Post-permit management method and system for pollutant discharge permits based on environmental compliance management

Abstract

The present invention discloses a post-permit management method and system for pollutant discharge permits based on environmental compliance management, which specifically relates to the field of environmental management technology. According to the types of pollutants discharged by the enterprise, primary pollutants and the secondary pollutants generated by them are determined, and a simulation model is constructed and corrected to predict the pollutant conversion rate under different environments, monitor the concentration of secondary pollutants, calculate the generation rate, and evaluate the tracking stability of the equipment to the generation process. The equipment data is compared with the predicted generation rate, and its tracking capability and response delay are analyzed. The real-time level of the equipment is determined by combining fuzzy logic, and the data is divided into real-time and non-real-time pollution data. By continuously collecting real-time data, the regulatory department monitors the emission dynamics in real time, judges the compliance of the emission, analyzes the abnormality of the non-real-time data, adjusts the monitoring frequency, ensures the timeliness and accuracy of the data, effectively solves the problem of complex pollution, and improves the regulatory capacity of pollutant generation and emission.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental management, and in particular to a post-permit management method and system for pollutant discharge permits based on environmental compliance management. Background Art

[0002] The pollutant discharge permit system is an important component of ecological and environmental management. It provides legal and institutional guarantees for regulating pollutant discharge and promoting environmental protection. Enterprises and institutions must discharge pollutants within the management framework of the pollutant discharge permit. The pollutant discharge permit is not only the basic certificate for enterprises and institutions to legally discharge pollutants, but also an important basis for environmental protection authorities to enforce the law. In order to achieve effective management of pollutant discharge units, environmental regulatory authorities have begun to establish a post-permit management technology system for pollutant discharge permits in recent years. Figure 1 As shown in the figure, the system aims to fully understand the actual pollution discharge situation of enterprises through various means, such as off-site audits, on-site inspections, regular reporting and self-monitoring. In addition, the core goal of post-permit management of pollutant discharge permits is to manage emission behaviors in a refined manner to ensure that the discharge of pollutants meets national and local environmental protection standards. By building a systematic post-permit supervision method for pollutant discharge permits, the pollution discharge behavior of pollutant discharge units can be monitored in real time, and illegal discharge behaviors can be discovered and dealt with in a timely manner to reduce environmental risks. In the existing technology, when post-permit management of pollutant discharge permits based on environmental compliance management, online monitoring equipment is installed at the enterprise's discharge port to monitor pollutant emissions in real time. These devices can continuously measure the types and concentrations of pollutants and transmit the data in real time to the monitoring platform of the environmental regulatory department. Once the emission data is abnormal or exceeds the standard, the system will automatically issue an alarm to facilitate timely corrective measures.

[0003] The existing technology has the following shortcomings:

[0004] Online monitoring equipment typically only detects specific types of pollutants, such as sulfur dioxide, nitrogen oxides, and particulate matter. However, some companies may emit multiple pollutants during their production processes. The chemical reactions or interactions between these pollutants in the environment may lead to more complex pollution problems, and these complex pollution problems are often not detected by online monitoring equipment. For example, certain emitted chemicals will produce secondary pollutants such as ozone or PM2.5 after photochemical reactions in the air. When online monitoring equipment only detects primary pollutants, it may ignore the generation process and impact of these secondary pollutants. At the same time, if online monitoring equipment fails to monitor these complex chemical reactions or secondary pollutants in a timely manner, it may cause environmental regulators to underestimate the severity of the pollution and miss the opportunity to intervene in potential high-risk environmental problems. In the long run, complex pollution may lead to regional air quality deterioration, increased acid rain, and even affect plant and human health. Summary of the Invention

[0005] The purpose of the present invention is to provide a post-permit management method and system for pollutant discharge permits based on environmental compliance management to address the deficiencies in the background technology.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solution: a post-permit management method for pollutant discharge permits based on environmental compliance management, comprising the following steps:

[0007] S1: Based on the types of pollutants emitted during the enterprise's production process, identify the target primary pollutants that undergo chemical reactions. Based on the known chemical reaction mechanisms, identify the secondary pollutants generated by the target primary pollutants and construct a reaction chain from primary pollutants to secondary pollutants.

[0008] S2: Install sensors capable of real-time monitoring of environmental conditions near the discharge outlets. Integrate the real-time monitoring data from these sensors with online monitoring data of pollutant concentrations. Use known pollutant reaction mechanisms to construct simulation models to predict the rate and amount of conversion of primary pollutants into secondary pollutants under different environmental conditions. Combined with existing online monitoring data, the models can be calibrated in real time.

[0009] S3: Install secondary pollutant monitoring equipment downstream of the emission outlet to monitor the concentration changes of secondary pollutants in the air in real time. By monitoring the concentration changes of primary and secondary pollutants and combining them with environmental conditions, calculate the generation rate of secondary pollutants and compare it with the concentration changes of primary pollutants to evaluate the tracking efficiency and stability of the online monitoring equipment in tracking the generation of secondary pollutants.

[0010] S4: Compare the monitoring data of the online monitoring equipment with the predicted secondary pollutant generation rate to analyze whether the equipment can track the generation of secondary pollutants. If the monitoring equipment fails to capture the change of secondary pollutants within the specified time, evaluate the response delay of the monitoring equipment to the change of secondary pollutants;

[0011] S5: Based on the tracking efficiency stability of the online monitoring equipment for the generation of secondary pollutants and the response delay of the monitoring equipment to changes in secondary pollutants, fuzzy logic is used to determine the real-time level of the online monitoring equipment in tracking and detecting chemical reactions of pollutants in the environment, and the pollutant concentration data detected by the online monitoring equipment is divided into real-time pollution data and non-real-time pollution data;

[0012] S6: Through the continuous collection of real-time pollution data, regulatory authorities can track the emission dynamics of pollutants in real time and determine whether the emissions comply with the requirements of the pollutant discharge permit in real time; for non-real-time pollution data, the degree of non-real-time anomalies within a fixed time period is analyzed. If the degree of non-real-time anomalies is high, the monitoring frequency of the online monitoring equipment is adjusted accordingly.

[0013] Preferably, in S3, the correlation between the concentration change of the primary pollutant and the secondary pollutant generation rate is analyzed and then the generation rate correlation index is calculated, wherein the calculation method of the generation rate correlation index is:

[0014] Define the primary pollutant concentration as X, which represents the primary pollutant concentration monitored at the discharge port; define the secondary pollutant generation rate as Y, which represents the secondary pollutant generation rate obtained through downstream monitoring; calculate the joint probability distribution p(x, y) of the primary pollutant concentration X and the secondary pollutant generation rate Y, as well as their respective marginal probability distributions p(x) and p(y); the joint probability distribution p(x, y) represents the probability that X and Y fall within a certain interval at the same time. By calculating the frequency of the discretized data pair (Xi, Yi), its joint distribution is calculated as follows: Marginal probability distribution p(x) and p(y): represent the probability that the primary pollutant concentration X and the secondary pollutant generation rate Y fall within the interval, respectively. The expressions are: The generation rate correlation index is calculated by calculating the difference between the joint probability distribution and the marginal probability distribution of two variables, and the expression is: Where PK is the generation rate correlation index.

[0015] Preferably, in S4, a reaction rate anomaly index is generated after analyzing the reaction rate change of the online monitoring equipment to the secondary pollutant generation change within the W time period. The method for obtaining the reaction rate anomaly index is:

[0016] C 生成(t) Expressed as the actual generation rate of secondary pollutants, the generation rate of the prediction model at time t; C 监测 (t) represents the change in the concentration of secondary pollutants captured by the online monitoring equipment at time t; T 延迟 As the delay time for the equipment to capture the change of secondary pollutants, the reaction speed R 设备 (t) represents the response rate of the online monitoring equipment to the changes in the generation of secondary pollutants, and the calculation expression is: Δt is the time interval, R 设备 (t) is the response speed of the online monitoring equipment at time t, ΔC 监测 (t) is the change in the concentration of secondary pollutants in the monitoring equipment within the time period t. For the actual generation rate, the generation rate R predicted by the model is used. 生成 (t) is compared, and the expression is: R 生成 (t) is the actual generation rate of secondary pollutants (predicted value), ΔC 生成 (t) is the predicted change in the concentration of secondary pollutants, and the reaction rate error ΔR(t) is calculated as follows: ΔR(t) = R 生成(t)-R 设备 (t); ΔR(t) represents the error between the response speed of the online monitoring device and the actual generation rate at time t. During the time period W, the response speed anomaly index of the monitoring device is calculated. The response speed anomaly index is calculated based on the fluctuation of the response speed error ΔR(t) of the device, and the expression is: Where RS is the reaction speed abnormality index.

[0017] Preferably, in S5, the generation rate correlation index and the reaction speed abnormality index are used as input items of the fuzzy logic, and the real-time level of the online monitoring equipment in tracking and detecting chemical reactions of pollutants in the environment is used as the output item of the fuzzy logic;

[0018] Fuzzify the generation rate correlation index, reaction speed anomaly index, and the real-time level of online monitoring equipment tracking and detecting chemical reactions of pollutants in the environment; construct fuzzy rules based on the input fuzzy set to derive the real-time level;

[0019] The specific numerical values ​​of the generation rate correlation index and the reaction speed abnormality index are fuzzified and mapped to fuzzy sets. The fuzzified data are expressed as a membership function.

[0020] According to the fuzzification results of the input items, the corresponding fuzzy rules are matched. Each rule will deduce the fuzzy value of the output item according to the membership degree of the input item;

[0021] The fuzzy values ​​of the output items obtained through fuzzy reasoning are aggregated and then defuzzified to obtain the final value of the real-time level.

[0022] Preferably, the real-time pollution data and non-real-time pollution data are divided, specifically: according to the final value of the real-time level obtained, it is compared with the preset real-time reference threshold: if the final value of the real-time level is greater than or equal to the preset real-time reference threshold, the pollutant concentration data collected by the online monitoring equipment is divided into real-time pollution data; if the final value of the real-time level is less than the preset real-time reference threshold, the pollutant concentration data collected by the online monitoring equipment is divided into non-real-time pollution data.

[0023] Preferably, in S6, within a fixed time period T, the deviation of the non-real-time data from the real-time data or the model predicted data is calculated, and the calculation formula of the non-real-time abnormality degree AE is: C 非实时 (t) is the non-real-time data captured by the online monitoring equipment at time t, C 预测 (t) is the theoretical pollutant concentration predicted by the model.

[0024] Preferably, the acquired non-real-time abnormality degree is compared with a gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is smaller than the second standard threshold, and the non-real-time abnormality degree is compared with the first standard threshold and the second standard threshold respectively;

[0025] If the non-real-time abnormality level is less than the first standard threshold, the non-real-time abnormality level is low, and a level 3 warning signal is generated, and the current frequency of the monitoring device remains unchanged;

[0026] If the degree of non-real-time anomaly is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the device has a moderate non-real-time anomaly. At this time, a second-level warning signal is generated and the monitoring frequency is increased to improve the device's response speed;

[0027] If the degree of non-real-time abnormality is greater than the second standard threshold, the degree of non-real-time abnormality of the equipment is high. At this time, a first-level warning signal is generated and the monitoring frequency is increased to ensure that the equipment can capture the rapid changes in pollutant generation.

[0028] The present invention also provides a post-permit management system for pollutant discharge permits based on environmental compliance management, which includes a reaction chain determination module, a simulation model correction module, a tracking efficiency evaluation module, an equipment response delay analysis module, a fuzzy logic real-time evaluation module, and a monitoring frequency adjustment module;

[0029] Reaction chain determination module: Based on the types of pollutants emitted during the enterprise's production process, the module determines the target primary pollutants that undergo chemical reactions. Based on the known chemical reaction mechanism, the module determines the secondary pollutants generated by the target primary pollutants and constructs a reaction chain from primary pollutants to secondary pollutants.

[0030] Simulation model calibration module: Sensors capable of real-time monitoring of environmental conditions are installed near the discharge outlet. The real-time monitoring data from these environmental condition sensors is integrated with online monitoring data of pollutant concentrations. Using known pollutant reaction mechanisms, a simulation model is constructed to predict the rate and amount of conversion of primary pollutants into secondary pollutants under different environmental conditions. The model is then calibrated in real time based on existing online monitoring data.

[0031] Tracking efficiency evaluation module: Secondary pollutant monitoring equipment is installed downstream of the emission outlet to monitor the concentration changes of secondary pollutants in the air in real time. By monitoring the concentration changes of primary and secondary pollutants and combining them with environmental conditions, the generation rate of secondary pollutants is calculated and compared with the concentration changes of primary pollutants to evaluate the stability of the online monitoring equipment's tracking efficiency of secondary pollutant generation.

[0032] Equipment response delay analysis module: This module compares the monitoring data of online monitoring equipment with the predicted secondary pollutant generation rate to analyze whether the equipment can track the generation of secondary pollutants. If the monitoring equipment fails to capture the change of secondary pollutants within the specified time, the module evaluates the response delay of the monitoring equipment to the change of secondary pollutants.

[0033] Fuzzy logic real-time evaluation module: Based on the stability of the online monitoring equipment's tracking efficiency of secondary pollutant generation and the monitoring equipment's response delay to changes in secondary pollutants, fuzzy logic is used to determine the real-time level of the online monitoring equipment's tracking and detection of chemical reactions of pollutants in the environment. The pollutant concentration data detected by the online monitoring equipment is divided into real-time pollution data and non-real-time pollution data.

[0034] Monitoring frequency adjustment module: Through the continuous collection of real-time pollution data, regulatory authorities can track the emission dynamics of pollutants in real time and determine whether the emissions comply with the requirements of the pollutant discharge permit in real time; for non-real-time pollution data, the degree of non-real-time anomalies within a fixed time period is analyzed. If the degree of non-real-time anomalies is high, the monitoring frequency of the online monitoring equipment is adjusted accordingly.

[0035] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0036] 1. This invention identifies primary pollutants and the secondary pollutants they generate, combines real-time environmental data with simulation models, predicts pollutant generation rates, and performs real-time corrections, effectively tracking complex pollution reaction chains. Downstream monitoring equipment further monitors secondary pollutant concentrations in real time. By calculating the generation rate correlation index and reaction rate anomaly index, the equipment's response speed and tracking stability are analyzed to ensure data accuracy and timeliness.

[0037] 2. This invention utilizes fuzzy logic technology to process the generation rate correlation index and the reaction speed anomaly index to determine the real-time level of online monitoring equipment and automatically classify pollutant data into real-time and non-real-time data. For non-real-time data with a high degree of anomaly, a multi-level threshold early warning mechanism is established to dynamically adjust the monitoring equipment frequency to ensure that the rapidly changing pollutant generation process can be captured. This not only improves the accuracy of pollutant emission supervision, but also provides timely warnings of possible emission violations, preventing environmental quality deterioration and helping enterprises and regulatory authorities to conduct more efficient pollution management. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0039] Figure 1 This is a framework diagram of the post-pollutant discharge permit supervision technology system.

[0040] Figure 2 Flow chart of the method of the present invention.

[0041] Figure 3 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0043] Example 1, please refer to Figure 2 and Figure 3 As shown, the post-permit management method for pollutant discharge permits based on environmental compliance management described in this embodiment includes the following steps:

[0044] S1: Based on the types of pollutants emitted during the enterprise's production process, identify the target primary pollutants that undergo chemical reactions. Based on the known chemical reaction mechanisms, identify the secondary pollutants generated by the target primary pollutants and construct a reaction chain from primary pollutants to secondary pollutants.

[0045] S2: Install sensors capable of real-time monitoring of environmental conditions near the discharge outlets. Integrate the real-time monitoring data from these sensors with online monitoring data of pollutant concentrations. Use known pollutant reaction mechanisms to construct simulation models to predict the rate and amount of conversion of primary pollutants into secondary pollutants under different environmental conditions. Combined with existing online monitoring data, the models can be calibrated in real time.

[0046] S3: Install secondary pollutant monitoring equipment downstream of the emission outlet to monitor the concentration changes of secondary pollutants in the air in real time. By monitoring the concentration changes of primary and secondary pollutants and combining them with environmental conditions, calculate the generation rate of secondary pollutants and compare it with the concentration changes of primary pollutants to evaluate the tracking efficiency and stability of the online monitoring equipment in tracking the generation of secondary pollutants.

[0047] S4: Compare the monitoring data of the online monitoring equipment with the predicted secondary pollutant generation rate to analyze whether the equipment can track the generation of secondary pollutants. If the monitoring equipment fails to capture the change of secondary pollutants within the specified time, evaluate the response delay of the monitoring equipment to the change of secondary pollutants;

[0048] S5: Based on the tracking efficiency stability of the online monitoring equipment for the generation of secondary pollutants and the response delay of the monitoring equipment to changes in secondary pollutants, fuzzy logic is used to determine the real-time level of the online monitoring equipment in tracking and detecting chemical reactions of pollutants in the environment, and the pollutant concentration data detected by the online monitoring equipment is divided into real-time pollution data and non-real-time pollution data;

[0049] S6: Through the continuous collection of real-time pollution data, regulatory authorities can track the emission dynamics of pollutants in real time and determine whether the emissions comply with the requirements of the pollutant discharge permit in real time; for non-real-time pollution data, the degree of non-real-time anomalies within a fixed time period is analyzed. If the degree of non-real-time anomalies is high, the monitoring frequency of the online monitoring equipment is adjusted accordingly.

[0050] In S1, among the pollutants emitted during the production process of the enterprise, primary pollutants (also called primary pollutants) refer to pollutants that the enterprise directly discharges into the environment, while secondary pollutants are harmful substances generated by these primary pollutants through physical and chemical reactions under atmospheric or other environmental conditions. According to the production process of the enterprise, identify the main primary pollutants it emits. These primary pollutants are usually discharged directly into the environment and may participate in subsequent chemical reactions to generate secondary pollutants. The following are some common primary pollutants:

[0051] Nitrogen oxides (NOx): Typically come from fossil fuel combustion processes such as power plants, motor vehicles, and industrial combustion facilities.

[0052] Volatile organic compounds (VOCs): Volatile emissions from solvents, chemical products, fuels, etc.

[0053] Sulfur dioxide (SO2): comes from the combustion of fuels such as coal and oil, especially sulfur-containing fuels.

[0054] Carbon monoxide (CO): A product of incomplete combustion, especially from motor vehicle emissions.

[0055] Ammonia (NH3): comes from agriculture, fertilizer use, and some industrial processes.

[0056] Chemical reactions of primary pollutants in the atmosphere produce secondary pollutants. Depending on the type of primary pollutant, the secondary pollutants it produces can be determined using known atmospheric chemical reaction mechanisms. For example, the following are some common primary pollutants and the secondary pollutants they produce:

[0057] Nitrogen oxides (NOx): Nitrogen oxides react with volatile organic compounds in the presence of sunlight to produce ozone (O3) and fine particulate matter (PM2.5).

[0058] Secondary pollutants generated: Ozone (O3): generated by the photochemical reaction of NO2 and oxygen (O2).

[0059] Fine particulate matter (PM2.5): PM2.5 is formed in the atmosphere through the formation of nitrates, when NO2 reacts with other substances to form nitrates.

[0060] Volatile organic compounds (VOCs): VOCs react photochemically with NOx in sunlight to form ozone and secondary organic aerosols (SOA).

[0061] Secondary pollutants generated: Ozone (O3): VOCs react with NOx to generate ozone.

[0062] Secondary organic aerosol (SOA): Volatile organic compounds condense in the atmosphere to form fine particulate matter (PM2.5) after oxidation reaction.

[0063] Sulfur dioxide (SO2) undergoes an oxidation process in the atmosphere to produce sulfuric acid, which combines with water vapor to form acid rain or combines with other particles to form PM2.5.

[0064] Secondary pollutants generated: sulfate particulate matter (PM2.5): SO2 is oxidized in the atmosphere to form sulfate, which in turn forms fine particulate matter.

[0065] Acid rain: SO2 is converted into sulfuric acid in the atmosphere, which combines with precipitation to form acid rain.

[0066] Carbon monoxide (CO): Carbon monoxide is converted into carbon dioxide (CO2) through oxidation reactions, indirectly contributing to the formation of photochemical smog. Secondary pollutants generated: Ozone: Although CO does not directly produce ozone, it can enhance photochemical reactions in the atmosphere, indirectly promoting ozone production.

[0067] Ammonia: NH3 reacts with acidic pollutants (such as nitric acid and sulfuric acid) to form ammonia salt particles.

[0068] Secondary pollutants generated: Ammonium nitrate: Ammonia reacts with nitric acid to produce ammonium nitrate, which is one of the main components of PM2.5.

[0069] Ammonium sulfate: Ammonia gas reacts with sulfuric acid to form ammonium sulfate particles.

[0070] The key to constructing a reaction chain from primary pollutants to secondary pollutants is to capture the chemical transformation process between pollutants and clearly show their generation paths under different conditions. The reaction chain can be constructed as follows:

[0071] Example reaction chain: nitrogen oxides (NOx) and volatile organic compounds (VOCs)

[0072] NOx emission → photochemical reaction → NO2 generation → NO2 decomposition and reaction with oxygen → ozone (O3) generation

[0073] VOCs emission → reaction with OH radicals → generation of intermediate products → reaction with NOx → generation of secondary organic aerosol (SOA)

[0074] NOx+VOCs → produce photochemical smog under light → generate ozone (O3) and PM2.5

[0075] Example reaction chain: Sulfur dioxide (SO2)

[0076] SO2 emission → oxidized by OH free radicals in the atmosphere → sulfuric acid generation → combined with water vapor → acid rain; SO2 emission → oxidized by OH free radicals → sulfate particles generated → PM2.5 generation;

[0077] Example reaction chain: Ammonia (NH3)

[0078] NH3 emission → reaction with nitric acid → ammonium nitrate generation → PM2.5 generation; NH3 emission → reaction with sulfuric acid → ammonium sulfate generation → PM2.5 generation;

[0079] Environmental conditions (such as temperature, humidity, light intensity, and wind speed) have a significant impact on the rate and amount of conversion of primary pollutants into secondary pollutants. For example:

[0080] Sunlight intensity: determines the activity of photochemical reactions, thereby affecting the generation of ozone and PM2.5.

[0081] Humidity: The higher the humidity, the more easily acidic gases (such as SO2 and NOx) dissolve in water, generating acid rain or secondary particulate matter.

[0082] Temperature: Under high temperature conditions, the rate of photochemical reactions accelerates and the amount of ozone produced increases; at low temperatures, particulate matter is more likely to condense and form PM2.5.

[0083] These factors should be incorporated into the construction of the reaction chain to ensure that the amount of secondary pollutants generated under different conditions can be accurately predicted.

[0084] Based on the reaction chain identified above, combined with data from online monitoring equipment and environmental sensors, the concentration changes of primary and secondary pollutants are dynamically monitored, and the reaction chain model is updated in real time. By simulating different environmental conditions, future pollutant generation trends can be predicted, helping businesses and regulators take appropriate environmental measures. By constructing the reaction chain from primary to secondary pollutants, a scientific basis for environmental compliance management can be provided.

[0085] In S2, sensors that can monitor environmental conditions in real time are installed near the discharge outlet, and their data are integrated with online monitoring data of pollutant concentrations. A simulation model is constructed through known pollutant reaction mechanisms, which can effectively predict the speed and amount of conversion of primary pollutants into secondary pollutants.

[0086] First, it is necessary to install sensors that can monitor environmental conditions in real time. These sensors will provide data that is closely related to the reaction rate of pollutants. Key environmental parameters include: Light intensity (UV intensity): Photochemical reactions such as ozone generation require the participation of sunlight, so UV intensity is an important parameter for predicting photochemical pollution. Temperature: Temperature affects the reaction rate in the atmosphere, and generally higher temperatures will accelerate chemical reactions. Humidity: High humidity may promote the conversion of acidic gases (such as SO2, NOx) into acid rain or secondary particulate matter. Wind speed and direction: These parameters affect the diffusion rate of pollutants and determine the residence time and concentration changes of pollutants in the air. Atmospheric pressure: affects the diffusion and deposition of pollutants, especially when the atmospheric pressure is low, pollutants are more likely to accumulate.

[0087] Online monitoring equipment is typically installed at emission outlets to monitor the emission concentrations of primary pollutants, such as NOx, SO2, and VOCs. To build predictive models, these pollutant concentration data must be integrated with data from environmental condition sensors. Through an Internet of Things (IoT) system or wireless sensor network, ensure that all environmental condition and pollutant concentration data are transmitted to a central database or cloud platform in real time. Match the timestamps of environmental condition sensors and pollutant concentrations to ensure that the data at each point in time corresponds to each other, forming a complete monitoring record. Invalid or abnormal data (such as equipment failure, transmission errors, etc.) is cleaned up to ensure the accuracy of the integrated data.

[0088] Based on the known atmospheric chemical reaction mechanisms, a simulation model is constructed to predict the rate and amount of transformation of primary pollutants into secondary pollutants under different environmental conditions. The model needs to take into account the types of pollutants and their reaction pathways. According to the primary pollutants emitted by the enterprise, important chemical reaction mechanisms are selected. Common mechanisms include: nitrogen oxides (NOx) and volatile organic compounds (VOCs) generate ozone (O3) and secondary organic aerosols (SOA) under sunlight. Sulfur dioxide (SO2) is oxidized in the atmosphere to generate sulfate (PM2.5) and form acid rain with water. Ammonia reacts with nitric acid or sulfuric acid to generate ammonium nitrate or ammonium sulfate particles (PM2.5). The influence of environmental parameters on the reaction rate needs to be introduced into the model. For example, when the temperature rises, the reaction rate often accelerates; when the UV intensity increases, the amount of ozone generated increases. Based on the principles of chemical kinetics, reaction rate equations for different pollutants under different environmental conditions are established to predict the amount of secondary pollutants generated. For example, the ozone generation rate It can be expressed as: Where k1 is the reaction rate constant, and f(T,UV) represents the effect of temperature and UV intensity on the reaction rate. Use known data (such as historical monitoring data) to verify the initial accuracy of the model and make adjustments.

[0089] In order to ensure that the model can accurately reflect the actual pollutant transformation process, it is necessary to calibrate the model based on the data from existing online monitoring equipment to ensure that the model predictions are consistent with the actual data. The specific steps are as follows:

[0090] Obtain secondary pollutant monitoring data: Install monitoring equipment downstream of the discharge port to monitor the concentration of secondary pollutants in real time, such as ozone (O3), PM2.5, SOA, etc.

[0091] Data comparison: Compare the secondary pollutant generation predicted by the model with the actual monitored secondary pollutant concentrations to analyze the model error.

[0092] Adjust reaction rate constants: If there is a deviation between the actual data and the model prediction, the reaction rate constant or environmental impact coefficient can be adjusted to make the model output closer to the actual situation.

[0093] Feedback mechanism: A dynamic feedback mechanism is introduced to continuously monitor the model’s performance. When the model’s prediction error exceeds a set threshold, an automatic correction process is triggered to enhance the model’s accuracy.

[0094] The calibrated simulation model can predict in real time the conversion rate of primary pollutants and the amount of secondary pollutants generated under different environmental conditions. This prediction helps businesses and regulators respond promptly to pollution risks exceeding standards. Based on the predictions, companies can adjust their production and emissions practices in real time to prevent secondary pollutant levels from exceeding standards. For example, if ozone peaks are predicted, companies can be required to reduce NOx and VOC emissions. If the model predicts that secondary pollutant concentrations are about to reach dangerous thresholds, the system automatically issues an alert, allowing regulators and businesses to take proactive measures to reduce emissions or strengthen pollution control.

[0095] In this application, by installing environmental condition sensors, integrating real-time monitoring data, building a simulation model based on pollutant reaction mechanisms, and performing real-time corrections based on online monitoring data, we can accurately predict the conversion rate and amount of primary pollutants into secondary pollutants. This solution improves the efficiency of enterprise pollution management and helps environmental regulators better respond to potential high-risk environmental pollution issues.

[0096] In S3, in order to effectively evaluate the generation rate of secondary pollutants and their tracking efficiency, secondary pollutant monitoring equipment needs to be installed downstream of the emission outlet. These devices should be able to monitor the concentration changes of key secondary pollutants in the air in real time, such as: Ozone (O3) sensor: used to monitor the concentration of ozone generated by photochemical reactions. PM2.5 particle sensor: monitors fine particulate matter, especially secondary particulate matter generated by the conversion of primary pollutants, such as sulfate, nitrate and organic aerosol (SOA). Secondary organic aerosol (SOA) monitoring equipment: used to monitor the concentration of organic aerosol generated by the reaction of volatile organic compounds.

[0097] Monitoring equipment should be installed a certain distance downstream of the discharge outlet to ensure it can effectively capture secondary pollutants generated by environmental reactions with primary pollutants. This distance should be determined based on local wind speed, direction, and other diffusion conditions. Multiple monitoring devices should be installed at different downstream locations to capture the diffusion range and concentration changes of secondary pollutants, providing a comprehensive understanding of their formation and accumulation.

[0098] Online monitoring equipment has been installed at the discharge outlets of primary pollutants to monitor the concentration of primary pollutants (such as NOx, SO2, VOCs, etc.) in real time. Secondary pollutant monitoring equipment at downstream locations will collect concentration data of secondary pollutants in real time. Record the emission concentrations of primary pollutants such as NOx, SO2, VOCs, etc. The data can come from the online monitoring equipment at the discharge outlet. Record the concentration changes of secondary pollutants such as ozone, PM2.5, SOA, etc. in real time. Integrate data from environmental condition sensors (such as light intensity, temperature, humidity, wind speed, wind direction), which will affect the generation rate of secondary pollutants.

[0099] By collecting the concentration data of primary and secondary pollutants in real time and combining it with environmental conditions, the generation rate of secondary pollutants can be calculated. The specific steps are as follows:

[0100] The generation rate of secondary pollutants is expressed as the rate of change of the concentration of secondary pollutants over time (usually in μg / m 3 / h or ppb / h). Generation rate calculation formula: C 二次污染物 (t2), C 二次污染物 (t1) is the concentration of secondary pollutants at two moments, and t2-t1 is the time interval. Since environmental conditions (such as temperature, UV light intensity, etc.) have a significant impact on the generation rate of secondary pollutants, a correction factor needs to be introduced when calculating the generation rate. The expression is: k is the reaction rate constant, based on known atmospheric reaction mechanisms. f(T, UV, H) represents the function of how environmental conditions (such as temperature T, light intensity UV, and humidity H) affect the formation rate. This function can be determined from experimental data or existing research.

[0101] Compare the changes in the concentration of primary pollutants with the generation rate of secondary pollutants and analyze their correlation. For example:

[0102] Ozone Generation: Increased NOx and VOC concentrations can lead to increased ozone generation rates, especially under strong light conditions. By comparing changes in NOx concentration with ozone generation rates, it is possible to determine whether ozone generation is proceeding as expected.

[0103] PM2.5 generation: Changes in SO2 and NH3 concentrations will affect the generation rate of sulfate and ammonium nitrate particulate matter (PM2.5). By comparing SO2 emission data with PM2.5 generation rates, the dynamic behavior of the reaction chain can be inferred.

[0104] Under similar environmental conditions, the generation rate of secondary pollutants should remain relatively stable. If the device detects large fluctuations under stable conditions, it may indicate data errors or insufficient sensitivity.

[0105] After analyzing the correlation between the concentration changes of primary pollutants and the generation rate of secondary pollutants, the generation rate correlation index is calculated. The higher the generation rate correlation index, the more effective the monitoring equipment is in capturing the generation process of secondary pollutants. The calculation method of the generation rate correlation index is:

[0106] Define the primary pollutant concentration as X, which represents the primary pollutant concentration monitored at the emission outlet, such as NOx, SO2, VOCs, etc. Define the secondary pollutant generation rate as Y, which represents the secondary pollutant generation rate obtained through downstream monitoring, such as the generation rate of ozone (O3), PM2.5, or SOA. Calculate the joint probability distribution p(x, y) of the primary pollutant concentration X and the secondary pollutant generation rate Y, as well as their respective marginal probability distributions p(x) and p(y); the joint probability distribution p(x, y) represents the probability that X and Y fall within a specific interval at the same time. By calculating the frequency of the discretized data pair (Xi, Yi), its joint distribution is calculated. The expression is: Marginal probability distributions p(x) and p(y): represent the probability that the primary pollutant concentration X and the secondary pollutant generation rate Y fall within a specific range, respectively. The expressions are: The generation rate correlation index is calculated by calculating the difference between the joint probability distribution and the marginal probability distribution of two variables, and the expression is: Where PK is the generation rate correlation index.

[0107] The larger the generation rate correlation index, the more efficient and stable the online monitoring equipment is in tracking the generation of primary and secondary pollutants. This means the equipment can effectively capture the relationship between changes in primary pollutant concentration and the generation rate of secondary pollutants, and can accurately reflect the chemical transformation process between pollutants even in complex nonlinear reaction environments. A high correlation index indicates that changes in primary pollutant concentration can effectively predict the generation of secondary pollutants, and that the equipment's monitoring data is consistent with the actual pollutant generation dynamics, reflecting the equipment's strong monitoring stability under different environmental conditions.

[0108] On the contrary, the smaller the generation rate correlation index, the poorer the efficiency and stability of the online monitoring equipment in tracking the generation of secondary pollutants. The equipment fails to accurately reflect the dynamic changes between primary and secondary pollutants, and may experience problems such as data lag, low monitoring accuracy, or delayed response to the pollutant generation process. In this case, the equipment may not be able to stably capture the transformation reaction of pollutants under complex environmental conditions, resulting in the underestimated or misjudgment of the secondary pollutant generation process. A low correlation index means that the equipment monitoring is unreliable and may not be able to provide real-time and accurate pollution data, affecting the effectiveness of environmental supervision.

[0109] In S4, real-time concentration data of secondary pollutants, such as ozone (O3), PM2.5, SOA, etc., are obtained from online monitoring equipment. The data should include detailed records at different time points to ensure that their dynamic changes can be analyzed. The generation rate of secondary pollutants is predicted by using a simulation model constructed using primary pollutant concentrations, environmental conditions (such as temperature, light intensity, humidity, etc.) and known pollutant reaction mechanisms. The predicted value should reflect the generation amount or generation rate of secondary pollutants in different time periods, expressed as: R 预测 (t) is the generation rate of secondary pollutants at time t, ΔC 预测 is the predicted concentration change of the secondary pollutant during the time period Δt.

[0110] Compare the changes in secondary pollutant concentrations captured by the online monitoring equipment with the predicted generation rate point by point, analyze whether the two are synchronized in time, and check whether the equipment can accurately capture the generation process of secondary pollutants. Compare the time series graphs of the monitoring data and the predicted generation rate. Observe whether the secondary pollutant concentration change curve of the equipment is consistent with the predicted generation rate trend. Calculate the difference between the monitoring data and the predicted generation rate using the expression: ΔR(t) = R 监测 (t)-R 预测 (t); where R 监测 (t) is the secondary pollutant generation rate measured by the online monitoring equipment. If ΔR(t) deviates over a long period of time, the equipment is failing to effectively track the generation of secondary pollutants. If the error between the monitored data and the predicted generation rate is small and the trend is consistent, the online monitoring equipment is effectively capturing the secondary pollutant generation process and has strong tracking capabilities. If the error is large, particularly during peak pollutant generation periods, the equipment fails to capture rapid changes, indicating insufficient tracking capabilities.

[0111] If the equipment fails to capture the changes in secondary pollutants within the specified time, the reaction speed of the online monitoring equipment to the changes in the generation of secondary pollutants is calculated. The reaction speed anomaly index is generated after analyzing the changes in the reaction speed of the online monitoring equipment to the changes in the generation of secondary pollutants within the time period W. The method for obtaining the reaction speed anomaly index is as follows:

[0112] C 生成(t) Expressed as the actual generation rate of secondary pollutants, the generation rate (predicted value) of the prediction model at time t; C 监测 (t) represents the change in the concentration of secondary pollutants (monitoring value) captured by the online monitoring equipment at time t; T 延迟 As the delay time for the equipment to capture the change of secondary pollutants, the reaction speed R 设备 (t) represents the response rate of the online monitoring equipment to the changes in the generation of secondary pollutants, and the calculation expression is: Δt is the time interval, R 设备 (t) is the response speed of the online monitoring equipment at time t, ΔC 监测 (t) is the change in the concentration of secondary pollutants in the monitoring equipment within the time period t. For the actual generation rate, the generation rate R predicted by the model is used. 生成 (t) is compared, and the expression is: R 生成 (t) is the actual generation rate of secondary pollutants (predicted value), ΔC 生成 (t) is the predicted change in the concentration of secondary pollutants, and the reaction rate error ΔR(t) is calculated as follows: ΔR(t) = R 生成 (t)-R 设备 (t); ΔR(t) represents the error between the response speed of the online monitoring device and the actual generation rate at time t. If ΔR(t) is small, it means that the device's response speed is consistent with the actual generation rate; conversely, a large error indicates that the device's response speed is lagging or inaccurate. During the W time period, the monitoring device's response speed anomaly index is calculated to measure whether the monitoring device's response speed is abnormal. The response speed anomaly index is calculated based on the fluctuation of the device's response speed error ΔR(t) and is expressed as: Where RS is the reaction speed abnormality index.

[0113] A smaller RS ​​value indicates that the monitoring device's response speed is close to the predicted generation rate, the response speed is stable, and the device can promptly capture changes in secondary pollutants. A larger RS ​​value indicates that the device's response to secondary pollutants is delayed, the device's response speed is unstable, or there is a large lag, making it unable to accurately track changes in the generation of secondary pollutants.

[0114] In S5, the generation rate correlation index PK and the reaction rate abnormality index RS are used as the input items of the fuzzy logic, and the real-time level P of the online monitoring equipment in tracking and detecting the chemical reactions of pollutants in the environment is used as the output item of the fuzzy logic;

[0115] Generation Rate Correlation Index (PK): This index measures the correlation between the generation of primary and secondary pollutants. A higher PK indicates that the monitoring equipment is able to effectively detect the generation of secondary pollutants.

[0116] Reaction Rate Abnormality Index (RS): This index measures the response speed of online monitoring equipment to the generation of secondary pollutants. A low RS indicates a fast and stable response; a high RS indicates a significant response delay.

[0117] Real-time performance level (P): This reflects the real-time performance of online monitoring equipment in monitoring pollutant formation and chemical reaction processes. The P level can be categorized as low, medium, or high, indicating the equipment's performance in real-time monitoring.

[0118] In order to convert the production rate correlation index PK and the reaction speed abnormality index RS into fuzzy inputs, it is necessary to define fuzzy sets for these inputs. Common fuzzy sets include categories such as "low", "medium", and "high".

[0119] Low: The PK value is low, indicating that the correlation between the primary pollutant and the secondary pollutant generation process is poor and the equipment tracking ability is weak.

[0120] Medium: The PK value is medium, and the device can partially capture the pollutant formation, but the performance is sometimes unstable.

[0121] High: The PK value is high, and the equipment can well capture the generation process of pollutants.

[0122] Fuzzification of reaction speed abnormality index RS:

[0123] Low: A low RS value indicates that the equipment response speed is normal and stable, and it can quickly track the generation of secondary pollutants.

[0124] Medium: The RS value is moderate. The device occasionally has a response delay, but generally can still capture changes.

[0125] High: The RS value is high, the device response delay is obvious, and the generation and changes of secondary pollutants cannot be captured in time.

[0126] Fuzzification of real-time level P:

[0127] Low real-time performance: The device cannot capture changes in chemical reactions in a timely manner, resulting in large delays.

[0128] Medium real-time performance: The device's tracking and response capabilities are moderate, and there may be occasional lags, but the overall response is relatively timely.

[0129] High real-time performance: The equipment can capture the generation and changes of secondary pollutants in real time and respond quickly.

[0130] Based on the input fuzzy set, a set of fuzzy rules is constructed to derive the real-time level P. The rules can be formulated based on the combination of PK and RS. Common examples of rules are as follows:

[0131] Rule 1: If PK is "high" and RS is "low", then P is "high real-time".

[0132] Rule 2: If PK is "medium" and RS is "medium", then P is "medium real-time".

[0133] Rule 3: If PK is "low" and RS is "high", then P is "low real-time".

[0134] Rule 4: If PK is "high" but RS is "high", then P is "medium real-time" (high relevance but large response delay, overall performance is medium).

[0135] The core of fuzzy reasoning is to use a rule base to perform reasoning based on the fuzzy set of input items through fuzzy logic operations (such as the "maximum-minimum method" or "product method") to obtain the fuzzy value of the output item P. The reasoning steps are as follows:

[0136] The specific values ​​of the generation rate correlation index PK and the reaction speed abnormality index RS are fuzzified and mapped to three fuzzy sets: "low", "medium", and "high". The fuzzified data are expressed as a membership function.

[0137] According to the fuzzification results of the input items, the corresponding fuzzy rules are matched. Each rule will deduce the fuzzy value of the output item P based on the membership degree of the input item.

[0138] The fuzzy values ​​of the output item P obtained by fuzzy reasoning are aggregated, and then defuzzified (such as the centroid method) to obtain the final value of the real-time level P.

[0139] The real-time pollution data and non-real-time pollution data are divided into:

[0140] According to the final value of the real-time level P, it is compared with the preset real-time reference threshold:

[0141] Real-time pollution data: If the final value of the real-time level P is greater than or equal to the pre-set real-time reference threshold, the pollutant concentration data collected by the online monitoring equipment will be classified as real-time pollution data, indicating that the monitoring equipment can accurately capture the dynamic changes in the generation of secondary pollutants. Therefore, these data can be used as reliable real-time data to reflect the current environmental pollution situation.

[0142] Non-real-time pollution data: If the final value of the real-time level P is less than the preset real-time reference threshold, the pollutant concentration data collected by the online monitoring equipment will be classified as non-real-time pollution data, indicating that the equipment is not tracking the generation of secondary pollutants in a timely manner, and the data may have large lags or errors, requiring further compensation or correction.

[0143] In S6, regulatory authorities continuously collect data on pollutant concentrations emitted by enterprises through online monitoring equipment. This data is marked as real-time pollution data by the system, indicating that the equipment can effectively capture changes in pollutants. This real-time data allows regulatory authorities to track pollutant emissions in real time and compare the data with the requirements of the pollutant discharge permit. If the real-time data indicates that pollutant emission concentrations exceed the standard, regulatory authorities can take timely measures, such as warning the enterprise or activating emission control systems.

[0144] Real-time pollution data immediately reflects whether a company's emissions comply with its permit requirements. By comparing emissions against permitted emission limits in real time, the system automatically determines whether emissions exceed the permitted limits and generates a corresponding compliance report. If real-time data indicates that emissions are approaching or exceeding the permitted limit, the system triggers an early warning mechanism, notifying regulators and the company to take urgent measures to prevent further emissions exceeding the permitted limit.

[0145] For non-real-time pollution data, since the equipment fails to capture pollutant changes within the specified time, it is necessary to analyze the degree of abnormality and adjust the equipment's monitoring frequency accordingly. The degree of abnormality in non-real-time data can be determined by analyzing data deviations and lags within a fixed time period.

[0146] In a fixed time period T, the deviation of non-real-time data relative to real-time data or model prediction data is calculated. The calculation formula for the non-real-time anomaly degree AE is: C 非实时 (t) is the non-real-time data captured by the online monitoring equipment at time t, C 预测 (t) is the theoretical pollutant concentration predicted by the model.

[0147] Comparing the acquired non-real-time abnormality degree with a gradient standard threshold, the gradient standard threshold including a first standard threshold and a second standard threshold, wherein the first standard threshold is less than the second standard threshold, and comparing the non-real-time abnormality degree with the first standard threshold and the second standard threshold respectively;

[0148] If the degree of non-real-time anomaly is less than the first standard threshold, it indicates that the device's non-real-time problem is not obvious and the degree of non-real-time anomaly is low. In this case, a third-level warning signal is generated, and the current frequency of the monitoring device can remain unchanged. The device's response speed and data accuracy meet regulatory requirements.

[0149] If the degree of non-real-time anomaly is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it indicates that the device is significantly non-real-time, with occasional data lags or inaccuracies. This indicates a moderate non-real-time anomaly, generating a Level 2 warning signal and appropriately increasing the monitoring frequency to improve the device's response speed. Data timeliness and accuracy can be improved by shortening the monitoring interval (for example, from every minute to every 30 seconds).

[0150] If the degree of non-real-time anomalies exceeds the second standard threshold, the equipment frequently experiences non-real-time issues, data lags or is severely biased, and the equipment cannot accurately reflect the dynamics of pollutant generation. This high degree of non-real-time anomalies generates a Level 1 warning signal, necessitating a significant increase in monitoring frequency to ensure the equipment can capture rapid changes in pollutant generation. At the same time, regulators should urge companies to maintain or calibrate their equipment to ensure its proper operation.

[0151] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal, and the importance of the second-level warning signal is greater than that of the third-level warning signal. Relevant personnel can take corresponding adjustment measures according to different warning signal levels.

[0152] After adjusting the monitoring frequency of the equipment, the new data needs to be verified to ensure that the adjusted frequency can improve the response speed of the equipment and reduce non-real-time problems. After the equipment frequency is adjusted, continuously collect new real-time data and non-real-time data to observe how the equipment captures changes in pollutant generation. Based on the newly collected data, recalculate the non-real-time anomaly degree AE to confirm whether the response speed of the equipment and data accuracy have improved. If the adjusted data quality is still not ideal, it may be necessary to further increase the monitoring frequency, or consider other technical means (such as adding monitoring points or replacing monitoring equipment) to solve non-real-time problems.

[0153] In this application, by continuously collecting real-time pollution data, regulatory authorities can track the emission dynamics of enterprises in real time to ensure that pollution discharge behavior complies with the requirements of the pollution discharge permit. For non-real-time pollution data, by analyzing the degree of non-real-time anomalies within a fixed time period, the response delay problem of the equipment can be evaluated, and the response speed of the equipment can be improved by adjusting the monitoring frequency. This method helps regulatory authorities to promptly identify problems in monitoring equipment and ensure the real-time and accuracy of pollutant emissions through dynamic adjustments, thereby effectively supervising the environmental compliance of enterprises.

[0154] In this embodiment, first, based on the types of pollutants discharged by the enterprise, the target primary pollutants and the secondary pollutants they generate are determined, and a pollutant reaction chain is constructed. Then, environmental condition sensors are installed near the discharge port, environmental data and online monitoring data are integrated in real time, and a simulation model is constructed and calibrated to predict the pollutant conversion rate. Monitoring equipment is installed downstream of the discharge port to monitor the concentration of secondary pollutants in real time, calculate the generation rate and evaluate the tracking efficiency of the monitoring equipment. By comparing the monitoring data and the predicted rate, the tracking capability and response delay of the equipment are analyzed. Combining the generation rate correlation index and the reaction speed anomaly index, fuzzy logic is used to determine the real-time level of the monitoring equipment, and the data is divided into real-time and non-real-time data. Finally, the regulatory authorities monitor emission compliance by continuously collecting real-time data, and perform anomaly analysis on non-real-time data. If the degree of anomaly is high, the monitoring frequency is adjusted.

[0155] Example 2: The post-permit management system for pollutant discharge permits based on environmental compliance management described in this example includes a reaction chain determination module, a simulation model correction module, a tracking efficiency evaluation module, an equipment response delay analysis module, a fuzzy logic real-time evaluation module, and a monitoring frequency adjustment module.

[0156] Reaction chain determination module: Based on the types of pollutants emitted during the enterprise's production process, the module determines the target primary pollutants that undergo chemical reactions. Based on the known chemical reaction mechanism, the module determines the secondary pollutants generated by the target primary pollutants and constructs a reaction chain from primary pollutants to secondary pollutants.

[0157] Simulation model calibration module: Sensors capable of real-time monitoring of environmental conditions are installed near the discharge outlet. The real-time monitoring data from these environmental condition sensors is integrated with online monitoring data of pollutant concentrations. Using known pollutant reaction mechanisms, a simulation model is constructed to predict the rate and amount of conversion of primary pollutants into secondary pollutants under different environmental conditions. The model is then calibrated in real time based on existing online monitoring data.

[0158] Tracking efficiency evaluation module: Secondary pollutant monitoring equipment is installed downstream of the emission outlet to monitor the concentration changes of secondary pollutants in the air in real time. By monitoring the concentration changes of primary and secondary pollutants and combining them with environmental conditions, the generation rate of secondary pollutants is calculated and compared with the concentration changes of primary pollutants to evaluate the stability of the online monitoring equipment's tracking efficiency of secondary pollutant generation.

[0159] Equipment response delay analysis module: This module compares the monitoring data of online monitoring equipment with the predicted secondary pollutant generation rate to analyze whether the equipment can track the generation of secondary pollutants. If the monitoring equipment fails to capture the change of secondary pollutants within the specified time, the module evaluates the response delay of the monitoring equipment to the change of secondary pollutants.

[0160] Fuzzy logic real-time evaluation module: Based on the stability of the online monitoring equipment's tracking efficiency of secondary pollutant generation and the monitoring equipment's response delay to changes in secondary pollutants, fuzzy logic is used to determine the real-time level of the online monitoring equipment's tracking and detection of chemical reactions of pollutants in the environment. The pollutant concentration data detected by the online monitoring equipment is divided into real-time pollution data and non-real-time pollution data.

[0161] Monitoring frequency adjustment module: Through the continuous collection of real-time pollution data, regulatory authorities can track the emission dynamics of pollutants in real time and determine whether the emissions comply with the requirements of the pollutant discharge permit in real time; for non-real-time pollution data, the degree of non-real-time anomalies within a fixed time period is analyzed. If the degree of non-real-time anomalies is high, the monitoring frequency of the online monitoring equipment is adjusted accordingly.

[0162] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0163] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0164] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0165] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A post-permit management method for pollutant discharge permits based on environmental compliance management, characterized by: The following steps are included: S1: Based on the types of pollutants emitted during the enterprise's production process, identify the target primary pollutants that undergo chemical reactions. Based on the known chemical reaction mechanisms, identify the secondary pollutants generated by the target primary pollutants and construct a reaction chain from primary pollutants to secondary pollutants. S2: Install sensors capable of real-time monitoring of environmental conditions near the discharge outlets. Integrate the real-time monitoring data from these sensors with online monitoring data of pollutant concentrations. Use known pollutant reaction mechanisms to construct simulation models to predict the rate and amount of conversion of primary pollutants into secondary pollutants under different environmental conditions. Combined with existing online monitoring data, the models can be calibrated in real time. S3: Install secondary pollutant monitoring equipment downstream of the emission outlet to monitor the concentration changes of secondary pollutants in the air in real time. By monitoring the concentration changes of primary and secondary pollutants and combining them with environmental conditions, calculate the generation rate of secondary pollutants and compare it with the concentration changes of primary pollutants to evaluate the tracking efficiency and stability of the online monitoring equipment in tracking the generation of secondary pollutants. S4: Compare the monitoring data of the online monitoring equipment with the predicted secondary pollutant generation rate to analyze whether the equipment can track the generation of secondary pollutants. If the monitoring equipment fails to capture the change of secondary pollutants within the specified time, evaluate the response delay of the monitoring equipment to the change of secondary pollutants; S5: Based on the tracking efficiency stability of the online monitoring equipment for the generation of secondary pollutants and the response delay of the monitoring equipment to changes in secondary pollutants, fuzzy logic is used to determine the real-time level of the online monitoring equipment in tracking and detecting chemical reactions of pollutants in the environment, and the pollutant concentration data detected by the online monitoring equipment is divided into real-time pollution data and non-real-time pollution data; S6: Through the continuous collection of real-time pollution data, regulatory authorities can track the emission dynamics of pollutants in real time and determine whether the emissions comply with the requirements of the pollutant discharge permit in real time; for non-real-time pollution data, the degree of non-real-time anomalies within a fixed time period is analyzed. If the degree of non-real-time anomalies is high, the monitoring frequency of the online monitoring equipment is adjusted accordingly.

2. The post-permit management method for pollutant discharge permits based on environmental compliance management according to claim 1, characterized in that: In S3, the correlation between the concentration change of primary pollutants and the generation rate of secondary pollutants is analyzed and the generation rate correlation index is calculated. The calculation method of the generation rate correlation index is: Define the primary pollutant concentration as X, which represents the primary pollutant concentration monitored at the discharge port; define the secondary pollutant generation rate as Y, which represents the secondary pollutant generation rate obtained through downstream monitoring; calculate the joint probability distribution p(x, y) of the primary pollutant concentration X and the secondary pollutant generation rate Y, as well as their respective marginal probability distributions p(x) and p(y); the joint probability distribution p(x, y) represents the probability that X and Y fall within a certain interval at the same time. By calculating the frequency of the discretized data pair (Xi, Yi), its joint distribution is calculated as follows: Marginal probability distribution p(x) and p(y): represent the probability that the primary pollutant concentration X and the secondary pollutant generation rate Y fall within the interval, respectively. The expressions are: The generation rate correlation index is calculated by calculating the difference between the joint probability distribution and the marginal probability distribution of two variables, and the expression is: Where PK is the generation rate correlation index.

3. The post-permit management method for pollutant discharge permits based on environmental compliance management according to claim 2, characterized in that: In S4, the reaction rate anomaly index is generated after analyzing the reaction rate change of the online monitoring equipment to the generation change of the secondary pollutants in the W time period. The method for obtaining the reaction rate anomaly index is: C 生成(t) Expressed as the actual generation rate of secondary pollutants, the generation rate of the prediction model at time t; C 监测 (t) represents the change in the concentration of secondary pollutants captured by the online monitoring equipment at time t; T 延迟 As the delay time for the equipment to capture the change of secondary pollutants, the reaction speed R 设备 (t) represents the response rate of the online monitoring equipment to the changes in the generation of secondary pollutants, and the calculation expression is: Δt is the time interval, R 设备 (t) is the response speed of the online monitoring equipment at time t, ΔC 监测 (t) is the change in the concentration of secondary pollutants in the monitoring equipment within the time period t. For the actual generation rate, the generation rate R predicted by the model is used. 生成 (t) is compared, and the expression is: R 生成 (t) is the actual generation rate of secondary pollutants (predicted value), ΔC 生成 (t) is the predicted change in the concentration of secondary pollutants, and the reaction rate error ΔR(t) is calculated as follows: ΔR(t) = R 生成 (t)-R 设备 (t); ΔR(t) represents the error between the response speed of the online monitoring device and the actual generation rate at time t. During the time period W, the response speed anomaly index of the monitoring device is calculated. The response speed anomaly index is calculated based on the fluctuation of the response speed error ΔR(t) of the device, and the expression is: Where RS is the reaction speed abnormality index.

4. The post-permit management method for pollutant discharge permits based on environmental compliance management according to claim 3 is characterized by: In S5, the generation rate correlation index and the reaction speed anomaly index are used as input items of the fuzzy logic, and the real-time level of the online monitoring equipment in tracking and detecting the chemical reactions of pollutants in the environment is used as the output item of the fuzzy logic; Fuzzify the generation rate correlation index, reaction speed anomaly index, and the real-time level of online monitoring equipment tracking and detecting chemical reactions of pollutants in the environment; construct fuzzy rules based on the input fuzzy set to derive the real-time level; The specific numerical values ​​of the generation rate correlation index and the reaction speed abnormality index are fuzzified and mapped to fuzzy sets. The fuzzified data are expressed as a membership function. According to the fuzzification results of the input items, the corresponding fuzzy rules are matched. Each rule will deduce the fuzzy value of the output item according to the membership degree of the input item; The fuzzy values ​​of the output items obtained through fuzzy reasoning are aggregated and then defuzzified to obtain the final value of the real-time level.

5. The post-permit management method for pollutant discharge permits based on environmental compliance management according to claim 4 is characterized by: The real-time pollution data and non-real-time pollution data are divided into: according to the final value of the real-time level obtained, it is compared with the pre-set real-time reference threshold: if the final value of the real-time level is greater than or equal to the pre-set real-time reference threshold, the pollutant concentration data collected by the online monitoring equipment is divided into real-time pollution data; if the final value of the real-time level is less than the pre-set real-time reference threshold, the pollutant concentration data collected by the online monitoring equipment is divided into non-real-time pollution data.

6. The post-permit management method for pollutant discharge permits based on environmental compliance management according to claim 1 is characterized by: In S6, within a fixed time period T, the deviation of non-real-time data from real-time data or model prediction data is calculated. The calculation formula for the non-real-time anomaly degree AE is: C 非实时 (t) is the non-real-time data captured by the online monitoring equipment at time t, C 预测 (t) is the theoretical pollutant concentration predicted by the model.

7. The post-permit management method for pollutant discharge permits based on environmental compliance management according to claim 6, characterized in that: Comparing the acquired non-real-time abnormality degree with a gradient standard threshold, the gradient standard threshold including a first standard threshold and a second standard threshold, wherein the first standard threshold is less than the second standard threshold, and comparing the non-real-time abnormality degree with the first standard threshold and the second standard threshold respectively; If the non-real-time abnormality level is less than the first standard threshold, the non-real-time abnormality level is low, and a level 3 warning signal is generated, and the current frequency of the monitoring device remains unchanged; If the degree of non-real-time anomaly is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the device has a moderate non-real-time anomaly. At this time, a second-level warning signal is generated and the monitoring frequency is increased to improve the device's response speed; If the degree of non-real-time abnormality is greater than the second standard threshold, the degree of non-real-time abnormality of the equipment is high. At this time, a first-level warning signal is generated and the monitoring frequency is increased to ensure that the equipment can capture the rapid changes in pollutant generation.

8. A post-permit management system for pollutant discharge permits based on environmental compliance management, for implementing the post-permit management method for pollutant discharge permits based on environmental compliance management as described in any one of claims 1 to 7, characterized in that: It includes reaction chain determination module, simulation model correction module, tracking efficiency evaluation module, equipment response delay analysis module, fuzzy logic real-time evaluation module and monitoring frequency adjustment module; Reaction chain determination module: Based on the types of pollutants emitted during the enterprise's production process, the module determines the target primary pollutants that undergo chemical reactions. Based on the known chemical reaction mechanism, the module determines the secondary pollutants generated by the target primary pollutants and constructs a reaction chain from primary pollutants to secondary pollutants. Simulation model calibration module: Sensors capable of real-time monitoring of environmental conditions are installed near the discharge outlet. The real-time monitoring data from these environmental condition sensors is integrated with online monitoring data of pollutant concentrations. Using known pollutant reaction mechanisms, a simulation model is constructed to predict the rate and amount of conversion of primary pollutants into secondary pollutants under different environmental conditions. The model is then calibrated in real time based on existing online monitoring data. Tracking efficiency evaluation module: Secondary pollutant monitoring equipment is installed downstream of the emission outlet to monitor the concentration changes of secondary pollutants in the air in real time. By monitoring the concentration changes of primary and secondary pollutants and combining them with environmental conditions, the generation rate of secondary pollutants is calculated and compared with the concentration changes of primary pollutants to evaluate the stability of the online monitoring equipment's tracking efficiency of secondary pollutant generation. Equipment response delay analysis module: This module compares the monitoring data of online monitoring equipment with the predicted secondary pollutant generation rate to analyze whether the equipment can track the generation of secondary pollutants. If the monitoring equipment fails to capture the change of secondary pollutants within the specified time, the module evaluates the response delay of the monitoring equipment to the change of secondary pollutants. Fuzzy logic real-time evaluation module: Based on the stability of the online monitoring equipment's tracking efficiency of secondary pollutant generation and the monitoring equipment's response delay to changes in secondary pollutants, fuzzy logic is used to determine the real-time level of the online monitoring equipment's tracking and detection of chemical reactions of pollutants in the environment. The pollutant concentration data detected by the online monitoring equipment is divided into real-time pollution data and non-real-time pollution data. Monitoring frequency adjustment module: Through the continuous collection of real-time pollution data, regulatory authorities can track the emission dynamics of pollutants in real time and determine whether the emissions comply with the requirements of the pollutant discharge permit in real time; for non-real-time pollution data, the degree of non-real-time anomalies within a fixed time period is analyzed. If the degree of non-real-time anomalies is high, the monitoring frequency of the online monitoring equipment is adjusted accordingly.

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