A method and system for analyzing multimodal large-scale models of water pollution in industrial parks.

Abstract

This invention relates to the field of large-scale model analysis methods, specifically disclosing an analysis method for a multimodal large-scale model of water pollution in industrial parks, comprising the following steps: S1: acquiring multimodal time-series data within the industrial park; S2: performing spatiotemporal alignment processing on the multimodal time-series data to construct a multimodal fusion tensor with spatiotemporal correlation; S3: inputting the multimodal fusion tensor into a pre-trained multimodal large-scale model; S4: calculating the pollution source probability of each node based on the pollution contribution matrix and combined with real-time hydrodynamic parameters within the park's pipe network, and generating a dynamic pollution source tracing map; S5: performing pollution risk classification and early warning based on the dynamic pollution source tracing map. This invention integrates multiple types of time-series data, including water quality monitoring, image monitoring, and enterprise production activities, and constructs a multimodal fusion tensor through spatiotemporal alignment and weighted fusion to comprehensively capture multidimensional features related to water pollution.

Description

Technical Field

[0001] This invention relates to the field of large model analysis methods, specifically to an analysis method and system for a multimodal large model of water pollution in industrial parks. Background Technology

[0002] Industrial parks, as concentrated areas of industrial production, are characterized by dense enterprise production activities, complex wastewater discharge processes, and numerous pipeline nodes, making them highly susceptible to water pollution problems. Water pollution not only damages the surrounding ecological environment but may also affect public health and safety. Therefore, accurate monitoring, rapid source tracing, risk early warning, and scientific management of water pollution in industrial parks have become key priorities and challenges in current environmental protection management.

[0003] Existing water pollution analysis technologies in industrial parks mostly rely on single types of monitoring data, making it difficult to integrate multi-dimensional information for comprehensive analysis. This results in insufficient data utilization and an inability to accurately capture the complex dynamic characteristics of water pollution. In terms of pollution source tracing, traditional methods are mostly based on empirical judgments or simple diffusion models, lacking in-depth fusion of multimodal data and the mining of implicit causal relationships. This leads to low accuracy and efficiency in source tracing, making it difficult to quickly locate core pollution sources and pollution diffusion paths.

[0004] Meanwhile, existing early warning mechanisms lack a tiered control logic, making it difficult to implement differentiated measures based on the degree of pollution risk, and easily leading to delayed warnings or over-treatment. In terms of governance decision-making, the inability to accurately quantify the causal relationship between enterprise production activities and water pollution results in weakly targeted governance measures, hindering the efficient eradication of pollution problems. Furthermore, the pollution characteristics of industrial parks and enterprise production activities are constantly changing; existing models are mostly static and cannot be dynamically updated. Long-term use leads to decreased adaptability and reduced analytical accuracy, making it difficult to meet the needs of long-term, precise water pollution control.

[0005] In summary, existing water pollution analysis technologies for industrial parks suffer from shortcomings such as insufficient data utilization, low source tracing accuracy, lack of targeted early warning, unreasonable governance decisions, and poor model adaptability. There is an urgent need for an analysis method and system that can integrate multimodal data, achieve accurate source tracing, hierarchical early warning, scientific decision-making, and dynamic adaptability to address the deficiencies of existing technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and system for analyzing a multimodal large-scale model of water pollution in industrial parks, thus solving the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for analyzing a multimodal large-scale model of water pollution in industrial parks, comprising the following steps: S1: Acquire multimodal time-series data within the industrial park. The multimodal time-series data includes water quality monitoring data, image monitoring data, and enterprise production activity data. The water quality monitoring data consists of continuous time-series monitoring values ​​of pH, COD, and ammonia nitrogen-related water quality indicators at the park's pipeline network nodes and discharge outlets. The image monitoring data consists of video and visual image acquisition data of sewage outlets and key nodes of the pipeline network. The enterprise production activity data consists of time-series statistical data on the production load, raw material consumption, and wastewater discharge flow of enterprises in the park. S2: Perform spatiotemporal alignment processing on the multimodal time series data to unify the data time dimension and spatial coordinate benchmark, and construct a multimodal fusion tensor with spatiotemporal correlation based on the park pipeline network topology. S3: Input the multimodal fusion tensor into the pre-trained multimodal large model, and calculate and output the pollution contribution matrix of each potential pollution emission node in the park to the downstream monitoring node or the total emission outlet through the implicit causal reasoning mechanism of the multimodal large model. S4: Based on the pollution contribution matrix and combined with the real-time hydrodynamic parameters in the park's pipe network, the probability of pollution sources at each node is calculated and a dynamic pollution source tracing map is generated. S5: Based on the dynamic pollution source map, conduct pollution risk classification and early warning, or combine enterprise production activity data to quantify the causal impact of pollution and generate targeted governance decision-making schemes.

[0008] Preferably, the construction of a multimodal fusion tensor with spatiotemporal correlation in S2 specifically includes the following steps: S21: Extract the pipeline network topology and construct a node association matrix. Based on the park's geographic information system map, extract the node distribution and pipeline connection relationships of the park's pipeline network, and construct a node association matrix. , where matrix elements Characterizing nodes in the park's pipeline network With nodes The direct connection relationship between nodes, if nodes With nodes If there is a direct pipe connection, then If there is no direct pipe connection, then ; S22: Extract multimodal features and construct a feature matrix. Based on the spatiotemporally aligned multimodal time-series data, filter the effective features of each monitoring node at the same time point and construct the feature matrix. , where the characteristic matrix Each row corresponds to a multimodal feature vector of a park node, and the column vectors correspond to the feature dimensions of each modality, thereby realizing feature integration of multimodal data; S23: Calculate the spatiotemporal weight matrix and fuse it to generate a multimodal fusion tensor, then calculate the spatiotemporal weight matrix using a spatiotemporal attention mechanism. And based on the spatiotemporal weight matrix For the feature matrix We perform weighted fusion to obtain the multimodal fusion tensor. The calculation formula is as follows: ; ; in, It represents the Hadamardi (or Hadama) stack; , Both are learnable linear transformation matrices, used to transform the feature matrix. Mapping to the query space and key space enables feature dimension adaptation; Characteristic matrix The dimension of the feature vectors; This is the normalized exponential function executed along the last dimension of the matrix; It is a multimodal fusion tensor with spatiotemporal correlation.

[0009] Preferably, in S3, the pollution contribution matrix is ​​calculated through an implicit causal reasoning mechanism, specifically including the following steps: S31: Deep feature extraction, converting the multimodal fusion tensor The data is input into a pre-defined dual-channel heterogeneous graph attention network, which is divided into a water quality-image modal channel and an enterprise production modal channel. The network learns the deep feature representations of the three modalities of data under the topology of the park's pipeline network, and finally outputs the hidden feature vectors of each node. S32: Pollution contribution calculation and matrix construction. Based on the hidden feature representation of each node, the potential pollution emission nodes in the park are calculated using a counterfactual reasoning framework. For downstream monitoring nodes Pollution contribution Summarize the pollution contribution of all nodes. This constitutes a pollution contribution matrix. The calculation formula is as follows: ; in, Potential pollution emission nodes Hidden layer feature vectors obtained through a dual-channel heterogeneous graph attention network; For downstream monitoring nodes The corresponding hidden layer feature vector; This represents a vector concatenation operation; The weight matrix is ​​a learnable matrix; It is a linear rectified activation function with leakage; It is the sigmoid activation function; The attention weight coefficients are those that integrate the network topology and multimodal data features.

[0010] Preferably, the attention weight coefficient The following steps are used to calculate: S41: Obtain the basic weight parameters and extract the spatiotemporal weight matrix obtained in step S23. Corresponding element in As the spatiotemporal prior weights of the pipeline network; simultaneously, the original attention coefficients are calculated using a graph attention mechanism. , as a data-driven attention weight; S42: Nonlinear weight fusion, employing a nonlinear fusion method to integrate spatiotemporal prior weights. With data-driven attention weights To achieve fusion, a balance coefficient is introduced. Control the weight distribution between the two; S43: Normalization processing: The fused weights are normalized to obtain the final attention weight coefficients. The calculation formula is as follows: ; in, This is an adjustable balance coefficient, with a value range of [0,1]. For nodes The set of neighboring nodes in the park's pipeline network topology, i.e., the nodes All nodes have direct pipe connections; original attention coefficients. The calculation formula is: ,in The learnable parameter vector in the graph attention mechanism is used to assign weights to the concatenated hidden layer feature vectors, thereby achieving an accurate assessment of the strength of the association between nodes.

[0011] Preferably, the generation of a dynamic pollution source map in S4 includes the following steps: S51: Construct a pollutant diffusion model based on the pollution contribution matrix. Combined with the park's pipeline network topology and real-time hydrodynamic parameters Constructing a physical-guided neural network model for pollutant diffusion The hydrodynamic parameters mentioned above This includes water flow velocity, hydraulic gradient, and cross-sectional area within the pipe network, used to simulate the diffusion patterns of pollutants within the pipe network; S52: Pollution source probability inversion calculation, inputting the actual water quality observation data of the park into the physical-guided pollutant diffusion neural network model. By using model inversion calculations, the probability distribution of each potential pollution emission node as a pollution source is obtained. ,in Represents a node This represents the probability value of a pollution source; the higher the probability value, the more likely the node is to be a pollution source. S53: Dynamic source tracing map generation, which will generate the probability distribution. The pollution contribution relationship between each node is overlaid on the park's geographic information system map, marking the spatial location of each node, the probability of pollution sources, and the contribution relationship between nodes, generating a visualized dynamic pollution source tracing map, and realizing an intuitive presentation of pollution sources.

[0012] Preferably, the probability distribution This is obtained by solving a constrained optimization problem, and the specific solution steps include: S61: Determine the objective function, aiming to minimize the error between the actual monitored concentration and the model predicted concentration. Combined with L1 regularization constraints, construct the constrained optimization objective function as follows: ; S62: Set constraints. Based on the physical meaning of the pollution source probability, set constraints to ensure the rationality of the solution results. The constraints are as follows: ; S63: Solve the optimization problem. Use the gradient descent algorithm to solve the above constrained optimization problem, and obtain the pollution source probability vector of each node that satisfies the constraints. That is, the probability distribution. ; in, This represents the pollutant concentration vector obtained from actual monitoring at each monitoring node in the park. The physical-guided neural network model simulates and predicts the pollutant concentration function. Let be the pollution source probability vector for each node to be solved, containing the pollution source probabilities of all nodes; For the park's pipeline network topology, These are real-time hydrodynamic parameters, all of which are model input parameters; is a non-negative L1 regularization coefficient; This represents the total number of pipeline nodes within the park. It is an L2 norm; The L1 norm is used to implement sparsity constraints on pollution source probabilities, making the pollution source probability of most nodes approach 0, while retaining only a few core pollution source nodes; the constraint conditions include... The probability of a single node being a pollution source is limited to between 0 and 1; The sum of the pollution source probabilities of all nodes is limited to no more than 1.

[0013] Preferably, in S5, pollution risk early warning is based on dynamic pollution source tracing maps, specifically including the following steps: S71: Set risk level thresholds. Based on the park's water pollution control standards, environmental regulations, and the park's pollution prevention and control capabilities, set two levels of risk level thresholds. and ,in , The threshold for Level 1 warning is... The threshold is set at level two. S72: Level 1 Early Warning Triggering and Response, Real-time Monitoring of Pollution Source Probability at Each Node in the Dynamic Pollution Source Tracing Map ,when satisfy When a Level 1 warning is triggered, the system will automatically perform the following operations: S721, extract the high-probability pollution source tracing path of the node and sort out the path of pollutants spreading from the node to the downstream monitoring node; S73: Level II Early Warning Triggering and Response, when the probability of a pollution source at a certain node in the dynamic pollution source tracing map... satisfy When a Level II warning is triggered, the system will simultaneously perform the following operations: activate the on-site audible and visual alarm devices in the park to remind on-site staff to take emergency measures; generate an emergency source tracing instruction, specifying the verification nodes, verification content, and time requirements; and initiate the on-site verification process for the pollution source, arranging staff to go to the node and related enterprises to conduct on-site sampling and verification, so as to control the spread of pollution in a timely manner.

[0014] Preferably, the generation of governance decision-making schemes in S5 also includes the following steps: S81: Quantification of Causal Impact. When the system triggers a Level 1 or Level 2 warning, based on the enterprise production activity data, a comparative causal inference model is invoked to quantify the causal impact of different production behaviors of enterprises in the park on the current sudden change in pollution contribution. The calculation formula is as follows: ; S82: Identification of major abnormal production behaviors, calculating the degree of causal impact of all enterprise production behaviors. ,extract The production behavior with the largest absolute value was identified as the main abnormal production behavior, which is the core factor causing the sudden change in pollution contribution. S83: Generation of governance decision-making schemes, which involves the following sub-steps: semantically and feature-matching the identified major abnormal production behaviors with the pre-set industrial park water pollution control measures database to select targeted treatment measures; compiling a detailed description of the abnormal behavior, including behavior type, implementation intensity, and duration; clarifying the implementation steps, responsible parties, completion deadlines, and precautions for the treatment measures; and integrating the above content to generate a complete governance decision-making scheme, providing actionable guidance for pollution control. in, The degree of causal impact of sudden changes in the contribution of enterprise production behavior to pollution; This represents the intervention operator in causal inference; For specific enterprise production activities variables; This indicates that the production activity is in an abnormal or high-intensity emission state. This indicates that the production activity is at the industry benchmark or normal emission level. By comparing the pollution contribution under the two conditions, the causal impact of the behavior is quantified. This is a set of covariates for historical pollution monitoring and enterprise production in the industrial park. It is a mathematical expectation operator used to calculate the expected value of the pollution contribution matrix under specific intervention conditions; This is a pollution contribution matrix, used to reflect the pollution impact relationships between nodes.

[0015] Preferably, the analysis method of the multimodal large model of water pollution in industrial parks further includes a model incremental update step to adapt to the dynamic changes in the characteristics of water pollution in the park and the production behavior of enterprises, specifically including the following steps: S91: Incremental dataset construction. New multimodal time-series data from the park is acquired according to a preset time period, including newly added water quality monitoring data, image monitoring data, and enterprise production activity data. After noise reduction and deduplication preprocessing of the new data, an incremental dataset is constructed. ; S92: Calculate data distribution differences, calculate incremental datasets. A measure of the difference in distribution between the training dataset and the historical training dataset in the multimodal fusion tensor feature space. The distribution difference measure is represented by KL divergence. The larger the KL divergence value, the greater the distribution difference between the two types of data and the worse the model fit. S93: Incremental Model Update and Map Reconstruction, Setting Distribution Difference Thresholds When the distribution difference measure Exceeding the preset threshold At that time, the online learning mechanism of the multimodal large model is triggered; with incremental datasets To train the model, the parameters were fine-tuned in small batches to prevent the model from forgetting historical knowledge. The pollution contribution matrix was recalculated using the updated multimodal model, and a new dynamic pollution source map was generated to ensure that the model always adapts to the changes in the pollution characteristics of the park.

[0016] An analysis system for a multimodal large-scale model of water pollution in industrial parks includes a data acquisition module, a data processing and fusion module, a model analysis module, and a decision-making and early warning module. The specific workflow of each module is as follows: S101: Data acquisition module workflow. The data acquisition module is configured to acquire multimodal time-series data within the industrial park through multi-source monitoring and acquisition equipment. The data acquisition module includes a water quality monitoring unit, an image monitoring unit, and an enterprise data acquisition unit. S102: Data processing and fusion module workflow. The data processing and fusion module is communicatively connected to the data acquisition module and configured to preprocess and spatiotemporally align multimodal time series data, and construct a multimodal fusion tensor with spatiotemporal correlation based on the park pipeline network topology. S103: Model Analysis Module Workflow. The model analysis module has a pre-trained multimodal large model embedded in it. It is connected to the data processing and fusion module and configured to receive the multimodal fusion tensor, calculate the pollution contribution matrix through the implicit causal reasoning mechanism, invert the probability distribution of pollution sources by combining real-time hydrodynamic parameters, and generate a dynamic pollution source tracing map. S104: Workflow of the decision-making and early warning module. The decision-making and early warning module is connected to the model analysis module and is configured to trigger graded pollution risk early warnings based on the probability of pollution sources in the dynamic pollution source tracing map. At the same time, it quantifies the degree of pollution causal impact of enterprise production behavior, matches the governance measures library, and generates governance decision schemes.

[0017] The present invention provides an analysis method and system for a multimodal large-scale model of water pollution in industrial parks. Addressing the shortcomings of existing technologies, it achieves precise, efficient, and dynamic control of water pollution in industrial parks through core designs such as multimodal data fusion, implicit causal reasoning, dynamic source tracing, hierarchical early warning, and incremental model updates. This results in the following beneficial effects: 1. Achieving efficient fusion and full utilization of multimodal data, overcoming the limitations of single-data type analysis in existing technologies: This invention integrates multiple types of time-series data, such as water quality monitoring, image monitoring, and enterprise production activities, and constructs a multimodal fusion tensor through spatiotemporal alignment and weighted fusion to comprehensively capture multi-dimensional features related to water pollution, providing comprehensive and accurate data support for subsequent analysis and improving the comprehensiveness and reliability of water pollution analysis.

[0018] 2. Improve the accuracy and efficiency of pollution source tracing, solving the problems of low accuracy and poor efficiency of traditional tracing methods: This invention uses the implicit causal reasoning mechanism of a multimodal large model, combined with the pipeline network topology and hydrodynamic characteristics, to inversely calculate the probability of pollution sources at each node and generate a dynamic pollution source tracing map. This can quickly locate the core pollution source, clearly identify the pollution diffusion path, and provide accurate source tracing basis for pollution control.

[0019] 3. Constructing a tiered early warning mechanism to achieve differentiated handling of pollution risks solves the problem of insufficient targeting of existing early warning mechanisms: This invention sets multi-level risk thresholds based on the probability of pollution sources, triggers different levels of early warnings and executes corresponding handling procedures, which not only avoids pollution spread caused by delayed early warnings, but also prevents resource waste caused by over-handling, and improves the timeliness and rationality of pollution risk management.

[0020] 4. Achieving precise and operational governance decisions, solving the problem of insufficient targeting of traditional governance measures: This invention quantifies the causal impact of enterprise production behavior on pollution by comparing causal reasoning models, identifies major abnormal production behaviors, matches targeted governance measures, and generates a complete governance decision plan, providing feasible guidance for the eradication of pollution and improving the efficiency and effectiveness of governance work.

[0021] 5. It has the ability to incrementally update the model, which solves the problem of poor adaptability of existing static models: This invention triggers online fine-tuning of the model by periodically constructing incremental datasets and calculating differences in data distribution, thereby realizing dynamic updates of model parameters and reconstruction of source tracing maps. This ensures that the model always adapts to the dynamic changes in the pollution characteristics of the park and the production behavior of enterprises, and guarantees the stability of long-term analysis accuracy.

[0022] 6. Modular system design, strong practicality and good scalability: The analysis system of this invention is divided into four modules: data acquisition, data processing and fusion, model analysis, and decision-making and early warning. Each module has a clear division of labor and smooth communication. The parameters of each module can be flexibly adjusted according to the actual management and control needs of the park, adapting to the water pollution control needs of industrial parks of different sizes and types, and facilitating promotion and application. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the analysis method of a multimodal large model of water pollution in industrial parks according to the present invention. Figure 2 This is a schematic diagram of the principle of the analysis system for a multimodal large model of water pollution in industrial parks according to the present invention. Figure 3 This is a block diagram illustrating the principle of the data acquisition module described in this invention. Detailed Implementation

[0024] The technical solutions of 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.

[0025] like Figures 1-3 As shown, this invention provides a technical solution: a method for analyzing a multimodal large-scale model of water pollution in industrial parks, comprising the following steps: S1: Acquire multimodal time-series data within the industrial park. The multimodal time-series data includes water quality monitoring data, image monitoring data, and enterprise production activity data. The water quality monitoring data consists of continuous time-series monitoring values ​​of pH, COD, and ammonia nitrogen-related water quality indicators at the park's pipeline network nodes and discharge outlets. The image monitoring data consists of video and visual image acquisition data of sewage outlets and key nodes of the pipeline network. The enterprise production activity data consists of time-series statistical data on the production load, raw material consumption, and wastewater discharge flow of enterprises in the park. S2: Perform spatiotemporal alignment processing on the multimodal time series data to unify the data time dimension and spatial coordinate benchmark, and construct a multimodal fusion tensor with spatiotemporal correlation based on the park pipeline network topology. S3: Input the multimodal fusion tensor into the pre-trained multimodal large model, and calculate and output the pollution contribution matrix of each potential pollution emission node in the park to the downstream monitoring node or the total emission outlet through the implicit causal reasoning mechanism of the multimodal large model. S4: Based on the pollution contribution matrix and combined with the real-time hydrodynamic parameters in the park's pipe network, the probability of pollution sources at each node is calculated and a dynamic pollution source tracing map is generated. S5: Based on the dynamic pollution source map, conduct pollution risk classification and early warning, or combine enterprise production activity data to quantify the causal impact of pollution and generate targeted governance decision-making schemes.

[0026] Specifically, the construction of a multimodal fusion tensor with spatiotemporal correlation in S2 includes the following steps: S21: Extract the pipeline network topology and construct a node association matrix. Based on the park's geographic information system map, extract the node distribution and pipeline connection relationships of the park's pipeline network, and construct a node association matrix. , where matrix elements Characterizing nodes in the park's pipeline network With nodes The direct connection relationship between nodes, if nodes With nodes If there is a direct pipe connection, then If there is no direct pipe connection, then ; S22: Extract multimodal features and construct a feature matrix. Based on the spatiotemporally aligned multimodal time-series data, filter the effective features of each monitoring node at the same time point and construct the feature matrix. , where the characteristic matrix Each row corresponds to a multimodal feature vector of a park node, and the column vectors correspond to the feature dimensions of each modality, thereby realizing feature integration of multimodal data; S23: Calculate the spatiotemporal weight matrix and fuse it to generate a multimodal fusion tensor, then calculate the spatiotemporal weight matrix using a spatiotemporal attention mechanism. And based on the spatiotemporal weight matrix For the feature matrix We perform weighted fusion to obtain the multimodal fusion tensor. The calculation formula is as follows: ; ; in, This represents the Hadamard product, which is the element-wise multiplication of two matrices of the same dimension at corresponding positions. , Both are learnable linear transformation matrices, used to transform the feature matrix. Mapping to the query space and key space enables feature dimension adaptation; Characteristic matrix The dimension of the feature vector is used to avoid dimensionality explosion during the calculation process; This is a normalization exponential function executed along the last dimension of the matrix, used to normalize the weight values ​​to the [0,1] interval, thereby achieving a reasonable allocation of weights; It is a multimodal fusion tensor with spatiotemporal correlation, which integrates the spatiotemporal features of multimodal data with the network topology correlation features.

[0027] The construction process of the multimodal fusion tensor in S2 serves to break down the spatiotemporal barriers and modal separations of multimodal data. Through a three-stage progressive processing, it achieves deep data integration and effective representation: S21 extracts the pipeline network topology and constructs a node association matrix A, clarifying the spatial connection relationships of each node in the park's pipeline network. This provides a physical topological benchmark for subsequent data fusion, ensuring the fusion process aligns with the actual pipeline network layout. S22 filters effective features based on the spatiotemporally aligned multimodal time-series data and constructs a feature matrix X. This achieves feature aggregation of three heterogeneous data types—water quality, images, and enterprise production—in the same spatiotemporal dimension, avoiding information fragmentation caused by inconsistent data dimensions and spatiotemporal asynchrony. S23 calculates the weight matrix through a spatiotemporal attention mechanism. and with the characteristic matrix Weighted fusion incorporates spatial correlation information of the pipeline network topology using the Hadamard product, dynamically assigns importance weights for each modality feature under different spatiotemporal conditions through softmax normalization, and leverages learnable matrices. , By achieving feature dimension adaptation, the risk of dimensionality explosion is effectively avoided, and the resulting multimodal fusion tensor is... It fully preserves the spatiotemporal characteristics and pipeline topology association attributes of multimodal data, providing a high-value and highly available unified feature input for subsequent implicit causal inference and pollution contribution calculation, which significantly improves the accuracy and efficiency of subsequent analysis processes.

[0028] In this embodiment, for example, an industrial park is selected as the application scenario. The park contains 100 pipeline monitoring nodes, 30 production enterprises, and a total pipeline length of 80 kilometers, covering various production industries such as chemical industry and machinery processing.

[0029] In step S21, based on the 1:1000 scale Geographic Information System (GIS) map of the park, the latitude and longitude coordinates and pipeline connection relationships of all pipeline nodes are extracted through a combination of image recognition and manual verification, constructing a 100×100-dimensional node association matrix. Node 1 is directly connected to Node 2, and Node 1 is directly connected to Node 5. , The matrix elements corresponding to node 1 and other nodes that are not directly connected are all 0. The matrix elements of the remaining nodes are assigned values ​​according to the same rules, thus completely restoring the physical connection topology of the park's pipeline network.

[0030] In step S22, spatiotemporal alignment processing is performed on the multimodal time-series data. Water quality monitoring data (pH, COD, ammonia nitrogen), image monitoring data (visual features of sewage outlet flow, pipeline damage identification features), and enterprise production activity data (production load, raw material consumption, wastewater discharge flow) are uniformly aligned to a 1-minute time granularity and a 1-meter spatial accuracy. Effective features without missing or abnormal features at the same time point are selected from each monitoring node, and a 100×30-dimensional feature matrix is ​​constructed. Each row corresponds to a multimodal feature vector of a pipeline node, and the column vectors correspond to 3 types of water quality indicators, 10 types of image features, and 17 types of enterprise production features, respectively, realizing the feature integration of multimodal data; In step S23, the feature matrix is ​​set. Dimension of feature vectors ,initialization , Given a 30×64 dimensional learnable linear transformation matrix, the spatiotemporal weight matrix is ​​calculated using a spatiotemporal attention mechanism. First, the feature matrix respectively with , Multiply to obtain the query matrix and key matrix. Transpose the key matrix and multiply it with the query matrix, then divide by 1 / 2. (Right now ) is scaled, and then associated with the node matrix. The Hadamard product is calculated, incorporating the pipeline topology information. Finally, the matrix is ​​normalized along its last dimension using the softmax function to obtain a 100×100 dimensional spatiotemporal weight matrix. The calculation formula is as follows: ; Then the spatiotemporal weight matrix With characteristic matrix Perform matrix multiplication to generate a multimodal fusion tensor. The calculation formula is as follows: ; Among them, the spatiotemporal weight matrix The weight values ​​are distributed in the interval [0,1]. The weight values ​​of nodes that are directly connected and have high spatiotemporal feature matching are closer to 1, and vice versa. The final generated 100×30-dimensional multimodal fusion tensor It not only includes the core features of multimodal data from each node, but also reflects the spatiotemporal correlation between nodes and the pipeline topology, providing accurate feature support for subsequent pollution contribution matrix calculation and pollution source tracing.

[0031] Specifically, S3 calculates the pollution contribution matrix through an implicit causal reasoning mechanism, which includes the following steps: S31: Deep feature extraction, converting the multimodal fusion tensor The data is input into a pre-defined dual-channel heterogeneous graph attention network, which is divided into a water quality-image modal channel and an enterprise production modal channel. The network learns the deep feature representations of the three modalities of data under the topology of the park's pipeline network, and finally outputs the hidden feature vectors of each node. S32: Pollution contribution calculation and matrix construction. Based on the hidden feature representation of each node, the potential pollution emission nodes in the park are calculated using a counterfactual reasoning framework. For downstream monitoring nodes Pollution contribution Summarize the pollution contribution of all nodes. This constitutes a pollution contribution matrix. The calculation formula is as follows: ; in, Potential pollution emission nodes The hidden layer feature vector obtained through the dual-channel heterogeneous graph attention network is used to characterize the multimodal deep features of the node. For downstream monitoring nodes The corresponding hidden layer feature vector is used to characterize the feature response of the downstream monitoring node; This represents a vector concatenation operation that merges the hidden feature vectors of upstream and downstream nodes into a one-dimensional vector, thereby achieving the association of features between upstream and downstream nodes. It is a learnable weight matrix used to perform linear transformations on the concatenated feature vectors, thereby strengthening the weights of key features. It is a linear rectified activation function with leakage, used to introduce nonlinear feature mapping and avoid the gradient vanishing problem; The sigmoid activation function is used to map the calculation results to the [0,1] interval, quantifying the relative magnitude of the pollution contribution. Attention weight coefficients, which integrate network topology and multimodal data features, are used to characterize nodes. With nodes The stronger the association between the elements, the larger the weighting coefficient.

[0032] The role of the implicit causal reasoning mechanism in S3 to calculate the pollution contribution matrix is ​​to overcome the limitations of traditional pollution contribution calculations that are "based solely on data correlation and lack causal mining." This enables the accurate quantification and attribution of the pollution impact of each potential pollution emission node on downstream monitoring nodes, providing a core basis for subsequent pollution source tracing and governance decisions. S31 will fuse multimodal tensors The input is a dual-channel heterogeneous graph attention network. By designing separate channels, it focuses on the water quality-image modality and the enterprise production modality respectively. Combined with the topology of the park's pipeline network, it deeply mines the deep features of various modal data, effectively avoiding the problems of insufficient representation of single modal features and interference from heterogeneous modal features. The output hidden feature vectors of each node can accurately characterize the pollution-related characteristics of the node, providing a high-quality feature foundation for subsequent causal inference.

[0033] S32 accurately calculates each potential pollution emission node based on hidden layer feature vectors and a counterfactual reasoning framework. For downstream monitoring nodes Pollution contribution Compared to traditional empirical calculations or simple statistical methods, this approach effectively isolates irrelevant factors and clarifies the causal relationships of pollution effects between nodes. Furthermore, by using the sigmoid activation function to quantify contributions to the [0,1] interval, it intuitively reflects the intensity of pollution effects at each node. The LeakyReLU activation function introduces a nonlinear mapping to avoid gradient vanishing, ensuring computational accuracy. Finally, it utilizes a learnable weight matrix. Strengthen the influence of key features, combined with attention weight coefficients By incorporating pipeline network topology information, a pollution contribution matrix is ​​finally constructed. It can clearly present the pollution impact relationship between all nodes and clarify the pollution contribution ratio of each potential pollution emission node. It solves the shortcomings of traditional methods that cannot accurately locate core pollution contribution nodes and are difficult to quantify the pollution transmission relationship between nodes. It provides a scientific and reliable quantitative basis for subsequent dynamic source tracing and precise treatment, and further improves the accuracy and pertinence of the entire water pollution analysis process.

[0034] In this embodiment, continuing the aforementioned industrial park application scenario, based on the already generated 100×30-dimensional multimodal fusion tensor... The pollution contribution matrix is ​​calculated through an implicit causal reasoning mechanism. The specific steps are as follows: In step S31, a dual-channel heterogeneous graph attention network is preset. This network is divided into a water quality-image modality channel and an enterprise production modality channel. The water quality-image modality channel is used to learn the deep correlation features between water quality monitoring data and image monitoring data, while the enterprise production modality channel is used to learn the intrinsic correlation features between enterprise production activity data and pollution emissions. The multimodal fusion tensor is then used to... After inputting the network, the network is combined with the park's pipeline topology (node ​​association matrix). The multimodal features of each node are hierarchically learned and fused to effectively filter redundant information and enhance pollution-related features. The final output is a hidden layer feature vector corresponding to 100 nodes, each with a 64-dimensional dimension. The hidden layer feature vector corresponding to the potential pollution emission node i is... The hidden feature vector corresponding to the downstream monitoring node j is ; In step S32, based on the hidden feature vectors of each node, the pollution contribution of each potential pollution emission node i to the downstream monitoring node j is calculated using a counterfactual reasoning framework. First of all, and Perform vector concatenation operation ( The two 64-dimensional hidden feature vectors are fused into a 128-dimensional one-dimensional vector, and then this concatenated vector is combined with a learnable weight matrix. (Initialized to 128×64 dimensions) Matrix multiplication is performed to complete the feature linear transformation to strengthen the weights of key contamination features. The result is then input into the LeakyReLU activation function for nonlinear mapping to avoid the gradient vanishing problem. The output is then input into the sigmoid activation function to map the result to the [0,1] interval, obtaining the initial contamination contribution. Finally, the initial contamination contribution is compared with the attention weight coefficients. Multiplying the 100×100 matrix (calculated in step S4 above) yields the final pollution contribution. The calculation formula is as follows: ; Summarize the pollution contribution between all 100 potential pollution emission points and 100 downstream monitoring points. Construct a 100×100 dimensional pollution contribution matrix. , where matrix elements This represents the pollution contribution of potential pollution emission node i to downstream monitoring node j. The closer the value is to 1, the greater the pollution impact of the potential pollution emission node on the downstream monitoring node, and vice versa. For example, potential pollution emission node 3 corresponds to downstream monitoring node 8. This indicates that node 3 contributes significantly to the pollution of node 8 and is one of the main sources of pollution at node 8. Meanwhile, node 3 corresponds to downstream monitoring node 20. This indicates that node 3 has a relatively small impact on the pollution of node 20. The pollution contribution matrix clearly shows the correlation of pollution impact among the nodes, providing direct quantitative support for subsequent accurate source tracing and targeted governance decisions.

[0035] Specifically, the attention weight coefficients The following steps are used to calculate: S41: Obtain the basic weight parameters and extract the spatiotemporal weight matrix obtained in step S23. Corresponding element in As the spatiotemporal prior weights of the pipeline network; simultaneously, the original attention coefficients are calculated using a graph attention mechanism. , as a data-driven attention weight; S42: Nonlinear weight fusion, employing a nonlinear fusion method to integrate spatiotemporal prior weights. With data-driven attention weights To achieve fusion, a balance coefficient is introduced. Control the weight distribution between the two; S43: Normalization processing: The fused weights are normalized to obtain the final attention weight coefficients. The calculation formula is as follows: ; in, This is an adjustable balance coefficient, with a value range of [0,1]. When only considering spatiotemporal prior information, when Only data-driven attention information is considered at this time, which is used to flexibly control the proportion of the two types of weights; For nodes The set of neighboring nodes in the park's pipeline network topology, i.e., the nodes All nodes have direct pipe connections; original attention coefficients. The calculation formula is: ,in The learnable parameter vector in the graph attention mechanism is used to assign weights to the concatenated hidden layer feature vectors, thereby achieving an accurate assessment of the strength of the association between nodes.

[0036] Attention weight coefficient The calculation steps aim to address the technical deficiency of traditional attention weight calculations, namely the disconnect between spatiotemporal prior information and data-driven features. This allows for the precise fusion of the spatiotemporal characteristics of the pipeline network topology with the correlation features of multimodal data, thus informing subsequent pollution contribution calculations. The precise calculations provide scientific and reliable weight support. Specifically, step S41 acquires spatiotemporal prior weights synchronously. With data-driven attention weights This approach retains the physical connectivity characteristics of the network topology in step S23 (spatiotemporal prior) while also uncovering the intrinsic correlations between multimodal hidden layer features (data-driven), effectively avoiding the correlation evaluation bias caused by a single weight type; step S42 introduces a balance coefficient. This method achieves non-linear fusion of two types of weights, allowing for flexible adjustment of their proportions based on the complexity of the industrial park's pipeline network and the required pollution monitoring accuracy. This adapts to the specific application scenarios of industrial parks with different business formats, solving the problem of fixed weights being unsuitable for dynamic pollution scenarios. Step S43 normalizes the fused weights to a reasonable range, eliminating calculation biases caused by differences in weight values ​​across different nodes and ensuring the comparability of attention weights between nodes. The final attention weight coefficients are then obtained. Capable of accurately depicting nodes With nodes The spatiotemporal correlation strength and data feature correlation between them not only conform to the actual topological layout of the park's pipeline network, but also accurately respond to the pollution correlation information reflected by multimodal data, effectively improving the subsequent pollution contribution matrix. The increased computational accuracy provides more targeted quantitative basis for tracing pollution sources and making decisions on pollution control, making up for the shortcomings of traditional weight calculations that ignore the synergistic effect of spatiotemporal topology and data features.

[0037] In this embodiment, continuing the aforementioned industrial park application scenario involving 100 pipeline monitoring nodes and 30 production enterprises, the 100×100-dimensional spatiotemporal weight matrix obtained in step S23 is used. The 64-dimensional hidden layer feature vectors of each node output in step S31 , Specifically, the above attention weight coefficients are implemented. Calculation steps: In S41, extract The corresponding element in the middle is as (e.g., the corresponding nodes 3 and 8) Simultaneously initialize the learnable parameter vector in the graph attention mechanism. 128-dimensional (and) (The concatenated vectors have the same dimension), through the formula The original attention coefficients were calculated. (e.g., the corresponding nodes 3 and 8) ); In S42, the balance coefficient is set. It emphasizes both spatiotemporal prior information and data-driven features, and... and Perform nonlinear fusion; In S43, taking node 3 as an example, its set of neighboring nodes... Including nodes 4, 6, and 9 that are directly connected to node 3, calculate the corresponding neighboring nodes. The values ​​are summed, and then the fusion weights corresponding to nodes 3 and 8 are divided by the summed value to obtain the final attention weight coefficients. Similarly, the relationships between all nodes can be calculated. A 100×100 dimensional attention weight coefficient matrix is ​​constructed. This matrix accurately reflects the spatiotemporal correlation and data feature correlation strength between nodes. Substituting this matrix into the pollution contribution calculation formula in step S32 can further improve... The calculation accuracy ensures the pollution contribution matrix. It can accurately reflect the pollution impact relationships between nodes.

[0038] Specifically, generating a dynamic pollution source map in S4 includes the following steps: S51: Construct a pollutant diffusion model based on the pollution contribution matrix. Combined with the park's pipeline network topology and real-time hydrodynamic parameters Constructing a physical-guided neural network model for pollutant diffusion The hydrodynamic parameters mentioned above This includes water flow velocity, hydraulic gradient, and cross-sectional area within the pipe network, used to simulate the diffusion patterns of pollutants within the pipe network; S52: Pollution source probability inversion calculation, inputting the actual water quality observation data of the park into the physical-guided pollutant diffusion neural network model. By using model inversion calculations, the probability distribution of each potential pollution emission node as a pollution source is obtained. ,in Represents a node This represents the probability value of a pollution source; the higher the probability value, the more likely the node is to be a pollution source. S53: Dynamic source tracing map generation, which will generate the probability distribution. The pollution contribution relationship between each node is overlaid on the park's geographic information system map, marking the spatial location of each node, the probability of pollution sources, and the contribution relationship between nodes, generating a visualized dynamic pollution source tracing map, and realizing an intuitive presentation of pollution sources. in, This is a pollution contribution matrix, representing the pollution impact relationships between nodes; This is the topology of the park's pipeline network, including geographical information such as node distribution and pipeline connection methods. These are real-time hydrodynamic parameters used to reflect the physical characteristics of water flow and pollutant diffusion within the pipe network; This is a physics-guided neural network model for pollutant diffusion, which integrates physical diffusion laws with data-driven modeling to improve the accuracy of inversion calculations. The probability distribution of pollution sources for each node is used to quantify the likelihood of each node being a pollution source. For a single node The probability of the pollution source is [0,1].

[0039] The generation steps of the dynamic pollution source map in S4 are designed to address the technical shortcomings of traditional pollution source tracing, such as "abstract results, lack of intuitiveness, failure to integrate physical diffusion laws, and inability to dynamically present pollution correlations." It enables the visualization, precision, and dynamic presentation of pollution source location, pollution diffusion path, and node correlation impact, providing intuitive and scientific source tracing support for water pollution control in the park.

[0040] Specifically, step S51 constructs a physically guided neural network model for pollutant diffusion. The pollution contribution matrix Pipeline topology With real-time hydrodynamic parameters The deep integration relies on data-driven mining of pollution correlations between nodes, and combines physical characteristics such as water flow velocity and hydraulic gradient to simulate the diffusion pattern of pollutants. This effectively avoids the source tracing bias caused by the pure data-driven model ignoring physical diffusion characteristics, and improves the accuracy and rationality of pollution source probability inversion.

[0041] Step S52 calculates the probability distribution of pollution sources through model inversion. This transforms abstract pollution contribution relationships into quantifiable probability values, clarifying the likelihood of each node being a pollution source, thus solving the problems of traditional source tracing being unable to quantify pollution source probabilities and locate core pollution sources.

[0042] Step S53 overlays the probability distribution and pollution contribution relationship onto a GIS map to generate a dynamic source tracing map. This map intuitively marks the location of nodes, the probability of pollution sources, and the correlation of contributions between nodes. It breaks through the limitations of traditional source tracing results being "abstract and difficult to interpret," enabling regulators to quickly identify core pollution sources, trace pollution diffusion paths, and clearly understand the pollution impact correlation between each node. At the same time, its dynamic characteristics can respond in real time to changes in water quality monitoring data and hydrological parameters, updating the source tracing results in a timely manner. This provides intuitive and accurate visualization support for subsequent graded early warning and precise governance decisions, making up for the shortcomings of traditional source tracing methods, such as lack of dynamic adaptability, low visualization level, and difficulty in implementation. It significantly improves the efficiency and pertinence of water pollution source tracing in the park, helping regulators to quickly deal with pollution hazards and accurately control pollution sources.

[0043] In this embodiment, the application scenario of an industrial park with 100 pipeline monitoring nodes, 30 production enterprises, and a total pipeline length of 80 kilometers is continued, based on the 100×100-dimensional pollution contribution matrix obtained above. Node association matrix Characterized pipeline topology The specific steps for generating the dynamic pollution source map described above are as follows: In S51, a physically guided neural network model for pollutant diffusion is constructed. Input pollution contribution matrix Pipeline topology Simultaneously, real-time hydrodynamic parameters of the park's pipeline network were collected. (The water flow velocity ranges from 0.8 to 1.2 m / s, the hydraulic gradient ranges from 0.002 to 0.005, and the cross-sectional area is divided into three categories: 0.5 m², 0.8 m², and 1.2 m², depending on the pipe specifications.) The model integrates physical diffusion laws with data-driven modeling to simulate the diffusion law of pollutants. In S52, real-time water quality observation data (real-time monitoring values ​​of indicators such as pH, COD, and ammonia nitrogen) from 100 pipeline monitoring nodes in the park are collected and input into the model. By performing constrained optimization inversion calculations, the probability distribution of pollution sources at each node is obtained. The pollution source probability of node 3 The probability of pollution sources at node 8 The probability of pollution sources at node 15 The probabilities of the remaining nodes are all below 0.5, clearly identifying nodes 3 and 15 as the core potential sources of pollution; In S53, the probability distribution The pollution contribution relationship between nodes (e.g., node 3 to node 8) The data is overlaid onto a 1:1000 scale GIS map of the park, marking the latitude and longitude coordinates of each node and the probability of pollution sources (distinguished by different color gradients: dark red for probabilities ≥0.8, orange for 0.5-0.8, and blue for <0.5). Arrow lines are used to mark the pollution contribution correlation and correlation strength between nodes, generating a visual dynamic pollution source tracing map. The map can be updated in real time with water quality observation data, hydrological parameters, and pollution source probabilities. Regulatory personnel can quickly identify nodes 3 and 15 as core pollution sources through the map, and clearly see that node 3 has a significant pollution impact on downstream node 8. This provides intuitive and accurate source tracing support for subsequent activation of secondary warning, on-site verification, and precise treatment.

[0044] Specifically, the probability distribution This is obtained by solving a constrained optimization problem, and the specific solution steps include: S61: Determine the objective function, aiming to minimize the error between the actual monitored concentration and the model predicted concentration. Combined with L1 regularization constraints, construct the constrained optimization objective function as follows: ; S62: Set constraints. Based on the physical meaning of the pollution source probability, set constraints to ensure the rationality of the solution results. The constraints are as follows: ; S63: Solve the optimization problem. Use the gradient descent algorithm to solve the above constrained optimization problem, and obtain the pollution source probability vector of each node that satisfies the constraints. That is, the probability distribution. ; in, The pollutant concentration vectors obtained from actual monitoring at each monitoring node in the park are acquired in real time by online water quality sensors. The physical-guided neural network model simulates and predicts pollutant concentration functions, and its output is the predicted concentration vector of each node, which is used to compare with the actual monitored concentration. Let be the pollution source probability vector for each node to be solved, containing the pollution source probabilities of all nodes; For the park's pipeline network topology, These are real-time hydrodynamic parameters, all of which are model input parameters; It is a non-negative L1 regularization coefficient used to induce sparse solutions, highlight core pollution source nodes, and avoid interference from redundant nodes; This represents the total number of pipeline nodes within the park. The L2 norm is used to measure the error between the actual monitored concentration and the model predicted concentration. The smaller the error, the higher the accuracy of the model prediction. The L1 norm is used to implement sparsity constraints on pollution source probabilities, making the pollution source probability of most nodes approach 0, while retaining only a few core pollution source nodes; the constraint conditions include... The probability of a pollution source for a single node is limited to between 0 and 1, which is within the range of probability values. The sum of the pollution source probabilities of all nodes is limited to no more than 1 to ensure the reasonableness of the solution results.

[0045] probability distribution The constrained optimization solution steps aim to address the technical shortcomings of traditional pollution source probability inversion, such as "unconstrained results leading to unreasonable outcomes, large errors, and lack of prominence of core pollution sources." This enables accurate and reasonable solutions for pollution source probabilities, providing a high-quality quantitative foundation for the generation of dynamic pollution source tracing maps.

[0046] Specifically, the constrained optimization objective function constructed in step S61 takes minimizing the error between the actual monitored concentration and the model predicted concentration as its core objective. It uses the L2 norm to accurately measure the deviation between the two, ensuring that the solved function... It can closely match real-world pollution scenarios, while incorporating L1 regularization constraints and non-negative coefficients. It effectively induces sparse solutions, which can highlight a few core pollution source nodes and suppress the interference of redundant nodes, thus solving the problems of fuzzy identification of pollution source nodes and difficulty in locking the core pollution source in traditional solutions. Step S62 sets constraints based on probabilistic physical principles, limiting the probability of a single node as a pollution source to the interval [0,1] and ensuring that the sum of the probabilities of all nodes does not exceed 1. This effectively avoids problems where the solution results exceed reasonable limits and do not conform to actual physical logic, ensuring... The scientific validity and reliability; Step S63 employs the gradient descent algorithm to solve the optimization problem, which boasts advantages such as fast convergence speed, high solution accuracy, and low computational cost. It can efficiently obtain probability vectors that satisfy the constraints. Compared to traditional optimization algorithms, it is better suited to complex industrial park scenarios with multiple nodes and multiple parameters, ultimately yielding a probability distribution. This method not only accurately quantifies the probability of each node as a pollution source, but also clarifies the core pollution source through sparsity constraints. It provides accurate and reasonable quantitative basis for the subsequent visualization of dynamic source tracing maps, the triggering of graded early warnings, and the formulation of precise governance decisions. This further enhances the scientific nature and feasibility of the entire water pollution source tracing process and makes up for the technical shortcomings of traditional pollution source probability solutions, such as lack of constraints, insufficient accuracy, and lack of prominence of core nodes.

[0047] In this embodiment, continuing the aforementioned industrial park application scenario, a physical-guided pollutant diffusion neural network model is constructed based on step S51. Actual monitoring concentration vectors of 100 pipeline monitoring nodes in the park (Data is collected in real time by online water quality sensors, covering concentration values ​​of indicators such as pH, COD, and ammonia nitrogen, arranged in a 100-dimensional vector according to node order), specifically implementing the above probability distribution. Constraint optimization solution steps: In S61, a constrained optimization objective function is constructed, and the L1 regularization coefficient is set. This is used to balance error minimization and sparsity constraints, with the objective function being... ,in This is the 100-dimensional pollutant concentration vector predicted by the model.

[0048] In S62, constraints are set, taking into account the 100 pipeline nodes in the park ( Based on the actual situation, the probability of pollution sources at a single node is constrained. ( ), and the sum of the probabilities of all nodes This ensures that the solution results conform to the probabilistic physical meaning.

[0049] In S63, the gradient descent algorithm is used to solve this constrained optimization problem, with a learning rate of 0.01 and 1000 iterations. The learning rate is adjusted iteratively. The values ​​of are taken until the objective function converges to its minimum value, ultimately yielding a 100-dimensional pollution source probability vector that satisfies the constraints. (i.e., probability distribution) ), of which node 3 Node 15 Node 8 The probabilities of the remaining 97 nodes are all below 0.1, and the sum of the probabilities of all nodes is 0.92 (≤1), which meets the constraint requirements. The solution result not only accurately matches the actual monitored concentration, but also highlights the two core pollution sources, nodes 3 and 15, through sparsity constraints, providing reliable quantitative support for subsequent dynamic source tracing map annotation and precise treatment.

[0050] Specifically, S5 uses a dynamic pollution source tracing map for pollution risk early warning, which includes the following steps: S71: Set risk level thresholds. Based on the park's water pollution control standards, environmental regulations, and the park's pollution prevention and control capabilities, set two levels of risk level thresholds. and ,in , The threshold for Level 1 warning is... The threshold is set at level two. S72: Level 1 Early Warning Triggering and Response, Real-time Monitoring of Pollution Source Probability at Each Node in the Dynamic Pollution Source Tracing Map ,when satisfy When a Level 1 warning is triggered, the system will automatically perform the following operations: S721, extract the high-probability pollution source tracing path of the node and sort out the path of pollutants spreading from the node to the downstream monitoring node; Retrieve information about the enterprises associated with this node, including enterprise name, production type, and wastewater discharge status; generate a report containing source tracing path, information about associated enterprises, and pollution trend analysis; push the report to the park's environmental protection supervision platform to remind supervisors to pay attention and verify it; S73: Level II Early Warning Triggering and Response, when the probability of a pollution source at a certain node in the dynamic pollution source tracing map... satisfy When a Level II warning is triggered, the system will simultaneously perform the following operations: activate the on-site audible and visual alarm devices in the park to alert on-site staff to take emergency measures; generate an emergency source tracing instruction, specifying the verification nodes, verification content, and time requirements; and initiate the on-site verification process for the pollution source, arranging staff to go to the node and related enterprises to conduct on-site sampling and verification, so as to control the spread of pollution in a timely manner. in, , This is a pollution risk level threshold; the specific value can be adjusted according to the actual situation of the park. An example setting is provided below. , ; Nodes in the dynamic pollution source map The probability of pollution sources is used to determine the pollution risk level; Level 1 warning corresponds to general pollution risk, Level 2 warning corresponds to major pollution risk, and different warning levels are matched with differentiated handling procedures to ensure that pollution risks are controlled in a timely manner.

[0051] The pollution risk early warning steps in S5 based on dynamic pollution source tracing maps aim to address the technical shortcomings of traditional pollution early warning systems, such as "lack of grading standards, single handling procedures, delayed response, and insufficient targeting." This enables precise grading of pollution risks, timely early warning, and differentiated handling, constructing a closed-loop management system of "early warning, tracking, verification, and control," and providing efficient and practical technical support for water pollution risk prevention and control in industrial parks.

[0052] Specifically, step S71 sets two threshold levels by combining the park's water pollution control standards, environmental regulations, and prevention and control capabilities. , ( This approach avoids the problems of over- or delayed warnings caused by a single threshold, and can differentiate control priorities based on the degree of pollution risk, adapting to the actual prevention and control capabilities of the park, ensuring that the warning standards are scientific, reasonable, and practical. Step S72, for Level 1 warnings (general pollution risk), automatically extracts the source tracing path, retrieves information from related enterprises, generates an analysis report, and pushes it to the regulatory platform, achieving accurate source tracing and information synchronization of pollution risks. This reminds regulatory personnel to conduct targeted verification, avoiding resource waste caused by over-handling and timely control of potential pollution hazards to prevent the spread and escalation of pollution. Step S73, for Level 2 warnings (major pollution risk), activates on-site audible and visual alarms, generates emergency tracking instructions, and initiates on-site verification procedures, achieving rapid response to pollution risks. In response to emergencies, the system aims to minimize pollution control response time, promptly control the spread of pollution, and reduce environmental impact and economic losses. The entire early warning process relies on the real-time updating capabilities of a dynamic pollution source map, enabling simultaneous responses to dynamic changes in pollution source probabilities. This allows for real-time updates of early warning information and dynamic adjustments to the handling process, solving the problem of traditional early warning methods being unable to adapt to dynamic pollution scenarios. Furthermore, the differentiated handling process balances control efficiency with rational resource utilization, ensuring that different levels of pollution risks are accurately and promptly controlled. This further improves the water pollution control system in industrial parks, enhancing the initiative, targeting, and efficiency of pollution risk prevention and control, and overcoming the technical shortcomings of traditional early warning methods, such as being crude, having a delayed response, and exhibiting disordered handling.

[0053] In this embodiment, the application scenario of an industrial park with 100 pipeline monitoring nodes and 30 production enterprises is continued. Based on the dynamic pollution source tracing map and the probability distribution of pollution sources at each node generated above, the application is further developed. The specific steps for implementing the above-mentioned pollution risk early warning are as follows: In S71, based on the park's environmental management standards, local environmental regulations, and the park's pollution prevention and control capabilities, two levels of risk level thresholds are set, as exemplarily defined. , ,in This is the Level 1 warning threshold, corresponding to general pollution risk. This is a Level II warning threshold, corresponding to a significant pollution risk.

[0054] In S72, the system monitors the pollution source probability of each node in the dynamic pollution source tracing map in real time. According to monitoring, node 22 Node 30 All satisfy If a Level 1 warning is triggered, the system will automatically extract high-probability pollution source tracing paths for nodes 22 and 30 (such as node 22→node 25→node 28, node 30→node 32→node 35), sort out the pollutant diffusion paths, retrieve information on machinery processing enterprise A associated with node 22 and chemical enterprise B associated with node 30 (including enterprise name, production type, wastewater discharge time and discharge volume, etc.), generate a warning report containing source tracing paths, information on associated enterprises and pollution trend analysis, and push it to the park's environmental protection supervision platform at the same time, reminding supervisory personnel to pay close attention and go to the site for verification.

[0055] In S73, node 3 Node 15 All satisfy Upon triggering a Level II warning, the system immediately activates the on-site audible and visual alarm devices in the park, alerting on-site staff to respond urgently. Simultaneously, it generates an emergency source tracing instruction, specifying the verification nodes as nodes 3 and 15 and related enterprises. The verification content includes whether there are abnormal emissions from the node's sewage outlets and the operation status of the related enterprises' production wastewater treatment facilities. The time requirement is to arrive at the site within 1 hour. Subsequently, the on-site verification process is initiated, and staff are arranged to carry sampling equipment to nodes 3 and 15 and related enterprises to conduct on-site sampling and facility verification work, and timely take control measures to prevent further spread of pollutants and ensure that pollution risks are quickly and effectively controlled.

[0056] Specifically, generating governance decision-making schemes in S5 also includes the following steps: S81: Quantification of Causal Impact. When the system triggers a Level 1 or Level 2 warning, based on the enterprise production activity data, a comparative causal inference model is invoked to quantify the causal impact of different production behaviors of enterprises in the park on the current sudden change in pollution contribution. The calculation formula is as follows: ; S82: Identification of major abnormal production behaviors, calculating the degree of causal impact of all enterprise production behaviors. ,extract The production behavior with the largest absolute value was identified as the main abnormal production behavior, which is the core factor causing the sudden change in pollution contribution. S83: Generation of governance decision-making schemes, which involves the following sub-steps: semantically and feature-matching the identified major abnormal production behaviors with the pre-set industrial park water pollution control measures database to select targeted treatment measures; compiling a detailed description of the abnormal behavior, including behavior type, implementation intensity, and duration; clarifying the implementation steps, responsible parties, completion deadlines, and precautions for the treatment measures; and integrating the above content to generate a complete governance decision-making scheme, providing actionable guidance for pollution control. in, The degree of causal impact of sudden changes in the contribution of enterprise production behavior to pollution. The larger the absolute value, the more significant the impact of this production activity on the abrupt change in pollution contribution. A positive value indicates that the behavior will increase the pollution contribution, while a negative value indicates that the behavior will decrease the pollution contribution. This refers to the intervention operator in causal inference, used to characterize human intervention in a firm's production behavior and exclude the influence of other confounding factors. For specific enterprise production activities, it represents a production operation or emission behavior of the enterprise, such as raw material input, production load adjustment, etc. This indicates that the production activity is in an abnormal or high-intensity emission state. This indicates that the production activity is at the industry benchmark or normal emission level. By comparing the pollution contribution under the two conditions, the causal impact of the behavior is quantified. This is a set of covariates for historical pollution monitoring and enterprise production in the industrial park, including historical water quality data and historical production data of enterprises, used to control for interfering factors and improve the accuracy of quantifying causal effects. It is a mathematical expectation operator used to calculate the expected value of the pollution contribution matrix under specific intervention conditions; This is a pollution contribution matrix, used to reflect the pollution impact relationships between nodes.

[0057] The steps in generating governance decision-making schemes in S5 are designed to address the technical shortcomings of traditional pollution control, such as "blind decision-making, weak targeted measures, lack of causal support, and inability to be implemented." By achieving a closed-loop governance model that "precisely identifies pollution causes, matches targeted measures, and clarifies implementation paths," S5 provides operable and scientifically reliable decision support for the precise governance of water pollution in industrial parks.

[0058] Specifically, step S81 quantifies the causal impact of firm production behavior on abrupt changes in pollution contribution by comparing causal inference models. With the help of intervention operators Excluding irrelevant confounding factors, and combining historical covariate sets Improving the accuracy of quantification breaks through the limitations of traditional governance that "only associates pollution phenomena without exploring the root causes of pollution," accurately quantifies the intensity and direction of the impact of different production behaviors on pollution mutations, and clarifies the intrinsic causal relationship between production behaviors and mutations in pollution contribution.

[0059] Step S82 involves extraction The production behavior with the largest absolute value accurately identifies the core abnormal behavior that causes a sudden change in pollution contribution, solving the problem of "inability to locate the core cause of pollution and coarse and general measures" in traditional governance, and providing a clear target for subsequent targeted governance.

[0060] Step S83 uses semantic and feature matching with a pre-set governance measures library to select suitable handling measures. It also clarifies key information such as details of abnormal behavior, implementation steps, responsible parties, and completion deadlines, transforming abstract causal analysis results into a complete governance plan that can be implemented and executed. This avoids the drawbacks of traditional governance measures being "vague, untargeted, and difficult to implement".

[0061] The entire decision-making process is closely linked to the aforementioned early warning, source tracing, and probability inversion steps. Relying on multimodal data and causal reasoning, it achieves a precise match between governance decisions and pollution causes. This ensures the scientific and targeted nature of governance measures, clarifies the specific requirements for implementation, effectively improves the efficiency and effectiveness of pollution control, reduces governance costs, and at the same time makes up for the technical shortcomings of traditional governance decisions, such as lack of causal support and poor operability. It further improves the closed-loop management system of "early warning, source tracing, decision-making, and governance" of water pollution in industrial parks.

[0062] In this embodiment, continuing the aforementioned industrial park application scenario involving 100 pipeline monitoring nodes and 30 production enterprises, based on the nodes (nodes 3, 15, 22, and 30) and related enterprises that triggered the early warning mentioned above, the above-mentioned governance decision-making scheme generation steps are specifically implemented as follows: In S81, when nodes 3 and 15 trigger a level-two warning and nodes 22 and 30 trigger a level-one warning, the production activity data of each related enterprise (including raw material input, production load, wastewater discharge flow, etc.) are retrieved. The comparative causal inference model is invoked, combined with the covariate set of historical pollution monitoring data of the industrial park and historical production data of the enterprises. (Including water quality monitoring data for the past 6 months, enterprise production records, etc.), quantify the causal impact of each enterprise's production behavior on sudden changes in pollution contribution. The calculation formula is: ,in These correspond to production behaviors such as raw material input and production load adjustment. For abnormal state, The status is normal; calculations show that the chemical company C associated with node 3 has a "raw material input exceeding the benchmark value by 30%" corresponding to... (A positive value indicates that this behavior significantly increases the pollution contribution.) For chemical company D associated with node 15, the corresponding "wastewater treatment facility shutdown for 1 hour" is... The node 22 associated with machining enterprise A has an "abnormally increased cutting fluid discharge flow rate". The chemical company B associated with node 30 has its "production load increased to 110%" .

[0063] In S82, extract the information from each enterprise. The production behaviors with the largest absolute values ​​were identified as follows: Chemical Company D "wastewater treatment facilities shut down for 1 hour", Chemical Company C "raw material input exceeded the benchmark value by 30%", Machining Company A "cutting fluid discharge flow rate increased abnormally", and Chemical Company B "production load increased to 110%" were identified as the main abnormal production behaviors that caused sudden changes in the pollution contribution of each related node.

[0064] In S83, the aforementioned major abnormal production behaviors are matched with the pre-set industrial park water pollution control measures database to select targeted treatment measures. Detailed descriptions of each abnormal behavior are compiled (e.g., chemical company D: wastewater treatment facilities shut down for 1 hour, resulting in direct discharge of untreated wastewater). The treatment steps are clarified (e.g., company D immediately restarts wastewater treatment facilities and performs emergency treatment on untreated wastewater; company C adjusts raw material input to the baseline value), the responsible parties (corresponding to the company's environmental protection manager and the park's environmental supervision specialist), the completion time limit (company D restarts facilities within 1 hour; company C adjusts raw material input within 2 hours), and precautions (emergency wastewater treatment must meet discharge standards). All of the above content is integrated to generate 4 targeted treatment decision plans, which are pushed to the corresponding companies and the park's supervision platform to provide clear and operable guidance for on-site pollution control and ensure that pollution is treated accurately and efficiently.

[0065] Specifically, the analysis method of the multimodal large model of water pollution in industrial parks also includes a model incremental update step to adapt to the dynamic changes in the characteristics of water pollution in the park and the production behavior of enterprises, which specifically includes the following steps: S91: Incremental dataset construction. New multimodal time-series data from the park is acquired according to a preset time period, including newly added water quality monitoring data, image monitoring data, and enterprise production activity data. After noise reduction and deduplication preprocessing of the new data, an incremental dataset is constructed. ; S92: Calculate data distribution differences, calculate incremental datasets. A measure of the difference in distribution between the training dataset and the historical training dataset in the multimodal fusion tensor feature space. The distribution difference measure is represented by KL divergence. The larger the KL divergence value, the greater the distribution difference between the two types of data and the worse the model fit. S93: Incremental Model Update and Map Reconstruction, Setting Distribution Difference Thresholds When the distribution difference measure Exceeding the preset threshold At that time, the online learning mechanism of the multimodal large model is triggered; with incremental datasets To provide training data, the model parameters were fine-tuned in small batches to prevent the model from forgetting historical knowledge. The pollution contribution matrix was recalculated using the updated multimodal model, and a new dynamic pollution source map was generated to ensure that the model always adapts to the changes in the pollution characteristics of the park. in, This is an incremental dataset containing newly added multimodal time-series data from the park, used for model updates; To measure the difference in distribution between the incremental dataset and the historical dataset, KL divergence is used. The value of KL divergence ranges from [0,+∞), and a value of 0 indicates that the two types of data are completely consistent. The preset distribution difference threshold, the specific value of which can be adjusted according to the model performance and the rate of change of pollution in the park, is used to determine whether a model update needs to be triggered; the online learning mechanism of the multimodal large model is used to achieve incremental fine-tuning of model parameters without retraining the entire model, thereby improving model update efficiency and reducing computational costs.

[0066] The incremental update step of the model aims to address the technical shortcomings of traditional multimodal large models, such as "static training, inability to adapt to dynamic changes, low update efficiency, and easy forgetting of historical knowledge." It ensures that the model always keeps pace with the dynamic changes in the characteristics of water pollution in the industrial park and the production behavior of enterprises, maintains the long-term accuracy of model analysis, source tracing, early warning and decision-making, and provides continuous and reliable technical support for the closed-loop management of water pollution in industrial parks.

[0067] Specifically, step S91 constructs an incremental dataset according to a preset cycle. By performing noise reduction and deduplication preprocessing on the newly added multimodal data, the effective pollution characteristics and production behavior information in the new data are preserved, while the interference of redundant and abnormal data on model updates is avoided. This provides a high-quality data foundation for incremental model updates and adapts to dynamic scenarios such as adjustments to the production conditions of enterprises in the park, changes in pollution characteristics, and updates to monitoring data.

[0068] Step S92 uses KL divergence to calculate the distribution difference between the incremental dataset and the historical training dataset. It can accurately quantify the degree of difference between two types of data in the multimodal fusion tensor feature space, providing a scientific and quantifiable basis for judging whether the model needs to be updated. This avoids the waste of computing resources caused by blind updates and also prevents problems such as decreased model adaptability and reduced analysis accuracy caused by excessive differences in data distribution and failure to update in time.

[0069] Step S93 involves setting a distribution difference threshold. This enables adaptive triggering of model updates, employing an online learning mechanism to fine-tune model parameters in small batches instead of retraining the entire model. This effectively avoids the model forgetting historical training knowledge (i.e., catastrophic forgetting), significantly improves model update efficiency, and reduces computational costs. At the same time, it recalculates the pollution contribution matrix and reconstructs the dynamic pollution source map, ensuring that the updated model can accurately match the current pollution characteristics and production behavior of the park, and continuously output accurate source tracing, early warning, and decision-making results.

[0070] The entire incremental update process runs through the entire water pollution control process, filling the technical gap of the traditional model's "one-time training and long-term reuse", ensuring that the model has dynamic adaptation capabilities, further improving the closed-loop control system of "early warning, source tracing, decision-making, treatment and updating" of water pollution in industrial parks, ensuring the continuity, accuracy and scalability of water pollution analysis and control work, and adapting to the dynamic changes in pollution characteristics and production behavior during the long-term operation of the park.

[0071] In this embodiment, continuing the aforementioned industrial park application scenario involving 100 pipeline monitoring nodes and 30 production enterprises, and based on the previously trained multimodal large-scale model of water pollution in industrial parks, the above-mentioned incremental update steps for the model are specifically implemented as follows: In S91, a preset time period of once a month is set to acquire newly added multimodal time-series data of the park monthly. This includes newly added water quality monitoring data (real-time monitoring values ​​of pH, COD, and ammonia nitrogen) from 100 pipeline monitoring nodes, newly added image monitoring data of pipeline outlets and pipes, and newly added production activity data (raw material input, production load, wastewater discharge flow, etc.) from 30 enterprises. The newly added data is denoised using wavelet denoising and deduplicated using data comparison. After removing abnormal and redundant data, a monthly incremental dataset is constructed. .

[0072] In S92, the incremental dataset is calculated. A measure of the difference in distribution between the model's historical training dataset and the multimodal fusion tensor feature space. (Using KL divergence as a characterization), a threshold for distribution difference was set. (Adjusted based on model performance and the rate of change in pollution in the park), the calculated incremental dataset for the current month and the historical dataset... Exceeding the preset threshold This triggers the model's online learning mechanism.

[0073] In S93, the incremental dataset built in the current month is used. For training data, a batch size of 32 and a learning rate of 0.001 were set to fine-tune the parameters of the multimodal large model in small batches. The focus was on fine-tuning the parameters related to the spatiotemporal attention mechanism and the implicit causal inference module to prevent the model from forgetting pollution features and network topology information from previous training. After the parameters were updated, the 100×100-dimensional pollution contribution matrix was recalculated using the updated multimodal large model. The pollution contribution of node 3 Updated from 0.82 to 0.85 (to adapt to the pollution characteristics after the adjustment of the raw material input process of Company C), node 15. The value was updated from 0.21 to 0.24. At the same time, the dynamic pollution source map was reconstructed, and the correlation between the pollution source probability and pollution contribution of each node was updated to ensure that the map can accurately reflect the current pollution distribution and transmission characteristics of the park. This allows the model to continuously adapt to the dynamic changes in the park's production behavior and pollution characteristics, providing accurate support for subsequent pollution risk early warning and governance decisions.

[0074] like Figure 2 and Figure 3 As shown, an analysis system for a multimodal large-scale model of water pollution in industrial parks includes a data acquisition module, a data processing and fusion module, a model analysis module, and a decision-making and early warning module. The specific workflow of each module is as follows: S101: Data Acquisition Module Workflow. The data acquisition module is configured to acquire multimodal time-series data within the industrial park through multi-source monitoring and acquisition equipment. The data acquisition module includes a water quality monitoring unit, an image monitoring unit, and an enterprise data acquisition unit. Specifically, it executes the following: S1011: The water quality monitoring unit uses online water quality sensors to collect continuous time-series monitoring values ​​of pH, COD, and ammonia nitrogen-related water quality indicators at nodes and discharge outlets in the park's pipeline network in real time, and transmits them to the data buffer. S1012: The image monitoring unit uses high-definition cameras and visual acquisition equipment to capture video and visual images of discharge outlets and key nodes in the pipeline network, performs image preprocessing, and transmits them to the data buffer. S1013: The enterprise data acquisition unit connects to the park's enterprise production management system to obtain time-series statistical data on enterprise production load, raw material consumption, and wastewater discharge flow, performs data format conversion, and transmits the data to the data buffer. S102: Data Processing and Fusion Module Workflow. The data processing and fusion module is communicatively connected to the data acquisition module and configured to preprocess and spatiotemporally align multimodal time-series data, and construct a spatiotemporally correlated multimodal fusion tensor based on the park's pipeline network topology. Specifically, it executes: S1021, preprocessing the multimodal time-series data in the data buffer by denoising, deduplication, and missing value imputation to ensure data validity; S1022, performing spatiotemporal alignment processing on the preprocessed multimodal data to unify the data's time dimension and spatial coordinate reference; S1023, constructing the multimodal fusion tensor based on the park's pipeline network topology according to steps S21-S23, and transmitting it to the model analysis module. S103: Model Analysis Module Workflow. The model analysis module embeds a pre-trained multimodal large model and connects to the data processing and fusion module. It is configured to receive the multimodal fusion tensor, calculate the pollution contribution matrix through an implicit causal inference mechanism, invert the pollution source probability distribution by combining real-time hydrodynamic parameters, and generate a dynamic pollution source tracing map. Specifically, it executes: S1031, receiving the multimodal fusion tensor transmitted by the data processing and fusion module; S1032, calculating the pollution contribution matrix through an implicit causal inference mechanism according to steps S31-S32; S1033, inverting the pollution source probability distribution and generating a dynamic pollution source tracing map by combining real-time hydrodynamic parameters according to steps S51-S53, and transmitting it to the decision-making and early warning module; simultaneously, performing incremental model updates periodically according to steps S91-S93 to ensure model adaptability. S104: Workflow of the Decision and Early Warning Module. The Decision and Early Warning Module communicates with the Model Analysis Module and is configured to trigger graded pollution risk early warnings based on the probability of pollution sources in the dynamic pollution source tracing map. Simultaneously, it quantifies the causal impact of enterprise production behavior on pollution, matches the governance measures library, and generates a governance decision plan. Specifically, it executes the following steps: S1041, Receive the dynamic pollution source tracing map transmitted by the Model Analysis Module; S1042, Following steps S71-S73, Trigger graded early warnings based on pollution source probabilities and execute corresponding early warning handling operations; S1043, When an early warning is triggered, Following steps S81-S83, Quantify the causal impact of enterprise production behavior, identify major abnormal production behaviors, match the governance measures library, and generate a governance decision plan; S1044, Push the early warning information and governance decision plan to the park's environmental protection supervision platform and relevant responsible entities.

[0075] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing a multimodal large-scale model of water pollution in industrial parks, characterized in that, Includes the following steps: S1: Acquire multimodal time-series data within the industrial park. The multimodal time-series data includes water quality monitoring data, image monitoring data, and enterprise production activity data. The water quality monitoring data consists of continuous time-series monitoring values ​​of pH, COD, and ammonia nitrogen-related water quality indicators at the park's pipeline network nodes and discharge outlets. The image monitoring data consists of video and visual image acquisition data of sewage outlets and key nodes of the pipeline network. The enterprise production activity data consists of time-series statistical data on the production load, raw material consumption, and wastewater discharge flow of enterprises in the park. S2: Perform spatiotemporal alignment processing on the multimodal time series data to unify the data time dimension and spatial coordinate benchmark, and construct a multimodal fusion tensor with spatiotemporal correlation based on the park pipeline network topology. S3: Input the multimodal fusion tensor into the pre-trained multimodal large model, and calculate and output the pollution contribution matrix of each potential pollution emission node in the park to the downstream monitoring node or the total emission outlet through the implicit causal reasoning mechanism of the multimodal large model. S4: Based on the pollution contribution matrix and combined with the real-time hydrodynamic parameters in the park's pipe network, the probability of pollution sources at each node is calculated and a dynamic pollution source tracing map is generated. S5: Based on the dynamic pollution source tracing map, conduct pollution risk classification and early warning, or combine enterprise production activity data to quantify the causal impact of pollution and generate targeted governance decision-making schemes; The generation of a dynamic pollution source map in S4 includes the following steps: S51: Construct a pollutant diffusion model based on the pollution contribution matrix. Combined with the park's pipeline network topology and real-time hydrodynamic parameters Constructing a physical-guided neural network model for pollutant diffusion The hydrodynamic parameters mentioned above This includes water flow velocity, hydraulic gradient, and cross-sectional area within the pipe network, used to simulate the diffusion patterns of pollutants within the pipe network; S52: Pollution source probability inversion calculation, inputting the actual water quality observation data of the park into the physical-guided pollutant diffusion neural network model. By using model inversion calculations, the probability distribution of each potential pollution emission node as a pollution source is obtained. ,in Represents a node This represents the probability value of a pollution source; the higher the probability value, the more likely the node is to be a pollution source. S53: Dynamic source tracing map generation, which will generate the probability distribution. The pollution contribution relationship between each node is overlaid on the park's geographic information system map, marking the spatial location of each node, the probability of pollution sources, and the contribution relationship between nodes, generating a visualized dynamic pollution source tracing map, and realizing an intuitive presentation of pollution sources. The probability distribution This is obtained by solving a constrained optimization problem, and the specific solution steps include: S61: Determine the objective function, aiming to minimize the error between the actual monitored concentration and the model predicted concentration. Combined with L1 regularization constraints, construct the constrained optimization objective function as follows: ； S62: Set constraints. Based on the physical meaning of the pollution source probability, set constraints to ensure the rationality of the solution results. The constraints are as follows: ； S63: Solve the optimization problem. Use the gradient descent algorithm to solve the above constrained optimization problem, and obtain the pollution source probability vector of each node that satisfies the constraints. That is, the probability distribution. ; in, This represents the pollutant concentration vector obtained from actual monitoring at each monitoring node in the park. The physical-guided neural network model simulates and predicts pollutant concentration functions, and its output is the predicted concentration vector of each node, which is used to compare with the actual monitored concentration. Let be the pollution source probability vector for each node to be solved, containing the pollution source probabilities of all nodes; For the park's pipeline network topology, These are real-time hydrodynamic parameters, all of which are model input parameters; is a non-negative L1 regularization coefficient; This represents the total number of pipeline nodes within the park. It is an L2 norm; The L1 norm is used to implement sparsity constraints on pollution source probabilities, making the pollution source probability of most nodes approach 0, while retaining only a few core pollution source nodes; the constraint conditions include... The probability of a single node being a pollution source is limited to between 0 and 1; The sum of the pollution source probabilities of all nodes is limited to no more than 1.

2. The analysis method and system for a multimodal large-scale model of water pollution in industrial parks according to claim 1, characterized in that, The construction of a multimodal fusion tensor with spatiotemporal correlation in S2 includes the following steps: S21: Extract the pipeline network topology and construct a node association matrix. Based on the park's geographic information system map, extract the node distribution and pipeline connection relationships of the park's pipeline network, and construct a node association matrix. , where matrix elements Characterizing nodes in the park's pipeline network With nodes The direct connection relationship between nodes, if nodes With nodes If there is a direct pipe connection, then If there is no direct pipe connection, then ; S22: Extract multimodal features and construct a feature matrix. Based on the spatiotemporally aligned multimodal time-series data, filter the effective features of each monitoring node at the same time point and construct the feature matrix. , where the characteristic matrix Each row corresponds to a multimodal feature vector of a park node, and the column vectors correspond to the feature dimensions of each modality, thereby realizing feature integration of multimodal data; S23: Calculate the spatiotemporal weight matrix and fuse it to generate a multimodal fusion tensor, then calculate the spatiotemporal weight matrix using a spatiotemporal attention mechanism. And based on the spatiotemporal weight matrix For the feature matrix We perform weighted fusion to obtain the multimodal fusion tensor. The calculation formula is as follows: ； ； in, It represents the Hadamardi (or Hadama) stack; , Both are learnable linear transformation matrices, used to transform the feature matrix. Mapping to the query space and key space enables feature dimension adaptation; Characteristic matrix The dimension of the feature vectors; This is the normalized exponential function executed along the last dimension of the matrix; It is a multimodal fusion tensor with spatiotemporal correlation.

3. The method for analyzing a multimodal large-scale model of water pollution in industrial parks according to claim 2, characterized in that, In S3, the pollution contribution matrix is ​​calculated through an implicit causal reasoning mechanism, specifically including the following steps: S31: Deep feature extraction, converting the multimodal fusion tensor The data is input into a pre-defined dual-channel heterogeneous graph attention network, which is divided into a water quality-image modal channel and an enterprise production modal channel. The network learns the deep feature representations of the three modalities of data under the topology of the park's pipeline network, and finally outputs the hidden feature vectors of each node. S32: Pollution contribution calculation and matrix construction. Based on the hidden feature representation of each node, the potential pollution emission nodes in the park are calculated using a counterfactual reasoning framework. For downstream monitoring nodes Pollution contribution Summarize the pollution contribution of all nodes. This constitutes a pollution contribution matrix. The calculation formula is as follows: ； in, Potential pollution emission nodes Hidden layer feature vectors obtained through a dual-channel heterogeneous graph attention network; For downstream monitoring nodes The corresponding hidden layer feature vector; This represents a vector concatenation operation; The weight matrix is ​​a learnable matrix; It is a linear rectified activation function with leakage; It is the sigmoid activation function; The attention weight coefficients are those that integrate the network topology and multimodal data features.

4. The method for analyzing a multimodal large-scale model of water pollution in industrial parks according to claim 3, characterized in that, The attention weight coefficient The following steps are used to calculate: S41: Obtain the basic weight parameters and extract the spatiotemporal weight matrix obtained in step S23. Corresponding element in As the spatiotemporal prior weights of the pipeline network; simultaneously, the original attention coefficients are calculated using a graph attention mechanism. , as a data-driven attention weight; S42: Nonlinear weight fusion, employing a nonlinear fusion method to integrate spatiotemporal prior weights. With data-driven attention weights To achieve fusion, a balance coefficient is introduced. Control the weight distribution between the two; S43: Normalization processing: The fused weights are normalized to obtain the final attention weight coefficients. The calculation formula is as follows: ； in, This is an adjustable balance coefficient, with a value range of [0,1]. For nodes The set of neighboring nodes in the park's pipeline network topology, i.e., the nodes All nodes have direct pipe connections; original attention coefficients. The calculation formula is: ,in The learnable parameter vector in the graph attention mechanism is used to assign weights to the concatenated hidden layer feature vectors, thereby achieving an accurate assessment of the strength of the association between nodes.

5. The method for analyzing a multimodal large-scale model of water pollution in industrial parks according to claim 4, characterized in that, S5 uses dynamic pollution source tracing maps for pollution risk early warning, which specifically includes the following steps: S71: Set risk level thresholds. Based on the park's water pollution control standards, environmental regulations, and the park's pollution prevention and control capabilities, set two levels of risk level thresholds. and ,in , The threshold for Level 1 warning is... The threshold is set at level two. S72: Level 1 Early Warning Triggering and Response, Real-time Monitoring of Pollution Source Probability at Each Node in the Dynamic Pollution Source Tracing Map ,when satisfy When a Level 1 warning is triggered, the system will automatically perform the following operations: S721, extract the high-probability pollution source tracing path of the node and sort out the path of pollutants spreading from the node to the downstream monitoring node; S73: Level II Early Warning Triggering and Response, when the probability of a pollution source at a certain node in the dynamic pollution source tracing map... satisfy When a Level II warning is triggered, the system will simultaneously perform the following operations: activate the on-site audible and visual alarm devices in the park to remind on-site staff to take emergency measures; generate an emergency source tracing instruction, specifying the verification nodes, verification content, and time requirements; and initiate the on-site verification process for the pollution source, arranging staff to go to the node and related enterprises to conduct on-site sampling and verification, so as to control the spread of pollution in a timely manner.

6. The method for analyzing a multimodal large-scale model of water pollution in industrial parks according to claim 5, characterized in that, The generation of governance decision-making schemes in S5 also includes the following steps: S81: Quantification of Causal Impact. When the system triggers a Level 1 or Level 2 warning, based on the enterprise production activity data, a comparative causal inference model is invoked to quantify the causal impact of different production behaviors of enterprises in the park on the current sudden change in pollution contribution. The calculation formula is as follows: ； S82: Identification of major abnormal production behaviors, calculating the degree of causal impact of all enterprise production behaviors. ,extract The production behavior with the largest absolute value was identified as the main abnormal production behavior, which is the core factor causing the sudden change in pollution contribution. S83: Generation of governance decision-making schemes, which involves the following sub-steps: semantically and feature-matching the identified major abnormal production behaviors with the pre-set industrial park water pollution control measures database to select targeted treatment measures; compiling a detailed description of the abnormal behavior, including behavior type, implementation intensity, and duration; clarifying the implementation steps, responsible parties, completion deadlines, and precautions for the treatment measures; and integrating the above content to generate a complete governance decision-making scheme, providing actionable guidance for pollution control. in, The degree of causal impact of sudden changes in the contribution of enterprise production behavior to pollution; This represents the intervention operator in causal inference; For specific enterprise production activities variables; This indicates that the production activity is in an abnormal or high-intensity emission state. This indicates that the production activity is at the industry benchmark or normal emission level. By comparing the pollution contribution under the two conditions, the causal impact of the behavior is quantified. This is a set of covariates for historical pollution monitoring and enterprise production in the industrial park. It is a mathematical expectation operator used to calculate the expected value of the pollution contribution matrix under specific intervention conditions; This is a pollution contribution matrix, used to reflect the pollution impact relationships between nodes.

7. The method for analyzing a multimodal large-scale model of water pollution in industrial parks according to claim 6, characterized in that, The analysis method of the multimodal large model of water pollution in industrial parks also includes an incremental model update step to adapt to the dynamic changes in the characteristics of water pollution in the park and the production behavior of enterprises. Specifically, it includes the following steps: S91: Incremental dataset construction. New multimodal time-series data from the park is acquired according to a preset time period, including newly added water quality monitoring data, image monitoring data, and enterprise production activity data. After noise reduction and deduplication preprocessing of the new data, an incremental dataset is constructed. ; S92: Calculate data distribution differences, calculate incremental datasets. A measure of the difference in distribution between the training dataset and the historical training dataset in the multimodal fusion tensor feature space. The distribution difference measure is represented by KL divergence. The larger the KL divergence value, the greater the distribution difference between the two types of data and the worse the model fit. S93: Incremental Model Update and Map Reconstruction, Setting Distribution Difference Thresholds When the distribution difference measure Exceeding the preset threshold At that time, the online learning mechanism of the multimodal large model is triggered; with incremental datasets To train the model, the parameters were fine-tuned in small batches to prevent the model from forgetting historical knowledge. The pollution contribution matrix was recalculated using the updated multimodal model, and a new dynamic pollution source map was generated to ensure that the model always adapts to the changes in the pollution characteristics of the park.

8. An analysis system for a multimodal large-scale model of water pollution in industrial parks, which is applied to the analysis method for a multimodal large-scale model of water pollution in industrial parks as described in claim 7, characterized in that, It includes a data acquisition module, a data processing and fusion module, a model analysis module, and a decision-making and early warning module. The specific workflow of each module is as follows: S101: Data acquisition module workflow. The data acquisition module is configured to acquire multimodal time-series data within the industrial park through multi-source monitoring and acquisition equipment. The data acquisition module includes a water quality monitoring unit, an image monitoring unit, and an enterprise data acquisition unit. S102: Data processing and fusion module workflow. The data processing and fusion module is communicatively connected to the data acquisition module and configured to preprocess and spatiotemporally align multimodal time series data, and construct a multimodal fusion tensor with spatiotemporal correlation based on the park pipeline network topology. S103: Model Analysis Module Workflow. The model analysis module has a pre-trained multimodal large model embedded in it. It is connected to the data processing and fusion module and configured to receive the multimodal fusion tensor, calculate the pollution contribution matrix through the implicit causal reasoning mechanism, invert the probability distribution of pollution sources by combining real-time hydrodynamic parameters, and generate a dynamic pollution source tracing map. S104: Workflow of the decision-making and early warning module. The decision-making and early warning module is connected to the model analysis module and is configured to trigger graded pollution risk early warnings based on the probability of pollution sources in the dynamic pollution source tracing map. At the same time, it quantifies the degree of pollution causal impact of enterprise production behavior, matches the governance measures library, and generates governance decision schemes.

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