area with only a very small amount of local sample data. This achieves rapid migration with few samples and greatly reduces the cost and cycle of large-scale promotion of advanced early warning technology.

[0021] 4. This invention transcends the traditional model that only provides macro-level risk levels. The predictive model can output multi-dimensional and refined information such as water level process lines, gridded inundation risk maps, and water depth. Combined with deep integration with geographic information systems, the system can generate dynamic and visualized risk products. More importantly, through a hybrid decision engine, the quantitative prediction results are deeply integrated with expert knowledge bases and emergency plans. This enables the automatic generation and accurate release of differentiated action guidelines for different regions and different targets, directly transforming early warning information into executable emergency instructions, significantly improving the intelligence level and decision-making efficiency of emergency response. Attached Figure Description

[0022] Figure 1 This is a diagram showing the overall system architecture of the flood control large model early warning system based on multi-source data fusion and dynamic optimization proposed in this invention. Figure 2 This is a schematic diagram of the data preprocessing and fusion module of the flood control large model early warning system based on multi-source data fusion and dynamic optimization proposed in this invention; Figure 3This is a schematic diagram of the reinforcement learning workflow of the dynamic optimization module of the flood control large model early warning system based on multi-source data fusion and dynamic optimization proposed in this invention. Figure 4 This is a schematic diagram of the workflow of the early warning decision-making and release module of the flood control large model early warning system based on multi-source data fusion and dynamic optimization proposed in this invention; Figure 5 This is a schematic diagram of the MAML-based meta-learning framework for the flood control large model early warning system based on multi-source data fusion and dynamic optimization proposed in this invention. Detailed Implementation

[0023] Reference Figures 1-5 A flood control large-scale model early warning system based on multi-source data fusion and dynamic optimization, such as Figure 1 As shown, the system adopts a three-layer architecture, including a data input layer, a core model processing layer, and a decision output layer. This architecture is implemented by five closely cooperating functional modules: a data acquisition module, a data preprocessing and fusion module, a prediction model module, a dynamic optimization module, and an early warning decision and release module. The correspondence between each module and the architecture layers is as follows: The data input layer consists of a data acquisition module, which is responsible for acquiring heterogeneous data from multiple sources.

[0024] The core model processing layer consists of a data preprocessing and fusion module, a prediction model module, and a dynamic optimization module, and is responsible for in-depth data processing, model prediction, and real-time optimization.

[0025] The decision output layer consists of an early warning decision and release module, which is responsible for generating and releasing early warning information.

[0026] Together, the system forms a complete intelligent processing chain from raw data input to final decision output, and achieves continuous model optimization through closed-loop feedback.

[0027] (a) Data Input Layer: This layer / module aims to construct a multidimensional spatiotemporal data system for flood event prediction, providing the necessary data foundation for subsequent processing. This layer consists of a multi-source heterogeneous data acquisition network, specifically including: Meteorological data: long-term weather forecast data with a time resolution of no more than 6 hours, such as the output of the Global Forecast System (GFS) or Weather Research and Forecasting Model (WRF); wide-area real-time meteorological observation data, such as meteorological satellite cloud images; high-resolution short-term meteorological data, such as reflectivity data from Doppler weather radar.

[0028] Hydrological data: Dynamic hydrological data such as water level and flow rate from Internet of Things (IoT) sensor networks, with a sampling frequency of no more than 1 minute.

[0029] Geospatial data: High-precision digital elevation models (DEMs) with a spatial resolution of no more than 5 meters; high-resolution satellite remote sensing imagery with a resolution of no more than 1 meter.

[0030] Socioeconomic data: gridded population density distribution data, location data of key infrastructure such as hospitals, schools, transportation hubs, and power plants.

[0031] (II) Core Model Processing Layer: This layer contains three core functional modules: 1. Data Preprocessing and Fusion Module: This module is the key link between raw data and advanced models. Its core task is to transform disorganized, multi-source, heterogeneous data into a unified, information-intensive feature representation. Its detailed workflow is as follows: Figure 2 As shown, it includes the following stages: 1.1 Multimodal Data Input and Preliminary Processing: Standardization, cleaning, interpolation, and spatiotemporal alignment of data from different sources and with different structures; specifically including: For gridded data, such as radar echo maps with a spatial resolution of no more than 1 kilometer, standardization is performed to remove format differences.

[0032] For time series data, such as water level / flow rate data, the sampling frequency should not exceed 1 minute, and spatiotemporal alignment and cleaning should be performed.

[0033] For high-frequency, noisy sensor data, such as real-time water levels collected by IoT devices, the Adaptive Unscented Kalman Filter (AUKF) algorithm is used for noise filtering and state estimation, outputting smooth and refined state values.

[0034] 1.2 Channel-Specific Deep Feature Extraction: A specialized deep learning model is used to perform deep feature mining on different types of data. This step transforms raw data from different sources into a unified, computable feature vector representation; specifically including: For gridded data such as radar reflectivity data, convolutional neural networks are used for processing. Through multi-layer convolution, spatial distribution features of rainfall, such as the shape and intensity of rainbands, are extracted.

[0035] For time series data such as water level and flow rate, recurrent neural networks such as Long Short-Term Memory (LSTM) or its variants, such as Gated Recurrent Unit (GRU), are used for processing, and their gating mechanism is used to capture time dependence and trends.

[0036] For high-frequency sensor data that has undergone denoising, an adaptive unscented Kalman filter algorithm is used for state estimation and denoising, and the output smooth and refined state values ​​are used as features.

[0037] 1.3 Core Dynamic Fusion: The feature vectors generated in the above steps—namely, the gridded spatial features extracted by CNN, the temporal features of the time series data extracted by LSTM / GRU, and the refined state features of the high-frequency sensors obtained by AUKF—are input together into the core fusion network based on the scaled dot product attention mechanism; its mathematical expression is as follows: , Among them, the query matrix Key matrix AND-value matrix The features are obtained by linearly transforming the feature vectors of all input data sources through independent fully connected layers, such as radar image features, water level time series features, and GFS model prediction features. Key matrix The dimension of the feature vector is used to scale the dot product result to ensure the stability of gradient calculation; Through this mechanism, the system can adaptively calculate the correlation between features from different data sources based on the specific needs of the current prediction task, generate a set of dynamic attention weights, and finally apply the weights to the value matrix. A weighted summation is performed to obtain a high-dimensional fusion feature vector that integrates spatiotemporal information and real-time status, and maximizes the information gain for the current prediction target. This vector serves as the unified input for the prediction model module. This process realizes the dimensionality normalization and dynamic weight generation of the feature vector.

[0038] 2. Predictive Model Module: Based on the fused feature vector output by the data fusion module, this module provides actionable predictive products that far exceed traditional single-risk-level predictions. Its core lies in scenario-based modeling and generalizable design, such as... Figure 1 As shown: 2.1 Core Model Architecture: The model adopts a deep reinforcement learning (DRL) model based on the Actor-Critic architecture, which has powerful decision-making and sequence prediction capabilities.

[0039] It should be noted that Actor-Critic is a hybrid algorithm framework for deep reinforcement learning. Its core idea is to combine the advantages of both policy-based and value-based methods to achieve more stable and efficient learning.

[0040] The architecture consists of two key neural networks: Actor: Function: Responsible for policy output, that is, directly determining the specific action to be taken based on the current environment state; Essence: It is a policy network whose goal is to find an optimal policy function that can obtain the maximum long-term reward through learning.

[0041] Critic: Function: Responsible for evaluating the value of the action chosen by the Actor, that is, judging whether performing an action is good or bad in the current state, and giving a quantitative score; Essence: It is a value network whose goal is to learn and estimate the value function as accurately as possible to provide guidance for the Actor's policy update.

[0042] Collaborative working mechanism and advantages: The fundamental advantage of this architecture is that Actors can update their strategies based on more stable and less biased value assessment signals provided by Critics, rather than directly utilizing the original environmental rewards, which may have high variance and high latency.

[0043] Specifically, the Critic provides a clear direction for the Actor by learning the value function: if the Critic evaluates the value of an action as higher than average, the Actor will adjust its policy to increase the probability of choosing that action in the future under that state; otherwise, it will decrease it. This approach significantly reduces the variance of the policy gradient, making the entire learning process converge faster and more stable.

[0044] In this technical solution, the Actor network is responsible for outputting specific flood forecast values ​​or model parameter adjustment actions, while the Critic network is responsible for evaluating the long-term cumulative reward brought about by the forecast or adjustment. The two work together to drive the entire early warning model to continuously and stably optimize towards the optimal early warning strategy.

[0045] It should be further explained that DRL (Deep Reinforcement Learning) is an advanced artificial intelligence method that combines the perception capabilities of deep learning with the decision-making capabilities of reinforcement learning.

[0046] Reinforcement Learning (RL): This is a learning paradigm in which an agent learns by interacting with the environment. The agent observes a state in the environment, then performs an action. The environment rewards or punishes the agent based on the action and moves to a new state. The agent's sole objective is to learn an optimal policy through trial and error to maximize the long-term cumulative reward.

[0047] Deep Learning (DL): This refers to the technology of automatically extracting and learning complex features from massive amounts of data using deep neural networks; its powerful non-linear representation capabilities make it particularly adept at processing high-dimensional and complex raw data such as images and time series.

[0048] The combination of the two (DRL): In traditional reinforcement learning, when the state space of the environment is very large and complex, such as in flood warning where the state consists of countless meteorological, hydrological, and geographical data points, it is difficult for the agent to learn effectively. Deep reinforcement learning (DRL) was developed to solve this problem. It uses deep neural networks to act as the brain of the agent, achieving two key functions: Approximate value function: evaluates the merits of the current state or state-action pair, i.e., what the Critic does in the Actor-Critic architecture.

[0049] Approximate policy function: directly outputs the best action to be taken based on the current state, i.e. what the Actor does.

[0050] 2.2 Flexible Backbone Network: Based on the physical characteristics of different early warning scenarios, the system has the ability to adaptively switch between scenarios and select the optimal neural network structure as the backbone. 2.2.1 For river basin early warning scenarios: Graph Neural Network (GNN) is selected as the backbone; This model abstracts hydrological stations, reservoirs, etc. as nodes of the graph and river segments as edges, naturally simulating the propagation and confluence of floods in complex river networks and other river network topology simulation processes; It not only incorporates long-term meteorological forecast data as a dynamic driving force, but also uses static geospatial data, such as river slope and roughness, as static attributes of the graph, thereby achieving accurate simulation of flood waves under long-term and physical constraints.

[0051] It should be noted that GNN (Graph Neural Network) is a deep learning model specifically designed for processing and learning graph structure data. Unlike traditional neural networks, such as CNN for image grids and RNN for linear sequences, GNN can directly model non-Euclidean space data composed of nodes and edges.

[0052] Core working mechanism: The core mechanism of GNN is message passing or neighborhood aggregation. Its working principle can be summarized as follows: Information aggregation: Each node in the graph collects information from all its directly connected neighbor nodes, i.e., features; Information update: Each node merges the aggregated neighbor information with its own original information and performs calculations through a neural network, such as a fully connected layer, to update its own state or representation.

[0053] This aggregation and update process is repeated multiple times, corresponding to the number of layers in a GNN, so that information can gradually spread from the nearest neighbors of a node to more distant nodes, thereby allowing each node to perceive the structural information of the entire graph and a wider range of contextual features.

[0054] Applications and advantages of this technical solution: For the scenario of flood early warning in river basins, the entire water system is naturally a complex graph structure: Nodes: can be accurately represented as hydrological monitoring stations, reservoirs, lakes or river confluences, each node has its own attributes, such as real-time water level, flow rate, reservoir capacity, etc.; Edges: can be represented as river segments connecting these nodes, each edge also has its own attributes, such as river segment length, gradient, roughness, cross-sectional area, etc.

[0055] Using GNN as the backbone network has unparalleled advantages: high structural fit, GNN can perfectly model the topology of river network, and its message passing mechanism naturally simulates the physical process of flood wave propagation, confluence and evolution from upstream nodes to downstream nodes; clear physical meaning, the model not only learns from data, but also learns the transmission law of water flow under the physical constraint of river network, so that the prediction results are not only accurate, but also have stronger physical interpretability.

[0056] 2.2.2 For urban flooding early warning scenarios: Convolutional Long Short-Term Memory Network (ConvLSTM) is selected as the backbone. This model combines the spatial information processing capability of Convolutional Neural Network (CNN) and the temporal series processing capability of Long Short-Term Memory Network (LSTM), and is good at extracting spatiotemporal features. It uses high-resolution short-term meteorological data as the core dynamic input and uses static geospatial data, such as DEM elevation and impermeability determined by land use type, as important underlying surface constraints. It can accurately predict the formation, collection and inundation process of surface runoff caused by short-term heavy rainfall.

[0057] It should be noted that ConvLSTM (Convolutional Long Short-Term Memory) is a special type of recurrent neural network (RNN) that deeply integrates the spatial feature extraction capabilities of convolutional neural networks (CNN) with the time series processing capabilities of long short-term memory networks (LSTM), and is specifically designed for modeling and predicting spatiotemporal sequence data.

[0058] Core Innovation and Working Mechanism: When processing time series data, the traditional LSTM network completes the internal state transitions through fully connected operations (matrix multiplication), which causes it to lose spatial information when processing data. The core innovation of ConvLSTM is that it replaces the matrix multiplication inside the LSTM unit with convolution operations. This means that at each time step, the model can not only remember and pass on historical information like LSTM, but also extract the spatial features of the current input through convolution kernels like CNN.

[0059] With this design, each memory cell of ConvLSTM no longer stores a single numerical value, but a two-dimensional feature map that can represent spatial information. Therefore, it can simultaneously capture and learn the dynamic evolution of data in both time and space dimensions.

[0060] Applications and advantages of this technical solution: For the scenario of urban flooding early warning, the input data, such as continuous Doppler radar echo maps, is a typical spatiotemporal sequence data - it has both the spatial distribution of each frame of the image and the sequence of image changes over time.

[0061] Using ConvLSTM as the backbone network has the following key advantages: Spatiotemporal coupled modeling: It can perfectly handle this type of data and understand the complex processes of rain clouds moving and deforming in space, as well as strengthening and weakening in time; Accurate physical process simulation: The formation of urban flooding, such as the collection and inundation of surface runoff, is itself a spatiotemporal evolution process. ConvLSTM can effectively simulate this process, using high-resolution short-term meteorological data, such as radar rainfall, as dynamic input, and combining it with static geospatial data, such as topographic elevation and impermeability, as physical constraints, thereby accurately predicting the risk distribution and inundation range of urban flooding in the next few hours.

[0062] 2.3. Rapid cross-regional adaptation capability based on model-independent meta-learning: To address the model's region dependency problem, this module integrates a model-independent meta-learning framework (MAML) during the training phase, such as... Figure 5 As shown, this framework trains the prediction model on a large number of flood tasks in different watersheds / cities to find an optimal initial parameter. This parameter has strong generalization ability and plasticity, so that when the model is deployed to a new region, it can start from this initial state with strong generalization ability and quickly adapt to achieve high accuracy with only a small amount of local data and a few gradient updates. This achieves learning to adapt to new regions with few samples. This capability greatly improves the model's portability and efficiency of large-scale deployment.

[0063] The inner and outer double-loop parameter update process to achieve this goal can be defined by the following formula: Inner loop: , External circulation: , Among them, meta-model parameters Optimization is achieved through a dual internal and external circulation system, such as... Figure 5 As shown: Task environment: includes tasks distributed Specific tasks of mid-sampling For example, a complete dataset of a historical flood event in a specific river basin, with each task dataset divided into support sets. For example, the first 70% of the data for that event, and the query set. For example, 30% of the data after the event.

[0064] Meta-learners: including meta-models and temporary adaptation model Its parameters These are the initial parameters to be optimized.

[0065] Internal and external dual-loop optimization process: Inner loop, rapid simulation of task-specific adaptation: for task distribution Any specific task in the sampling , using its support set Meta-model Perform one or more gradient descent iterations to obtain the temporarily adapted parameters. The preferred range for the inner loop learning rate α is 10. −3 Up to 10 −2 ; Outer loop, meta-optimization: using the query set of this task In the adapted model The loss is calculated on top of the original model, but the gradient of this loss will be used to update the original meta-model parameters. The key point is that the gradient of this loss will be used to update the original meta-model parameters, rather than temporary parameters.

[0066] Optimization goal and process direction: Accumulate the gradient across a large number of different tasks and use a meta-learning rate. Update Preferred range 10 −4 Up to 10 −3 Finally, an optimal initial parameter was found. Make the meta-model parameters The convergence is to a meta-initial state with strong generalization ability; the data flow of the entire process is: task environment → inner loop (support set fine-tuning) → outer loop (query set evaluation) → meta-model optimization, the goal of which is to enable the model to learn and adapt to new regions with few samples.

[0067] When the model is deployed to a completely new, sparsely dataed region, it can start from this optimal initial state. Starting with only a small amount of local sample data, the model can quickly adapt to the unique characteristics of a new region and achieve high prediction accuracy through one or several gradient updates, thereby fundamentally improving the model's portability and efficiency for large-scale deployment.

[0068] It should be further explained that MAML (Model-Agnostic Meta-Learning) is an advanced meta-learning algorithm framework. Its core goal is to train a model that can quickly adapt to new tasks. It aims to solve the fundamental problem that traditional deep learning models require a large amount of data and computing resources to retrain when facing new environments or data-sparse scenarios. Its key characteristics can be understood by breaking them down from its name: Meta-learning: This is a "learning to learn" paradigm. Its training goal is not to make the model proficient in a specific task, but to enable the model to master a set of general learning capabilities. By training on a large number of different tasks, the model can learn a set of "meta-knowledge" or an excellent "initial state". Model-Agnostic: This is a key advantage, meaning that the MAML framework is not limited to any specific neural network architecture. Whether it's a CNN for images, an RNN for sequences, or the GNN and ConvLSTM mentioned in this solution, they can all serve as its base model. This versatility makes its application extremely broad.

[0069] Significance of this technical solution: In this invention, the introduction of the MAML framework is to fundamentally solve the core problem of poor cross-regional generalization ability of early warning models. By training the model on a large number of historical flood events from different geographical environments and climate characteristics, the resulting early warning model has extremely strong "plasticity". When the model needs to be deployed to a brand-new watershed or city that lacks long-term observation data, it no longer needs to be trained from scratch. It only needs to be fine-tuned once or twice using a very small amount of local sample data, i.e., the inner loop, to quickly adapt to the characteristics of the new environment and achieve high prediction accuracy, thereby greatly reducing the application cost and solving the bottleneck of large-scale technology promotion.

[0070] 2.4 Multi-dimensional output: The model output is no longer a single risk level, but includes multi-dimensional and operable detailed information such as the water level process line of key river sections at specific future time points, the gridded inundation risk probability map covering the warning area, and the water depth prediction of key flood-prone points. This information provides a quantitative basis for the subsequent classification of warning levels, such as the four-level warning of blue, yellow, orange, and red.

[0071] 3. Dynamic Optimization Module: This module proposes an innovative online adaptive optimization framework that endows the early warning model with the ability to learn and self-evolve in real time. The framework abstracts and models the entire early warning process, that is, the model continuously makes predictions and receives feedback based on real-time data streams, into a Markov Decision Process (MDP), thereby transforming the early warning problem into a sequential decision problem that can be solved through reinforcement learning.

[0072] 3.1 Core Algorithm: The advanced dual-delay deep deterministic policy gradient (TD3) algorithm is preferred as the core online optimization engine. This algorithm effectively solves the problems of overestimation of value and training instability that are common in traditional reinforcement learning algorithms by introducing key technologies such as target policy smoothing and truncated double Q learning.

[0073] 3.2 Core Working Mechanisms and Processes: such as Figure 3 As shown, this module constructs a reinforcement learning closed loop for the interaction between the agent and the environment.

[0074] Environment: The State is represented by high-dimensional feature data output by the data fusion module, which comprehensively describes the current flood risk situation; Reward represents the immediate feedback to the agent's actions, calculated based on the deviation between the prediction result and the actual observation value. Accurate predictions receive positive rewards, while incorrect predictions receive negative rewards, i.e., penalties; The reward function is negatively correlated with the mean square error of the prediction and applies a high-weighted penalty term to situations that lead to underreporting of critical infrastructure, as defined by socioeconomic data, in the region.

[0075] The agent consists of a policy network and a dual critic network. The policy network acts as the decision-maker, receiving the environmental state and directly outputting a specific predictive action. The dual critic network acts as the evaluator, responsible for judging the quality of the action taken by the policy network, that is, evaluating the long-term value that the action can bring. The dual network is used to obtain a more stable and accurate value assessment.

[0076] Closed-loop optimization process: Observe the current situation: The intelligent agent observes the environment and obtains the current state through its sensors, namely the data acquisition and fusion module; Take action: The policy network outputs a predicted action based on the observed state; Evaluating the effectiveness of an action: The environment provides a reward for the action, and a dual-critic network also evaluates the value of the action; Guided policy optimization: The evaluation results of the dual-critic network are used to guide the policy network to optimize. If an action receives a high evaluation, the policy network will adjust its parameters to increase the probability of taking that action in a similar state in the future; otherwise, it will decrease it. This continuous feedback and optimization cycle drives the agent to learn continuously, making its prediction policy better and better. Target value calculation: Its core mechanism lies in calculating a stable and reliable target Q value using the following formula, denoted as This is used to efficiently and accurately guide the updating of the policy network. , Among them, the target value It is an estimate of the long-term returns of states and actions, the target value. The calculation relies on instant rewards Compared to conservative estimates of future value, instant rewards Designed as a function inversely proportional to the mean squared error (MSE) of the prediction error, and imposing an additional, highly weighted penalty term when prediction bias leads to underreporting in areas where critical infrastructure is located; discount factor It is used to balance the importance of immediate rewards and long-term returns. The value ranges from 0.9 to 0.99; to suppress overestimation of the Q-value, this invention employs two independent target Q-networks. and The smaller of the two Q values ​​is then used as an estimate of the future value.

[0077] Target action By target policy network Add a clipped noise to the output. To enhance the exploratory nature of the strategy, its expression is: The noise standard deviation Cutting range .

[0078] (III) Decision Output Layer: Corresponding to the early warning decision and release module, as the final output of the system, this layer / module is responsible for transforming the refined risk information output by the model into executable action instructions and maximizing the value of all previously collected data. Its detailed workflow is as follows: Figure 4 As shown, it is divided into two stages: decision-making and generation, and presentation and release.

[0079] Decision-making and generation phase: Model input: Receives the raw output from the prediction model module, mainly including the risk probability and the dynamic threshold adjusted in real time by the dynamic optimization module; Integrate prediction results: Integrate and process the input raw risk information; Hybrid Decision Engine: This is the core of decision-making. It not only relies on the quantitative prediction of the model, but also integrates a rule base that encapsulates hydrological expert knowledge and historical disaster thresholds for comprehensive judgment. Its built-in expert rule base includes the following rules: If the predicted flooding area covers critical infrastructure or high-density population areas, based on socio-economic data, the warning level of the area will be automatically raised and a specific emergency plan will be triggered. Determine the warning level and scope: Through the comprehensive judgment of the hybrid decision engine, the warning level of this warning event is finally determined, such as blue, yellow, orange, red, and the precise scope of impact.

[0080] Presentation and Release Phase: Deep integration with Geographic Information System (GIS): Deeply coupling the identified risk scope with the Geographic Information System (GIS); Generate visual early warning products: Automatically generate visual early warning products, such as dynamic risk heat maps and flood range simulation maps, with spatial resolution up to the street level. The location of critical infrastructure and high-density areas is clearly overlaid on this map, providing clear and focused decision-making basis for emergency command. Multi-channel distribution gateway: As a unified information outlet, it is responsible for disseminating formatted early warning information through multiple channels; Dissemination to the public and departments through all channels: Once an alert is triggered, the system will prioritize and precisely push the alert information to relevant management departments and the public based on population density and critical infrastructure priorities in socio-economic data. The alert information will be accurately and efficiently delivered to relevant management departments and the public through channels such as SMS, application (APP) push, emergency broadcasts, and application programming interfaces (APIs) to government emergency platforms, and will also provide evacuation route suggestions, thus completing the entire alert dissemination process.

[0081] (iv) System Flow and Feedback: such as Figure 1 As shown, the overall data flow of the system is: data input layer → core model processing layer → decision output layer, marked by solid arrows; simultaneously, the system constructs a crucial closed-loop feedback mechanism: the dynamic optimization module receives prediction errors and real-time data as feedback, and... Figure 1 The dashed arrows indicate that the TD3 algorithm is used to continuously optimize the model parameters, enabling the model to achieve online adaptive learning.

[0082] Reference Figures 1-5 A flood control large-scale model early warning method based on multi-source data fusion and dynamic optimization includes the following steps: Step 1: Construct a multi-dimensional spatiotemporal data system: Through a data acquisition network, multi-source heterogeneous data for flood forecasting are acquired, including: long-term meteorological forecast data with a time resolution of no more than 6 hours, such as the output of GFS or WRF models; wide-area real-time meteorological observation data, such as meteorological satellite cloud images; high-resolution short-term meteorological data, such as reflectivity data from Doppler weather radar; underlying surface and hydrological status data, including dynamic hydrological data from IoT sensor networks, with a sampling frequency of no more than 1 minute and a spatial resolution of no more than 5 meters, as well as static geospatial data such as gridded population density and the location of critical infrastructure.

[0083] Step 2: Data Preprocessing and Dynamic Fusion The process involves processing and fusing the multi-source data obtained in step one, and the corresponding workflow is as follows: Figure 2 : The source data are cleaned, interpolated, spatiotemporally aligned, and standardized. High-frequency noisy data are denoised using an adaptive unscented Kalman filter. Convolutional neural networks are used to extract the spatial distribution features of gridded data, such as radar charts. Long Short-Term Memory networks or their variants are used to extract the time dependence features of time-series data, such as water levels. The extracted feature vectors from each source data are input into a fusion network based on a scaled dot product attention mechanism. Dynamic weights are adaptively generated according to the current prediction task, and a unified high-dimensional fusion feature vector is generated through weighted summation. The calculation formula is as follows: .

[0084] Step 3: Scenario-based risk prediction based on deep reinforcement learning: Based on the fused feature vector obtained in step two, a refined risk prediction is performed: A deep reinforcement learning model based on the Actor-Critic architecture is used as the core of the prediction. The backbone network is adaptively selected based on the early warning scenario: for river basin early warning, a graph neural network is selected to simulate the river network topology; for urban flooding early warning, a convolutional long short-term memory network is selected to extract spatiotemporal features. Incorporating a cross-regional rapid adaptation strategy: During the model training phase, a model-independent meta-learning framework is applied, and through internal and external double-loop optimization, the model obtains a meta-initial parameter with strong generalization ability, thereby supporting rapid adaptation in new regions using a small amount of data; The model outputs multi-dimensional and actionable prediction results, including: water level process lines at key river sections at specific future time points, gridded inundation risk probability maps, and predictions of water depth at key flood-prone points.

[0085] Step 4: Dynamic online optimization based on reinforcement learning: The early warning process is modeled as a Markov decision process and subjected to online adaptive optimization; its corresponding process is... Figure 3 : We employ a dual-delay deep deterministic policy gradient algorithm as the optimization engine, leveraging its target policy smoothing and dual-Q learning mechanism to enhance stability. We construct an interactive closed loop between the agent and the environment, where the agent includes a policy network and a dual-critic network. The state of the environment is a fusion feature, and the reward is based on the prediction error, enabling continuous prediction, evaluation, and policy optimization. The target Q-value is calculated using the following formula to update the network: Instant rewards It is negatively correlated with prediction error and imposes a high-weight penalty on underreporting in key areas.

[0086] Step 5: Intelligent Early Warning Decision-Making and Multi-Channel Dissemination: The process of converting forecast results into executable early warning commands and issuing them corresponds to... Figure 4 : Decision-making and generation: Utilizing a hybrid decision engine, the risk probability and dynamic threshold output by the model are integrated, and an expert rule base is incorporated, such as rules that associate key facilities and high-density population areas, to comprehensively assess and determine the final warning level and precise spatial range; Presentation and dissemination: Through deep integration with geographic information systems, the warning area is generated into visual products such as dynamic risk heat maps and inundation area maps. Then, through multi-channel distribution gateways, information is prioritized and regionally targeted based on socio-economic data. Warning information and evacuation suggestions are efficiently and accurately disseminated to the public and emergency management departments through various channels such as SMS, APP, emergency broadcasts, and API interfaces.

[0087] Through the synergistic effect of the above-mentioned technical solutions, this invention achieves the following significant and quantifiable beneficial effects, which directly stem from its core technological innovation: Significantly Improved Prediction Accuracy: The prediction accuracy of this invention far surpasses that of traditional models. Through multi-source data fusion based on an attention mechanism, the system can capture a more comprehensive and critical combination of disaster-causing factors, thereby significantly reducing prediction errors. Experimental data shows that the mean square error (MSE) of this system for flood process prediction can be stably reduced to below 0.05, while the MSE of traditional hydrological or statistical models is usually in the higher range of 0.15 to 0.3. In retrospective tests on historical flood events in multiple different scenarios, the comprehensive early warning accuracy of this invention, taking into account indicators such as hit rate and false alarm rate, exceeds 95%, demonstrating excellent reliability. This effect is mainly attributed to the rich information foundation provided by the data fusion module.

[0088] Optimization of Response Speed ​​and Timeliness: This system possesses near real-time response capabilities. The dynamic optimization mechanism based on the TD3 algorithm enables the model to respond to changes in real-time data streams within seconds. The end-to-end response time for a single early warning calculation is reduced to less than 5 seconds. Compared to the computation cycle of traditional models, which often takes tens of minutes or even hours, this invention significantly improves timeliness. More importantly, this rapid response capability can advance the effective early warning time—the time difference between issuing the warning and the occurrence of the disaster—by an average of 1 to 2 hours, gaining invaluable time for disaster prevention and mitigation, personnel evacuation, and material preparation. This effect is primarily attributed to the online learning capability of the dynamic optimization module.

[0089] Superior Regional Adaptability: This invention fundamentally solves the basic weaknesses of traditional models, such as poor generalization ability and difficulty in cross-regional deployment. The introduction of the Meta-Learning (MAML) framework enables the model to "learn how to learn". In practical applications, the model can quickly adapt to the geographical and climatic characteristics of new regions with very little local data (few-shot learning). In transfer tests targeting different terrain scenarios, such as plains and rivers, mountain watersheds and urban built-up areas, the difference between the model's prediction accuracy in the new region and its accuracy in the original training region was successfully controlled within 5%, demonstrating unprecedentedly strong transferability. This effect is mainly attributed to the introduction of the Meta-Learning framework.

[0090] Providing comprehensive and feasible decision support: This invention transcends the limitations of traditional early warning systems that only provide a single risk level. The system not only outputs the risk level, but also generates a series of intuitive and rich visualization products through deep integration with the GIS platform, such as dynamic flood risk heat maps and inundation range simulation maps under different probability scenarios. This provides emergency management departments with a more comprehensive and scientific basis for decision-making than simple alarms. For example, it can support advanced applications such as dynamic planning of emergency evacuation routes and optimization of rescue resource allocation, transforming early warning information into actionable plans. This effect is mainly attributed to the powerful functions of the early warning decision and release module.

[0091] The technical solution of the present invention will be described in detail below using three specific application scenarios as examples, so that those skilled in the art can better understand and implement the present invention.

[0092] Example 1: River Basin Flood Warning Application scenario: This embodiment addresses the flood early warning needs of a large river basin with multiple tributaries, a complex water system, and multiple hydrological monitoring stations along its course.

[0093] Data Integration and Model Configuration: The system first integrates real-time outflow data from upstream reservoirs, IoT water level sensor data from various hydrological stations along the river (sampling frequency down to the minute level), 72-hour rainfall prediction sequences from GFS numerical weather prediction covering the entire basin, and high-precision basin DEM data through the data acquisition module. In the prediction model module, a graph neural network (GNN) is selected as the core prediction model. Specifically, each hydrological monitoring station and reservoir is abstracted as a node in the graph, and the river segments connecting them are abstracted as edges. The features of the nodes include real-time water level and flow, while the features of the edges can include river segment length and slope. The structure of GNN naturally fits the topological relationship of the river network, and can very effectively learn and simulate the evolution, superposition, and attenuation of flood waves in complex river networks.

[0094] System Output and Technical Effects: The system can predict the future water level and flow process lines of key downstream control sections 24 to 72 hours in advance. At the same time, the early warning decision module, combined with DEM data, can generate inundation range maps of high-risk areas and accurately mark weak sections where dike overflow or breach may occur. In retrospective simulations of several major flood events in the basin's history, this embodiment, compared with traditional hydrological models based on physical processes (such as HEC-RAS), reduced the mean square error (MSE) of flood peak water level prediction by about 30%, and also significantly reduced the prediction error of flood peak occurrence time.

[0095] Example 2: Urban Flooding (Rainwater) Early Warning Application scenario: This embodiment addresses the flood warning needs of a modern metropolis with a dense population, highly impermeable underlying surface (such as a large amount of concrete and asphalt pavement), and a complex underground drainage network.

[0096] Data Integration and Model Configuration: The system integrates radar rainfall nowcasting data (1-3 hours ahead) with a spatial resolution of 1km×1km, real-time water level sensor data deployed at urban flood-prone areas and key nodes of the main pipeline network (such as storm drains), and refined urban DEM data. The innovation lies in the system's real-time API access to social media platforms, automatically extracting text and image information posted by citizens containing geographic location information and keywords (such as "waterlogging," "flooding," and "urban flooding") using natural language processing technology, serving as supplementary verification data for ground conditions. In the prediction model module, a Convolutional Long Short-Term Memory (ConvLSTM) network is used as the prediction core. This model can simultaneously handle dynamic changes in data across time and space, making it highly suitable for simulating the formation and accumulation of surface runoff caused by short-duration heavy rainfall.

[0097] System Output and Technical Performance: The system can generate urban flooding risk heat maps with spatial resolution accurate to the street level for the next 1 to 3 hours. The early warning decision module further overlays and analyzes this risk map with real-time traffic data, dynamically generating optimal emergency rescue vehicle routes and suggested evacuation routes for citizens. In tests on multiple rainstorm events in the city, the system achieved an accuracy rate of over 95% in identifying severely flooded areas. For water level prediction at key water accumulation points, the prediction error was successfully controlled within 0.1 meters in 92% of cases, far exceeding the 68% accuracy rate of traditional rainfall threshold-based or simplified hydrological models.

[0098] Example 3: Early warning of sudden flash floods in mountainous areas Application scenario: This embodiment is designed for typical small mountain watersheds with steep terrain, rapid water flow, and high susceptibility to flash floods caused by localized short-term heavy rainfall. Such scenarios have extremely high requirements for the timeliness of early warning.

[0099] Data Integration and Model Configuration: Considering the suddenness of flash floods, the system's data integration focuses on several key rapidly changing variables that most directly reflect flash flood risk: minute-level real-time rainfall intensity data from automatic weather stations in mountainous areas, soil moisture sensor data deployed in upstream gullies (to reflect previous soil moisture content, i.e., runoff capacity), and vegetation cover data interpreted from high-resolution remote sensing images (affecting surface runoff velocity). In terms of model configuration, this embodiment has undergone targeted optimization: In the data fusion module, prior knowledge is applied to the attention mechanism to give extremely high weight to the combination of the two features "short-term heavy rainfall" and "high soil saturation," as these are the core physical factors triggering flash floods; In the dynamic optimization module, the reward function of the TD3 agent is specifically designed to impose a significant penalty on "false negatives" to reflect the extreme risk avoidance principle of "better to report false than to miss" in flash flood warnings during model training.

[0100] System output and technical effects: Compared with the traditional early warning method based on critical rainfall threshold, the average advance time of flash flood warning in this embodiment has increased by more than 25%. More importantly, after testing on a large number of historical and simulated flash flood scenarios, this system achieved zero missed reports for all potentially disastrous key flood events, demonstrating extremely high reliability and providing more adequate protection for the safe evacuation of people.

[0101] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A flood control large-scale model early warning system based on multi-source data fusion and dynamic optimization, comprising a data input layer, a core model processing layer, and a decision output layer, characterized in that: The data input layer, consisting of a data acquisition module, is used to construct a multi-dimensional spatiotemporal data system and collect multi-source heterogeneous data, including long-term meteorological forecast data, wide-area real-time meteorological observation data, high-resolution short-term meteorological data, underlying surface and hydrological status data, and socio-economic data. The core model processing layer consists of a data preprocessing and fusion module, a prediction model module, and a dynamic optimization module, which are used to process, fuse, predict, and optimize multi-source heterogeneous data online. The decision output layer consists of an early warning decision and release module, which is used to convert the prediction results into executable early warning instructions and release them through multiple channels.

2. The flood control large-scale model early warning system based on multi-source data fusion and dynamic optimization according to claim 1, characterized in that, The data preprocessing and fusion module includes: The feature extraction unit is used to clean, interpolate and spatiotemporally align the source data, and uses a convolutional neural network to extract the spatial features of the gridded data, a long short-term memory network or its variants to extract the temporal features of the time series data, and an adaptive unscented Kalman filter algorithm to process high-frequency noisy sensor data. The fusion unit employs a scaled dot product attention mechanism, taking the feature vector generated by the feature extraction unit as input, adaptively calculating the correlation between features from different data sources and generating dynamic attention weights. Through weighted summation, a high-dimensional fusion feature vector integrating spatiotemporal information and real-time state is generated.

3. The flood control large-scale model early warning system based on multi-source data fusion and dynamic optimization according to claim 1, characterized in that, The prediction model module includes: Deep reinforcement learning model based on Actor-Critic architecture; The scene-adaptive backbone network selection unit is used to select either a graph neural network or a convolutional long short-term memory network as the backbone network according to the warning scene. The model-independent meta-learning framework integration unit is used to find an optimal meta-initial parameters during the model training phase through an inner and outer dual-loop optimization mechanism, enabling the model to quickly adapt using a small amount of local data when deployed to a new region, thus possessing few-sample learning capabilities.

4. The flood control large-scale model early warning system based on multi-source data fusion and dynamic optimization according to claim 3, characterized in that, For river basin early warning scenarios, graph neural networks are selected to simulate river network topology; for urban flooding early warning scenarios, convolutional long short-term memory networks are selected to extract spatiotemporal features.

5. The flood control large-scale model early warning system based on multi-source data fusion and dynamic optimization according to claim 1, characterized in that, The dynamic optimization module is used to abstract and model the early warning process as a Markov decision process, and adopts a dual-delay deep deterministic policy gradient algorithm as the online optimization engine. This module constructs a reinforcement learning closed loop for agent-environment interaction, where the agent includes a policy network and a dual critic network. The environmental state is represented by the fused feature vector output by the data fusion module. The reward is calculated based on the deviation between the prediction result and the actual observation value, and a high-weight penalty term is applied to situations that lead to underreporting of critical infrastructure areas. Through this closed loop, the system can learn and self-correct online based on real-time data streams, thereby achieving dynamic optimization of the model.

6. The flood control large-scale model early warning system based on multi-source data fusion and dynamic optimization according to claim 1, characterized in that, The early warning decision-making and release module includes: The hybrid decision engine is used to integrate quantitative prediction results from the prediction model module, dynamic thresholds, and the built-in expert rule base, which contains rules for raising early warning levels and triggering emergency plans based on socio-economic data. The geographic information system deep integration unit is used to couple the determined early warning range with the geographic information system to generate visualization products such as dynamic risk heat maps and flooding range simulation maps. A multi-channel distribution gateway is used to prioritize and precisely push early warning information based on socio-economic data, and distribute it through channels such as SMS, application push, emergency broadcast, and application programming interface.

7. A flood control large-scale model early warning method based on multi-source data fusion and dynamic optimization, and a flood control large-scale model early warning system based on multi-source data fusion and dynamic optimization according to any one of claims 1 to 6, characterized in that, Includes the following steps: Step 1: Construct a multi-dimensional spatiotemporal data system and collect heterogeneous data from multiple sources; Step 2: Clean, interpolate, align and standardize the multi-source heterogeneous data, and use a scaling dot product attention mechanism to dynamically fuse the processed data to generate a high-dimensional fusion feature vector. Step 3: Based on the high-dimensional fused feature vector, a deep reinforcement learning model based on the Actor-Critic architecture is used for risk prediction, and a graph neural network or a convolutional long short-term memory network is selected as the backbone network according to the warning scenario. Step 4: Model the early warning process as a Markov decision process, and use the double-delay deep deterministic policy gradient algorithm to perform online adaptive optimization of risk prediction; Step 5: Transform the optimized forecast results into early warning instructions, generate visualized early warning products based on geographic information systems, and accurately release them through multiple channels based on socio-economic data.

8. The flood control large-scale model early warning method based on multi-source data fusion and dynamic optimization according to claim 7, characterized in that, In step three, during the model training phase, a model-independent meta-learning framework is adopted, and the optimal initial meta-parameters are found through an inner and outer double-loop optimization mechanism, enabling the prediction model to gain rapid cross-regional adaptability. When the prediction model is deployed to a new region, it can be quickly fine-tuned using initial parameters and a small amount of local sample data to achieve learning adaptation with few samples.

9. The flood control large-scale model early warning method based on multi-source data fusion and dynamic optimization according to claim 7, characterized in that, In step four, an interactive closed loop between the agent and the environment is constructed, where the environmental state is represented by a high-dimensional fused feature vector. Instant rewards are calculated based on the deviation between the predicted results and the actual observed values, and penalties are imposed on underreporting in areas where critical infrastructure is located. By utilizing a dual-delay deep deterministic policy gradient algorithm, the parameters of the prediction model are updated based on immediate rewards and conservative estimates of future value, enabling real-time learning and self-correction.

10. The flood control large-scale model early warning method based on multi-source data fusion and dynamic optimization according to claim 7, characterized in that, In step five, a hybrid decision engine is used to integrate the prediction results with the expert rule base for analysis. The expert rule base contains rules that automatically raise the warning level and trigger emergency plans when the predicted flooding area covers critical infrastructure or high-density population areas. The identified early warning area is deeply integrated with the geographic information system to generate dynamic risk heat maps and inundation area simulation maps; Based on regional priorities in socioeconomic data, differentiated information is disseminated through SMS, application push notifications, emergency broadcasts, and application programming interfaces.

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