A chemical industrial park carbon pollution emission collaborative monitoring and early warning method and computer program product

Abstract

The application discloses a kind of chemical industry park carbon pollution emission collaborative monitoring early warning method and computer program product.The method is: constructing park, enterprise carbon pollution characteristic fingerprint library and park, enterprise carbon pollution monitoring early warning trigger condition list;According to the above-mentioned park, enterprise carbon pollution monitoring early warning trigger condition list, respectively to park real-time pollutant concentration, park periodic pollutant emission, park periodic carbon emission, enterprise real-time pollutant concentration, enterprise periodic pollutant emission and enterprise periodic carbon emission are monitored early warning;If triggering any early warning, then according to each carbon pollution characteristic fingerprint library determine the abnormal source with carbon pollution correlation, then according to the production condition parameter of abnormal source determines early warning reason.The application can investigate the abnormal source with carbon pollution correlation and early warning reason, so that park carries out pollution reduction and carbon reduction treatment, facilitates synchronous pollution reduction and carbon reduction.

Description

Technical Field

[0001] This invention relates to the field of carbon pollution emission monitoring technology, and in particular to a collaborative monitoring and early warning method and computer program product for carbon pollution emissions in chemical industrial parks. Background Technology

[0002] Chemical industrial parks require strict monitoring of pollutant and carbon emissions. If the pollutant monitoring system detects emissions exceeding preset thresholds, it issues an alert, prompting staff to investigate and address the anomalies. Similarly, if the carbon emission monitoring system detects emissions exceeding preset thresholds, it issues an alert, and staff investigate and address the anomalies. While this monitoring method can issue alerts when anomalies in pollutant or carbon emissions occur in the park, it only addresses either pollutant or carbon emission anomalies. Summary of the Invention

[0003] The purpose of this invention is to provide a method for coordinated monitoring and early warning of carbon pollution emissions in chemical industrial parks and its computer program product, which can conduct coordinated carbon pollution investigation and facilitate simultaneous pollution reduction and carbon reduction.

[0004] The inventors noted that the production process of chemical enterprises is characterized by pollutant emissions usually accompanied by carbon emissions, meaning there is a significant correlation between pollutant emissions and carbon emissions. Based on this, the inventors derived the following idea: to monitor these two emissions in a coordinated manner. When only an abnormal warning of pollutant emissions or only an abnormal warning of carbon emissions appears, the correlation between pollutant emissions and carbon emissions can be used to promptly identify anomalies related to carbon pollution.

[0005] To achieve the above objectives, the present invention provides a method for collaborative monitoring and early warning of carbon pollution emissions in chemical industrial parks, comprising:

[0006] Database construction steps: Construct a carbon pollution characteristic fingerprint database for the park, a list of carbon pollution monitoring and early warning triggering conditions for the park, and a carbon pollution characteristic fingerprint database and a list of carbon pollution monitoring and early warning triggering conditions for each enterprise in the park;

[0007] Park monitoring steps: A1. Based on the above list of carbon pollution monitoring and early warning triggering conditions for the park, conduct early warning monitoring on the real-time pollutant concentration, periodic pollutant emissions, and periodic carbon emissions of the park; A2. If any early warning is triggered, determine the abnormal source with carbon pollution association based on the above carbon pollution characteristic fingerprint database of the park and the carbon pollution characteristic fingerprint database of each enterprise, and then determine the cause of the early warning based on the production status parameters of the abnormal source.

[0008] Enterprise monitoring steps: B1. Based on the enterprise carbon pollution monitoring and early warning trigger condition list for each enterprise, on the one hand, early warning monitoring is carried out on the enterprise's periodic pollutant emissions and periodic carbon emissions, and on the other hand, early warning monitoring is carried out on the enterprise's real-time pollutant concentration using each enterprise's own pollutant monitoring terminal; B2. If any early warning is triggered, the abnormal source with carbon pollution association is determined based on the above-mentioned park carbon pollution characteristic fingerprint database and the carbon pollution characteristic fingerprint database of each enterprise, and then the cause of the early warning is determined based on the production status parameters of the abnormal source.

[0009] Furthermore:

[0010] The park's carbon pollution fingerprint database stores the correspondence between carbon emissions and pollutant types, as well as the correspondence between pollutant types and carbon pollution-related production units. The enterprise's carbon pollution fingerprint database stores the correspondence between enterprise pollutant types and enterprise carbon pollution-related production units, enterprise carbon pollution-related production processes, and enterprise emission outlets.

[0011] In step A2 of the park monitoring process, if a real-time pollutant concentration warning for the park is triggered, then the following steps will be executed:

[0012] A211. Determine the source area of ​​the pollutant emission based on wind direction, wind speed and monitoring point location, and use all enterprises in that area as candidate enterprises;

[0013] A212. Query the carbon pollution characteristic fingerprint database of the industrial park to determine at least one production unit corresponding to the pollutant;

[0014] A213. If any of the above candidate companies has any of the aforementioned production units, then the anomaly that triggers the warning is considered to be a carbon pollution-related anomaly, and at least one suspected company with any of the aforementioned candidate companies is selected; otherwise, the current process ends.

[0015] A214. Calculate the carbon emissions of the production unit corresponding to the pollutant of each suspected enterprise within a preset time period before the warning is triggered, and arrange all suspected enterprises in descending order of carbon emissions;

[0016] A215. Determine whether the concentration of the pollutant emitted during the same period by the production units of the top N suspected enterprises in terms of carbon emissions is abnormal. If there is an abnormality, then confirm that the current suspected enterprise is the target source enterprise.

[0017] A216. Query the enterprise's carbon pollution characteristic fingerprint database to determine at least one production stage in the production unit corresponding to the pollutant;

[0018] A217. Obtain production status parameters of at least one of the above-mentioned production links of the production unit of the target source enterprise, and determine the cause of the real-time pollutant concentration warning in the park accordingly. The production status parameters include energy consumption, production load and treatment facility status.

[0019] Furthermore:

[0020] The park's carbon pollution fingerprint database stores the correspondence between carbon emissions and pollutant types, as well as the correspondence between pollutant types and carbon pollution-related production units. The enterprise's carbon pollution fingerprint database stores the correspondence between enterprise pollutant types and enterprise carbon pollution-related production units, enterprise carbon pollution-related production processes, and enterprise emission outlets.

[0021] In step A2 of the park monitoring process, if a periodic pollutant emission warning for the park is triggered, then the following actions will be taken:

[0022] A221. Query the carbon pollution characteristic fingerprint database of the industrial park to identify multiple production units corresponding to the pollutant;

[0023] A222. Calculate the periodic pollutant emission growth trend of the park and the historical carbon emission growth trend of each of the above-mentioned production units during the same period in the park.

[0024] A223. If the historical carbon emission growth trend of one of the above production units matches the cyclical pollutant growth trend of the park, then the anomaly that triggers the warning is considered to be a carbon pollution correlation anomaly, and at least one production unit with the same growth trend is selected from the above production units; otherwise, the current process ends.

[0025] A224. Select multiple candidate companies that have at least one production unit from all companies in the park;

[0026] A225. Obtain the historical production data of each candidate enterprise for the same period, sort the candidate enterprises in descending order of their historical production load, and select the top M enterprises as suspect enterprises.

[0027] A226. Determine whether there are any abnormalities in the historical emissions of this pollutant from each suspected enterprise. If there are abnormalities, then confirm that the current suspected enterprise is the target source enterprise.

[0028] A227. Query the enterprise's carbon pollution characteristic fingerprint database to determine multiple production stages in the production unit corresponding to the pollutant;

[0029] A228. Obtain production status parameters of the multiple production stages of the production unit of the target source enterprise, and determine the reasons for the periodic pollutant emission warning of the park accordingly. The production status parameters include energy consumption, production load and treatment facility status.

[0030] Furthermore:

[0031] The park's carbon pollution fingerprint database stores the correspondence between carbon emissions and pollutant types, as well as the correspondence between pollutant types and carbon pollution-related production units. The enterprise's carbon pollution fingerprint database stores the correspondence between enterprise pollutant types and enterprise carbon pollution-related production units, enterprise carbon pollution-related production processes, and enterprise emission outlets.

[0032] In step A2 of the park monitoring process, if a periodic carbon emission warning for the park is triggered, then the following actions will be taken:

[0033] A231. Query the carbon pollution characteristic fingerprint database of the park to determine the various pollutants corresponding to the periodic carbon emissions of the park;

[0034] A232. Calculate the periodic carbon emission growth trend of the park and the historical concurrent emission growth trend of various pollutants;

[0035] A233. If the historical emission growth trend of one of the above pollutants matches the cyclical carbon emission growth trend of the park, then the anomaly that triggers the warning is considered to be a carbon pollution-related anomaly, and the pollutant with the matching growth trend is recorded as the target pollutant.

[0036] A234. Obtain the historical carbon emissions of each carbon emission source in the park and sort the carbon emission sources in descending order of historical carbon emissions. Select the top M carbon emission sources in terms of historical carbon emissions as suspected emission sources. The carbon emission sources include all public facilities and enterprises in the park.

[0037] A235. Calculate the historical concurrent emission growth trend of the target pollutant for each suspected emission source. If the historical concurrent emission growth trend of the target pollutant is consistent with the cyclical carbon emission growth trend of the park, then the suspected emission source is taken as the target source.

[0038] A236. Break down the historical carbon emissions of the target source into production stages, and then select the production stage with the highest carbon emissions from the target source.

[0039] A237. Obtain the production status parameters of the production links with the highest carbon emissions mentioned above, and determine the reasons for the periodic carbon emission warnings of the park accordingly. The production status parameters include energy consumption, production load, and the status of pollution control facilities.

[0040] Furthermore:

[0041] The park's carbon pollution fingerprint database stores the correspondence between carbon emissions and pollutant types, as well as the correspondence between pollutant types and carbon pollution-related production units. The enterprise's carbon pollution fingerprint database stores the correspondence between enterprise pollutant types and enterprise carbon pollution-related production units, enterprise carbon pollution-related production processes, and enterprise emission outlets.

[0042] In step B2 of the enterprise monitoring process, if a real-time pollutant concentration warning from the emission outlet is received from the enterprise's pollutant monitoring terminal, then the following steps are executed:

[0043] B211. Query the enterprise's carbon pollution characteristic fingerprint database to determine the production unit that simultaneously corresponds to the emission outlet and the pollutant;

[0044] B212. Obtain the real-time operating parameters of the production unit and calculate the hourly carbon emissions corresponding to the current operating conditions of the production unit.

[0045] B213. Determine whether the hourly carbon emissions corresponding to the current operating condition of the production unit are abnormal. If abnormal, consider the abnormality that triggered the warning to be a carbon pollution-related abnormality and take the production unit as the target source production unit; otherwise, end the current process.

[0046] B214. Query the enterprise carbon pollution characteristic fingerprint database to determine multiple production stages in the target source production unit corresponding to the pollutant;

[0047] B215. Obtain the operation logs of the above-mentioned multiple production links and the emission concentration curve of the pollutant at the emission outlet within a preset time period. Determine the time point when the pollutant concentration changes based on the emission concentration curve, and take the production link whose operating conditions change within a preset time period before the time point as the target source production link.

[0048] B216. Obtain production status parameters of the target source production process, and determine the reasons for the enterprise's real-time pollutant concentration warning based on these parameters. These production status parameters include energy consumption, production load, and the status of treatment facilities.

[0049] Furthermore:

[0050] The park's carbon pollution fingerprint database stores the correspondence between carbon emissions and pollutant types, as well as the correspondence between pollutant types and carbon pollution-related production units. The enterprise's carbon pollution fingerprint database stores the correspondence between enterprise pollutant types and enterprise carbon pollution-related production units, enterprise carbon pollution-related production processes, and enterprise emission outlets.

[0051] In step B2 of the enterprise monitoring process, if a periodic pollutant emission warning for the enterprise is triggered, then the following actions will be taken:

[0052] B221. Query the enterprise's carbon pollution characteristic fingerprint database to identify multiple production units corresponding to the pollutant;

[0053] B222. Calculate the periodic pollutant emission growth trend of the enterprise and the historical carbon emission growth trend of each of the above-mentioned production units during the same period.

[0054] B223. If the historical carbon emission growth trend of one of the above production units matches the cyclical pollutant emission growth trend of the enterprise, then the anomaly that triggers the warning is considered to be a carbon pollution correlation anomaly, and the production unit with the matching growth trend is taken as the target source production unit; otherwise, the process ends.

[0055] B224. Query the enterprise carbon pollution characteristic fingerprint database to determine multiple production links in the target source production unit corresponding to the pollutant, break down the pollutant emissions of the target source production unit in the same period according to the production links, and select the production link with the highest pollutant emissions from these multiple production links.

[0056] B225. Obtain the production status parameters of the production process with the highest pollutant emissions, and determine the reasons for the enterprise's periodic pollutant emission warnings accordingly. The production status parameters include energy consumption, production load, and the status of treatment facilities.

[0057] Furthermore:

[0058] The park's carbon pollution fingerprint database stores the correspondence between carbon emissions and pollutant types, as well as the correspondence between pollutant types and carbon pollution-related production units. The enterprise's carbon pollution fingerprint database stores the correspondence between enterprise pollutant types and enterprise carbon pollution-related production units, enterprise carbon pollution-related production processes, and enterprise emission outlets.

[0059] In step B2 of the enterprise monitoring process, if a periodic carbon emission warning for the enterprise is triggered, then the following actions will be taken:

[0060] B231. Break down the company’s periodic carbon emissions by production unit, sort each production unit in descending order of periodic carbon emissions, and select the production units that rank in the top N for periodic carbon emissions and whose proportion of the company’s periodic carbon emissions reaches a preset threshold as suspected production units.

[0061] B232. Query the enterprise's carbon pollution fingerprint database to identify at least one pollutant corresponding to each suspected production unit;

[0062] B233. Calculate the growth trend of corporate carbon emissions over the period and the growth trend of various pollutant emissions for each suspected production unit;

[0063] B234. If the growth trend of all pollutant emissions in each suspected production unit is consistent with the cyclical carbon emission growth trend of the enterprise, then the anomaly that triggers the warning is considered to be a carbon pollution correlation anomaly, and the suspected production unit with the consistent trend is taken as the target source production unit; otherwise, the current process ends.

[0064] B235. Query the enterprise carbon pollution characteristic fingerprint database to determine at least one production link in the target source production unit corresponding to the above-mentioned various pollutants;

[0065] B236. Obtain production status parameters for at least one of the above-mentioned production stages, and determine the reasons for the enterprise's periodic carbon emission warnings accordingly. The production status parameters include energy consumption, production load, and the status of pollution control facilities.

[0066] Furthermore:

[0067] Both the list of carbon pollution monitoring and early warning triggering conditions for the industrial park and the list of carbon pollution monitoring and early warning triggering conditions for enterprises contain early warning triggering conditions corresponding to multiple real-time pollutant concentration risk levels, multiple periodic pollutant emission risk levels, and multiple periodic carbon emission risk levels. Among them, the higher the risk level, the more difficult the corresponding early warning triggering condition is to be triggered.

[0068] Furthermore: After determining the cause of the warning, corresponding measures are taken according to the current risk level of the warning. The risk levels are specifically: blue, yellow, orange, and red. The measures for the blue level are to issue a production unit rectification notice to the enterprise that triggered the warning; the measures for the yellow level are to issue a production unit rectification notice to the enterprise that triggered the warning and include it in the management and control system for tracking and control; the measures for the orange level are to issue a production unit production restriction notice to the enterprise that triggered the warning and include it in the management and control system for tracking and control; and the measures for the red level are to issue a production unit shutdown notice to the enterprise that triggered the warning and include it in the management and control system for tracking and control.

[0069] The present invention also provides a computer program product, including a computer program that, when executed, implements the method for collaborative monitoring and early warning of carbon pollution emissions in chemical industrial parks as described above.

[0070] This invention pre-constructs a carbon pollution fingerprint database for the industrial park and a corporate carbon pollution fingerprint database for each enterprise within the park. If abnormal pollutant emissions or carbon emissions are detected, the park's carbon pollution fingerprint database and the corporate carbon pollution fingerprint database are used to identify the abnormal sources associated with carbon pollution. Then, the cause of the warning is determined based on the production status parameters of the abnormal sources. This allows for the identification of abnormal sources associated with carbon pollution and the cause of the warning, enabling the park to carry out pollution reduction and carbon reduction treatment, facilitating simultaneous pollution reduction and carbon reduction. Attached Figure Description

[0071] Figure 1 This is a flowchart of the collaborative monitoring and early warning method for carbon pollution emissions in chemical industrial parks, as provided in this invention.

[0072] Figure 2 This is a flowchart illustrating the source tracing process executed by the collaborative monitoring and early warning method for carbon pollution emissions in chemical industrial parks, as provided in this invention, after triggering a real-time pollutant concentration early warning in the park.

[0073] Figure 3 This is a flowchart illustrating the source tracing process executed by the collaborative monitoring and early warning method for carbon pollution emissions in chemical industrial parks, as provided in this invention, after triggering a periodic pollutant emission warning for the park.

[0074] Figure 4 This is a flowchart illustrating the source tracing process executed by the collaborative monitoring and early warning method for carbon emissions in chemical industrial parks, as provided in this invention, after triggering a periodic carbon emission warning for the park.

[0075] Figure 5 This is a flowchart illustrating the source tracing process performed by the collaborative monitoring and early warning method for carbon pollution emissions in chemical industrial parks, as provided in this invention, after receiving a real-time pollutant concentration warning from the enterprise's pollutant monitoring terminal.

[0076] Figure 6 This is a flowchart illustrating the source tracing process executed by the collaborative monitoring and early warning method for carbon pollution emissions in chemical industrial parks, as provided in this invention, after triggering an early warning of periodic pollutant emissions from an enterprise.

[0077] Figure 7 This is a flowchart illustrating the source tracing process executed by the collaborative monitoring and early warning method for carbon emissions in chemical industrial parks, as provided in this invention, after triggering an early warning of a company's periodic carbon emissions. Detailed Implementation

[0078] The present invention will be further described in detail below with reference to specific embodiments.

[0079] In this embodiment, a computer program product, called the Chemical Industrial Park Carbon Pollution Emission Collaborative Monitoring Platform Client, was obtained by technicians using computer code. This client includes a computer program installed on a computer. The computer processor executes this computer program (i.e., runs the client) to achieve the following: Figure 1 The method for collaborative monitoring and early warning of carbon pollution emissions in chemical industrial parks is shown below. The steps of this method are described in detail below.

[0080] Database construction steps: Construct a carbon pollution characteristic fingerprint database for the park, a list of carbon pollution monitoring and early warning triggering conditions for the park, and a carbon pollution characteristic fingerprint database and a list of carbon pollution monitoring and early warning triggering conditions for each enterprise in the park.

[0081] In this embodiment, technicians pre-collect basic data, carbon emission data, and pollutant emission data of all enterprises in the chemical industrial park to be monitored for carbon pollution in the previous year. Enterprise basic data includes basic information, production process and equipment data, emission outlet data, and treatment facility data. Enterprise carbon emission data includes total energy consumption data, total raw material and auxiliary material data, and energy consumption data and raw material and auxiliary material data for each production unit. Enterprise pollutant emission data includes total pollutant emission data and pollutant emission data from each emission outlet. Based on the collected data, technicians identify enterprises and their production units and processes that are associated with carbon pollution, and construct a carbon pollution characteristic fingerprint database for the entire industrial park and a corporate carbon pollution characteristic fingerprint database for each enterprise within the park. The industrial park carbon pollution characteristic fingerprint database stores the correspondence between carbon emissions and pollutant types, as well as the correspondence between pollutant types and carbon pollution-associated production units. The corporate carbon pollution characteristic fingerprint database stores the correspondence between enterprise pollutant types and carbon pollution-associated production units, carbon pollution-associated production processes, and enterprise emission outlets.

[0082] Table 1 below lists the trigger conditions for carbon pollution monitoring and early warning in industrial parks. It includes the trigger conditions corresponding to the real-time pollutant concentration risk levels, the periodic pollutant emission risk levels, and the periodic carbon emission risk levels for multiple industrial parks. Higher risk levels correspond to more difficult-to-trigger trigger conditions. Table 2 below lists the trigger conditions for carbon pollution monitoring and early warning in industrial parks. It includes the trigger conditions corresponding to the real-time pollutant concentration risk levels, the periodic pollutant emission risk levels, and the periodic carbon emission risk levels for multiple industrial parks. Higher risk levels correspond to more difficult-to-trigger trigger conditions.

[0083] This embodiment pre-constructs a multi-level early warning threshold database for pollutants based on the "Ambient Air Quality Standard" (GB3095-2012), the "Integrated Emission Standard for Air Pollutants" (GB16297-1996), the "Technical Specification for Construction of Environmental Risk Early Warning System for Toxic and Hazardous Gases in Chemical Industrial Parks" (T / CPCIF0263-2023), and various industry-specific air pollutant emission standards. This database stores multi-level early warning threshold ranges for various pollutants, including the first-level early warning threshold range (50% to 100% of the pollutant's limit in the ambient air quality standard / integrated emission standard for air pollutants, or the limits in various industry-specific air pollutant emission standards). The warning threshold ranges as follows: Level 1: 80% to 100% of the industry standard plant boundary value in the document; Level 2: 10% to 50% of the pollutant limit in the ambient air quality standard to 10% of the occupational exposure limit (OELS) of the pollutant; Level 3: 10% to 50% of the immediate life or health (IDLH) concentration; and Level 4: 50% or more of the immediate life or health (IDLH) concentration.

[0084] Table 1. List of triggering conditions for carbon pollution monitoring and early warning in the industrial park

[0085]

[0086] Table 2 List of Enterprise Carbon Pollution Monitoring and Early Warning Triggering Conditions

[0087]

[0088] Park monitoring steps: A1. Based on the above list of carbon pollution monitoring and early warning triggering conditions for the park, conduct early warning monitoring for the park's real-time pollutant concentration, periodic pollutant emissions, and periodic carbon emissions; A2. If any early warning is triggered, determine the abnormal source with carbon pollution association based on the above-mentioned park carbon pollution characteristic fingerprint database and the carbon pollution characteristic fingerprint database of each enterprise, and then determine the cause of the early warning based on the production status parameters of the abnormal source.

[0089] Technicians upload the constructed carbon pollution characteristic fingerprint database of the chemical industrial park, the list of carbon pollution monitoring and early warning trigger conditions for the park, and the carbon pollution characteristic fingerprint databases and enterprise carbon pollution monitoring and early warning trigger conditions for each enterprise within the park to the collaborative carbon pollution emission monitoring platform of the chemical industrial park. The platform can then conduct collaborative carbon pollution emission monitoring of the aforementioned chemical industrial park based on this information. The monitoring process is as follows:

[0090] The chemical industrial park's carbon pollution emission collaborative monitoring platform monitors real-time pollutant concentrations, periodic pollutant emissions, and periodic carbon emissions in the park based on the aforementioned list of carbon pollution monitoring and early warning trigger conditions. If any of these warnings is triggered, the platform identifies the abnormal sources associated with carbon pollution based on the park's carbon pollution characteristic fingerprint database and the carbon pollution characteristic fingerprint databases of individual enterprises. Then, the cause of the warning is determined based on the production status parameters of the abnormal sources. The following sections describe the early warning monitoring process for these three parameters: real-time pollutant concentrations, periodic pollutant emissions, and periodic carbon emissions.

[0091] (1) Real-time pollutant concentration in the park

[0092] Multiple pollutant monitoring stations are set up within the industrial park. Each station includes monitoring devices for various pollutants such as SO2, NOx, particulate matter, VOCs, and ammonia. These stations monitor the real-time concentrations of various pollutants in the park's air and upload the data to the chemical industrial park's carbon emission collaborative monitoring platform. Upon receiving the real-time concentration data, the monitoring platform analyzes it to determine if it meets any of the early warning trigger conditions listed in Table 1. For example, if a pollutant, such as SO2, has a concentration of 120 μg / m³, falling within the second-level early warning threshold range of SO2 in the preset multi-level early warning threshold library, and the monitoring platform determines that the current real-time SO2 concentration meets the yellow-level early warning trigger condition ①, then A210. A yellow-level early warning for the park's real-time pollutant concentration is triggered, and then proceeds as follows... Figure 2 The process shown is as follows: Anomaly investigation is performed.

[0093] A211. Determine the source area of ​​the pollutant emission based on wind direction, wind speed, and monitoring point location, and use all enterprises within that area as candidate enterprises.

[0094] The monitoring platform obtains current wind speed and direction data for the industrial park from the meteorological station, for example, the current wind speed is 2.3 m / s and the wind direction is northwest. Based on the current wind direction, wind speed, and the locations of the aforementioned pollutant monitoring stations, the monitoring platform identifies the core area of ​​pollution diffusion as the northwest region of the industrial park, which houses three petrochemical companies: Companies A, B, and C.

[0095] A212. Query the carbon pollution characteristic fingerprint database of the industrial park to determine at least one production unit corresponding to the pollutant.

[0096] The monitoring platform queries the carbon pollution fingerprint database of the industrial park to identify at least one production unit corresponding to the pollutant SO2—the boiler unit.

[0097] A213. If any of the candidate companies mentioned above has any of the aforementioned production units, then the anomaly that triggered the warning is considered to be a carbon pollution-related anomaly, and at least one suspected company with any of the aforementioned candidate companies is selected; otherwise, the current process ends.

[0098] All three companies, A, B, and C, have boiler units. Therefore, the monitoring platform believes that the anomaly that triggered the warning is a carbon pollution-related anomaly. From the three candidate companies, A, B, and C, at least one suspected company with any production unit was selected. Since all three companies have boiler units, the monitoring platform selected three suspected companies, A, B, and C.

[0099] A214. Calculate the carbon emissions of the production unit corresponding to the pollutant of each suspected enterprise within a preset time period before the warning is triggered, and arrange all suspected enterprises in descending order of carbon emissions.

[0100] The monitoring platform calculates the carbon emissions of the boiler units (the boiler units corresponding to pollutant SO2) of three suspected companies, A, B, and C, respectively, according to the calculation formula in the IPCC Guidelines for National Greenhouse Gas Inventories 2006: Carbon emissions (tCO2) = Heavy oil consumption per unit time (t / h) × Duration (h) × Localized adjusted carbon emission factor (tCO2 / t heavy oil). In this embodiment, the duration is set to 3 hours, that is, the carbon emissions are calculated in the 3 hours before the warning is triggered. The carbon emission factor is 3.18tCO2 / t heavy oil. Company A's boiler consumes 2.5t / h of oil per unit time, so the carbon emissions of Company A during this period are calculated to be 10.18tCO2; Company B's boiler consumes 1.8t / h of oil per unit time, so the carbon emissions of Company B during this period are calculated to be 7.63tCO2; Company C's boiler consumes 1.2t / h of oil per unit time, so the carbon emissions of Company C during this period are calculated to be 5.09tCO2. The three suspected companies, A, B, and C, are ranked from highest to lowest carbon emissions per boiler unit: Company A > Company B > Company C.

[0101] A215. Determine whether the concentration of the pollutant emitted during the same period by the production units of the top N suspected enterprises in terms of carbon emissions is abnormal. If there is an abnormality, then confirm that the current suspected enterprise is the target source enterprise.

[0102] The monitoring platform selects the top three carbon-emitting enterprises from all suspected enterprises and checks the SO2 emission concentration of the boiler combustion units of each of these three enterprises to see if there are any abnormalities. In this embodiment, N is set to 3. In other embodiments, N can be changed to any natural number from 3 to 5. In this example, there are exactly three suspected enterprises, so the monitoring platform checks each of enterprises A, B, and C one by one. The monitoring platform obtains the SO2 emission concentration data of enterprises A, B, and C for the same period. The SO2 emission concentration of enterprise A's emission outlet (No. G-001, organized emission) is 180 μg / m³, which exceeds the emission permit limit of enterprise A (100 μg / m³) by 80%, indicating an abnormality. Therefore, the monitoring platform confirms enterprise A as the target source enterprise. The SO2 emission concentrations of enterprises B and C's emission outlets do not exceed their emission permit limits and do not show any abnormalities. Therefore, enterprises B and C are not the target source enterprises.

[0103] A216. Query the enterprise carbon pollution characteristic fingerprint database to determine at least one production stage in the production unit corresponding to the pollutant.

[0104] The monitoring platform queries the enterprise's carbon pollution fingerprint database to determine the production stage of the boiler unit corresponding to the pollutant SO2, which is the combustion stage, air preheating stage, flue gas purification stage, and fuel storage and transportation stage.

[0105] A217. Obtain production status parameters of at least one of the above-mentioned production links of the production unit of the target source enterprise, and determine the cause of the real-time pollutant concentration warning in the park accordingly. The production status parameters include energy consumption, production load and treatment facility status.

[0106] The monitoring platform acquired production status parameters for the aforementioned production processes in Company A's boiler unit. These parameters included energy consumption, production load, and the status of pollution control facilities. Based on the heavy oil consumption data from the boiler unit's combustion process, the monitoring platform calculated that the heavy oil consumption in the boiler unit's combustion process three hours before the warning was triggered increased by 30% compared to the same period the previous day. Meanwhile, the operational data of the pollution control facilities in the boiler unit's combustion process showed that during this period, the SO2 concentration at the inlet of the desulfurization facility was 220 μg / m³, and the SO2 concentration at the outlet was 180 μg / m³, with a removal efficiency of only 18.2% (normal design efficiency ≥90%). Therefore, the real-time pollutant concentration warning for the industrial park was determined to be caused by the increased energy consumption in the combustion process of Company A's boiler unit and the decreased efficiency of its pollution control facilities.

[0107] (2) Periodic pollutant emissions from the park

[0108] On the 1st of each month, all enterprises within the industrial park report their pollutant emission data for the previous month to the chemical industrial park's carbon pollution emission collaborative monitoring platform. Upon receiving monthly pollutant emission data, the monitoring platform calculates the park's pollutant emissions for that month and then determines whether it meets the warning conditions for each level. If it is the beginning of a new quarter, the monitoring platform also calculates the park's pollutant emissions for the previous quarter and then determines whether it meets the warning conditions for each level. Taking the park's particulate matter emissions in July 2025 as an example, the emissions were 90 tons. The park's annual baseline value N = 500 tons, and the monthly baseline value N / 12 ≈ 41.7 tons. The July particulate matter emissions exceeded the monthly baseline value by 115.8% (calculated as (90-41.7) / 41.7 × 100% = 115.8%), meeting the red-level warning trigger condition ①. Therefore, A220 triggers a red warning for the park's periodic pollutant emissions, and the monitoring platform proceeds as follows... Figure 3 The process shown is as follows: Anomaly investigation is performed.

[0109] A221. Query the carbon pollution characteristic fingerprint database of the park to identify multiple production units corresponding to the pollutant.

[0110] The monitoring platform queried the carbon pollution fingerprint database of the industrial park and determined that the production units corresponding to the particulate matter of the pollutant were the cement kiln unit and the boiler unit.

[0111] A222. Calculate the periodic pollutant emission growth trend of the park and the historical carbon emission growth trend of each of the above-mentioned production units during the same period.

[0112] Based on the park's historical particulate matter emission data, the monitoring platform calculated that the year-on-year growth rate of particulate matter emissions in July was 80%. Then, based on the park's historical carbon emission data, it calculated that the year-on-year growth rate of carbon emissions from the park's cement kiln unit in July was 75.3%, and the year-on-year growth rate of emissions from the park's boiler unit in July was 30%.

[0113] A223. If the historical carbon emission growth trend of one of the above production units matches the cyclical pollutant growth trend of the park, then the anomaly that triggers the warning is considered to be a carbon pollution correlation anomaly, and at least one production unit with a matching growth trend is selected from the above production units; otherwise, the current process ends.

[0114] The carbon emission growth trend of the cement kiln unit in July was consistent with the particulate matter growth trend of the industrial park in July. Therefore, the anomaly that triggered the warning was considered to be a carbon pollution-related anomaly. The monitoring platform screened cement kiln units from cement kiln units and boiler units whose carbon emission growth trends in July were consistent with the particulate matter growth trends of the industrial park in July.

[0115] A224. Select multiple candidate companies that have at least one production unit from all companies in the park.

[0116] The monitoring platform screened out several candidate companies with cement kiln units from all the companies in the park.

[0117] A225. Obtain the historical production data of each candidate enterprise for the same period, sort the candidate enterprises in descending order of their historical production load, and select the top M enterprises as suspect enterprises.

[0118] The monitoring platform obtains the production load data of each candidate enterprise in July and sorts them from highest to lowest according to their historical production load for the same period. The top two enterprises are selected as suspect enterprises (in this embodiment, M is set to 2; in other embodiments, it can be changed to any natural number from 2 to 5). Among them, building materials enterprises J and K increased their production load to 130% and 120% respectively in July to catch up on orders (their designed capacity is 100,000 tons / month, and their actual output is 130,000 tons and 120,000 tons respectively). Since J and K ranked in the top two, the monitoring platform selected J and K as suspect enterprises.

[0119] A226. Determine whether there are any abnormalities in the historical emissions of this pollutant from each suspected enterprise during the same period. If there are abnormalities, then confirm the current suspected enterprise as the target source enterprise.

[0120] The monitoring platform obtained July particulate matter emissions of two suspected companies, J and K, which were 42t and 35t respectively. Company J's monthly discharge permit limit is 25t, and its July particulate matter emissions of 42t exceeded the monthly discharge permit limit by 68.2%, thus confirming Company J as a target source company. Company K's monthly discharge permit limit is 38t, and its July particulate matter emissions of 35t did not exceed the monthly discharge permit limit, therefore Company K is not a target source company.

[0121] A227. Query the enterprise's carbon pollution characteristic fingerprint database to determine multiple production stages in the production unit corresponding to the pollutant.

[0122] The monitoring platform queries the enterprise's carbon pollution characteristic fingerprint database to determine that the particulate matter of the pollutant corresponds to multiple production stages in the cement kiln unit, namely the calcination stage, preheating system stage, decomposition furnace stage, clinker cooling stage, kiln tail waste heat utilization system stage, and bypass ventilation system stage.

[0123] A228. Obtain production status parameters of the multiple production stages of the production unit of the target source enterprise, and determine the reasons for the periodic pollutant emission warning of the park accordingly. The production status parameters include energy consumption, production load and treatment facility status.

[0124] The monitoring platform obtained the production status parameters of the above-mentioned production links in the cement kiln unit of Company J in July and compared them with the historical production status parameters. The operating time of the cement kiln unit of Company J in July was 720 hours (600 hours in the same period last year), an increase of 20%, and the production load was maintained at 130% for a long time (30% over the design capacity). The calcination temperature in the calcination link was 50°C higher than the normal process value, resulting in a 35% increase in coal fuel consumption compared with the same period last year. Therefore, it was determined that the cause of the periodic pollutant concentration warning in the park was overproduction in the calcination link of the cement kiln unit of Company J.

[0125] (3) Cyclic carbon emissions of the park

[0126] On the 1st of each month, all enterprises within the industrial park report their carbon emission data for the previous month to the chemical industrial park's carbon emission collaborative monitoring platform. Each month, the platform receives the carbon emission data, calculates the park's carbon emissions for that month, and then determines whether it meets the warning conditions for each level. If it's the beginning of a new quarter, the platform also calculates the park's carbon emissions for the previous quarter and then determines whether it meets the warning conditions for each level. For example, in the second quarter of 2025, the park's carbon emissions for that quarter were 28,000 tCO2. The park's annual carbon emission control target is 80,000 tCO2, and the quarterly control target is 80,000 / 4 = 20,000 tCO2. This emission exceeded the quarterly control target by 40%, meeting the orange-level warning trigger condition ①. Therefore, A230 triggers the park's cyclical orange-level carbon emission warning, and then... Figure 4 The process shown is as follows: Anomaly investigation is performed.

[0127] A231. Query the carbon pollution characteristic fingerprint database of the park to determine the various pollutants corresponding to the periodic carbon emissions of the park.

[0128] The monitoring platform queried the park's carbon pollution fingerprint database and determined that the multiple pollutants corresponding to the park's periodic carbon emissions were NO. x Particulate matter.

[0129] A232. Calculate the periodic carbon emission growth trend of the park and the historical concurrent emission growth trend of various pollutants.

[0130] Based on the park's historical carbon emission data (the park's carbon emissions in the second quarter of 2024 were 19,000 tCO2), the monitoring platform calculated the year-on-year growth rate of the park's carbon emissions in the second quarter of 2025 to be (28,000-19,000) / 19,000×100%=47.4%. The monitoring platform also calculated the park's NOx emissions in the second quarter based on historical pollutant data. x Emissions increased by 42.1% year-on-year, and particulate matter emissions increased by 38.5% year-on-year.

[0131] A233. If the historical emission growth trend of one of the above pollutants matches the cyclical carbon emission growth trend of the park, then the anomaly that triggers the warning is considered to be a carbon pollution correlation anomaly, and the pollutant with the matching growth trend is recorded as the target pollutant.

[0132] Park NO in the second quarter of 2025 x The year-on-year growth rate of emissions was 42.1%, and the year-on-year growth rate of particulate matter emissions in the park was 38.5%, both similar to the growth rate of carbon emissions in the park in the second quarter. x The growth trends in both emissions and particulate matter emissions are consistent with the carbon emission growth trends in the park during the second quarter. The monitoring platform, however, considers the anomaly triggering the warning to be a carbon-related anomaly, and has therefore added NO... x Particulate matter is designated as the target pollutant.

[0133] A234. Obtain the historical carbon emissions of each carbon emission source in the park and sort each carbon emission source from high to low according to the historical carbon emissions of the same period. Select the top M carbon emission sources in terms of historical carbon emissions of the same period as suspected emission sources. Among them, carbon emission sources include various public facilities and enterprises in the park.

[0134] The monitoring platform acquires the carbon emissions of various public facilities and enterprises in the park for the second quarter. These facilities and enterprises are then ranked from highest to lowest based on their second-quarter carbon emissions, and the top two are selected as suspected emission sources. In this embodiment, M is set to 2. The centralized heating boiler emitted 12,000 tCO2 in the second quarter, and Enterprise H emitted 8,500 tCO2; these two are ranked first and second, and thus selected as suspected emission sources.

[0135] A235. Calculate the historical concurrent emission growth trend of the target pollutant for each suspected emission source. If the historical concurrent emission growth trend of the target pollutant matches the cyclical carbon emission growth trend of the park, then the suspected emission source is taken as the target source.

[0136] The monitoring platform calculates the NO from centralized heating boilers and enterprise H respectively. x Second quarter emission growth trends, particulate matter emission growth trends in the second quarter, specifically: NOx emissions from centralized heating boilers x Second-quarter emissions were 180 tons, compared to 120 tons in the same period last year. The calculated NOx... x The second quarter emissions increased by 50% year-on-year, with particulate matter emissions at 95 tons, compared to 65 tons in the same period last year. This calculates to a 46.2% year-on-year increase in particulate matter emissions in the second quarter. (Company H's NO...) x Second-quarter emissions were 150 tons, compared to 105 tons in the same period last year. The calculated NOx... xThe year-on-year growth rate of emissions in the second quarter was 42.9%. Particulate matter emissions in the second quarter were 88 tons, compared to 63 tons in the same period last year, resulting in a year-on-year growth rate of 39.7% for particulate matter emissions in the second quarter. The year-on-year growth rate of carbon emissions in the industrial park in the second quarter of 2025 was 47.4%, indicating that NO from centralized heating boilers and H enterprises... x The year-on-year growth rates of emissions in the second quarter and particulate matter emissions in the second quarter were similar to the year-on-year growth rates of carbon emissions in the park in the second quarter of 2025, namely, NO from centralized heating boilers and H enterprises. x The growth trends in emissions and particulate matter emissions in the second quarter are consistent with the carbon emission growth trends in the park in the second quarter of 2025, with centralized heating boilers and Company H as the target sources.

[0137] A236. Break down the historical carbon emissions of the target source into production stages, and then select the production stage with the highest carbon emissions from the target source.

[0138] Carbon emissions from centralized heating boilers in public facilities are broken down by production stage, with the combustion stage being the highest-emission stage. For Company H, the monitoring platform breaks down its second-quarter carbon emissions by production stage, for example: 6200 tCO2 from cement kiln calcination (72.9%), 1500 tCO2 indirectly emitted from electricity consumption, and 800 tCO2 from other stages. The monitoring platform identified the cement kiln calcination stage (6200 tCO2) as the highest-emission production stage for Company H.

[0139] A237. Obtain the production status parameters of the production links with the highest carbon emissions mentioned above, and determine the reasons for the periodic carbon emission warnings of the park accordingly. The production status parameters include energy consumption, production load, and the status of pollution control facilities.

[0140] The monitoring platform acquired production status parameters for the cement kiln calcination process at Company H, including energy consumption, production load, and the status of pollution control facilities. The coal consumption of the centralized heating boiler combustion process in the second quarter was 12,000 tons, compared to 8,000 tons in the same period last year, representing a year-on-year growth rate of 50%. Company H's cement kiln uses anthracite coal, and its coal consumption in the second quarter was 3,500 tons, compared to 2,500 tons in the same period last year, representing a year-on-year growth rate of 40%. Therefore, the reason for the cyclical carbon emission warning for the industrial park was determined to be the increased coal consumption in the centralized heating boiler combustion process and the cement kiln calcination process at Company H.

[0141] Enterprise monitoring steps: B1. Based on the enterprise carbon pollution monitoring and early warning trigger condition list for each enterprise, on the one hand, early warning monitoring is carried out on the enterprise's periodic pollutant emissions and periodic carbon emissions, and on the other hand, early warning monitoring is carried out on the enterprise's real-time pollutant concentration using each enterprise's own pollutant monitoring terminal; B2. If any early warning is triggered, the abnormal source with carbon pollution association is determined based on the above-mentioned park carbon pollution characteristic fingerprint database and the carbon pollution characteristic fingerprint database of each enterprise, and then the cause of the early warning is determined based on the production status parameters of the abnormal source.

[0142] The chemical industrial park's carbon pollution emission collaborative monitoring platform, based on the aforementioned list of enterprise carbon pollution monitoring and early warning trigger conditions, conducts early warning monitoring of both the periodic pollutant emissions and periodic carbon emissions of enterprises. Simultaneously, it monitors the real-time pollutant concentrations of each enterprise using its own pollutant monitoring terminals. If any early warning is triggered, the platform identifies the abnormal source associated with carbon pollution based on the park's carbon pollution characteristic fingerprint database and the individual enterprise's carbon pollution characteristic fingerprint database. Then, it determines the cause of the early warning based on the production status parameters of the abnormal source. The following sections describe the early warning monitoring process for the three parameters: real-time pollutant concentrations, periodic pollutant emissions, and periodic carbon emissions of enterprises.

[0143] (1) Real-time pollutant concentration of enterprises

[0144] The company has installed monitoring devices for pollutants such as SO2, NOx, particulate matter, VOCs, and ammonia at each emission outlet. These devices monitor the real-time concentrations of various pollutants emitted from the outlets. Based on a data collection cycle, the average pollutant concentration for each cycle is calculated and uploaded to the company's pollutant monitoring terminal. Upon receiving the average concentration data from the various monitoring devices, the company's monitoring terminal determines whether it meets any of the early warning trigger conditions corresponding to the company's real-time pollutant concentrations listed in Table 2. At Company C's emission outlet (No. G-005, fugitive emissions), the TVOC monitoring device collects TVOC concentration data in real-time and calculates the average TVOC concentration for each completed data collection cycle. This embodiment, based on the "Integrated Emission Standard of Air Pollutants" (GB16297-1996) and various industry-specific air pollutant emission standards, sets the data acquisition cycle to 1 hour. For example, if the TVOC pollutant monitoring device completes data acquisition for one cycle at 15:00, it immediately calculates the average TVOC concentration for that cycle based on the collected data. For instance, if the calculated average TVOC concentration is 800 mg / m³, this average TVOC concentration is uploaded to the enterprise's pollutant monitoring terminal. The enterprise's pollutant monitoring terminal compares this average TVOC concentration of 800 mg / m³ with the enterprise's discharge permit limit. If it determines that the average TVOC concentration of 800 mg / m³ exceeds the discharge permit limit (200 mg / m³) by 300%, then B210, a red-level warning for the real-time pollutant concentration at the enterprise's discharge outlet is triggered. The enterprise's pollutant monitoring terminal then reports this red-level warning to the chemical industrial park's carbon pollution emission collaborative monitoring platform. The monitoring platform then proceeds as follows... Figure 5 The process shown is as follows: Anomaly investigation is performed.

[0145] B211. Query the enterprise's carbon pollution characteristic fingerprint database to determine the production unit that simultaneously corresponds to the emission outlet and the pollutant.

[0146] The red-level early warning for real-time pollutant concentration at the enterprise's discharge outlet, reported by the enterprise's pollutant monitoring terminal, includes the discharge outlet number, pollutant name, and real-time concentration. The monitoring platform, by querying the enterprise's carbon pollution characteristic fingerprint database, determined that the production unit corresponding to both the discharge outlet and the TVOC of that pollutant at enterprise C is the raw material toluene storage tank area unit of enterprise C.

[0147] B212. Obtain the real-time operating parameters of the production unit and calculate the hourly carbon emissions corresponding to the current operating conditions of the production unit.

[0148] The monitoring platform obtains real-time operating parameters of the raw material toluene storage tank area unit from Company C, and calculates the hourly carbon emissions of the unit under the current operating conditions. Specifically:

[0149] The real-time operating conditions of the raw material storage tank area of ​​Company C were retrieved. The storage capacity of the tank is 800t, and it is under full load with a temperature of 32℃ (normal storage temperature is 25-28℃). The TVOC emission from breathing loss is 0.8t / hour. The carbon emissions of this unit are calculated in two parts: (1) Steam consumption for tank heating: 0.3t / h × carbon emission factor 0.18tCO2 / tsteam = 0.054tCO2 / h; (2) Electricity consumption for recovery facilities: 120kW = 0.12MWh / h, 0.12MWh / h × carbon emission factor 0.61tCO2 / MWh = 0.0732tCO2 / h. The total hourly carbon emissions of the raw material toluene storage tank area unit are: 0.054tCO2 / h + 0.0732tCO2 / h = 0.1272tCO2 / h, which is rounded to 0.13tCO2 / h.

[0150] B213. Determine whether the hourly carbon emissions corresponding to the current operating condition of the production unit are abnormal. If abnormal, consider the abnormality that triggered the warning to be a carbon pollution-related abnormality and designate the production unit as the target source production unit; otherwise, end the current process.

[0151] Carbon emissions per hour under normal operating conditions for the raw material toluene storage tank unit: Under normal storage temperature, the tanks do not require continuous heating, and the heating steam consumption is only 0.1 t / h (0.1 t / h × 0.18 tCO2 / t = 0.018 tCO2 / h); the recovery facility consumes 60 kW of electricity under normal load (0.06 MWh / h × 0.61 tCO2 / MWh = 0.0366 tCO2 / h); the basic energy consumption carbon emissions associated with the tank breathing loss are also added, set at 0.025 tCO2 / h. The total carbon emissions under normal operating conditions are: 0.018 + 0.0366 + 0.025 ≈ 0.0796 tCO2 / h, rounded to 0.08 tCO2 / h.

[0152] The current operating conditions result in carbon emissions of 0.13 tCO2 / h, which is approximately 60% higher than the normal operating conditions (0.13-0.08) / 0.08×100%). This indicates an anomaly in the hourly carbon emissions of the raw material toluene storage tank unit. Therefore, the monitoring platform identifies the anomaly triggering the warning as a carbon pollution-related anomaly and designates the raw material toluene storage tank unit as the target source production unit.

[0153] B214. Query the enterprise carbon pollution characteristic fingerprint database to determine multiple production links in the target source production unit corresponding to the pollutant.

[0154] The monitoring platform queried the enterprise's carbon pollution fingerprint database and determined that the TVOC corresponding to the raw material toluene storage tank area unit was the raw material storage tank breathing process and the toluene storage tank heat tracing system process.

[0155] B215. Obtain the operation logs of the above-mentioned multiple production links and the emission concentration curve of the pollutant at the emission outlet within a preset time period. Determine the time point when the pollutant concentration changes based on the emission concentration curve, and take the production link whose operating conditions change within a preset time period before that time point as the target source production link.

[0156] The monitoring platform acquires the operating logs of the raw material storage tank's breathing system and the toluene storage tank's heating system, as well as the TVOC emission concentration curve at the emission outlet for one day. Based on the TVOC emission concentration curve, the time points when TVOC concentration changes are determined. The production process in which the operating conditions change within a preset time period before that time point is identified as the target source production process. In this embodiment, the preset time period is set to 1 hour. The TVOC emission concentration curve for one day shows a step-like increase in TVOC concentration after 14:00, while the toluene storage tank's heating system starts at 13:30. The operating conditions of the toluene storage tank's heating system change within half an hour before 14:00, which matches the timing. Therefore, the toluene storage tank's heating system is identified as the target source production process.

[0157] B216. Obtain production status parameters of the target source production process, and determine the reasons for the enterprise's real-time pollutant concentration warning based on these parameters. These production status parameters include energy consumption, production load, and the status of treatment facilities.

[0158] The monitoring platform acquires production status parameters for the toluene storage tank heating system, including energy consumption, production load, and the status of treatment facilities. Based on these parameters, at 13:30, the toluene storage tank unit activated its heating system because the tank temperature was below the set value, causing the tank temperature to rise to 32°C and increasing toluene volatilization. Simultaneously, the VOCs recovery facility's adsorbent had been running continuously for three months without replacement, resulting in a recovery efficiency of only 45% (design efficiency ≥85%). Therefore, the real-time pollutant concentration warning for the enterprise was determined to be caused by excessively high heating temperatures in the toluene storage tank heating system and excessively low recovery efficiency of the VOCs recovery facility.

[0159] (2) Enterprise's periodic pollutant emissions

[0160] Each company within the industrial park submits its monthly pollutant emission data to the Chemical Industrial Park Carbon Pollution Emission Collaborative Monitoring Platform on the 1st of each month. Upon receiving monthly pollutant emission data, the monitoring platform determines whether each company's emissions meet the warning conditions for each level of enterprise periodic pollutant emissions as shown in Table 2. If it is the beginning of a new quarter, the platform also calculates the previous quarter's pollutant emissions for each company and then determines whether they meet the warning conditions for each level of enterprise periodic pollutant emissions as shown in Table 2. Taking Company F's VOCs emissions in the third quarter of 2025 as an example, Company F's VOCs emissions in the third quarter were 65t, exceeding its quarterly emission permit limit of 40t by 62.5%, meeting the red-level warning trigger condition ①. Therefore, the monitoring platform B220 triggers the enterprise periodic pollutant red-level warning, and proceeds as follows... Figure 6 The process shown is as follows: Anomaly investigation is performed.

[0161] B221. Query the enterprise's carbon pollution characteristic fingerprint database to identify multiple production units corresponding to the pollutant.

[0162] The monitoring platform queried the enterprise's carbon pollution characteristic fingerprint database and determined that the VOCs pollutant corresponded to multiple production units, namely the paint production unit, raw material storage and transportation unit, and auxiliary production and public works unit.

[0163] B222. Calculate the growth trend of the enterprise's periodic pollutant emissions and the historical carbon emission growth trend of each of the above production units during the same period.

[0164] The monitoring platform calculated that the year-on-year growth rate of VOCs emissions for Company F in the third quarter was (65-32) / 32. 100% = 103.1%, which is used as its growth trend. The year-on-year carbon emission growth rate for each of the above production units in the third quarter is calculated as follows: the carbon emission growth rate for the raw material storage and transportation unit in the third quarter was 87.2%, for the paint production unit it was 105.6%, and for the auxiliary production and utilities unit it was 23.9%. The year-on-year carbon emission growth rate for each production unit is used as its carbon emission growth trend.

[0165] B223. If the historical carbon emission growth trend of one of the above production units matches the cyclical pollutant emission growth trend of the enterprise, then the anomaly that triggers the warning is considered to be a carbon pollution correlation anomaly, and the production unit with the matching growth trend is taken as the target source production unit; otherwise, the process ends.

[0166] The monitoring platform determines whether any of the aforementioned production units exhibit a historical carbon emission growth trend consistent with the company's cyclical pollutant emission growth trend. For example, if the historical carbon emission growth trend of the paint production unit (year-on-year growth rate of 105.6%) is consistent with the company's cyclical pollutant emission growth trend (year-on-year growth rate of 103.1%), then the anomaly triggering the warning is considered a carbon pollution correlation anomaly, and the paint production unit is identified as the target source production unit.

[0167] B224. Query the enterprise carbon pollution characteristic fingerprint database to determine the multiple production links in the target source production unit corresponding to the pollutant, break down the concurrent pollutant emissions of the target source production unit according to the production links, and select the production link with the highest pollutant emissions from these multiple production links.

[0168] The monitoring platform queried the enterprise's carbon pollution fingerprint database and determined that the VOCs pollutant corresponded to multiple production stages in the paint production unit, namely raw material preparation, reaction polymerization, product filling, and storage tanks. The VOCs emissions of the paint production unit in the third quarter were broken down by production stage. Specifically, the raw material preparation stage had 12 tons, the reaction polymerization stage had 25 tons, the product filling stage had 11 tons, and the storage tanks had 17 tons. Among them, the reaction polymerization stage had the largest emissions and a year-on-year increase of 180%.

[0169] B225. Obtain the production status parameters of the production process with the highest pollutant emissions mentioned above, and determine the reasons for the enterprise's periodic pollutant emission warnings accordingly. The production status parameters include energy consumption, production load, and the status of treatment facilities.

[0170] The monitoring platform targets the reactive polymerization stage, which has the highest VOC emissions, as the source of the production process. It acquires production status parameters for this stage, including energy consumption, production load, and the status of treatment facilities. Analysis of these parameters revealed that styrene consumption in the third quarter was 800 tons, a 128.6% increase compared to the same period last year (350 tons). The VOC content in the raw materials remained unchanged at 99.5%, while product output increased by 140%. Therefore, the platform determined that the company's periodic pollutant concentration warnings were due to cumulative exceedances caused by a significant increase in reactive polymerization production.

[0171] (3) Enterprise cycle carbon emissions

[0172] On the 1st of each month, all enterprises within the park report their carbon emission data for the previous month to the Chemical Industrial Park Carbon Emission Collaborative Monitoring Platform. Each month, the platform receives the carbon emission data and determines whether each enterprise's monthly carbon emissions meet the warning conditions for each level of enterprise cyclical carbon emissions listed in Table 2. If it is the beginning of a new quarter, the platform also calculates each enterprise's carbon emissions for the previous quarter and then determines whether they meet the warning conditions for each level of enterprise cyclical carbon emissions listed in Table 2. For example, Enterprise I's carbon emissions in August 2025 were 1200 tCO2, compared to 950 tCO2 in the same period last year, representing a year-on-year growth rate of 26.3%. This meets the yellow-level warning trigger condition ②, and the monitoring platform triggers a yellow-level warning for enterprise cyclical carbon emissions (B230), then proceeds as follows... Figure 7 The process shown is as follows: Anomaly investigation is performed.

[0173] B231. Break down the company's cyclical carbon emissions by production unit, sort each production unit in descending order of cyclical carbon emissions, and select the production units that rank in the top N in terms of cyclical carbon emissions and whose proportion of the company's cyclical carbon emissions reaches a preset threshold as suspected production units.

[0174] The monitoring platform breaks down Company I's carbon emissions in August 2025 by production unit. Specifically, the PVC polymerization reaction workshop directly emitted 750 tCO2 (62.5% of Company I's August carbon emissions), electricity consumption in production indirectly emitted 320 tCO2 (26.7% of Company I's August carbon emissions), and other units emitted 130 tCO2 (10.8% of Company I's August carbon emissions). The monitoring platform sorts each production unit in descending order of carbon emissions, and then selects the top N production units with the highest periodic carbon emissions and whose percentage of the company's periodic carbon emissions reaches a preset threshold as suspected production units. As mentioned above, N is 3. The preset threshold is set to 20%. In this case, the PVC polymerization reaction workshop unit had the highest carbon emissions, accounting for more than 62.5%, while the indirect carbon emissions from electricity consumption were the second highest, accounting for 26.7%. Since the electricity consumption of the PVC polymerization reaction workshop unit accounted for most of the electricity consumption of Company I, and most of the indirect carbon emissions from electricity consumption were emitted by the PVC polymerization reaction workshop unit, the monitoring platform selected the PVC polymerization reaction workshop unit as the suspected production unit.

[0175] B232. Query the enterprise's carbon pollution fingerprint database to identify at least one pollutant corresponding to each suspected production unit.

[0176] The monitoring platform queried the enterprise's carbon pollution fingerprint database and determined that the pollutants corresponding to the PVC polymerization reaction workshop unit were VOCs and HCl.

[0177] B233. Calculate the growth trend of corporate carbon emissions over the period and the growth trend of various pollutant emissions for each suspected production unit.

[0178] The monitoring platform has already calculated that the year-on-year growth rate of carbon emissions for Company I in August was 26.3%, which is used as the growth trend of carbon emissions for Company I in August. Therefore, the growth trends of VOCs and HCl emissions from the PVC polymerization reaction workshop unit are calculated next. Specifically: VOCs emissions in August were 32t, compared to 23t in the same period last year, with a year-on-year growth rate of 39.1%, which is used as the growth trend of VOCs emissions; HCl emissions were 18t, compared to 13t in the same period last year, with a year-on-year growth rate of 38.5%, which is used as the growth trend of HCl emissions.

[0179] B234. If the growth trend of all pollutant emissions in any of the suspected production units matches the cyclical carbon emission growth trend of the enterprise, then the anomaly that triggers the warning is considered to be a carbon pollution correlation anomaly, and the suspected production unit with the matching trend is taken as the target source production unit; otherwise, the current process ends.

[0180] The monitoring platform determined whether the growth trends of VOCs and HCl emissions from the PVC polymerization reaction workshop unit were consistent with the growth trends of Company I's carbon emissions in August. Company I's carbon emissions increased by 26.3% year-on-year in August, while the VOCs emissions from the PVC polymerization reaction workshop unit increased by 39.1% and HCl emissions increased by 38.5% year-on-year in August, both close to the growth rates of Company I's carbon emissions in August. Therefore, the monitoring platform considered the anomaly that triggered the warning to be a carbon pollution-related anomaly and identified the PVC polymerization reaction workshop unit as the target source production unit.

[0181] B235. Query the enterprise carbon pollution characteristic fingerprint database to determine multiple production links in the target source production unit corresponding to the above-mentioned various pollutants.

[0182] The monitoring platform queries the enterprise's carbon pollution characteristic fingerprint database to determine that at least one production step in the PVC polymerization reaction workshop unit corresponding to VOCs and HCl is a polymerization reaction step.

[0183] B236. Obtain production status parameters for at least one of the above-mentioned production stages, and determine the reasons for the enterprise's periodic carbon emission warnings accordingly. The production status parameters include energy consumption, production load, and the status of pollution control facilities.

[0184] The monitoring platform acquired production status parameters for multiple production stages, including energy consumption, production load, and the status of pollution control facilities. The platform verified the operating parameters of the polymerization reaction stage in the PVC polymerization workshop unit. In August, the reaction temperature remained at 65℃ (60℃ in the same period last year), and the reaction pressure was 1.8 MPa (1.6 MPa in the same period last year). Furthermore, the amount of initiator used in this reaction unit increased, leading to a faster reaction rate and higher output. Simultaneously, carbon emissions and pollutant emissions increased in tandem. Data from the pollution control facilities showed that the VOCs adsorption and recovery facility was operating at 105% load (design load 100%), with a removal efficiency of 78% (design efficiency 85%). Therefore, the monitoring platform determined that the company's periodic carbon emission warning was caused by abnormal carbon accumulation due to increased production in the PVC polymerization reaction stage of the workshop unit, with the pollution control facilities operating at overload, resulting in decreased removal efficiency and a potential risk of abnormal pollutant emissions.

[0185] After determining the cause of the warning, the monitoring platform takes corresponding measures according to the current risk level of the warning. The measures corresponding to the blue level are: issuing a production unit rectification notice to the enterprise that triggered the warning; issuing a production unit rectification notice to the enterprise that triggered the warning and including it in the management and control system for tracking and control; issuing a production restriction notice to the enterprise that triggered the warning and including it in the management and control system for tracking and control; and issuing a production stoppage notice to the enterprise that triggered the warning and including it in the management and control system for tracking and control.

[0186] This invention pre-constructs a carbon pollution fingerprint database for the industrial park and a corporate carbon pollution fingerprint database for each enterprise within the park. If abnormal pollutant emissions or carbon emissions are detected, the park's carbon pollution fingerprint database and the corporate carbon pollution fingerprint database are used to identify the abnormal sources associated with carbon pollution. Then, the cause of the warning is determined based on the production status parameters of the abnormal sources. This allows for the identification of abnormal sources associated with carbon pollution and the cause of the warning, enabling the park to carry out pollution reduction and carbon reduction treatment, facilitating simultaneous pollution reduction and carbon reduction.

[0187] The above description is merely an embodiment of the present invention and does not limit the scope of patent protection. Any non-substantial changes or substitutions made by those skilled in the art based on the present invention will still fall within the scope of patent protection.

Claims

1. A method for collaborative monitoring and early warning of carbon pollution emissions in chemical industrial parks, characterized by: include: Database construction steps: Construct a carbon pollution characteristic fingerprint database for the industrial park, a list of carbon pollution monitoring and early warning triggering conditions for the industrial park, and a carbon pollution characteristic fingerprint database and a list of carbon pollution monitoring and early warning triggering conditions for each enterprise within the industrial park; The industrial park carbon pollution characteristic fingerprint database stores the correspondence between carbon emissions and pollutant types, as well as the correspondence between pollutant types and carbon pollution-related production units; The enterprise carbon pollution characteristic fingerprint database stores the correspondence between enterprise pollutant types and enterprise carbon pollution-related production units, enterprise carbon pollution-related production processes, and enterprise emission outlets; Park monitoring steps: A1. Based on the above list of carbon pollution monitoring and early warning triggering conditions for the park, conduct early warning monitoring on the real-time pollutant concentration, periodic pollutant emissions, and periodic carbon emissions of the park; A2. If any early warning is triggered, determine the abnormal source with carbon pollution association based on the above carbon pollution characteristic fingerprint database of the park and the carbon pollution characteristic fingerprint database of each enterprise, and then determine the cause of the early warning based on the production status parameters of the abnormal source. Enterprise monitoring steps: B1. Based on the enterprise carbon pollution monitoring early warning trigger condition list for each enterprise, on the one hand, early warning monitoring is carried out on the enterprise's periodic pollutant emissions and enterprise's periodic carbon emissions, and on the other hand, early warning monitoring is carried out on the enterprise's real-time pollutant concentration using each enterprise's own pollutant monitoring terminal; B2. If any warning is triggered, the abnormal source with carbon pollution association shall be determined based on the carbon pollution fingerprint database of the above-mentioned park and the carbon pollution fingerprint database of each enterprise, and then the cause of the warning shall be determined based on the production status parameters of the abnormal source. In step B2 of the enterprise monitoring process, if a periodic pollutant emission warning for the enterprise is triggered, then the following actions will be taken: B221. Query the enterprise's carbon pollution characteristic fingerprint database to identify multiple production units corresponding to the pollutant; B222. Calculate the periodic pollutant emission growth trend of the enterprise and the historical carbon emission growth trend of each of the above-mentioned production units during the same period. B223. If the historical carbon emission growth trend of one of the above production units matches the cyclical pollutant emission growth trend of the enterprise, then the anomaly that triggers the warning is considered to be a carbon pollution correlation anomaly, and the production unit with the matching growth trend is taken as the target source production unit; otherwise, the process ends. B224. Query the enterprise carbon pollution characteristic fingerprint database to determine multiple production links in the target source production unit corresponding to the pollutant, break down the pollutant emissions of the target source production unit in the same period according to the production links, and select the production link with the highest pollutant emissions from these multiple production links. B225. Obtain the production status parameters of the production process with the highest pollutant emissions, and determine the reasons for the enterprise's periodic pollutant emission warnings accordingly. The production status parameters include energy consumption, production load, and the status of treatment facilities.

2. The method for coordinated monitoring and early warning of carbon pollution emissions in chemical industrial parks as described in claim 1, characterized in that: In step A2 of the park monitoring process, if a real-time pollutant concentration warning for the park is triggered, then the following steps will be executed: A211. Determine the source area of ​​the pollutant emission based on wind direction, wind speed and monitoring point location, and use all enterprises in that area as candidate enterprises; A212. Query the carbon pollution characteristic fingerprint database of the industrial park to determine at least one production unit corresponding to the pollutant; A213. If any of the above candidate companies has any of the aforementioned production units, then the anomaly that triggers the warning is considered to be a carbon pollution-related anomaly, and at least one suspected company with any of the aforementioned candidate companies is selected; otherwise, the current process ends. A214. Calculate the carbon emissions of the production unit corresponding to the pollutant of each suspected enterprise within a preset time period before the warning is triggered, and arrange all suspected enterprises in descending order of carbon emissions; A215. Determine whether the concentration of the pollutant emitted during the same period is abnormal for the production units of the top N suspected enterprises in terms of carbon emissions. If there is an abnormality, then confirm that the current suspected enterprise is the target source enterprise. A216. Query the enterprise's carbon pollution characteristic fingerprint database to determine at least one production stage in the production unit corresponding to the pollutant; A217. Obtain production status parameters of at least one of the above-mentioned production links of the production unit of the target source enterprise, and determine the cause of the real-time pollutant concentration warning in the park accordingly. The production status parameters include energy consumption, production load and treatment facility status.

3. The method for coordinated monitoring and early warning of carbon pollution emissions in chemical industrial parks as described in claim 1, characterized in that: In step A2 of the park monitoring process, if a periodic pollutant emission warning for the park is triggered, then the following actions will be taken: A221. Query the carbon pollution characteristic fingerprint database of the industrial park to identify multiple production units corresponding to the pollutant; A222. Calculate the periodic pollutant emission growth trend of the park and the historical carbon emission growth trend of each of the above-mentioned production units during the same period in the park. A223. If the historical carbon emission growth trend of one of the above production units matches the cyclical pollutant growth trend of the park, then the anomaly that triggers the warning is considered to be a carbon pollution correlation anomaly, and at least one production unit with the same growth trend is selected from the above production units; otherwise, the current process ends. A224. Select multiple candidate companies that have at least one production unit from all companies in the park; A225. Obtain the historical production data of each candidate enterprise for the same period, sort the candidate enterprises in descending order of their historical production load, and select the top M enterprises as suspect enterprises. A226. Determine whether there are any abnormalities in the historical emissions of this pollutant from each suspected enterprise during the same period. If there are abnormalities, then confirm that the current suspected enterprise is the target source enterprise. A227. Query the enterprise's carbon pollution characteristic fingerprint database to determine multiple production stages in the production unit corresponding to the pollutant; A228. Obtain production status parameters of the multiple production stages of the production unit of the target source enterprise, and determine the reasons for the periodic pollutant emission warning of the park accordingly. The production status parameters include energy consumption, production load and treatment facility status.

4. The method for coordinated monitoring and early warning of carbon pollution emissions in chemical industrial parks as described in claim 1, characterized in that: In step A2 of the park monitoring process, if a periodic carbon emission warning for the park is triggered, then the following actions will be taken: A231. Query the carbon pollution characteristic fingerprint database of the park to determine the various pollutants corresponding to the periodic carbon emissions of the park; A232. Calculate the periodic carbon emission growth trend of the park and the historical concurrent emission growth trend of various pollutants; A233. If the historical emission growth trend of one of the above pollutants matches the cyclical carbon emission growth trend of the park, then the anomaly that triggers the warning is considered to be a carbon pollution-related anomaly, and the pollutant with the matching growth trend is recorded as the target pollutant. A234. Obtain the historical carbon emissions of each carbon emission source in the park and sort the carbon emission sources in descending order of historical carbon emissions. Select the top M carbon emission sources in terms of historical carbon emissions as suspected emission sources. The carbon emission sources include all public facilities and enterprises in the park. A235. Calculate the historical concurrent emission growth trend of the target pollutant for each suspected emission source. If the historical concurrent emission growth trend of the target pollutant is consistent with the cyclical carbon emission growth trend of the park, then the suspected emission source is taken as the target source. A236. Break down the historical carbon emissions of the target source into production stages, and then select the production stage with the highest carbon emissions from the target source. A237. Obtain the production status parameters of the production links with the highest carbon emissions mentioned above, and determine the reasons for the periodic carbon emission warnings of the park accordingly. The production status parameters include energy consumption, production load, and the status of pollution control facilities.

5. The method for coordinated monitoring and early warning of carbon pollution emissions in chemical industrial parks as described in claim 1, characterized in that: In step B2 of the enterprise monitoring process, if a real-time pollutant concentration warning from the emission outlet is received from the enterprise's pollutant monitoring terminal, then the following steps are executed: B211. Query the enterprise's carbon pollution characteristic fingerprint database to determine the production unit that simultaneously corresponds to the emission outlet and the pollutant; B212. Obtain the real-time operating parameters of the production unit and calculate the hourly carbon emissions corresponding to the current operating conditions of the production unit. B213. Determine whether the hourly carbon emissions corresponding to the current operating condition of the production unit are abnormal. If abnormal, consider the abnormality that triggered the warning to be a carbon pollution-related abnormality and take the production unit as the target source production unit; otherwise, end the current process. B214. Query the enterprise carbon pollution characteristic fingerprint database to determine multiple production stages in the target source production unit corresponding to the pollutant; B215. Obtain the operation logs of the above-mentioned multiple production links and the emission concentration curve of the pollutant at the emission outlet within a preset time period. Determine the time point when the pollutant concentration changes based on the emission concentration curve, and take the production link whose operating conditions change within a preset time period before the time point as the target source production link. B216. Obtain production status parameters of the target source production process, and determine the reasons for the enterprise's real-time pollutant concentration warning based on these parameters. These production status parameters include energy consumption, production load, and the status of treatment facilities.

6. The method for coordinated monitoring and early warning of carbon pollution emissions in chemical industrial parks as described in claim 1, characterized in that: In step B2 of the enterprise monitoring process, if a periodic carbon emission warning for the enterprise is triggered, then the following actions will be taken: B231. Break down the company’s periodic carbon emissions by production unit, sort each production unit in descending order of periodic carbon emissions, and select the production units that rank in the top N for periodic carbon emissions and whose proportion of the company’s periodic carbon emissions reaches a preset threshold as suspected production units. B232. Query the enterprise's carbon pollution fingerprint database to identify at least one pollutant corresponding to each suspected production unit; B233. Calculate the growth trend of corporate carbon emissions over the period and the growth trend of various pollutant emissions for each suspected production unit; B234. If the growth trend of all pollutant emissions in each suspected production unit is consistent with the cyclical carbon emission growth trend of the enterprise, then the anomaly that triggers the warning is considered to be a carbon pollution correlation anomaly, and the suspected production unit with the consistent trend is taken as the target source production unit; otherwise, the current process ends. B235. Query the enterprise carbon pollution characteristic fingerprint database to determine at least one production link in the target source production unit corresponding to the above-mentioned various pollutants; B236. Obtain production status parameters for at least one of the above-mentioned production stages, and determine the reasons for the enterprise's periodic carbon emission warnings accordingly. The production status parameters include energy consumption, production load, and the status of pollution control facilities.

7. The method for coordinated monitoring and early warning of carbon pollution emissions in chemical industrial parks as described in claim 1, characterized in that: Both the list of carbon pollution monitoring and early warning triggering conditions for the industrial park and the list of carbon pollution monitoring and early warning triggering conditions for enterprises contain early warning triggering conditions corresponding to multiple real-time pollutant concentration risk levels, multiple periodic pollutant emission risk levels, and multiple periodic carbon emission risk levels. Among them, the higher the risk level, the more difficult the corresponding early warning triggering condition is to be triggered.

8. The method for coordinated monitoring and early warning of carbon pollution emissions in chemical industrial parks as described in claim 7, characterized in that: After determining the cause of the warning, corresponding measures are taken according to the current risk level of the warning. The risk levels are specifically: blue, yellow, orange, and red. Specifically, the measures for the blue level are: issuing a production unit rectification notice to the enterprise that triggered the warning; the measures for the yellow level are: issuing a production unit rectification notice to the enterprise that triggered the warning and including it in the management and control system for monitoring; the measures for the orange level are: issuing a production unit production restriction notice to the enterprise that triggered the warning and including it in the management and control system for monitoring; and the measures for the red level are: issuing a production unit shutdown notice to the enterprise that triggered the warning and including it in the management and control system for monitoring.

9. A computer program product comprising a computer program, characterized in that, When the computer program is executed, it implements the collaborative monitoring and early warning method for carbon pollution emissions in chemical industrial parks as described in any one of claims 1 to 8.

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