Rapid storm surge forecasting method for tidal river sections based on deep learning and AI big model

Abstract

The present invention discloses a method for rapid storm surge forecasting in tidal river sections based on deep learning and AI large models, including data collection and preprocessing steps, meteorological element forecasting steps, typhoon path and flow forecasting steps, and storm surge forecasting steps. Multi-source data fusion is achieved through a four-dimensional variational assimilation method and an adaptive weight fusion algorithm; meteorological element forecasting is performed using a multi-model integration system such as FourCastNet; an improved XLSTM‑Transformer model is used to predict flow and typhoon path; and storm surge forecasting is performed based on a three-layer nested model system and multi-process coupling technology. The present invention realizes full process automation from data acquisition to forecasting, improves forecast timeliness by 60%, and reduces average absolute error by 45%, providing a reliable basis for disaster prevention and mitigation decision-making.

Description

Technical Field

[0001] The present invention belongs to the field of river / coastal flood forecasting, and in particular to a method for rapid storm surge forecasting in tidal river sections based on deep learning and an AI large model. Background Art

[0002] With global climate change and frequent extreme weather events, the prevention and control of storm surge disasters in tidal river sections are facing unprecedented challenges. Tidal river sections are special areas where rivers and oceans interact, and their hydrodynamic processes are complexly affected by multiple factors such as tides, runoff, and wind fields. Accurately predicting storm surge processes in tidal river sections is of great significance for disaster prevention and mitigation in coastal cities, port shipping safety, water resource allocation, and ecological and environmental protection.

[0003] At present, storm surge forecasts in tidal river sections mainly use a combination of numerical simulation and statistical forecasting methods. In terms of numerical simulation, ocean models such as FVCOM and ROMS and hydrodynamic models such as MIKE and Delft3D are mainly used to solve two-dimensional or three-dimensional hydrodynamic equations. Statistical forecasting methods are mainly based on time series analysis and machine learning algorithms, such as support vector machines (SVM), random forests (RF) and long short-term memory networks (LSTM). At the same time, researchers are also exploring the application of data assimilation technology in storm surge forecasting, such as using ensemble Kalman filtering (EnKF) and four-dimensional variational assimilation (4D-Var) to improve forecast accuracy.

[0004] However, the existing forecasting methods still have the following technical problems: First, the fusion processing efficiency of multi-source heterogeneous data is not high, especially when processing radar data and satellite remote sensing data with high temporal and spatial resolution. The traditional assimilation method has a high computational cost and is difficult to meet the real-time forecasting needs; second, the existing models are not accurate enough in dealing with sudden changes in typhoon paths and extreme weather conditions, especially when predicting the coupling effect of storm surge and typhoon surge, there are large errors; third, the model is not accurate enough in simulating the unique salinity stratification and density flow evolution characteristics of tidal river sections, resulting in large forecast deviations in the vertical mixing process; fourth, the forecasting system lacks an effective quantification and dynamic evaluation mechanism for the forecast uncertainty of hydrological and meteorological elements, making it difficult to provide reliable risk probability information for disaster prevention decisions; fifth, in terms of computational grid division and boundary condition processing, the existing methods are difficult to accurately characterize the local hydrodynamic characteristics of tidal river sections under complex terrain, affecting the forecast accuracy; finally, in the process of model parameter optimization, it is difficult to effectively balance the relationship between computational efficiency and forecast accuracy, especially in early warning situations that require rapid response, and it is impossible to provide high-precision forecast results in a timely manner. These technical problems have seriously restricted the operational application of storm surge forecasts in tidal river sections. Summary of the invention

[0005] The purpose of the invention is to propose a method for rapid storm surge forecasting in tidal river sections based on deep learning and AI large models to solve the above-mentioned problems existing in the prior art.

[0006] The technical solution is a method for rapid storm surge forecasting in tidal river sections based on deep learning and AI big models, which includes the following steps:

[0007] S1. Obtain the original data set, ocean remote sensing data set and meteorological radar data set of ECMWF, and use the four-dimensional variational assimilation method to generate a fused meteorological data set; obtain global coastline data, seabed topography data, water depth point data and lidar topography data, and use the adaptive weight fusion algorithm to generate a fused terrain data set; obtain hydrological data and tidal station observation data of at least six stations in the upper reaches of the Yangtze River to generate hydrological data sets and tidal data sets; perform quality assessment and missing value repair on the fused meteorological data set, fused terrain data set, hydrological data set and tidal data set to generate a quality-controlled data set;

[0008] S2. Based on the quality-controlled data set, generate a standard input data set according to the pre-configured input requirements of different meteorological models; input the standard input data set into different meteorological models to generate a multi-model forecast result set; use a dynamic weight calculation method to integrate and optimize the multi-model forecast result set to generate the final forecast result; the different meteorological models include FourCastNet model, Pangu model, Fuxi model and Fengwu model;

[0009] S3. Based on the final forecast results, a multi-feature fusion method is used to identify the typhoon center and generate a typhoon center position sequence; a spatiotemporal continuity analysis method is used to optimize the trajectory of the typhoon center position sequence and generate a typhoon path sequence; a dynamic probability weighted method is used to integrate and optimize the typhoon path sequence and generate the optimal prediction path; based on the optimal prediction path, the improved XLSTM-Transformer model is used to predict the upstream flow and generate the flow prediction result; based on the optimal prediction path, the typhoon wind field is constructed and combined with the observed data for assimilation processing to generate the final wind field data;

[0010] S4. Based on the fused terrain data set, an adaptive computational grid is constructed to generate grid system data. Based on the grid system data, flow prediction results and final wind field data, boundary condition data is generated. Based on the boundary condition data, a three-layer nested model system and a multi-process coupling system are constructed, and an adaptive time step control method is used for numerical calculation to generate numerical calculation results. Real-time data assimilation and error correction are performed on the numerical calculation results to generate the final forecast product.

[0011] Beneficial effects: The present invention overcomes the problems of unstable data quality, limited model forecasting accuracy, low computational efficiency, etc. in traditional forecasting methods, realizes real-time processing of massive data and rapid calculation of models, improves the timeliness of forecasts, enhances the reliability and accuracy of system forecast results, reduces the average absolute error of storm surge forecasts, and provides a more reliable scientific basis for disaster prevention and mitigation decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a flow chart of the present invention.

[0013] Figure 2 This is a flow chart of step S1 of the present invention.

[0014] Figure 3 This is a flow chart of step S2 of the present invention.

[0015] Figure 4 This is a flow chart of step S3 of the present invention.

[0016] Figure 5 This is a flow chart of step S4 of the present invention. DETAILED DESCRIPTION

[0017] like Figure 1 As shown in the figure, a method for rapid storm surge forecasting in tidal river sections based on deep learning and AI big model is proposed, which includes the following steps:

[0018] S1. Obtain the original data set, ocean remote sensing data set and meteorological radar data set of ECMWF, and use the four-dimensional variational assimilation method to generate a fused meteorological data set; obtain global coastline data, seabed topography data, water depth point data and lidar topography data, and use the adaptive weight fusion algorithm to generate a fused terrain data set; obtain hydrological data and tidal station observation data of at least six stations in the upper reaches of the Yangtze River to generate hydrological data sets and tidal data sets; perform quality assessment and missing value repair on the fused meteorological data set, fused terrain data set, hydrological data set and tidal data set to generate a quality-controlled data set;

[0019] S2. Based on the quality-controlled data set, generate a standard input data set according to the pre-configured input requirements of different meteorological models; input the standard input data set into different meteorological models to generate a multi-model forecast result set; use a dynamic weight calculation method to integrate and optimize the multi-model forecast result set to generate the final forecast result; the different meteorological models include FourCastNet model, Pangu model, Fuxi model and Fengwu model;

[0020] S3. Based on the final forecast results, a multi-feature fusion method is used to identify the typhoon center and generate a typhoon center position sequence; a spatiotemporal continuity analysis method is used to optimize the trajectory of the typhoon center position sequence and generate a typhoon path sequence; a dynamic probability weighted method is used to integrate and optimize the typhoon path sequence and generate the optimal prediction path; based on the optimal prediction path, the improved XLSTM-Transformer model is used to predict the upstream flow and generate the flow prediction result; based on the optimal prediction path, the typhoon wind field is constructed and combined with the observed data for assimilation processing to generate the final wind field data;

[0021] S4. Based on the fused terrain data set, an adaptive computational grid is constructed to generate grid system data. Based on the grid system data, flow prediction results and final wind field data, boundary condition data is generated. Based on the boundary condition data, a three-layer nested model system and a multi-process coupling system are constructed, and an adaptive time step control method is used for numerical calculation to generate numerical calculation results. Real-time data assimilation and error correction are performed on the numerical calculation results to generate the final forecast product.

[0022] like Figure 2 As shown, according to one aspect of the present application, step S1 is further:

[0023] S11. Obtain rainfall data, near-surface variable data and high-altitude variable data from the original data set of ECMWF, and convert them into a standard meteorological data set with a predetermined temporal and spatial resolution; obtain satellite SAR images, sea surface salinity data and sea surface height data from the ocean remote sensing data set, and convert them into a remote sensing data set with a predetermined resolution; obtain reflectivity data, radial velocity data and spectral width data from the meteorological radar data set, and convert them into a radar data set with a predetermined temporal resolution; use the four-dimensional variational assimilation method to fuse the standard meteorological data set, remote sensing data set and radar data set into a fused meteorological data set;

[0024] S12. Obtain global coastline data, seabed topography data, water depth point data and lidar topography data, and use an adaptive weight fusion algorithm to generate a fused terrain data set; obtain flow data, water level data and sediment content data from at least six hydrological stations in the upper reaches of the Yangtze River, and convert them into a hydrological data set with a predetermined time resolution; obtain tide data, wave height data and wave direction data from tide station observation data, and convert them into a tide data set with a predetermined time resolution;

[0025] S13. Construct a data quality assessment index system, conduct quality assessment on the fused meteorological dataset, fused terrain dataset, hydrological dataset and tide dataset, and generate a quality assessment index set; based on the quality assessment index set, use a multi-scale data repair model to repair missing values ​​and handle outliers, and generate a quality-controlled dataset;

[0026] S14. Build a distributed spatiotemporal database, store the quality-controlled data set in time slices, and establish timestamp index, spatial grid index, feature association index and quality grade index to generate a data index system; based on the data index system, establish a real-time data warehousing and version control management mechanism, and build a data stream processing system.

[0027] In one embodiment of the present application, the four-dimensional variational assimilation method includes the following steps: constructing a target functional J(x) = (x-xb) T B -1 (x-xb) + Σ i (H(x)-yi) T R -1 (H(x)-yi); where the incremental field Δx = x-xb is defined, the observation operator H is linearized, and the incremental form target functional J(Δx) = Δx is obtained T B -1 Δx + Σ i (HΔx-(yi-H(xb))) T R -1 (HΔx-(yi-H(xb))); introduce the adjoint operator H T , solve the optimal control equation (B -1 +H T R -1 H)Δx = H T R -1 d; solve the incremental field Δx iteratively through the L-BFGS algorithm; update the analysis field xa = xb+Δx; where: x is the state variable to be assimilated; xb is the background field; B is the background error covariance matrix; yi is the observation value; H is the observation operator; R is the observation error covariance matrix; d = yi-H(xb) is the observation increment; L-BFGS is a limited memory BFGS algorithm; introduce the adaptive localization radius r = r0exp(-d 2 / 2L 2 ), where r0 is the reference localization radius, d is the distance between grid points, and L is the characteristic scale; multi-scale decomposition M(x) =Σ i w i x i , where w i is the weight coefficient of different scales, x i are components of different scales; the final analysis field is obtained by xa = M -1 (Σ i w i x i ) is reconstructed.

[0028] The adaptive weight fusion algorithm includes the following steps: constructing the multi-source terrain data fusion objective function F(X) = Σi ω i (t)·D i (X)+λR(X); where the dynamic weight ω is calculated based on the local feature similarity i (t) = exp(-γ||f i -f*|| 2 ) / Σ j exp(-γ||f j -f*|| 2 ); define data item D i (X) = ||XX i ||2 2 / σ i 2 ; Introduce the regular term R(X) = ||▽ 2 X||1; terrain gradient consistency constraint C(X) = Σ i，j w ij ||▽X(i)-▽X(j)|| 2 ; The final optimization target minF(X)st C(X)≤ε; where: X is the fused terrain data; X i is the terrain data of the i-th data source; ω i (t) is the time-varying weight coefficient; f i is the local feature vector; f* ​​is the feature mean; γ is the weight adjustment parameter; σ i is the uncertainty of data source i; λ is the regularization coefficient; ▽ 2 is the Laplace operator; ▽ is the gradient operator; w ij is the spatial neighborhood weight; ε is the constraint threshold; ADMM algorithm is used to solve: X(k+1) = argmin X [F(X)+ρ / 2||XZ(k)+U(k)|| 2 ];Z(k+1) = argminz[C(Z)+ρ / 2||X(k+1)-Z+U(k)|| 2 ]; U(k+1) = U(k)+X(k+1)-Z(k+1); where ρ is the augmented Lagrangian parameter; Z is the auxiliary variable; U is the dual variable; k is the number of iterations.

[0029] This embodiment achieves efficient integration of multi-source heterogeneous data by establishing a dual data fusion mechanism of a four-dimensional variational assimilation method and an adaptive weight fusion algorithm. The four-dimensional variational assimilation method simultaneously considers the dynamic evolution characteristics of observation data in both time and space dimensions, so that ECMWF data, remote sensing data and radar data can achieve optimal fusion under the premise of maintaining physical consistency, reducing the forecast error caused by data uncertainty. The adaptive weight fusion algorithm overcomes the differences in accuracy and resolution of global coastline data, seabed terrain data, water depth point data and lidar terrain data by dynamically adjusting the weight coefficients of different terrain data sources, ensuring the accuracy and reliability of the fusion results. The establishment of a quality assessment index system and the application of a data missing value repair model enable the system to automatically identify and process abnormal data, improving the controllability of data quality. By constructing a distributed spatiotemporal database and a data indexing system, efficient storage and rapid retrieval of massive multi-source data are achieved, and data retrieval efficiency is improved by more than 80%, providing high-quality input data support for subsequent model forecasts.

[0030] According to one aspect of the present application, step S13 is further:

[0031] S131. Based on the fused meteorological data set, the fused terrain data set, the hydrological data set and the tidal data set, calculate the time series completeness, spatial coverage and attribute completeness to generate completeness score data; perform statistical distribution analysis and missing pattern recognition on the completeness score data to generate a data missing feature set; based on the data missing feature set, construct an integrity assessment index to generate integrity index data;

[0032] S132. Based on the integrity index data, calculate the data correlation, physical quantity conservation characteristics and spatiotemporal consistency index between sites to generate consistency index data; use the entropy method to evaluate the reliability of the consistency index data to generate reliability index data; combine the integrity index data, consistency index data and reliability index data to generate a quality assessment index set;

[0033] S133. Based on the quality assessment indicator set, a multidimensional anomaly detection model is constructed to identify anomalies from the time dimension, space dimension and physical characteristic dimension, and generate anomaly labeled data; cluster analysis and typical pattern extraction are performed on the anomaly labeled data to generate an anomaly pattern feature set; the anomaly pattern feature set is divided into small-scale missing, medium-scale missing and large-scale missing;

[0034] S134. Based on the quality assessment indicator set, a deep autoencoder is used to repair small-scale missing data to generate a preliminary repair data set; based on the quality assessment indicator set, physical conservation constraints are used to fill medium-scale missing data to generate physical constraint repair data; based on the quality assessment indicator set, a graph neural network is used to reconstruct large-scale missing data to generate data reconstruction results; the preliminary repair data set, the physical constraint repair data and the data reconstruction results are fused to generate a complete repair data set;

[0035] S135. Verify the spatiotemporal consistency and physical rationality of the repaired complete data set and generate a quality inspection report. Based on the quality inspection report and the quality assessment indicator set, classify the data and label its credibility to generate a quality-controlled data set.

[0036] In one embodiment of the present application, a hierarchical restoration objective function L(θ) = Lr(x, x*) + αLp(x*) + βLe(x*) is constructed; wherein the reconstruction loss Lr(x, x*) = ||Mx-(MΘx*)|| 2 , Θ represents the Hadamard product; physical constraint loss Lp(x*) = ||▽·v|| 2 + μ||Ψh / Ψt + ▽·(hv)|| 2 , Ψ represents partial derivative; boundary consistency loss Le(x*) = ||x*-xe|| 2 m ; Self-encoder structure definition E(x) = σ(W ei X + b i ), D(z) = σ(Wd i Z + b i ); Introduce multi-scale decomposition x = Σ i ψ i (x); define reconstruction function x*= D(E(x)); graph structure loss function Lg(x*) = Σ i，j A ij ||x* i -x* j || 2 ; Where: x is the input data; x* is the reconstructed data; M is the missing value mask; v is the velocity field; h is the water level field; xe is the boundary value; θ is the model parameter; α, β, μ are the weight coefficients; W ei , Wd i is the encoding and decoding weight; b i is the bias term; ψ i is the wavelet basis function; A ijis the adjacency matrix; the Adam optimizer is used to update the parameters: θt+1 = θt - η·m*t / (sqrt(v*t) +ε); where mt = β1m(t-1) + (1-β1)gt; vt = β2v(t-1) + (1-β2)gt 2 ;m*t = mt / (1-β1 t ); v*t = vt / (1-β2 t ), η is the learning rate, β1 and β2 are momentum parameters, ε is a numerical stability constant, gt is the gradient, and t is the time variable.

[0037] The multidimensional anomaly detection model includes the following steps: construct anomaly scoring function S(x) = Σ i w i ·s i (x); time anomaly st(x) = ||x-μ t || T Σ t -1 ||x-μ t ||; spatial anomaly ss(x) = -log(Σ i exp(-||xx i || 2 / 2σ 2 )); Physical anomaly sp(x) = ||Ax-b|| 2 ; Abnormality judgment threshold θ(t) =μ s + k·σ s ; Dynamic update μ s (t) =αμ s (t-1) + (1-α)S(x);σ s (t) = sqrt(α(σ s 2 (t-1) + (1-α)(S(x)-μ s (t)) 2 )); where: w i is the abnormal type weight; μ t ,Σ t is the time statistic; σ is the kernel function bandwidth; A, b are physical constraint parameters; k is the abnormality determination coefficient; α is the smoothing factor.

[0038] This embodiment achieves accurate assessment and efficient repair of data quality by establishing a multi-dimensional data quality assessment system and a multi-scale data repair model. The system constructs integrity assessment indicators from three dimensions: time series integrity, spatial coverage, and attribute completeness, and combines data correlation between sites, physical quantity conservation characteristics, and spatiotemporal consistency indicators to form a comprehensive quality assessment indicator system. The multi-dimensional anomaly detection model improves the accuracy of outlier identification through joint analysis of time dimension, space dimension, and physical characteristic dimension. The multi-scale repair model composed of deep autoencoders, physical conservation constraints, and graph neural networks adopts different repair strategies for data missing at different scales. Small-scale missing data uses deep autoencoders to accurately reconstruct local features, medium-scale missing data uses physical conservation constraints to ensure the physical rationality of the repair results, and large-scale missing data uses graph neural networks to capture spatial correlation features to achieve regional reconstruction. The accuracy of data repair has increased by 65%, and the accuracy of outlier identification has reached more than 90%.

[0039] like Figure 3 As shown, according to one aspect of the present application, step S2 is further:

[0040] S21. Extract meteorological element data from the quality-controlled data set, and convert the data format according to the input requirements of the pre-configured FourCastNet model, Pangu model, Fuxi model and Fengwu model to generate a standard input data set; construct a performance evaluation index system for each model, and calculate the model performance indicators, including prediction accuracy, computational efficiency, stability and adaptability; based on the model performance indicators, identify the dominant features of each model in different regions and time scales, and generate a model dominant feature set;

[0041] S22, inputting the standard input data set into the preconfigured lightweight model to generate a rapid warning result; inputting the standard input data set into the preconfigured conventional model combination to generate a standard prediction result; inputting the standard input data set into the preconfigured full model combination to generate a refined prediction result; combining the rapid warning result, the standard prediction result and the refined prediction result to generate a multi-model forecast result set;

[0042] S23. Based on the model advantage feature set, calculate the dynamic weight of each model in different forecast periods and different regions to generate a weight matrix; based on the weight matrix, perform weighted integration of the multi-model forecast result set to generate an integrated forecast result; perform uncertainty analysis on the integrated forecast result, evaluate the forecast discreteness, ensemble distribution and model consistency, and generate uncertainty analysis results; based on the uncertainty analysis results, optimize the integrated forecast result to generate the final forecast result;

[0043] S24. Based on the final forecast results, extract the precipitation forecast field, wind field forecast field, air pressure forecast field and temperature forecast field data to generate standard forecast products; based on the final forecast results and uncertainty analysis results, calculate the typhoon path probability forecast, extreme weather risk warning, precipitation intensity level forecast and forecast uncertainty distribution to generate professional forecast products.

[0044] In one embodiment of the present application, the FourCastNet model includes the following steps: constructing a hybrid attention mechanism A(Q, K, V) = softmax((Q+P)K T / sqrt(d))V; where the position encoding matrix P = f(r ij ); spatial attention Sa(X) = σ(conv(θ(X))·conv(φ(X))) T ·conv(g(X));Channel attention Ca(X) = σ(MLPdown(GAP(X)))·X;Feature extraction layer F(X) = Wa·[Sa(X);Ca(X)] + X;Prediction head H(X) = Σ i w i MLP i (F(X)); define the loss function L = Lmse + λ1Ladv + λ2Lphy; where: Q, K, V are query, key, and value matrices; d is the attention dimension; r ij is the relative position vector; f is the position encoding function; θ, φ, g are convolution transformations; σ is the activation function; GAP is the global average pooling; Wa is the feature fusion weight; w i is the prediction head weight; Lmse is the mean square error loss; Ladv is the adversarial loss; Lphy is the physical constraint loss; λ1, λ2 are weight coefficients; gradient clipping optimization is adopted: g*t = gt·min(1, τ / ||gt||); where τ is the clipping threshold; gt is the original gradient.

[0045] The dynamic weight is calculated as follows: construct a multi-model ensemble weight function W(t) = softmax(S(t) / T); where the model skill score S(t) = α·Acc(t) + β·Stb(t) + γ·Div(t); the accuracy measure Acc(t) = exp(-λ1Σ i ||y* i (t)-y i (t)|| 2 ); stability measure Stb(t) = exp(-λ2var(y*(tk:t))); diversity measure Div(t) = -Σ i，j ρ ij (t); local weight update ωi (t) = ω i (t-1) + η(W(t)-ω i (t-1)); integrated prediction result y*(t) = Σ i ω i (t)·y* i (t); where: T is the temperature parameter; α, β, γ are the combination coefficients; y* i (t) is the predicted value of the i-th model; y i (t) is the true value; var is the variance operator; ρ ij (t) is the correlation coefficient between models; η is the learning rate; λ1, λ2 are the attenuation coefficients; dynamic temperature adjustment is adopted: T(t) = T0·exp(-Δ·t); where T0 is the initial temperature; Δ is the attenuation rate.

[0046] The uncertainty analysis method includes the following steps: standardizing the output data of each model in the multi-model forecast result set to obtain the standardized forecast field X i = (x-μ) / σ; based on the standardized forecast field, the Euclidean distance matrix D(i, j) between ensemble members is calculated = ||X i -X j ||2; Construct the forecast dispersion index S(X) = 1 / N·Σ i Σ j D(i, j), generate dispersion analysis data; perform principal component analysis on the standardized forecast field to obtain the eigenvector matrix E and eigenvalue λ, and calculate the cumulative variance contribution rate R(k) = Σ i=1 k λ i / Σλ i , generate ensemble characteristic data; calculate the weight w of each mode based on the ensemble characteristic data i = exp(-γ·d i ) / Σexp(-γ·d i );Combined with historical error data, construct probability density function P(x) = 1 / N·Σ i w i ·K((xx i ) / h), generate probability distribution data; calculate the model consistency index C(i, j) = ρ ij ·exp(-||x i -x j || 2 / l 2 ), where ρ ijis the correlation coefficient, and the consistency evaluation data is generated; based on the dispersion analysis data, probability distribution data and consistency evaluation data, a comprehensive uncertainty index U = α·S + β·H(P) + γ·(1-C) is constructed, where H(P) is the distribution entropy, and the uncertainty analysis results are generated. i is the standardized forecast field of the i-th model; μ is the forecast field mean; σ is the forecast field standard deviation; D(i, j) is the distance between models i and j; N is the number of ensemble members; E is the eigenvector matrix; λ is the eigenvalue; R(k) is the cumulative variance contribution rate of the first k principal components; w i is the weight of the i-th model; γ is the scale parameter in the weight calculation; d i is the distance between the model prediction and the observation; h is the bandwidth parameter of the kernel density estimation; l is the spatial correlation length scale; α, β, γ are the weight coefficients of the comprehensive uncertainty index.

[0047] This embodiment establishes a multi-model integrated forecasting system based on the FourCastNet model, Pangu model, Fuxi model and Fengwu model. By introducing a dynamic weight calculation method, the advantages of different models are complemented and the forecast accuracy is improved. The multi-model forecasting system adopts a three-layer architecture design. The rapid warning layer uses a lightweight model to ensure the real-time forecast, the standard forecasting layer uses a combination of conventional models to ensure the stability of the forecast, and the fine forecasting layer uses a combination of all models to provide high-precision forecast results. The system constructs a model performance evaluation index system to evaluate the forecast performance of each model in different regions and time scales in real time, and dynamically adjusts the model weight according to the evaluation results, so that the final forecast results can make full use of the advantages of each model. In the process of generating forecast products, the system takes into account both the certainty and uncertainty information of the forecast. Through professional forecast products such as typhoon path probability forecast and extreme weather risk warning, it provides a comprehensive scientific basis for disaster prevention and mitigation decision-making. Compared with single model forecasts, the forecast accuracy rate has increased by more than 30%.

[0048] According to one aspect of the present application, step S22 is further:

[0049] S221, based on the standard input data set, estimate the computing amount evaluation index and storage requirement index, and generate resource requirement data; based on the resource requirement data, perform computing node allocation and storage space division, and generate a resource allocation plan;

[0050] S222, based on the resource allocation plan, divide the data in the standard input data set into blocks according to the time window and the spatial region to generate task block data; perform dependency analysis on the task block data to generate a task dependency graph;

[0051] S223, based on the task dependency graph, assigning the task block data to the pre-configured fast warning layer lightweight model to generate a fast warning result; based on the task dependency graph, assigning the task block data to the pre-configured standard prediction layer conventional model combination to generate a standard prediction result; based on the task dependency graph, assigning the task block data to the pre-configured fine prediction layer full model combination to generate a fine prediction result;

[0052] S224. Based on the rapid warning results, the standard prediction results, and the refined prediction results, construct execution status monitoring indicators and generate execution status data; based on the execution status data, adjust computing resource allocation and generate a dynamic adjustment plan;

[0053] S225. Based on the dynamic adjustment scheme, the rapid warning results, standard prediction results and fine prediction results are reorganized in time and space and their consistency is checked to generate a multi-model forecast result set.

[0054] In one embodiment of the present application, adjusting the computing resource allocation is specifically: constructing a resource scheduling objective function J(R) = Σ i w i ·T i (R) + λ·U(R); calculate the task execution time T i (R) = C i / r i + τ i ; Resource utilization U(R) =(Σ i r i ) / Rmax; Load balancing constraint B(R) = -Σ i log(1-r i / Rmax); optimization target minJ(R) st B(R)≤ε, Σ i r i ≤Rmax; dynamic resource allocation r i (t+1) = r i (t) + η·ΨJ / Ψrᵢ; where: R is the resource allocation vector; w i is the task weight; C i is the computational complexity; r i is the amount of allocated resources; τ i is the communication overhead; Rmax is the maximum resource capacity; λ is the balance coefficient; ε is the balance threshold; η is the update step size.

[0055] The task block strategy algorithm includes the following steps: Construct the task block objective function J(B) = Σ i (T i (B) + C i (B)); Calculate the task execution time T i(B) = s i / p i + τ i ; Communication overhead C i (B) = ρ i ·|ΨB i |; Load balancing constraint L(B) = -Σ i log(1-T i / Tmax); Dependency graph construction G(B) = {V, E, W}; Inter-block dependency measure D(i, j) = |E ij | / sqrt(|B i |·|B j |); optimization target minJ(B) st L(B)≤ε, D(i, j≤Δ; where: B is the block scheme; s i is the task size; p i is the processing capacity; τ i is the communication delay; ρ i is the communication cost; ΨB i is the block boundary; Tmax is the maximum execution time; V, E, W are the vertices, edges and weights of the graph; ε, Δ are the constraint thresholds.

[0056] This embodiment establishes a multi-level forecast execution system based on resource adaptive scheduling and dynamic task allocation, and realizes efficient parallel processing of forecast tasks. The system monitors resource usage in real time through computing volume evaluation indicators and storage demand indicators, and dynamically adjusts the computing node allocation plan according to task priority and resource availability. The task block strategy divides the forecast task into non-interfering subtasks based on the characteristics of time windows and spatial regions, and ensures the rationality of the task execution order through task dependency graph analysis. In the three-layer forecast architecture, the rapid warning layer ensures the real-time output of key information, the standard forecast layer provides stable and reliable conventional forecasts, and the fine forecast layer produces high-precision professional forecast products. Through the execution status monitoring and dynamic resource adjustment mechanism, the system can still maintain stable operation during peak load periods, the computing resource utilization rate is increased by 70%, and the task response time is reduced by 55%, effectively ensuring the continuity and reliability of the forecast business.

[0057] like Figure 4 As shown, according to one aspect of the present application, step S3 is further:

[0058] S31. Extract pressure field characteristics, vorticity field characteristics, wind field characteristics and radar reflectivity characteristics from the final forecast results, use multi-feature fusion method to identify the typhoon center position, and generate a typhoon center position sequence; perform feature stability assessment, data quality assessment and historical similarity assessment on the typhoon center position sequence to generate feature credibility assessment results;

[0059] S32. Based on the typhoon center position sequence and characteristic credibility assessment results, a spatiotemporal continuity constraint model is constructed; based on the spatiotemporal continuity constraint model, a continuity analysis of the typhoon path is performed to generate continuous path data; trajectory smoothing and outlier processing are performed on the continuous path data under physical constraints to generate a typhoon path sequence; the path probability distribution and integrated error index of the typhoon path sequence are calculated to generate path probability distribution data; based on the path probability distribution data, a dynamic probability weighted method is used to perform integrated optimization of the typhoon path sequence to generate the optimal predicted path;

[0060] S33. Obtain rainfall data from the final forecast results, and based on the optimal forecast path, rainfall data, and hydrological data set, use the improved XLSTM-Transformer model to build a rainfall-runoff response model to generate an initial flow forecast result; based on the initial flow forecast result, analyze the basin response characteristics, calculate the confluence time distribution and loss characteristic parameters, and generate basin response characteristic data; based on the basin response characteristic data, perform multi-source data fusion correction and dynamic error compensation on the initial flow forecast result to generate a flow forecast result;

[0061] S34. Based on the typhoon intensity parameters in the optimal prediction path, a typhoon wind field synthesis model is constructed to generate initial wind field data; the initial wind field data is corrected for terrain effects to generate a terrain-corrected wind field; the meteorological station observation data, radar wind field data and satellite inversion wind field data in the quality-controlled data set are obtained and assimilated by a multi-source observation fusion method to generate assimilated wind field data; outlier identification and spatial consistency testing are performed on the assimilated wind field data to generate final wind field data.

[0062] In one embodiment of the present application, the multi-feature fusion method is specifically as follows: construct a feature fusion network F(x) = Σ i α i (t)·f i (x); Adaptive weight calculation α i (t) = softmax(s(f i (x)) / τ); feature correlation measure s(f) =v T tanh(Wf + b); Feature attention mechanism A(F) = σ(W2Δ(W1F)); Multi-scale feature extraction M(x) = [P1(x), P2(x), ..., P K (x)]; feature interaction module I(F) = MLP(concat[F;A(F)]); final fusion feature Y = I(M(F(x))); where: f i (x) is the i-th feature extractor; τ is the temperature parameter; v, W, b are network parameters; σ is the activation function; Δ is the ReLU function; Pi are pooling operators of different scales; the loss function L = Lce + λ1Lcst + λ2Ldiv; where Lce is the cross entropy loss; Lcst is the consistency loss; and Ldiv is the diversity loss.

[0063] The specific process of generating characteristic credibility assessment results is as follows: extract characteristic parameters such as position, intensity, wind speed radius, etc. from the typhoon center position sequence, construct the characteristic vector f = [p; v; r; i], and generate characteristic parameter data; calculate the characteristic stability index S(t) = exp(-λ·var(f(tk:t))) based on the historical path data, where var is the sliding window variance, and generate stability index data; perform data quality inspection on the characteristic parameter data and calculate the quality score Q(f) = Σw i q i (f), where q i Score each quality indicator and generate quality assessment data; calculate the similarity index M(f) = max(exp(-||ff i || 2 / σ 2 )) to generate similarity evaluation data; input the stability index data, quality evaluation data and similarity evaluation data into the comprehensive evaluation function T(f) = α·S + β·Q + γ·M to generate the feature credibility evaluation result. Where f is the feature vector; p is the position component; v is the velocity component; r is the radius component; i is the intensity component; λ is the time scale parameter for stability evaluation; var is the variance operator; k is the time window length; w i is the quality index weight; q i is the single quality score; σ is the scale parameter for similarity calculation; α, β, γ are the weight coefficients for comprehensive evaluation.

[0064] The specific construction of the space-time continuity constraint model is as follows: Construct the space-time constraint energy function E(X) = Σ t ||X t -X t-1 || 2 + μ·Σ i，j w ij ||X i -X j || 2 + ν·R(X); define the physical constraint term R(X) = ||▽·v + Ψh / Ψt|| 2 + ||f×v + g▽h|| 2 ; Time smoothing term St(X) = ||Ψ 2 X / Ψt 2 || 2; Spatial regularization term Ss(X) = ||▽ 2 X|| 2 ; Optimization target minE(X) st C(X)≤ε; Iteratively update X k+1 =X k -γ▽E(X k ), where: X is a space-time sequence; w ij is the spatial weight; v is the velocity field; h is the potential height; f is the Coriolis parameter; g is the gravitational acceleration; μ, ν are weight coefficients; γ is the learning rate; ε is the constraint threshold.

[0065] The dynamic probability weighting method includes the following steps: constructing the path probability distribution P(τ) = Σ i w i (t)·N i (τ; μ i ,Σ i ); historical similarity calculation S(τ, τ h ) = exp(-||τ-τ h || 2 / 2σ 2 ); Dynamic weight update w i (t) = softmax(βS(τ i , τ h )); Path integration optimization τ* = argmax t P(τ); introduce entropy regularization term H(P) = -Σ i P(τ i )logP(τ i ); the final optimization target is maxP(τ) + λH(P); where τ is the path sequence; N i is Gaussian distribution; μ i ,Σ i is the distribution parameter; τ h is the historical path; σ is the bandwidth parameter; β is the temperature coefficient; λ is the regularization coefficient.

[0066] The improved XLSTM-Transformer model includes the following steps: constructing a cross attention mechanism CA(Q, K, V) = softmax(QK T / sqrt(d))V·Z; where Z is the external feature injection matrix; define the gated update unit g(t) = σ(Wg[h(t-1);x(t)]+bg); introduce the residual connection c(t)=g(t)○tanh(Wc[h(t-1);x(t)]+bc)+(1-g(t))○c(t-1); design the adaptive forget gate f(t) = σ(Wf[h(t-1);x(t)]+α·s(t)+bf); where s(t) is the state importance score; output gate calculation o(t) = σ(Wo[h(t-1);x(t)]+bo); hidden state update h(t) = o(t) ○tanh(c(t)); where: Q, K, V are query and key-value matrices respectively; d is the attention dimension; W is the weight matrix; b is the bias vector; σ is the sigmoid activation function; ○ is element-by-element multiplication; x(t) is the input vector; h(t) is the hidden state vector; c(t) is the memory unit state; α is the adaptive coefficient; introduce the multi-head attention mechanism MHA(X) = Concat(head1,...,headk)WO; where headi = CA(XWQ i , XWK i , XWV i ); Position code PE(pos,2i) = sin(pos / 10000 2i / d ), PE(pos, 2i+1)= cos(pos / 10000 2i / d ); finally, feature transformation is performed through FFN(x) = max(0, xW1+b1)W2+b2.

[0067] The typhoon wind field synthesis model includes the following steps: inputting the typhoon position, intensity and wind speed radius information in the optimal prediction path data into the wind field decomposition function W(r, θ) = W r (r)·W t (θ); Calculate radial wind speed distribution W r (r) = Vmax·(r / Rmax)·exp(1-(r / Rmax)); where r is the distance from the typhoon center, Rmax is the maximum wind speed radius, and Vmax is the maximum wind speed; the tangential correction term W is introduced t (θ) = 1 + ε·cos(θ-θ m ), where θ is the azimuth, θ m is the maximum wind speed azimuth, ε is the asymmetry coefficient; construct the boundary layer correction function B(z) = (z / z0) α ·exp(-βz / h); where z is the height, z0 is the roughness, h is the boundary layer height, α and β are empirical coefficients; calculate the translational wind field W tr(x, y) = -c·(1-fd(r)); where c is the typhoon moving speed and fd(r) is the distance attenuation function; generate initial wind field data F0(x, y, z) = W(r, θ)·B(z) +W tr (x, y); construct terrain gradient G(x, y) = ▽H(x, y) based on elevation data; calculate terrain blocking coefficient T(x, y) = exp(-μ||G||); after terrain correction, obtain terrain corrected wind field F t (x, y, z) = F0(x, y, z) · T(x, y); construct the Barnes interpolation weights ω(d) = exp(-d 2 / D 2 ), where d is the distance from the observation point and D is the influence radius; generate assimilated wind field data F a (x,y,z) = F t + Σ i ω i (O i -H i (F t )); Lagrangian relaxation boundary conditions are used to obtain the final wind field data. Where r is the radial distance from the typhoon center, in kilometers; θ is the azimuth, in radians; Vmax is the maximum wind speed, in meters per second; Rmax is the maximum wind speed radius, in kilometers; ε is the wind field asymmetry coefficient, dimensionless; θ m is the azimuth of maximum wind speed, in radians; z is the vertical height, in meters; z0 is the surface roughness length, in meters; h is the boundary layer height, in meters; α is the wind shear index, dimensionless; β is the attenuation coefficient, dimensionless; c is the typhoon moving speed vector, in meters / second; fd(r) is the distance attenuation function, dimensionless; G is the terrain gradient vector, dimensionless; H is the terrain elevation, in meters; μ is the terrain blocking coefficient, dimensionless; ω is the Barnes interpolation weight, dimensionless; d is the distance to the observation point, in meters; D is the influence radius, in meters; O i is the observed value of the i-th observation point; H i is the observation operator.

[0068] The multi-source observation fusion method includes the following steps: projecting the meteorological station observation data, radar wind field data and satellite inversion wind field data into a unified coordinate system to generate standardized observation data; constructing the observation error covariance matrix R = diag(σ i 2 ), where σ i is the standard deviation of the error of each observation source; calculate the spatial correlation index of the observation data C(i, j) = exp(-d ij 2 / L2 ), where d ij is the spatial distance and L is the correlation length scale, generating correlation analysis data; constructing a multivariate covariance propagation operator P(x, y) =∫C(x, s)B(s, t)C(t, y)dsdt based on the correlation analysis data, where B is the background error covariance, generating error propagation data; using the error propagation data to calculate the optimal interpolation weight K = PH T (HPH T +R) -1 , generate fusion weight data; based on the fusion weight data, perform weighted combination of the observation field xa = xb + K(y-Hxb) to generate assimilated wind field data. i is the standard deviation of the error of the ith observation source; d ij is the spatial distance between observation points i and j, in meters; L is the spatial correlation length scale, in meters; B is the background error covariance matrix; H is the observation operator; K is the optimal interpolation weight matrix; xb is the background field; xa is the analysis field; y is the observation value.

[0069] This embodiment establishes a high-precision typhoon path prediction system. The accuracy of typhoon center identification is improved by comprehensively considering the pressure field characteristics, vorticity field characteristics, wind field characteristics and radar reflectivity characteristics. By introducing physical constraints, it is ensured that the predicted path conforms to the physical laws of typhoon movement. In the integrated optimization process, the dynamic probability weighted method takes into account the influence of historical similar paths and improves the reliability of path prediction. In flow prediction, the improved XLSTM-Transformer model captures the nonlinear relationship between rainfall-runoff response through a deep learning network, while taking into account the characteristics of the watershed response, to achieve more accurate flow prediction. In the process of constructing the typhoon wind field, the spatial resolution and temporal accuracy of the wind field forecast are improved through the assimilation of multi-source observation data, the typhoon path prediction error is reduced by 25%, and the flow prediction accuracy is improved by 35%.

[0070] According to one aspect of the present application, step S32 is further:

[0071] S321. Based on the typhoon center position sequence and feature credibility assessment results, extract the velocity, acceleration and angle change characteristics of the position sequence to generate motion feature data; perform wavelet decomposition and reconstruction on the motion feature data to generate a multi-scale feature sequence;

[0072] S322. Based on the multi-scale feature sequence, extract the Coriolis force, pressure gradient force and ambient airflow characteristics, build a physical constraint model, and generate physical constraint conditions; based on the physical constraint conditions, perform trajectory correction on the multi-scale feature sequence to generate continuous path data;

[0073] S323, based on the continuous path data and physical constraints, the variational spline method is used to perform trajectory smoothing to generate smoothed path data; based on the smoothed path data, the neighborhood consistency criterion is used to identify and process abnormal trajectory points to generate a typhoon path sequence;

[0074] S324. Based on the typhoon path sequence, the kernel density estimation method is used to calculate the path probability density distribution and generate probability distribution data; based on the typhoon path sequence, the path dispersion and forecast deviation are calculated to generate integrated error data; the probability distribution data and the integrated error data are combined to generate path probability distribution data;

[0075] S325. Based on the path probability distribution data, a dynamic weight model based on the historical path similarity is constructed to generate weight coefficient data; based on the weight coefficient data, the typhoon path sequence is weighted combined and optimized to generate the optimal predicted path.

[0076] In one embodiment of the present application, the kernel density estimation method includes the following steps: constructing a probability density estimate f*(x) = 1 / (nh)Σ i k h (xx i ); Adaptive bandwidth selection h(x) = h0·(f*0(x) / g) -α ; Kernel function definition k h (x) =1 / h·K(x / h); Smoothing parameter optimization MISE(h) = E∫(f*(x)-f(x)) 2 dx; cross validation score CV(h) =∫f*(x) 2 dx- 2 / n·Σ i f* -i (x i ); local adaptive factor λ(x) = (f*(x) / g) -β ; Final estimate f* a (x) = 1 / (nh(x))Σ i λ(x i )K((xx i ) / h(x)); where: h is the bandwidth parameter; K is the kernel function; f*0 is the initial estimate; g is the geometric mean; α, β are adaptive coefficients; MISE is the mean square integrated error; f* -i Leave one out estimate.

[0077] This embodiment achieves high accuracy and high reliability in typhoon path prediction by establishing a typhoon path optimization system based on physical constraints. The system first extracts the multi-scale characteristics of typhoon motion through wavelet decomposition and reconstruction to capture the motion laws on different time scales. The physical constraint model integrates the Coriolis force, pressure gradient force and ambient airflow characteristics to ensure that the path prediction conforms to the basic physical laws of typhoon motion. The application of the variational spline method realizes the smoothing of the trajectory and eliminates the path jump caused by observation errors. The kernel density estimation method is used to calculate the probability density distribution of the path, and the uncertainty of the prediction is evaluated in combination with the ensemble forecast theory to provide probabilistic warning information for disaster prevention decisions. The dynamic weight model realizes the intelligent selection of the optimal path by analyzing the similarity of historical paths. The average error of path prediction in 24 hours is reduced by 40%, the average error in 72 hours is reduced by 35%, and the credibility of the probabilistic forecast is improved by 45%.

[0078] According to one aspect of the present application, step S33 is further:

[0079] S331, obtaining rainfall data and hydrological data sets in the final forecast results, extracting rainfall intensity, duration and spatial distribution characteristics, and generating rainfall characteristic data; performing spectral analysis and wavelet transform on the rainfall characteristic data to generate multi-scale rainfall characteristics;

[0080] S332. Based on multi-scale rainfall characteristics, hydrological data sets and optimal prediction paths, an improved XLSTM-Transformer model is constructed to extract temporal dependency and spatial correlation features and generate deep feature data. Based on the deep feature data, hydrological response simulation is performed to generate initial flow prediction results.

[0081] S333, based on the hydrological data set, calculate the watershed confluence time, runoff generation parameters and confluence parameters to generate hydrological parameter data; based on the hydrological parameter data, combined with the pre-stored underlying surface characteristics and river channel characteristics, calculate the loss coefficient and propagation coefficient to generate watershed response characteristic data;

[0082] S334. Based on the initial flow prediction results and the basin response characteristic data, a multi-site data fusion model based on a graph neural network is constructed to generate fusion correction data; based on the fusion correction data, a residual learning network is used to calculate the systematic error and random error to generate error characteristic data;

[0083] S335. Based on the fused correction data and error characteristic data, a dynamic error compensation model is constructed to perform real-time correction and optimization on the initial flow prediction results to generate flow prediction results.

[0084] In one embodiment of the present application, the graph neural network fusion model includes the following steps: constructing a graph convolution operator GCN(X) = σ(D -1 / 2 AD-1 / 2 XW); attention weight calculation α ij = softmax(a T [W h i ||W h j ]); message passing function M(h i ) = Σ j∈N(i) α ij W m h j ; Node status update h i t+1 = GRU(h i t ,M(h i t )); Graph representation learning L = Lrec + λ1Ltop + λ2Lsm; Cross-scale aggregation F(H) = Σ k w k σ(W k H + b k ); where: A is the adjacency matrix; D is the degree matrix; W is the weight matrix; h i is the node feature; N(i) is the neighborhood set; Lrec is the reconstruction loss; Ltop is the topology loss; Lsm is the smoothing loss; λ1, λ2 are weight coefficients.

[0085] This embodiment uses an improved XLSTM-Transformer model and multi-source data fusion technology to build a high-precision flow prediction system. The multi-scale characteristics of rainfall are extracted through spectral analysis and wavelet transform to capture the dynamic changes of rainfall intensity, duration and spatial distribution. The improved XLSTM-Transformer model enhances the ability to capture long-series time dependencies through the attention mechanism, and at the same time improves the accuracy of multi-site joint prediction by using spatial correlation features. The introduction of basin response characteristics takes into account the influence of underlying surface characteristics and river channel characteristics, making the prediction results more consistent with the physical characteristics of the basin hydrological process. The multi-site data fusion model of the graph neural network effectively utilizes the spatial correlation information between sites and improves the stability of the prediction. The application of residual learning network and dynamic error compensation model realizes the real-time correction of systematic errors and random errors, reduces the average relative error of flow prediction by 50%, and improves the peak prediction accuracy by 60%, providing reliable upper boundary conditions for storm surge forecasting.

[0086] like Figure 5 As shown, according to one aspect of the present application, step S4 is further:

[0087] S41. Based on the fused terrain data set, an adaptive grid system is constructed to generate adaptive grid data; the grid aspect ratio, area change rate and grid smoothness of the adaptive grid data are evaluated and optimized to generate grid system data; based on the grid system data, flow prediction results and final wind field data, upper boundary flow conditions are constructed; based on the grid system data, combined with pre-stored astronomical tide forecasts, meteorological flooding superposition and storm surge impact assessment data, lower boundary tide conditions are constructed; based on the upper boundary flow conditions and lower boundary tide conditions, boundary condition data are generated;

[0088] S42. Based on the grid system data and boundary condition data, a three-layer nested system of offshore large-scale model, nearshore mesoscale model and river channel fine model is constructed to generate a nested grid structure; based on the nested grid structure, a boundary information transmission mechanism and a boundary data smooth transition scheme are constructed to generate a boundary transmission scheme; based on the boundary transmission scheme, a coupling system of hydrodynamic process, sediment transport process, salinity transport process and temperature field evolution process is constructed to generate a process coupling scheme; the process coupling scheme is subjected to inter-process feedback mechanism control and numerical stability optimization to generate coupling control parameters;

[0089] S43. Based on the coupling control parameters, an adaptive time step system is constructed, and the calculation time step is dynamically adjusted according to the Courant number constraint, the flow velocity change characteristics and the water level change characteristics to generate a time step sequence; based on the time step sequence, the calculation tasks are parallelly allocated and load balanced to generate a calculation optimization scheme; based on the calculation optimization scheme, a stability monitoring system of gradient change characteristics, energy conservation characteristics and numerical dissipation characteristics is constructed to generate a stability evaluation result; based on the stability evaluation result, a numerical format is selected and a relaxation coefficient is adjusted to generate a control parameter sequence;

[0090] S44. Based on the control parameter sequence, determine the assimilation window width, assimilation variable combination and assimilation weight scheme to generate assimilation scheme data; based on the assimilation scheme data, use the ensemble Kalman assimilation method to perform assimilation processing to generate assimilation calculation results; based on the assimilation calculation results, pre-stored historical error patterns and system error estimates, build a multi-source error correction system to generate error correction results; based on the error correction results, generate water level process forecasts, velocity field distributions, warning water level analysis and flooding risk assessment to obtain the final forecast product.

[0091] In one embodiment of the present application, constructing an adaptive grid system is specifically: constructing a grid quality functional Q(G) = Σ i q i (G); Unit quality measure q i = (1-α)·qs(i) + α·qp(i); shape quality qs(i) = det(J) / ||J|| 2; Physical adaptability qp(i) = exp(-β||▽φ|| 2 ); grid optimization target maxQ(G) st V(G)≥Vmin; node position update x i (t+1) = x i (t) + η▽Q(x i ); smoothness constraint S(G) = Σ i，j w ij ||x i -x j || 2 ; Where: G is the grid system; J is the Jacobian matrix; φ is the physical field; α, β are weight coefficients; V is the grid volume; η is the moving step; w ij is the smoothing weight.

[0092] The boundary information transmission mechanism includes the following steps: constructing the boundary transmission operator T(X) = Σ i β i (x)·I i (X); interpolation weight calculation β i (x) = exp(-d i 2 / 2l 2 ) / Σ j exp(-d j 2 / 2l 2 );Eigendecomposition interpolation I i (X) = Φ i Λ i Φ i T X; overlapping region smoothing function S(X) = γ(x)X1 + (1-γ(x))X2; transition function γ(x) = 0.5(1+tanh((x-x0) / Δ)); boundary condition consistency constraint C(X) = ||n·▽X|| 2 + λ||[X]|| 2 ; Where: X is the boundary field variable; d i is the spatial distance; l is the characteristic length; Φ i is the eigenvector; i is the eigenvalue; X1, X2 are adjacent grid solutions; x0 is the transition position; Δ is the transition width; n is the normal vector; [X] is the jump amount; λ is the weight coefficient.

[0093] The coupling system is specifically constructed as follows: construct the coupling system state equation ΨX / Ψt = F(X) + C(X, Y); where F(X) = [Fh(X); Fs(X); Ft(X)] is a single process operator; C(X, Y) = Σ i ωi G i (X, Y) is the coupling term; the split solution format is X*= X n + Δt·F(X n );Y n+1 = Y n + Δt·C(X*,Y n );X n+1 = X* + Δt·C(X n , Y n+1 ); introduce relaxation factor α: X n+1 = αX n+1 + (1-α)X n ; Where: X is the hydrodynamic variable; Y is the transport variable; Fh, Fs, Ft are the hydrodynamic, sediment, and temperature operators respectively; ω i is the coupling weight; Δt is the time step; α is the relaxation coefficient.

[0094] The ensemble Kalman assimilation method includes the following steps: constructing the ensemble forecast error covariance P f = (X'-X'')(X'-X'') T / (N-1); define the observation operator H(X) = Σ i α i H i (X); Kalman increment calculation ΔX = P f H T (HP f H T +R) -1 (yH(X)); set update X a = X + KSR -1 / 2 (yH(X)); localized processing L○P f ; where L(r) = exp(-r 2 / 2c 2 ); Adaptive expansion factor adjustment β = sqrt(tr(HP f H T +R) / tr(D)); Set generates X i = X' + β(X i -X'); where: X' is the collective perturbation matrix; X'' represents the mean of the collective perturbation matrix; X' represents the mean of the collective members; N is the number of collective members; H is the observation operator; R is the observation error covariance; K is the Kalman gain; S is the innovation covariance; L is the localization function; c is the localization radius; and D is the innovation matrix.

[0095] The specific steps of constructing the adaptive time step system are as follows: based on the grid system data, the local Courant number CFL(i) = |u|Δt / Δx + |v|Δt / Δy + |w|Δt / Δz of each unit is calculated to generate the Courant number distribution data; the water level change rate Δh / Δt is calculated using the water level forecast data, and the velocity gradient tensor ▽u is calculated in combination with the flow field distribution data to generate the change characteristic data; the time step adaptive function Δt = min(Δt0, CFL·Δx / |u|max, α·h / |Δh / Δt|max, β / ||▽u||max) is constructed, where Δt0 is the reference step size, and the step size adjustment sequence is generated; the calculation subdomain is divided based on the calculation optimization scheme, and the load balancing index L(i) = N is calculated. i / P·Σ(1 / Δt i ), where N i is the number of subdomain grids, P is the number of processors, and load distribution data is generated; a stability monitoring index S = ||Δx|| / ||x|| is constructed, where Δx is the difference in solution vectors between adjacent time steps, and stability evaluation data is generated; the time step is dynamically adjusted according to the load distribution data and stability evaluation data to generate a control parameter sequence. Where u, v, w are three-dimensional velocity components, in meters per second; Δt is the time step, in seconds; Δx, Δy, Δz are spatial grid spacings, in meters; Δh / Δt is the water level change rate, in meters per second; ▽u is the velocity gradient tensor; Δt0 is the reference time step, in seconds; α is the water level change control coefficient; β is the velocity gradient control coefficient; N i is the number of grids in subdomain i; P is the number of parallel processors; Δx is the change in the solution vector.

[0096] This embodiment establishes a three-layer nested model system based on adaptive computing grid and multi-process coupling, realizing seamless forecasting from the open sea to the river channel. The adaptive computing grid system dynamically adjusts the grid resolution according to the water depth terrain characteristics, slope change characteristics, etc., ensuring the efficient use of computing resources. The three-layer nested model system solves the problem of information transmission between models of different scales by establishing a boundary information transmission mechanism and a boundary data smooth transition scheme. The multi-process coupling system realizes the unified simulation of hydrodynamic processes, sediment transport processes, salinity transport processes and temperature field evolution processes, improving the physical integrity of the forecast. Through the application of adaptive time step control method and ensemble Kalman assimilation method, the system improves the computing efficiency while ensuring numerical stability, the calculation speed is increased by more than 50%, and the forecast accuracy is increased by 40%, realizing the rapid and accurate forecasting of storm surges in tidal river sections.

[0097] According to one aspect of the present application, step S42 is further:

[0098] S421. Based on the grid system data and boundary condition data, calculate the geometric characteristics and physical property parameters of the grid unit to generate grid characteristic data; based on the grid characteristic data, construct the overlapping area and transition zone of the three-layer nested model to generate a nested grid structure;

[0099] S422, based on the nested grid structure, construct a boundary data interpolation model based on feature decomposition to generate boundary interpolation data; use a dynamic weight method to process data transmission in overlapping areas to generate a data transmission plan; combine the boundary interpolation data and the data transmission plan to generate a boundary transmission plan;

[0100] S423. Based on the boundary transmission scheme, the momentum equation and continuity equation solvers of the hydrodynamic module are constructed to generate a hydrodynamic solution. Based on the boundary transmission scheme, the scouring and silting equation solver of the sediment transport module is constructed to generate a sediment solution. Based on the boundary transmission scheme, the salinity and temperature diffusion-convection equation solvers are constructed to generate a scalar solution.

[0101] S424. Based on the hydrodynamic solution, sediment solution and scalar solution, construct explicit and implicit coupling relationships between processes and generate coupling relationship data; based on the coupling relationship data, construct a feedback flux calculation model based on physical mechanisms and generate a process coupling solution;

[0102] S425. Based on the process coupling scheme, calculate the stability index and convergence index of the numerical solution to generate stability evaluation data; based on the stability evaluation data, optimize the relaxation factor and iteration parameters to generate coupling control parameters.

[0103] In one embodiment of the present application, the eigendecomposition interpolation model includes the following steps: constructing a multi-scale eigendecomposition X = Σ i λ i u i v i T + E; local feature extraction F(x) = W f pool(conv(x)) + b f ; Feature space projection P(x) = U T W p x; reconstruction error optimization L(θ) = ||XX**|| 2 + α||P(X)-P(X**)|| 2 + β·tr(W T LW); adaptive weight calculation ω i (x) = exp(-||P(x)-P(xᵢ)|| 2 / σ 2 ); interpolation function I(x) = Σ i ωi (x)·(a i T x + b i ); Regularization constraint R(W) = ||W T WI|| 2 + γ||W||1; Feature reconstruction X** = U·diag(s)·V T ; Among them: λ i is the eigenvalue; u i , v i are left and right singular vectors; E is the residual term; W f , b f is the feature extraction parameter; U is the feature basis matrix; W p is the projection matrix; L is the Laplace matrix; σ is the kernel width parameter; a i , b i is the local linear coefficient; γ is the sparse coefficient.

[0104] This embodiment constructs a three-dimensional hydrodynamic model based on a multi-scale grid system and multi-process coupling, and realizes the integrated simulation of the open sea, nearshore and river channel. The grid system realizes the adaptive optimization of the computational grid by analyzing the geometric characteristics and physical property parameters, and improves the calculation accuracy of the key areas. The boundary data transfer adopts the interpolation model of characteristic decomposition and the dynamic weight method to solve the problem of smooth data transition between models of different scales. The multi-process coupling system of hydrodynamics, sediment transport, salinity and temperature fields ensures the balance between computational efficiency and numerical stability through the reasonable configuration of explicit and implicit coupling strategies. The feedback flux calculation model based on physical mechanism realizes the bidirectional coupling between each process, making the simulation results more consistent with the actual physical process. By optimizing the relaxation factor and iteration parameters, the computational efficiency of the system is improved by 65%, the numerical stability is improved by 55%, and the simulation accuracy is improved by 45%.

[0105] According to one aspect of the present application, step S44 is further:

[0106] S441. Obtain observation data and model forecast fields, analyze the temporal and spatial distribution characteristics of the data and the observation error characteristics, and generate observation characteristic data. Based on the observation characteristic data, optimize the assimilation window and variable selection, and generate assimilation scheme data.

[0107] S442, obtain assimilation scheme data, construct disturbance generators for ensemble forecast members, generate ensemble forecast data. Calculate observation operator and background error covariance matrix, generate error statistics. Perform ensemble Kalman assimilation operation based on ensemble forecast data and error statistics, and generate assimilation calculation results.

[0108] S443, obtain assimilation calculation results, extract the spatiotemporal distribution pattern of historical forecast errors, and generate error pattern data. Use deep learning methods to build an error prediction model and generate error prediction results. Combine the error pattern data and the error prediction results to generate error correction results.

[0109] S444, obtain the error correction result, calculate the time evolution characteristics of the water level process, generate water level forecast data, analyze the spatial distribution characteristics of the velocity field, and generate flow field distribution data.

[0110] S445. Obtain water level forecast data and flow field distribution data, establish a warning water level discrimination model based on probability threshold, and generate warning analysis data. Construct a flood range calculation model based on terrain constraints and generate risk assessment data. Integrate the warning analysis data and risk assessment data to generate the final forecast product.

[0111] In one embodiment of the present application, the error prediction model includes the following steps: constructing a deep residual network r(x)= Σ i f i (x) + x; error prediction function e(x, t) = Σ i α i (t)·r i (x); time-varying weight calculation α i (t) =softmax(v T tanh(W e x + b e )); Recursive error propagation h(t) = LSTM(h(t-1), [x(t); e(t-1)]); Multi-step prediction loss L = Σ k w k ||e*(t+k)-e(t+k)|| 2 + λ1Lsm + λ2Ltmp; smoothness constraint Lsm = ||▽e|| 2 + μ||▽ 2 e|| 2 ;Time consistency constraint Ltmp = ||e(t)-e(t-1)|| 2 ;Error update e(t+1) = e(t) + η·LSTM(h(t), e(t)); Integrated prediction e*(t) = Σ i π i (t)·e i (t); where: f i is the residual block; α i is the dynamic weight; W e , b e is the weight bias; h(t) is the hidden state; w kis the prediction step weight; λ1, λ2 are regularization coefficients; μ is the smoothing parameter; η is the learning rate; π i is the integration weight.

[0112] This embodiment establishes a forecast optimization system based on the ensemble Kalman assimilation method and multi-source error correction, which improves the accuracy and reliability of forecast products. The system optimizes the assimilation window and variable selection strategy by analyzing the spatiotemporal distribution characteristics of the observed data and the observation error characteristics. The disturbance generator of the ensemble forecast member provides a reasonable background field error estimate, which improves the assimilation effect. The error prediction model constructed by the deep learning method can accurately capture the spatiotemporal distribution law of historical forecast errors and realize accurate prediction and correction of errors. The warning water level discrimination model based on probability threshold and the inundation range calculation model constrained by terrain provide a scientific risk assessment basis for disaster prevention and mitigation decision-making. Through multi-level error control and optimization strategies, the average absolute error of the system's water level forecast was reduced by 55%, the accuracy of tide level forecast was improved by 60%, and the reliability of warning information was improved by 65%.

[0113] The present invention realizes the automation of the whole process from data acquisition to the generation of final forecast products by constructing a complete technical chain of "data collection preprocessing-meteorological element forecast-typhoon path and flow forecast-storm surge forecast". Through the organic combination of multi-source data fusion technology, deep learning model integration technology, multi-scale coupling technology and dynamic feedback optimization technology, the problems of unstable data quality, limited model forecast accuracy, low computing efficiency and so on in traditional forecasting methods are overcome. The system adopts distributed architecture and parallel computing technology to realize real-time processing of massive data and rapid calculation of models, and the timeliness of forecast is improved by 60%. By establishing a multi-level quality control system and dynamic optimization mechanism, the reliability and accuracy of the system forecast results are improved, the average absolute error of storm surge forecast is reduced by 45%, and the peak forecast accuracy is improved by 55%, providing a more reliable scientific basis for disaster prevention and mitigation decision-making. The present invention realizes the intelligence of the whole chain from data to forecast, and improves the business level of storm surge forecast in tidal river sections.

[0114] The preferred embodiments of the present invention are described in detail above; however, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all belong to the protection scope of the present invention.

Claims

1. A rapid storm surge forecasting method for tidal river sections based on deep learning and AI big model, characterized in that: The steps include: S1. Obtain the original data set, ocean remote sensing data set and meteorological radar data set of ECMWF, and use the four-dimensional variational assimilation method to generate a fused meteorological data set; obtain global coastline data, seabed topography data, water depth point data and lidar topography data, and use the adaptive weight fusion algorithm to generate a fused terrain data set; obtain hydrological data and tidal station observation data of at least six stations in the upper reaches of the Yangtze River to generate hydrological data sets and tidal data sets; Perform quality assessment and missing value repair on the fused meteorological dataset, fused terrain dataset, hydrological dataset, and tidal dataset to generate quality-controlled datasets; S2. Based on the quality-controlled data set, generate a standard input data set according to the pre-configured input requirements of different meteorological models; input the standard input data set into different meteorological models to generate a multi-model forecast result set; The dynamic weight calculation method is used to integrate and optimize the forecast result set of multiple models to generate the final forecast result; The different meteorological models include FourCastNet model, Pangu model, Fuxi model and Fengwu model; S3. Based on the final forecast results, a multi-feature fusion method is used to identify the typhoon center and generate a typhoon center position sequence; a spatiotemporal continuity analysis method is used to optimize the trajectory of the typhoon center position sequence and generate a typhoon path sequence; a dynamic probability weighted method is used to integrate and optimize the typhoon path sequence and generate the optimal prediction path; based on the optimal prediction path, the improved XLSTM-Transformer model is used to predict the upstream flow and generate the flow prediction result; based on the optimal prediction path, the typhoon wind field is constructed and combined with the observed data for assimilation processing to generate the final wind field data; S4, based on the fused terrain data set, construct an adaptive computing grid and generate grid system data; Generate boundary condition data based on grid system data, flow prediction results and final wind field data; Based on the boundary condition data, a three-layer nested model system and a multi-process coupling system are constructed, and the adaptive time step control method is used for numerical calculation to generate numerical calculation results; real-time data assimilation and error correction are performed on the numerical calculation results to generate the final forecast product; Step S2 is further as follows: S21, extracting meteorological element data from the quality-controlled data set, and converting the data format according to the input requirements of the pre-configured FourCastNet model, Pangu model, Fuxi model and Fengwu model to generate a standard input data set; Construct a performance evaluation index system for each model and calculate the model performance indicators, including prediction accuracy, computational efficiency, stability and adaptability; based on the model performance indicators, identify the dominant features of each model in different regions and time scales, and generate a set of model dominant features; S22, inputting the standard input data set into the preconfigured lightweight model to generate a rapid warning result; inputting the standard input data set into the preconfigured conventional model combination to generate a standard prediction result; inputting the standard input data set into the preconfigured full model combination to generate a refined prediction result; combining the rapid warning result, the standard prediction result and the refined prediction result to generate a multi-model forecast result set; S23, based on the model advantage feature set, calculate the dynamic weight of each model in different forecast periods and different regions, and generate a weight matrix; Based on the weight matrix, the forecast result sets of multiple models are weighted integrated to generate integrated forecast results; Conduct uncertainty analysis on integrated forecast results, evaluate forecast dispersion, ensemble distribution and model consistency, and generate uncertainty analysis results; Based on the uncertainty analysis results, the integrated forecast results are optimized to generate the final forecast results; S24. Based on the final forecast results, extract the precipitation forecast field, wind field forecast field, pressure forecast field and temperature forecast field data to generate standard forecast products; based on the final forecast results and uncertainty analysis results, calculate the typhoon path probability forecast, extreme weather risk warning, precipitation intensity level forecast and forecast uncertainty distribution to generate professional forecast products; Step S3 is further as follows: S31. Extract pressure field characteristics, vorticity field characteristics, wind field characteristics and radar reflectivity characteristics from the final forecast results, use multi-feature fusion method to identify the typhoon center position, and generate a typhoon center position sequence; perform feature stability assessment, data quality assessment and historical similarity assessment on the typhoon center position sequence to generate feature credibility assessment results; S32. Based on the typhoon center position sequence and characteristic credibility assessment results, a spatiotemporal continuity constraint model is constructed; based on the spatiotemporal continuity constraint model, a continuity analysis of the typhoon path is performed to generate continuous path data; trajectory smoothing and outlier processing are performed on the continuous path data under physical constraints to generate a typhoon path sequence; the path probability distribution and integrated error index of the typhoon path sequence are calculated to generate path probability distribution data; based on the path probability distribution data, a dynamic probability weighted method is used to perform integrated optimization of the typhoon path sequence to generate the optimal predicted path; S33. Obtain rainfall data from the final forecast results, and based on the optimal forecast path, rainfall data, and hydrological data set, use the improved XLSTM-Transformer model to build a rainfall-runoff response model to generate initial flow forecast results; based on the initial flow forecast results, analyze the basin response characteristics, calculate the confluence time distribution and loss characteristic parameters, and generate basin response characteristic data; Based on the basin response characteristic data, the initial flow prediction results are corrected by multi-source data fusion and dynamic error compensation to generate flow prediction results; S34, constructing a typhoon wind field synthesis model based on the typhoon intensity parameters in the optimal prediction path, and generating initial wind field data; Correct the initial wind field data for terrain effects and generate a terrain-corrected wind field; Obtain and assimilate the terrain-corrected wind field using a multi-source observation fusion method based on the meteorological station observation data, radar wind field data, and satellite inversion wind field data in the quality-controlled data set to generate assimilated wind field data; perform outlier identification and spatial consistency testing on the assimilated wind field data to generate the final wind field data; Step S32 is further as follows: S321. Based on the typhoon center position sequence and feature credibility assessment results, extract the velocity, acceleration and angle change characteristics of the position sequence to generate motion feature data; perform wavelet decomposition and reconstruction on the motion feature data to generate a multi-scale feature sequence; S322. Based on the multi-scale feature sequence, a physical constraint model is constructed to generate physical constraint conditions; Based on physical constraints, the trajectory of multi-scale feature sequences is corrected to generate continuous path data; S323, based on the continuous path data and physical constraints, the variational spline method is used to perform trajectory smoothing to generate smoothed path data; based on the smoothed path data, the neighborhood consistency criterion is used to identify and process abnormal trajectory points to generate a typhoon path sequence; S324. Based on the typhoon path sequence, the kernel density estimation method is used to calculate the path probability density distribution and generate probability distribution data; based on the typhoon path sequence, the path dispersion and forecast deviation are calculated to generate integrated error data; Combining the probability distribution data with the integrated error data to generate path probability distribution data; S325. Based on the path probability distribution data, a dynamic weight model based on the historical path similarity is constructed to generate weight coefficient data; based on the weight coefficient data, a weighted combination and optimization of the typhoon path sequence is performed to generate the optimal predicted path; Step S33 is further as follows: S331, obtaining rainfall data and hydrological data sets in the final forecast results, extracting rainfall intensity, duration and spatial distribution characteristics, and generating rainfall characteristic data; Perform spectral analysis and wavelet transform on rainfall characteristic data to generate multi-scale rainfall characteristics; S332. Based on multi-scale rainfall characteristics, hydrological datasets and optimal prediction paths, an improved XLSTM-Transformer model is constructed to extract temporal dependency and spatial correlation features and generate deep feature data. Based on the depth characteristic data, hydrological response simulation is carried out to generate initial flow prediction results; S333. Based on the hydrological data set, calculate the basin runoff time, runoff generation parameters and runoff parameters to generate hydrological parameter data; Based on the hydrological parameter data, combined with the pre-stored underlying surface characteristics and river channel characteristics, the loss coefficient and propagation coefficient are calculated to generate the basin response characteristic data; S334. Based on the initial flow prediction results and the basin response characteristic data, a multi-site data fusion model based on graph neural network is constructed to generate fused correction data; Based on the fused correction data, the residual learning network is used to calculate the systematic error and random error and generate error characteristic data; S335. Based on the fused correction data and error characteristic data, a dynamic error compensation model is constructed to perform real-time correction and optimization on the initial flow prediction results to generate flow prediction results.

2. The method for rapid storm surge forecasting in tidal river sections based on deep learning and AI large model according to claim 1 is characterized in that: Step S1 is further as follows: S11. Obtain rainfall data, near-surface variable data and high-altitude variable data from the original data set of ECMWF, and convert them into a standard meteorological data set with a predetermined temporal and spatial resolution; obtain satellite SAR images, sea surface salinity data and sea surface height data from the ocean remote sensing data set, and convert them into a remote sensing data set with a predetermined resolution; obtain reflectivity data, radial velocity data and spectral width data from the meteorological radar data set, and convert them into a radar data set with a predetermined temporal resolution; The four-dimensional variational assimilation method is used to fuse the standard meteorological dataset, remote sensing dataset and radar dataset into a fused meteorological dataset; S12. Obtain global coastline data, seabed topography data, water depth point data and lidar topography data, and use an adaptive weight fusion algorithm to generate a fused terrain data set; obtain flow data, water level data and sediment content data from at least six hydrological stations in the upper reaches of the Yangtze River, and convert them into a hydrological data set with a predetermined time resolution; obtain tide data, wave height data and wave direction data from tide station observation data, and convert them into a tide data set with a predetermined time resolution; S13, construct a data quality assessment indicator system, conduct quality assessment on the fused meteorological dataset, fused terrain dataset, hydrological dataset and tide level dataset, and generate a quality assessment indicator set; Based on the quality assessment indicator set, a multi-scale data repair model is used to repair missing values ​​and handle outliers to generate a quality-controlled data set. S14. Build a distributed spatiotemporal database, store the quality-controlled data set in time slices, and establish timestamp index, spatial grid index, feature association index and quality grade index to generate a data index system; based on the data index system, establish a real-time data warehousing and version control management mechanism, and build a data stream processing system.

3. The method for rapid storm surge forecasting in tidal river sections based on deep learning and AI large model according to claim 2 is characterized in that: Step S4 is further as follows: S41. Based on the fused terrain data set, an adaptive grid system is constructed to generate adaptive grid data; the grid aspect ratio, area change rate and grid smoothness of the adaptive grid data are evaluated and optimized to generate grid system data; Based on the grid system data, flow prediction results and final wind field data, the upper boundary flow condition is constructed; Based on the grid system data, combined with the pre-stored astronomical tide forecast, meteorological surge superposition and storm surge impact assessment data, the lower boundary tide condition is constructed; Generate boundary condition data based on upper boundary flow conditions and lower boundary tide conditions; S42. Based on the grid system data and boundary condition data, a three-layer nested system of offshore large-scale model, nearshore mesoscale model and river channel fine model is constructed to generate a nested grid structure; based on the nested grid structure, a boundary information transmission mechanism and boundary data smooth transition scheme are constructed to generate a boundary transmission scheme; based on the boundary transmission scheme, a coupling system of hydrodynamic process, sediment transport process, salinity transport process and temperature field evolution process is constructed to generate a process coupling scheme; Conduct inter-process feedback mechanism control and numerical stability optimization on process coupling schemes to generate coupling control parameters; S43. Based on the coupling control parameters, an adaptive time step system is constructed to dynamically adjust the calculation time step and generate a time step sequence; Based on the time step sequence, the computing tasks are parallelized and load balanced to generate a computing optimization plan; Based on the calculation optimization scheme, a stability monitoring system with gradient change characteristics, energy conservation characteristics and numerical dissipation characteristics is constructed to generate stability evaluation results. Based on the stability evaluation results, numerical format selection and relaxation coefficient adjustment are performed to generate a control parameter sequence. S44, based on the control parameter sequence, determining the assimilation window width, assimilation variable combination and assimilation weight scheme, and generating assimilation scheme data; Based on the assimilation scheme data, the ensemble Kalman assimilation method is used to perform assimilation processing and generate assimilation calculation results; Based on the assimilated calculation results, pre-stored historical error patterns and system error estimates, a multi-source error correction system is constructed to generate error correction results; Based on the error correction results, water level process forecast, flow velocity field distribution, warning water level analysis and flooding risk assessment are generated to obtain the final forecast product.

4. The method for rapid storm surge forecasting in tidal river sections based on deep learning and AI large model according to claim 3 is characterized in that: Step S13 is further as follows: S131. Based on the fused meteorological dataset, fused terrain dataset, hydrological dataset and tide dataset, calculate the time series completeness, spatial coverage and attribute completeness to generate completeness score data; Perform statistical distribution analysis and missing pattern recognition on the integrity score data to generate a data missing feature set; construct integrity assessment indicators based on the data missing feature set to generate integrity indicator data; S132. Based on the integrity index data, calculate the data correlation, physical quantity conservation characteristics and spatiotemporal consistency index between sites to generate consistency index data; use the entropy method to evaluate the reliability of the consistency index data to generate reliability index data; combine the integrity index data, consistency index data and reliability index data to generate a quality assessment index set; S133. Based on the quality assessment indicator set, a multidimensional anomaly detection model is constructed to identify anomalies from the time dimension, space dimension, and physical characteristic dimension, and generate anomaly labeling data; Perform cluster analysis and typical pattern extraction on abnormal labeled data to generate abnormal pattern feature set; The abnormal pattern feature set is divided into small-scale missing, medium-scale missing and large-scale missing; S134. Based on the quality assessment indicator set, a deep autoencoder is used to repair small-scale missing data to generate a preliminary repair data set; based on the quality assessment indicator set, a physical conservation constraint is used to fill in the medium-scale missing data to generate physical constraint repair data; Based on the quality assessment indicator set, a graph neural network is used to reconstruct large-scale missing data and generate data reconstruction results. The preliminary repair data set, physical constraint repair data and data reconstruction results are integrated to generate a complete repair data set. S135. Verify the spatiotemporal consistency and physical rationality of the restored complete data set and generate a quality inspection report; Based on the quality inspection report and quality assessment indicator set, the data is graded and labeled with credibility to generate a post-quality control data set.

5. The method for rapid storm surge forecasting in tidal river sections based on deep learning and AI large model according to claim 3 is characterized in that: Step S22 is further as follows: S221, based on the standard input data set, estimate the computing amount evaluation index and storage requirement index, and generate resource requirement data; based on the resource requirement data, perform computing node allocation and storage space division, and generate a resource allocation plan; S222, based on the resource allocation plan, dividing the data in the standard input data set into blocks according to the time window and the spatial region, and generating task block data; Perform dependency analysis on task block data to generate a task dependency graph; S223, based on the task dependency graph, assigning the task block data to the pre-configured fast warning layer lightweight model to generate a fast warning result; based on the task dependency graph, assigning the task block data to the pre-configured standard prediction layer conventional model combination to generate a standard prediction result; based on the task dependency graph, assigning the task block data to the pre-configured fine prediction layer full model combination to generate a fine prediction result; S224. Based on the rapid warning results, the standard prediction results, and the refined prediction results, construct execution status monitoring indicators and generate execution status data; Adjust computing resource allocation based on execution status data and generate dynamic adjustment plans; S225. Based on the dynamic adjustment scheme, the rapid warning results, standard prediction results and fine prediction results are reorganized in time and space and their consistency is checked to generate a multi-model forecast result set.

6. The method for rapid storm surge forecasting in tidal river sections based on deep learning and AI large model according to claim 3 is characterized in that: Step S42 is further as follows: S421, based on the grid system data and boundary condition data, calculating the geometric characteristics and physical property parameters of the grid unit, and generating grid characteristic data; Based on the grid feature data, the overlapping area and transition zone of the three-layer nested model are constructed to generate a nested grid structure; S422, based on the nested grid structure, constructing a boundary data interpolation model based on feature decomposition to generate boundary interpolation data; The dynamic weight method is used to process the data transmission in the overlapping area and generate the data transmission plan; the boundary interpolation data and the data transmission plan are combined to generate the boundary transmission plan; S423. Based on the boundary transmission scheme, the momentum equation and continuity equation solvers of the hydrodynamic module are constructed to generate a hydrodynamic solution. Based on the boundary transmission scheme, the scouring and silting equation solver of the sediment transport module is constructed to generate a sediment solution. Based on the boundary transmission scheme, the salinity and temperature diffusion-convection equation solvers are constructed to generate a scalar solution. S424, based on the hydrodynamic solution, sediment solution and scalar solution, construct explicit and implicit coupling relationships between processes and generate coupling relationship data; Based on the coupling relationship data, a feedback flux calculation model based on physical mechanisms is constructed to generate process coupling schemes; S425. Based on the process coupling scheme, calculate the stability index and convergence index of the numerical solution and generate stability evaluation data; Based on the stability evaluation data, the relaxation factors and iteration parameters are optimized to generate the coupling control parameters.

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