Post-license management method and system for pollution discharge license based on environmental compliance management

Abstract

The invention discloses a pollution discharge license post-license management method and system based on environmental compliance management, and particularly relates to the technical field of environmental management. Primary pollutants and secondary pollutants generated by the primary pollutants are determined according to the types of pollutants discharged by an enterprise, and a simulation model is constructed and corrected to predict pollutant conversion rates in different environments, monitor the concentration of the secondary pollutants, calculate the generation rate and evaluate the tracking stability of equipment to the generation process. Comparing the equipment data with the predicted generation rate, analyzing the tracking capability and response delay of the equipment, determining the real-time level of the equipment in combination with fuzzy logic, and dividing the data into real-time pollution data and non-real-time pollution data; real-time data is continuously collected, a supervision department monitors the emission dynamic state in real time, the pollution discharge compliance is judged, the abnormal degree of non-real-time data is analyzed, the monitoring frequency is adjusted, the timeliness and accuracy of the data are ensured, the problem of combined pollution is effectively solved, and the supervision capacity for pollutant generation and emission is improved.

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 part of ecological environmental management. It provides legal and institutional guarantees for regulating pollutant discharge behaviors and promoting environmental protection. In recent years, with the increasingly stringent environmental protection requirements in my country, the pollutant discharge behaviors of enterprises and institutions must be carried out under the management framework of pollutant discharge permits. Pollutant discharge permits are 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, in recent years, environmental regulatory authorities have begun to build a post-permit management technology system for pollutant discharge permits. Please refer to Figure 1 As shown in the figure, the system aims to fully grasp the actual pollution discharge situation of enterprises through various means, such as off-site audits, on-site inspections, regular reports 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 emission of pollutants meets national and local environmental protection standards. By building a systematic post-permit supervision method for pollutant discharge permits, the emission behavior of pollutant discharge units can be monitored in real time, illegal emission behaviors can be discovered and dealt with in a timely manner, and environmental risks can be reduced. In the prior art, when post-permit management of pollutant discharge permits based on environmental compliance management is carried out, online monitoring equipment is installed at the enterprise's discharge port to monitor the emission of pollutants in real time. These devices can continuously measure the types and concentrations of pollutants and transmit the data to the monitoring platform of the environmental regulatory department in real time. Once the emission data is abnormal or exceeds the standard, the system will automatically issue an alarm to facilitate timely corrective measures.

[0003] The prior art has the following deficiencies:

[0004] Online monitoring equipment usually only detects specific types of pollutants, such as sulfur dioxide, nitrogen oxides, particulate matter, etc. However, some companies may emit multiple pollutants during the production process. The chemical reactions or interactions between these pollutants in the environment may lead to more complex pollution problems, and these complex pollutions are often not detected by online monitoring equipment. For example: some emitted chemicals will generate 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 the online monitoring equipment fails to monitor these complex chemical reactions or secondary pollutants in a timely manner, it may cause environmental regulatory authorities 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 deterioration of air quality, increase in acid rain, and even affect the health of plants and humans. 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 object, the present invention provides the following technical solution: a post-permit management method for pollutant discharge permit based on environmental compliance management, comprising the following steps:

[0007] S1: According to the types of pollutants discharged during the production process of the enterprise, determine the target primary pollutants that undergo chemical reactions, determine the secondary pollutants generated by the target primary pollutants based on the known chemical reaction mechanism, and construct the reaction chain from primary pollutants to secondary pollutants;

[0008] S2: Install sensors that can monitor environmental conditions in real time near the discharge outlet, integrate the real-time monitoring data of the environmental condition sensors with the online monitoring data of pollutant concentrations, use known pollutant reaction mechanisms to build a simulation model to predict the speed and amount of transformation of primary pollutants into secondary pollutants under different environmental conditions, and calibrate the model in real time in combination with existing online monitoring data;

[0009] S3: Install secondary pollutant monitoring equipment downstream of the discharge port to monitor the concentration changes of secondary pollutants in the air in real time. By monitoring the concentration changes of primary and secondary pollutants, combined 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 on 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 changes of secondary pollutants within the specified time, evaluate the response delay of the monitoring equipment to the changes 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 the changes of secondary pollutants, the real-time level of the online monitoring equipment in tracking and detecting the chemical reactions of pollutants in the environment is determined by using fuzzy logic, 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 meet 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 generation rate of the secondary pollutant 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. The expression is: Marginal probability distribution p(x) and p(y): They 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 abnormality 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, and the method for obtaining the reaction rate abnormality 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 change of secondary pollutant generation, 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 generated, 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. In the W time period, the abnormal reaction speed index of the monitoring device is calculated. The abnormal reaction speed index is calculated based on the fluctuation of the response speed error ΔR(t) of the device. 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 tracking and detecting the chemical reaction of the 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 the fuzzy set, and 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 device 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 device 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 prediction 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 less 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 third-level warning signal is generated at this time, and the current frequency of the monitoring device remains unchanged;

[0026] If the non-real-time abnormality 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 abnormality. At this time, a secondary warning signal is generated and the monitoring frequency is increased to improve the response speed of the device;

[0027] If the non-real-time abnormality level is greater than the second standard threshold, the non-real-time abnormality level 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, including 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: According to the types of pollutants discharged during the production process of the enterprise, the target primary pollutants that undergo chemical reactions are determined. According to the known chemical reaction mechanism, the secondary pollutants generated by the target primary pollutants are determined, and the reaction chain from primary pollutants to secondary pollutants is constructed;

[0030] Simulation model correction module: Install sensors that can monitor environmental conditions in real time near the discharge outlet, integrate the real-time monitoring data of the environmental condition sensors with the online monitoring data of pollutant concentrations, and use known pollutant reaction mechanisms to build simulation models to predict the speed and amount of transformation of primary pollutants into secondary pollutants under different environmental conditions. Combined with existing online monitoring data, the model is corrected in real time;

[0031] Tracking efficiency evaluation module: Install secondary pollutant monitoring equipment downstream of the emission port 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 environmental conditions, calculate the generation rate of secondary pollutants and compare it with the concentration changes of primary pollutants to evaluate the tracking efficiency stability of the online monitoring equipment for the generation of secondary pollutants.

[0032] Equipment response delay analysis 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 changes of secondary pollutants within the specified time, evaluate the response delay of the monitoring equipment to the changes of secondary pollutants.

[0033] Fuzzy logic real-time evaluation module: 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 the changes of secondary pollutants, the real-time level of the online monitoring equipment in tracking and detecting the chemical reactions of pollutants in the environment is determined by using fuzzy logic, and 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 meet 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. The present invention identifies primary pollutants and the secondary pollutants they generate, combines real-time environmental condition data and simulation models, predicts pollutant generation rates and performs real-time corrections, and effectively tracks complex pollution reaction chains. The installed downstream monitoring equipment further monitors the concentration of secondary pollutants in real time, and calculates the generation rate correlation index and reaction speed anomaly index, analyzes the response speed and tracking stability of the equipment, and ensures the accuracy and timeliness of the data.

[0037] 2. The present invention uses fuzzy logic technology to process the generation rate correlation index and the reaction speed abnormality index, determine the real-time level of the online monitoring equipment, and automatically divide the pollutant data into real-time and non-real-time data. For non-real-time data with a high degree of abnormality, by setting a multi-level threshold warning mechanism, the frequency of the monitoring equipment is dynamically adjusted to ensure that the rapidly changing pollutant generation process can be captured, which not only improves the accuracy of pollutant emission supervision, but also timely warns of possible emission exceeding the standard, avoids environmental quality deterioration, and helps 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 drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded 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 The figure is a 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] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are 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 permit based on environmental compliance management described in this embodiment includes the following steps:

[0044] S1: According to the types of pollutants discharged during the production process of the enterprise, determine the target primary pollutants that undergo chemical reactions, determine the secondary pollutants generated by the target primary pollutants based on the known chemical reaction mechanism, and construct the reaction chain from primary pollutants to secondary pollutants;

[0045] S2: Install sensors that can monitor environmental conditions in real time near the discharge outlet, integrate the real-time monitoring data of the environmental condition sensors with the online monitoring data of pollutant concentrations, use known pollutant reaction mechanisms to build a simulation model to predict the speed and amount of transformation of primary pollutants into secondary pollutants under different environmental conditions, and calibrate the model in real time in combination with existing online monitoring data;

[0046] S3: Install secondary pollutant monitoring equipment downstream of the discharge port to monitor the concentration changes of secondary pollutants in the air in real time. By monitoring the concentration changes of primary and secondary pollutants, combined 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 on 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 changes of secondary pollutants within the specified time, evaluate the response delay of the monitoring equipment to the changes 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 the changes of secondary pollutants, the real-time level of the online monitoring equipment in tracking and detecting the chemical reactions of pollutants in the environment is determined by using fuzzy logic, 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 meet 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 discharged during the production process of the enterprise, primary pollutants (also called primary pollutants) refer to pollutants directly discharged into the environment by the enterprise, 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 the combustion of fossil fuels, 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 (SO 2 ): 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 (NH 3 ): From agriculture, fertilizer use, and some industrial processes.

[0056] Chemical reactions of primary pollutants in the atmosphere will generate secondary pollutants. Depending on the type of primary pollutant, the secondary pollutants generated can be determined through known atmospheric chemical reaction mechanisms. For example, the following are some common primary pollutants and their generated secondary pollutants:

[0057] Nitrogen oxides (NOx): Nitrogen oxides react with volatile organic compounds under the action of sunlight to produce ozone (O 3 ) and fine particulate matter (PM2.5).

[0058] Secondary pollutants generated: ozone (O 3 ): Pass NO 2 With oxygen (O 2 ) are produced in photochemical reactions.

[0059] Fine particulate matter (PM2.5): through the formation of nitrates, NO 2 After reacting with other substances to form nitrates, PM2.5 is formed in the atmosphere.

[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 (O 3 ): VOCs react with NOx to produce 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 (SO 2 ) undergoes an oxidation process in the atmosphere to generate sulfuric acid, which combines with water vapor to form acid rain or combines with other particles to generate PM2.5.

[0064] Secondary pollutants generated: sulfate particles (PM2.5): SO 2 Sulfates are formed through oxidation in the atmosphere, which in turn form fine particulate matter.

[0065] Acid Rain: SO 2 It is converted into sulfuric acid in the atmosphere and combines with precipitation to form acid rain.

[0066] Carbon monoxide (CO): Carbon monoxide is converted into carbon dioxide (CO 2 ), indirectly promoting the formation of photochemical smog. Secondary pollutants generated: Ozone: Although CO does not directly generate ozone, it can enhance photochemical reactions in the atmosphere and indirectly promote ozone generation.

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

[0068] Secondary pollutants generated: Ammonium nitrate: Ammonia reacts with nitric acid to form 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 in the following way:

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

[0072] NOx emission → photochemical reaction → NO 2 Generate → NO 2 Decomposes and reacts with oxygen → ozone (O 3 )generate

[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 (O 3 ) and PM2.5

[0075] Example reaction chain: Sulfur dioxide (SO 2 )

[0076] SO 2 Emission → Oxidation by OH radicals in the atmosphere → Production of sulfuric acid → Combination with water vapor → Acid rain; SO 2 Emission → Oxidation by OH radicals → Generation of sulfate particles → PM2.5 generation;

[0077] Example reaction chain: Ammonia (NH 3 )

[0078] NH 3 Emission → reaction with nitric acid → ammonium nitrate generation → PM2.5 generation; NH 3 Emission → reaction with sulfuric acid → ammonium sulfate generation → PM2.5 generation;

[0079] Environmental conditions (such as temperature, humidity, light intensity, 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 acidic gases (such as SO 2 , NOx) are more easily soluble 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; under low temperatures, particulate matter is more likely to condense to 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] According to the reaction chain determined above, combined with the data from online monitoring equipment and environmental sensors, the concentration changes of primary pollutants and secondary pollutants are dynamically monitored, and the reaction chain model is updated in real time. By simulating different environmental conditions, the generation trend of future pollutants can be predicted, and enterprises and regulatory authorities can be helped to take corresponding environmental protection measures. By constructing a reaction chain from primary pollutants to secondary pollutants, a scientific basis can be provided for environmental compliance management.

[0085] In S2, sensors capable of real-time monitoring of environmental conditions are installed near the discharge outlets, 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 speed up chemical reactions. Humidity: High humidity may promote the formation of acidic gases (such as SO 2 , NOx) into acid rain or secondary particulate matter. Wind speed and direction: These parameters affect the diffusion rate of pollutants, 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 usually installed at the emission port to monitor the emission concentration of primary pollutants, such as NOx, SO 2, VOCs, etc. In order to build a prediction model, these pollutant concentration data need to be integrated with the data from environmental condition sensors. Through the Internet of Things (IoT) system or wireless sensor network, ensure that all environmental conditions and pollutant concentration data can be transmitted to the 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 time point corresponds to each other to form a complete monitoring record. Clean invalid or abnormal data (such as equipment failure, transmission error, etc.) 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 speed 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 (O 3 ) and secondary organic aerosol (SOA). Sulfur dioxide (SO 2 ) is oxidized in the atmosphere to form sulfate (PM2.5) and forms acid rain with water. Ammonia reacts with nitric acid or sulfuric acid to form ammonium nitrate or ammonium sulfate particles (PM2.5). The model needs to introduce the influence of environmental parameters on the reaction rate. For example, when the temperature rises, the reaction rate often speeds up; when the UV intensity increases, the amount of ozone generated increases. According to the principles of chemical kinetics, the reaction rate equations of 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 k 1 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 of existing online monitoring equipment to ensure that the model prediction is 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 (O 3 ), 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 model output can be made closer to the actual situation by adjusting the reaction rate constant or environmental impact coefficient.

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

[0094] The calibrated simulation model can predict in real time the transformation rate of primary pollutants and the amount of secondary pollutants generated under different environmental conditions. This prediction will help enterprises and regulatory authorities to respond to the risk of excessive pollution in a timely manner. According to the prediction results, the production and emission behavior of enterprises can be adjusted in real time to prevent the excessive secondary pollutants. For example, when the ozone peak is predicted, enterprises can be required to reduce the emission of NOx and VOCs. Once the model predicts that the concentration of secondary pollutants is about to reach the dangerous threshold, the system will automatically issue an early warning, and regulatory authorities and enterprises can take measures in advance 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 the pollutant reaction mechanism, and combining online monitoring data for real-time correction, the conversion rate and generation amount of primary pollutants to secondary pollutants can be accurately predicted. This solution improves the efficiency of enterprise pollution management and helps environmental regulatory authorities better deal with potential high-risk environmental pollution problems.

[0096] In S3, in order to effectively evaluate the generation rate of secondary pollutants and their tracking efficiency, it is necessary to install secondary pollutant monitoring equipment at the 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 (O 3 ) 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 transformation 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 at a certain distance downstream of the discharge port to ensure that secondary pollutants generated by environmental reactions of primary pollutants can be effectively captured. This distance should be determined based on local wind speed, wind direction and other diffusion conditions. Multiple monitoring devices should be set up at different downstream points to capture the diffusion range and concentration changes of secondary pollutants, so as to fully understand their generation and accumulation.

[0098] Online monitoring equipment has been installed at the primary pollutant discharge outlets to monitor primary pollutants (such as NOx, SO 2, VOCs, etc.) concentration. The secondary pollutant monitoring equipment at the downstream location will collect the concentration data of secondary pollutants in real time. Record NOx, SO 2 The emission concentration of primary pollutants such as , VOCs, etc., the data can come from the online monitoring equipment at the emission outlet. Real-time record of concentration changes of secondary pollutants such as ozone, PM2.5, SOA, etc. Integrate the data of environmental condition sensors (such as light intensity, temperature, humidity, wind speed, wind direction), these conditions will affect the generation rate of secondary pollutants.

[0099] By collecting the concentration data of primary and secondary pollutants in real time and combining them 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 (t 2 ), C 二次污染物 (t 1 ) are the secondary pollutant concentrations at two moments, t 2 -t 1 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 effect of environmental conditions (such as temperature T, light intensity UV, humidity H) on the generation rate. This function can be determined through experimental data or existing studies.

[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 VOCs concentrations will lead to an increase in the ozone generation rate, especially under strong light conditions. By comparing the change in NOx concentration with the ozone generation rate, it can be determined whether the ozone generation process is in line with expectations.

[0103] PM2.5 generation: SO 2 and NH 3 Changes in concentration will affect the generation rate of sulfate and ammonium nitrate particulate matter (PM2.5). 2 By comparing the emission data with the PM2.5 generation rate, the dynamic behavior of the reaction chain can be deduced.

[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 that the device has data errors or insufficient sensitivity.

[0105] After analyzing the correlation between the concentration change of primary pollutants and the generation rate of secondary pollutants, the generation rate correlation index is calculated. The higher the generation rate correlation index is, 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, SO 2 , VOCs, etc. The secondary pollutant generation rate is defined as Y, which represents the secondary pollutant generation rate obtained through downstream monitoring, such as ozone (O 3 ), PM2.5 or SOA generation rate. Calculate the joint probability distribution p(x,y) of primary pollutant concentration X and 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 into a certain 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 distribution 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, and 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 is, the higher the tracking efficiency and stability of the online monitoring equipment for the generation process of primary pollutants and secondary pollutants. This means that the equipment can effectively capture the relationship between the change in primary pollutant concentration and the generation rate of secondary pollutants, and can even accurately reflect the chemical transformation process between pollutants in a complex nonlinear reaction environment. A high correlation index indicates that the concentration change of primary pollutants can well predict the generation of secondary pollutants, and the monitoring data of the equipment is consistent with the actual pollutant generation dynamics, which reflects the strong monitoring stability of the equipment under different environmental conditions.

[0108] On the contrary, the smaller the generation rate correlation index is, the poorer the tracking efficiency and stability of the online monitoring equipment for the generation of secondary pollutants. The equipment fails to accurately reflect the dynamic changes between primary pollutants and secondary pollutants, and may have 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 misjudged secondary pollutant generation process. A low correlation index means that 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 (O 3 ), PM2.5, SOA, etc. The data should include detailed records at different time points to ensure that its dynamic changes can be analyzed. The generation rate of secondary pollutants is predicted by using a simulation model built 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 the concentration of secondary pollutants 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, the expression is: 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 for a long time, it means that the equipment fails to effectively track the generation of secondary pollutants. If the error between the monitoring data and the predicted generation rate is small and the trend is consistent, it means that the online monitoring equipment can effectively capture the generation process of secondary pollutants and has strong tracking ability. If the error is large, especially when the pollutant generation peaks, the equipment fails to capture rapid changes, indicating that the equipment's tracking ability is insufficient.

[0111] If the equipment fails to capture the changes of 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, and the reaction speed abnormality 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 W time period. The method for obtaining the reaction speed abnormality index is:

[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 change of secondary pollutant generation, 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 generated, 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 response speed of the device is consistent with the actual generation rate; conversely, a large error indicates that the device response speed is lagging or inaccurate. In the W time period, the response speed anomaly index of the monitoring device is calculated to measure whether the response speed of the monitoring device is abnormal. 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.

[0113] The smaller the RS value, the closer the response speed of the monitoring equipment is to the predicted generation rate, the more stable the response speed is, and the equipment can timely capture the changes of secondary pollutants. The larger the RS value, the greater the delay in the equipment's response to secondary pollutants, the more unstable the equipment's response speed is, or there is a large lag, and it is unable to accurately track the generation changes of secondary pollutants.

[0114] In S5, the generation rate correlation index PK and the reaction speed abnormality index RS are used as the input items of the fuzzy logic, and the real-time level P of the online monitoring equipment for 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: used to measure the correlation between the generation process of primary pollutants and secondary pollutants. The higher the PK, the more effective the monitoring equipment is in capturing the generation of secondary pollutants.

[0116] Reaction speed abnormality index RS: It is used to measure the response speed of online monitoring equipment to the generation of secondary pollutants. If RS is low, it means that the equipment's response speed is fast and stable; if RS is high, it means that the equipment's response delay is large.

[0117] Real-time level P: reflects the real-time performance of online monitoring equipment in pollutant generation and chemical reaction processes. Level P can be divided into low, medium and high levels, indicating the performance of the equipment in real-time monitoring.

[0118] In order to convert the generation 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 equipment can partially capture the pollutant formation, but sometimes the performance is unstable.

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

[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 can quickly track the generation of secondary pollutants.

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

[0125] High: The RS value is high, the equipment 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 has moderate tracking and response capabilities, and sometimes there will be 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 rule examples 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, and overall medium performance).

[0135] The core of fuzzy reasoning is to use the rule base to reason based on the fuzzy set of input items through fuzzy logic operations (such as "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 of "low", "medium" and "high". The fuzzified data are expressed as a membership function.

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

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

[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 obtained, 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 preset 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 does not track 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, the regulatory authorities obtain real-time data on the concentration of pollutants emitted by enterprises through continuous collection of online monitoring equipment. These data are marked as real-time pollution data by the system, indicating that the equipment can effectively capture changes in pollutants. Through real-time data, regulatory authorities can track pollutant emissions in real time and compare the data with the provisions of the pollutant discharge permit. If the real-time data shows that the concentration of pollutant emissions exceeds the standard, the regulatory authorities can take timely measures, such as warning the enterprise or activating the emission control system.

[0144] Real-time pollution data can immediately reflect whether the company's emissions meet the requirements of the emission permit. Through real-time comparison with the permitted emission limit, the system will automatically determine whether the emissions exceed the limit and generate a corresponding compliance report. If the real-time data shows that the emissions are close to or exceed the upper limit of the emission permit, the system will trigger an early warning mechanism to notify the regulatory authorities and the company to take emergency measures to prevent further excessive emissions.

[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 monitoring frequency of the equipment accordingly. The degree of abnormality of non-real-time data can be analyzed by analyzing the data deviation and lag 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 of 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.

[0147] 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 less 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;

[0148] If the non-real-time abnormality is less than the first standard threshold, it means that the non-real-time problem of the equipment is not obvious and the non-real-time abnormality is low. At this time, a third-level warning signal is generated and the current frequency of the monitoring equipment can remain unchanged. The response speed and data accuracy of the equipment meet the regulatory requirements.

[0149] If the degree of non-real-time abnormality is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the device is more non-real-time, data lag or inaccuracy occurs occasionally, and the device has a moderate non-real-time abnormality. At this time, a secondary warning signal is generated, and the monitoring frequency is appropriately increased to improve the response speed of the device. The timeliness and accuracy of the data can be improved by shortening the monitoring interval (for example, from once every minute to once every 30 seconds).

[0150] If the degree of non-real-time anomaly is greater than the second standard threshold, the equipment frequently encounters non-real-time problems, the data is lagging or seriously biased, and it cannot accurately reflect the generation dynamics of pollutants. The degree of non-real-time anomaly of the equipment is high. At this time, a first-level warning signal is generated, and the monitoring frequency needs to be greatly increased to ensure that the equipment can capture the rapid changes in pollutant generation. At the same time, the regulatory authorities should urge enterprises to maintain or calibrate the equipment to ensure the normal operation of the equipment.

[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 the equipment's capture of changes in pollutant generation. Based on the newly collected data, recalculate the non-real-time degree of anomaly 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 the 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, according to the types of pollutants discharged by the enterprise, the target primary pollutants and the secondary pollutants generated by them are determined, and the 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 simulation models are constructed and corrected 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. Combined with 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 department monitors emission compliance by continuously collecting real-time data, performs anomaly analysis on non-real-time data, and adjusts the monitoring frequency if the degree of anomaly is high.

[0155] Embodiment 2, the pollutant discharge permit post-license management system based on environmental compliance management described in this embodiment 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: According to the types of pollutants discharged during the production process of the enterprise, the target primary pollutants that undergo chemical reactions are determined. According to the known chemical reaction mechanism, the secondary pollutants generated by the target primary pollutants are determined, and the reaction chain from primary pollutants to secondary pollutants is constructed;

[0157] Simulation model correction module: Install sensors that can monitor environmental conditions in real time near the discharge outlet, integrate the real-time monitoring data of the environmental condition sensors with the online monitoring data of pollutant concentrations, and use known pollutant reaction mechanisms to build simulation models to predict the speed and amount of transformation of primary pollutants into secondary pollutants under different environmental conditions. Combined with existing online monitoring data, the model is corrected in real time;

[0158] Tracking efficiency evaluation module: Install secondary pollutant monitoring equipment downstream of the emission port 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 environmental conditions, calculate the generation rate of secondary pollutants and compare it with the concentration changes of primary pollutants to evaluate the tracking efficiency stability of the online monitoring equipment for the generation of secondary pollutants.

[0159] Equipment response delay analysis 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 changes of secondary pollutants within the specified time, evaluate the response delay of the monitoring equipment to the changes of secondary pollutants.

[0160] Fuzzy logic real-time evaluation module: 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 the changes of secondary pollutants, the real-time level of the online monitoring equipment in tracking and detecting the chemical reactions of pollutants in the environment is determined by using fuzzy logic, and 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 meet 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 formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0163] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0164] Those of ordinary skill 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 to be beyond the scope of this application.

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

Claims

1. A post-permit management method for pollutant discharge permits based on environmental compliance management, characterized by: The steps include: S1: According to the types of pollutants discharged during the production process of the enterprise, determine the target primary pollutants that undergo chemical reactions, determine the secondary pollutants generated by the target primary pollutants based on the known chemical reaction mechanism, and construct the reaction chain from primary pollutants to secondary pollutants; S2: Install sensors that can monitor environmental conditions in real time near the discharge outlet, integrate the real-time monitoring data of the environmental condition sensors with the online monitoring data of pollutant concentrations, use known pollutant reaction mechanisms to build a simulation model to predict the speed and amount of transformation of primary pollutants into secondary pollutants under different environmental conditions, and calibrate the model in real time in combination with existing online monitoring data; S3: Install secondary pollutant monitoring equipment downstream of the discharge port to monitor the concentration changes of secondary pollutants in the air in real time. By monitoring the concentration changes of primary and secondary pollutants, combined 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 on 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 changes of secondary pollutants within the specified time, evaluate the response delay of the monitoring equipment to the changes 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 the changes of secondary pollutants, the real-time level of the online monitoring equipment in tracking and detecting the chemical reactions of pollutants in the environment is determined by using fuzzy logic, 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 meet 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 permit based on environmental compliance management according to claim 1 is characterized by: In S3, the correlation between the concentration change of primary pollutants and the generation rate of secondary pollutants is analyzed and then 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. The expression is: Marginal probability distribution p(x) and p(y): They 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 permit based on environmental compliance management according to claim 2 is characterized by: In S4, the reaction rate abnormality 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 abnormality 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 change of secondary pollutant generation, 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 generated, 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. In the W time period, the abnormal reaction speed index of the monitoring device is calculated. The abnormal reaction speed index is calculated based on the fluctuation of the response speed error ΔR(t) of the device. The expression is: Where RS is the reaction speed abnormality index.

4. The post-permit management method for pollutant discharge permit based on environmental compliance management according to claim 3 is characterized by: 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 tracking and detecting the chemical reaction 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 the fuzzy set, and 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 permit 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 permit 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 of 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.

7. The post-permit management method for pollutant discharge permit based on environmental compliance management according to claim 6 is characterized by: 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 less 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; If the non-real-time abnormality level is less than the first standard threshold, the non-real-time abnormality level is low, and a third-level warning signal is generated at this time, and the current frequency of the monitoring device remains unchanged; If the non-real-time abnormality 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 abnormality. At this time, a secondary warning signal is generated and the monitoring frequency is increased to improve the response speed of the device; If the non-real-time abnormality level is greater than the second standard threshold, the non-real-time abnormality level 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-management system for pollutant discharge permits based on environmental compliance management, used to implement the post-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: According to the types of pollutants discharged during the production process of the enterprise, the target primary pollutants that undergo chemical reactions are determined. According to the known chemical reaction mechanism, the secondary pollutants generated by the target primary pollutants are determined, and the reaction chain from primary pollutants to secondary pollutants is constructed; Simulation model correction module: Install sensors that can monitor environmental conditions in real time near the discharge outlet, integrate the real-time monitoring data of the environmental condition sensors with the online monitoring data of pollutant concentrations, and use known pollutant reaction mechanisms to build simulation models to predict the speed and amount of transformation of primary pollutants into secondary pollutants under different environmental conditions. Combined with existing online monitoring data, the model is corrected in real time; Tracking efficiency evaluation module: Install secondary pollutant monitoring equipment downstream of the emission port 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 environmental conditions, calculate the generation rate of secondary pollutants and compare it with the concentration changes of primary pollutants to evaluate the tracking efficiency stability of the online monitoring equipment for the generation of secondary pollutants. Equipment response delay analysis 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 changes of secondary pollutants within the specified time, evaluate the response delay of the monitoring equipment to the changes of secondary pollutants. Fuzzy logic real-time evaluation module: 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 the changes of secondary pollutants, the real-time level of the online monitoring equipment in tracking and detecting the chemical reactions of pollutants in the environment is determined by using fuzzy logic, and 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 meet 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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