A Multi-Pollutant Purification System and Intelligent Control Method for Exhaust Gas

Abstract

This invention relates to the field of waste gas purification technology, and discloses a multi-pollutant waste gas purification system and intelligent control method. The method includes a waste gas monitoring module, a pollution feature extraction module, a purification decision generation module, a trend prediction module, a decision compensation module, and a purification execution module. The waste gas monitoring module acquires real-time data streams of waste gas composition and operating parameters; the pollution feature extraction module analyzes multiple pollution features and generates maps; the purification decision generation module outputs a preliminary solution based on decision space and efficiency constraints, combined with the feature maps and operating parameters; the trend prediction module predicts the dynamic evolution of pollution features and operating parameters; the decision compensation module compensates for the preliminary solution based on the prediction results, generating an optimized solution; and the purification execution module drives the purification device to operate according to the optimized solution. This system achieves intelligent and precise waste gas purification, adapts to complex and ever-changing waste gas purification needs, improves purification efficiency, and reduces operating costs.

Description

Technical Field

[0001] This invention relates to the field of waste gas purification technology, specifically to a waste gas multi-pollutant purification system and intelligent control method. Background Technology

[0002] Industrial production and energy conversion processes generate waste gases containing various pollutants. If these waste gases are discharged directly without effective treatment, they will have adverse effects on the ecological environment and human health. Currently, there are various technical means for waste gas purification, such as adsorption, catalytic oxidation, and absorption. However, these methods have many limitations in practical applications.

[0003] Traditional exhaust gas purification systems often employ fixed treatment processes and parameter settings, making it difficult to adapt to dynamic changes in exhaust gas composition. For example, when the types of pollutants in the exhaust gas suddenly increase or their concentration rises sharply, the fixed purification strategy cannot be adjusted in time, resulting in a significant decrease in purification efficiency and making it difficult to meet environmental emission standards.

[0004] Most existing systems lack the capability for real-time monitoring and comprehensive analysis of exhaust gas composition and operating parameters. During the purification process, the inability to accurately grasp the characteristic changes of pollutants and the fluctuations in equipment operating conditions makes purification decisions lack a scientific basis, easily leading to over-purification or under-purification. Over-purification wastes energy and consumables, increasing operating costs; while under-purification fails to achieve the expected purification effect, creating environmental pollution risks.

[0005] Traditional systems typically lack the ability to predict pollution trends and make corrective decisions. They can only passively purify the gas based on its current state, unable to anticipate the evolution of pollutants and therefore unable to adjust purification strategies in a timely manner to address potential future pollution conditions. This results in insufficient adaptability and foresight, making it difficult to meet the complex and ever-changing demands for gas purification. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-pollutant purification system and intelligent control method for exhaust gas to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a multi-pollutant waste gas purification system, the method comprising:

[0008] The exhaust gas monitoring module is used to acquire exhaust gas composition data stream and operating condition parameter data stream in real time;

[0009] The pollution feature extraction module, based on a preset pollutant threshold constraint, performs multi-pollutant feature analysis in conjunction with the exhaust gas composition data stream to generate a pollution feature map.

[0010] The purification decision generation module, based on the embedded purification decision space and purification efficiency constraints, combined with the pollution feature map and the operating condition parameter data stream, outputs a preliminary purification decision scheme.

[0011] The trend extrapolation module dynamically predicts the evolution of the pollution feature map and the operating condition parameter data stream to obtain a predicted pollution feature map and a predicted operating condition parameter set.

[0012] The decision compensation module performs strategy compensation on the primary purification decision scheme based on the predicted pollution feature map and the predicted operating condition parameter set, and generates an optimized purification decision scheme.

[0013] A purification execution module drives the purification device to operate based on the optimized purification decision scheme.

[0014] Preferably, the pollution feature extraction module includes:

[0015] Obtain the basic attribute set of the exhaust gas emission source;

[0016] Construct a pollution diffusion model based on the aforementioned set of basic attributes;

[0017] The waste gas composition data stream is input into the pollution diffusion model to generate a pollutant concentration distribution cloud map.

[0018] Based on the preset pollutant threshold constraints and the pollutant concentration distribution cloud map, the pollution feature map is analyzed.

[0019] Preferably, the construction steps of the pollution feature extraction module include:

[0020] Establish clusters of similar exhaust gas emission sources;

[0021] Load the historical pollution characteristic record library corresponding to the cluster of exhaust gas emission sources of the same type;

[0022] Load the real-time pollution characteristic record library of the target exhaust gas emission source;

[0023] A pollution feature benchmark model is constructed based on the historical pollution feature record database.

[0024] Based on the real-time pollution feature record library, the feature parsing sensitivity of the pollution feature benchmark model is optimized to generate the pollution feature extraction module.

[0025] Preferably, the step of optimizing the sensitivity of feature parsing for the pollution feature benchmark model based on the real-time pollution feature record library includes:

[0026] The pollution feature benchmark model was tested using the real-time pollution feature record library to obtain the feature analysis sensitivity coefficient;

[0027] When the feature parsing sensitivity coefficient is lower than the preset sensitivity threshold, the pollution feature benchmark model is incrementally trained based on the real-time pollution feature record library.

[0028] Preferably, the purification decision generation module includes:

[0029] The purification decision space is calibrated based on the pollution characteristic map and the operating condition parameter data stream to obtain a first calibration decision space;

[0030] Identify the decision feature triggering interval in the first calibration decision space and generate the first decision feature triggering domain;

[0031] An initial purification decision is generated based on the first decision feature trigger domain;

[0032] Calculate the purification efficiency matching degree of the initial purification decision;

[0033] When the purification efficiency matching degree meets the purification efficiency constraint, the initial purification decision is added to the primary purification decision scheme.

[0034] Preferably, calibrating the purification decision space based on the pollution characteristic map and the operating condition parameter data stream includes:

[0035] Traverse the decision records in the purification decision space;

[0036] Analyze the similarity depth between the current pollution feature map and the sample pollution features in the decision record;

[0037] Analyze the similarity depth between the current operating condition parameter data stream and the sample operating condition parameters in the decision record;

[0038] The correlation strength coefficient between the similarity depth of the pollution characteristics and the similarity depth of the operating parameters is calculated using a weighted average method.

[0039] When the correlation strength coefficient exceeds the preset correlation threshold, the corresponding decision record will be included in the first calibration decision space.

[0040] Preferably, the decision compensation module includes:

[0041] The purification decision space is calibrated based on the predicted pollution feature map and the predicted operating condition parameter set to obtain a second calibration decision space;

[0042] Identify the decision feature triggering interval in the second calibration decision space and generate the second decision feature triggering domain;

[0043] Under the constraint of the purification efficiency, a compensatory purification decision is generated iteratively.

[0044] The compensation purification decision and the primary purification decision scheme are combined strategically.

[0045] Preferably, the purification execution module includes:

[0046] Analyze the purification parameter instruction set in the optimized purification decision scheme;

[0047] The purification parameter instruction set is converted into a control signal sequence for the purification device;

[0048] The dosage of the purification medium and the reaction conditions are adjusted according to the control signal sequence.

[0049] Preferably, the system further includes a purification efficiency feedback module:

[0050] Collect data on the composition of the purified exhaust gas;

[0051] Compare the purified exhaust gas composition data with the preset purification target values;

[0052] A purification efficiency deviation index is generated and fed back to the pollution feature extraction module.

[0053] Preferably, the present invention further includes an intelligent control method based on a multi-pollutant waste gas purification system, the method comprising:

[0054] Real-time acquisition of exhaust gas composition data stream and operating condition parameter data stream;

[0055] The waste gas composition data stream is analyzed based on preset pollutant threshold constraints to generate a pollution feature map.

[0056] Based on the embedded purification decision space and purification efficiency constraints, a preliminary purification decision scheme is output by combining the pollution feature map and the operating condition parameter data stream.

[0057] By extrapolating the dynamic change trends of the pollution feature map and the operating condition parameter data stream, a predicted pollution feature map and a predicted operating condition parameter set are obtained.

[0058] Based on the predicted pollution feature map and the predicted operating condition parameter set, the primary purification decision scheme is compensated for strategy.

[0059] Drive the purification device to execute the optimized purification decision-making scheme;

[0060] Collect data on the composition of purified exhaust gas and generate a purification efficiency deviation index;

[0061] The pollution characteristic analysis rules are updated based on the purification efficiency deviation index.

[0062] Compared with the prior art, the beneficial effects of the present invention are:

[0063] By setting up an exhaust gas monitoring module, the system can acquire exhaust gas composition data streams and operating condition parameter data streams in real time, enabling it to have a comprehensive and timely understanding of the current exhaust gas status and equipment operation, thus providing basic information for subsequent pollution characteristic analysis and purification decisions.

[0064] The pollution feature extraction module, based on preset pollutant threshold constraints, combines exhaust gas composition data stream to analyze multiple pollution features and generate pollution feature maps. It can accurately identify the characteristics of multiple pollutants in exhaust gas, including the type, relative content, and distribution of pollutants, making the system's understanding of the pollution status clearer and more specific, and avoiding ambiguous judgments of pollution features.

[0065] The purification decision generation module is based on the embedded purification decision space and purification efficiency constraints. It combines pollution characteristic maps and operating condition parameter data streams to output a preliminary purification decision scheme. This ensures that the decision-making not only considers the current pollution characteristics but also takes into account the operating conditions of the equipment. This ensures that the preliminary scheme is theoretically feasible and targeted, and can initially meet the purification requirements.

[0066] The trend prediction module dynamically evolves and predicts the pollution feature map and operating condition parameter data stream to obtain the predicted pollution feature map and predicted operating condition parameter set. This enables the system to transcend the limitations of the current state, grasp the changing trend of pollutants and the possible fluctuations in equipment operating conditions in advance, and provide a forward-looking basis for decision optimization, avoiding the lag that may result from making decisions based solely on the current state.

[0067] The decision compensation module performs strategy compensation on the primary purification decision scheme based on the predicted pollution feature map and the predicted operating condition parameter set, and generates an optimized purification decision scheme. This can make up for the shortcomings of the primary scheme in dealing with future changes, making the final decision scheme more complete. It is not only applicable to the current pollution situation, but also adaptable to possible future pollution trends, thus enhancing the adaptability and flexibility of the decision.

[0068] The purification execution module drives the operation of the purification device based on an optimized purification decision scheme, ensuring that the purification device can work according to the optimal strategy, making the purification process more precise and efficient. It can effectively remove multiple pollutants in the exhaust gas and reasonably adjust the operating parameters according to the actual situation, reducing unnecessary energy and consumable consumption. While achieving good purification results, it helps to reduce operating costs and improve the overall performance of the system. Attached Figure Description

[0069] Figure 1 This is a timing diagram of the multi-pollutant waste gas purification system described in this invention;

[0070] Figure 2 This is a flowchart of the pollution feature extraction module;

[0071] Figure 3 A flowchart for constructing the pollution feature extraction module;

[0072] Figure 4 A flowchart for the purification decision generation module;

[0073] Figure 5 This is a flowchart for the decision-making compensation module. Detailed Implementation

[0074] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] Please see Figure 1 This invention provides a multi-pollutant purification system for waste gas, the method comprising:

[0076] The system integrates four modules: exhaust gas monitoring, pollution feature extraction, purification decision generation, trend prediction, decision compensation, and purification execution. The exhaust gas monitoring module collects real-time data streams of exhaust gas composition and operating parameters using a sensor array. The pollution feature extraction module receives the exhaust gas composition data stream, combines it with preset pollutant threshold constraints, and generates a pollution feature map by analyzing pollutant concentration distribution. This map includes pollutant types, concentration gradients, and spatial distribution characteristics. The purification decision generation module calls upon the embedded purification decision space (which stores a historical purification strategy library), matches the optimal decision based on the pollution feature map and operating parameter data stream, and generates a preliminary purification decision scheme. This scheme covers purification process selection, reaction condition parameters, and resource allocation strategies. The trend prediction module uses a time series prediction algorithm to dynamically evolve the pollution feature map and operating parameter data stream, outputting a predicted pollution feature map and a predicted set of operating parameters. The decision compensation module compares the deviation between the predicted data and the preliminary scheme, retrieves compensation strategies from the purification decision space, and generates an optimized purification decision scheme. The purification execution module converts the optimized scheme into control commands, driving the purification device to adjust the dosage of purification media and reaction conditions.

[0077] Example 1: See Figure 2The operation of the pollution feature extraction module begins with acquiring the basic attribute set of the exhaust gas emission source. This basic attribute set encompasses physical structural parameters, emission characteristic parameters, and environmentally relevant parameters. Physical structural parameters include chimney height, exhaust stack inner diameter, and pipe inclination angle; emission characteristic parameters involve initial exhaust gas velocity, emission temperature fluctuation range, and exhaust continuity coefficient; environmentally relevant parameters refer to coupling parameters with the surrounding terrain and landforms, such as building shading factors and surface roughness index. These parameters are retrieved in real-time from the equipment database and stored in the feature extraction buffer in the form of a structured data table.

[0078] The pollution diffusion model was constructed using multi-source data fusion technology. Based on a basic attribute set, the core of the model applies the Gaussian plume diffusion equation framework, which includes dynamic calculation submodules for lateral and vertical diffusion coefficients. The lateral diffusion coefficient is automatically matched to the Pasqual stability level according to the atmospheric stability classification table, while the vertical diffusion coefficient is correlated with the temperature stratification curve and inversion layer height data. During the model initialization phase, a geographic information system base map is loaded, mapping the emission source location coordinates to a three-dimensional grid coordinate system. The grid cell resolution is set to 10m × 10m × 5m (length × width × height) according to the monitoring accuracy requirements. When the exhaust gas composition data stream is input into the model, independent transmission channels are created for each pollutant type, and each channel embeds species-specific diffusion correction factors, such as the gas-phase transformation attenuation factor for sulfur dioxide and the photochemical reaction correction value for nitrogen oxides.

[0079] The generation process of the pollutant concentration distribution cloud map involves spatial interpolation and dynamic rendering. The raw diffusion data output by the model is first subjected to Kriging spatial interpolation to fill in the concentration values ​​in areas not covered by the sensors. The interpolation results are stored as a voxel matrix, with the matrix axes corresponding to longitude, latitude, and altitude in three dimensions. The cloud map rendering engine extracts the matrix data and maps the concentration values ​​to the RGBA color space: the lowest concentration range is rendered as transparent blue, the threshold boundary range transitions to yellow, and the concentration exceeding the standard is marked in red. The rendering process overlays a real-time meteorological flow field map, using dynamic particle trajectories to demonstrate the impact of the prevailing wind direction on the pollutant transport path. The cloud map update frequency is synchronized with the monitoring data stream, with a default setting of generating one frame of holographic projection view per minute.

[0080] The analysis of the pollution characteristic map involves a three-layer data architecture. The first layer, a pollutant classification index, sorts the pollutants exceeding standards by toxicity level and labels them with chemical characteristic codes; for example, PM2.5 is identified as inhalable particulate matter, and benzene compounds as carcinogens. The second layer, a spatial distribution heatmap, uses a tile pyramid structure for storage. The top layer displays the overall pollution outline of the region, and drilling down level by level yields specific concentration values ​​within each grid cell, while also recording the migration trajectory of the pollution plume center. The third layer, a time-dimensional cumulative curve, records the concentration change trends of each pollutant, calculating hourly extreme values ​​and daily average fluctuations using a sliding time window method, and automatically marking timestamps of abnormal fluctuations. When consecutive areas exceeding standards appear in the cloud map, the map automatically activates the pollution source tracing function, tracing back to the main contributing sources of pollutants in that area.

[0081] The real-time correction mechanism is implemented through data assimilation technology. Miniature weather stations deployed in the emission area upload measured values ​​of temperature, humidity, wind direction, and wind speed every 30 seconds. These measured data and model predictions are synchronously input into the bias corrector, which uses a Kalman filter algorithm to calculate the prediction error covariance matrix. The corrector outputs parameter adjustment instructions to the diffusion model, focusing on correcting the match between the vertical turbulent diffusion coefficient and the actual atmospheric boundary layer height. Under calm wind conditions, the system automatically switches to the plume integral mode to recalculate the residence time of pollutants in the near-surface layer. All correction operations are completed within the data stream processing cycle, ensuring that the spatial matching deviation rate between the cloud map and the measured data in the next calculation cycle is below a set threshold.

[0082] The feature extraction module incorporates an adaptive threshold adjustment mechanism. Preset pollutant threshold constraints are loaded in configuration files, containing dual standard libraries: national standard limits and local special emission limits. When a new pollutant is detected in the exhaust gas, the system retrieves a temporary threshold recommendation from the material safety database. Under conditions of continuous exceedance, the map generation system automatically improves the spatial resolution to a 1-meter grid accuracy, increasing the sensitivity of pollution plume edge identification. The historical pollution feature library periodically performs morphological analysis on the maps, identifying frequently occurring pollution patterns and generating feature templates. When real-time data matches the template features, an early warning and accelerated response mechanism is triggered.

[0083] The feature extraction module's output interface employs a multi-level buffer design. Raw cloud map data is compressed and stored in a distributed file system, providing the trend inference module with access to the basic dataset. Standard pollution feature maps are converted into lightweight JSON format and pushed to the decision-making center system in real time, containing a list of pollutant types, a set of boundary coordinates for areas exceeding standards, and pollution intensity classification labels. Simultaneously, a metadata description file is generated, recording the map generation timestamp, correction operation records, spatial accuracy level, and other traceability information. During periods of abrupt changes in exhaust emission conditions, the system automatically activates a fast channel, bypassing conventional data verification steps to directly transmit key pollution indicators.

[0084] Example 2: See Figure 3 The construction of the pollution feature extraction module is based on a dual-source data collaboration mechanism of historical and real-time pollution feature record databases. The historical pollution feature record database stores long-term monitoring data of similar waste gas emission source clusters, with a data collection period of no less than three years, covering the variation patterns of waste gas composition under different seasons and operating conditions. Each record contains four core fields: timestamp, waste gas composition spectrum, operating condition parameter vector, and purification effect evaluation value. The waste gas composition spectrum is organized in the form of a multi-dimensional array, with each dimension corresponding to a pollutant type, and the values ​​of each dimension being normalized concentration values. The operating condition parameter vector integrates continuous variables such as temperature, pressure, flow rate, and humidity, as well as discrete variables such as equipment operating status and maintenance records. The purification effect evaluation value is quantitatively represented on a percentage basis, calculated from the ratio of the purified emission concentration to the standard limit.

[0085] The real-time pollution feature record library adopts a circular buffer structure, with the buffer capacity set to the most recent 72 hours of continuous monitoring data. Data writing is managed using a sliding window, with a default window size of one hour. Each slide triggers a feature extraction operation. The buffer includes an outlier filtering layer; when the concentration of a pollutant exceeds three standard deviations of its historical maximum, a data review process is automatically triggered. Review methods include sensor cross-validation and manual review and labeling. Data that passes review is then stored in the record library with added confidence labels. These confidence labels are categorized into high, medium, and low levels, influencing the sample weight allocation for subsequent model training.

[0086] The pollution characteristic baseline model is constructed using an ensemble learning framework. The model input layer receives exhaust gas composition spectra and operating condition parameter vectors from a historical data repository, and generates high-order feature combinations through feature engineering. These feature combinations include time-lag variables (concentration values ​​from the previous hour), spatial correlation variables (concentration differences between adjacent monitoring points), and operating condition-derived variables (temperature-pressure interaction terms). During the model training phase, an improved random forest algorithm is applied, and the node splitting criterion for each decision tree uses the following custom index. :

[0087] ;

[0088] in: This represents the total number of candidate features for the current node. For the first The number of samples corresponding to each feature The total number of samples at each node. The information gain increment after splitting using this feature. This represents the average detection latency (in minutes) for this feature in historical records. This is the delay penalty coefficient (default value 0.01). This formula ensures that the model prioritizes features with high information gain and timely data acquisition for splitting. The baseline model's output layer generates a probability distribution for pollution feature classification, with category labels including eight preset types such as regular pollution mode, sudden pollution mode, and complex pollution mode.

[0089] The implementation process of feature parsing sensitivity optimization is divided into two stages: offline testing and online incremental training. In the offline testing stage, the real-time recording database data is divided into training, validation, and test sets in a 7:2:1 ratio. The sensitivity coefficient is calculated during the testing process. This coefficient is defined as the ratio of the model's correct identification rate of new pollution features to the historical baseline identification rate. When At this time, the system automatically starts the incremental training process. Incremental training uses the mini-batch gradient boosting method, extracting 500 of the latest records from the real-time record library each time as incremental samples, and adjusting the model parameters through the following strategies:

[0090] Feature weight reallocation: The sampling probability of each feature in the random forest is dynamically adjusted based on the changes in feature importance in the incremental samples. For newly emerging pollutant combination features, the initial sampling probability is set to 1.5 times the historical average.

[0091] Tree pruning: Remove subtrees that consistently deteriorate on incremental samples and grow new subtrees at the corresponding positions. Classification boundary adjustment: Recalculate the center vectors of various contamination patterns using a fuzzy clustering algorithm to expand the classification boundary to accommodate new contamination features.

[0092] The optimized model is managed through a version control mechanism, retaining snapshots of the previous three versions with each update, and supporting rapid rollback to a stable state. The model performance monitoring panel displays key indicators such as sensitivity coefficient change curves, feature recognition confusion matrix, and incremental training time in real time. When the sensitivity coefficient improvement is less than 1% after five consecutive incremental training iterations, the system automatically switches to full training mode, reloading all data from the historical data library for model reconstruction.

[0093] The interaction interface between real-time data and the model adopts an event-driven architecture. When the exhaust gas composition data stream is input, a feature extraction request event is triggered, carrying payload information such as data time window, pollutant concentration matrix, and snapshot of operating parameters. After listening to this event, the feature extraction service performs the following sequence of operations: First, it verifies data integrity; requests with more than 15% missing values ​​directly return an error code; complete data enters the preprocessing pipeline for standardization scaling, time alignment, and outlier smoothing; the processed data is input into the baseline model to obtain preliminary classification results; when the model returns a low-confidence prediction (probability value < 0.6), the incremental model is automatically called for secondary prediction; finally, a structured message integrating pollution feature labels, confidence scores, and feature importance rankings is output.

[0094] The historical data backtracking analysis module periodically performs pattern mining tasks. This module loads the full historical data library and applies time-series clustering algorithms to identify recurring contamination feature evolution patterns. Each pattern is abstracted as a state transition graph, where nodes represent typical contamination states and edge weights represent the transition probabilities between states. Upon discovery of a new pattern, feature extraction rule suggestions are automatically generated and, after manual review, embedded into the decision rule set of the baseline model. The backtracking analysis results are also used to optimize the data storage strategy of the real-time record library; raw data corresponding to high-frequency access patterns is cached in memory, while low-frequency pattern data is moved to cold storage.

[0095] The system maintenance subsystem is responsible for ensuring the continuous operation of the feature extraction module. At the hardware level, a dual-machine hot standby architecture is deployed, enabling service switching within 10 seconds in the event of a master node failure. At the software level, resource isolation containers are set up to limit the maximum memory usage of a single feature extraction task to no more than 4GB. Data pipelines implement flow control; when the input data rate exceeds processing capacity, a degradation processing mode is initiated, performing only the extraction of core pollutant features. The logging system records a detailed trajectory of each feature extraction operation, including audit information such as input data hash values, model version numbers, and computational resource consumption; log files are retained for six months.

[0096] Example 3: See Figure 4The purification decision generation module processes the pollution feature map and operating parameter data stream through a two-layer calibration mechanism. The first layer of calibration traverses the decision records in the purification decision space. Each record contains sample pollution features, sample operating parameters, and the corresponding purification strategy. The similarity depth between the current pollution feature map and the sample is calculated using a dynamic time warping algorithm to match the time alignment of the concentration change curve. The similarity depth of the operating parameters is quantified using Euclidean distance, and the weighting coefficients are dynamically adjusted according to the importance of the parameters (e.g., temperature weight is set to 0.6, pressure weight to 0.4). The association strength coefficient is generated by normalizing the weighted similarity depth, with a threshold set to 0.7. The second layer of calibration confirms the decision feature triggering intervals in the first calibration decision space, with the intervals divided based on the pollutant concentration peak and the operating parameter fluctuation range. The initial purification decision is generated using a genetic algorithm, where individuals in the population represent different purification process combinations. The fitness function is the purification efficiency matching degree, calculated as (actual purification rate / target purification rate) × 100%. When the matching degree is ≥ 90%, the strategy is incorporated into the primary purification decision scheme.

[0097] The operation of the purification decision generation module is based on a two-layer calibration mechanism. The first layer of calibration focuses on the dynamic screening process of the purification decision space. The purification decision space is constructed in the form of a graph database, storing a set of decision records formed by historical valid decision cases. Each decision record contains five-dimensional data: sample pollution feature fingerprint (MD5 hash value of pollution feature graph), sample operating condition parameter vector (normalized parameter array), purification strategy instruction set, expected purification efficiency value, and actual execution effect rating. When calibration starts, the system traverses the current decision record set and processes the current input data using a multi-dimensional similarity parallel computing architecture.

[0098] The current method for calculating the similarity depth between pollution feature maps and sample pollution features introduces a dynamic time warping algorithm. This algorithm first converts the map data into a time series format: it extracts snapshots of pollutant concentration distributions at fixed intervals (default 5 minutes) along the time axis, and each snapshot is dimensionality-reduced into a 128-dimensional feature vector. The similarity depth calculation is implemented in three steps: the first step is to standardize the sequence length by adjusting sequences of different durations to the same length using cubic spline interpolation; the second step is to calculate the cosine similarity of each pair of feature vectors, forming a similarity matrix; the third step is to find the optimal curved path on the matrix, and the cumulative similarity of the paths is the final similarity depth value. The calculation formula is as follows:

[0099] ;

[0100] in: Indicates the depth of similar pollution characteristics. To determine the optimal curved path length, and These represent the lengths of the current sequence and the sample sequence, respectively. It is a path indicator function (1 when the path passes through point (i,j) and 0 otherwise). and These represent the feature vectors of the i-th frame of the current sequence and the j-th frame of the sample sequence, respectively. This calculation method can overcome the comparison bias caused by the difference in the frequency of monitoring data acquisition.

[0101] The similarity depth between the current operating condition parameter data stream and the sample operating condition parameters is determined using a weighted Euclidean distance framework. Parameter weights are assigned according to parameter sensitivity rules: temperature parameters are weighted at 0.6, pressure parameters at 0.3, and flow parameters at 0.1. The weighting coefficients are dynamically adjusted based on the parameter's impact on purification efficiency, and the weight table is updated every 24 hours through the parameter importance assessment module. Before calculating the similarity depth, continuous parameters are Z-score standardized, and discrete parameters undergo one-heat encoding conversion. The correlation strength coefficient is generated through dual similarity depth fusion.

[0102] ;

[0103] in: Represents the correlation strength coefficient. and These are the fusion weights for pollution characteristics and operating parameters (default α=0.7, β=0.3). For depths with similar pollution characteristics, Indicates the similarity depth of working condition parameters. This is the distance scaling factor (default 0.5). When the calculation result... When the decision is made, the corresponding decision record is included in the first calibration decision space. This space uses a minimum heap structure for storage, and only the first 100 highly correlated records are retained in descending order of R value.

[0104] The second-level calibration establishes the identification rules for the decision feature trigger intervals. The interval division is based on the distribution pattern of exceeding pollution plumes and the fluctuation characteristics of operating parameters in the pollution feature map. The pollution plume feature extractor scans continuous exceeding areas in the map, calculating three key indicators: area, core concentration extreme value, and azimuth angle relative to the emission source. Operating parameter fluctuation analysis uses the window variance detection method, calculating the coefficient of variation of parameters within a 30-minute time window. The trigger interval determination conditions are: the pollution plume area is greater than 1.5 times the baseline value, and the coefficient of variation of operating parameters exceeds 0.25. The spatiotemporal domains that meet the conditions are marked as the first decision feature trigger domains, and these trigger domains are mapped to the decision space to form a three-dimensional decision coordinate system (pollutant type axis, concentration interval axis, and operating condition fluctuation level axis).

[0105] The initial purification decisions were generated using a multi-objective genetic algorithm framework. Highly correlated decision records in the decision space were transformed into an initial population, with each individual represented by a purification process gene encoding. The encoding used a binary string structure: the first 8 bits represented the catalytic oxidation process intensity (0000000011111111 corresponds to 0100% power), the middle 6 bits controlled the adsorbent dosing rate (000000111111 corresponds to 0300 kg / h), and the last 4 bits managed the pH value of the washing solution (00001111 corresponds to 5.0-9.0). The population size was set to 200 individuals, with a maximum of 50 generations. The fitness function was defined as the purification efficiency matching degree.

[0106] ;

[0107] In the formula: The fitness of an individual θ is represented. To achieve the expected purification efficiency, It is the target purification rate. This is a predicted energy consumption value. The maximum allowable energy consumption of the system. Represents the equipment loss coefficient. This is the theoretical minimum loss value. Weighting coefficient. , , These correspond to the priority levels of purification effect, energy efficiency, and equipment lifespan (default 0.8, 0.15, 0.05). Genetic operations include two-point crossover (probability 0.7) and site mutation (probability 0.01), and the selection mechanism adopts a tournament selection strategy.

[0108] The rule for determining whether an individual has met the fitness assessment criteria is: when the optimal individual... and At that time, the corresponding purification strategy for that individual is adopted. The decision-making generator decodes the binary gene into executable instructions: catalytic oxidation power = first 8 bits of the gene × 100 / 255 (%), adsorbent dosage = middle 6 bits × 300 / 63 (kg / h), and washing solution pH = last 4 bits × 4 / 15 + 5.0. Simultaneously, a constraint detection module is added to verify whether the instruction parameters exceed the equipment's safe operating boundaries. The final generated primary purification decision scheme includes four core data points: strategy encoding, expected purification efficiency, resource consumption prediction, and equipment wear estimation, which are stored in the decision-making scheme cache pool.

[0109] The decision-making scheme cache pool employs a versioned management mechanism. Each scheme is accompanied by a scheme fingerprint, which is jointly generated by the input feature hash and decision parameters. When the similarity depth between new input data and historical decision scenarios exceeds a threshold, the system directly calls the cached scheme and executes a rapid correction process: fine-tuning the adsorbent dosage using current operating parameters (±5% adjustment range) and calibrating the washing solution pH value (±0.3 adjustment range). The cache pool implements an LRU replacement strategy, retaining the 100 most recent high-efficiency decision-making schemes. A scheme failure detector continuously monitors the actual performance of each scheme, and automatically removes the scheme from the cache pool when the purification efficiency matching degree is lower than 0.85 after three consecutive executions.

[0110] The module output interface is designed as a bidirectional data channel. The main channel transmits structured primary purification decision schemes, including a set of executable process control parameters; the auxiliary channel outputs a decision analysis report, recording a list of associated decision record indexes, three-dimensional coordinates of decision feature trigger domains, and an evolutionary trajectory graph of the genetic algorithm. An anomaly handling mechanism is built into the scheme generation stage: if no compliant individuals are produced after 50 generations of evolution, the system switches to a conservative strategy mode, activating preset purification plans according to pollution type, and simultaneously sending a decision failure alarm to the operation and maintenance system. All output data includes a timestamp and decision sequence number, establishing a complete scheme traceability chain.

[0111] Example 4: See Figure 5 The decision compensation module is implemented based on a dynamic calibration mechanism of predicted pollution characteristic maps and predicted operating condition parameter sets. Taking a chemical plant's waste gas treatment system as an example, the system detected abnormal fluctuations in sulfur dioxide concentration at 8:00 AM, and the trend projection module predicted a pollution peak would occur within the next two hours. At this time, a preliminary purification decision plan has been generated, including activated carbon adsorption device operating parameters of 200 kg / h dosage and catalytic oxidation temperature set at 350℃. After the decision compensation module is activated, it first loads the predicted dataset, which contains time-series formatted predicted pollutant concentration values ​​and operating condition parameter change curves.

[0112] The spatial grid labeling method was used to analyze the predicted pollution characteristic map. The emission area was divided into 10m × 10m grid cells, and each cell recorded the predicted concentration value for a future time slice (one slice every 15 minutes). The system identified grid cells numbered G-17 to G-23 as forming a continuous exceeding area at 9:30, with the core concentration reaching 2.3 times the standard limit. The predicted operating condition parameter set showed that the exhaust gas temperature would rise from 280℃ to 320℃ during the same period, and the fan speed would need to be increased by 15% to maintain system pressure balance. When these data were input into the second calibration decision space, a spatial reconstruction process was triggered: records in the historical decision records with a similarity of more than 70% to the predicted concentration distribution pattern and a matching degree of more than 65% to the operating condition change trend were screened out, forming a temporary decision database containing 32 decision records.

[0113] The generation of the second decision feature trigger domain depends on the coupled analysis of pollution diffusion dynamics and equipment response characteristics. The system creates a trigger domain determination table and updates the correlation status between pollution characteristics and equipment parameters in each area in real time, as shown in Table 1.

[0114] Table 1: Trigger Domain Determination Table.

[0115] ;

[0116] Trigger priority is determined by a weighted score based on the exceedance multiple and parameter change rate; areas with a score exceeding 80 are marked as "emergency." For area G-18, the system retrieved three relevant historical decisions from the temporary decision database: Case A increased the activated carbon dosage to 240 kg / h and supplemented with alkaline spraying; Case B maintained the original adsorption capacity but increased the catalytic temperature to 380℃; Case C used a combination of these two measures but reduced the fan speed by 10%. The decision compensation module initiated a multi-dimensional evaluation, calculating the adaptability index of each case under the current predicted conditions.

[0117] The generation process of the compensation purification decision is implemented through phased iterative optimization. The first phase adjusts the activated carbon dosing parameters: based on the predicted concentration gradient curve, the system is tentatively adjusted in increments of 5 kg / h within the 200-250 kg / h range, with simulations calculating the change in pollutant adsorption efficiency after each adjustment. When the dosage reaches 230 kg / h, the simulation shows that the predicted residual concentration in region G-18 at 9:30 can be reduced to 1.2 times the standard limit. The second phase optimizes the catalytic oxidation conditions: based on the predicted exhaust gas temperature rise curve, the optimal reaction temperature window is recalculated, and the original 350℃ is adjusted to 365℃ to compensate for the impact of temperature increase on catalytic efficiency. The third phase balances system parameters: detecting that an increase in fan speed might lead to an increase in pressure drop in the scrubbing tower, the bypass valve opening is adjusted to 45% to maintain stable system pressure differential.

[0118] The strategy fusion process employs a decision tree conflict resolution mechanism. When a conflict arises between the baseline adsorption capacity (200 kg / h) in the initial scheme and the incremental demand (230 kg / h) in the compensation scheme, the system activates a compromise algorithm: a transitional scheme of 215 kg / h is executed for the first 30 minutes, switching to 230 kg / h once the predicted concentration reaches its peak; the catalytic temperature adopts a linear heating strategy, increasing by 5°C every 10 minutes from the initial 350°C until the target temperature is reached. The fused optimized scheme generates an execution timetable, with control command switching accurate to the minute level.

[0119] 09:00-09:20: Activated carbon 215kg / h + catalysis 355℃ + fan speed 105%; 09:20-09:40: Activated carbon 230kg / h + catalysis 365℃ + fan speed 110%; After 09:40: Return to baseline parameters based on actual monitoring data.

[0120] The real-time adaptive monitoring system continuously collected key indicators during the execution of the plan. Portable monitors deployed in the G-18 area uploaded actual concentration data every 5 minutes, performing deviation analysis between the actual and predicted values. When the actual concentration exceeded the predicted value by 15% at 9:28, the system immediately triggered dynamic compensation: executing the peak response plan 2 minutes in advance and activating an additional backup adsorption tower. Simultaneously, temperature sensor feedback indicated that the actual heating rate of the catalytic bed was lower than expected, and the system automatically added 5kW of auxiliary power to the electric heater to maintain the temperature rise curve.

[0121] The historical decision database update mechanism is activated after the compensation process is completed. After verification of the actual implementation effect data of this optimization scheme, new decision records are generated and stored in the database. These records include fields such as predicted feature map fingerprints, actual operating condition parameter matrices, compensation strategy instruction sets, and final purification efficiency. The system specifically marks key operational nodes in this compensation: the suppression effect of a 230 kg / h dosage on peak concentration, byproduct control performance at a catalytic temperature of 365℃, and the coordinated adjustment parameters of fan speed and bypass valve. The analysis results of differences between the new records and the original cases are used to adjust the similarity calculation weights in the decision space, enhancing the sensitivity to identifying sudden pollution characteristics.

[0122] The anomaly handling subsystem provides safety assurance for the compensation process. When predicted data indicates a potential exceedance of the equipment's design capacity (e.g., the predicted concentration exceeds three times the limit), the system automatically switches to emergency mode: immediately sending an early warning signal to the central control room and simultaneously executing the preset highest-level purification plan (maximum adsorption capacity + full activation of backup equipment). All compensation operations are subject to safety constraints, such as activated carbon dosage not exceeding 120% of the equipment nameplate value and catalytic temperature not exceeding the material's tolerance limit. Before each parameter adjustment, the system cross-validates the available margin of the actuators to ensure that operational instructions are implemented within safety boundaries.

[0123] Example 5: The operation of the purification execution module begins with the instruction parsing process for optimizing the purification decision scheme. The system loads the scheme file, which adopts a hierarchical coding structure: the top layer is the global execution strategy code, identifying the priority order of the purification targets for this operation; the middle layer contains equipment group control parameter packages, each parameter package being associated with an identifier for a specific purification device; the bottom layer is the timing control instruction block, defining the time nodes and transition curves for parameter adjustments. The parsing engine decomposes the file content layer by layer, classifying and mapping the parameter instructions to the device controller address space. Taking the parameter package parsing of the activated carbon adsorption device as an example, the target dosage is extracted as 215 kg / h, the dosage rate change gradient is 5 kg / min, and the maximum allowable instantaneous error is ±3%. The parameter package of the catalytic oxidation unit includes a temperature setpoint of 365℃, a heating rate limit of 15℃ / min, and an identifier for the isothermal maintenance period.

[0124] The generation of the control signal sequence follows industrial protocol conversion standards. Each parameter command is first converted into a standard control message, with the message header containing the device address code, command type identifier, and payload length. The dosing control signal uses a pulse frequency modulation mechanism: the target value of 215 kg / h is converted into a pulse frequency reference value of 1.2 kHz, which is associated with the speed-regulating motor drive current. The temperature control signal uses analog output mode, converting the 365℃ target value into a 4-20mA current loop signal, corresponding to the PLC output module channel address 0x3F5A. The timing instruction block drives the signal sequence generator, arranging control events according to the timeline set in the scheme: at 09:00:00, a frequency gradient command is issued to increase the motor speed from 800 rpm to 950 rpm; at 09:20:00, a second frequency jump to 1050 rpm is triggered, with each event marked with a 5-second execution tolerance window.

[0125] The purification unit's drive subsystem implements a distributed control architecture. The field controller network is divided into three levels: the first-level controller is a central command distributor deployed in the main control cabinet of the purification system; the second-level controller is an equipment cluster coordinator, with each purification unit configured with an independent coordination node; and the third-level controller is an execution terminal, directly connected to the drivers and sensors. The central command distributor breaks down the total control signal sequence into equipment sub-sequences and distributes them to the coordination nodes via industrial Ethernet. After receiving the command, the adsorption unit coordinator generates a speed control closed loop locally: Hall sensors collect motor speed in real time, with data refreshed every 200ms; the PID controller compares the actual speed with the target value and outputs a PWM duty cycle correction signal to the drive circuit. The catalytic oxidation unit coordinator performs temperature interlock control: when the thermocouple detects that the actual bed temperature is 2°C lower than the set value, it automatically increases the thyristor conduction angle and activates the auxiliary heater.

[0126] The purification medium adjustment mechanism employs a composite execution mechanism. The activated carbon storage silo's feeding mechanism is equipped with dual-stage control: coarse adjustment mode uses a variable frequency screw conveyor for speed control, while fine adjustment mode adjusts the feeding cross-section via a pneumatic gate. When a control signal is triggered, coarse adjustment mode is first activated to quickly approach the target dosage; when the actual dosage enters the range of ±5% of the target value, fine adjustment mode is switched. The spray tower alkali supply system implements dual pressure-flow regulation: a plunger pump receives the basic flow signal, while a differential pressure sensor monitors the nozzle atomization status; when the pipeline pressure exceeds 0.6 MPa, the reflux valve automatically activates to release pressure and maintain the injection pressure within a stable range.

[0127] The purification efficiency feedback module deploys a multi-channel monitoring network. The system sets up three parallel sampling points on the exhaust manifold of the purification unit, each equipped with a different type of sensor array. Sensor array 1 is equipped with a non-dispersive infrared analyzer to continuously measure SO2 and NOx concentrations; array 2 is equipped with a hydrogen flame ionization detector to monitor total VOCs; and array 3 is equipped with a laser scattering particulate counter to capture suspended particles in the PM2.5-PM10 range. All sensors sample synchronously via a data acquisition unit, with a default sampling period of 60 seconds, and a 10-second high-speed sampling mode activated at pollution peaks. Monitoring data is verified in real-time: when the readings of the three sensor groups for the same pollutant differ by more than 15%, a calibration gas injection procedure is triggered to recalibrate the sensors.

[0128] The purification efficiency evaluation system establishes a standardized comparison process. A pre-set database of purification target values ​​stores control standards for different pollutants, including instantaneous limits (e.g., SO2 peak concentration ≤ 150 mg / m³) and moving average limits (e.g., 24-hour average PM10 ≤ 100 μg / m³). After each sampling window closes, the data processing unit automatically associates the timestamp of the current sample with the purification plan execution timeline to generate a time-matched efficiency evaluation table. The comparison algorithm uses direct threshold judgment for instantaneous values ​​and calculates forward cumulative curve values ​​for moving averages. The system generates a deviation index vector, with each dimension representing the extent to which a specific pollutant exceeds the standard within a specified time period: for example, the SO2 index is calculated as the difference between the measured maximum value and the limit, and the particulate matter index is calculated as the ratio of the 24-hour average to the limit.

[0129] Feedback-loop control enables dynamic parameter updates. Deviation index vectors are pushed to the threshold constraint library of the pollution feature extraction module via a data bus. The update logic includes two modes: in normal mode, a threshold fine-tuning algorithm is executed, and the corresponding threshold is automatically reduced by 5% when the same pollutant exceeds the standard for three consecutive sampling cycles; in emergency mode, threshold reconstruction is triggered, and a temporary threshold rule is created and an emergency analysis level is activated when the concentration of a sudden increase in pollutant exceeds 120% of the historical maximum. Change records in the threshold constraint library generate update notification files, containing four metadata items: original threshold, new threshold, effective time, and change reason code. This file is synchronously distributed to the purification decision space management system, triggering a re-evaluation process for historical decision records.

[0130] The equipment status monitoring network performs a holographic record of the purification process. The system has a built-in equipment operation log storage device, recording four types of operational events along a timeline: control command issuance time and parameter details, actuator response time and action confirmation signals, monitoring value curves of key sensor elements, and trigger records of safety interlock devices. Log storage employs a tiered compression strategy: routine operating data retains its original time-series record for 30 days, while abnormal event data is marked for permanent storage. The maintenance and diagnostic interface periodically scans equipment status codes. When the activated carbon saturation status flag is detected, an adsorbent replacement recommendation is added to the performance evaluation report. A continuous increase in catalyst bed pressure loss triggers the catalyst activity detection process, and the results are automatically correlated with the parameters of the equipment loss prediction model.

[0131] A communication fault-tolerance mechanism ensures the reliability of the control link. The main control signal channel adopts a dual-network redundancy architecture, automatically switching to the PROFIBUS backup channel when the Ethernet signal delay exceeds 500ms. Data transmission implements a triple verification mechanism: message-level CRC cyclic redundancy check, application-layer data hash check, and transaction-level execution confirmation feedback. The purification execution module is equipped with a command timeout monitor; any control signal that does not receive a device response confirmation will be resent after 2 seconds. If it fails three times consecutively, the device is considered offline and the backup device takeover procedure is initiated. A secure communication protocol encrypts and signs critical control commands, with the key being rotated and updated every 24 hours to prevent unauthorized command injection.

[0132] The system is equipped with multi-layered emergency control safety boundaries. The hardware layer features independent limit protection devices: if the adsorption tower temperature exceeds the safety threshold, the physical cooling system is directly triggered; if the bag filter pressure differential is too high, compressed air pulse backflushing is automatically activated. The software layer includes a parameter permissible range verification module, which performs parameter range verification before the control signal is issued: if the activated carbon dosage instruction value exceeds the equipment's rated value by 90%-110%, it is automatically truncated to the permissible boundary value; if the catalytic temperature setpoint is higher than the material's tolerance temperature, the automatic calculation process for the safety cooling coefficient is initiated. An emergency stop criterion is embedded at the end of the control chain: when the concentration of purified emissions exceeds the emergency limit for 15 consecutive minutes, the system forcibly switches to the preset highest intensity purification mode.

[0133] This implementation constructs a complete decision-making, execution, and feedback control chain. The purification execution module ensures accurate transmission of control intentions through fine-grained command parsing and protocol conversion; the distributed control system enables collaborative operation of the equipment cluster; the performance feedback system establishes a scientific evaluation index system; and the closed-loop update mechanism continuously optimizes the pollution characteristic analysis rules. All technical aspects are designed with traceability in mind: every control command, every status change, and every threshold adjustment retains a complete operational trajectory and decision-making basis record. Equipment status monitoring and safety protection mechanisms provide technical support for long-term continuous operation, and communication fault-tolerant design enhances applicability in industrial settings. The entire implementation process contains no subjective evaluation content; all descriptions are based on verifiable technical features and working principles.

[0134] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0135] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-pollutant waste gas purification system, characterized in that, The system includes: The exhaust gas monitoring module is used to acquire exhaust gas composition data stream and operating condition parameter data stream in real time; The pollution feature extraction module, based on a preset pollutant threshold constraint, performs multi-pollutant feature analysis in conjunction with the exhaust gas composition data stream to generate a pollution feature map. The purification decision generation module, based on the embedded purification decision space and purification efficiency constraints, combined with the pollution feature map and the operating condition parameter data stream, outputs a preliminary purification decision scheme. The trend extrapolation module dynamically predicts the evolution of the pollution feature map and the operating condition parameter data stream to obtain a predicted pollution feature map and a predicted operating condition parameter set. The decision compensation module performs strategy compensation on the primary purification decision scheme based on the predicted pollution feature map and the predicted operating condition parameter set, and generates an optimized purification decision scheme. A purification execution module, which drives the purification device to operate based on the optimized purification decision scheme; The purification decision generation module includes: The purification decision space is calibrated based on the pollution characteristic map and the operating condition parameter data stream to obtain a first calibration decision space; Identify the decision feature triggering interval in the first calibration decision space and generate the first decision feature triggering domain; An initial purification decision is generated based on the first decision feature trigger domain; Calculate the purification efficiency matching degree of the initial purification decision; When the purification efficiency matching degree meets the purification efficiency constraint, the initial purification decision is added to the primary purification decision scheme. The step of calibrating the purification decision space based on the pollution feature map and the operating condition parameter data stream includes: Traverse the decision records in the purification decision space; Analyze the similarity depth between the current pollution feature map and the sample pollution features in the decision record; Analyze the similarity depth between the current operating condition parameter data stream and the sample operating condition parameters in the decision record; The correlation strength coefficient between the similarity depth of the pollution characteristics and the similarity depth of the operating parameters is calculated using a weighted average method. When the correlation strength coefficient exceeds the preset correlation threshold, the corresponding decision record will be included in the first calibration decision space.

2. The system as described in claim 1, characterized in that, The pollution feature extraction module includes: Obtain the basic attribute set of the exhaust gas emission source; Construct a pollution diffusion model based on the aforementioned set of basic attributes; The waste gas composition data stream is input into the pollution diffusion model to generate a pollutant concentration distribution cloud map. Based on the preset pollutant threshold constraints and the pollutant concentration distribution cloud map, the pollution feature map is analyzed.

3. The system as described in claim 1, characterized in that, The construction steps of the pollution feature extraction module include: Establish clusters of similar exhaust gas emission sources; Load the historical pollution characteristic record library corresponding to the cluster of exhaust gas emission sources of the same type; Load the real-time pollution characteristic record library of the target exhaust gas emission source; A pollution feature benchmark model is constructed based on the historical pollution feature record database. Based on the real-time pollution feature record library, the feature parsing sensitivity of the pollution feature benchmark model is optimized to generate the pollution feature extraction module.

4. The system as described in claim 3, characterized in that, The feature parsing sensitivity optimization of the pollution feature benchmark model based on the real-time pollution feature record library includes: The pollution feature benchmark model was tested using the real-time pollution feature record library to obtain the feature analysis sensitivity coefficient; When the feature parsing sensitivity coefficient is lower than the preset sensitivity threshold, the pollution feature benchmark model is incrementally trained based on the real-time pollution feature record library.

5. The system as described in claim 1, characterized in that, The decision compensation module includes: The purification decision space is calibrated based on the predicted pollution feature map and the predicted operating condition parameter set to obtain a second calibration decision space; Identify the decision feature triggering interval in the second calibration decision space and generate the second decision feature triggering domain; Under the constraint of the purification efficiency, a compensatory purification decision is generated iteratively. The compensation purification decision and the primary purification decision scheme are combined strategically.

6. The system as described in claim 1, characterized in that, The purification execution module includes: Analyze the purification parameter instruction set in the optimized purification decision scheme; The purification parameter instruction set is converted into a control signal sequence for the purification device; The dosage of the purification medium and the reaction conditions are adjusted according to the control signal sequence.

7. The system as described in claim 1, characterized in that, The system also includes a purification efficiency feedback module: Collect data on the composition of the purified exhaust gas; Compare the purified exhaust gas composition data with the preset purification target values; A purification efficiency deviation index is generated and fed back to the pollution feature extraction module.

8. An intelligent control method based on a multi-pollutant waste gas purification system, characterized in that, The method includes: Real-time acquisition of exhaust gas composition data stream and operating condition parameter data stream; The waste gas composition data stream is analyzed based on preset pollutant threshold constraints to generate a pollution feature map. Based on the embedded purification decision space and purification efficiency constraints, a preliminary purification decision scheme is output by combining the pollution feature map and the operating condition parameter data stream. By extrapolating the dynamic change trends of the pollution feature map and the operating condition parameter data stream, a predicted pollution feature map and a predicted operating condition parameter set are obtained. Based on the predicted pollution feature map and the predicted operating condition parameter set, the primary purification decision scheme is compensated for strategy. Drive the purification device to execute the optimized purification decision-making scheme; Collect data on the composition of purified exhaust gas and generate a purification efficiency deviation index; The pollution characteristic analysis rules are updated based on the purification efficiency deviation index. The purification decision space is calibrated based on the pollution characteristic map and the operating condition parameter data stream to obtain a first calibration decision space; Identify the decision feature triggering interval in the first calibration decision space and generate the first decision feature triggering domain; An initial purification decision is generated based on the first decision feature trigger domain; Calculate the purification efficiency matching degree of the initial purification decision; When the purification efficiency matching degree meets the purification efficiency constraint, the initial purification decision is added to the primary purification decision scheme. Traverse the decision records in the purification decision space; Analyze the similarity depth between the current pollution feature map and the sample pollution features in the decision record; Analyze the similarity depth between the current operating condition parameter data stream and the sample operating condition parameters in the decision record; The correlation strength coefficient between the similarity depth of the pollution characteristics and the similarity depth of the operating parameters is calculated using a weighted average method. When the correlation strength coefficient exceeds the preset correlation threshold, the corresponding decision record will be included in the first calibration decision space.

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