Multi-scale atmospheric pollutant and greenhouse gas emission data assimilation and fusion modeling method

Abstract

The invention discloses a multi-scale atmospheric pollutant and greenhouse gas emission data assimilation and fusion modeling method, and belongs to the field of atmospheric pollution monitoring. According to the method, the accuracy and physical interpretability of emission estimation are improved through adaptive fusion and high-precision complementation of multi-source observation data in combination with collaborative assimilation of a physical model and a deep intelligent model; and by adopting multi-scale feature dynamic fusion and a hierarchical graph neural network, high-resolution and refined emission spatial-temporal distribution modeling is realized. And meanwhile, Bayesian optimization and transfer learning are adopted to realize continuous adaptive optimization and knowledge generalization of model parameters. The method effectively solves the problems of heterogeneity, data missing, model migration, insufficient generalization ability and the like of multi-source data, and can be widely applied to the fields of atmospheric environment management, carbon emission monitoring and the like.

Description

Technical Field

[0001] This invention belongs to the field of air pollution monitoring, and more specifically, it relates to a method for assimilation and fusion modeling of air pollutant and greenhouse gas emission data at multiple scales. Background Technology

[0002] In recent years, with the accelerating pace of urbanization and the increasing complexity of industrial structures, the emission of air pollutants and greenhouse gases has posed serious threats to the ecological environment and human health. Accurately and dynamically monitoring, assessing, and predicting the spatiotemporal distribution of air pollutants (such as PM2.5, NOx, SO2, and volatile organic compounds) and major greenhouse gases (such as CO2 and CH4) has become a significant challenge in environmental science, public policy, and urban governance. Traditional methods for establishing and extrapolating atmospheric emission data mainly rely on ground-based monitoring stations, emission inventory statistics, and physical model extrapolation. These methods have significant limitations in terms of spatial resolution, data completeness, and real-time performance. The number of ground-based monitoring stations is limited and mostly concentrated in urban core areas, making it difficult to cover key emission source areas such as rural areas, remote areas, or industrial clusters. Furthermore, some automated and manual emission inventory statistics are limited by issues such as statistical frequency, reporting delays, and untimely data updates, resulting in emission inventory data that fails to accurately reflect the actual emission situation.

[0003] Current mainstream emission modeling methods are mostly based on physical or statistical models. Physical models (such as atmospheric diffusion models and source inversion models) can quantitatively describe the transport and transformation processes of emission sources and atmospheric pollutants. However, when there are significant uncertainties in parameter settings, boundary conditions, and input data, their modeling accuracy and generalization ability are limited, and model calibration and parameter tuning rely heavily on human experience. Statistical modeling methods focus on directly fitting the spatiotemporal distribution characteristics of pollutants using observational data, lacking effective modeling of physical mechanisms, and often suffer from problems such as "black box" nature and poor generalization. In recent years, with the advancement of artificial intelligence technology, especially deep learning and graph neural networks, intelligent emission modeling methods have gradually emerged. These methods can automatically extract complex high-dimensional spatiotemporal features and achieve automatic learning and prediction of pollutant emission patterns. However, limited by factors such as the size and distribution of training samples, data noise, and missing data, the models are prone to bottlenecks such as overfitting, poor transferability, and unstable performance in new regions.

[0004] Furthermore, actual air pollution and greenhouse gas emissions exhibit significant multi-scale characteristics: they show periodic variations on time scales such as daily, weekly, and monthly, as well as spatial heterogeneity across urban, suburban, local, and even micro-regional areas. How to fully integrate physical mechanisms and observational data characteristics at different scales in modeling, and improve the model's responsiveness to complex spatiotemporal heterogeneity and sudden extreme events, is a key challenge that urgently needs to be overcome in the field of emissions estimation. At the same time, facing the urgent needs of regional environmental governance, emissions modeling also needs to possess good model generalization and adaptive evolution capabilities, enabling rapid migration and application to new spatial regions or new observational data sources, continuously absorbing new data, and continuously improving model accuracy to support scientific decision-making and governance.

[0005] In summary, existing technologies face the following core challenges in the field of atmospheric pollutant and greenhouse gas emission modeling: (1) The heterogeneity of multi-source observation data leads to difficulties in information fusion, data gaps, and noise, making traditional completion and correction methods unable to efficiently handle complex scenarios; (2) Physical models and data-driven models each have their limitations, making it difficult to fully leverage the synergistic effect of multi-source information and physical mechanisms, resulting in insufficient modeling accuracy and spatial timeliness; (3) Current models have limited ability to fuse multi-scale spatial-temporal features, weak generalization and transfer capabilities, and are unable to cope with the challenges of new regions, new data sources, or extreme emission events. Therefore, it is urgent to develop a new high-resolution emission modeling method that can integrate multi-source heterogeneous observation data, fuse multi-scale physical and deep intelligent assimilation, and possess key capabilities such as adaptive completion, hierarchical dynamic correction, and transfer generalization, in order to break through the current technical bottlenecks and better serve refined environmental governance and carbon emission management. Summary of the Invention

[0006] This invention aims to address the core technical challenges in existing atmospheric pollutant and greenhouse gas emission modeling, including high heterogeneity of multi-source observation data, severe data gaps and noise, difficulty in coordinating physical and data-driven models, insufficient fusion of multi-scale spatiotemporal features, and weak model generalization and transfer capabilities. It proposes a novel emission modeling method that integrates multi-source heterogeneous observation, multi-scale assimilation, and embedded intelligent optimization. This method enables dynamic, intelligent, and accurate modeling of high-resolution emission data in complex scenarios, effectively improving the accuracy, timeliness, and adaptability of emission estimation, and meeting the needs of scientific decision-making and refined governance.

[0007] To achieve the above objectives, the present invention employs the following technical solution:

[0008] The method includes:

[0009] Multi-source heterogeneous observation data acquisition and adaptive preprocessing automatically acquire heterogeneous emission observation data from various categories, including ground monitoring, remote sensing images, UAV observations, and social perception. A multi-scale adaptive missing data completion-hierarchical dynamic correction fusion algorithm is adopted to dynamically determine the data credibility and spatial weight based on multi-level spatiotemporal characteristics. A dynamic embedding and filling mechanism based on variational Bayesian inference is used to adaptively complete missing data points. Combined with a hierarchical self-correction module, errors and noise are suppressed to obtain highly consistent, multi-scale, and complete observation data.

[0010] By dynamically coupling atmospheric physical emission model parameters with observational data, adopting a hierarchical nested variational assimilation mechanism, designing a generative adversarial deep de-embedding network, and using neural networks to adaptively learn the hidden correlation between observational data and physical model residuals, the collaborative correction of observational, physical, and statistical data is achieved, and emission inventory parameters are automatically adjusted in real time. Based on multi-model uncertainty calculation and anomaly detection mechanism, abnormal emission sources are identified and corrected.

[0011] Cross-scale feature fusion and high-precision modeling are performed. A hierarchical spatial transformation-embedded attention hybrid graph neural network is constructed to perform hierarchical mapping and alignment of emission features at different scales. A hierarchical self-attention mechanism is introduced to realize dynamic information flow regulation of multi-scale subgraphs. Combined with graph structure adaptive sparse connection and multi-level feature fusion, high-resolution and cross-scale fine estimation of emission distribution of air pollutants and greenhouse gases is achieved.

[0012] Based on the spatial-temporal error inversion of the model output, a Bayesian optimization framework is adopted to achieve automatic adjustment of model parameters through assimilation and fusion. A knowledge transfer generalization strategy is designed to dynamically update the model structure and parameters through transfer learning and feedback optimization, so as to realize knowledge transfer between regions and rapid adaptation to new regions, and continuously improve the accuracy and timeliness of emission estimation.

[0013] In one approach, the multi-scale adaptive missing completion-hierarchical dynamic correction fusion algorithm includes:

[0014] Based on feature statistics of multi-level spatiotemporal windows, the credibility and spatial weight of each observation data source at different scales are dynamically calculated.

[0015] To address missing data, a variational Bayesian inference method is employed, treating the original observations as a probability model with unknown parameters. By introducing auxiliary latent variables, the missing values ​​are filled in by maximizing the lower bound of evidence.

[0016] Hierarchical self-calibration minimizes reconstruction error by using local weighted mean and adaptive fusion coefficients, dynamically suppresses anomalous biases and noise, and improves the quality of observation data.

[0017] In one scheme, the hierarchical nested variational assimilation mechanism between the atmospheric physical emission model parameters and the observation data includes:

[0018] The parameters of the physical emission model are recursively nested and optimized at different spatial / temporal scales, with higher-scale results used as lower-scale prior inputs, to achieve layer-by-layer parameter tuning from global to local.

[0019] By using a generative adversarial deep de-embedding network, the residuals of the physical model are input to generate a deep de-embedding vector of heterogeneous observation-physical-statistical information. The complex error correlation structure is captured by adversarial loss optimization, thereby achieving deep fusion and correction of multi-source data.

[0020] In one approach, the online adjustment of emission inventory parameters by the model specifically includes:

[0021] Based on the joint uncertainty measurement and dynamic threshold of physical models, deep networks and real observations, the system automatically identifies emission anomalies and locates the sources of abnormal emissions, enabling real-time correction of emission inventory parameters and adaptive adjustment of the model.

[0022] In one embodiment, the hierarchical spatial transformation-embedded attention hybrid graph neural network includes:

[0023] Graph structures are constructed at three spatial scales: region, city, and micro-region. Spatial transformation operators are designed to map and align multi-scale node features, and multi-scale dependencies between region, city, and micro-region are modeled.

[0024] A hierarchical self-attention mechanism is introduced to dynamically weight and fuse the features of subgraph nodes at each scale, highlighting key emission units and features;

[0025] Adjacency probability matrix is ​​used to filter true adjacency relationships to achieve adaptive sparse connectivity of graph structure. Multi-level feature concatenation and residual connectivity are combined to output the final emission modeling features.

[0026] In one approach, the Bayesian optimization and transfer generalization strategy based on space-time error includes:

[0027] Using the spatial-temporal error of emission modeling as the objective function, automatic optimization of model parameters is achieved through Gaussian process modeling;

[0028] By employing transfer learning strategies such as feature alignment, adaptive scaling, and domain discrimination, emission modeling knowledge from the source region is transferred to the target region, enabling rapid adaptation to new regions or new data sources.

[0029] By utilizing continuous alternating feedback optimization and transfer learning, dynamic closed-loop tuning and real-time incremental training of model structure and parameters can be achieved.

[0030] In one approach, the method integrates multi-source heterogeneous data acquisition and processing, multi-scale physical-deep hybrid assimilation, hierarchical spatial transformation and graph neural networks with embedded attention, and an adaptive feedback iterative optimization mechanism to achieve dynamic, cross-scale, high-resolution, and high-precision intelligent modeling of atmospheric pollutants and greenhouse gas emissions.

[0031] Beneficial effects of this invention:

[0032] This invention significantly improves the completeness and consistency of atmospheric pollutant and greenhouse gas emission data through adaptive fusion and high-precision completion of multi-source heterogeneous observation data. Building upon this, it employs synergistic assimilation of physical and deep intelligent models, significantly enhancing the accuracy, physical interpretability, and ability to characterize complex nonlinear emission processes in emission modeling. Simultaneously, it innovatively introduces multi-scale feature dynamic fusion and hierarchical graph neural networks, achieving high-resolution and precise estimation of emission information at different levels, including regional, urban, and micro-regional levels. Through Bayesian optimization and transfer learning mechanisms, the model can continuously adaptively optimize parameters, rapidly generalize to new regions and new data sources, and effectively address the challenges of changing scenarios and extreme emission events. Overall, this invention overcomes the technical bottlenecks of existing emission modeling methods in data fusion, model accuracy, generalization ability, and adaptability, providing more efficient, intelligent, and reliable technical support for atmospheric environmental governance and carbon emission management. Attached Figure Description

[0033] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0034] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0035] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete.

[0036] like Figure 1As shown, a multi-scale data assimilation and fusion modeling method for atmospheric pollutant and greenhouse gas emissions is presented, with the following specific steps:

[0037] Step 1: Acquisition and Adaptive Preprocessing of Multi-Source Heterogeneous Observation Data

[0038] This system automatically acquires multi-category, heterogeneous emission monitoring data, including ground monitoring, remote sensing imagery, UAV observations, and social perception. A "multi-scale adaptive missing data completion-hierarchical dynamic correction fusion" algorithm is proposed. It utilizes multi-level spatiotemporal feature mining to dynamically determine the credibility and spatial weight of various data types. A dynamic embedding and imputation mechanism based on variational Bayesian inference is employed to achieve high-precision adaptive completion of missing data points while maintaining consistency across multiple sources.

[0039] First, a multi-source data collaborative acquisition system is constructed. This system consists of ground monitoring stations, remote sensing satellites, unmanned aerial vehicles (UAVs), and crowdsourcing devices, automatically and synchronously collecting multi-category, heterogeneous observation data on air pollutants and greenhouse gas emissions. All collected data are then uniformly projected onto a common spatiotemporal coordinate system (t, x, y) to form a heterogeneous spatiotemporal dataset. Where d i Let s represent the i-th observation. i Indicates the data source category, (t i ,x i ,y i () represents the observation time and spatial coordinates, N This represents the total number of observations.

[0040] Subsequently, a "multi-scale adaptive missing completion-hierarchical dynamic correction fusion" algorithm is proposed. First, multi-level spatiotemporal feature mining is performed on dataset D. This is achieved by constructing multi-scale spatiotemporal windows. (where I is the scale layer, K) l (where is the number of windows at this scale), the sliding window method is used to statistically analyze the distribution, variance, and missing rate of data at each scale. The confidence level C of each source observation at scale layer l is defined. i,l With spatial weight α i,l :

[0041]

[0042] in, Indicates data source s i In window w l The observed variance, Let N(i,l) be the standard deviation threshold, and N(i,l) represent the set of indices of relevant observations within the window. This dynamically yields the data reliability weights for different sources and different spatial-temporal scales.

[0043] To address missing data, a dynamic embedding imputation mechanism based on Variational Bayesian Inference is employed. The original data is treated as following a probabilistic model p(d) with unknown parameters. i |θ), and the missing data is filled in by maximizing the variational lower bound. Specifically, for each missing observation point d m Introduce auxiliary latent variable z m (e.g., spatial-temporal embedding vectors) optimize variational distribution q(z) m |φ) maximizes the lower bound of evidence (ELBO):

[0044]

[0045] Where, d obs D represents the observed sample set within the current window. KL It is the Kullback-Leibler divergence, used to constrain the distribution of latent variables to not deviate from the prior p(z). m ). Final missing values Obtained by maximum a posteriori estimation, and the scale dynamic weights α of multi-source data are fused during imputation. i,l Achieve consistency.

[0046] To further improve the quality of data at different spatiotemporal scales, a "hierarchical self-correction module" is designed. For each scale layer l, error bias reduction and noise suppression are applied to the preprocessed data. Let the original observation value be d. i Using hierarchical correction function Corrected data The calculation is as follows:

[0047]

[0048] in, This represents the local weighted mean within the k-th window at scale level l. These are the fusion coefficients adaptively learned by minimizing the reconstruction error. They are updated alternately. By dividing the data into windows, abnormal deviations and noise are dynamically suppressed, thereby improving the resolution and integrity of the final data.

[0049] This series of steps efficiently enables highly reliable preprocessing and missing data completion of multi-source heterogeneous observation data at multiple scales, laying a solid foundation for subsequent multi-scale assimilation and fusion modeling.

[0050] Step 2: Dynamically couple the parameters of the atmospheric physical emission model with the observation data to construct a multi-scale parameter tuning mechanism;

[0051] By introducing a hierarchical and nested variational assimilation approach, atmospheric physical emission model parameters are dynamically coupled with observational data to construct a multi-scale parameter tuning mechanism. A Generative Adversarial Deep Decomposition Network (GADDN) is designed to adaptively learn the hidden correlations between observational data and physical model residuals, achieving collaborative correction of observational, physical, and statistical data. The emission inventory is adjusted online in real time, and based on a joint model uncertainty metric and anomaly detection mechanism, abnormal emission sources are automatically identified and corrected.

[0052] First, a "multi-scale nested variational assimilation-generative hybrid network" model framework is proposed. The core idea of ​​this framework is to integrate the parameter set of the physical emission model (denoted as θ). l (where l represents the scale level) and observation data from multiple scales and sources. Dynamic coupling in space and time. For each layer l, a cost function is constructed.

[0053]

[0054] in, Let M be the prior parameters at scale l, B be the prior covariance matrix, and M be the prior parameters at scale l. l (θ l H is the physical emission model prediction at this scale. l (·) represents the mapping of the observation operator to the observation space, and R is the observation error covariance matrix. Multi-scale nested assimilation involves recursively optimizing θ at different scales from coarse to fine. l The results from the upper layer serve as the prior inputs for the lower layer, thereby enabling fine-tuning of global and local parameters.

[0055] Next, a Generative Adversarial Deep Decomposition Network (GADDN) was designed. This network consists of a generator G... ψ Discriminator D ω The generator is composed of physical model residuals r = d obs -H l (M l (θ l Using () as input, it generates a deep embedding vector z of heterogeneous information from three categories: observation, physics, and statistics. Its goal is to capture the complex structure of observation errors and uncertainties in the physical model. Its adversarial training loss is...

[0056]

[0057] Where ψ and ω are the generator and discriminator parameters, respectively, and p data It is a true joint distribution, p res This represents the residual distribution of the physical model. After training, z = G ψ (r) is used to correct model parameters or reconstruct observations, achieving deep coupling correction of observation data, physical model output, and statistical characteristics.

[0058] Then, at each time step or upon acquiring a new observation, the system embeds the GADDN output into the physical model parameter update, adjusting the emission inventory online in real time. Specifically, a multi-model uncertainty measurement and anomaly detection mechanism is employed to compare the physical model predictions, corrected outputs, and actual observations to construct a joint uncertainty function.

[0059] U(x) = γ1·Var model (x)+γ2·Var GADDN (x)+γ3·Var obs (x)

[0060] Among them, Var model Var GADDN Var obs These represent the uncertainties in the physical model, the deep generative network, and actual observations, respectively, with γ1, γ2, and γ3 being weighting coefficients. When U(x) exceeds the dynamic threshold, an abnormal emission identification mechanism is automatically triggered to locate the abnormal emission source and correct the corresponding emission inventory parameters, ensuring the adaptability and accuracy of the entire system.

[0061] This "multi-scale nested variational assimilation-generative hybrid network" model highly integrates the scientific priors of physical models with the data-driven advantages of deep learning, enabling dynamic, high-precision, and multi-scale collaborative assimilation of atmospheric pollutant and greenhouse gas emission data. This provides accurate state input and physical consistency assurance for subsequent high-resolution emission modeling.

[0062] Step 3: Cross-scale feature fusion and high-precision modeling

[0063] We propose a "Hierarchical Spatial Transformation-Embedded Attention Hybrid GNN" (HSTA-HGNN). This GNN utilizes spatial transformation operators to perform hierarchical mapping and alignment of emission features at different scales, explicitly modeling multi-scale relationships between regions, cities, and micro-regions. An embedded hierarchical self-attention mechanism dynamically regulates the information flow of subgraphs at each scale, enhancing the representational ability of important emission features. By combining adaptive sparse connectivity of the graph structure with multi-level feature fusion, we achieve refined emission modeling across spatial scales, faithfully reflecting both local and overall emission distributions.

[0064] First, a hierarchical spatial transformation operator is designed to handle emission characteristics at different spatial scales. The original multi-scale emission characteristic data is organized into a three-level graph structure at the regional, city, and micro-regional levels, denoted as G, respectively. L =(V L E L ), G M =(V M E M ), G S =(V S E S ), where V * E is a set of vertices (representing spatial units). * Let T be an edge set (representing neighborhood relations), with superscripts (L, M, S) corresponding to "region," "city," and "micro-region," respectively. Spatial transformation operator T. a→b (·) is used to represent the node features at scale a. Mapping to scale b:

[0065]

[0066] in, For node features at input scale a, M j It is the set of neighborhoods corresponding to node j mapped to scale \(b\). σ(·) represents the spatial transformation weights, and σ(·) is the activation function. This mechanism achieves feature alignment and information transfer across multiple scales, explicitly modeling the dependencies between spatial units at different levels (from large to small).

[0067] Next, a hierarchical self-attention mechanism is introduced into the feature representation of each scale map to achieve dynamic fusion of features across levels and spaces. For each node at the current scale c... Its neighborhood Feature aggregation employs attention weighting:

[0068]

[0069] Among them, W c For linear transformation parameters, For self-attention weights, defined as

[0070] Where [·||·] denotes vector concatenation, a c This represents the attention parameter vector to be learned. By embedding a self-attention mechanism in each layer, key nodes and features are dynamically highlighted, improving the model's ability to represent and distinguish heterogeneous emission features.

[0071] Furthermore, by combining an adaptive sparse connectivity strategy for graph structures and multi-level feature fusion, high-fidelity cross-scale modeling is achieved. Specifically, this is achieved by learning the adjacency probability matrix P. c By filtering out the truly important node connections, an adaptive sparse graph structure is formed:

[0072]

[0073] Where τ is an adjustable threshold and 1 / {·} is an indicator function. Multi-level feature fusion utilizes concatenation and residual connections to combine output features h at different scales. L h M h S Integrate into final emission modeling features h fused :

[0074]

[0075] Where φ(·) is a nonlinear mapping function, The original input features are used. This hierarchical structure captures both macroscopic overall relationships and preserves fine-grained spatial differences, enabling high-precision and high-availability emission distribution modeling.

[0076] Through the above-mentioned "hierarchical spatial transformation-embedded attention hybrid graph neural network" architecture, multi-scale spatial information is effectively integrated, giving the model robust expression capabilities for local anomalies, global trends and scale shifts, and enabling high-resolution, cross-scale, and precise estimation of atmospheric pollutants and greenhouse gas emissions.

[0077] Step 4: Based on the spatial-temporal error inversion of the model output, use the Bayesian optimization framework to automatically adjust the assimilation and fusion model parameters to improve the data consistency of subsequent iterations.

[0078] We design migration and generalization strategies for greenhouse gas and pollutant emissions to achieve inter-regional knowledge transfer and rapid adaptation to new regions. Through continuous and alternating feedback optimization and transfer learning, we dynamically update the model structure and parameters, enhance the model's adaptability to new data sources and extreme emission events, and continuously improve the accuracy and timeliness of high-resolution emission estimation.

[0079] First, based on the spatial-temporal error inversion of the model output, the Bayesian Optimization (BO) framework is used to achieve automatic tuning of the assimilation and fusion model parameters. Let the set of model parameters be λ, and the model output be... The actual observation is y obs (t,x,y). Define the space-time error function as:

[0080]

[0081] Where T, X, and Y represent the number of spatial grid points and the number of time steps, respectively. Bayesian optimization aims at E(λ) and constructs a surrogate model using a Gaussian Process (GP): E(λ)~GP(μ(λ),K(λ,λ′)), where μ(λ) is the mean function and K(·,·) is the kernel function. BO uses an acquisition function (such as Expected Improvement, EI) to select the optimal parameter λ in each round. * :

[0082] λ * =argmax λ EI(λ)

[0083] The model automatically iterates and optimizes parameters, driving the convergence of global spatial-temporal errors and improving the consistency of subsequent data.

[0084] Secondly, to achieve inter-regional knowledge transfer and rapid adaptation to new regions, a migration generalization strategy for greenhouse gas and pollutant emissions is designed. Let the source region model parameters be θ. S The target region model parameters are θ T Extracting shared representational features F from the source region. S =f share (D S The transfer is achieved through feature alignment and adaptive scaling.

[0085] θ T ←θ S +α·(F T -F S )

[0086] Where α is the adaptive migration coefficient, F T The target region features are used. The migration process utilizes a neighborhood discriminator D.dom Minimize the difference in representation distribution between the source and target regions to achieve rapid generalization adjustment of model parameters and features, significantly reducing the risk of cold start in new regions under unsupervised or weakly supervised conditions.

[0087] Finally, through continuous alternating feedback optimization and transfer learning, dynamic updates are achieved in the model structure and parameter space. Specifically, after each round of Bayesian optimization, the optimization results and their error distribution are used as supervision signals for transfer learning to update the model parameters in the target region or new data source; the model that has undergone transfer adaptation then undergoes a new round of Bayesian optimization iteration. This mechanism can be described as follows:

[0088]

[0089] in, (k) This indicates that the k-th round of iteration involves alternating optimization and transfer learning. For novel data sources or extreme emission events, the model can absorb new knowledge in real time through incremental training, enhancing generalization and adaptability, and continuously improving the spatial timeliness and accuracy of high-resolution emission estimation.

[0090] It achieves dynamic closed-loop optimization and knowledge generalization throughout the entire emission modeling process, effectively supporting efficient and intelligent modeling of air pollutant and greenhouse gas emission data in complex scenarios.

[0091] This method integrates multi-source heterogeneous data, multi-scale physical-deep hybrid assimilation, hierarchical spatial transformation and graph neural networks with embedded attention, as well as an adaptive feedback optimization mechanism. It breaks through the accuracy and efficiency bottlenecks of existing algorithms, realizes high-resolution dynamic modeling of carbon emissions and pollutants, and provides strong support for scientific decision-making and governance.

[0092] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0093] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assimilation and fusion modeling of atmospheric pollutant and greenhouse gas emission data at multiple scales, characterized in that: The method includes: Multi-source heterogeneous observation data acquisition and adaptive preprocessing automatically acquire heterogeneous emission observation data from various categories, including ground monitoring, remote sensing images, UAV observations, and social perception. A multi-scale adaptive missing data completion-hierarchical dynamic correction fusion algorithm is adopted to dynamically determine the data credibility and spatial weight based on multi-level spatiotemporal characteristics. A dynamic embedding and filling mechanism based on variational Bayesian inference is used to adaptively complete missing data points. Combined with a hierarchical self-correction module, errors and noise are suppressed to obtain highly consistent, multi-scale, and complete observation data. By dynamically coupling atmospheric physical emission model parameters with observational data, adopting a hierarchical nested variational assimilation mechanism, designing a generative adversarial deep de-embedding network, and using neural networks to adaptively learn the hidden correlation between observational data and physical model residuals, the collaborative correction of observational, physical, and statistical data is achieved, and emission inventory parameters are automatically adjusted in real time. Based on multi-model uncertainty calculation and anomaly detection mechanism, abnormal emission sources are identified and corrected. Cross-scale feature fusion and high-precision modeling are performed. A hierarchical spatial transformation-embedded attention hybrid graph neural network is constructed to perform hierarchical mapping and alignment of emission features at different scales. A hierarchical self-attention mechanism is introduced to realize dynamic information flow regulation of multi-scale subgraphs. Combined with graph structure adaptive sparse connection and multi-level feature fusion, high-resolution and cross-scale fine estimation of emission distribution of air pollutants and greenhouse gases is achieved. Based on the spatial-temporal error inversion of the model output, a Bayesian optimization framework is adopted to achieve automatic adjustment of model parameters through assimilation and fusion. A knowledge transfer generalization strategy is designed to dynamically update the model structure and parameters through transfer learning and feedback optimization, so as to realize knowledge transfer between regions and rapid adaptation to new regions, and continuously improve the accuracy and timeliness of emission estimation.

2. The method for assimilation and fusion modeling of multi-scale atmospheric pollutant and greenhouse gas emission data according to claim 1, characterized in that: The multi-scale adaptive missing completion-hierarchical dynamic correction fusion algorithm includes: Based on feature statistics of multi-level spatiotemporal windows, the credibility and spatial weight of each observation data source at different scales are dynamically calculated. To address missing data, a variational Bayesian inference method is employed, treating the original observations as a probability model with unknown parameters. By introducing auxiliary latent variables, the missing values ​​are filled in by maximizing the lower bound of evidence. Hierarchical self-calibration minimizes reconstruction error by using local weighted mean and adaptive fusion coefficients, dynamically suppresses anomalous biases and noise, and improves the quality of observation data.

3. The method for assimilation and fusion modeling of multi-scale atmospheric pollutant and greenhouse gas emission data according to claim 1, characterized in that: The hierarchical nested variational assimilation mechanism between atmospheric physical emission model parameters and observational data includes: The parameters of the physical emission model are recursively nested and optimized at different spatial / temporal scales, with higher-scale results used as lower-scale prior inputs, to achieve layer-by-layer parameter tuning from global to local. By using a generative adversarial deep de-embedding network, the residuals of the physical model are input to generate a deep de-embedding vector of heterogeneous observation-physical-statistical information. The complex error correlation structure is captured by adversarial loss optimization, thereby achieving deep fusion and correction of multi-source data.

4. The method for assimilation and fusion modeling of multi-scale atmospheric pollutant and greenhouse gas emission data according to claim 1, characterized in that: The online adjustment of emission inventory parameters in the model specifically includes: Based on the joint uncertainty measurement and dynamic threshold of physical models, deep networks and real observations, the system automatically identifies emission anomalies and locates the sources of abnormal emissions, enabling real-time correction of emission inventory parameters and adaptive adjustment of the model.

5. The method for assimilation and fusion modeling of multi-scale atmospheric pollutant and greenhouse gas emission data according to claim 1, characterized in that: The hierarchical spatial transformation-embedded attention hybrid graph neural network includes: Graph structures are constructed at three spatial scales: region, city, and micro-region. Spatial transformation operators are designed to map and align multi-scale node features, and multi-scale dependencies between region, city, and micro-region are modeled. A hierarchical self-attention mechanism is introduced to dynamically weight and fuse the features of subgraph nodes at each scale, highlighting key emission units and features; Adjacency probability matrix is ​​used to filter true adjacency relationships to achieve adaptive sparse connectivity of graph structure. Multi-level feature concatenation and residual connectivity are combined to output the final emission modeling features.

6. The method for assimilation and fusion modeling of multi-scale atmospheric pollutant and greenhouse gas emission data according to claim 1, characterized in that: The Bayesian optimization and transfer generalization strategy based on space-time error includes: Using the spatial-temporal error of emission modeling as the objective function, automatic optimization of model parameters is achieved through Gaussian process modeling; By employing feature alignment, adaptive scaling, and domain discrimination transfer learning strategies, emission modeling knowledge from the source region is transferred to the target region, enabling rapid adaptation to new regions or novel data sources. By utilizing continuous alternating feedback optimization and transfer learning, dynamic closed-loop tuning and real-time incremental training of model structure and parameters can be achieved.

7. The method for assimilation and fusion modeling of multi-scale atmospheric pollutant and greenhouse gas emission data according to claim 1, characterized in that: The method described integrates multi-source heterogeneous data acquisition and processing, multi-scale physical-deep hybrid assimilation, hierarchical spatial transformation and graph neural networks with embedded attention, and adaptive feedback iterative optimization mechanism to achieve dynamic, cross-scale, high-resolution, and high-precision intelligent modeling of atmospheric pollutants and greenhouse gas emissions.

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