Efficient and accurate pollution source monitoring quality control system and method

Abstract

The invention relates to the field of environment monitoring, and discloses an efficient and accurate pollution source monitoring quality control system and method, and the system comprises a multi-modal data collection module, a high-dimensional data modeling module, a time-space correlation analysis module, a time sequence prediction module, a multi-task learning module and an anomaly detection feedback module. The method comprises the following steps: collecting data of pollutants such as air, water quality and noise, performing high-dimensional modeling and optimization on the data, and capturing space-time propagation paths of the pollutants among different sensor nodes; the system predicts the short-term change and the long-term trend of the pollutant concentration in real time; the multi-task learning model is used for predicting concentration changes of multiple pollutants at the same time, multi-scale decomposition is carried out on a time sequence through wavelet transform, abnormal changes in data are detected, and a feedback control strategy is generated. According to the invention, the precision and real-time performance of pollution source monitoring are improved, the dynamic change of multiple pollutants can be effectively handled, and the high efficiency and reliability of environment quality monitoring are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a highly efficient and accurate pollution source monitoring quality control system and method. Background Art

[0002] In the current field of environmental monitoring, the demand for monitoring sources of air, water quality, noise, etc. is increasing, especially in industrial parks, transportation hubs and cities, where real-time monitoring and effective control of pollutants have become particularly important. Traditional pollution monitoring systems mostly use a single type of sensor with a limited data collection range and can only handle the monitoring of a single pollutant, making it difficult to effectively cope with complex multi-source pollution environments. At the same time, existing systems often fail to fully utilize the collected multi-dimensional data, resulting in serious data redundancy and information omissions. In addition, the dynamic propagation of pollutants has significant spatiotemporal characteristics, and existing technologies cannot accurately capture this dynamic change, thus limiting the effectiveness of monitoring systems in predicting and preventing pollution.

[0003] The existing monitoring system has obvious deficiencies in dealing with the joint analysis of multi-source pollutants, dynamic trend prediction, and response to abnormal events. Especially when dealing with the spread of pollutants in different time and space dimensions, the system lacks an effective spatiotemporal correlation analysis mechanism and cannot fully capture the diffusion path of pollutants. At the same time, existing prediction models usually only predict short-term or single pollutants, and it is difficult to take into account the long-term trend of pollutants or the joint prediction of multiple pollutants. In addition, the anomaly detection mechanism is often limited to a single threshold alarm mode, which cannot accurately identify complex anomalies in multi-dimensional data, nor can it generate response strategies in a timely manner, resulting in limited response speed and decision-making ability of the system. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an efficient and accurate pollution source monitoring quality control system and method, which realizes real-time monitoring, accurate prediction and abnormal response of multiple pollutants through multimodal data acquisition, spatiotemporal correlation analysis and multi-task learning, thereby improving the accuracy and efficiency of pollution source monitoring.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an efficient and accurate pollution source monitoring quality control system, comprising:

[0006] A multimodal data acquisition module, used to collect different types of pollutant data from multiple sensor nodes, where the sensor nodes include air quality monitoring sensors, wastewater monitoring sensors, and noise monitoring sensors;

[0007] High-dimensional data modeling module, used to perform topological data analysis and sparse representation optimization on the collected multimodal data, reduce data redundancy, and extract efficient features;

[0008] The spatiotemporal correlation analysis module is used to build a spatiotemporal correlation model of multiple sensor nodes based on graph neural networks and analyze the dynamic propagation path of pollutants between multiple monitoring points;

[0009] Time series forecasting module, used to predict future pollutant concentration trends based on adaptive time series models and seasonal ARIMA models;

[0010] A multi-task learning module for joint prediction of concentration changes of multiple pollutants at the same time;

[0011] The anomaly detection feedback module is used to detect abnormal fluctuations in pollutant data through multi-scale wavelet transform and generate feedback control signals based on the detection results.

[0012] Preferably, the high-dimensional data modeling module includes:

[0013] The topological data analysis unit is used to perform high-dimensional topological modeling on multimodal data. It constructs the topological features of complex extracted data by Rips, including:

[0014] Multimodal datasets collected by sensors Convert to Rips Complex R ∈ (X), the Rips complex is constructed in the following manner:

[0015]

[0016] Among them, X is the data set, dist(x i ,x j ) is the data point x i and x j The distance between them, ∈ is the preset distance threshold, and σ is a set containing several data points;

[0017] The persistent homology calculation unit is used to calculate the persistent homology of the data set. The persistent homology is analyzed by calculating the persistence of topological features at different scales. The persistence is defined as:

[0018] Pers(H k )=(b k ,d k )

[0019] Among them, H k is the k-dimensional homology group, b k and d k They represent the birth time and death time of topological features respectively;

[0020] The sparse representation unit is used to optimize the sparse representation of high-dimensional data. The dimensionality reduction and denoising of the data are performed by solving the sparse representation optimization problem. The sparse representation optimization formula is:

[0021]

[0022] Where X is the multimodal data matrix, D is the dictionary matrix, Z is the sparse representation matrix, and λ is the regularization parameter for controlling sparsity;

[0023] The efficient feature extraction unit is used to extract efficient features after sparse representation to ensure that the extracted features can effectively characterize the spatiotemporal distribution and changes of pollution source data.

[0024] Preferably, the spatiotemporal correlation analysis module includes:

[0025] The graph structure building unit is used to model the data of the multimodal sensor network as a graph structure G = (V, E), where V represents the set of sensor nodes, E represents the spatiotemporal association between nodes, and each node v i Corresponding to a sensor, the node feature is expressed as where h i is the multidimensional feature vector of the i-th node;

[0026] The graph neural network construction unit is used to establish a graph neural network model based on the constructed graph structure G. The graph neural network is used to iteratively update the node features through the graph convolution layer. The update formula is:

[0027]

[0028] in, is the node v at the l+1th layer i The updated features of is the set of neighbor nodes of node i, W (l) is the weight matrix of the lth layer, σ is the activation function, and AGG represents the aggregation function;

[0029] The aggregation function unit is used to realize the aggregation of node features in the message passing process of the graph neural network. The aggregation function includes mean aggregation and maximum pooling aggregation. The specific aggregation operations are:

[0030]

[0031] in, represents the number of neighbor nodes of node i, h j is the feature vector of neighbor node j;

[0032] The dynamic graph neural network unit is used to process the dynamic changes of the sensor network, construct a dynamic graph neural network, and dynamically update the sensor node features according to the time step t. The update formula of the node features over time is:

[0033]

[0034] in, is the node v at the l+1th layer and time step t+1 i Features, Indicates that at time t, node v i The set of neighbor nodes, W (l) is the dynamic weight matrix of the lth layer, σ is the activation function;

[0035] The spatiotemporal propagation analysis unit is used to determine the spatiotemporal propagation path of pollutants in the sensor network and extract the dynamic spatiotemporal correlation characteristics of pollution sources by analyzing the changes in node characteristics after the graph neural network completes multiple feature updates.

[0036] Preferably, the time series prediction module includes:

[0037] Adaptive Kalman filter unit for dynamic prediction of pollutant concentrations based on real-time sensor data and historical pollution data;

[0038] The seasonal ARIMA model unit is used to process pollutant data with seasonal periodic changes. The specific formula of the seasonal ARIMA model is:

[0039] φ p (B s )·φ p (B)(1-B) d (1-B s ) D y t =Θ q (B s )·θ q (B)∈ t

[0040] Where B is the lag operator, s is the seasonal period, d is the non-seasonal difference order, D is the seasonal difference order, Φ p (B s ) and φ p (B) are seasonal and non-seasonal autoregressive coefficients, Θ q (B s ) and θ q (B) are seasonal and non-seasonal moving average coefficients, ∈ t is the error term;

[0041] The prediction correction unit is used to perform prediction correction based on the short-term prediction results of the Kalman filter and the long-term trend prediction of the seasonal ARIMA model, combining the two to generate the final pollutant concentration prediction value.

[0042] Preferably, the multi-task learning module includes:

[0043] The task-sharing feature extraction unit is used to extract shared features from the monitoring data of different pollutants. By constructing a joint feature space, the model can learn shared feature representations from the monitoring data of each pollutant and form an underlying feature extraction network.

[0044] The task-specific branch unit is used to construct task-specific prediction branches based on the shared feature extraction and the characteristics of different pollutants. Each branch corresponds to the prediction task of a pollutant. The task-specific branch further processes the shared features through an independent neural network layer to generate the prediction results of specific pollutants.

[0045] The multi-task loss function optimization unit is used to optimize the system's joint prediction ability for multiple pollutants by constructing a joint loss function of multi-task learning. The joint loss function is:

[0046]

[0047] Where T is the number of tasks, is the loss function of the i-th task, α i is the weight of the i-th task;

[0048] The output integration unit is used to integrate the pollutant prediction results generated by each task-specific branch, and finally output the concentration prediction values ​​of multiple pollutants. The output is the concentration change trends of multiple pollutants predicted simultaneously, which is used for quality control and decision support of pollution sources.

[0049] Preferably, the multi-task learning module further includes:

[0050] The task weight adaptive adjustment unit is used to adjust the weight α of each task according to the changes in the monitoring data of each pollutant and the actual needs during the multi-task learning process. i Dynamic adjustments are made to ensure that the prediction accuracy of key pollutants is optimized first when pollutant concentrations change dramatically. The adaptive adjustment of task weights follows the following rules:

[0051]

[0052] in, is the task weight at time t, β is the learning rate, is the impact of task i on the loss function at time t.

[0053] Preferably, the abnormality detection feedback module includes:

[0054] Multiscale anomaly detection unit, used to perform multiscale analysis on the time series of pollutant concentrations based on multiscale wavelet transform and identify abnormal fluctuations in monitoring data;

[0055] The Bayesian anomaly detection unit is used to perform probability analysis of abnormal events using Bayesian statistical methods, and calculate the probability that the pollutant concentration exceeds the normal range at a certain moment based on the historical distribution of sensor data and current observations;

[0056] A threshold alarm unit is used to automatically generate an alarm signal according to a set pollutant concentration threshold when the multi-scale wavelet transform and Bayesian anomaly detection unit detect an anomaly. The alarm signal can trigger the feedback control mechanism of the system, including sending a notification to the system management personnel and triggering the start and stop of the automatic control equipment;

[0057] The feedback control strategy unit is used to generate a feedback control strategy according to the abnormality detection result.

[0058] Preferably, when the feedback control strategy unit detects an abnormal pollution event, the system automatically adjusts the monitoring frequency and monitoring range of the sensor. The feedback control strategy includes dynamically increasing the monitoring density of key areas and reducing the monitoring frequency of non-critical areas. The generation of the feedback control strategy is based on the following optimization objectives:

[0059]

[0060] Among them, C is the feedback control strategy, R i (C) represents the risk level of control strategy C for the i-th monitoring area, M i (C) is the resource consumption of the monitoring area, α i and β i Represent the weights of risk level and resource consumption respectively.

[0061] Preferably, the abnormality detection feedback module further includes:

[0062] The automated response unit is used to automatically adjust the sensor configuration, monitoring frequency, transmission rate and data processing priority after generating the feedback control strategy, and execute the system control instructions through automated equipment to achieve real-time response to abnormal conditions of pollution sources.

[0063] The present invention also provides an efficient and accurate pollution source monitoring quality control method, comprising the following steps:

[0064] Collect pollutant data on air, water quality and noise;

[0065] Use topological data analysis methods to model high-dimensional multimodal pollutant data, and use sparse representation methods to reduce and optimize data dimensionality;

[0066] A graph neural network model is used to build a spatiotemporal correlation model of pollutants to capture the dynamic propagation path of pollutants between sensor nodes;

[0067] Based on historical monitoring data, an adaptive Kalman filter is used to make real-time predictions of pollutant concentrations, and a seasonal ARIMA model is used to predict long-term trends;

[0068] Use multi-task learning models to simultaneously predict concentration changes of multiple pollutants;

[0069] The time series of pollutant data is decomposed at multiple scales through wavelet transform to detect abnormal changes in the data and adjust the monitoring strategy based on the feedback results.

[0070] The present invention provides an efficient and accurate pollution source monitoring quality control system and method. It has the following beneficial effects:

[0071] 1. The present invention collects multiple pollutant data such as air quality, water quality and noise simultaneously through a multimodal sensor network to ensure the breadth and depth of monitoring. Combined with topological data analysis and sparse representation technology, the system can extract key features from high-dimensional data and effectively remove redundancy and noise, thereby significantly improving the accuracy of data processing and the accuracy of monitoring results.

[0072] 2. This invention introduces graph neural network (GNN) to model the spatiotemporal relationship between sensor nodes. The system can capture the propagation paths and correlations of pollutants in different time and space. The accuracy of spatiotemporal correlation analysis improves the ability to identify pollutant diffusion paths and helps managers better understand the dynamic changes of pollution sources.

[0073] 3. The adaptive Kalman filter of the present invention is used for short-term prediction of pollutant concentrations and can be dynamically adjusted according to real-time data, while the seasonal ARIMA model is used for long-term trend prediction, especially for pollutants with significant seasonal changes. The combination of the two enables the system to have a high prediction ability in dealing with both short-term fluctuations and long-term trends of pollutants.

[0074] 4. The present invention adopts a multi-task learning model to simultaneously predict the concentration changes of multiple pollutants, using the commonality between tasks to improve prediction accuracy and reduce redundant calculations. This method enables the system to efficiently handle the joint prediction tasks of multiple pollutants and significantly improve the overall computing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0076] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] Please see attached Figure 1 , the embodiment of the present invention provides an efficient and accurate pollution source monitoring quality control system, which can realize real-time monitoring and quality control of various pollutants such as air, wastewater, noise, etc. Through multimodal sensor network data acquisition, high-dimensional modeling, spatiotemporal correlation analysis, time series prediction and multi-task learning technology are combined to accurately capture the trend of pollutant concentration changes, detect abnormal conditions and automatically generate feedback control signals. The present invention not only improves the accuracy of pollution source monitoring, but also significantly enhances the real-time response capability and data processing efficiency of the system, and has broad application prospects. The specific implementation of each module of the system of the present invention is described in detail below.

[0079] Multimodal data acquisition module

[0080] The multimodal data acquisition module provided in the embodiment of the present invention is the basis of the entire pollution source monitoring quality control system, which is used to obtain relevant data of environmental pollutants from different types of sensor nodes and transmit them to the subsequent data processing module in real time. The module can simultaneously collect information on multiple pollutants, such as air quality, water pollution and noise data, to ensure that the monitoring system can fully cover multiple pollution sources.

[0081] In this embodiment, the specific implementation of the multimodal data acquisition module is as follows:

[0082] 1. Sensor node layout and type: In this embodiment, the multimodal data acquisition module includes multiple sensor nodes, which are arranged at different locations of the pollution source. These nodes are used to collect different types of pollutant data. Specifically, the sensor nodes include:

[0083] Air quality monitoring sensors are used to collect concentration data of particulate matter (such as PM2.5, PM10) and gaseous pollutants (such as NO2, SO2, CO, etc.) in the air;

[0084] Wastewater monitoring sensors to monitor pollutant concentrations in water, such as heavy metal ions, chemical oxygen demand (COD), biological oxygen demand (BOD), and other toxic substances;

[0085] Noise monitoring sensors are used to measure the noise level (in decibels) in the environment, especially in industrial areas or areas with heavy traffic.

[0086] 2. Multimodal data acquisition and transmission:

[0087] In this embodiment, each sensor node collects the raw data of its corresponding pollutant in real time. In order to ensure the data transmission efficiency and real-time performance of the system, a sensor network architecture based on wireless communication technology, such as LoRa, NB-IoT, 4G / 5G and other communication technologies, is adopted to ensure the rapid transmission of data between each node and the central processing unit, avoiding the complex wiring and maintenance problems caused by wired connections.

[0088] 3. Spatiotemporal synchronization of multimodal data:

[0089] In this embodiment, the collected data not only includes different types of pollutant data, but also includes the sensor's geographical location information and timestamp. Each sensor node will be accompanied by the specific time and geographical coordinates of its data collection, so that the subsequent data analysis module can perform spatiotemporal correlation analysis.

[0090] 4. Preliminary processing of multimodal data:

[0091] In this embodiment, after collecting data, the sensor nodes perform a certain degree of preliminary processing to reduce redundancy and noise that may be generated during data transmission. Each node pre-processes the data locally, such as data denoising, missing value filling, data standardization, etc., to ensure that the data uploaded to the central processing unit maintains a high quality.

[0092] 5. Synchronous structure of multimodal data:

[0093] In this embodiment, multiple sensor nodes are synchronized through a unified clock to ensure the consistency of the collected data in time and space. The clock synchronization of sensor data can be achieved through GPS signals or other time synchronization protocols to ensure that the time of all sensor nodes is consistent.

[0094] 6. Integration and transmission of multimodal data:

[0095] In this embodiment, the data collected by multiple sensor nodes are uploaded to the central processing unit through wireless transmission technology. The transmission protocol supports multiple modes, including real-time data transmission based on event triggering and batch transmission of data collected at regular intervals. The specific mode is set according to the needs of the monitoring area. For example, when the pollutant concentration fluctuates greatly, the sensor can switch to the event trigger mode to send early warning data to the central system in real time; when the pollutant concentration is relatively stable, the regular batch transmission mode can be used to save transmission resources.

[0096] 7. Multimodal Data Representation:

[0097] In this embodiment, in order to effectively organize and store the multimodal data collected by the sensor nodes, each pollutant data is represented as a high-dimensional vector xi before transmission, which includes the pollutant concentration, collection timestamp, sensor location information, etc. The data format is defined as follows:

[0098] x i =[d i,1 ,d i,2 ,…,d i,k ,t i ,l i ]

[0099] Among them, d i,j represents the monitoring value of the i-th sensor on the j-th type of pollutant, t i Indicates the time of data collection, l i Represents the geographic coordinates of the sensor. This high-dimensional data representation provides the necessary foundation for the subsequent high-dimensional data modeling module and ensures that the data can remain structured and usable during the analysis process.

[0100] 8. Multimodal data collaborative processing:

[0101] In this embodiment, multiple sensor nodes can work together in a networking manner. The system will dynamically adjust the sensor nodes according to the characteristics of different monitoring areas to ensure the comprehensiveness and accuracy of data collection. For example, for monitoring a large industrial area, the air quality sensor may share some information with the wastewater sensor in order to more accurately capture possible pollution diffusion sources.

[0102] The multimodal data acquisition module in this embodiment realizes comprehensive pollutant data acquisition through air quality monitoring sensors, wastewater monitoring sensors and noise monitoring sensors.

[0103] High-dimensional data modeling module

[0104] Since the pollutant data collected by sensors are usually multidimensional and redundant, appropriate data modeling and optimization can significantly improve the efficiency and accuracy of subsequent data analysis. This module uses topological data analysis (TDA) and sparse representation optimization to extract key features from pollutant data, reduce data redundancy, and provide efficient feature representation for subsequent spatiotemporal correlation analysis, prediction, and control.

[0105] In this embodiment, the specific implementation of the high-dimensional data modeling module is as follows:

[0106] 1. Topological Data Analysis (TDA):

[0107] In this embodiment, a topological data analysis method is used to process the high-dimensional features of multimodal pollutant data. By analyzing the topological structure of the pollutant data, the global and local associations hidden in the data set can be discovered. Specifically, the system first performs topological structure modeling on the data collected by the sensor.

[0108] Rips complex construction: Assume that the multimodal data set collected by the sensor is X = {x1, x2, ..., x n},in represents the high-dimensional representation of the i-th data point, n is the number of samples, and d is the feature dimension. To analyze the topological features in the data, Rips complex is used to construct the topological structure of the data, which is defined as follows:

[0109]

[0110] Among them, X is the data set, dist(x i ,x j ) is the data point x i and x j The distance between them, ∈ is the preset distance threshold, and σ is a set of several data points. Through Rips complexity, the system can capture the connection relationship and local structure in the data set.

[0111] Persistent homology calculation: After the Rips complex construction, this embodiment calculates the persistent homology of the topological structure to extract stable topological features in high-dimensional data. Persistent homology identifies persistent features in the data by tracking changes in the topological structure. Specifically, the persistence of the k-dimensional homology group is defined as:

[0112] Pers(H k )=(b k ,d k )

[0113] Among them, H k is the k-dimensional homology group, b k and d k They represent the birth time and extinction time of topological features, respectively. By calculating persistent features of different dimensions, the system can identify important structures in pollutant data and remove transient noise information.

[0114] 2. Sparse representation optimization:

[0115] In this embodiment, the system performs sparse representation optimization on the data after topological data analysis. The high-dimensional data collected by the sensor usually contains a large amount of redundant information. Through sparse representation, a small number of representative important features can be extracted while reducing noise interference. The sparse representation optimization process is achieved through the following steps:

[0116] Sparse representation model construction: Assume that the multimodal data matrix is Where n is the number of samples and d is the feature dimension. The sparse representation optimization goal is to find a sparse coefficient matrix Z and a dictionary matrix D so that X can be approximated by the product of D and Z while ensuring that Z is sparse. The optimization goal is:

[0117]

[0118] Among them, X is the multimodal data matrix, D is the dictionary matrix, Z is the sparse representation matrix, and λ is the regularization parameter that controls sparsity. By minimizing this objective function, the system can generate a sparse representation Z that can efficiently represent the original data.

[0119] Sparse decoding and dimensionality reduction: After completing the sparse representation optimization, the system uses the dictionary matrix D and the sparse representation matrix Z to decode and reduce the data. Sparse representation not only effectively reduces data redundancy, but also retains key information in the data. Since the amount of data collected by the sensor network is huge and has high noise, sparse representation can remove noise without losing the main information, thereby improving the processability of the data.

[0120] 3. Data format conversion and efficient feature extraction:

[0121] In this embodiment, after topological data analysis and sparse representation optimization, the system will convert the multimodal data format to generate a feature vector representation suitable for subsequent analysis. The data of each sensor node is finally represented as an efficient feature vector v i , which contains the main characteristics of the data, timestamp and geographic location information. The final representation of the data is as follows:

[0122] v i =[f i,1 ,f i,2 ,…,f i,m ,t i ,l i ]

[0123] Among them, f i,j represents the jth feature of the i-th sample extracted, t i is the timestamp, l i The sensor’s geographic location information. The efficient feature representation in this format provides a basis for subsequent spatiotemporal correlation analysis and time series prediction.

[0124] The high-dimensional data modeling module in this embodiment can effectively extract key features from multimodal pollutant data through topological data analysis and sparse representation optimization technology.

[0125] Spatiotemporal correlation analysis module

[0126] The multimodal data acquisition module provides a large amount of sensor data with spatiotemporal labels, and through the spatiotemporal correlation analysis module, the system can build a pollutant propagation model in different time and space based on these data. This module mainly uses graph neural network (GNN) technology to model, combines the spatial position, timestamp and pollutant concentration changes of sensor nodes, identifies the spatiotemporal dynamic correlation of pollutants, and provides a basis for subsequent prediction and control decisions.

[0127] In this embodiment, the specific implementation of the spatiotemporal correlation analysis module is as follows:

[0128] 1. Construction of sensor network graph structure:

[0129] In this embodiment, the multimodal sensor network is firstly modeled as a graph structure G = (V, E), where:

[0130] V is the set of sensor nodes, each node v i Represents a sensor node, and the node characteristics include the pollutant concentration, timestamp and location information collected by the sensor;

[0131] E is an edge set, which represents the spatial or temporal association between sensor nodes. The weight of the edge can be calculated based on the geographical distance or temporal correlation between sensor nodes.

[0132] For any two sensor nodes v i and v j , if the geographical distance between the two is dist(v i ,v j ) is less than a preset threshold ∈ s , then add an edge between these two nodes in graph G:

[0133] E ij ={(v i ,v j )||dist(v i ,v j )≤∈ s}

[0134] Among them, ∈ s Indicates the spatial distance threshold. In addition, the association in the time dimension is measured by the time difference. If the sampling time difference between two nodes is less than a certain time threshold ∈ t, you can also add edges to it.

[0135] 2. Graph neural network construction and feature update:

[0136] In this embodiment, the constructed graph structure is analyzed through the graph neural network to identify the propagation law of pollutants in the time and space dimensions. The core of the graph neural network is the aggregation and update of node features. The initial features of each sensor node are: Including the pollutant concentration, timestamp and geographic location information of the sensor node, the initial feature is expressed as:

[0137]

[0138] Among them, d i Represents node v i The collected pollutant concentration data, t i Indicates the data collection time, l i Indicates the geographic location of the sensor.

[0139] In each layer of the graph neural network, node features are aggregated and updated with neighbor node features to reflect their spatiotemporal associations. i The feature update formula at the l+1th layer is:

[0140]

[0141] in, is the l+1th layer node v i Features, is node v i The neighbor node set, AGG is the aggregation function, usually using mean aggregation:

[0142]

[0143] in, represents the number of neighbor nodes of node i, h j is the feature vector of neighbor node j. Through iterative updates of multi-layer graph convolution, the system can capture the spatiotemporal correlation characteristics between sensor nodes and identify the propagation trend of pollutants between different nodes.

[0144] 3. Dynamic update of dynamic graph neural network:

[0145] In this embodiment, the propagation path of pollutants changes dynamically over time, so a dynamic graph neural network (DGNN) is needed to dynamically adjust and update the graph structure of the sensor network. The feature update formula of the dynamic graph neural network is:

[0146]

[0147] in, is the node v at the l+1th layer and time step t+1 i Features, Indicates that at time t, node vi The definition of neighbor nodes may be adjusted dynamically over time to capture the time-varying propagation law of pollutants. (l) is the dynamic weight matrix of the lth layer, and σ is the activation function.

[0148] 4. Spatiotemporal propagation analysis and result output:

[0149] In this embodiment, after updating the multi-layer graph neural network and dynamic feature propagation, the system can identify the dynamic propagation path of pollutants between different monitoring points and generate a spatiotemporal dependency graph between sensor nodes. By analyzing the spatiotemporal propagation path, the system can predict the diffusion direction of pollutants, determine potential pollution sources, and evaluate the degree of pollution impact on different areas.

[0150] The results of this spatiotemporal correlation analysis will be output to the subsequent control decision module to dynamically adjust the monitoring strategy or trigger the corresponding control mechanism to ensure that the diffusion of pollutants is within a controllable range.

[0151] The spatiotemporal correlation analysis module in this embodiment constructs and analyzes the spatiotemporal relationship of a multimodal sensor network based on graph neural network technology, and can effectively capture the dynamic propagation path of pollutants between different monitoring points.

[0152] Time Series Forecasting Module

[0153] This module predicts the changing trend of pollutant concentrations in the future time period based on the historical pollutant data collected by the sensor network. This module combines the adaptive time series model (ATSM) and the seasonal ARIMA model to cope with short-term fluctuations and long-term seasonal trends in pollutant concentrations. Through dynamic adjustment and adaptive modeling of data, the system can effectively capture the changing patterns of pollutant concentrations over time, providing strong support for the prediction and prevention of pollutants.

[0154] In this embodiment, the specific implementation of the time series prediction module is as follows:

[0155] 1. Adaptive Time Series Model (ATSM):

[0156] In this embodiment, an adaptive time series model is first used to process short-term fluctuations in pollutant concentrations. The adaptive Kalman filter is the core of the model and is used to dynamically adjust the system's prediction of future pollutant concentrations based on pollutant concentration data collected in real time by sensors.

[0157] State prediction: Let x t is the pollutant concentration state at time t, and the system is predicted by the following state equation:

[0158]

[0159] in, is the predicted value at time t, A is the state transfer matrix, is the state of the previous moment, B is the control input matrix, u t is the control input at time t (such as the impact of external factors on pollutants).

[0160] Covariance prediction: The system also updates the error covariance to quantify the uncertainty of the prediction results. The update formula of the covariance is:

[0161] P t|t-1 =A·P t-1 ·A T +Q

[0162] Among them, P t|t-1 is the prediction covariance matrix at time t, P t-1 is the covariance matrix of the previous moment, and Q is the covariance matrix of the process noise, which quantifies the uncertainty in the model prediction process.

[0163] Kalman gain calculation: After receiving the actual observation value of the sensor, the system calculates the Kalman gain K t , used to update the prediction based on actual data. The calculation formula of Kalman gain is:

[0164] K t =P t|t-1 ·H T ·(H·P t|t-1 ·H T +R) -1

[0165] Among them, H is the observation matrix, R is the observation noise covariance matrix, K t Indicates the degree to which the system adjusts the prediction model based on the current observations.

[0166] State update: combined with actual observation value z t , the system corrects the predicted pollutant concentration, and the state update formula is:

[0167]

[0168] in, is the corrected predicted value of pollutant concentration, z t is the actual observation value at time t. Covariance update: The system updates the covariance matrix according to the corrected state:

[0169] P t =(IK t ·H)·Pt|t-1

[0170] Among them, P t is the updated covariance at time t, and I is the identity matrix.

[0171] The adaptive Kalman filter can dynamically adjust the prediction model according to the real-time data of the sensor, adapt to the changes in environmental conditions, and improve the accuracy of short-term predictions.

[0172] 2. Seasonal ARIMA Model:

[0173] In this embodiment, the system also uses a seasonal ARIMA model to process the long-term trend of pollutant concentrations, especially for pollutants with seasonal fluctuation characteristics (such as pollutants affected by climate and seasonal changes).

[0174] Model structure: The seasonal ARIMA model models time series data with seasonality and long-term trends by combining autoregression, difference, and moving average processes. The basic formula of the model is:

[0175] Φ p (B s )·φ p (B)(1-B) d (1-B s ) D y t =Θ q (B s )·θ q (B)∈ t

[0176] in:

[0177] B is the lag operator, define B k y t =y t-k ;

[0178] s is the seasonal period;

[0179] d is the non-seasonal difference order, and D is the seasonal difference order;

[0180] φ p (B s ) and φ p (B) Autoregressive coefficient polynomials for seasonal and non-seasonal respectively;

[0181] Θ q (B s ) and θ q (B) is the seasonal and non-seasonal moving average coefficient;

[0182] ∈ t is the error term.

[0183] Long-term trend forecasting: Through the seasonal ARIMA model, the system can identify and predict seasonal fluctuations in pollutant concentrations. For example, in winter, due to the rising trend of PM2.5 caused by heating, the ARIMA model can identify this seasonal pattern and provide early warning.

[0184] 3. Combination of short-term and long-term forecasts:

[0185] In this embodiment, the system uses a combination of an adaptive Kalman filter and a seasonal ARIMA model to predict pollutant concentrations. The adaptive Kalman filter is used to predict rapid changes in pollutants in the short term, while the seasonal ARIMA model is used to deal with long-term trends and seasonal fluctuations. The combination of the two enables the system to simultaneously deal with short-term abnormal fluctuations and long-term trends of pollutants.

[0186] Forecast result correction: The system generates the final pollutant concentration forecast value by weighting the short-term forecast results and the long-term trend forecast results. It is expressed as:

[0187]

[0188] in, is the short-term prediction result of the adaptive Kalman filter, is the long-term forecast result of the seasonal ARIMA model, and α is the parameter for adjusting the weights of short-term and long-term forecasts.

[0189] The time series prediction module in this embodiment combines the adaptive time series model and the seasonal ARIMA model to achieve accurate prediction of pollutant concentrations.

[0190] Multi-task learning module

[0191] Due to the wide variety of pollutants and their correlation, traditional single-task prediction methods cannot effectively utilize the common information between different pollutants. The multi-task learning module achieves joint prediction of multiple pollutant concentrations through a shared feature extraction network and task-specific branch models. This module can improve the prediction accuracy and efficiency of the system, reduce redundant calculations, and ensure that the system has strong adaptability when facing multiple pollutant monitoring tasks.

[0192] In this embodiment, the specific implementation of the multi-task learning module is as follows:

[0193] 1. Task-shared feature extraction unit:

[0194] In this embodiment, the system first extracts shared features from data of different pollutant types, and uses the common feature mining capabilities of multi-task learning to share the underlying representation between tasks. The pollutant data collected by each sensor contains multi-dimensional features, such as the concentration, timestamp, and spatial location information of the pollutant. i To represent the data of the i-th pollutant, the system first processes the input data of all pollutants through a shared feature extraction network to extract the underlying feature representation of each pollutant. The representation of shared feature extraction is:

[0195] F shared =FeatureExtractor(X)

[0196] Where X represents the input matrix F of all pollutant data shared Representing the shared underlying feature representation, the feature extraction network contains multiple convolutional layers or fully connected layers, aiming to extract the most representative features from the pollutant data.

[0197] The goal of the shared feature extraction network is to fully explore the common information between different types of pollutants and reduce repeated calculations and the number of model parameters.

[0198] 2. Task-specific branch units:

[0199] Based on the shared feature extraction, the system in this embodiment constructs a task-specific prediction branch for each pollutant task. Each task branch is responsible for predicting the concentration change of a specific pollutant. The branch network further extracts task-specific feature representations based on the shared features and performs concentration prediction. Specifically, the prediction representation of the i-th pollutant is:

[0200] y i =f i (F shared )

[0201] Among them, f i is the prediction branch network of the i-th task, including a specific convolutional layer or a fully connected layer, F shared The output of the shared feature extraction network is used. Each prediction branch is optimized according to the characteristics of the pollutant to ensure that the concentration change of each pollutant is accurately predicted.

[0202] 3. Multi-task loss function optimization:

[0203] In this embodiment, the system optimizes the prediction model of each pollutant by constructing a multi-task joint loss function. The multi-task loss function contains the loss values ​​of multiple tasks and adjusts the priority of different tasks through weight coefficients. The joint loss function of multi-task learning is defined as follows:

[0204]

[0205] Where T is the number of tasks, α i is the weight of the i-th task, by minimizing The system can optimize the prediction models of multiple pollutants at the same time and adjust the priority of each task according to actual needs. The loss function for the i-th task is usually the mean square error (MSE) or cross entropy loss, expressed as:

[0206]

[0207] Among them, y i,j is the true concentration value of the i-th task, is the predicted value of the i-th task, n i is the number of samples for task i.

[0208] 4.Task weight adaptive adjustment unit:

[0209] In this embodiment, the task weight α i The task weights are adjusted adaptively based on the importance of different pollutant tasks to ensure that the prediction accuracy of key pollutants is optimized first when the pollutant concentration changes greatly. The adaptive adjustment of task weights is based on the impact of each task loss function on the total loss. The adjustment rules are as follows:

[0210]

[0211] in, is the task weight at time step t, β is the learning rate, It indicates the contribution of the loss of task i to the total loss. Through this adaptive adjustment, the system can automatically identify which pollutant prediction tasks are more important at present and dynamically adjust the optimization strategy.

[0212] 5. Prediction results integration:

[0213] In this embodiment, the pollutant concentration prediction values ​​generated by each task-specific branch will be integrated and output to form a joint prediction result for multiple pollutants. The prediction value of each pollutant is finally expressed as:

[0214]

[0215] in, is the predicted concentration of the ith pollutant. The final joint prediction result is output to the control and decision-making module of the system for use in pollution source monitoring and control strategy formulation.

[0216] The multi-task learning module in this embodiment can effectively handle multiple pollutant prediction tasks simultaneously through the design of shared feature extraction and task-specific branches.

[0217] Anomaly detection feedback module

[0218] The anomaly detection feedback module detects abnormal fluctuations in the data, so that the system can promptly capture possible pollution accidents or abnormal emission behaviors, and generate feedback control signals based on the detection results to trigger corresponding control measures. This module mainly relies on multi-scale wavelet transform and Bayesian anomaly detection to achieve multi-scale analysis and probabilistic anomaly detection of data, and combines the feedback control mechanism to ensure that the system can respond to abnormal situations in real time.

[0219] In this embodiment, the specific implementation of the abnormality detection feedback module is as follows:

[0220] 1. Multiscale wavelet transform for anomaly detection:

[0221] In this embodiment, the system first uses multi-scale wavelet transform to analyze the time series data of pollutant concentration. Multi-scale wavelet transform can effectively capture data changes on different time scales and is suitable for detecting short-term abnormal fluctuations and long-term trend deviations in pollutant concentration.

[0222] The system performs wavelet transform on the time series data f(t)f(t)f(t), and the transformation formula is as follows:

[0223]

[0224] Among them, W ψ (a,b) are wavelet coefficients, a is the scale parameter, b is the translation parameter, ψ is the mother wavelet function, and f(t) is the sequence of pollutant concentration changes over time. By adjusting the scale parameter a, the system can simultaneously analyze data fluctuations on different time scales and identify short-term and long-term anomalies.

[0225] The coefficients W after wavelet transformation ψ (a, b) represents the change of data at different time scales. The system detects anomalies by analyzing the amplitude change of wavelet coefficients. When the wavelet coefficient exceeds the preset threshold, the system determines it as an abnormal point and records the time and amplitude of the abnormality.

[0226] 2. Bayesian Anomaly Detection Model:

[0227] In order to further improve the accuracy of anomaly detection, the present embodiment introduces a Bayesian anomaly detection model to determine whether the pollutant concentration at a certain time point deviates from the normal range by performing a probability analysis on the pollutant concentration data.

[0228] The core of Bayesian anomaly detection is to calculate the probability of abnormal event A occurring under given observation data B. The Bayesian formula is as follows:

[0229]

[0230] Among them, P(A|B) represents the posterior probability of anomaly A occurring under observed data B, P(B|A) represents the probability of observing data B when an abnormal event occurs, P(A) is the prior probability of the abnormal event, and P(B) is the total probability of observing the current data B.

[0231] The system calculates the posterior probability of abnormal events by performing Bayesian inference on the current sensor observation data. When P(A|B) exceeds the preset threshold, the system determines that the current pollutant concentration is abnormal, which may be caused by a pollution emission accident or equipment failure. The Bayesian model can perform dynamic inference based on the distribution of historical data and current observations, improving the accuracy and robustness of anomaly detection.

[0232] 3.Threshold alarm unit:

[0233] In this embodiment, the system sets different pollutant concentration thresholds for each sensor. When wavelet transform and Bayesian detection are judged as abnormal, the system will trigger an alarm based on the specific pollutant type and environmental background of the sensor. The alarm mechanism includes:

[0234] Send alarm signals to the monitoring center via wireless network;

[0235] Start the pollution control equipment on site (such as emergency shutdown, pollutant interception device, etc.).

[0236] The specific threshold value θ is set based on the type of pollutant and national or local environmental standards, and the formula is as follows:

[0237]

[0238] Among them, d i is the pollutant concentration monitored by sensor i, and θ is the alarm threshold of the pollutant.

[0239] 4. Feedback control strategy generation:

[0240] When the system detects abnormal pollutant concentration, the system in this embodiment will automatically generate a feedback control strategy. The feedback control strategy is mainly used to dynamically adjust the working state of the sensor or control the emission of the pollution source to reduce the risk of pollutant diffusion. The optimization goal of feedback control is to minimize the impact of pollution while making rational use of resources. The specific optimization goal formula is as follows:

[0241]

[0242] in:

[0243] C is the feedback control strategy;

[0244] R i (C) represents the risk level of control strategy C for the i-th monitoring area;

[0245] M i (C) Resource consumption in the monitoring area (e.g., energy consumption due to increased sensor operating frequency);

[0246] α i and β i Represent the weights of risk level and resource consumption respectively.

[0247] Feedback control strategies include increasing the sampling frequency of sensors, adding monitoring equipment, or emergency treatment of pollution sources. The system automatically generates the optimal control strategy based on the monitored abnormality and environmental conditions, and executes it through automated equipment.

[0248] 5. Automated Response Unit:

[0249] In this embodiment, the system is equipped with an automated response unit for executing the feedback control strategy. The response unit can dynamically adjust the configuration, monitoring frequency, and monitoring range of the sensor according to the generated feedback signal. For example, when the pollutant concentration in a certain area exceeds the standard, the system will automatically increase the sensor sampling frequency in that area and reduce the monitoring frequency in other areas to save resources. At the same time, the system can automatically start or shut down related pollution control equipment to achieve rapid response and control of pollution sources.

[0250] The anomaly detection feedback module in this embodiment can accurately capture abnormal fluctuations in pollutant concentrations through multi-scale wavelet transform and Bayesian anomaly detection method, and combines threshold alarm and feedback control mechanism to ensure that the system can respond to abnormal situations in a timely and effective manner.

[0251] In general, the present invention collects environmental data through multiple sensor networks, combines topological data analysis, sparse representation, graph neural network and adaptive time series model, and realizes dynamic monitoring and concentration prediction of multiple pollutants. The system can optimize the prediction accuracy of multiple pollutants through multi-task learning model, and quickly respond to abnormal changes of pollutants with the help of multi-scale anomaly detection and feedback control mechanism, ensuring the real-time and accuracy of monitoring.

[0252] Please see attached Figure 2 The present invention also provides an efficient and accurate pollution source monitoring quality control method, comprising the following steps:

[0253] S1. Collect pollutant data of air, water quality and noise;

[0254] S2. Use topological data analysis methods to perform high-dimensional modeling on multimodal pollutant data, and use sparse representation methods to reduce and optimize the data dimension;

[0255] S3, using the graph neural network model to build a spatiotemporal correlation model of pollutants to capture the dynamic propagation path of pollutants between sensor nodes;

[0256] S4. Based on historical monitoring data, an adaptive Kalman filter is used to make real-time predictions of pollutant concentrations, and a seasonal ARIMA model is used to predict long-term trends;

[0257] S5. Use a multi-task learning model to simultaneously predict the concentration changes of multiple pollutants;

[0258] S6. Perform multi-scale decomposition of the time series of pollutant data through wavelet transform, detect abnormal changes in the data, and adjust the monitoring strategy based on the feedback results.

[0259] Each step of the method of the present invention is implemented based on the functions of the aforementioned system. The technical details and key algorithms involved have been fully described in the system embodiment and will not be repeated here.

[0260] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An efficient and accurate pollution source monitoring quality control system, characterized in that: include: A multimodal data acquisition module, used to collect different types of pollutant data from multiple sensor nodes, where the sensor nodes include air quality monitoring sensors, wastewater monitoring sensors, and noise monitoring sensors; High-dimensional data modeling module, used to perform topological data analysis and sparse representation optimization on the collected multimodal data, reduce data redundancy, and extract efficient features; The spatiotemporal correlation analysis module is used to build a spatiotemporal correlation model of multiple sensor nodes based on graph neural networks and analyze the dynamic propagation path of pollutants between multiple monitoring points; Time series forecasting module, used to predict future pollutant concentration trends based on adaptive time series models and seasonal ARIMA models; A multi-task learning module for joint prediction of concentration changes of multiple pollutants at the same time; The anomaly detection feedback module is used to detect abnormal fluctuations in pollutant data through multi-scale wavelet transform and generate feedback control signals based on the detection results.

2. According to claim 1, a highly efficient and accurate pollution source monitoring quality control system is characterized in that: The high-dimensional data modeling module includes: The topological data analysis unit is used to perform high-dimensional topological modeling on multimodal data. It constructs the topological features of complex extracted data by Rips, including: Multimodal datasets collected by sensors Convert to Rips Complex R ∈ (X), the Rips complex is constructed in the following manner: Among them, X is the data set, dist(x i , x j ) is the data point x i and x j The distance between them, ∈ is the preset distance threshold, and σ is a set containing several data points; The persistent homology calculation unit is used to calculate the persistent homology of the data set. The persistent homology is analyzed by calculating the persistence of topological features at different scales. The persistence is defined as: Pers(H k )=(b k ,d k ) Among them, H k is the k-dimensional homology group, b k and d k They represent the birth time and death time of topological features respectively; The sparse representation unit is used to optimize the sparse representation of high-dimensional data. The dimensionality reduction and denoising of the data are performed by solving the sparse representation optimization problem. The sparse representation optimization formula is: Where X is the multimodal data matrix, D is the dictionary matrix, Z is the sparse representation matrix, and λ is the regularization parameter for controlling sparsity; The efficient feature extraction unit is used to extract efficient features after sparse representation to ensure that the extracted features can effectively characterize the spatiotemporal distribution and changes of pollution source data.

3. According to claim 1, a highly efficient and accurate pollution source monitoring quality control system is characterized in that: The spatiotemporal correlation analysis module comprises: The graph structure building unit is used to model the data of the multimodal sensor network as a graph structure G = (V, E), where V represents the sensor node set, E represents the spatiotemporal association between nodes, and each node v i Corresponding to a sensor, the node feature is expressed as where h i is the multidimensional feature vector of the i-th node; The graph neural network construction unit is used to establish a graph neural network model based on the constructed graph structure G. The graph neural network is used to iteratively update the node features through the graph convolution layer. The update formula is: in, is the node v at the l+1th layer i The updated features of is the set of neighbor nodes of node i, W (l) is the weight matrix of the lth layer, σ is the activation function, and AGG represents the aggregation function; The aggregation function unit is used to realize the aggregation of node features in the message passing process of the graph neural network. The aggregation function includes mean aggregation and maximum pooling aggregation. The specific aggregation operations are: in, represents the number of neighbor nodes of node i, h j is the feature vector of neighbor node j; The dynamic graph neural network unit is used to process the dynamic changes of the sensor network, construct a dynamic graph neural network, and dynamically update the sensor node features according to the time step t. The update formula of the node features over time is: in, is the node v at the l+1th layer and time step t+1 i Features, Indicates that at time t, node v i The set of neighbor nodes, W (l) is the dynamic weight matrix of the lth layer, σ is the activation function; The spatiotemporal propagation analysis unit is used to determine the spatiotemporal propagation path of pollutants in the sensor network and extract the dynamic spatiotemporal correlation characteristics of pollution sources by analyzing the changes in node characteristics after the graph neural network completes multiple feature updates.

4. According to claim 1, a highly efficient and accurate pollution source monitoring quality control system is characterized in that: The time series prediction module includes: Adaptive Kalman filter unit for dynamic prediction of pollutant concentrations based on real-time sensor data and historical pollution data; The seasonal ARIMA model unit is used to process pollutant data with seasonal periodic changes. The specific formula of the seasonal ARIMA model is: F p (B s )·φ p (B)(1-B) d (1-B s ) D y t =Θ q (B s )·θ q (B)∈ t Where B is the lag operator, s is the seasonal period, d is the non-seasonal difference order, D is the seasonal difference order, Φ p (B s ) and φ p (B) are seasonal and non-seasonal autoregressive coefficients, Θ q (B s ) and θ q (B) are seasonal and non-seasonal moving average coefficients, ∈ t is the error term; The prediction correction unit is used to perform prediction correction based on the short-term prediction results of the Kalman filter and the long-term trend prediction of the seasonal ARIMA model, combining the two to generate the final pollutant concentration prediction value.

5. According to claim 1, a highly efficient and accurate pollution source monitoring quality control system is characterized in that: The multi-task learning module includes: The task-sharing feature extraction unit is used to extract shared features from the monitoring data of different pollutants. By constructing a joint feature space, the model can learn shared feature representations from the monitoring data of each pollutant and form an underlying feature extraction network. The task-specific branch unit is used to construct task-specific prediction branches based on the shared feature extraction and the characteristics of different pollutants. Each branch corresponds to the prediction task of a pollutant. The task-specific branch further processes the shared features through an independent neural network layer to generate the prediction results of specific pollutants. The multi-task loss function optimization unit is used to optimize the system's joint prediction ability for multiple pollutants by constructing a joint loss function of multi-task learning. The joint loss function is: Where T is the number of tasks, is the loss function of the i-th task, α i is the weight of the i-th task; The output integration unit is used to integrate the pollutant prediction results generated by each task-specific branch, and finally output the concentration prediction values ​​of multiple pollutants. The output is the concentration change trends of multiple pollutants predicted simultaneously, which is used for quality control and decision support of pollution sources.

6. According to claim 5, a highly efficient and accurate pollution source monitoring quality control system is characterized in that: The multi-task learning module also includes: The task weight adaptive adjustment unit is used to adjust the weight α of each task according to the changes in the monitoring data of each pollutant and the actual needs during the multi-task learning process. i Dynamic adjustments are made to ensure that the prediction accuracy of key pollutants is optimized first when pollutant concentrations change dramatically. The adaptive adjustment of task weights follows the following rules: in, is the task weight at time t, β is the learning rate, is the impact of task i on the loss function at time t.

7. The efficient and accurate pollution source monitoring quality control system according to claim 1 is characterized in that: The abnormality detection feedback module comprises: Multiscale anomaly detection unit, used to perform multiscale analysis on the time series of pollutant concentrations based on multiscale wavelet transform and identify abnormal fluctuations in monitoring data; The Bayesian anomaly detection unit is used to perform probability analysis of abnormal events using Bayesian statistical methods, and calculate the probability that the pollutant concentration exceeds the normal range at a certain moment based on the historical distribution of sensor data and current observations; A threshold alarm unit is used to automatically generate an alarm signal according to a set pollutant concentration threshold when the multi-scale wavelet transform and Bayesian anomaly detection unit detect an anomaly. The alarm signal can trigger the feedback control mechanism of the system, including sending a notification to the system management personnel and triggering the start and stop of the automatic control equipment; The feedback control strategy unit is used to generate a feedback control strategy according to the abnormality detection result.

8. The efficient and accurate pollution source monitoring quality control system according to claim 7 is characterized in that: When the feedback control strategy unit detects an abnormal pollution event, the system automatically adjusts the monitoring frequency and monitoring range of the sensor. The feedback control strategy includes dynamically increasing the monitoring density of key areas and reducing the monitoring frequency of non-key areas. The generation of the feedback control strategy is based on the following optimization objectives: Among them, C is the feedback control strategy, R i (C) represents the risk level of control strategy C for the i-th monitoring area, M i (C) is the resource consumption of the monitoring area, α i and β i Represent the weights of risk level and resource consumption respectively.

9. The efficient and accurate pollution source monitoring quality control system according to claim 7 is characterized in that: The anomaly detection feedback module also includes: The automated response unit is used to automatically adjust the sensor configuration, monitoring frequency, transmission rate and data processing priority after generating the feedback control strategy, and execute the system control instructions through automated equipment to achieve real-time response to abnormal conditions of pollution sources.

10. An efficient and accurate pollution source monitoring quality control method, applied to the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Collect pollutant data on air, water quality and noise; Use topological data analysis methods to model high-dimensional multimodal pollutant data, and use sparse representation methods to reduce and optimize data dimensionality; A graph neural network model is used to build a spatiotemporal correlation model of pollutants to capture the dynamic propagation path of pollutants between sensor nodes; Based on historical monitoring data, an adaptive Kalman filter is used to make real-time predictions of pollutant concentrations, and a seasonal ARIMA model is used to predict long-term trends; Use multi-task learning models to simultaneously predict concentration changes of multiple pollutants; The time series of pollutant data is decomposed at multiple scales through wavelet transform to detect abnormal changes in the data and adjust the monitoring strategy based on the feedback results.

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