Intelligent real-time monitoring and over-standard early warning method for waste gas emission

Abstract

The application relates to the technical field of intelligent monitoring, and discloses a kind of intelligent waste gas emission real-time monitoring and over-standard early warning method, including multi-source data acquisition and preprocessing, dynamic threshold construction, double-track anomaly identification and concentration prediction, hierarchical early warning and pollution tracing, data storage and compliance reporting and the like steps.By synchronously collecting multi-source data and carrying out whole-process preprocessing, combining working conditions, weather and equipment health degree to construct a dynamic threshold system, using a rule engine and an isolation forest algorithm to realize double-track anomaly identification, realizing minute-level prediction of waste gas concentration through an LSTM neural network, using a GNN model to realize accurate positioning of pollution sources, constructing a hierarchical early warning and closed-loop disposal mechanism, and realizing compliance reporting according to HJ212 protocol, accurate monitoring, active early warning and closed-loop management of waste gas emission are realized, the environmental protection compliance ability and pollution prevention and control level of enterprises are improved, and the practicality is strong, and it can be widely applied to various industrial waste gas emission scenes.

Description

Technical Field

[0001] This application relates to the technical field of intelligent monitoring, and in particular to an intelligent method for real-time monitoring and early warning of exceeding emission standards for exhaust gases. Background Technology

[0002] With increasingly stringent environmental regulations, industrial waste gas emission monitoring has become a core component of corporate compliance and environmental governance. Currently, various industrial enterprises generally use online monitoring equipment to monitor waste gas emissions. However, existing waste gas monitoring and early warning methods still have many technical shortcomings and cannot meet the needs of precise and proactive environmental management.

[0003] On the one hand, the accuracy of monitoring data is difficult to guarantee. The complex working conditions of high temperature, high humidity and high dust in industrial sites can easily lead to condensation in sampling pipelines and sensor drift. In addition, the existing solutions lack a full-process preprocessing and dynamic calibration mechanism, which can easily cause data distortion. At the same time, the lack of uniform standards for equipment from multiple manufacturers and the incompatibility of data formats further reduce the availability of data and affect the scientific nature of compliance judgment and governance decisions.

[0004] On the other hand, early warning mechanisms are passive and poorly adaptable. Existing early warning systems mostly rely on fixed regulatory thresholds, failing to dynamically adjust to production load, weather conditions, and equipment health status. This makes them unsuitable for changing operating conditions and weakly capable of identifying hidden exceedances such as data drift and periodic fluctuations, resulting in only post-event alerts and missed opportunities for early intervention. Furthermore, current technologies lack concentration prediction capabilities, hindering proactive risk prevention. The "monitoring-early warning-source tracing-treatment-verification" process is disconnected, making it difficult to quickly locate pollution sources after exceedances, lacking a closed-loop treatment mechanism, leading to low efficiency in problem rectification and a high risk of environmental penalties. Summary of the Invention

[0005] To address the aforementioned technical issues, this application provides an intelligent method for real-time monitoring and early warning of excessive emissions of exhaust gases.

[0006] Firstly, this application provides an intelligent method for real-time monitoring and early warning of excessive emissions of exhaust gas, employing the following technical solution: A method for real-time monitoring and early warning of excessive emissions of intelligent exhaust gas includes the following steps: S1. Simultaneously collect multi-source monitoring data and auxiliary data related to exhaust gas emissions, and preprocess the raw data to obtain a standardized dataset; S2. Based on the preset benchmark threshold, combined with the correlation coefficients of working conditions, weather and health status of monitoring equipment, the real-time dynamic threshold and predictive warning threshold adapted to the changes in the scenario are calculated by the preset algorithm. S3. The rule engine and the isolated forest algorithm are used to identify explicit and implicit anomalies in the monitoring data, respectively; a concentration prediction model is built based on the LSTM neural network, and the standardized dataset is input to predict the exhaust gas concentration for a preset time period in the future. S4. Based on the comparison results of real-time monitoring values, correction values, predicted values ​​and various thresholds, trigger multi-level early warnings, locate pollution sources and push disposal plans through GNN model combined with multi-source data, and conduct real-time verification of rectification effects to form closed-loop management. S5 caches and stores monitoring and related data for different durations at the edge and in the cloud respectively, and automatically reports compliant data and related records to the ecological and environmental supervision platform in accordance with standard protocols.

[0007] Optionally, in step S1, the multi-source monitoring data includes organized emission outlet CEMS monitoring data and unorganized emission sensor monitoring data at the plant boundary; The auxiliary data includes pollution control facility operating data, production load data, and on-site meteorological data; for organized emission outlets, specific technologies are used to monitor the concentration of gaseous pollutants and particulate matter, and related parameters of the exhaust gas are monitored simultaneously; for unorganized emissions at the plant boundary, dedicated sensors are used to monitor the concentration of characteristic pollutants. The preprocessing process includes outlier filtering, missing value supplementation, multi-source data synchronization, sensor drift correction, and data standardization. The data standardization converts monitoring data of different magnitudes and units into a uniform magnitude.

[0008] Optionally, in step S2, the correlation coefficients of the working condition, weather and monitoring equipment health status are the production load coefficient, the weather correction coefficient and the sensor health coefficient, respectively. The dynamic threshold and the prediction and early warning threshold are calculated by a preset formula, and each coefficient is dynamically adjusted according to the actual scenario parameters.

[0009] Optionally, in step S3, the rule engine presets multiple explicit anomaly identification rules, and the isolated forest algorithm generates random trees, extracts segmented feature sequences, calculates feature importance, and filters out anomalies.

[0010] Optionally, in step S4, the multi-level early warning is a four-level early warning. Each level of early warning is triggered according to the correspondence between the monitored value, the predicted value and the threshold. The GNN model locates the pollution source by constructing the association between nodes and edges and calculating the pollution contribution of nodes.

[0011] Optionally, in step S5, the time for caching the original data at the edge is not less than a preset threshold, the time for storing the full amount of data in the cloud meets compliance requirements, the data reporting adopts a standard protocol, and the data is parsed and processed before being reported and various monitoring reports are generated for querying.

[0012] Optionally, the preset algorithm includes: ; In the formula, For real-time dynamic thresholds, The benchmark threshold stipulated by environmental regulations, This is the production load factor, determined based on the ratio of the enterprise's actual production load to its rated load. The higher the production load, the higher the production load. The closer the value is to 1.2, the closer it is to 0.8; This is a meteorological correction factor, determined based on the on-site wind speed and direction. The higher the wind speed and the more important the downwind monitoring point... The closer the value is to 0.7, the more important it is for the upwind monitoring point. The closer the value is to 1.3; The sensor health coefficient is determined based on the sensor drift; the smaller the drift, the better. The closer the value is to 1.0, the greater the drift. The closer the value is to 0.9.

[0013] Optionally, the preset algorithm further includes: ; In the formula, To predict the warning threshold, set it to a real-time dynamic threshold. 90% of the data is used to detect rising trends in exhaust gas concentration in advance, allowing sufficient time for subsequent intervention operations.

[0014] In summary, this application includes at least one of the following beneficial technical effects: 1. This application effectively solves the problems of data distortion and equipment incompatibility in existing technologies by acquiring multi-source data and preprocessing the entire process. Combined with a dynamic calibration mechanism and standardized processing, it ensures the accuracy and reliability of monitoring data, providing solid data support for subsequent early warning and source tracing. By constructing a dynamic threshold system that integrates regulatory limits, operating parameters, meteorological conditions, and equipment health, it overcomes the limitations of traditional fixed thresholds, adapts to complex and ever-changing industrial production scenarios, and improves the adaptability and accuracy of early warning. The dual-track anomaly recognition mode combining rule engine and isolated forest algorithm can not only quickly capture explicit anomalies but also effectively identify implicit anomalies. Combined with minute-level concentration prediction achieved by LSTM neural network, it realizes the transformation from passive alarm to active prediction and early warning, and can push intervention signals in advance to eliminate the risk of exceeding standards in its infancy. The application of GNN model realizes rapid and accurate location of pollution sources. Combined with a graded early warning and closed-loop disposal mechanism, it can quickly push disposal plans and verify the rectification effect, greatly improving the disposal efficiency of exceeding standards and forming a complete pollution prevention and control system.

[0015] 2. This application achieves automatic compliant data reporting through the HJ212 protocol, meeting the needs of enterprises for environmental compliance and regulatory authorities for source tracing. Meanwhile, model optimization and equipment calibration steps ensure the long-term stability and reliability of the system. The entire method does not require complex hardware modifications, is compatible with existing monitoring equipment, has low implementation costs, and is highly scalable. It can be widely applied to various industrial waste gas emission scenarios, helping enterprises improve their environmental compliance capabilities, reduce the risk of environmental penalties, and optimize the operation of pollution control facilities, achieving a dual improvement in environmental protection and economic benefits. It has strong practicality and promotional value. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the intelligent real-time monitoring and early warning method for exhaust gas emissions in this application. Detailed Implementation

[0017] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0018] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0019] This application discloses an intelligent method for real-time monitoring and early warning of excessive emissions of exhaust gas, referring to... Figure 1 This includes the following steps: S1. Simultaneously collect multi-source monitoring data and auxiliary data related to exhaust gas emissions, and preprocess the raw data to obtain a standardized dataset; S2. Based on the preset benchmark threshold, combined with the correlation coefficients of working conditions, weather and health status of monitoring equipment, the real-time dynamic threshold and predictive warning threshold adapted to the changes in the scenario are calculated by the preset algorithm. S3. The rule engine and the isolated forest algorithm are used to identify explicit and implicit anomalies in the monitoring data, respectively; a concentration prediction model is built based on the LSTM neural network, and the standardized dataset is input to predict the exhaust gas concentration for a preset time period in the future. S4. Based on the comparison results of real-time monitoring values, correction values, predicted values ​​and various thresholds, trigger multi-level early warnings, locate pollution sources and push disposal plans through GNN model combined with multi-source data, and conduct real-time verification of rectification effects to form closed-loop management. S5 caches and stores monitoring and related data for different durations at the edge and in the cloud respectively, and automatically reports compliant data and related records to the ecological and environmental supervision platform in accordance with standard protocols.

[0020] Specifically, this embodiment takes the monitoring and early warning of exhaust gas emissions from a petrochemical enterprise as an example to elaborate on the intelligent real-time monitoring and early warning method for exceeding emission standards described in this invention. This enterprise has two organized emission outlets (flues) and one unorganized emission area at the plant boundary. The main pollutants emitted include SO2 and NO. x Particulate matter and VOCs are pollutants. Pollution control facilities include desulfurization towers, dust collectors, and VOCs adsorption devices. The specific implementation steps are as follows: CEMS systems were installed at two organized emission outlets, using DOAS technology to monitor SO2 and NO. x For concentration monitoring, laser scattering method is used to monitor particulate matter concentration. Simultaneously, flow rate, temperature, humidity, and O2 sensors are installed to monitor the flow rate, temperature, humidity, and O2 concentration of exhaust gas. The sampling pipeline is designed with 120℃ heat tracing to prevent VOCs condensation. Four VOCs micro-sensors (PID) and two particulate matter sensors are deployed in the fugitive emission area at the plant boundary, covering the upwind, downwind, and central areas of the plant, respectively, to monitor VOCs and PM2.5 concentrations. Current, flow rate, and differential pressure sensors are installed on the desulfurization tower, dust collector, and VOCs adsorption device to collect operating data of the pollution control facilities (fan current, desulfurizing agent flow rate, dust collector differential pressure, and adsorption device airflow). A weather station is installed within the plant area to collect meteorological data such as wind speed, wind direction, temperature, and humidity. Production load sensors are installed in the production workshop to collect real-time production load data for each production line.

[0021] All monitoring devices and sensors are connected to an industrial-grade DTU edge gateway. The edge gateway synchronously collects the above-mentioned multi-source raw data, uses the 3σ criterion to filter out outliers (such as negative particulate matter concentration or sudden SO2 concentrations far exceeding the normal range), uses linear interpolation to supplement missing values ​​(such as data loss of several minutes due to temporary sensor failure), and achieves synchronization of multi-source data through timestamp alignment to ensure that monitoring data, operating condition data, and meteorological data at the same point in time correspond and match. An adaptive algorithm is used to correct sensor drift in real time, and finally, a standardized formula is used. All data are standardized by mapping data of different magnitudes and units to the [0,1] interval, resulting in a standardized dataset. This refers to a specific raw monitoring data point (e.g., SO2 concentration of 50 mg / m³). This refers to the historical maximum value of this type of data (e.g., SO2 concentration of 200 mg / m³). The historical minimum value of this type of data (e.g., SO2 concentration 0 mg / m³) is used to calculate a standardized value of 0.25, which facilitates subsequent data fusion and model calculation.

[0022] Based on GB31571-2015 "Emission Standard of Pollutants for Petroleum Refining Industry", the baseline thresholds for each pollutant are determined. SO2 is 100 mg / m³, NO x The concentrations are: 150 mg / m³ for particulate matter, 30 mg / m³ for particulate matter, and 60 mg / m³ for VOCs. The production load factor is determined based on the company's production load. When the production line is running at full capacity, Take 1.2; when the production line load is 80% of the rated load, Take 1.0; when the production line load is 60% of the rated load, Take 0.8.

[0023] Meteorological correction coefficients were determined based on on-site meteorological data. When the wind speed is 3 m / s and the monitoring point is downwind, Take 0.7; when the wind speed is 1 m / s and the monitoring point is upwind, Take 1.3; when the wind speed is 2 m / s and the monitoring point is at the center of the factory area, Take 1.0.

[0024] Determining the sensor health coefficient based on sensor drift. When the sensor drift is 2%, Take 0.98; when the sensor drift is 4%, Take 0.92; when the sensor has no drift, Take 1.0.

[0025] Through dynamic threshold formula Calculate real-time dynamic thresholds, such as when the SO2 baseline threshold is reached. The production line is operating at full capacity. Wind speed 3 m / s, monitoring point downwind ( ), sensor drift 2% ( When SO2 is in its real-time dynamic threshold, Predicted early warning threshold When the predicted SO2 value reaches 74.09 mg / m³, a prediction warning is triggered, allowing time for intervention.

[0026] The rule engine has 12 preset explicit anomaly rules, including: monitoring data exceeding the baseline threshold, data being negative, data remaining constant for 10 consecutive minutes, data changing by more than 50% within 5 minutes, device communication interruption for more than 3 minutes, data sudden drop due to sampling pipeline blockage, and sensor failure with no data output. When the monitoring data meets any of these rules, the rule engine immediately marks it as an explicit anomaly. For example, when the real-time monitoring value of SO2 reaches 105 mg / m³ (exceeding the baseline threshold of 100 mg / m³), the rule engine marks the data as an explicit anomaly and triggers an early warning preparation.

[0027] The Isolation Forest algorithm is used to identify latent anomalies. The specific implementation process is as follows: During the random tree generation process, random segmentation features (such as SO2 concentration, production load, and wind speed) are stored at each tree node. For each SO2 concentration monitoring data point, the segmentation feature sequence used by its branch path on each tree is obtained. A feature sequence length threshold of 10 is set, and feature sequences shorter than 10 are retained. Based on the position and frequency of the feature in the sequence, a formula is used to... Calculate the importance of each feature (where The importance of feature f for data point d. , For data point d, feature f appears as the terminating feature in all segmentation feature sequences. It is the sum of the number of times each feature appears as a terminating feature. , For data point d, feature f is the sum of the number of times it appears in all segmented feature sequences. Features are sorted from high to low importance. Anomalies are selected based on the anomaly probability output by the isolated forest. The core features of the anomalies are then output in combination with the sorted feature sequences. For example, if SO2 concentration rises slowly for 20 minutes (drift) and the core influencing feature is an increase in production load, the data is marked as a latent anomaly.

[0028] Regarding the construction and training of the LSTM concentration prediction model: One year of multi-source monitoring data from the enterprise was collected and preprocessed according to the method in step 1 to obtain the training dataset. The training dataset was divided into a training set and a test set in a 7:3 ratio. Model parameters were initialized with 100 iterations, a learning rate of 0.001, an Adam optimizer, and MAE loss function. The training set was input into the LSTM model (the input layer receives historical hourly data on SO2 concentration, production load, desulfurizer flow rate, temperature, and wind speed; the first LSTM layer has 64 hidden units, the second LSTM layer has 32 hidden units, the fully connected layer weighted fusion features, and the output layer outputs predicted SO2 concentration values ​​for the next 1-60 minutes) for iterative training. After each training round, the model performance was verified using the test set, and the number of hidden units and the learning rate were adjusted until the model's prediction accuracy met the requirements. After training, an online learning mechanism was introduced to adjust the model parameters in real time based on newly collected real-time data to ensure prediction accuracy. For example, if the current hourly SO2 concentration data (average 80 mg / m³), production load (90%), desulfurizer flow rate (5 m³ / h), temperature (25℃), and wind speed (2 m / s) are input, the model outputs a predicted SO2 concentration of 88 mg / m³ for the next 30 minutes, which is close to 1.07 times the dynamic threshold of 82.32 mg / m³, triggering a yellow alert.

[0029] Based on the comparison results of real-time monitoring values, standardized correction values, predicted values, dynamic thresholds, and predicted early warning thresholds, the corresponding level of early warning is triggered: When the real-time SO2 monitoring value reaches 80mg / m³ (80% of the baseline threshold), a blue (attention) warning is triggered. The system will remind the company's environmental protection specialist through a pop-up window on the web platform and push suggestions for optimizing the desulfurizing agent flow. When the predicted SO2 value is 88 mg / m³ (which will exceed the dynamic threshold of 82.32 mg / m³ within 15 minutes), a yellow (warning) alert is triggered. The system will push the warning information to the company's environmental protection manager and production line supervisor via SMS and APP, and activate the first-level emergency response plan (such as increasing the desulfurizing agent flow). When the real-time SO2 monitoring value reaches 90mg / m³ (exceeding the dynamic threshold of 82.32mg / m³) and lasts for 5 minutes, an orange (emergency) warning is triggered. The system issues emergency warnings through multiple channels, automatically increases the desulfurizing agent flow in the desulfurization tower, limits the production line load to 80%, and pushes warning information to the company's head and the environmental protection department's regional specialist. When the real-time SO2 monitoring value reaches 180mg / m³ (more than twice the benchmark threshold) and remains so for 1 minute, a red (especially urgent) warning is triggered. The system automatically triggers the production shutdown plan, immediately reports to the environmental protection department, initiates emergency response, and pushes the warning information to the company's legal representative and the environmental protection department's responsible leader.

[0030] Construction and application of GNN pollution source tracing model: The model nodes are constructed, including 2 organized emission outlets, 3 production lines, 3 sets of pollution control equipment (desulfurization tower, dust collector, VOCs adsorption device), and 1 meteorological monitoring point. The edges between nodes include physical associations (e.g., production line 1 connects to emission outlet 1, desulfurization tower corresponds to emission outlet 1) and data associations (e.g., correlation between SO2 concentration and production line 1 load, correlation between SO2 concentration and desulfurization tower flow rate). Historical operating data and monitoring data of each node are collected, model parameters are initialized, and the node feature dimensions are set to 5 (monitoring concentration, load, flow rate, temperature, and wind speed). Edge weights are set according to the strength of the association (physical association). (Weight 0.8, data association weight 0.2); The gradient descent algorithm is used to train the model. Through node feature propagation and updating, the pollution contribution of each node is calculated. After training, when the SO2 concentration at emission point 1 exceeds the standard, real-time monitoring data and operating condition data are input, and the model outputs the pollution contribution of each node. The contribution of production line 1 is 85%, the desulfurization tower is 10%, and the meteorological monitoring point is 5%, thus identifying production line 1 as the core pollution source. The system pushes a treatment plan: reduce the load of production line 1, increase the desulfurizing agent flow rate of the desulfurization tower, and check whether the desulfurization tower spray device is normal. After the treatment is completed, the edge terminal monitors the SO2 concentration at emission point 1 in real time, and the cloud verifies the rectification effect. If the SO2 concentration drops below the dynamic threshold, the warning is lifted; if it does not meet the standard, the warning is upgraded and a treatment plan is pushed again, forming a closed-loop management.

[0031] The edge gateway (industrial-grade DTU) caches raw data for 30 days for local data backtracking and emergency queries. When monitoring equipment malfunctions, historical raw data can be queried through the edge gateway to troubleshoot the cause of the malfunction. The cloud uses public cloud servers to store all data (including standardized datasets, early warning records, handling logs, and rectification review results) for 3 years to meet the compliance requirements for environmental law enforcement traceability and enterprise data retention.

[0032] Data reporting uses the HJ212-2017 protocol based on TCP. The specific implementation process is as follows: Using the NetJet network framework, a TCP protocol receiving server is created by instantiating a server, setting the port to 8000, and configuring the processor word processing program. The processor is instantiated through the channel initializer and its initialization channel is overwritten to match the socket channel. The channel processing program chain is obtained, and a parser is set to convert the raw message data into a readable string in UTF-8 format. A common data receiving class is defined (containing attribute request code qn, system code st, command code cn, access password pw, site identifier mn, response identifier flag, collection time datetime, and command parameter cp). Each group of data is separated by a semicolon, and the data is divided into string arrays by semicolons. The divided string arrays are parsed in a loop. The current string is split by a comma, and then by an equal sign to form a group of data. The attribute code of the data is determined, and the data is set into the monitoring item data class (containing attribute monitoring item code, monitoring value, monitoring value type, and monitoring item status) according to the rules. The parsed readable string is converted into JSON format for program recognition. Finally, the data is automatically reported to the local ecological and environmental protection department's monitoring platform according to the requirements of the HJ212 protocol. Meanwhile, emission trend reports, early warning statistics reports, and pollution control efficiency reports are generated in the cloud, supporting real-time queries by enterprise environmental management personnel and regulatory authorities.

[0033] The cloud platform updates the parameters of the LSTM concentration prediction model and the GNN pollution source tracing model weekly. Combining the latest monitoring data, operational data, and treatment results, it adjusts parameters such as the number of hidden units, learning rate, and edge weights to optimize model performance and ensure the accuracy of prediction and source tracing. All monitoring sensors and the CEMS system are calibrated monthly using standard gases (SO2 50mg / m³, NO...). x (100mg / m³) Complete zero-point and range calibration, record calibration data, adjust sensor parameters, and ensure the stability of monitoring equipment and data accuracy; conduct a comprehensive inspection of the entire monitoring and early warning system every quarter, check the operating status of sensors, edge gateways, and cloud servers, investigate algorithm vulnerabilities, and promptly resolve equipment failures and system problems to ensure long-term stable operation of the system.

[0034] When the rule engine or isolated forest algorithm identifies abnormal data, the system automatically associates the monitoring equipment (such as the SO2 sensor at emission outlet No. 1), the collection time (such as 14:30 on XX / XX / 2026), the operating parameters (such as 90% load of production line No. 1 and desulfurizing agent flow rate of 4m³ / h), and the meteorological data (such as wind speed of 2m / s and temperature of 25℃) corresponding to the abnormal data. It analyzes the cause of the abnormal data. For example, if it identifies a sudden drop in SO2 data, the associated data shows that the temperature of the sampling pipeline has dropped to 80℃ (below the heating temperature of 120℃). It determines that the cause of the abnormality is condensation in the sampling pipeline and pushes the handling suggestion: check the heating device of the sampling pipeline and restart the heating system. The company's environmental management personnel should handle the situation in a timely manner according to the suggestion to reduce the impact of data distortion on monitoring and early warning.

[0035] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for real-time monitoring and early warning of excessive emissions of intelligent exhaust gas, characterized in that, Includes the following steps: S1. Simultaneously collect multi-source monitoring data and auxiliary data related to exhaust gas emissions, and preprocess the raw data to obtain a standardized dataset; S2. Based on the preset benchmark threshold, combined with the correlation coefficients of working conditions, weather and health status of monitoring equipment, the real-time dynamic threshold and prediction warning threshold adapted to the changes in the scenario are calculated by the preset algorithm. S3. Use a rule engine and an isolated forest algorithm to identify explicit and implicit anomalies in the monitoring data, respectively. A concentration prediction model is built based on an LSTM neural network, and a standardized dataset is input to predict the concentration of exhaust gas over a preset time period in the future. S4. Based on the comparison results of real-time monitoring values, correction values, predicted values ​​and various thresholds, trigger multi-level early warnings, locate pollution sources and push disposal plans through GNN model combined with multi-source data, and conduct real-time verification of rectification effects to form closed-loop management. S5 caches and stores monitoring and related data for different durations at the edge and in the cloud respectively, and automatically reports compliant data and related records to the ecological and environmental supervision platform in accordance with standard protocols.

2. The intelligent real-time monitoring and early warning method for exhaust gas emissions according to claim 1, characterized in that, In step S1, the multi-source monitoring data includes organized emission outlet CEMS monitoring data and unorganized emission sensor monitoring data at the plant boundary; The auxiliary data includes pollution control facility operating data, production load data, and on-site meteorological data; for organized emission outlets, specific technologies are used to monitor the concentration of gaseous pollutants and particulate matter, and related parameters of the exhaust gas are monitored simultaneously; for unorganized emissions at the plant boundary, dedicated sensors are used to monitor the concentration of characteristic pollutants. The preprocessing process includes outlier filtering, missing value supplementation, multi-source data synchronization, sensor drift correction, and data standardization. The data standardization converts monitoring data of different magnitudes and units into a uniform magnitude.

3. The intelligent real-time monitoring and early warning method for exhaust gas emissions according to claim 1, characterized in that, In step S2, the correlation coefficients of the working condition, weather and monitoring equipment health status are the production load coefficient, the weather correction coefficient and the sensor health coefficient, respectively. The dynamic threshold and the prediction and early warning threshold are calculated by a preset formula, and each coefficient is dynamically adjusted according to the actual scenario parameters.

4. The intelligent real-time monitoring and early warning method for exhaust gas emissions according to claim 1, characterized in that, In step S3, the rule engine presets multiple explicit anomaly identification rules, and the isolated forest algorithm generates random trees, extracts segmented feature sequences, calculates feature importance, and filters out anomalies.

5. The intelligent real-time monitoring and early warning method for exhaust gas emissions according to claim 1, characterized in that, In step S4, the multi-level early warning is a four-level early warning. Each level of early warning is triggered according to the correspondence between the monitored value, the predicted value and the threshold. The GNN model locates the pollution source by constructing the association between nodes and edges and calculating the pollution contribution of nodes.

6. The intelligent real-time monitoring and early warning method for exhaust gas emissions according to claim 1, characterized in that, In step S5, the time for caching the original data at the edge is not less than a preset threshold, the time for storing the full amount of data in the cloud meets compliance requirements, the data reporting adopts a standard protocol, and the data is parsed and processed before being reported and various monitoring reports are generated for querying.

7. The intelligent real-time monitoring and early warning method for exhaust gas emissions according to claim 1, characterized in that, The preset algorithm includes: ； In the formula, For real-time dynamic thresholds, The benchmark threshold stipulated by environmental regulations, This is the production load factor, determined based on the ratio of the enterprise's actual production load to its rated load. The higher the production load, the higher the production load. The closer the value is to 1.2, the closer it is to 0.8; This is a meteorological correction factor, determined based on the on-site wind speed and direction. The higher the wind speed and the more important the downwind monitoring point... The closer the value is to 0.7, the more important it is for the upwind monitoring point. The closer the value is to 1.3; The sensor health coefficient is determined based on the sensor drift; the smaller the drift, the better. The closer the value is to 1.0, the greater the drift. The closer the value is to 0.

9.

8. The intelligent real-time monitoring and early warning method for exhaust gas emissions according to claim 7, characterized in that, The preset algorithm also includes: ； In the formula, To predict the warning threshold, set it to a real-time dynamic threshold. 90% of the data is used to detect rising trends in exhaust gas concentration in advance, allowing sufficient time for subsequent intervention operations.

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