A high-efficiency and precise pollution source monitoring quality control system and method

Abstract

The application relates to the field of environmental monitoring, and discloses a high-efficiency and high-precision pollution source monitoring quality control system and method, which comprises a multi-modal data acquisition module, a high-dimensional data modeling module, a space-time correlation analysis module, a time series prediction module, a multi-task learning module and an abnormality detection feedback module; pollution data such as air, water quality and noise is acquired, high-dimensional modeling and optimization are performed on the data, and the space-time propagation path of the pollution between different sensor nodes is captured; the system can predict the short-term change and long-term trend of the concentration of the pollution in real time; the multi-task learning model is used to simultaneously predict the concentration change of multiple pollutants, wavelet transformation is performed on the time series for multi-scale decomposition, abnormal changes in the data are detected, and a feedback control strategy is generated. The application improves the accuracy and real-time performance of pollution source monitoring, can effectively cope with the dynamic change of multiple pollutants, and ensures the efficiency and reliability of environmental quality monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to an 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 pollution sources such as air, water quality, and noise is increasing. Real-time monitoring and effective control of pollutants are becoming particularly important, especially in industrial parks, transportation hubs, and cities. Traditional pollution monitoring systems mostly use a single type of sensor, with a limited data collection range and only capable of monitoring a single pollutant, making it difficult to effectively cope with complex environments with multiple sources of pollution. At the same time, existing systems often fail to fully utilize the multi-dimensional data collected, resulting in significant data redundancy and information omissions. Furthermore, the dynamic propagation of pollutants has significant spatiotemporal characteristics, and existing technologies are unable to accurately capture these dynamic changes, thus limiting the effectiveness of monitoring systems in predicting and preventing pollution.

[0003] Existing monitoring systems have significant shortcomings in terms of joint analysis of multi-source pollutants, dynamic trend prediction, and response to abnormal events. In particular, when dealing with the spread of pollutants across different temporal and spatial dimensions, the system lacks an effective spatiotemporal correlation analysis mechanism, making it unable to fully capture the pollutant diffusion path. Furthermore, existing prediction models typically only focus on short-term or single pollutant forecasts, making it difficult to account for long-term pollutant trends or the joint prediction of multiple pollutants. Furthermore, anomaly detection mechanisms are often limited to a single threshold alarm mode, unable to accurately identify complex anomalies in multi-dimensional data or generate timely response strategies, resulting in limited system response speed and decision-making capabilities. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides an efficient and accurate pollution source monitoring quality control system and method. Through multimodal data acquisition, spatiotemporal correlation analysis and multi-task learning, it can achieve real-time monitoring, accurate prediction and abnormal response of multiple pollutants, 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 for collecting different types of pollutant data from multiple sensor nodes, including 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 collected multimodal data, reduce data redundancy, and extract efficient features;

[0008] a spatio-temporal correlation analysis module configured to construct a spatio-temporal correlation model of the multi-sensor nodes based on a graph neural network, and analyze dynamic propagation paths of the pollutants between the multiple monitoring points;

[0009] a time series prediction module configured to predict future pollutant concentration variation trends based on an adaptive time series model and a seasonal ARIMA model;

[0010] a multi-task learning module configured to simultaneously jointly predict concentration variations of multiple pollutants;

[0011] an anomaly detection feedback module configured to detect abnormal fluctuations in the pollutant data through multi-scale wavelet transform, and generate a feedback control signal according to a detection result.

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

[0013] a topological data analysis unit configured to perform high-dimensional topological modeling on the multi-modal data, and extract topological features of the data by constructing Rips complexes, including:

[0014] converting a multi-modal data set collected by the sensor into a Rips complex , the Rips complex being constructed in the following manner:

[0015]

[0016] wherein, is a data set, is a data point and is a distance between data points, is a preset distance threshold, is a set containing a plurality of data points;

[0017] a persistent homology calculation unit configured to calculate persistent homology of the data set, the persistent homology being analyzed by calculating persistence of the topological features at different scales, the persistence being defined as:

[0018]

[0019] wherein, is a dimensional homology group, and respectively represent a birth time and a death time of the topological features;

[0020] a sparse representation unit configured to perform sparse representation optimization on the high-dimensional data, and reduce dimensions and denoise the data by solving a sparse representation optimization problem, a sparse representation optimization formula being:

[0021]

[0022] in, is the multimodal data matrix, is the dictionary matrix, is a sparse representation matrix, is the regularization parameter to control 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] Graph structure building unit, used to model the data of multimodal sensor networks as a graph structure ,in represents the set of sensor nodes, Represents the spatiotemporal association between nodes, each node Corresponding to a sensor, the node feature is expressed as ,in For the Multidimensional feature vector of nodes;

[0026] Graph neural network building unit, used for building graph structures based on A graph neural network model is established. The graph neural network is used to iteratively update node features through the graph convolution layer. The update formula is:

[0027]

[0028] in, For the Layer Node The updated features of For nodes The set of neighbor nodes of For the The weight matrix of the layer, is the activation function, Represents an aggregate function;

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

[0030]

[0031] in, Representation node The number of neighbor nodes, Neighbor nodes The eigenvector of

[0032] Dynamic graph neural network unit is used to process the dynamic changes of sensor networks and build dynamic graph neural network according to time steps. Dynamically update the sensor node characteristics. The update formula of the node characteristics over time is:

[0033]

[0034] in, For the Layer, time step Time Node Features, Indicates time Time Node The set of neighbor nodes of For the The dynamic weight matrix of the layer, 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 features 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]

[0040] in, is the lag operator, For seasonal cycles, is the non-seasonal differencing order, is the seasonal difference order, and are seasonal and non-seasonal autoregressive coefficients, respectively. and and are seasonal and non-seasonal moving average coefficients respectively, 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-shared feature extraction unit is used to extract shared features from monitoring data of different pollutant types. 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 characteristics of different pollutants based on the shared feature extraction. 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 prediction results for 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 for multi-task learning. The joint loss function is:

[0046]

[0047] in, is the number of tasks, For the The loss function of each task, For the The weight of each task;

[0048] The output integration unit is used to integrate the pollutant prediction results generated by each task-specific branch and ultimately output the concentration prediction values ​​of multiple pollutants. The output is the simultaneously predicted concentration change trends of multiple pollutants, 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 of each task according to the changes in the monitoring data of each pollutant and the actual needs during the multi-task learning process. Dynamic adjustments are made to ensure that the prediction accuracy of key pollutants is prioritized when pollutant concentrations change dramatically. The adaptive adjustment of task weights follows the following rules:

[0051]

[0052] in, For time The task weight at the moment, is a learning rate, is a task at time the impact on the loss function.

[0053] Preferably, the anomaly detection feedback module comprises:

[0054] a multi-scale anomaly detection unit for performing multi-scale analysis on the time series of pollutant concentration based on multi-scale wavelet transform, identifying abnormal fluctuations in the monitoring data;

[0055] a Bayesian anomaly detection unit for performing probability analysis of abnormal events by Bayesian statistical method, calculating the probability of the pollutant concentration exceeding the normal range at a certain time based on the historical distribution of sensor data and the current observation;

[0056] a threshold alarm unit for automatically generating an alarm signal according to the set pollutant concentration threshold when the multi-scale wavelet transform and the Bayesian anomaly detection unit detect an anomaly, the alarm signal being able to trigger the feedback control mechanism of the system, including sending a notification to the system administrator and triggering the start and stop of the automatic control device;

[0057] a feedback control strategy unit for generating a feedback control strategy according to the anomaly detection result.

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

[0059]

[0060] wherein, is a feedback control strategy, denotes the control strategy the risk level of the first monitoring area, is the resource consumption of the monitoring area, and denote the weights of the risk level and the resource consumption, respectively.

[0061] Preferably, the anomaly detection feedback module further comprises:

[0062] an automated response unit for automatically adjusting the configuration of the sensor, the monitoring frequency, the transmission rate, and the priority of data processing after the feedback control strategy is generated, and executing the control instructions of the system through automated devices to realize real-time response to abnormal 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 the data dimension;

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

[0067] Based on historical monitoring data, an adaptive Kalman filter is used to predict pollutant concentrations in real time, 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 into multiple scales through wavelet transform to detect abnormal changes in the data and adjust the monitoring strategy according to 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. This invention uses a multimodal sensor network to simultaneously collect data on multiple pollutants, including air quality, water quality, and noise, ensuring both breadth and depth of monitoring. Combining topological data analysis and sparse representation techniques, the system can extract key features from high-dimensional data, effectively removing redundancy and noise, thereby significantly improving the accuracy of data processing and the precision of monitoring results.

[0072] 2. This invention uses a graph neural network (GNN) to model the spatiotemporal relationships between sensor nodes. This system can capture the propagation paths and correlations of pollutants across time and space. The precision of spatiotemporal correlation analysis improves the ability to identify pollutant diffusion paths, helping managers better understand the dynamics of pollution sources.

[0073] 3. The adaptive Kalman filter in this invention is used for short-term pollutant concentration prediction, dynamically adjusting based on real-time data, while the seasonal ARIMA model is used for long-term trend prediction, particularly for pollutants with significant seasonal variations. The combination of these two methods provides a system with high predictive capabilities for both short-term fluctuations and long-term trends in pollutants.

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

[0075] Figure 1 is a schematic diagram of the system architecture of the application;

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

[0077] The technical solutions in the embodiments of the application will be described in detail below with reference to the accompanying drawings of the application. Obviously, the described embodiments are only a part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0078] Please refer to the accompanying drawings of the application Figure 1 The embodiments of the application provide 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 multi-modal sensor network data acquisition, high-dimensional modeling, spatio-temporal correlation analysis, time series prediction and multi-task learning technology, the concentration change trend of pollutants is accurately captured, abnormal conditions are detected and feedback control signals are automatically generated. The application 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 a wide application prospect. The specific implementation of each module of the system of the application is described in detail below.

[0079] Multi-modal data acquisition module

[0080] The multi-modal data acquisition module provided by the embodiments of the application 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. This module can simultaneously collect information of multiple pollutants such as air quality, water pollution and noise data, ensuring that the monitoring system can comprehensively cover multiple pollution sources.

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

[0082] 1. Sensor node arrangement and type: In this embodiment, the multi-modal data acquisition module includes multiple sensor nodes arranged at different positions of the pollution source, which are used to collect data of different types of pollutants. Specifically, the sensor nodes include:

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

[0084] Wastewater monitoring sensors, used to monitor the concentration of pollutants 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 raw data on its corresponding pollutant in real time. To ensure efficient and real-time data transmission, a sensor network architecture based on wireless communication technologies, such as LoRa, NB-IoT, and 4G / 5G, is adopted. This ensures rapid data transmission between each node and the central processing unit, avoiding the complex wiring and maintenance issues associated with 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 geographic location information and timestamp. Each sensor node is accompanied by the specific time and geographic 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, sensor nodes perform a certain degree of preliminary processing after collecting data to reduce redundancy and noise that may be generated during data transmission. Each node performs local data preprocessing, such as data denoising, missing value filling, and data normalization, to ensure that the data uploaded to the central processing unit maintains high quality.

[0092] 5. Synchronous structure of multimodal data:

[0093] In this embodiment, multiple sensor nodes are synchronized by 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, data collected by multiple sensor nodes is uploaded to a central processing unit via wireless transmission technology. The transmission protocol supports multiple modes, including event-triggered real-time data transmission and scheduled batch transmission of collected data. The specific mode is set according to the needs of the monitoring area. For example, if pollutant concentrations fluctuate significantly, the sensor can switch to event-triggered mode to transmit early warning data to the central system in real time; while when pollutant concentrations are relatively stable, scheduled batch transmission mode can be used to conserve transmission resources.

[0096] 7. Multimodal Data Representation:

[0097] In this embodiment, 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]

[0099] in, Indicates the The sensor in the Monitoring values ​​for pollutants of this type, Indicates the time of data collection, 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 collaborate through networking. The system dynamically adjusts sensor nodes based on the characteristics of different monitoring areas to ensure comprehensive and accurate data collection. For example, when monitoring a large industrial area, air quality sensors might share some information with wastewater sensors to more accurately identify potential sources of pollution.

[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] Because pollutant data collected by sensors is often multidimensional and highly 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 characteristics of multimodal pollutant data. By analyzing the topological structure of the pollutant data, it is possible to discover hidden global and local correlations in the data set. Specifically, the system first performs topological structure modeling on the data collected by the sensors.

[0108] Rips complex construction: Assume that the multimodal dataset collected by the sensor is ,in Indicates the A high-dimensional representation of the data points, is the sample size, 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] in, For the dataset, For data points and The distance between is the preset distance threshold, is a set of data points. By using Rips, the system can capture the connectivity and local structures in the dataset.

[0111] Persistent Homology Computation: After the Rips complex construction, this example computes persistent homology on the topology to extract stable topological features from high-dimensional data. Persistent homology identifies persistent features in the data by tracking changes in the topology. Specifically, the persistence of the k-dimensional homology group is defined as:

[0112]

[0113] in, for dimensional homology group, and Birth time and death time represent the topological features, respectively. By calculating the persistence features in different dimensions, the system can identify the important structures in the pollutant data and remove the 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. High-dimensional data collected by sensors usually contains a large amount of redundant information. By sparse representation, a small number of representative important features can be extracted, while noise interference is reduced. The process of sparse representation optimization is achieved through the following steps:

[0116] Sparse representation model construction: Let the multi-modal data matrix be where is the number of samples, is the feature dimension. The sparse representation optimization goal is to find the sparse coefficient matrix and the dictionary matrix , so that can be approximated by the product of and , while ensuring that is sparse. The optimization goal is:

[0117]

[0118] where is the multi-modal data matrix, is the dictionary matrix, is the sparse representation matrix, is the regularization parameter that controls sparsity. By minimizing this objective function, the system can generate a sparse representation which can efficiently represent the original data.

[0119] Sparse decoding and dimensionality reduction: After completing sparse representation optimization, the system decodes and reduces the dimension of the data using the dictionary matrix and the sparse representation matrix . Sparse representation not only effectively reduces data redundancy, but also preserves key information in the data. Due to the large amount of data collected by the sensor network and the high noise, through sparse representation, noise can be removed without losing main information, improving data processability.

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

[0121] In this embodiment, after topological data analysis and sparse representation optimization, the system will perform format conversion on the multi-modal data to generate feature vector representation suitable for subsequent analysis. The data of each sensor node is finally represented as an efficient feature vector , where the data contains the main features, timestamps, and geographical location information. The final representation of the data is as follows:

[0122]

[0123] where, represents the th feature of the th sample extracted, is the timestamp, is the geographical location information of the sensor. The efficient feature representation of this format provides a foundation 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 multi-modal pollutant data through topological data analysis and sparse representation optimization techniques.

[0125] Spatiotemporal correlation analysis module

[0126] The multi-modal data acquisition module provides a large amount of sensor data with spatiotemporal labels, and through the spatiotemporal correlation analysis module, the system can construct a pollutant propagation model at different times and spaces based on these data. This module mainly uses graph neural network (GNN) technology for modeling, combining the spatial position of the sensor node, the timestamp, and the pollutant concentration change to identify the spatiotemporal dynamic correlation of the pollutant, providing 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. Graph structure construction of sensor network:

[0129] In this embodiment, the multi-modal sensor network is first modeled as a graph structure , where:

[0130] is the set of sensor nodes, each node represents a sensor node, and the node features include the pollutant concentration collected by the sensor, the timestamp, and the location information;

[0131] is the edge set, representing the spatial or temporal correlation between sensor nodes, and the weight of the edge can be calculated according to the geographical distance between sensor nodes or the correlation in time.

[0132] For any two sensor nodes and , if the geographical distance between them is less than a certain preset threshold , then an edge is added to the graph Add an edge between these two nodes:

[0133]

[0134] in, 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 , 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 by graph neural network to identify the propagation pattern of pollutants in time and space. The core of 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] in, Representation node The collected pollutant concentration data, Indicates the data collection time. Indicates the geographic location of the sensor.

[0139] In each layer of the graph neural network, node features are aggregated and updated with neighboring node features to reflect their spatiotemporal associations. In the The feature update formula of the layer is:

[0140]

[0141] in, It is Layer Node Features, is a node The set of neighbor nodes of It is an aggregation function, usually using mean aggregation:

[0142]

[0143] in, Representation node The number of neighbor nodes, Neighbor nodes 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, For the Layer, time step Time Node Features, Indicates time Time Node The definition of neighbor nodes may be adjusted dynamically over time to capture the time-varying propagation law of pollutants. For the The dynamic weight matrix of the layer, is the activation function.

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

[0149] In this embodiment, through multi-layer graph neural network updates and dynamic feature propagation, the system can identify the dynamic propagation paths of pollutants between different monitoring points and generate a spatiotemporal dependency graph between sensor nodes. By analyzing these spatiotemporal propagation paths, the system can predict the spread of pollutants, identify potential pollution sources, and assess the impact of pollution in 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 uses historical pollutant data collected by the sensor network to predict trends in pollutant concentrations over future time periods. It combines an adaptive time series model (ATSM) with a seasonal ARIMA model to account for both short-term fluctuations and long-term seasonal trends in pollutant concentrations. Through dynamic data adjustment and adaptive modeling, the system effectively captures patterns in pollutant concentrations over time, providing strong support for pollution prediction and prevention.

[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 example, an adaptive time series model is first used to address short-term fluctuations in pollutant concentrations. The adaptive Kalman filter is the core of this model, dynamically adjusting the system's predictions of future pollutant concentrations based on real-time pollutant concentration data collected by sensors.

[0157] State prediction: For time The pollutant concentration state at the moment is predicted by the system through the following state equation:

[0158]

[0159] in, For time The predicted value at time, is the state transition matrix, is the state at the previous moment, is the control input matrix, For time Control inputs at all times (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 covariance update formula is:

[0161]

[0162] in, It's time The forecast covariance matrix at time , is the covariance matrix at the previous moment, 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 , used to update the prediction based on actual data. The calculation formula of Kalman gain is:

[0164]

[0165] in, is the observation matrix, is the observation noise covariance matrix, Indicates the degree to which the system adjusts the forecast model based on the current observations.

[0166] Status update: combined with actual observations , the system corrects the predicted pollutant concentration, and the state update formula is:

[0167]

[0168] in, is the corrected predicted value of pollutant concentration, For time The actual observed value at time.

[0169] Covariance update: The system updates the covariance matrix based on the corrected state:

[0170]

[0171] in, For time The updated covariance at time t, is the identity matrix.

[0172] The adaptive Kalman filter can dynamically adjust the prediction model based on the real-time data of the sensor, adapt to the changes in environmental conditions, and improve the accuracy of short-term predictions.

[0173] 2. Seasonal ARIMA Model:

[0174] 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).

[0175] Model structure: The seasonal ARIMA model combines autoregressive, differencing, and moving average processes to model time series data with seasonality and long-term trends. The basic formula of the model is:

[0176]

[0177] in:

[0178] is the lag operator, and we define ;

[0179] It is a seasonal cycle;

[0180] is the non-seasonal differencing order, is the seasonal difference order;

[0181] and Autoregressive coefficient polynomials for seasonal and non-seasonal respectively;

[0182] and is the seasonal and non-seasonal moving average coefficient;

[0183] is the error term.

[0184] Long-term trend forecasting: Using seasonal ARIMA models, the system can identify and predict seasonal fluctuations in pollutant concentrations. For example, in winter, PM2.5 levels rise due to heating. The ARIMA model can identify this seasonal pattern and provide early warning.

[0185] 3. Combination of short-term and long-term forecasts:

[0186] 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, short-term changes in pollutants, while the seasonal ARIMA model is used to address long-term trends and seasonal fluctuations. This combination enables the system to simultaneously address both short-term abnormal fluctuations and long-term trends in pollutants.

[0187] Forecast result correction: The system generates the final pollutant concentration prediction value by weighting the short-term prediction results and long-term trend prediction results. Expressed as:

[0188]

[0189] in, is the short-term prediction result of the adaptive Kalman filter, is the long-term forecast result of the seasonal ARIMA model, Parameters for adjusting the weights of short-term and long-term forecasts.

[0190] 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.

[0191] Multi-task learning module

[0192] Due to the wide variety of pollutants and their interrelationships, traditional single-task prediction methods cannot effectively leverage the shared information across different pollutants. The multi-task learning module utilizes a shared feature extraction network and task-specific branching models to jointly predict the concentrations of multiple pollutants. This module improves the system's prediction accuracy and efficiency, reduces redundant computation, and ensures strong adaptability to diverse pollutant monitoring tasks.

[0193] In this embodiment, the specific implementation of the multi-task learning module is as follows:

[0194] 1. Task-shared feature extraction unit:

[0195] 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. Indicates the The system first processes all the input data of pollutants through a shared feature extraction network to extract the underlying feature representation of each pollutant. The representation of shared feature extraction is:

[0196]

[0197] in, Input matrix representing all pollutant data 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.

[0198] 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.

[0199] 2. Task-specific branch units:

[0200] Based on the shared feature extraction, the system in this embodiment builds 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 of each pollutant is expressed as:

[0201]

[0202] in, For the a prediction branch network for each task, including specific convolutional layers or fully connected layers, The output of the shared feature extraction network is shared by each prediction branch. Each prediction branch is optimized specifically for the characteristics of the pollutant, ensuring accurate prediction of the concentration changes of each pollutant.

[0203] 3. Multi-task loss function optimization:

[0204] In this embodiment, the system optimizes the prediction model for 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:

[0205]

[0206] where, is the number of tasks, is the weight of the th task, and by minimizing , the system can simultaneously optimize the prediction models of multiple pollutants and adjust the priority of each task according to actual needs, is the loss function of the th task, which is usually mean square error (MSE) or cross-entropy loss, represented as:

[0207]

[0208] where, is the true concentration value of the th task, is the predicted value of the th task, is the number of samples for task .

[0209] 4. Task weight adaptive adjustment unit:

[0210] In this embodiment, the task weight is adaptively adjusted according to the importance of different pollutant tasks to ensure that the prediction accuracy of key pollutants is prioritized in cases where the concentration of pollutants changes significantly. The adaptive adjustment of task weights is based on the influence of each task loss function on the total loss, and the adjustment rule is as follows:

[0211]

[0212] where, is the task weight at time step , is the learning rate, indicates the task Through this adaptive adjustment, the system can automatically identify which pollutant prediction tasks are more important at the moment and dynamically adjust the optimization strategy.

[0213] 5. Prediction results integration:

[0214] In this embodiment, the pollutant concentration prediction values ​​generated by each task-specific branch are integrated and output to form a joint prediction result for multiple pollutants. The prediction value of each pollutant is finally expressed as:

[0215]

[0216] in, For the The final joint prediction results are output to the system's control and decision-making module for pollution source monitoring and control strategy formulation.

[0217] The multi-task learning module in this embodiment can effectively process multiple pollutant prediction tasks simultaneously through the design of shared feature extraction and task-specific branches.

[0218] Anomaly detection feedback module

[0219] The anomaly detection feedback module detects unusual fluctuations in data, enabling the system to promptly identify potential pollution incidents or unusual emissions. Based on the detection results, it generates feedback control signals, triggering appropriate control measures. This module primarily relies on multiscale wavelet transforms and Bayesian anomaly detection to perform multiscale data analysis and probabilistic anomaly detection. Combined with feedback control mechanisms, this ensures the system can respond to anomalies in real time.

[0220] In this embodiment, the specific implementation of the anomaly detection feedback module is as follows:

[0221] 1. Multiscale wavelet transform for anomaly detection:

[0222] In this embodiment, the system first analyzes the time series data of pollutant concentrations using a multi-scale wavelet transform. The multi-scale wavelet transform can effectively capture data changes at different time scales and is suitable for detecting short-term abnormal fluctuations and long-term trend deviations in pollutant concentrations.

[0223] The system performs wavelet transform on the time series data f(t)f(t)f(t). The transformation formula is as follows:

[0224]

[0225] in, is the wavelet coefficient, is the scale parameter, is the translation parameter, is the mother wavelet function, is a sequence of pollutant concentration changes over time. By adjusting the scale parameter ,The system can simultaneously analyze data fluctuations on different time scales and identify short-term and long-term anomalies.

[0226] Wavelet transformed coefficients The system detects anomalies by analyzing the amplitude changes of wavelet coefficients, which represent changes in data at different time scales. When a wavelet coefficient exceeds a preset threshold, the system identifies it as an anomaly and records the time and amplitude of the anomaly.

[0227] 2. Bayesian Anomaly Detection Model:

[0228] In order to further improve the accuracy of anomaly detection, this 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.

[0229] The core of Bayesian anomaly detection is to calculate the given observation data Abnormal events The probability of occurrence. The Bayesian formula is as follows:

[0230]

[0231] in, Indicates that the observed data The following exception occurs The posterior probability of Indicates that data is observed when an abnormal event occurs The probability of is the prior probability of an abnormal event, The current data is observed The total probability of .

[0232] The system calculates the posterior probability of abnormal events by performing Bayesian inference on the current sensor observation data. When the preset threshold is exceeded, the system determines that the current pollutant concentration is abnormal, possibly caused by a pollution discharge accident or equipment failure. The Bayesian model can dynamically infer the distribution of historical data and current observations, improving the accuracy and robustness of anomaly detection.

[0233] 3.Threshold alarm unit:

[0234] In this embodiment, the system sets different pollutant concentration thresholds for each sensor. When wavelet transform and Bayesian detection determine an abnormality, the system triggers an alarm based on the specific pollutant type and environmental background of the sensor. The alarm mechanism includes:

[0235] Send alarm signals to the monitoring center via wireless network;

[0236] Start on-site pollution control equipment (such as emergency shutdown, pollutant interception devices, etc.).

[0237] Specific threshold The setting is based on the type of pollutant and national or local environmental standards, and the formula is as follows:

[0238]

[0239] in, For sensors The concentration of pollutants monitored, is the alarm threshold of the pollutant.

[0240] 4. Feedback control strategy generation:

[0241] When the system detects abnormal pollutant concentrations, in this embodiment, it automatically generates a feedback control strategy. This feedback control strategy is primarily used to dynamically adjust the operating status of sensors or control emissions from pollution sources to reduce the risk of pollutant spread. The optimization goal of feedback control is to minimize the impact of pollution while rationally utilizing resources. The specific optimization objective formula is as follows:

[0242]

[0243] in:

[0244] It is a feedback control strategy;

[0245] Represents control strategy For the first Risk level of each monitoring area;

[0246] Resource consumption in the monitoring area (such as energy consumption due to increased sensor operating frequency);

[0247] and Represent the weights of risk level and resource consumption respectively.

[0248] Feedback control strategies include increasing sensor sampling frequency, deploying additional monitoring equipment, or implementing emergency measures for pollution sources. The system automatically generates the optimal control strategy based on the degree of monitored anomalies and environmental conditions, and executes it through automated equipment.

[0249] 5. Automated Response Unit:

[0250] In this embodiment, the system is equipped with an automated response unit to implement feedback control strategies. Based on the generated feedback signals, the response unit can dynamically adjust sensor configuration, monitoring frequency, and monitoring range. For example, if pollutant concentrations in a certain area exceed the standard, the system will automatically increase the sensor sampling frequency in that area and reduce the monitoring frequency in other areas to conserve resources. Furthermore, the system can automatically activate or deactivate relevant pollution control equipment, enabling rapid response and control of pollution sources.

[0251] 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 mechanisms to ensure that the system can respond to abnormal situations in a timely and effective manner.

[0252] In summary, this invention collects environmental data through a multi-sensor network and combines topological data analysis, sparse representation, graph neural networks, and adaptive time series models to achieve dynamic monitoring and concentration prediction of multiple pollutants. The system optimizes multi-pollutant prediction accuracy through a multi-task learning model. Furthermore, it leverages multi-scale anomaly detection and feedback control mechanisms to rapidly respond to unusual pollutant changes, ensuring real-time and accurate monitoring.

[0253] Please see the attached Figure 2 The present invention also provides an efficient and accurate pollution source monitoring quality control method, comprising the following steps:

[0254] S1. Collect pollutant data on air, water quality and noise;

[0255] S2. Use topological data analysis methods to perform high-dimensional modeling of multimodal pollutant data and use sparse representation methods to reduce and optimize the data dimension;

[0256] S3. Use the graph neural network model to build a spatiotemporal correlation model of pollutants to capture the dynamic propagation path of pollutants between sensor nodes;

[0257] S4. Based on historical monitoring data, an adaptive Kalman filter is used to predict pollutant concentrations in real time, and a seasonal ARIMA model is used to predict long-term trends.

[0258] S5. Use multi-task learning models to simultaneously predict the concentration changes of multiple pollutants;

[0259] 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.

[0260] Each step of the method of the present invention is based on the functional implementation 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.

[0261] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An efficient and accurate pollution source monitoring quality control system, characterized by: include: A multimodal data acquisition module for collecting different types of pollutant data from multiple sensor nodes, including 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 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 paths 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; Multi-task learning module for joint prediction of concentration changes of multiple pollutants; Anomaly detection feedback module, which is used to detect abnormal fluctuations in pollutant data through multi-scale wavelet transform and generate feedback control signals based on the detection results; The multi-task learning module includes: The task-shared feature extraction unit is used to extract shared features from monitoring data of different pollutant types. 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 characteristics of different pollutants based on the shared feature extraction. 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 prediction results for 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 for multi-task learning. The joint loss function is: in, is the number of tasks, For the The loss function of each task, For the The weight of each task; An output integration unit is used to integrate the pollutant prediction results generated by each task-specific branch and ultimately output the concentration prediction values ​​of multiple pollutants. The output is the change trend of multiple pollutant concentrations predicted simultaneously, which is used for quality control and decision support of pollution sources; The task weight adaptive adjustment unit is used to adjust the weight of each task according to the changes in the monitoring data of each pollutant and the actual needs during the multi-task learning process. Dynamic adjustments are made to ensure that the prediction accuracy of key pollutants is prioritized when pollutant concentrations change dramatically. The adaptive adjustment of task weights follows the following rules: in, For time The task weight at the moment, is the learning rate, For the task In time The impact of time on the loss function.

2. The efficient and accurate pollution source monitoring quality control system according to claim 1 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 extracts the topological features of the data by constructing Rips complex, including: Multimodal datasets collected by sensors Convert to Rips Complex , the Rips complex is constructed in the following way: in, For the dataset, For data points and The distance between is the preset distance threshold, is a set of 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. Persistence is defined as: in, for dimensional homology group, and They represent the birth time and extinction time of topological features respectively; The sparse representation unit is used to optimize the sparse representation of high-dimensional data. It reduces the dimension and denoises the data by solving the sparse representation optimization problem. The sparse representation optimization formula is: in, is the multimodal data matrix, is the dictionary matrix, is a sparse representation matrix, is the regularization parameter to control 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. The efficient and accurate pollution source monitoring quality control system according to claim 1 is characterized in that: The spatiotemporal correlation analysis module includes: Graph structure building unit, used to model the data of multimodal sensor networks as a graph structure ,in represents the set of sensor nodes, Represents the spatiotemporal association between nodes, each node Corresponding to a sensor, the node feature is expressed as ,in For the Multidimensional feature vector of nodes; Graph neural network building unit, used for building graph structures based on A graph neural network model is established. The graph neural network is used to iteratively update node features through the graph convolution layer. The update formula is: in, For the Layer Node The updated features of For nodes The set of neighbor nodes of For the The weight matrix of the layer, is the activation function, Represents an aggregate function; The aggregation function unit is used to aggregate node features in the message passing process of the graph neural network. The aggregation functions include mean aggregation and maximum pooling aggregation. The specific aggregation operations are: in, Representation node The number of neighbor nodes, Neighbor nodes The eigenvector of Dynamic graph neural network unit is used to process the dynamic changes of sensor networks and build dynamic graph neural network according to time steps. Dynamically update the sensor node features. The update formula of the node features over time is: in, For the Layer, time step Time Node Features, Indicates time Time Node The set of neighbor nodes of For the The dynamic weight matrix of the layer, 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 features after the graph neural network completes multiple feature updates.

4. The efficient and accurate pollution source monitoring quality control system according to claim 1 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: in, is the lag operator, For seasonal cycles, is the non-seasonal differencing order, is the seasonal difference order, and are seasonal and non-seasonal autoregressive coefficients, respectively. and and are seasonal and non-seasonal moving average coefficients respectively, 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. The efficient and accurate pollution source monitoring quality control system according to claim 1 is characterized in that: The anomaly detection feedback module includes: Multi-scale anomaly detection unit, used to perform multi-scale analysis on the time series of pollutant concentrations based on multi-scale wavelet transform to identify abnormal fluctuations in monitoring data; The Bayesian anomaly detection unit is used to perform probabilistic analysis of abnormal events using Bayesian statistical methods. Based on the historical distribution of sensor data and current observations, it calculates the probability that the pollutant concentration exceeds the normal range at a certain moment. A threshold alarm unit is used to automatically generate an alarm signal based on the set pollutant concentration threshold when the multi-scale wavelet transform and Bayesian anomaly detection unit detects an anomaly. The alarm signal can trigger the system's feedback control mechanism, including sending a notification to system management personnel and triggering the start and stop of automatic control equipment; The feedback control strategy unit is used to generate a feedback control strategy based on the anomaly detection result.

6. The efficient and accurate pollution source monitoring quality control system according to claim 5 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-critical areas. The feedback control strategy is generated based on the following optimization objectives: in, is the feedback control strategy, Represents control strategy For the first The risk level of each monitoring area, To monitor resource consumption in a region, and Represent the weights of risk level and resource consumption respectively.

7. The efficient and accurate pollution source monitoring quality control system according to claim 5 is characterized in that: The anomaly detection feedback module further includes: The automated response unit is used to automatically adjust the sensor configuration, monitoring frequency, transmission rate, and data processing priority of the system after generating the feedback control strategy, and execute the system's control instructions through automated equipment to achieve real-time response to abnormal conditions at pollution sources.

8. An efficient and accurate pollution source monitoring quality control method, applied to the system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Collect pollutant data on air, water quality and noise; Use topological data analysis methods to perform high-dimensional modeling of multimodal pollutant data, and use sparse representation methods to reduce and optimize the data dimension; A graph neural network model is used to build a spatiotemporal correlation model of pollutants to capture the dynamic propagation paths of pollutants between sensor nodes. Based on historical monitoring data, an adaptive Kalman filter is used to predict pollutant concentrations in real time, 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 into multiple scales through wavelet transform to detect abnormal changes in the data and adjust the monitoring strategy according to the feedback results.

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