Abnormity early warning method, system and equipment for sewage treatment system and storage medium

Abstract

The invention discloses a sewage treatment system abnormity early warning method, system and equipment and a storage medium, and relates to the technical field of sewage treatment. The variety, concentration and time-space distribution of VOCs and malodorous gas in each process unit are monitored in real time through a multi-modal sensing technology, dynamic characteristic parameters are extracted, and a dynamic association model of coupling of an LSTM neural network and a random forest is constructed in combination with inflow water quality and process operation data. The model can accurately identify fault types such as water inlet toxicity impact and aeration abnormity, output graded early warning signals and position an abnormal source. According to the invention, traditional monitoring hysteresis is broken through, early warning is carried out 4-6 hours in advance, intrusive sampling is not needed, the operation and maintenance cost is reduced, and the impact resistance and operation stability of a sewage treatment system are improved.

Description

A method, system, device and storage medium for early warning of abnormalities in a wastewater treatment system Technical Field

[0001] This invention relates to the field of wastewater treatment technology, specifically to a method, system, equipment, and storage medium for early warning of abnormalities in a wastewater treatment system. Background Technology

[0002] Fluctuations in influent quality and quantity, or changes in water temperature, often affect the operating parameters of wastewater treatment plants, making it difficult to meet the metabolic needs of microorganisms. This leads to insufficient microbial activity, deterioration of sludge properties, and ultimately, a significant impact on the operational efficiency of the wastewater treatment system. Therefore, establishing an efficient online monitoring and early warning system for wastewater treatment systems is of great practical significance for timely detection of operational issues.

[0003] In terms of online monitoring and early warning of sewage treatment systems, existing technical measures include: (1) adding online water quality monitoring at the inlet end to monitor the fluctuation characteristics of influent water quality in real time. However, water quality monitoring instruments can only characterize water quality characteristics and cannot predict the operation status of sewage treatment systems. In addition, they will also increase investment costs. (2) adding monitoring instruments in each biological unit, such as process status parameters such as pH, MLSS (sludge concentration), and ORP (oxidation-reduction potential). However, these indicators cannot directly reflect abnormal microbial metabolism (such as sludge acidification, insufficient DO, etc.), resulting in lag in regulation.

[0004] (3) Regularly sample and test each biological unit, and determine whether the treatment system is operating normally by microscopic examination of sludge or community structure analysis. However, microscopic examination has the problems of low accuracy and inability to provide timely early warning of abnormalities, and community structure analysis has the disadvantages of high cost and long detection cycle.

[0005] (4) Installing offline detection indicators such as chemical oxygen demand (COD) or ammonia nitrogen in the sewage treatment plant, but it also has problems such as high lag and inability to provide real-time early warning of process abnormalities (such as sludge bulking and insufficient aeration).

[0006] Wastewater treatment plants, as vital urban infrastructure, not only purify water but also become major sources of odor and volatile organic compounds (VOCs). During wastewater treatment, microbial metabolic activities produce VOCs and odorous gases (such as hydrogen sulfide, ammonia, and thiols), exhibiting significant spatial variations in emissions. Different treatment units display distinct emission characteristics. For example: anaerobic units primarily release hydrogen sulfide and thiols, reflecting the state of organic matter decomposition; anoxic units release dimethyl sulfide and dimethyl disulfide, related to denitrification efficiency; and aerobic units produce aldehydes, ketones, and low-molecular-weight organic acids, indicating nitrifying bacteria activity.

[0007] Furthermore, changes in their composition and concentration directly reflect treatment efficiency and system health. Since these pollutants not only severely impact the surrounding air quality but also pose a potential threat to human health, direct emissions of VOCs and odorous gases into residential areas can cause anything from mild discomfort and increased susceptibility to illness to severe poisoning or serious diseases. This has become a critical issue urgently needing resolution in the field of environmental engineering. Therefore, current research on VOCs and odorous gases in wastewater treatment plants primarily focuses on their collection and treatment methods. For example, CN119455651A discloses a biological filter and a method for treating VOCs and odorous gases, which can be widely used to treat different components of VOCs and odorous gases in industrial plants, wastewater treatment, food processing industries, and garbage collection. It can effectively remove VOCs and odorous gases from waste gas and improve the air environment in polluted industrial and residential areas. CN110813087A discloses a method and system for treating high-concentration VOCs waste gas. By dissolving most of the high-concentration VOCs waste gas in water, the difficult-to-treat high-concentration organic waste gas is converted into easily treatable organic wastewater. The remaining water-insoluble organic components are treated by photocatalytic degradation and activated carbon adsorption, achieving complete treatment of high-concentration VOCs waste gas. The effluent and gas meet emission standards, significantly reducing treatment difficulty and cost. CN118150787A discloses an intelligent monitoring system and method for treating wastewater odor. It automatically determines the severity of odor by collecting physical parameters of wastewater odor in real time and activates appropriate treatment equipment, thereby effectively eliminating or reducing wastewater odor.

[0008] Wastewater treatment diagnostics mainly include the diagnosis of influent shock and the diagnosis of abnormal wastewater treatment operations. Influent shock diagnosis typically involves a sudden increase in the wastewater load received by the wastewater treatment plant, especially an excessively high organic load, which may lead to a surge in the release of odorous gases (such as hydrogen sulfide and ammonia) and volatile organic compounds (VOCs). This is because excessive input of organic matter may result in incomplete decomposition during microbial metabolism, thereby producing more VOCs and odorous gases.

[0009] Influent shocks typically cause rapid changes in VOCs or odorous gas concentrations within a short period. Abnormal fluctuations in the concentration of certain gases may indicate that the system has been subjected to a shock.

[0010] Wastewater treatment plant operational anomalies include microbial metabolic abnormalities and anaerobic / aerobic imbalances. Wastewater treatment plant operation typically relies on microbial metabolic processes. If changes in the wastewater's chemical composition or improper operation reduce microbial metabolic efficiency, it can lead to incomplete decomposition of organic matter, producing large amounts of undegraded volatile organic compounds (VOCs) and odorous gases. This can be detected by monitoring changes in VOC concentrations.

[0011] Insufficient oxygen supply in wastewater treatment plants can lead to excessive anaerobic conditions, producing more harmful gases such as hydrogen sulfide and methane. Changes in the composition of VOCs and odorous gases may be due to insufficient oxygen supply or uneven mixing during treatment, resulting in alterations in the microbial community and metabolic processes.

[0012] If the gas concentration rises abnormally during the treatment process, it may indicate a problem in some part of the wastewater treatment system (such as sedimentation tanks, anaerobic tanks, or aeration tanks). For example, insufficient aeration or abnormalities in the sludge return system may cause a surge in gas concentration.

[0013] In wastewater treatment, microbial metabolic activity is directly related to the efficiency of organic matter degradation, while the release of VOCs and odorous gases are "byproducts" of this process. When influent water quality changes abruptly (such as high load of organic matter or shock from toxic substances) or process conditions become unbalanced (such as insufficient dissolved oxygen or sludge aging), the microbial community structure and metabolic pathways change, leading to significant anomalies in the generation rate, concentration, or composition ratio of specific gases. As a direct product of microbial metabolism in wastewater treatment, the generation of VOCs is closely related to the type of wastewater treatment, environmental parameters, and the operating status of the wastewater treatment system.

[0014] Dynamic monitoring of VOCs and odorous gases can serve as an "olfactory sensor" for the operational status of wastewater treatment plants. Through characteristic gas identification and multi-parameter modeling, it can effectively diagnose influent shocks, process anomalies, and even microbial community imbalances. However, there is currently no systematic technical solution that combines VOCs fingerprinting with machine learning models to achieve real-time anomaly location and tiered early warning. Therefore, this invention proposes a method, system, equipment, and storage medium for anomaly early warning in wastewater treatment systems to improve the accuracy and timeliness of anomaly early warning. Summary of the Invention

[0015] This invention proposes an abnormal early warning method, system, equipment and storage medium for a wastewater treatment system. The method includes the following steps: S1. Constructing a gas "fingerprint spectrum" under different operating conditions through historical data or experimental simulation. For example, VOCs are mainly alcohols and ketones during normal operation, but the proportion of benzene compounds increases under shock load.

[0016] S2. Real-time monitoring of the types, concentrations, and spatiotemporal distribution of VOCs and odorous gases released from each process unit of the wastewater treatment plant.

[0017] S3. Extract the dynamic characteristic parameters of the gas, the characteristic parameters including at least one of the following: the rate of change of characteristic gas concentration, the ratio of key gas types, and spatial migration correlation.

[0018] S4. Construct a dynamic correlation model based on gas characteristic parameters and wastewater treatment process parameters, and obtain anomaly diagnosis rules through machine learning algorithm training.

[0019] S5. Input the real-time gas data into the dynamic correlation model, output the water inlet impact type, process anomaly level and location information, and trigger the graded early warning strategy.

[0020] In the preferred embodiment, the real-time monitoring in step S2 employs single-modal and multi-modal sensing technologies, specifically including: optimizing the deployment of a non-contact infrared spectral sensor array on the top of each biological unit pool based on the flow characteristics of each reaction unit, for detecting the concentration of characteristic gases (H2S, NH3, CH3SH, etc.).

[0021] Periodic sampling gas chromatography-mass spectrometry (GC-MS) is used for full-spectrum analysis of VOCs, identification of characteristic components (such as benzene series compounds and sulfur-containing compounds), and linkage with infrared sensors: when the infrared sensor detects a sudden increase in H2S concentration, it triggers GC-MS to perform expedited sampling, and identifies complex VOCs such as benzene series compounds and halogenated hydrocarbons by comparison with the NIST mass spectrometry library.

[0022] Infrared sensors provide real-time concentration data, while GC-MS provides detailed VOCs species information, forming a comprehensive monitoring network. The two sensors are cross-calibrated, and the single-gas readings of the infrared sensors are periodically calibrated using full-spectrum data from the GC-MS to correct drift errors.

[0023] If the concentration of a single gas (such as H2S) suddenly increases but other sulfur-based gases do not change synchronously, mass spectrometry verification is triggered to rule out sensor false alarms.

[0024] In a preferred embodiment, the dynamic feature parameter extraction method in step S3 includes: the ratio of key gas types, defining the concentration ratio (S / N ratio) of sulfur-based gases (H2S, CH3SH) and nitrogen-based gases (NH3, trimethylamine), and determining that sulfur-containing organic matter impact occurs when the S / N ratio exceeds a threshold.

[0025] The spatial migration correlation is used to locate the source of anomalies by using the time-delay correlation coefficient of gas concentration between different process units. For example, influent shock is usually accompanied by a sudden increase in gas concentration within a few hours (related to hydraulic residence time), and process anomalies (such as aeration failure) may cause the gas concentration to deviate from the baseline continuously.

[0026] In the preferred embodiment, the construction of the dynamic correlation model in step S4 includes: input variables: gas characteristic parameters, influent COD / TP / TN / NH3-N, dissolved oxygen (DO), sludge concentration (MLSS), reflux ratio, ORP, etc.

[0027] Data preprocessing: Data from different sampling frequencies are unified to a 1-minute timestamp. Low-frequency data (such as COD) is filled with linear interpolation, while high-frequency data (such as DO) is downsampled to a 1-minute interval by sliding window averaging.

[0028] Model Architecture: An LSTM neural network is used to capture temporal features, including short-term fluctuations (such as a sudden drop in DO and a sudden increase in H2S) and long-term trends (such as a slow increase in MLSS) in gas concentrations and process parameters. This is combined with a random forest algorithm for multi-parameter coupled analysis. Gas data is integrated with process parameters such as DO, pH, COD, and sludge concentration (MLSS) to establish an anomaly diagnostic model through machine learning. Current VOCs and odor gas concentrations are compared with historical data to determine if any abnormal changes exist. These changes can provide a basis for judging fluctuations in influent water quality, changes in microbial activity, or equipment malfunctions.

[0029] Output variables: Abnormality type label (toxic shock, carbon-nitrogen ratio imbalance, aeration abnormality), severity score (0-100%).

[0030] In the preferred embodiment, the graded early warning strategy in step S5 includes: Level 1 early warning: when the gas characteristics deviate from the baseline value by 10%-30%, the process parameters are automatically adjusted (the aeration rate is automatically adjusted and an emergency carbon source is added); Level 2 early warning: when the deviation is 30%-50%, the backup treatment unit is activated and the operation and maintenance personnel are notified to intervene; Level 3 early warning: when the deviation is >50%, the upstream pipeline monitoring system is linked to trace the pollution source and the emergency treatment agent is added.

[0031] Based on the aforementioned method for early warning of anomalies in a wastewater treatment system based on VOCs fingerprint spectrum, an early warning system is proposed, comprising: (1) a feature spectrum module, which generates fingerprint spectra of different gases based on historical data; (2) a gas monitoring module, which integrates multiple types of sensors and sampling devices; (3) a feature calculation module, which incorporates dynamic parameter extraction algorithms and data preprocessing methods; (4) a model analysis module, which carries a pre-trained anomaly diagnosis model; and (5) an early warning output module, which generates a visual report and controls the early warning.

[0032] The gas monitoring module also includes an anti-interference unit and a self-cleaning unit. The anti-interference unit is formed by coating the sensor surface with a hydrophobic nano-coating to reduce the interference of water mist on the optical sensor. The self-cleaning unit is equipped with a high-pressure gas backflushing device to periodically remove the sludge particles accumulated on the sensor probe.

[0033] The functions of the feature calculation module include: performing wavelet noise reduction on the original sensor data to remove high-frequency noise (commonly used methods include wavelet transform denoising filtering, Kalman filtering, adaptive filtering, etc.); performing feature dimensionality reduction on the GC-MS full-spectrum data using principal component analysis; calculating the dynamic threshold of the gas concentration change rate, and dynamically correcting the gas concentration alarm threshold according to seasons and influent types to avoid misjudgment.

[0034] The functions of the model analysis module include: Anomaly simulator: generating virtual gas data based on digital twin technology for model training and verification; Model interpreter: outputting the ranking of feature importance and visually displaying the key parameters affecting anomaly diagnosis.

[0035] Compared with the prior art, the beneficial effects of the present invention include: advancing the early warning time, with high accuracy and strong timeliness. Compared with traditional DO monitoring, water quality monitoring, microbial monitoring, etc., it can detect microbial metabolic abnormalities 4-6 hours earlier, and gas changes may indicate abnormalities earlier than traditional water quality parameters (such as COD, ammonia nitrogen).

[0036] The integration of the hierarchical early warning strategy and the automatic control logic (such as the three-level early warning linked to pipe network monitoring) goes beyond simple data alarms.

[0037] Precisely locate problems, define the sulfur-nitrogen gas ratio (S / N ratio) as the influent type discrimination index, and the technical effect is remarkable. The accuracy of anomaly unit judgment is high, and the gas characteristics of different treatment units can help locate the problem link (for example, an increase in H2S in the primary sedimentation tank indicates an impact of influent sulfide, and an increase in NH3 in the aeration tank indicates nitrification collapse).

[0038] Greatly reduce the operation and maintenance costs (compared with traditional manual patrol and off-line detection, the operation and maintenance costs can be reduced by about 30%), and avoid the losses caused by unplanned shutdowns.

[0039] There is no need for invasive sampling, and non-contact sensors can be deployed on the top of the enclosed space to reduce interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention will be further described below in conjunction with the drawings and embodiments.

[0041] FIG. 1 is a flowchart of the implementation process of the method of the present invention.

[0042] FIG. 2 is a flowchart of anomaly early warning and disposal of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] Example 1: A municipal sewage treatment plant has a designed daily treatment capacity of 50,000 m 3 , and adopts the process of "primary sedimentation tank + A² / O biological tank + secondary sedimentation tank". There are a small number of industrial enterprises within the service scope, and there is a risk of illegal discharge of industrial wastewater.

[0044] According to the present invention, a method, system, equipment and storage medium for abnormal early warning of a sewage treatment system are shown in Figure 1. The overall flowchart is as follows: S1. Construct a gas "fingerprint spectrum" under different operating conditions through historical data or experimental simulation. For example, VOCs are mainly alcohols and ketones during normal operation, but the proportion of benzene compounds increases under shock load.

[0045] A gas fingerprint profile was established using one year of historical operating data and three simulated chlorinated organic compound (COC) shock experiments. Under normal operating conditions, the VOCs in the primary sedimentation tank were mainly low-molecular-weight alcohols and ketones, with a baseline H2S concentration of 0.8-1.2 mg / m³. 3 When subjected to impact from chlorinated organic compounds, the fingerprint characteristics are the presence of chlorobenzene VOCs, a sharp increase in H2S concentration, and an S / N ratio (sulfur-to-nitrogen gas concentration ratio) > 3.5.

[0046] S2. Real-time monitoring of the types, concentrations, and spatiotemporal distribution of VOCs and odorous gases released from each process unit of the wastewater treatment plant.

[0047] A non-contact infrared spectral sensor array, using a Gasera TDLAS infrared sensor, was deployed at the top of the primary sedimentation tank. This sensor can accurately detect H2S and NH3. An Agilent 8890 / 5977B gas chromatography-mass spectrometry (GC-MS) system was also installed, with routine sampling and analysis every 2 hours. Expedited sampling was triggered when the infrared sensor detected a sudden change in gas concentration. At 9:00 AM one morning, the infrared sensor detected a rapid increase in H2S concentration, reaching 4.5 mg / m³ at 11:00 AM. 3 This triggers GC-MS expedited sampling.

[0048] S3. Extract the dynamic characteristic parameters of the gas, the characteristic parameters including at least one of the following: the rate of change of characteristic gas concentration, the ratio of key gas types, and spatial migration correlation.

[0049] Dynamic feature parameters were extracted using the feature calculation module. The H2S concentration increased by 300% within 2 hours, with a rate of change of 1.85 mg / (m³). 3 After PCA dimensionality reduction, the GC-MS detection data confirmed the presence of abnormal VOCs such as chlorobenzene and o-dichlorobenzene. The S / N ratio was calculated to be 4.2, which far exceeded the threshold of 3.5. Combined with spatial migration correlation analysis, the abnormal gas was only detected in the primary sedimentation tank and did not diffuse to the subsequent biological tank.

[0050] S4. Construct a dynamic correlation model based on gas characteristic parameters and wastewater treatment process parameters, and obtain anomaly diagnosis rules through machine learning algorithm training.

[0051] The extracted gas characteristic parameters were input into a dynamic correlation model with process parameters such as influent pH (real-time monitoring value was 5.2, a significant decrease from the baseline value of 7.2) and COD. The model used an LSTM neural network to capture the temporal characteristics of the sudden increase in H2S concentration, combined with a random forest algorithm for multi-parameter analysis, and output the anomaly type label as "chlorinated organic toxicity shock" with a severity score of 85%.

[0052] S5. Input the real-time gas data into the dynamic correlation model, output the water inlet impact type, process anomaly level and location information, and trigger the graded early warning strategy.

[0053] When a Level 3 warning is triggered, the system automatically links with the upstream pipeline monitoring system to trace the pollution source and locates it to a drainage branch pipe in an industrial park. At the same time, the emergency regulating tank is activated to introduce the polluted influent into the regulating tank for water quality buffering. Alkaline agents are added to adjust the pH to neutral to prevent highly toxic wastewater from entering the A² / O biological tank and causing the activated sludge system to collapse.

[0054] Example 2: A wastewater treatment plant in an industrial park uses a process of "bar screen + anoxic tank + aerobic tank + secondary sedimentation tank" with a treatment capacity of 20,000 m³. 3 / day, during the hot summer season, filamentous sludge bulking is prone to occur, and it is necessary to rely on the various modules of the early warning system to achieve accurate early warning and disposal, as shown in Figure 2.

[0055] According to the present invention, a wastewater treatment system anomaly early warning system based on VOCs fingerprinting is constructed, taking into account the conditions of this wastewater treatment plant, as follows: 1. Feature Spectrum Module: Generating fingerprint spectra of different gases based on historical data. Based on 18 months of historical operating data and 5 sludge bulking simulation experiments, gas fingerprint spectra of each unit are constructed. Under normal operating conditions in the aerobic tank, VOCs are mainly pentanal and low-molecular-weight organic acids, with a baseline concentration of dimethyl sulfide (DMS) of 0.2-0.4 mg / m³. 3 The S / N ratio (sulfur-based / nitrogen-based gases) is 0.8-1.0; under sludge bulking conditions, the fingerprint characteristic is that the DMS concentration continuously exceeds 0.6 mg / m³. 3 The S / N ratio is 1.2-1.6, and it is positively correlated with the sludge volume index (SVI) (R). 2 =0.89).

[0056] 2. Gas monitoring module: Integrating multiple types of sensors and sampling devices, a non-contact infrared sensor array is deployed at the top of the aerobic tank, using a Baseline-MoconIR sensor to detect DMS, H2S, and NH3. 3, The system was equipped with a PerkinElmerClarus 680GC-MS. The sensor surface is coated with a hydrophobic nano-coating to resist water mist interference, and a high-pressure gas backflushing device is used to clean the probe weekly. For three consecutive days, the infrared sensor continuously monitored a DMS concentration of 0.7 mg / m³.3 0.9 mg / m 3 1.1 mg / m 3 This triggers GC-MS expedited sampling.

[0057] 3. Feature calculation module: Built-in dynamic parameter extraction algorithm and data preprocessing method perform wavelet denoising on raw sensor data to remove high-frequency noise. Dimensionality reduction of GC-MS full-spectrum data is performed using PCA to extract core features, and the calculated daily average DMS change rate is 0.27 mg / (m²). 3 •d), S / N ratio 1.5, spatial migration correlation analysis showed that the abnormal gas was concentrated in the aerobic pool and there was no cross-unit diffusion, thus identifying the source of the anomaly.

[0058] 4. The model analysis module, equipped with a pre-trained anomaly diagnosis model, inputs gas characteristic parameters and process parameters such as MLSS (4100 mg / L) and DO (1.7 mg / L) into the pre-trained model. The LSTM neural network captures the long-term trend of continuously rising DMS, and the random forest algorithm couples multi-parameter analysis to output the label "filamentous sludge bulking" with a severity score of 48%. The model interpreter visualizes DMS concentration and DO as key influencing parameters.

[0059] 5. Early warning output module: Generates a visual report and controls the triggering of a secondary early warning. The generated visual report is pushed to maintenance personnel, synchronously linking with the process control system to automatically adjust the aeration rate from 3.0 m³ / h. 3 / (m 2 •h) increased to 4.2m 3 / (m 2 (h), start the PAC dosing device (dosage 25 mg / L). After 5 days, the DMS concentration dropped to 0.35 mg / m³. 3 SVI recovered to 140 mL / g, and the system operated stably.

[0060] In summary, this invention, through the construction of VOCs fingerprint profiles for each process unit, multi-module collaborative monitoring, and intelligent analysis, has achieved significant results in practical applications: it successfully and accurately identifies abnormal operating conditions such as chlorinated organic toxicity shocks and filamentous sludge bulking 4-6 hours in advance; through spatial correlation analysis, it pinpoints problematic units such as the primary sedimentation tank and aerobic tank; and the linkage control system automatically adjusts process parameters such as aeration rate and chemical dosing to quickly curb the development of abnormalities and avoid risks such as activated sludge system collapse and effluent quality exceeding standards. Simultaneously, relying on mature industrial sensors and a tiered monitoring model, it achieves non-invasive, low-cost operation and maintenance, adapts to different wastewater treatment scenarios, significantly improves the system's resilience and operational stability, and ensures the continuous and efficient operation of the treatment process.

Claims

1. A method, system, equipment, and storage medium for early warning of abnormalities in a wastewater treatment system, characterized in that, Includes the following steps: S1. Construct gas "fingerprints" under different operating conditions through historical data or experimental simulations. For example, VOCs are mainly alcohols and ketones during normal operation, but the proportion of benzene compounds increases under shock load. S2. By deploying gas monitoring devices in each process unit, monitor and collect in real time the types, concentrations, and spatiotemporal distribution data of VOCs and odorous gases released from each process unit of the wastewater treatment plant; S3. Extract the dynamic characteristic parameters of the gas, including at least one of the following: the rate of change of characteristic gas concentration, the proportional relationship of key gas types, and spatial migration correlation. S4. Construct a dynamic correlation model based on gas characteristic parameters and wastewater treatment process parameters, and obtain anomaly diagnosis rules through machine learning algorithm training; S5. Input real-time gas data into the dynamic correlation model, output influent impact type, process anomaly level and location information, and trigger a graded early warning strategy.

2. The method, system, equipment, and storage medium for abnormal early warning of a sewage treatment system according to claim 1, characterized in that, The real-time monitoring in step S2 employs single-modal and multi-modal sensing technologies, specifically including: optimizing the deployment of non-contact infrared spectral sensor arrays on the top of each biological unit tank based on the flow characteristics of each reaction unit, for detecting characteristic gas concentrations; periodically sampling gas chromatography-mass spectrometry (GC-MS) for full-spectrum analysis of VOCs types, identifying characteristic components, and linking with the infrared sensor. When the infrared sensor detects a sudden increase in H2S concentration, it triggers GC-MS expedited sampling, and identifies complex VOCs such as benzene series compounds and halogenated hydrocarbons through NIST mass spectrometry library comparison; the infrared sensor provides real-time concentration data, and the GC-MS provides detailed VOCs type information, forming a comprehensive monitoring network. The two sensors are cross-calibrated, and the full-spectrum data of the GC-MS is used to periodically calibrate the single gas readings of the infrared sensor to correct drift errors; if the concentration of a single gas increases sharply but other sulfur series gases do not change synchronously, mass spectrometry verification is triggered to eliminate sensor false alarms.

3. The method, system, equipment, and storage medium for abnormal early warning of a sewage treatment system according to claim 1, characterized in that, The dynamic feature parameter extraction method in step S3 includes: S3.1 defining the ratio of key gas types, defining the concentration ratio of sulfur-based gases to nitrogen-based gases (S / N ratio), and determining that sulfur-containing organic matter impact occurs when the S / N ratio exceeds the threshold; S3.2 locating spatial migration correlation, and locating the source of the anomaly by using the time-delay correlation coefficient of gas concentration between different process units.

4. The method, system, equipment, and storage medium for abnormal early warning of a sewage treatment system according to claim 1, characterized in that, The construction of the dynamic correlation model in step S4 includes: S4.1 Input variables, such as gas characteristic parameters, influent COD / TP / TN / NH3-N, dissolved oxygen (DO), sludge concentration (MLSS), reflux ratio, and ORP; S4.2 Data preprocessing, unifying data from different sampling frequencies to a 1-minute timestamp, using linear interpolation to fill low-frequency data, and downsampling high-frequency data to a 1-minute interval through a sliding window average; S4.3 Confirming the model architecture, using an LSTM neural network to capture temporal features, capturing short-term fluctuations and long-term trends of gas concentration and process parameters, and combining it with a random forest algorithm for multi-parameter coupling analysis; S4.4 Combining gas data with process parameters such as DO, pH, COD, and sludge concentration (MLSS), and establishing an anomaly diagnosis model through machine learning; S4.5 Comparing the current VOCs and odor gas concentrations with historical data to determine if there are any abnormal changes, which can provide a basis for judging fluctuations in influent water quality, changes in microbial activity, or equipment failures; S4.6 Output variables, anomaly type labels and severity scores from 0-100%.

5. The abnormal early warning method, system, equipment, and storage medium for a wastewater treatment system according to claim 1, characterized in that, The graded early warning strategy in step S5 includes: Level 1 early warning, when the gas characteristics deviate from the baseline value by 10%-30%, triggering the self-adjustment of process parameters; Level 2 early warning, when the deviation is 30%-50%, activating the backup treatment unit and notifying the operation and maintenance personnel to intervene; Level 3 early warning, when the deviation is >50%, linking the upstream pipeline monitoring system to trace the pollution source and initiating the addition of emergency treatment agents.

6. An early warning system implementing the method as described in any one of claims 1-5, characterized in that, include: The gas monitoring module integrates multiple types of sensors and sampling devices to collect VOCs and odor gas data from each process unit in real time. The feature calculation module has a built-in dynamic parameter extraction algorithm and data preprocessing method, which is used to extract dynamic feature parameters from the collected data. The model analysis module is equipped with a pre-trained anomaly diagnosis model, which is used to receive feature parameters and process parameters and output diagnostic results. The early warning output module generates a visual report and controls the early warning, which is used to generate early warning information and trigger control commands based on the diagnostic results.

7. The wastewater treatment system anomaly early warning system based on VOCs fingerprinting as described in claim 6, characterized in that, The gas monitoring module also includes an anti-interference unit and a self-cleaning unit; the anti-interference unit is to coat the sensor surface with a hydrophobic nano-coating to reduce the interference of water mist on the optical sensor; the self-cleaning unit is equipped with a high-pressure gas backflushing device to periodically remove the sludge particles accumulated on the sensor probe.

8. The wastewater treatment system anomaly early warning system based on VOCs fingerprinting as described in claim 6, characterized in that, The features calculation module includes the following functions: performing wavelet noise reduction on the raw sensor data to remove high-frequency noise; performing feature dimensionality reduction on the GC-MS full-spectrum data using principal component analysis; calculating the dynamic threshold of the gas concentration change rate, and dynamically correcting the gas concentration alarm threshold according to the season and water inflow type to avoid misjudgment.

9. The wastewater treatment system anomaly early warning system based on VOCs fingerprinting as described in claim 6, characterized in that, The functions of the model analysis module include: anomaly simulation, generating virtual gas data based on digital twin technology for model training and validation; and model interpretation, outputting feature importance ranking and visually displaying key parameters affecting anomaly diagnosis.

10. An electronic device and a computer-readable storage medium, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a wastewater treatment system anomaly early warning method based on VOCs fingerprinting as described in any one of claims 1-5.

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