Yangtze River Delta composite extreme weather ozone pollution early warning model construction method

Abstract

The invention relates to the technical field of environmental monitoring and atmospheric pollution early warning, in particular to a Yangtze River Delta composite extreme weather ozone pollution early warning model construction method, which comprises the following steps of S1, acquiring high-resolution meteorological data and pollutant concentration data of a Yangtze River Delta region to form an original data set; and S2, carrying out missing value interpolation and abnormal value elimination on the meteorological data and the pollutant data, and carrying out grid alignment according to time and space to generate a unified spatial-temporal characteristic matrix. According to the method, by collecting Yangtze Delta high-resolution weather and pollutant data, performing data cleaning, bimodal feature coding and joint representation modeling, predicting the ozone concentration and generating regional early warning through multi-layer Transform self-adaptive attention, the problems that traditional ozone early warning mostly depends on a single-modal prediction model, and the reliability of the ozone early warning is greatly improved are solved. And due to the lack of multi-modal space-time dependent capture, the problem of early warning information lag is caused.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring and air pollution early warning technology, and in particular to a method for constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta. Background Technology

[0002] Ozone, as a typical secondary pollutant, is influenced by both meteorological conditions and the concentration of precursor substances, exhibiting significant spatiotemporal nonuniformity and complex dynamic variations. Traditional ozone early warning systems mostly rely on single-mode prediction models, which suffer from delayed warning information due to the lack of multimodal spatiotemporal dependency capture. Therefore, there is an urgent need to establish an intelligent early warning model that can effectively integrate meteorological and pollutant information and capture the spatiotemporal variation patterns of ozone pollution under complex extreme weather conditions, providing scientific and timely decision support for regional air quality management. Summary of the Invention

[0003] To address the above shortcomings, this invention provides a method for constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta region. This method aims to improve upon the problem that traditional ozone early warning systems mostly rely on single-mode prediction models, which suffer from delayed early warning information due to the lack of multi-mode spatiotemporal dependency capture.

[0004] In a first aspect, the present invention provides the following technical solution: a method for constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta, comprising the following steps:

[0005] S1. Collect high-resolution meteorological data and pollutant concentration data of the Yangtze River Delta region to form the raw dataset;

[0006] S2. Impute missing values ​​and remove outliers from the meteorological and pollutant data, and align them with time and spatial grids to generate a unified spatiotemporal feature matrix;

[0007] S3. Input the spatiotemporal feature matrix into the weather encoder and pollution encoder respectively to generate weather latent representation and pollution latent representation;

[0008] S4. Dynamically adjust the fusion weights of the meteorological and pollution implicit representations based on the mutual information between them to generate a joint representation;

[0009] S5. Input the joint representation into a multi-layer Transformer model, capture the spatiotemporal dependency between weather and pollution through an adaptive attention mechanism, and generate the model output.

[0010] S6. During model training, optimize the encoder and Transformer model parameters, and introduce information bottleneck constraints to compress redundant features and enhance key features.

[0011] S7. Using the trained model, predict the ozone concentration of each spatial grid within the future time window, and generate regional ozone pollution early warning information based on the predicted values, including the time window, spatial grid, and contributions of the main driving meteorological factors.

[0012] By adopting the above technical solution, high-resolution meteorological and pollutant data in the Yangtze River Delta region are collected, and data cleaning, bimodal feature encoding and joint representation modeling are performed. Ozone concentration is predicted and regional early warning is generated through multi-layer Transformer adaptive attention. This improves the problem that traditional ozone early warning mostly relies on single-modal prediction models and lacks multimodal spatiotemporal dependency capture, resulting in delayed early warning information.

[0013] Preferably, the data acquisition includes:

[0014] High-resolution meteorological data on temperature, humidity, wind speed, wind direction, shortwave and longwave radiation intensity, and boundary layer height within the Yangtze River Delta region were acquired, and spatial interpolation was performed based on observation station grids and satellite remote sensing data.

[0015] Data on ozone, nitrogen oxides, and volatile organic compound concentrations are collected and supplemented using mobile monitoring equipment and historical databases.

[0016] The meteorological and pollutant data are uniformly time-stamped and spatially grid-mapped to form the original dataset, which provides a foundation for the subsequent construction of the feature matrix.

[0017] Preferably, the data cleaning and spatiotemporal alignment include:

[0018] Missing values ​​in meteorological and pollution data are processed by linear interpolation and Lagrange interpolation to generate continuous time series;

[0019] By removing outliers through distributional statistical analysis and anomaly detection, and replacing the outliers with the nearest mean, clean spatiotemporal data is obtained.

[0020] Data processed by timestamp and spatial grid alignment is unified into a standardized feature matrix from different sources for input into the encoder module.

[0021] Preferably, the feature encoder includes:

[0022] The standardized spatiotemporal feature matrix is ​​input into the meteorological encoder, which consists of multiple convolutional layers and feedforward fully connected layers, and is used to extract the spatiotemporal features of meteorological modes.

[0023] The standardized spatiotemporal feature matrix is ​​input into the pollution encoder, which has the same structure as the weather encoder, and is used to extract the spatiotemporal features of pollutant concentration.

[0024] By mapping meteorological and pollution latent representations to a shared latent space, a high-dimensional feature vector that can be directly fused across modalities is formed.

[0025] Preferably, the generation of the joint representation includes:

[0026] The mutual information between meteorological and pollution implicit representations is calculated to evaluate the complementarity and redundancy of different modes.

[0027] Based on the mutual information results, a dynamic weighting mechanism is adopted to adjust the fusion weights of meteorological and pollution modes, ensuring that the joint representation contains key information while suppressing redundant features;

[0028] By generating joint representations through weighted concatenation and gating mechanisms, optimized multimodal inputs are provided for subsequent Transformer models.

[0029] Preferably, the Transformer dynamic modeling and adaptive attention include:

[0030] The joint representation is input into the multi-layer Transformer encoder-decoder structure for sequence modeling;

[0031] In each self-attention layer, attention allocation is dynamically adjusted through mutual information feedback, enabling the model to adaptively focus on important features of meteorological and pollution modes at different time steps and spatial grids.

[0032] Contextual information is passed between the encoder and decoder to capture the time delay effect of meteorological conditions on pollutant diffusion, thereby generating a predicted sequence output.

[0033] Preferably, the model training and information bottleneck constraints include:

[0034] During model training, gradient descent is used to optimize the parameters of the weather encoder, pollution encoder, and Transformer model.

[0035] By introducing information bottleneck constraints, feature compression and key feature enhancement are achieved by maximizing the information correlation between the joint representation and the prediction results and minimizing redundant modal information.

[0036] A stable, compressed joint representation is generated, providing high-quality input for future ozone concentration prediction.

[0037] Preferably, the ozone concentration prediction and early warning generation includes:

[0038] The trained model is used to predict the ozone concentration of each spatial grid within a specified future time window.

[0039] Threshold determination is performed on the predicted values ​​to generate regional ozone pollution early warning information;

[0040] The warning information indicates the warning time window and spatial grid range, and the contribution of the main driving meteorological factors is marked by attention weight mapping.

[0041] Preferably, the joint representation generation and adaptive attention mechanism extension includes:

[0042] Based on the existing joint representation generation, new meteorological variables or other pollutant features are input into the meteorological encoder or pollution encoder and mapped to the shared latent space;

[0043] Mutual information is calculated for the extended meteorological and pollution latent representations, and the fusion weights are dynamically adjusted based on the mutual information results to generate an extended joint representation.

[0044] The extended joint representation is input into a multi-layer Transformer encoder-decoder structure, and the attention weight allocation is adjusted in the adaptive attention mechanism so that the model adaptively focuses on the spatiotemporal dependencies between the new features and the original features at different time steps and spatial grids.

[0045] By utilizing the extended joint representation, the spatiotemporal variations of ozone pollution under complex and combined extreme weather conditions are captured, and an optimized prediction sequence output is generated, providing adaptive and scalable support for the generation of early warning information.

[0046] The present invention has the following beneficial effects:

[0047] 1. In this invention, high-resolution meteorological and pollutant data from the Yangtze River Delta region are collected, and data cleaning, bimodal feature encoding and joint representation modeling are performed. Ozone concentration is predicted and regional early warnings are generated through multi-layer Transformer adaptive attention. This improves the problem that traditional ozone early warnings mostly rely on single-modal prediction models and lack multimodal spatiotemporal dependency capture, resulting in delayed early warning information.

[0048] 2. In this invention, a joint representation is generated by introducing a mutual information dynamic weighting mechanism, thereby effectively integrating meteorological and pollution modal features. This improves the problem that traditional ozone prediction methods, which mostly rely on empirical fusion methods, cannot distinguish between key information and redundant features, resulting in insufficient prediction accuracy.

[0049] 3. In this invention, by inputting the joint representation into a multi-layer Transformer model and employing an adaptive attention mechanism, the spatiotemporal dependency between meteorology and pollution is captured, thereby improving the problem that traditional ozone prediction mostly uses fixed spatiotemporal weight models, which cannot adaptively focus on key features, resulting in a lag in the response to pollution diffusion time series.

[0050] 4. In this invention, by introducing information bottleneck constraints to optimize encoder and Transformer parameters during model training, redundant features are compressed and key features are strengthened. This improves the problem that traditional multimodal predictions mostly do not perform feature compression processing, resulting in insufficient model stability and prediction reliability due to redundant information. Attached Figure Description

[0051] Figure 1 This is a flowchart of the method for constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta region, as proposed in this invention.

[0052] Figure 2 This is a structural diagram of the data acquisition and preprocessing module of the method for constructing an ozone pollution early warning model for combined extreme weather in the Yangtze River Delta proposed in this invention.

[0053] Figure 3 This is a flowchart of the data cleaning and spatiotemporal alignment process for the method of constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta proposed in this invention.

[0054] Figure 4 This is a schematic diagram of the feature encoder structure of the method for constructing an ozone pollution early warning model for combined extreme weather in the Yangtze River Delta proposed in this invention.

[0055] Figure 5 This is a schematic diagram illustrating the joint representation generation and dynamic weight adjustment principle of the method for constructing a combined extreme weather ozone pollution early warning model in the Yangtze River Delta region proposed in this invention.

[0056] Figure 6 This is a block diagram of the Transformer dynamic modeling and adaptive attention mechanism for the ozone pollution early warning model construction method for the Yangtze River Delta complex extreme weather proposed in this invention. Detailed Implementation

[0057] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Example 1:

[0059] In the first embodiment of the present invention, the present invention provides a method for constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta region, such as... Figures 1-6 As shown, it includes the following steps:

[0060] S1. Collect high-resolution meteorological data and pollutant concentration data of the Yangtze River Delta region to form the raw dataset;

[0061] Furthermore, data collection includes:

[0062] High-resolution meteorological data on temperature, humidity, wind speed, wind direction, shortwave and longwave radiation intensity, and boundary layer height within the Yangtze River Delta region were acquired, and spatial interpolation was performed based on observation station grids and satellite remote sensing data.

[0063] Data on ozone, nitrogen oxides, and volatile organic compound concentrations are collected and supplemented using mobile monitoring equipment and historical databases.

[0064] Meteorological and pollutant data are uniformly time-stamped and spatially grid-mapped to form the original dataset, which provides the foundation for subsequent feature matrix construction.

[0065] Specifically, this step is used to acquire the basic data required for ozone pollution prediction under complex extreme weather conditions in the Yangtze River Delta region, ensuring that subsequent feature matrix construction and model training can be based on high-resolution, multimodal meteorological and pollutant information. Through a systematic and unified data acquisition method, a raw dataset that can be directly used as encoder input is formed.

[0066] Meteorological data acquisition involved obtaining high-resolution meteorological data for the region, including temperature (T), humidity (H), wind speed (V), wind direction (θ), shortwave radiation (S), longwave radiation (L), and boundary layer height (BLH). Data sources included ground-based observation grids and satellite remote sensing data. For data from different spatial sources, spatial interpolation methods were used to map discrete data to a unified spatial grid, forming a meteorological feature matrix X. met The formula is as follows: Where (x) i ,y j ) represents the spatial grid position, t k d represents a point in time. m w is the value corresponding to the m-th observation data source. m For spatial interpolation weights, satisfying

[0067] Pollutant data collection included concentrations of ozone (O3), nitrogen oxides (NO and NO2), and volatile organic compounds (VOCs). Data sources included fixed monitoring stations, mobile monitoring equipment, and historical databases. Missing or blank spatial areas were filled using spatial interpolation and neighbor-point weighted supplementation methods to form a pollutant characteristic matrix X. poll : Where N ij Indicates position (x) i ,y j The set of available monitoring points in the surrounding area, c n (t k ) represents the nth monitoring point at time t k concentration value, wn For spatial weights.

[0068] Aligning time and space, the meteorological matrix X met With pollutant matrix X poll According to the unified timestamp t k and spatial grid (x i ,y j Alignment is performed to obtain the final original dataset D. raw :

[0069] D raw ={X met (x i ,y j ,t k ),X poll (x i ,y j ,t k )∣i=1..I,j=1..J,k=1..K};

[0070] This dataset is used directly as input to the subsequent bimodal encoder.

[0071] Input / output flow: Inputs include: ground meteorological observation data; satellite remote sensing meteorological data; fixed and mobile pollution monitoring data; and historical pollution database.

[0072] Processing: Spatial interpolation (meteorology and pollutants); weighted fusion; unified mapping of temporal and spatial grids.

[0073] Output: Original dataset D raw Including the aligned meteorological matrix X met and pollutant matrix X poll This is used for data cleaning and feature matrix construction in step S2.

[0074] It provides a complete, high-resolution, multimodal data foundation for subsequent feature matrix construction; ensures spatial consistency and temporal continuity of different data sources, provides directly usable input for joint representation generation and Transformer spatiotemporal modeling; and establishes reliable data support for the model to capture ozone concentration changes under complex extreme weather conditions.

[0075] S2. Impute missing values ​​and remove outliers from meteorological and pollutant data, and align them with time and spatial grids to generate a unified spatiotemporal feature matrix;

[0076] Furthermore, data cleaning and spatiotemporal alignment include:

[0077] Missing values ​​in meteorological and pollution data are processed by linear interpolation and Lagrange interpolation to generate continuous time series;

[0078] By removing outliers through distributional statistical analysis and anomaly detection, and replacing the outliers with the nearest mean, clean spatiotemporal data is obtained.

[0079] Data processed by timestamp and spatial grid alignment is unified into a standardized feature matrix from different sources for input into the encoder module.

[0080] Specifically, this step is used to improve the quality and standardize the raw dataset obtained in step S1, ensuring that the meteorological and pollutant data are continuous and complete in time and space, and that outliers are removed, thereby forming a unified spatiotemporal feature matrix that can be directly input into the bimodal encoder, providing a reliable foundation for joint representation generation and subsequent model training.

[0081] Missing value imputation for the original meteorological matrix X met and pollutant matrix X poll For missing data points, a continuous time series is generated using linear interpolation and Lagrange interpolation methods. Let the time series be {x(t1), x(t2), ..., x(t...}}. k If x(t) m (Missing information) The linear interpolation formula is:

[0082]

[0083] Lagrange interpolation is used for higher-order continuity, and the formula is: Where n is the number of selected interpolation points, ensuring that the interpolation results are continuous over time.

[0084] Outlier removal and replacement utilizes distributional statistical analysis to identify outliers in meteorological and pollutant data. Let the data sequence be {x1, x2, ..., x...}. N The condition for determining an anomaly is: |x i -μ|>k·σ; where μ is the sequence mean, σ is the standard deviation, and k is a set threshold. For x that is judged as abnormal... i Replace with the mean of the nearest time point: Generate a clean and continuous spatiotemporal data matrix X clean .

[0085] Aligning the temporal and spatial grids, the cleaned meteorological matrix and pollutant matrix According to the unified timestamp t k and spatial grid (x i ,y j Alignment yields the normalized feature matrix X. std :

[0086]

[0087] This matrix serves as the input to the dual-modal encoder, ensuring that meteorological and pollutant characteristics are synchronized in space and time.

[0088] Input / output flow: Input: Original meteorological matrix X met Original pollutant matrix X poll .

[0089] Processing: Linear interpolation and Lagrange interpolation are used to generate continuous time series; outlier detection and replacement with nearest-neighbor means are performed; timestamps and spatial grids are aligned uniformly.

[0090] Output: Standardized unified spatiotemporal feature matrix X std It can directly input weather encoders and pollution encoders.

[0091] To ensure data continuity and integrity, the impact of missing values ​​on model training is eliminated; outliers are removed to reduce noise interference in the generation of joint representations; a unified and standardized feature matrix is ​​formed to achieve synchronization of meteorological and pollutant multimodal data, providing high-quality input for subsequent joint representation and Transformer modeling; and a reliable foundation is established for capturing the spatiotemporal changes of ozone concentration under complex extreme weather conditions.

[0092] S3. Input the spatiotemporal feature matrix into the weather encoder and pollution encoder respectively to generate the weather latent representation and the pollution latent representation;

[0093] Furthermore, the feature encoder includes:

[0094] The standardized spatiotemporal feature matrix is ​​input into the meteorological encoder, which consists of multiple convolutional layers and feedforward fully connected layers, and is used to extract the spatiotemporal features of meteorological modes.

[0095] The standardized spatiotemporal feature matrix is ​​input into the pollution encoder, which has the same structure as the weather encoder, and is used to extract the spatiotemporal features of pollutant concentration.

[0096] By mapping meteorological and pollution latent representations to a shared latent space, a high-dimensional feature vector that can be directly fused across modalities is formed.

[0097] Specifically, this step is used to extract multimodal features from the standardized spatiotemporal feature matrix generated by S2, realize the generation of implicit representations of meteorological and pollutant information, and map them to a shared latent space, providing directly fused high-dimensional features for cross-modal joint representation generation. This step ensures that the structural information of spatiotemporal features and the complementarity between modes are fully expressed, promoting the spatiotemporal dependency modeling of subsequent Transformer models.

[0098] Meteorological encoder feature extraction, which involves standardizing the spatiotemporal feature matrix X std Input weather encoder εmet This encoder consists of multiple convolutional layers and feedforward fully connected layers, used to extract the latent features of meteorological modes in space and time. The convolution processing formula is: in K (l) For the l-th convolutional kernel, b (l) For bias, f conv The activation function is denoted by *, which represents the convolution operation. The convolution output is mapped to the meteorological hidden representation Z through a feedforward fully connected layer. met : Where L is the total number of convolutional layers, f fc This is the activation function for the fully connected layer.

[0099] Contamination encoder feature extraction, which involves standardizing the spatiotemporal feature matrix X std Input contamination encoder ε poll The encoder structure is consistent with that of the weather encoder, and it is used to extract the latent features of pollutant concentration modes in time and space to obtain the pollution latent representation Z. poll : This step ensures that different modal features are represented in the same dimensional space, while preserving spatiotemporal structural information.

[0100] Shared latent space mapping, the meteorological latent representation Z met And pollution hidden representation Z poll Mapped to the unified shared latent space Z shared To enable cross-modal fusion: Where φ map For shared space mapping functions, This represents vector concatenation or gated weighting operations. The mapped high-dimensional vectors can be directly used to generate joint representations, ensuring the complementarity of multimodal information and the suppression of redundant features.

[0101] Input / output flow: Input: Standardized unified spatiotemporal feature matrix X std Processing: Feature extraction via convolutional and fully connected layers for the weather encoder; feature extraction via convolutional and fully connected layers for the pollution encoder; latent representation mapping to a shared latent space and fusion. Output: Weather latent representation Z. met ; Contamination hidden representation Z poll Cross-modal shared implicit representation Z shared , used for joint representation generation.

[0102] Extracting high-dimensional spatiotemporal latent features enables structured representation of meteorological and pollutant modes; achieving cross-modal information fusion in a shared latent space ensures key features are preserved and redundancy is suppressed; providing high-quality features that can be directly input for joint representation generation promotes the Transformer model to accurately capture spatiotemporal dependencies; ensuring that multimodal spatiotemporal information is scalable and adaptable to meet the ozone pollution prediction needs under complex and extreme weather conditions.

[0103] S4. Dynamically adjust the fusion weights of the meteorological and pollution implicit representations based on the mutual information between them to generate a joint representation;

[0104] Furthermore, generating the joint representation includes:

[0105] The mutual information between meteorological and pollution implicit representations is calculated to evaluate the complementarity and redundancy of different modes.

[0106] Based on the mutual information results, a dynamic weighting mechanism is adopted to adjust the fusion weights of meteorological and pollution modes, ensuring that the joint representation contains key information while suppressing redundant features;

[0107] By generating joint representations through weighted concatenation and gating mechanisms, optimized multimodal inputs are provided for subsequent Transformer models.

[0108] Specifically, this step involves cross-modal fusion of the meteorological and pollution latent representations generated in step S3. Through dynamic mutual information weight adjustment, a joint representation is generated. This joint representation retains key information from both modes, suppresses redundant features, and provides optimized multimodal input for the Transformer model, thereby improving the accuracy and adaptability of ozone concentration prediction for complex extreme weather events.

[0109] Mutual information calculation, let the meteorological implicit representation be Z. met The pollution is represented by Z. poll The complementarity and redundancy between the two are assessed using mutual information metrics:

[0110]

[0111] Where p(z) m ,z p p(z) represents the joint distribution probability of the meteorological latent representation and the pollution latent representation. m ) and p(z p The two modalities are respectively distributed at their respective edges. Higher mutual information indicates more redundant features and less complementary information between the two modalities.

[0112] A dynamic weighting mechanism is used to determine the meteorological mode weights w based on the mutual information calculation results. met With pollution mode weights w poll The weights satisfy the normalization condition:

[0113]

[0114] By dynamically adjusting the weights, the contribution of redundant information is reduced, and the sensitivity of the joint representation to key features is improved.

[0115] Joint representation generation combines the weighted implicit representations into a joint representation Z through concatenation and gating mechanisms. joint :

[0116]

[0117] in This represents vector concatenation, where g(·) represents the gating function used to dynamically filter and enhance key information. The joint representation Z... joint As input to the Transformer model, it ensures that multimodal features are fully fused and redundant features are suppressed.

[0118] Input / output flow: Input: Weather implicit representation Z met ; Contamination hidden representation Z poll Processing: Calculate the mutual information I(Z) between the two modes. met Z poll Weights w are dynamically generated based on mutual information results. met With w poll Weighted concatenation and application of a gating function generate a joint representation Z. joint Output: Joint representation Z joint It can be directly used as input for a Transformer model.

[0119] It retains key information on meteorological and pollutant modes, achieving redundancy feature suppression; a dynamic weighting mechanism ensures the joint representation is highly adaptable under different temporal and spatial conditions; it provides optimized input for multimodal Transformer modeling, improving the model's ability to capture ozone pollution under complex extreme weather conditions; and it supports future expansion to include new meteorological variables or other pollutant inputs, achieving scalability of the joint representation.

[0120] S5. Input the joint representation into the multi-layer Transformer model, capture the spatiotemporal dependency between weather and pollution through an adaptive attention mechanism, and generate the model output.

[0121] Furthermore, Transformer dynamic modeling and adaptive attention include:

[0122] The joint representation is input into the multi-layer Transformer encoder-decoder structure for sequence modeling;

[0123] In each self-attention layer, attention allocation is dynamically adjusted through mutual information feedback, enabling the model to adaptively focus on important features of meteorological and pollution modes at different time steps and spatial grids.

[0124] Contextual information is passed between the encoder and decoder to capture the time delay effect of meteorological conditions on pollutant diffusion, thereby generating a predicted sequence output.

[0125] Specifically, this step is used to utilize the joint representation Z generated by S4. joint Multimodal spatiotemporal sequence modeling is performed, and an adaptive attention mechanism is used to capture the temporal and spatial dependencies between meteorological and pollution modes, generating sequence outputs that can be used for ozone concentration prediction. This step ensures that the model can dynamically focus on key features at different time steps and spatial grids, while capturing the time delay effect of meteorological conditions on pollutant diffusion, providing a foundation for high-precision early warning.

[0126] Transformer encoder-decoder input will jointly represent Z joint Input a multi-layer Transformer encoder-decoder structure. Let the joint representation sequence length be T, the number of spatial grids be N, and the feature vector at each time step be... Z seq =z1,z2,...,z T ,z t ∈Z joint The encoder performs feature mapping on the entire sequence through a self-attention mechanism to generate a context representation H. enc :

[0127] H enc =MultiHead(Z seq Z seq Z seq );

[0128] MultiHead(·) represents the multi-head self-attention function, which is used to capture global dependencies within a sequence.

[0129] Adaptive attention weight adjustment is implemented, where attention allocation in each self-attention layer is adjusted based on mutual information feedback between meteorological and pollution modes. Let the attention matrix be... Modulate attention weights using mutual information: Where ⊙ represents element-wise multiplication, and M is used to reduce the contribution of redundant information to attention and increase the weight of key modal features.

[0130] Encoder-decoder context passing, the decoder receives the encoder output H enc Combined with the predictions from previous time steps, sequence decoding is performed to generate the predicted feature sequence Y. pred Ypred =Decoder(H enc ,Y prev The decoder captures the time delay effect through self-attention and encoder-decoder attention, enabling the model to reflect the lag effect of meteorological conditions on pollutant diffusion.

[0131] Input / output flow: Input: Joint representation sequence Z joint Processing: A multi-layer Transformer encoder extracts spatiotemporal features; a self-attention layer performs adaptive weight allocation through mutual information modulation; the encoder output and the predicted sequence from the previous time step are input into the decoder; the decoder generates the predicted feature sequence for the future time step. Output: Predicted sequence Y pred It contains feature representations of future time windows for each spatial grid, used for S7 prediction and early warning generation.

[0132] It captures the spatiotemporal dependencies between meteorological and pollution modes, enabling multimodal dynamic modeling; the adaptive attention mechanism can automatically reduce the contribution of redundant features and improve the model's attention to key features; contextual information transmission ensures that the model captures the time delay effect of pollutant diffusion; and it provides accurate multimodal sequence input for subsequent ozone concentration prediction, enhancing prediction reliability and early warning availability.

[0133] S6. During model training, optimize the encoder and Transformer model parameters, and introduce information bottleneck constraints to compress redundant features and enhance key features.

[0134] Furthermore, model training and information bottleneck constraints include:

[0135] During model training, gradient descent is used to optimize the parameters of the weather encoder, pollution encoder, and Transformer model.

[0136] By introducing information bottleneck constraints, feature compression and key feature enhancement are achieved by maximizing the information correlation between the joint representation and the prediction results and minimizing redundant modal information.

[0137] A stable, compressed joint representation is generated, providing high-quality input for future ozone concentration prediction.

[0138] Specifically, this step trains the multimodal joint representation model constructed in steps S3 to S5. Gradient optimization is used to jointly adjust the encoder and Transformer model parameters, while introducing information bottleneck constraints to compress redundant features and strengthen key features, thereby generating a stable, high-quality joint representation suitable for prediction. This step ensures that the model retains key information while suppressing redundant features, providing reliable input for ozone concentration prediction in step S7.

[0139] Gradient descent optimization, let the joint representation be Z joint The model predicts the output as Y. pred The true value is Y true By defining the loss function For encoder parameters θ enc and Transformer parameter θ trans Optimize: Where η is the learning rate, the error is minimized by iteratively updating the model parameters.

[0140] Information bottleneck constraint, introducing an information bottleneck constraint function.

[0141]

[0142] Where I(Z) joint ;Y true I(Z) represents the mutual information between the joint representation and the prediction result, ensuring that key information is preserved; joint Z input ) represents the mutual information between the joint representation and the original input, used to suppress redundant features; β is the adjustment coefficient. The optimization objective is to maximize the prediction relevance and minimize the redundant information, achieving joint representation compression and feature enhancement.

[0143] The joint training process combines the loss function with information bottleneck constraints: Optimization via gradient descent To obtain a stable joint representation Where λ is the information bottleneck constraint weight.

[0144] Input / output flow, Input: Joint representation Z joint True ozone concentration Y true Processing: Gradient descent optimizes the encoder and Transformer model parameters; calculates information bottleneck constraints, compresses redundant features, and enhances key features; combines the prediction loss and information bottleneck constraints to generate the total loss function, and iteratively trains the model. Output: Stable, compressed joint representation. It can be directly used for predicting ozone concentrations in future time windows.

[0145] The model jointly optimizes the parameters of the meteorological encoder, pollution encoder, and Transformer model to improve the model's ability to extract key spatiotemporal features; information bottleneck constraints reduce redundant modal information and ensure that the joint representation is compact and stable; it provides high-quality multimodal feature inputs to provide a reliable data foundation for future ozone concentration prediction and regional early warning generation; it ensures that the model has stable prediction capabilities at different time steps and spatial grids, while enhancing its sensitivity to features under extreme complex weather conditions.

[0146] S7. Use the trained model to predict the ozone concentration of each spatial grid within the future time window, and generate regional ozone pollution early warning information based on the predicted values, including the time window, spatial grid and the contribution of the main driving meteorological factors.

[0147] Furthermore, ozone concentration prediction and early warning generation includes:

[0148] The trained model is used to predict the ozone concentration of each spatial grid within a specified future time window.

[0149] Threshold determination is performed on the predicted values ​​to generate regional ozone pollution early warning information;

[0150] The warning information indicates the warning time window and spatial grid range, and the contribution of the main driving meteorological factors is marked by attention weight mapping.

[0151] Specifically, this step is used to train a stable joint representation using S6. The ozone concentration of each spatial grid within a future time window is predicted, and regional ozone pollution early warning information is generated based on the prediction results. This step ensures that the prediction results fully cover the warning area in both time and space, and at the same time, the main driving meteorological factors are identified through attention weighting, providing an actionable decision-making basis for regional environmental management.

[0152] Future time window prediction, assuming the future time window length is T. f The number of spatial grids is N, and the joint representation input is The predicted output is Where f θ This represents the completed joint model, including the encoder and Transformer modules. Prediction results. The ozone concentration corresponding to time step t and spatial grid n.

[0153] Threshold determination and early warning generation: The predicted value is used to determine the regional threshold, assuming the threshold is...

[0154]

[0155] Where P t,n =1 indicates that an ozone pollution warning has occurred at this time step and in the grid. This is achieved through the matrix. Generate regional early warning information.

[0156] The main driving meteorological factor contribution annotation utilizes the attention weight A in step S5. adapt Mapping to each spatial grid and time step, calculate the contribution of each meteorological factor to the prediction results:

[0157]

[0158] Where C f,t,n Z represents the contribution value of the f-th meteorological factor at time step t and grid n, where H is the number of attention heads and Z is the number of attention heads. met,h,f Let h be the attention value of the h-th meteorological factor to the f-th meteorological factor.

[0159] Early warning information is output, forming matrix P and contribution matrix C, and the regional early warning information is output, including: time window T. j Spatial grid range N; Contribution of main driving meteorological factors C.

[0160] Input / output flow, Input: Stable joint representation Region threshold Processing: Input the joint representation into the trained model to generate predictions for future time steps. A binary early warning matrix P is generated by thresholding the predicted values; the contribution matrix C of the main driving meteorological factors is calculated by combining attention weight mapping. Output: Regional ozone pollution early warning information, including time window, spatial grid range, and contribution of the main driving meteorological factors.

[0161] Accurate prediction of ozone concentration in each spatial grid within a future time window ensures the integrity of early warning coverage; threshold determination enables automated generation of regional early warnings, improving response efficiency; attention mapping labels the contribution of meteorological factors, providing interpretability and offering a scientific basis for environmental management and decision-making; combined with multimodal joint representation, the model's adaptability and scalability to ozone pollution under complex extreme weather conditions are enhanced.

[0162] Joint representation generation and adaptive attention mechanism extensions include:

[0163] Based on the existing joint representation generation, new meteorological variables or other pollutant features are input into the meteorological encoder or pollution encoder and mapped to the shared latent space;

[0164] Mutual information is calculated for the extended meteorological and pollution latent representations, and the fusion weights are dynamically adjusted based on the mutual information results to generate an extended joint representation.

[0165] The joint representation input to the multi-layer Transformer encoder-decoder structure will be extended, and the attention weight allocation in the adaptive attention mechanism will be adjusted so that the model can adaptively focus on the spatiotemporal dependencies between the new features and the original features at different time steps and spatial grids.

[0166] By utilizing the extended joint representation, the spatiotemporal variations of ozone pollution under complex and combined extreme weather conditions are captured, and an optimized prediction sequence output is generated, providing adaptive and scalable support for the generation of early warning information.

[0167] Specifically, this step introduces new meteorological variables and other pollutant features based on the original joint representation generation, realizing the expansion of the joint representation and dynamic adjustment of the adaptive attention mechanism. By performing cross-modal mutual information calculation and dynamic weighting on the expanded features, the model can capture the spatiotemporal dependence of ozone pollution under complex and combined extreme weather conditions, providing adaptive and scalable support for the predicted sequence output, improving the model's sensitivity and scalability to new features, while ensuring the high quality and reliability of the prediction results.

[0168] Expand the joint representation of inputs by adding a meteorological variable X. met,new and characteristics of newly added pollutants X poll,new Inputting the weather encoder and pollution encoder respectively, and mapping them to the shared latent space, an extended weather latent representation is generated. and extended pollution hidden representation Where f enc,met and f enc,poll For the completed training of weather and pollution encoders.

[0169] Mutual information calculation and dynamic weighting: Mutual information calculation is performed on the extended implicit representation.

[0170]

[0171] Based on the mutual information results, the dynamic weighting coefficients α and β are calculated for joint representation generation:

[0172]

[0173] in To expand the joint representation, we ensure that the newly added feature information is included and suppress redundant features.

[0174] The adaptive attention mechanism adjusts the attention allocation at each time step and on the spatial grid by extending the joint representation of the input multi-layer Transformer encoder-decoder.

[0175]

[0176] Where Q and K are the query and key vectors, respectively, and d k Given the vector dimension, the function g(·) is used to dynamically adjust the attention weights based on mutual information, enabling the model to adaptively focus on the importance of extended features and original features at different time steps and spatial grids.

[0177] Predicted sequence generation involves passing contextual information between the encoder and decoder to generate an extended joint representation of the predicted sequence output. It can be used to generate regional ozone pollution early warning information. The output includes time step, spatial grid, and contribution information of each feature.

[0178] Input / output flow: Input: Original joint representation Z joint New meteorological variable X met,new New pollutant characteristics X poll,new Processing: An extended encoder generates an extended latent representation; mutual information computation and dynamic weighting generate an extended joint representation; a multi-layer Transformer encoder-decoder performs adaptive attention modeling; and a predicted sequence is generated as output. Output: Extended joint representation. Optimized prediction sequence Contribution mapping matrix to newly added features.

[0179] The model introduces new meteorological and pollutant features to achieve scalability and flexibility in joint representation; dynamic weighting of mutual information ensures the integrity of joint representation information and minimizes redundant features; an adaptive attention mechanism enhances the model's sensitivity to key features at different time steps and spatial grids; it captures the spatiotemporal changes of ozone pollution under complex and extreme weather conditions, providing high-quality predictive input for early warning generation; and it ensures that the model can still generate stable and reliable prediction sequences under extended feature environments, improving the model's transferability and application value.

[0180] Example 2:

[0181] In the Yangtze River Delta region, dense urban areas, concentrated industrial emissions, and active vehicle exhaust emissions, coupled with the influence of the land-sea confluence climate, lead to frequent extreme weather events such as high temperatures, strong sunlight, and stagnant atmospheres. This causes ozone pollution to accumulate rapidly in urban and suburban areas, forming high-concentration pollution zones. Environmental management departments need to predict ozone concentrations in real time and issue early warnings to guide traffic control and industrial emission restrictions. However, existing methods face several technical challenges: First, meteorological and pollutant data come from diverse sources with inconsistent time steps and spatial resolutions, leading to information redundancy and difficulty in reflecting the driving role of meteorology in ozone formation. Second, ozone concentrations change rapidly under extreme weather conditions, making it difficult for traditional models to capture time delay effects and local spatial diffusion characteristics. Furthermore, with the upgrading of observation indicators and monitoring technologies, newly added meteorological factors or pollutant characteristics cannot be directly applied to existing models. Simultaneously, high-dimensional spatiotemporal data contain a large amount of redundant information, lacking effective methods to identify key driving factors, thus limiting prediction accuracy and early warning interpretability. Finally, existing methods mostly use regional average predictions, failing to generate fine-grained grid-level early warning information and unable to clearly identify the contribution of major driving meteorological factors to pollution. To solve the above problems, this invention provides an image recognition system, the structure of which is as follows: Figure 1 As shown. The specific implementation process of this system is as follows:

[0182] Specifically, high-resolution meteorological data and pollutant concentration data from the Yangtze River Delta region were collected to form the raw dataset. Meteorological data included temperature, humidity, wind speed, wind direction, shortwave and longwave radiation intensity, and boundary layer height, spatially interpolated using observation station grids and satellite remote sensing data. Pollutant data included ozone, nitrogen oxides, and volatile organic compound concentrations, supplemented by mobile monitoring equipment and historical databases. The raw dataset, with standardized timestamps and spatial grid mapping, was output to provide a foundation for subsequent feature matrix construction.

[0183] Missing values ​​were imputed and outliers were removed from meteorological and pollutant data to generate a unified spatiotemporal feature matrix. Missing values ​​were processed using linear and Lagrange interpolation, while outliers were identified through distributional statistical analysis and replaced with the nearest neighbor mean. The processed data were aligned according to timestamps and spatial grids, unifying data from different sources into a standardized feature matrix, providing reliable input for the encoder to extract latent representations.

[0184] A unified spatiotemporal feature matrix is ​​input into the meteorological encoder and the pollution encoder, respectively, to extract meteorological and pollution latent representations. The encoder consists of multiple convolutional layers and feedforward fully connected layers for extracting spatiotemporal features. The two latent representations are mapped to a shared latent space to form a high-dimensional feature vector that can be directly fused across modalities, providing a foundation for joint representation generation.

[0185] The mutual information between the meteorological and pollution latent representations is calculated, and the fusion weights are dynamically adjusted based on the mutual information results to generate a joint representation. Through weighted concatenation and gating mechanisms, key information is preserved and redundant features are suppressed, providing optimized input for the Transformer model.

[0186] The joint representation is input into a multi-layer Transformer encoder-decoder structure to capture the spatiotemporal dependency between meteorological conditions and pollutants, generating a predicted sequence output. Within each self-attention layer, attention weights are dynamically adjusted through mutual information feedback, enabling the model to adaptively focus on key features at different time steps and spatial grids. Contextual information is passed between the encoder and decoder to capture the time-delay effect of meteorological conditions on pollutant dispersion.

[0187] During training, gradient descent is used to optimize the parameters of the weather encoder, pollution encoder, and Transformer model. Information bottleneck constraints are introduced to compress redundant features and enhance key features. This generates a stable, compressed joint representation, providing high-quality input for future ozone concentration prediction.

[0188] The trained model is used to predict ozone concentrations in each spatial grid within a future time window, and regional ozone pollution early warning information is generated based on the predictions, including the time window, spatial grid, and contributions of major driving meteorological factors. Attention weight mapping is used to label the major driving factors, providing refined early warning information for management departments.

[0189] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for constructing an early warning model for ozone pollution during complex extreme weather in the Yangtze River Delta region, characterized in that, Includes the following steps: S1. Collect high-resolution meteorological data and pollutant concentration data of the Yangtze River Delta region to form the raw dataset; S2. Impute missing values ​​and remove outliers from the meteorological and pollutant data, and align them with time and spatial grids to generate a unified spatiotemporal feature matrix; S3. Input the spatiotemporal feature matrix into the weather encoder and pollution encoder respectively to generate weather latent representation and pollution latent representation; S4. Dynamically adjust the fusion weights of the meteorological and pollution implicit representations based on the mutual information between them to generate a joint representation; S5. Input the joint representation into a multi-layer Transformer model, capture the spatiotemporal dependency between weather and pollution through an adaptive attention mechanism, and generate the model output. S6. During model training, optimize the encoder and Transformer model parameters, and introduce information bottleneck constraints to compress redundant features and enhance key features. S7. Using the trained model, predict the ozone concentration of each spatial grid within the future time window, and generate regional ozone pollution early warning information based on the predicted values, including the time window, spatial grid, and contributions of the main driving meteorological factors.

2. The method for constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta as described in claim 1, characterized in that, The data collection includes: High-resolution meteorological data on temperature, humidity, wind speed, wind direction, shortwave and longwave radiation intensity, and boundary layer height within the Yangtze River Delta region were acquired, and spatial interpolation was performed based on observation station grids and satellite remote sensing data. Data on ozone, nitrogen oxides, and volatile organic compound concentrations are collected and supplemented using mobile monitoring equipment and historical databases. The meteorological and pollutant data are uniformly time-stamped and spatially grid-mapped to form the original dataset, which provides a foundation for the subsequent construction of the feature matrix.

3. The method for constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta as described in claim 1, characterized in that, The data cleaning and spatiotemporal alignment include: Missing values ​​in meteorological and pollution data are processed by linear interpolation and Lagrange interpolation to generate continuous time series; By removing outliers through distributional statistical analysis and anomaly detection, and replacing the outliers with the nearest mean, clean spatiotemporal data is obtained. Data processed by timestamp and spatial grid alignment is unified into a standardized feature matrix from different sources for input into the encoder module.

4. The method for constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta according to claim 1, characterized in that, The feature encoder includes: The standardized spatiotemporal feature matrix is ​​input into the meteorological encoder, which consists of multiple convolutional layers and feedforward fully connected layers, and is used to extract the spatiotemporal features of meteorological modes. The standardized spatiotemporal feature matrix is ​​input into the pollution encoder, which has the same structure as the weather encoder, and is used to extract the spatiotemporal features of pollutant concentration. By mapping meteorological and pollution latent representations to a shared latent space, a high-dimensional feature vector that can be directly fused across modalities is formed.

5. The method for constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta according to claim 1, characterized in that, The generated joint representation includes: The mutual information between meteorological and pollution implicit representations is calculated to evaluate the complementarity and redundancy of different modes. Based on the mutual information results, a dynamic weighting mechanism is adopted to adjust the fusion weights of meteorological and pollution modes, ensuring that the joint representation contains key information while suppressing redundant features; By generating joint representations through weighted concatenation and gating mechanisms, optimized multimodal inputs are provided for subsequent Transformer models.

6. The method for constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta according to claim 1, characterized in that, The Transformer dynamic modeling and adaptive attention include: The joint representation is input into the multi-layer Transformer encoder-decoder structure for sequence modeling; In each self-attention layer, attention allocation is dynamically adjusted through mutual information feedback, enabling the model to adaptively focus on important features of meteorological and pollution modes at different time steps and spatial grids. Contextual information is passed between the encoder and decoder to capture the time delay effect of meteorological conditions on pollutant diffusion, thereby generating a predicted sequence output.

7. The method for constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta according to claim 1, characterized in that, The model training and information bottleneck constraints include: During model training, gradient descent is used to optimize the parameters of the weather encoder, pollution encoder, and Transformer model. By introducing information bottleneck constraints, feature compression and key feature enhancement are achieved by maximizing the information correlation between the joint representation and the prediction results and minimizing redundant modal information. A stable, compressed joint representation is generated, providing high-quality input for future ozone concentration prediction.

8. The method for constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta according to claim 1, characterized in that, The ozone concentration prediction and early warning generation includes: The trained model is used to predict the ozone concentration of each spatial grid within a specified future time window. Threshold determination is performed on the predicted values ​​to generate regional ozone pollution early warning information; The warning information indicates the warning time window and spatial grid range, and the contribution of the main driving meteorological factors is marked by attention weight mapping.

9. The method for constructing an ozone pollution early warning model for complex extreme weather in the Yangtze River Delta according to claim 1, characterized in that, The joint representation generation and adaptive attention mechanism extension includes: Based on the existing joint representation generation, new meteorological variables or other pollutant features are input into the meteorological encoder or pollution encoder and mapped to the shared latent space; Mutual information is calculated for the extended meteorological and pollution latent representations, and the fusion weights are dynamically adjusted based on the mutual information results to generate an extended joint representation. The extended joint representation is input into a multi-layer Transformer encoder-decoder structure, and the attention weight allocation is adjusted in the adaptive attention mechanism so that the model adaptively focuses on the spatiotemporal dependencies between the new features and the original features at different time steps and spatial grids. By utilizing the extended joint representation, the spatiotemporal variations of ozone pollution under complex and combined extreme weather conditions are captured, and an optimized prediction sequence output is generated, providing adaptive and scalable support for the generation of early warning information.

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