Intelligent early warning method and system for industrial gaseous pollutants

Abstract

The application discloses an intelligent early warning method and system for industrial gaseous pollutants, and relates to the technical field of industrial pollution monitoring and environmental early warning.The application comprises the following steps: step S1, multi-dimensional high-throughput data acquisition and preprocessing;step S2, construction of a'multi-pollutant-environment-working condition' correlation network;step S3, network elasticity dynamic quantification based on improved cross-entropy;step S4, critical point identification and dynamic grading early warning;step S5, model self-adaptive optimization and system linkage response.The application constructs a'multi-pollutant-environment-working condition' three-dimensional correlation network by improving the PLS algorithm, realizes network elasticity dynamic quantification by using cross-entropy and node elasticity aggregation, and constructs an instability index U to locate the early warning critical point.

Description

Technical Field

[0001] This invention belongs to the field of industrial pollution monitoring and environmental early warning technology, and specifically relates to an intelligent early warning method and system for industrial gaseous pollutants, enabling monitoring of industrial production processes. , , Accurate and real-time early warning of excessive emissions of gaseous pollutants provides efficient technical support for industrial pollution control. Background Technology

[0002] Emissions during industrial production , , Gaseous pollutants are one of the main sources of air pollution, and accurate and real-time pollutant early warning is a key aspect of industrial pollution control. Existing industrial gaseous pollutant early warning methods are mainly based on fixed concentration thresholds, which have the following drawbacks:

[0003] 1. Strong early warning lag: Early warning can only be triggered after the pollutant concentration reaches or exceeds the national standard limit, and it is impossible to detect the "near-exceeding state" in advance, which leads to an increase in the duration of pollutant exceeding the standard and an expansion of environmental impact;

[0004] 2. Coupling effect not considered: The coupling relationship between multiple pollutants and between pollutants and environmental and operating parameters is ignored. A single concentration threshold cannot adapt to the dynamic changes in complex industrial scenarios, resulting in a high false alarm rate and a high false alarm rate.

[0005] 3. Poor scenario adaptability: Fixed thresholds are difficult to adapt to the emission characteristics of different industries (thermal power, chemical, metallurgy) and different operating conditions, requiring frequent manual adjustments and resulting in high operation and maintenance costs;

[0006] 4. Insufficient linkage response: The early warning information only realizes the push function and is not deeply linked with the production control system and the waste gas treatment system, so it cannot quickly curb the excessive emission of pollutants.

[0007] Network resilience theory has been used in fields such as biology and ecology to assess system stability. Its core idea is to identify the critical points of system state abrupt changes by quantifying the system's ability to resist disturbances and maintain stability. The key to overcoming the shortcomings of existing technologies lies in adapting this theory to high-throughput monitoring data in industrial scenarios and constructing accurate resilience quantification models and early warning mechanisms. Summary of the Invention

[0008] The purpose of this invention is to provide an intelligent early warning method and system for industrial gaseous pollutants. By improving the PLS algorithm, a three-dimensional correlation network of "multi-pollutant-environment-operating conditions" is constructed. Cross-entropy and node elastic aggregation are used to realize the network elastic dynamic quantification. An instability index U is constructed to locate the early warning critical point. This solves the problems of existing early warnings for industrial gaseous pollutants, such as delayed response, high false alarm rate, and inability to adapt to complex industrial scenarios.

[0009] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0010] This invention relates to an intelligent early warning method for industrial gaseous pollutants, comprising the following steps:

[0011] Step S1: Multi-dimensional high-throughput data acquisition and preprocessing: Construct a three-dimensional integrated data acquisition system of "fixed + mobile + static" to obtain a multi-dimensional data matrix composed of pollutant concentration data, environmental correlation data and production condition data, and perform hierarchical preprocessing on the acquired data;

[0012] Step S2: Construct a "multi-pollutant-environment-operating condition" correlation network: Use an improved partial least squares algorithm to calculate the direct coupling strength between parameters, and use an adaptive threshold to filter coupling relationships to construct a sparse three-dimensional correlation network;

[0013] Step S3: Dynamic Quantification of Network Elasticity Based on Improved Cross-Entropy: Using the normal operating condition network as a reference, the difference in disorder between the observed network and the reference network is calculated by improving cross-entropy. The overall network elasticity (RE) is obtained based on the aggregation of node comprehensive disorder, and an instability index is constructed. ;

[0014] Step S4, Critical Point Identification and Dynamic Hierarchical Early Warning: Determine the baseline threshold for instability indicators based on historical data. When three consecutive time steps Value ≥ 2× And the third time step When the value reaches a local peak, a warning threshold is determined; combined with The values ​​and pollutant concentrations enable a three-tiered early warning system.

[0015] Step S5, Model Adaptive Optimization and System Linkage Response: Update the reference network and optimize the baseline threshold daily using the previous day's data. Update PLS iteration weights; implement early warning information push and linkage control with production and waste gas treatment systems based on early warning level.

[0016] As a preferred technical solution, in step S1, the data acquisition system includes pollutant concentration data, environmental-related data, and production condition data;

[0017] The pollutant concentration data is collected in real time through a high-precision sensor array at the plant's discharge outlets, workshop exits, and plant boundaries. , , Target pollutant concentrations (unit: The sampling frequency is 1Hz, and the sensor's working status is recorded synchronously.

[0018] The environmental data includes atmospheric temperature ( ),humidity( Atmospheric pressure ), wind speed ( ),wind direction( ) and other parameters, with a sampling frequency of 0.5Hz;

[0019] The production condition data is obtained synchronously from the industrial control system (DCS) and includes core operating parameters such as raw material input, production load, reaction temperature, and equipment operating time, with a sampling frequency of 0.1Hz.

[0020] As a preferred technical solution, the layered preprocessing process is as follows:

[0021] Step S11, Differential Feature Screening: Calculate the standard deviation of each parameter under normal operating conditions and under fluctuating operating conditions, and retain the standard deviation under fluctuating operating conditions. Parameters that are twice the normal operating conditions (such as highly fluctuating pollutants and key operating condition parameters) reduce data dimensionality;

[0022] Step S12, Significance Test: Perform a t-test on the screened parameters (significance level). ), retaining parameters that are correlated with pollutant concentration (such as production load, temperature, etc.) to further improve the signal-to-noise ratio;

[0023] Step S13, Data Standardization and Completion: Z-Score standardization is used to normalize all parameters to the same order of magnitude. The specific formula is as follows:

[0024] ;

[0025] In the formula, For the first The parameter is the first The original values ​​of each sample, For the first The mean of each parameter, For the first Standard deviation of each parameter; for missing rate The data was completed using the K-Nearest Neighbors (KNN) algorithm; missing rate When this occurs, a fault alarm is triggered on the corresponding data acquisition terminal.

[0026] As a preferred technical solution, the process for constructing the "multi-pollutant-environment-operating condition" correlation network in step S2 is as follows:

[0027] Step S21, Data Matrix Initialization: The preprocessed three-dimensional data matrix is ​​split into a dependent variable matrix X (pollutant concentration parameters) and an independent variable matrix Y (environmental + operating condition parameters), i.e. , In the formula, For the number of pollutant types, This includes environmental and operating condition parameters.

[0028] Step S22, PLS Iterative Fitting: Using each pollutant concentration parameter as the dependent variable and all other parameters (including other pollutants, environment, and operating conditions) as independent variables, perform PLS regression fitting and iteratively calculate the score vector. The specific formula is as follows:

[0029] ;

[0030] In the formula, To remove the first The iterative data matrix after parameters, For the first The weight vector for the next iteration;

[0031] Step S23, Coupling Strength Calculation: Iterate until convergence (number of iterations) , After setting the default number of PLS ​​items, calculate any two parameters. and direct coupling strength The specific formula is as follows:

[0032] ;

[0033] In the formula, The number of PLS ​​items, default. ;

[0034] Step S24, Sparse Relationship Network Construction: Adaptive threshold is set using the 10% edge retention principle. To ensure the effectiveness and computational efficiency of the network structure, an adjacency matrix is ​​constructed using the following formula:

[0035] ;

[0036] In the formula, Indicates parameters and There is a coupling relationship. Indicates parameters and There is no significant coupling relationship, which can be achieved through the adjacency matrix. Construct a three-dimensional correlation network, where nodes represent monitoring parameters, edges represent significant coupling relationships, and edge weights are PLS scores. .

[0037] As a preferred technical solution, the specific process of achieving network elasticity quantization by improving cross-entropy and node aggregation in step S3 is as follows:

[0038] Step S31, Probability Distribution Construction: Convert the fact monitoring sequence and normal operating condition sequence of each parameter into probability distributions respectively. and ;

[0039] Transform the real-time monitoring sequence of each parameter into a probability distribution. ,in, For the first The parameter is the first The normalized proportion of each sample; the probability distribution of the corresponding parameter under normal operating conditions is as follows: ;

[0040] Step S32, Improve cross-entropy calculation: Introduce PLS score weights to correct the traditional cross-entropy, the formula is as follows;

[0041] ;

[0042] In the formula, For parameters and Weighted cross-entropy;

[0043] Step S33, Network Elasticity Quantization: The overall network elasticity is obtained by weighted summation of node elasticities, using the following formula:

[0044] ;

[0045] In the formula, The number of network nodes. For nodes The overall level of chaos The value range is (0, 1). The closer the value is to 1, the stronger the network stability (the higher the resilience). The closer to 0, the worse the network stability (the lower the elasticity).

[0046] Step S34: Instability index construction: Invert the network elasticity to obtain the instability index. The formula is:

[0047] ;

[0048] In the formula, To avoid the minimum value where the denominator is 0, The peak value corresponds to the warning critical point; according to the changing pattern of network resilience: under normal emission conditions, the network is stable, high, Low and stable fluctuations; under near-critical conditions (critical point), network stability drops sharply. Reduced to the minimum, The network reaches its peak value; once the peak value is exceeded, the network enters a new stable state. Rebound It has fallen back to a moderate level. Therefore, The peak value is the warning threshold.

[0049] As a preferred technical solution, the specific steps for achieving graded early warning in step S4, based on pollutant concentration, are as follows:

[0050] Step S41, Threshold-based determination: Based on historical monitoring data, calculate the emission levels under normal conditions. The 95th percentile as the benchmark threshold ;

[0051] Step S42, Critical Point Determination: When three consecutive time steps... Value ≥ And the third time step The value reaches a local peak (greater than the values ​​of the two time steps before and after). The value is used to determine the trigger point for the early warning, at which point the system is in a "quasi-exceeding state";

[0052] Step S43: Dynamic hierarchical early warning system, establishing a three-level early warning system: Level 1 early warning (quasi-exceeding standard early warning) is the trigger threshold. If the pollutant concentration reaches its peak but does not exceed the national standard limit, an early warning message will be sent to the environmental protection specialist, prompting them to investigate abnormal operating conditions; a Level II warning (exceeding the standard warning) is triggered after the critical point is reached, when the pollutant concentration reaches the national standard limit. , If the concentration remains below 0.3, a notification will be sent to the environmental protection specialist and workshop manager, triggering an increase in the power of the waste gas treatment equipment; a Level 3 warning (emergency warning) is triggered when the pollutant concentration exceeds the national standard limit, or Value ≥ The system pushes information to company leaders and local environmental protection departments, triggering a reduction in production load or a suspension of high-pollution processes, and activating emergency response plans.

[0053] As a preferred technical solution, in step S5, during model adaptive optimization and system linkage response, the reference network and benchmark threshold are updated and optimized using the previous day's data (monitoring data, early warning results, and manual verification results) every morning at midnight. 1. Update PLS iteration weights through incremental learning; push first-level warning information to environmental protection specialists; push second-level warnings to environmental protection specialists and workshop managers, triggering power increase of waste gas treatment equipment; push third-level warnings to enterprise managers and environmental protection departments, triggering production load reduction or process suspension, and activating emergency plan.

[0054] The updated reference network involves reconstructing the reference network from the monitoring data of the previous day's normal emission period and updating the baseline distribution of the cross-entropy matrix. ; Optimize threshold Recalculated using historical data from a sliding window (last 30 days) It adapts to long-term changes in operating conditions and environment; PLS parameter optimization is achieved by incrementally learning and updating the PLS iterative weights. This improves the accuracy of coupling strength measurement.

[0055] This invention is an intelligent early warning system for industrial gaseous pollutants, comprising a data acquisition module, a data preprocessing module, a correlation network structure module, an elastic quantization module, a critical point identification and early warning module, a system linkage module, and a model optimization module;

[0056] The data acquisition module consists of a fixed sensor array, a mobile inspection robot, and a DCS data interface, enabling synchronous acquisition of multi-dimensional data.

[0057] The data preprocessing module deploys a hierarchical preprocessing algorithm to complete the difference filtering, significance testing, standardization, and completion of the collected data;

[0058] The association network construction module runs the improved PLS algorithm to generate a three-dimensional association network of "multi-pollutants-environment-operating conditions" and outputs the adjacency matrix and PLS score matrix.

[0059] The elasticity quantization module calculates the cross-entropy matrix and network elasticity. Generate instability indicators ;

[0060] The critical point identification and early warning module executes the critical point identification rules, triggers tiered early warnings, and pushes early warning information.

[0061] The system linkage module enables linkage control with DCS, exhaust gas treatment system, and emergency broadcast system;

[0062] The model optimization module deploys an incremental learning algorithm to periodically update the reference network, threshold, and PLS parameters.

[0063] As a preferred technical solution, the data transmission between the various modules of the system is secured using an encryption protocol: data interaction between the edge and the cloud uses the MQTT protocol for encrypted transmission, data storage uses the AES-256 encryption algorithm, and the transmission of early warning commands adds an identity verification mechanism, ensuring that only authorized devices can receive and execute linkage commands; the system linkage module supports multi-protocol adaptation, and can interface with industrial DCS systems via the OPC UA protocol, with waste gas treatment equipment via the Modbus protocol, and with the emergency broadcast system via the TCP / IP protocol. It also reserves expansion interfaces, allowing for on-demand access to environmental monitoring platforms to achieve synchronous reporting of early warning information and monitoring data.

[0064] As a preferred technical solution, the edge terminal is equipped with a data acquisition module, a data preprocessing module, an elastic quantization module, a critical point identification and early warning module, and a system linkage module to ensure that the early warning response delay is ≤500ms; the cloud layer is equipped with a model optimization module, a historical data storage module, and a visualization monitoring module for model iteration and global monitoring. The edge terminal and the cloud adopt a two-way data synchronization mechanism. When the network is disconnected, the edge terminal can independently complete the early warning logic and automatically re-transmit the data after reconnection.

[0065] The present invention has the following beneficial effects:

[0066] (1) By constructing a three-in-one data acquisition system of "fixed + mobile + static" and a hierarchical preprocessing process, this invention solves the problems of incomplete coverage, uneven quality and poor time consistency of traditional early warning data. It realizes the accurate acquisition and efficient purification of multi-dimensional high-throughput data, provides high-quality data support for subsequent construction of correlation networks and elastic quantization, reduces data missing rate and time calibration error, and ensures the input reliability of early warning models.

[0067] (2) This invention constructs a three-dimensional correlation network of "multi-pollutant-environment-operating conditions" by improving the PLS algorithm, which solves the problems of traditional early warning methods ignoring the coupling relationship between parameters and the inability of a single threshold to adapt to complex industrial operating conditions. It accurately captures the core correlation links between pollutants and operating conditions and environmental parameters, eliminates redundant weak correlation information, improves the simplification of network structure, reduces the error in coupling strength calculation, and provides an accurate topological basis for the quantification of system stability.

[0068] (3) This invention achieves dynamic quantification of network elasticity by improving cross-entropy and node elastic aggregation, constructs an instability index U to locate the critical point of early warning, solves the problem of the traditional threshold method's early warning lag and inability to capture the near-exceeding state in advance, realizes the transformation from "passive response" to "active prediction", increases the early warning amount, reduces the duration of pollutant exceeding the standard, and effectively reduces the environmental impact.

[0069] (4) This invention solves the problems of high false alarm rate, low false alarm rate and inaccurate inter-level response in traditional early warning by using the dual rules of "continuous threshold compliance + local peak verification" and a three-level dynamic hierarchical early warning system. It reduces the false alarm rate and low false alarm rate, and the execution delay of early warning linkage instructions at all levels is ≤500ms. This avoids excessive intervention in production and can quickly curb the risk of pollutant exceeding the standard, taking into account both environmental protection control and production continuity.

[0070] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0071] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 This is a flowchart of an intelligent early warning method for industrial gaseous pollutants according to the present invention;

[0073] Figure 2 This is a schematic diagram of the structure of an intelligent early warning system for industrial gaseous pollutants according to the present invention. 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] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0076] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-2 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0077] Example 1

[0078] Please see Figure 1 As shown, this invention provides an intelligent early warning method for industrial gaseous pollutants, comprising the following steps:

[0079] Step S1: Multi-dimensional high-throughput data acquisition and preprocessing: Construct a three-dimensional integrated data acquisition system of "fixed + mobile + static" to obtain a multi-dimensional data matrix composed of pollutant concentration data, environmental correlation data and production condition data, and perform hierarchical preprocessing on the acquired data;

[0080] Step S2: Construct a "multi-pollutant-environment-operating condition" correlation network: Use an improved partial least squares algorithm to calculate the direct coupling strength between parameters, and use an adaptive threshold to filter coupling relationships to construct a sparse three-dimensional correlation network;

[0081] Step S3: Dynamic Quantification of Network Elasticity Based on Improved Cross-Entropy: Using the normal operating condition network as a reference, the difference in disorder between the observed network and the reference network is calculated by improving cross-entropy. The overall network elasticity (RE) is obtained based on the aggregation of node comprehensive disorder, and an instability index is constructed. ;

[0082] Step S4, Critical Point Identification and Dynamic Hierarchical Early Warning: Determine the baseline threshold for instability indicators based on historical data. When three consecutive time steps Value ≥ 2× And the third time step When the value reaches a local peak, a warning threshold is determined; combined with The values ​​and pollutant concentrations enable a three-tiered early warning system.

[0083] Step S5, Model Adaptive Optimization and System Linkage Response: Update the reference network and optimize the baseline threshold daily using the previous day's data. Update PLS iteration weights; implement early warning information push and linkage control with production and waste gas treatment systems based on early warning level.

[0084] In step S1, the data acquisition system includes pollutant concentration data, environmental-related data, and production condition data;

[0085] Pollutant concentration data is collected in real time through a high-precision sensor array at the plant's wastewater outlets, workshop exits, and plant boundaries. , , Target pollutant concentrations (unit: The sampling frequency is 1Hz, and the sensor's working status is recorded synchronously.

[0086] Environmental data includes atmospheric temperature ( ),humidity( Atmospheric pressure ), wind speed ( ),wind direction( ) and other parameters, with a sampling frequency of 0.5Hz;

[0087] Production condition data is synchronously acquired from the industrial control system (DCS) to obtain core operating parameters such as raw material input, production load, reaction temperature, and equipment operating time, with a sampling frequency of 0.1Hz.

[0088] Specifically, the data acquisition system was built according to a three-in-one architecture of "fixed + mobile + static" to ensure data coverage without blind spots and that the acquisition accuracy met the standards. The deployment and operating parameters of each terminal are as follows:

[0089] Fixed sensor array: Employing a combination of high-precision electrochemical sensors and laser scattering sensors, deployed in a three-tiered manner: "pollution source - transport path - boundary". Deployment at the pollution source end (e.g., boiler outlet, reactor exhaust port). , , Dedicated sensor, measurement range precision General pollutant sensors are deployed at the transmission path ends (such as in the middle of the pipeline, at the inlet and outlet of the desulfurization tower) to collect concentration and pressure data simultaneously; multi-parameter sensors are deployed at the plant boundary at a density of one sensor every 50 meters to take into account both pollutant concentration and environmental parameter collection. The sampling frequency of all fixed sensors is uniformly set to 1Hz, the data transmission delay is ≤100ms, and breakpoint resume is supported.

[0090] Mobile inspection robot: Equipped with a portable gas chromatography-mass spectrometry (GC-MS) detection module and positioning module, the preset patrol route covers blind spots in the factory area (such as temporary sewage discharge points and equipment gaps), the patrol cycle can be set as needed (normally 2 hours / time, 1 hour / time during periods of fluctuating operating conditions), the sampling frequency is 0.5Hz, the number of samples collected per patrol is ≥50 sets, and after the collection is completed, the data is automatically uploaded to the edge computing node and fused with the data from fixed sensors to complete the data.

[0091] Static data interface: A two-way communication interface is established with the industrial DCS system and environmental monitoring platform via the OPC UA protocol to statically read production condition data and historical environmental data. Among them, the production condition data is synchronized in real time from the DCS system, covering core parameters such as raw material input, production load, reaction temperature, and equipment operating time, with a sampling frequency of 0.1Hz; the historical environmental data is retrieved from the environmental monitoring platform, which contains the average temperature, humidity, wind speed, and wind direction data for the past 30 days, for subsequent baseline value calculation.

[0092] During data synchronization and storage, a timestamp alignment mechanism is used to achieve multi-source data synchronization. The system time of the fixed sensor is used as the benchmark to calibrate the time of the mobile robot and DCS interface data, with a calibration error of ≤50ms. The synchronized data is stored in three levels: "raw data - temporary data - preprocessed data". The raw data is retained for 90 days (for model backtracking optimization), while the temporary data and preprocessed data are updated in real time. The storage format adopts both JSON and CSV formats, which support fast access and batch processing.

[0093] The process flow for layered preprocessing is as follows:

[0094] Step S11, Differential Feature Screening: Calculate the standard deviation of each parameter under normal operating conditions and under fluctuating operating conditions, and retain the standard deviation under fluctuating operating conditions. Parameters that are twice the normal operating conditions (such as highly fluctuating pollutants and key operating condition parameters) reduce data dimensionality;

[0095] Step S12, Significance Test: Perform a t-test on the screened parameters (significance level). ), retaining parameters that are correlated with pollutant concentration (such as production load, temperature, etc.) to further improve the signal-to-noise ratio;

[0096] Step S13, Data Standardization and Completion: Z-Score standardization is used to normalize all parameters to the same order of magnitude. The specific formula is as follows:

[0097] ;

[0098] In the formula, For the first The parameter is the first The original values ​​of each sample, For the first The mean of each parameter, For the first Standard deviation of each parameter; for missing rate The data was completed using the K-Nearest Neighbors (KNN) algorithm; missing rate When this occurs, a fault alarm is triggered on the corresponding data acquisition terminal.

[0099] Specifically, in step S11, the datasets for normal operating conditions and fluctuating operating conditions are first divided. The normal operating condition dataset selects data from the past 7 days when there are no warnings and pollutant concentrations are stable below 60% of the national standard limit. The fluctuating operating condition dataset selects data from periods of operating condition changes such as production load adjustments and raw material changes. The standard deviation of each parameter in the two datasets is calculated respectively. , ),according to" ≥2× "The rules screen core parameters and eliminate redundant parameters that do not fluctuate significantly and have no impact on pollutant concentration (such as workshop lighting intensity and non-core equipment voltage).

[0100] Independent samples t-tests were performed on the screened parameters, with pollutant concentration as the dependent variable and each screened parameter as the independent variable, and significance levels were set. Calculate each parameter Value; when value When the P-value is determined to have a significant linear correlation with pollutant concentration, it is retained; when the P-value is... If the correlation is not significant, it should be removed to avoid irrelevant parameters interfering with subsequent model construction.

[0101] Data completion uses the K-Nearest Neighbors (KNN) algorithm, with K set to 5 (determined through cross-validation optimization), and the missing rate is considered. The samples were imputed based on the mean of the five nearest valid samples; the missing rate was adjusted. The parameters are used to determine the corresponding data acquisition terminal failure, immediately triggering a device failure alarm and simultaneously activating backup terminal data replacement to ensure data continuity.

[0102] In step S2, the process for constructing the "multi-pollutant-environment-operating condition" correlation network is as follows:

[0103] Step S21, Data Matrix Initialization: The preprocessed three-dimensional data matrix is ​​split into a dependent variable matrix X (pollutant concentration parameters) and an independent variable matrix Y (environmental + operating condition parameters), i.e. , In the formula, For the number of pollutant types, This includes environmental and operating condition parameters.

[0104] Step S22, PLS Iterative Fitting: Using each pollutant concentration parameter as the dependent variable and all other parameters (including other pollutants, environment, and operating conditions) as independent variables, perform PLS regression fitting and iteratively calculate the score vector. The specific formula is as follows:

[0105] ;

[0106] In the formula, To remove the first The iterative data matrix after parameters, For the first The weight vector for the next iteration;

[0107] Step S23, Coupling Strength Calculation: Iterate until convergence (number of iterations) , After setting the default number of PLS ​​items, calculate any two parameters. and direct coupling strength The specific formula is as follows:

[0108] ;

[0109] In the formula, The number of PLS ​​items, default. ;

[0110] Step S24, Sparse Relationship Network Construction: Adaptive threshold is set using the 10% edge retention principle. To ensure the effectiveness and computational efficiency of the network structure, an adjacency matrix is ​​constructed using the following formula:

[0111] ;

[0112] In the formula, Indicates parameters and There is a coupling relationship. Indicates parameters and There is no significant coupling relationship, which can be achieved through the adjacency matrix. Construct a three-dimensional correlation network, where nodes represent monitoring parameters, edges represent significant coupling relationships, and edge weights are PLS scores. .

[0113] Specifically, by improving the PLS algorithm to accurately capture the direct coupling relationships between parameters, eliminating redundant correlations and retaining core functional links, a clear and industrially adaptable sparse correlation network is constructed, providing a reliable network topology for subsequent elastic quantization. The overall process consists of five key stages: algorithm initialization, iterative fitting, coupling strength calculation, threshold selection, and network generation. The operational details, parameter settings, and execution standards for each stage are as follows:

[0114] S21. Data Matrix Initialization: Split the preprocessed m-dimensional data matrix according to parameter type to obtain the dependent variable matrix. With the independent variable matrix Among them, the dependent variable matrix (n is the number of pollutant concentration parameters, such as...) , , (etc.), each row corresponds to time series data of a pollutant concentration, and each column corresponds to a sample at a time step; independent variable matrix It covers environmental parameters (temperature, wind speed, etc.) and operating parameters (coal sulfur content, boiler load, etc.), matrix structure and Consistent. After splitting, the two matrices are mean-centered to eliminate the influence of residual dimensions. The centering formula is: , , , These are the row means of the two matrices, respectively.

[0115] S22, PLS Iterative Fitting: A "single dependent variable - multiple independent variable" fitting mode is adopted to ensure that the coupling relationship between each pollutant concentration and all other parameters is accurately captured. The specific operation is as follows: ① Select... A specific pollutant concentration parameter is used as the target dependent variable. , and all remaining parameters (including Other pollutants in the middle The comprehensive independent variable matrix consists of environmental and operating condition parameters. ② Initialize the weight vector (dimensions and) (with consistent row counts), calculate the score vector for the first iteration. ③ Based on score vector Update the weight vector (normalize it to avoid magnitude deviation); ④ Repeat steps ②-③ to iteratively calculate the score vector. With weight vector until satisfied Or the number of iterations reaches Stop the iteration and retain the final score vector. ( (The score vectors from the first three iterations are used for subsequent calculations). Complete the above process step by step. The iterative fitting of all pollutant concentration parameters yields a complete set of score vectors.

[0116] S23. Coupling Strength Calculation: Based on the score vector obtained through iteration, calculate the direct coupling strength between any two parameters (including those between pollutants and between pollutants and the environment / operating conditions). The core principle is to measure the degree of correlation through vector similarity, using the following formula:

[0117] ;

[0118] in, To remove the first The iterative data matrix after parameters (after centering) For the first The normalized weight vector for the next iteration; calculation of coupling strength. After the calculation is completed, the coupling strength matrix is ​​obtained, and the matrix elements are... The value range is [-1, 1]. The closer the absolute value is to 1, the stronger the direct coupling relationship between the two parameters. Positive numbers indicate positive correlation, and negative numbers indicate negative correlation.

[0119] S24. Sparse Relational Network Construction: Adaptive Threshold Setting Adaptive Thresholds Using the "10% Edge Retention Principle" Eliminating weak coupling relationships and simplifying the network structure involves the following steps: ① Adjusting the coupling strength matrix. Take the absolute value of all off-diagonal elements to obtain the absolute value matrix. ; ② will The elements are sorted from largest to smallest, and the values ​​corresponding to the top 10% quantiles are selected as the adaptive threshold. (Ensure the network retains only core strong coupling relationships, while avoiding an overly sparse network); ③ Construct an adjacency matrix, with the following rules for determining matrix elements:

[0120] ;

[0121] in, Indicates parameters and There is a coupling relationship. Indicates parameters and There is no significant coupling relationship, which can be achieved through the adjacency matrix. Construct a three-dimensional correlation network, where nodes represent monitoring parameters, edges represent significant coupling relationships, and edge weights are PLS scores. Ultimately based on the adjacency matrix Generate a 3D correlation network with m monitoring parameters as nodes, significant coupling relationships as edges, and edge weights reflecting the correlation strength and positivity. The network output format supports JSON and graphical topology structures, and can be directly connected to subsequent elastic quantization modules.

[0122] In specific implementation, input the five core parameter data after S1 preprocessing ( Concentration, sulfur content of coal, boiler load, ambient temperature, wind speed), sample size For each group (corresponding to 1000 seconds of continuous monitoring data), a three-dimensional correlation network was constructed according to the above process. The specific process and results are as follows:

[0123] Matrix initialization: dependent variable matrix for Concentration time series data (1×1000), independent variable matrix For time-series data (4×1000) of four parameters—coal sulfur content, boiler load, ambient temperature, and wind speed—mean centering was performed on the two matrices. The average concentration was 0, and the mean deviation of other parameters was ≤0.01.

[0124] PLS iterative fitting: with Concentration is the target dependent variable, and the matrix of independent variables is integrated. 4×1000 dimensions; initialize weight vector Iteratively calculate the score vector and weight vector: Score vector of the first iteration The mean is 0.12, and the weight vector is from the second iteration. The weight vector in the third iteration The difference between the weight vectors of two consecutive iterations is 0.03, which is less than the convergence threshold. Stop the iteration and retain the score vectors from the first three iterations. .

[0125] Coupling strength calculation: Based on the retained score vector, the coupling strength between the five parameters is calculated, and the results are as follows: Corresponding sulfur content of coal (Strong positive correlation) Corresponding boiler load (Strong positive correlation) Corresponding ambient temperature (weak positive correlation) Corresponding wind speed (Weak negative correlation), boiler load corresponding to sulfur content in coal (Very weak correlation), the absolute value of the coupling strength between the remaining parameters is ≤0.1.

[0126] Sparse network construction: The absolute value matrix of coupling strength consists of 20 off-diagonal elements. After sorting them from largest to smallest, the minimum value corresponding to the first 10% (2 elements) is 0.75. Therefore, an adaptive threshold is used. Construct an adjacency matrix Retaining relationships with an absolute coupling strength ≥ 0.25, the final generated 3D network contains 5 nodes and 4 edges with edge weights of 0.82, 0.75, 0.31, and 0.28, respectively. After removing extremely weak correlations between coal sulfur content and boiler load, as well as other redundant links, the network structure is simple and the core coupling relationships are clearly defined, accurately reflecting... The interaction patterns between concentration and key influencing factors provide a reliable input for subsequent elasticity quantification.

[0127] In step S3, the specific process of achieving network elasticity quantization by improving cross-entropy and node aggregation is as follows:

[0128] Step S31, Probability Distribution Construction: Convert the fact monitoring sequence and normal operating condition sequence of each parameter into probability distributions respectively. and ;

[0129] Transform the real-time monitoring sequence of each parameter into a probability distribution. ,in, For the first The parameter is the first The normalized proportion of each sample; the probability distribution of the corresponding parameter under normal operating conditions is as follows: ;

[0130] Step S32, Improve cross-entropy calculation: Introduce PLS score weights to correct the traditional cross-entropy, the formula is as follows;

[0131] ;

[0132] In the formula, For parameters and Weighted cross-entropy;

[0133] Step S33, Network Elasticity Quantization: The overall network elasticity is obtained by weighted summation of node elasticities, using the following formula:

[0134] ;

[0135] In the formula, The number of network nodes. For nodes The overall level of chaos The value range is (0, 1). The closer the value is to 1, the stronger the network stability (the higher the resilience). The closer to 0, the worse the network stability (the lower the elasticity).

[0136] Step S34: Instability index construction: Invert the network elasticity to obtain the instability index. The formula is:

[0137] ;

[0138] In the formula, To avoid the minimum value where the denominator is 0, The peak value corresponds to the warning critical point; according to the changing pattern of network resilience: under normal emission conditions, the network is stable, high, Low and stable fluctuations; under near-critical conditions (critical point), network stability drops sharply. Reduced to the minimum, The network reaches its peak value; once the peak value is exceeded, the network enters a new stable state. Rebound It has fallen back to a moderate level. Therefore, The peak value is the warning threshold.

[0139] Specifically, step S3, based on the three-dimensional correlation network constructed in S2, uses normal operating conditions as a benchmark. It quantifies the difference in disorder between the real-time network and the benchmark network by improving cross-entropy, then obtains the overall network elasticity through node elastic aggregation. Finally, it constructs an instability index to locate the early warning critical point, providing core quantitative basis for subsequent early warning judgment. The overall process consists of five key steps: benchmark reference construction, probability distribution transformation, improved cross-entropy calculation, network elastic aggregation, and instability index generation. The operational details, parameter standards, and execution logic of each step are as follows:

[0140] Benchmark Network and Data Construction (Preparation): First, select continuous and stable normal operating condition data as the benchmark, and remove data from abnormal periods such as operating condition fluctuations and equipment maintenance. Based on the selected normal operating condition data, repeat step S2 to construct the reference association network (including the reference adjacency matrix). Reference coupling strength matrix This ensures that the reference network and the real-time network topology are consistent; at the same time, it extracts the time series data of each parameter under normal operating conditions as the benchmark sample for subsequent probability distribution construction.

[0141] Probability distribution construction: The interval partitioning method is used to transform the real-time monitoring sequence and the normal operating condition sequence into discrete probability distributions respectively. (Real-time) and (Benchmark), ensuring uniform distribution dimensions, specific operations: ① For each parameter Take the maximum value of the normal operating condition sequence. Minimum value Division There are equidistant intervals, and the range of each interval is... , (To avoid boundary value overflow); ② Statistically analyze the parameters in the real-time monitoring sequence. The frequency of the value falling within each interval Calculate the probability ( (For real-time data sample size), to obtain the real-time probability distribution ③ Similarly, count the frequency of the normal operating condition sequence in each interval. Calculate the baseline probability ( (using the baseline data sample size) to obtain the baseline probability distribution ④ Normalize the probability distribution to ensure , If the probability of a certain interval is 0, supplement with the minimum value. Avoid making logarithmic calculations meaningless.

[0142] Introducing the coupling strength obtained in step S2 As a weight, it corrects the deficiency of traditional cross-entropy, which only reflects the difference of a single parameter, and realizes the weighted influence of the correlation between parameters on the degree of difference; it uses the node comprehensive disorder degree to back-map the node elasticity, and then obtains the overall network elasticity through weighted summation. This reflects the stability of the entire monitoring system. Specific operations include: ① Calculating the stability of each node. Overall level of chaos ① The disorder is the sum of the weighted cross-entropy between the node and all significantly related nodes. Higher disorder indicates a more severe deviation of the node from the baseline state. ② Node resilience mapping: transforming node disorder into node resilience. The value range is (0, 1), and the closer the node elasticity is to 1, the more stable the node state; ③ Overall network elastic aggregation: assign weights according to the node degree (number of associated edges) to avoid isolated nodes affecting the overall result. ; ( For nodes The degree, that is The Middle ④ Window smoothing calculation: based on the sliding window length. Calculate real-time network resilience and to Window smoothing is performed to obtain the final network elasticity time series, with values ​​ranging from (0, 1). The closer the value is to 1, the stronger the stability of the entire network.

[0143] In practice, data from normal operating conditions are selected. concentration Boiler load sulfur content of coal Construct a reference correlation network, referencing the coupling strength matrix. With real-time networks Consistent, baseline probability distribution Divided into 20 intervals, such as Concentration range is .

[0144] Probability distribution transformation: Concentration (parameter) For example, the frequencies of 100 samples falling into each interval in the real-time sequence are: 5, 8, 12, 15, 18, 13, 9, 7, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0. After normalization, we get... The baseline sequence consists of 1000 samples that are evenly distributed across all intervals. The probability of each interval is approximately 0.05.

[0145] Improved cross-entropy calculation: concentration( ) and sulfur content of coal ( For example, coupling strength Calculate the weighted cross-entropy: Similarly, other correlation parameters are calculated. There are no related parameters (such as the sulfur content of coal and ambient temperature). Finally, a 5×5 cross-entropy matrix is ​​obtained.

[0146] Network elasticity quantification: ① Overall node disorder: The concentration node degree is 4. The disorder levels of other nodes are as follows: sulfur content of coal. Boiler load Ambient temperature Wind speed ② Node elasticity: , , 9, , ③ Weight Calculation: The node degrees are 4, 1, 1, 1, 1 respectively, with a total degree of 8, and the weights are... , ④ Network resilience: (Elastic value at the critical point).

[0147] Instability indicators and critical points: This value (The corrected actual calculated value is 4.02, echoing Example 1), and it is a local peak value, thus triggering the warning threshold. concentration The level has not yet exceeded the standard, thus enabling early warning.

[0148] This step improves the correlation characteristics of cross-entropy fusion parameters, and the network elasticity and instability indicators accurately reflect changes in system state, providing a quantitative basis for subsequent graded early warning. It is also adapted to the fluctuation characteristics of time series data in industrial scenarios, and its stability and accuracy meet the needs of practical applications.

[0149] In step S4, the specific steps for implementing graded early warning based on pollutant concentration are as follows:

[0150] Step S41, Threshold-based determination: Based on historical monitoring data, calculate the emission levels under normal conditions. The 95th percentile as the benchmark threshold ;

[0151] Step S42, Critical Point Determination: When three consecutive time steps... Value ≥ And the third time step The value reaches a local peak (greater than the values ​​of the two time steps before and after). The value is used to determine the trigger point for the early warning, at which point the system is in a "quasi-over-limit state";

[0152] Step S43: Dynamic hierarchical early warning system, establishing a three-level early warning system: Level 1 early warning (quasi-exceeding standard early warning) is the trigger threshold. If the pollutant concentration reaches its peak but does not exceed the national standard limit, an early warning message will be sent to the environmental protection specialist, prompting them to investigate abnormal operating conditions; a Level II warning (exceeding the standard warning) is triggered after the critical point is reached, when the pollutant concentration reaches the national standard limit. , If the concentration remains below 0.3, a notification will be sent to the environmental protection specialist and workshop manager, triggering an increase in the power of the waste gas treatment equipment; a Level 3 warning (emergency warning) is triggered when the pollutant concentration exceeds the national standard limit, or Value ≥ The system pushes information to company leaders and local environmental protection departments, triggering a reduction in production load or a suspension of high-pollution processes, and activating emergency response plans.

[0153] Specifically, instability indicators generated based on S3 Time-series data and pollutant concentration data are integrated through a closed-loop logic of "benchmark threshold calibration - precise critical point determination - tiered early warning triggering - dynamic tracking and adjustment" to achieve a complete process from critical point identification to tiered response. This ensures timely early warning while avoiding false alarms and missed alarms, adapting to dynamic changes in industrial operating conditions. The overall process consists of four key stages: benchmark threshold determination, critical point determination, dynamic tiered early warning, and early warning tracking and cancellation. The operational details, parameter standards, and execution logic of each stage are as follows:

[0154] ① Historical data screening: Screen data from the past 30 days of historical data, selecting data for continuous normal operating periods (must meet the following conditions: pollutant concentration ≤ 70% of national standard limit, network elasticity). The criteria for threshold calculation are as follows: ① Mean ≥ 0.6, operating condition fluctuation range ≤ 10%, no abnormal operating conditions such as equipment maintenance or raw material switching; ② Quantile calculation: For the screened normal operating condition (U) value sequence, linear interpolation is used to complete a small number of missing values ​​(missing rate ≤ 0.5%), and the 95th quantile is calculated as the baseline threshold. The formula is ,in, Under normal operating conditions Value sequence; ③ Threshold verification and correction: take normal operating conditions Value Mean ,like Then it is corrected to (To avoid extreme outliers causing the threshold to be too high); if Then it is corrected to (To avoid false alarms caused by excessively low thresholds); ④ Dynamic threshold update: Updated daily as the S5 model adaptively optimizes, and updated synchronously. Historical threshold records are retained for retrospective analysis of early warning effectiveness.

[0155] S42. Critical Point Determination: Employing a dual rule of "continuous threshold compliance + local peak verification," the system accurately identifies critical points where system states undergo sudden changes, avoiding accidental triggering due to instantaneous fluctuations. Specific operations include: ① Continuous Threshold Verification: Real-time traversal... Value time series sequence, when the first , , Three consecutive time steps All values ​​satisfy , , ① Complete the first level of verification; ② Local peak verification: with the first level of verification as an example. Taking time step as the target, the window length is determined. of Value subsequence ,like The maximum value of this subsequence (i.e. , , , ), complete the second verification; ③ Critical point confirmation: after both verifications pass, determine the first The time step is the warning threshold, recording the value at that moment. Value (peak value) Values, pollutant concentrations, and corresponding operating parameters serve as the core basis for graded early warning; ④ Anti-repeated triggering mechanism: After the critical point is triggered, the early warning judgment window is locked for 3 minutes, and the critical point will not be judged repeatedly during this period to avoid multiple triggers of the same abnormal operating condition.

[0156] Based on the critical point triggering state, pollutant concentration level and A three-tiered early warning system is established based on value changes. Each level of warning is triggered independently and responds in a coordinated manner, supporting escalation and downgrade between levels. Specific rules are as follows:

[0157] Level 1 Warning (Quasi-Exceedance Warning): Triggered by the confirmation of the critical point. The value reaches a local peak, but the pollutant concentration is ≤80% of the national standard limit (not exceeding the limit line); Action to take: ① Push the early warning information: Simultaneously push the warning information to the environmental protection specialist through the system platform, SMS, and WeChat, including the critical point time, ① Peak value, current concentration, related operating parameters (such as coal sulfur content, boiler load), and verification suggestions; ② Linkage control: only triggers information push, does not interfere with production and waste gas treatment systems, and reserves time for manual verification and adjustment; ③ Tracking frequency: updated once every 10 seconds. value, Values ​​and concentration data are continuously tracked to monitor changes in operating conditions.

[0158] Level II Warning (Exceeding Standard Warning): The triggering condition is that a Level I warning has been triggered and one of the following conditions is met: a) The pollutant concentration rises to the national standard limit. b) Network resilience Continuously below 0.3 and for a duration ; Operational steps: ① Early warning escalation push: Information is simultaneously pushed to environmental protection specialists and workshop managers, clearly indicating the concentration range or Abnormal duration; ② Linkage control: Automatically send adjustment commands to the waste gas treatment system (such as increasing the spray volume of the desulfurization tower and increasing the power of the activated carbon adsorption device), and at the same time push operating condition adjustment suggestions to the DCS system (such as reducing the production load and adjusting the raw material ratio); ③ Tracking frequency: Increase to update data once every 5 seconds and provide real-time feedback on the adjustment effect.

[0159] Level III Warning (Emergency Warning): Triggered by meeting any of the following conditions: a) Pollutant concentration exceeds national standard limits; b) U value ≥ (The system stability is severely unbalanced, which is likely to cause exceedances); c) Within 10 minutes after the Level II warning is triggered, the concentration does not decrease and continues to rise; Execute the following actions: ① Warning upgrade push: The information is pushed to the enterprise leader and environmental protection department supervisors at the same time, and the emergency broadcast is activated to notify the on-site personnel; ② Linkage control: Automatically trigger the forced reduction of production load (to below 60% of the normal load). If the concentration still rises, the corresponding process is suspended, and the waste gas treatment backup system is activated at the same time to ensure pollutant emission reduction; ③ Emergency record: Record the linkage operation log and concentration change curve in real time to provide a basis for subsequent emergency review.

[0160] In step S5, during model adaptive optimization and system linkage response, the reference network and baseline threshold are updated and optimized daily at midnight using the previous day's data (monitoring data, early warning results, and manual verification results). 1. Update PLS iteration weights through incremental learning; push first-level warning information to environmental protection specialists; push second-level warnings to environmental protection specialists and workshop managers, triggering power increase of waste gas treatment equipment; push third-level warnings to enterprise managers and environmental protection departments, triggering production load reduction or process suspension, and activating emergency plan.

[0161] The updated reference network involves reconstructing the reference network from the monitoring data of the previous day's normal emission period and updating the baseline distribution of the cross-entropy matrix. ; Optimize threshold Recalculated using historical data from a sliding window (last 30 days) It adapts to long-term changes in operating conditions and environment; PLS parameter optimization is achieved by incrementally learning and updating the PLS iterative weights. , improve the accuracy of coupling strength measurement.

[0162] Embodiment 2

[0163] Refer to Figure 2 As shown, the present invention is an intelligent early warning system for industrial gaseous pollutants, which can be used to execute the method content of Embodiment 1 and subsequent Embodiment 3 of the present invention, including: a data acquisition module, a data preprocessing module, an associated network structure module, an elastic quantization module, a critical point identification and early warning module, a system linkage module, and a model optimization module;

[0164] The data acquisition module consists of a fixed sensor array, a mobile inspection robot, and a DCS data interface, and realizes multi-dimensional data synchronous acquisition;

[0165] The data preprocessing module deploys a hierarchical preprocessing algorithm to complete the differential screening, significance test, normalization, and complementation of the acquired data;

[0166] The associated network construction module runs an improved PLS algorithm to generate a three-dimensional "multi-pollutant - environment - working condition" associated network, and outputs an adjacency matrix and a PLS score matrix;

[0167] The elastic quantization module calculates the cross-entropy matrix and network elasticity , and generates an instability index ;

[0168] The critical point identification and early warning module executes the critical point identification rule, triggers hierarchical early warning, and pushes early warning information;

[0169] The system linkage module realizes the linkage control with the DCS, the waste gas treatment system, and the emergency broadcast system;

[0170] The model optimization module deploys an incremental learning algorithm to regularly update the reference network, threshold, and PLS parameters.

[0171] The data transmission between the system modules uses an encryption protocol to ensure security: the data interaction between the edge side and the cloud uses encrypted transmission of the MQTT protocol, the data storage uses the AES-256 encryption algorithm, and the early warning instruction transmission adds an identity verification mechanism, and only authorized devices can receive and execute the linkage instruction; the system linkage module supports multi-protocol adaptation, can be docked with the industrial DCS system through the OPC UA protocol, docked with the waste gas treatment equipment through the Modbus protocol, and docked with the emergency broadcast system through the TCP / IP protocol, and at the same time reserves an expansion interface, and can be connected to the environmental protection supervision platform as needed to realize the synchronous reporting of early warning information and monitoring data.

[0172] The edge layer deploys a data acquisition module, a data preprocessing module, an elastic quantization module, a critical point identification and early warning module, and a system linkage module to ensure that the early warning response latency is ≤500ms. The cloud layer deploys a model optimization module, a historical data storage module, and a visualization monitoring module for model iteration and global monitoring. The edge and cloud layers adopt a two-way data synchronization mechanism. When the network is down, the edge can independently complete the early warning logic and automatically re-transmit the data after the network is connected.

[0173] Example 3 (A thermal power plant) Intelligent early warning application)

[0174] A thermal power plant is equipped with two 600MW generating units. The main pollutant emitted is SO2, with the national standard limit (GB 13223-2011) being 200 mg / m³. Production conditions fluctuate significantly, greatly affected by the sulfur content of the coal and boiler load. The ambient temperature and humidity range within the plant area is [range missing]. , Wind speed This embodiment is implemented using the method of the present invention. Intelligent early warning.

[0175] Fixed sensor arrays are deployed at the boiler outlet, desulfurization tower inlet, and plant boundary to collect data. Concentration (sampling frequency 1Hz); the mobile inspection robot patrols every 2 hours to collect data on blind spots in the factory area. Concentration (sampling frequency 0.5Hz); operating parameters such as sulfur content of coal, boiler load, and main steam pressure are acquired through the DCS interface (sampling frequency 0.1Hz), and ambient temperature, humidity, wind speed, and other parameters are collected simultaneously (sampling frequency 0.5Hz).

[0176] The core parameters were selected using the "2 standard deviation method": Concentration, sulfur content of coal, boiler load, ambient temperature, and wind speed; t-test (α=0.05) verifies that all the above parameters are consistent with... Concentration was significantly correlated; Z-Score normalization was used, and missing data (missing rate 1.2%) were filled in using the KNN algorithm.

[0177] The PLS parameter is set to the number of iterations. PLS item count ; Perform coupling strength calculation: Calculation results Coupling strength between concentration and sulfur content in coal Coupling strength with boiler load Coupling strength with ambient temperature Coupling strength with wind speed The sparse network construction uses the "10% edge retention principle" to determine the threshold. The constructed 3D interconnected network contains 5 nodes and 4 significantly coupled edges. - Sulfur content of coal, - Boiler load, -Ambient temperature, -Wind speed);

[0178] Reference network construction: using 7 consecutive days of normal operating condition data ( concentration Boiler load Construct a reference network;

[0179] Elasticity and U-value Calculation: Network elasticity is calculated from real-time monitoring data. Time series curves and Value timing curve, under normal operating conditions The mean is 0.72. The mean value is 1.39, which is the baseline threshold. (95th percentile);

[0180] When the unit load increased from 70% to 90%, the sulfur content of the coal increased from 0.8% to 1.2%. At that time, three consecutive time steps The values ​​were 3.72, 3.85, and 4.02 respectively (all...). ), and the third time step The value (4.02) is a local peak (two time steps before and after). The values ​​are 3.61 and 3.58 respectively, triggering the warning threshold. concentration (Not exceeding national standards).

[0181] Level 1 warning triggered: The system pushes a warning message to the environmental protection specialist, prompting them to check the sulfur content of the coal and the boiler load;

[0182] Operating condition adjustment tracking: Environmental inspectors found that the sulfur content of the coal exceeded the standard and notified the fuel department to adjust the coal blending ratio; at t=15min, Concentration increased to (National Standard) ), The value dropped to 0.28, triggering a level-two warning.

[0183] Level 2 Early Warning Linkage: The system pushes information to the workshop supervisor and automatically sends instructions to the desulfurization tower control system to increase the spray volume from... Upgraded to ;

[0184] Warning effect: , Concentration dropped to , It rose back to 0.65. The value dropped to 1.52, and the warning was lifted; this warning detected the near-exceeding state 23 minutes in advance, preventing further spread. Emissions exceeding concentration standards.

[0185] The reference network is updated daily at midnight using the previous day's data, and the calculations are then performed. By incrementally learning and updating the PLS iteration weights, the optimization error in coupling strength measurement was reduced from 3.2% to 1.5%.

[0186] It is worth noting that the various units included in the above system embodiments are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0187] Furthermore, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware, and the corresponding program can be stored in a computer-readable storage medium.

[0188] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A smart early warning method for industrial gaseous pollutants, characterized in that, Includes the following steps: Step S1: Multi-dimensional high-throughput data acquisition and preprocessing: Construct a three-dimensional integrated data acquisition system of "fixed + mobile + static" to obtain a multi-dimensional data matrix composed of pollutant concentration data, environmental correlation data and production condition data, and perform hierarchical preprocessing on the acquired data; Step S2: Construct a "multi-pollutant-environment-operating condition" correlation network: Use an improved partial least squares algorithm to calculate the direct coupling strength between parameters, and use an adaptive threshold to filter coupling relationships to construct a sparse three-dimensional correlation network; Step S3: Dynamic Quantification of Network Elasticity Based on Improved Cross-Entropy: Using the normal operating condition network as a reference, the difference in disorder between the observed network and the reference network is calculated by improving cross-entropy. The overall network elasticity (RE) is obtained based on the aggregation of node comprehensive disorder, and an instability index is constructed. ; Step S4, Critical Point Identification and Dynamic Hierarchical Early Warning: Determine the baseline threshold for instability indicators based on historical data. When three consecutive time steps Value ≥ 2× And the third time step When the value reaches a local peak, a warning threshold is determined; combined with The values ​​and pollutant concentrations enable a three-tiered early warning system. Step S5, Model Adaptive Optimization and System Linkage Response: Update the reference network and optimize the baseline threshold daily using the previous day's data. Update PLS iteration weights; implement early warning information push and linkage control with production and waste gas treatment systems based on early warning level.

2. The intelligent early warning method for industrial gaseous pollutants according to claim 1, characterized in that, In step S1, the data acquisition system includes pollutant concentration data, environmental data, and production condition data. The pollutant concentration data is collected in real time through a high-precision sensor array at the plant's discharge outlets, workshop exits, and plant boundaries. , , The target pollutant concentration was sampled at a frequency of 1 Hz, and the sensor's operating status was recorded simultaneously. The environmental data includes atmospheric temperature, humidity, atmospheric pressure, wind speed, and wind direction; The production status data, including raw material input, production load, reaction temperature, and equipment operating time, are synchronously obtained from the industrial control system.

3. The intelligent early warning method for industrial gaseous pollutants according to claim 1, characterized in that, The process flow for the layered preprocessing is as follows: Step S1: Filtering of differential features: Calculate the standard deviation of each parameter under normal operating conditions and under fluctuating operating conditions, and retain the standard deviation under fluctuating operating conditions. Parameters twice that of normal operating conditions; Step S2, Significance test: Perform a t-test on the screened parameters and retain the parameters that are correlated with pollutant concentration; Step S3, Data Standardization and Completion: Use Z-Score standardization to normalize all parameters to the same order of magnitude.

4. The intelligent early warning method for industrial gaseous pollutants according to claim 1, characterized in that, In step S2, the process for constructing the "multi-pollutant-environment-operating condition" correlation network is as follows: Step S21, Data matrix initialization: Split the preprocessed three-dimensional data matrix into a dependent variable matrix X and an independent variable matrix Y; Step S22, PLS Iterative Fitting: Using each pollutant concentration parameter as the dependent variable and all other parameters as independent variables, perform PLS regression fitting and iteratively calculate the score vector. The specific formula is as follows: ； In the formula, To remove the first The iterative data matrix after parameters, For the first The weight vector for the next iteration; Step S23, Coupling Strength Calculation: After iteration to convergence, calculate any two parameters. and direct coupling strength The specific formula is as follows: ； In the formula, The number of PLS ​​items, default. ; Step S24, Sparse Relationship Network Construction: Adaptive threshold is set using the 10% edge retention principle. Construct the adjacency matrix using the following formula: ； In the formula, Indicates parameters and There is a coupling relationship. Indicates parameters and No coupling relationship, through adjacency matrix Construct a three-dimensional interconnected network, where nodes represent monitoring parameters, edges represent coupling relationships, and edge weights are... .

5. The intelligent early warning method for industrial gaseous pollutants according to claim 1, characterized in that, In step S3, the specific process for achieving network elasticity quantization by improving cross-entropy and node aggregation is as follows: Step S31, Probability Distribution Construction: Convert the fact monitoring sequence and normal operating condition sequence of each parameter into probability distributions respectively. and ; Step S32, Improve cross-entropy calculation: Introduce PLS score weights to correct the traditional cross-entropy, the formula is as follows; ； In the formula, For parameters and Weighted cross-entropy; Step S33, Network Elasticity Quantization: The overall network elasticity is obtained by weighted summation of node elasticities, using the following formula: ； In the formula, The number of network nodes. For nodes The overall level of chaos The value ranges from (0, 1), and the closer it is to 1, the stronger the network stability. Step S34, Instability Index Construction: Invert the network elasticity to obtain the instability index U, the formula is: ； In the formula, To avoid the minimum value where the denominator is 0, The peak value corresponds to the warning threshold.

6. The intelligent early warning method for industrial gaseous pollutants according to claim 1, characterized in that, In step S4, the specific steps for implementing graded early warning based on pollutant concentration are as follows: Step S41, Threshold-based determination: Based on historical monitoring data, calculate the emission levels under normal conditions. The 95th percentile as the benchmark threshold ; Step S42, Critical Point Determination: When three consecutive time steps... Value ≥ And the third time step When the value reaches a local peak, a warning threshold is determined. Step S43: Dynamic hierarchical early warning system, establishing a three-level early warning system: Level 1 early warning is the trigger threshold. The pollutant concentration reaches its peak but does not exceed the national standard limit; a Level II warning is triggered after the critical point is reached and the pollutant concentration reaches the national standard limit. , The concentration remains below 0.3; a Level III alert is issued when the pollutant concentration exceeds the national standard limit, or Value ≥ .

7. The intelligent early warning method for industrial gaseous pollutants according to claim 1, characterized in that, In step S5, during model adaptive optimization and system linkage response, the reference network and baseline threshold are updated and optimized using the previous day's data every morning. Update the PLS iteration weights through incremental learning; The Level 1 warning information will be sent to the environmental protection specialist. The Level 2 warning was sent to the environmental protection specialist and workshop manager, triggering an increase in the power of the waste gas treatment equipment. The Level 3 warning is sent to the company's management and environmental protection department, triggering a reduction in production load or a suspension of processes, and activating the emergency plan.

8. An intelligent early warning system for industrial gaseous pollutants according to any one of claims 1-7, comprising a data acquisition module, a data preprocessing module, a correlation network structure module, an elastic quantization module, a critical point identification and early warning module, a system linkage module, and a model optimization module, characterized in that: The data acquisition module consists of a fixed sensor array, a mobile inspection robot, and a DCS data interface, enabling synchronous acquisition of multi-dimensional data. The data preprocessing module deploys a hierarchical preprocessing algorithm to complete the difference filtering, significance testing, standardization, and completion of the collected data; The associated network construction module runs the improved PLS algorithm to generate a three-dimensional associated network of "multi-pollutant-environment-operating condition" and outputs the adjacency matrix and PLS score matrix. The elasticity quantization module calculates the cross-entropy matrix and network elasticity. Generate instability indicators ; The critical point identification and early warning module executes the critical point identification rules, triggers tiered early warnings, and pushes early warning information. The system linkage module enables linkage control with DCS, exhaust gas treatment system, and emergency broadcast system; The model optimization module deploys an incremental learning algorithm to periodically update the reference network, threshold, and PLS parameters.

9. The intelligent early warning system for industrial gaseous pollutants according to claim 8, characterized in that, The system employs encrypted protocols to ensure data security between its modules: data interaction between the edge and cloud terminals uses the MQTT protocol for encrypted transmission, data storage uses the AES-256 encryption algorithm, and the transmission of early warning commands includes an identity verification mechanism, ensuring that only authorized devices can receive and execute linkage commands. The system's linkage module supports multi-protocol adaptation, enabling it to interface with industrial DCS systems via the OPC UA protocol, with waste gas treatment equipment via the Modbus protocol, and with the emergency broadcast system via the TCP / IP protocol. It also reserves expansion interfaces for on-demand access to environmental monitoring platforms, enabling the synchronous reporting of early warning information and monitoring data.

10. The intelligent early warning system for industrial gaseous pollutants according to claim 8, characterized in that, The edge layer is equipped with a data acquisition module, a data preprocessing module, an elastic quantization module, a critical point identification and early warning module, and a system linkage module to ensure that the early warning response delay is ≤500ms. The cloud layer is equipped with a model optimization module, a historical data storage module, and a visualization monitoring module for model iteration and global monitoring. The edge layer and the cloud layer adopt a two-way data synchronization mechanism. When the network is disconnected, the edge layer can independently complete the early warning logic and automatically re-transmit the data after reconnection.

Images