An ambient air quality influence dynamic simulation and pollution tracing big data system

Abstract

The application provides an environmental air quality influence dynamic simulation and pollution tracing big data system, which comprises a data acquisition and preprocessing unit, a pollution simulation unit, a dynamic demonstration unit and a tracing analysis unit; the data acquisition and preprocessing unit is used for acquiring atmospheric pollutant emission source data and meteorological data of a set enterprise in real time and performing preprocessing; the pollution simulation unit simulates pollutant diffusion processes of a plurality of pollution sources in a preset time period by using the preprocessed source data through a CALPUFF diffusion model; the dynamic demonstration unit realizes dynamic demonstration according to the simulation results of the situation simulation unit; the tracing analysis unit is used for analyzing pollution contribution weights of each pollution source and can analyze and predict the pollution contribution weights of the enterprise by simulating any time interval, different atmospheric pollutant emission situations and parameters and using the pollution simulation unit. The environmental air quality influence dynamic simulation and pollution tracing big data system is used for real-time acquisition of atmospheric pollutant emission source data, working condition data and meteorological data of a key pollutant discharge enterprise or an industrial park.

Description

Technical Field

[0001] This invention relates to the field of atmospheric pollution assessment technology, and in particular to a big data system for dynamic simulation of the impact of ambient air quality and pollution source tracing. Background Technology

[0002] To improve ambient air quality and control the emission of air pollutants from enterprises, many technical means can be used as support and assistance to explain how pollutants discharged by enterprises affect air quality. At present, the ecological and environmental authorities have established an ambient air quality monitoring network and set up monitoring stations in important locations to monitor the concentration of various pollutants in order to track changes in air quality over a long period of time. For enterprises or industrial parks, especially key enterprises or industrial parks under pollution control, automatic continuous emission monitoring equipment (CEMS) is required to be installed at major emission outlets, and monitoring data is required to be uploaded to the competent authorities. However, the problems with this management method at present include: (1) only key emission outlets are monitored at the emission end, without covering all emission sources (i.e., emission intensity of a certain pollutant) of the controlled enterprises or industrial parks; (2) only emission outlets and air quality monitoring stations are monitored separately, but the impact of the specific emission behavior of the emission outlets on the monitoring points (or other locations of concern, such as residential areas, schools, etc.) is not clear, or the connection between emission source intensity and air quality has not been established.

[0003] In environmental management practice, atmospheric pollutant diffusion models are typically introduced during environmental impact assessments for construction projects, or when management departments conduct regional air pollution source strength analysis, ambient air quality assessments, or the development of relevant plans and implementation schemes. These models are used to simulate and predict the impact of pollution discharge activities on regional ambient air quality. Generally, these simulations use static, averaged source strengths, which cannot achieve dynamic, real-time impact studies. Currently, users of WRF and CALPUFF models typically only handle single scenarios, standardizing and averaging prediction parameters, and in most cases, parameters are manually entered. Summary of the Invention

[0004] This invention provides a big data system for dynamic simulation of ambient air quality impacts and pollution source tracing, solving the problem that current methods only utilize static average source strength for atmospheric pollutant diffusion simulation, and meteorological data generally only use historical archived meteorological data, making dynamic monitoring and assessment impossible. The technical solution is as follows:

[0005] A big data system for dynamic simulation of ambient air quality impacts and pollution source tracing includes a data acquisition and preprocessing unit, a pollution simulation unit, a dynamic demonstration unit, and a source tracing analysis unit. The data acquisition unit acquires real-time data on the emission source strength of designated enterprises. The pollution simulation unit uses the emission source strength data to simulate the pollutant diffusion process of several pollution sources over a preset time period using a diffusion model. The dynamic demonstration unit dynamically demonstrates the simulation results from the scenario simulation unit. The source tracing analysis unit analyzes the pollution contribution weight of each pollution source and can analyze and predict the pollution contribution weight of enterprises using the pollution simulation unit by simulating arbitrary time intervals and different atmospheric pollutant emission scenarios and parameters.

[0006] The data acquisition unit is used to acquire the atmospheric pollutant emission source intensity data of the designated enterprise in real time, including the following steps:

[0007] S1: For emission outlets where continuous pollution source monitoring equipment is installed, the emission source strength of air pollutants is obtained through the continuous monitoring equipment, including the emission concentration, flue gas flow rate, emission rate, flue gas temperature, and flue gas humidity of different air pollutants;

[0008] S2: For emission outlets that do not have continuous pollution source monitoring equipment installed and use variable frequency fans, the concentration of atmospheric pollutant emissions is obtained through regular manual monitoring. The flue gas flow rate under different operating conditions is obtained by fitting the variable frequency data of the fan with the flue gas flow rate, and then the source strength of atmospheric pollutant emissions is obtained.

[0009] S3: For emission outlets that do not have continuous pollution source monitoring equipment installed and use fixed-frequency fans, the concentration of atmospheric pollutant emissions is obtained through periodic manual monitoring, and the flue gas flow rate is obtained through actual measurement, thereby obtaining the intensity of atmospheric pollutant emission sources.

[0010] The enterprise uses variable frequency fans for exhausting air pollutants. The operating frequency obtained is the variable frequency data of the variable frequency fan. By collecting air pollutant emission data at 10Hz, 20Hz, 30Hz and 40Hz and performing linear regression processing, the emission parameters of the variable frequency fan at different operating frequencies can be obtained by monitoring the wind speed of the exhaust flow at different frequencies. For the exhaust outlet using a fixed frequency fan, the corresponding pollutant emission parameters are constant.

[0011] It also includes an environmental data unit connected to the pollution simulation unit, which is used to collect meteorological and preset terrain data of a set area in real time.

[0012] The source tracing analysis unit is used to analyze the pollution contribution weight of each pollution source, including the following steps:

[0013] S11: Statistical analysis of emission intensity data of various air pollutants in the region within a specified time period;

[0014] S12: Calculate the hourly dynamic air pollutant emission source intensity data for each enterprise / pollution source within a set time period;

[0015] S13: Through pollution diffusion model simulation, calculate and obtain grid concentration field data of different pollutants for each emission source within a set time period;

[0016] S14: Calculate the pollution contribution weight of each enterprise (or each pollution source).

[0017] The source tracing analysis unit simulates arbitrary time intervals and different atmospheric pollutant emission scenarios and parameters, and uses the pollution simulation unit to analyze and predict the pollution contribution weight of enterprises, including the following steps:

[0018] S21: Setting emission scenarios and emission parameters within a set time interval means directly obtaining emission source strength data under the actual operating conditions of the enterprise, or setting pollution load according to the scenario for all or part of the enterprise's production processes, thereby obtaining emission source strength data under that scenario.

[0019] S22: Statistical data on dynamic atmospheric pollutant emission source strength over a specified time interval;

[0020] S23: Calculate the source intensity data of air pollutants at each emission source at a set time interval;

[0021] S24: Through pollution diffusion model simulation, calculate and obtain grid concentration field data of different pollutants for each emission source at a set time interval;

[0022] S25: Calculate the pollution contribution weight of each enterprise (or each emission source).

[0023] Under different emission scenarios or parameter conditions, the source tracing analysis unit obtains multiple sets of simulation results through the pollution simulation unit, and analyzes the dynamic pollution contribution ratio of each pollution source under different simulation results.

[0024] When the internal emission characteristics of an enterprise change, it is necessary to collect the emission concentration, exhaust flow rate, emission ratio, and operating frequency of exhaust fans of each emission source after the change, so as to obtain the updated first emission parameters. By simulating the updated emission parameters and analyzing the changes in their pollution contribution, an assessment can be made on whether the internal workflow of the enterprise has been optimized.

[0025] The dynamic demonstration unit displays the following:

[0026] (1) The display data acquisition unit acquires the atmospheric pollutant emission source strength data of the set enterprise in real time. The atmospheric pollutant emission source strength data includes the overall atmospheric pollutant emission source strength data, as well as the atmospheric pollutant emission source strength data of each production process and each pollution source (emission concentration, exhaust flow rate, emission temperature, emission velocity, operating frequency of the exhaust fan, etc.).

[0027] (2) Display a dynamic schematic diagram of the diffusion of enterprise pollution sources using the diffusion model of the pollution simulation unit, and mark the diffusion trend with different colors;

[0028] (3) By selecting different processes or different pollution sources, display the dynamic schematic diagram of diffusion using the diffusion model of the pollution simulation unit, and mark the diffusion trend with different colors;

[0029] (4) By selecting the time interval and setting the emission scenario or parameters, the dynamic schematic diagram of the diffusion of the source tracing analysis unit using the diffusion model of the pollution simulation unit is displayed, and the diffusion trend is marked with different colors, as well as the dynamic schematic diagram of the diffusion of different pollution sources can be displayed.

[0030] The emission intensity of each pollution source is simulated using the dynamic intensity that varies over time, and the dynamic pollution contribution ratio of each pollution source is analyzed to identify the main contributing sources at different times.

[0031] The aforementioned big data system for dynamic simulation of ambient air quality impacts and pollution source tracing captures real-time emission source strength data, operating condition data, and meteorological data for key polluting enterprises or industrial parks. Through the CALPUFF model, it achieves automatic, real-time, and continuous simulation and prediction, as well as a series of extended functions. Attached Figure Description

[0032] Figure 1 This is a flowchart of the big data system for dynamic simulation of environmental air quality impact and pollution source tracing. Detailed Implementation

[0033] The aforementioned big data system for dynamic simulation of environmental air quality impact and pollution source tracing utilizes the hardware and network conditions of the enterprise platform server to build a brand-new system. It can automatically capture meteorological data, enterprise operating data, and enterprise emission data in real time, and automatically preprocess and input them into the prediction model to carry out simulation work.

[0034] The aforementioned big data system for dynamic simulation of ambient air quality impacts and pollution source tracing includes the following units:

[0035] 1. Data acquisition and preprocessing unit, used to acquire real-time data on the atmospheric pollutant emission source intensity of designated enterprises.

[0036] Previously, different companies used different standards for data acquisition. Therefore, the data acquisition unit of this invention needs to acquire data of a unified standard to meet consistent requirements. Regarding data acquisition, in addition to the source strength data of key emission outlets that the ecological and environmental authorities require companies to continuously monitor and upload, source strength data of emission outlets under the company's self-controlled continuous monitoring and those under non-continuous monitoring have been added, achieving full coverage of the source strength of air pollutants from enterprises. Furthermore, by classifying the fans at non-continuously monitored emission outlets, acquiring frequency conversion data, and performing linear processing, dynamic updating and management of emission source strength data for non-continuously monitored emission outlets have been achieved.

[0037] The data acquisition unit is used to acquire the atmospheric pollutant emission source intensity data of the designated enterprise in real time, including the following steps:

[0038] S1: For emission outlets where continuous pollution source monitoring equipment is installed, the emission source strength of air pollutants is obtained through the continuous monitoring equipment, including the emission concentration, flue gas flow rate, emission rate, flue gas temperature, and flue gas humidity of different air pollutants;

[0039] For emissions sources that require continuous monitoring and data upload by the management department, CEMS equipment has been installed. In this case, the real-time emission concentration of each pollutant and the exhaust flow rate of the emission outlet are continuously and automatically monitored, and emission source strength data can be obtained directly. In addition, for exhaust outlets that do not need to be uploaded to the management department but are continuously monitored by the enterprise itself, CEMS equipment is also installed, and the data can be obtained directly in the same way.

[0040] CEMS stands for Continuous Emission Monitoring System, an integrated device used to continuously monitor the concentration of gaseous pollutants (such as sulfur dioxide and nitrogen oxides) and particulate matter emitted from stationary pollution sources (such as boilers and industrial furnaces) and to calculate total emissions. This system collects data in real time and transmits it to environmental protection authorities, enabling dynamic monitoring of the emission process and serving as a crucial technical means for environmental regulation.

[0041] S2: For emission outlets that do not have continuous pollution source monitoring equipment installed and use variable frequency fans, the concentration of atmospheric pollutant emissions is obtained through regular manual monitoring. The flue gas flow rate under different operating conditions is obtained by fitting the variable frequency data of the fan with the flue gas flow rate, and then the source strength of atmospheric pollutant emissions is obtained.

[0042] Furthermore, for exhaust outlets without CEMS, the pollutant concentration can be obtained through regular manual monitoring, but the exhaust flow rate is not monitored regularly.

[0043] For the exhaust port of the variable frequency fan, the exhaust flow rate at different frequencies is manually monitored and then linearized to obtain the relationship function between frequency and exhaust flow rate. The system can collect the frequency parameters of the fan and calculate the exhaust flow rate through the function.

[0044] S3: For emission outlets that do not have continuous pollution source monitoring equipment installed and use fixed-frequency fans, the concentration of atmospheric pollutant emissions is obtained through periodic manual monitoring, and the flue gas flow rate is obtained through actual measurement, thereby obtaining the intensity of atmospheric pollutant emission sources.

[0045] Furthermore, for the exhaust port of the fixed-frequency fan, its constant exhaust flow rate is obtained through manual monitoring, and then the latest periodically manually monitored concentration is used as the emission concentration input to its model.

[0046] The source intensity data for different pollutants are mainly measured for three types: sulfur dioxide, nitrogen oxides, and particulate matter; other pollutants can also be added as needed for management or research.

[0047] S3: A gas flow sensor is installed at the emission outlet for air pollutants to collect the exhaust flow rate of the outlet.

[0048] S4: Based on the emission concentration and exhaust flow rate of a certain air pollutant, obtain the emission source strength of the air pollutant, and obtain the first emission parameter by correlating the emission source strength of the air pollutant with the operating frequency of the exhaust fan at the emission outlet.

[0049] At a certain time period, the product of the emission concentration and the exhaust flow rate is the emission source strength data. The ratio of the emission source strength to the operating frequency of the exhaust fan is the first emission parameter. Considering that different emission outlets use exhaust fans with variable frequency and fixed frequency, different processing is required:

[0050] (1) If a variable frequency fan is used at the discharge outlet, the obtained operating frequency is the variable frequency data of the variable frequency fan. By collecting exhaust flow data at 10Hz, 20Hz, 30Hz and 40Hz, and performing linear regression based on a single monitoring, the emission parameters of the variable frequency fan at different operating frequencies can be obtained by monitoring the exhaust flow at different frequencies. That is, at a certain moment, the exhaust flow is proportional to the operating frequency, and the first emission parameter obtained is a function proportional to the operating frequency.

[0051] (2) If a fixed-frequency fan is used at the discharge outlet, the exhaust flow rate is a fixed amount, and the corresponding emission parameters are constant.

[0052] The exhaust flow rate of the discharge port of a fixed-frequency fan is constant, so the emission source strength can be directly calculated, and its corresponding first emission parameter can be determined.

[0053] S5: Obtain the source strength data of atmospheric pollutants from this type of emission outlet by using the operating frequency and the first emission parameter.

[0054] II. Pollution Simulation Unit: This unit uses atmospheric pollutant emission source strength data to simulate the pollutant diffusion process from several pollution sources over a preset time period using a pollution diffusion model. To analyze the diffusion process, an environmental data unit connected to the pollution simulation unit is also required. This environmental data unit is used to collect meteorological / topographical data for a designated area in real time.

[0055] The meteorological / topographic data is acquired through meteorological departments and networks, automatically obtaining GFS (Global Forecast System) and MODIS (topographic data). The meteorological data parameters include surface temperature, relative humidity, wind speed, wind direction, air pressure, radiation intensity, mixing layer height, and precipitation. The topographic data parameters include land use type data and ground elevation data. The data is preprocessed using the WRF Preprocessing System (WPS).

[0056] The pollution simulation unit, based on the CALPUFF diffusion model and combined with real-time data acquired by the data acquisition unit, can perform multi-frequency, multi-scenario simulations of the pollutant diffusion process from several pollution sources within a preset time period. The CALPUFF model is a non-steady-state Lagrange plume model system that can simulate the transport, transformation, and removal of pollutants in the atmospheric environment when the three-dimensional flow field changes with time and space. CALPUFF is suitable for simulation ranges from tens of meters to hundreds of kilometers, including terrain processing at the sub-grid scale, such as the impact of complex terrain. It also includes long-distance simulation calculation functions, such as dry deposition, wet deposition, chemical transformation of pollutants, and the impact of particulate matter concentration on visibility. The CALPUFF model system can handle continuous emission sources and intermittent emission situations, and can track the spatial and temporal changes of particle particles with the flow field.

[0057] 3. Dynamic Demonstration Unit: This unit dynamically demonstrates the simulation results from the scenario simulation unit.

[0058] The dynamic demonstration unit displays the following:

[0059] (1) The display data acquisition unit acquires the atmospheric pollutant emission source strength data of the set enterprise in real time. The atmospheric pollutant emission source strength data includes the overall pollutant gas emission data, as well as the atmospheric pollutant emission source strength data of each production process and each pollution source (emission concentration, exhaust flow rate, emission temperature, emission velocity, operating frequency of the exhaust fan, etc.).

[0060] (2) Display a dynamic schematic diagram of the diffusion of enterprise pollution sources using the diffusion model of the pollution simulation unit, and mark the diffusion trend with different colors;

[0061] (3) By selecting different processes or different pollution sources, display dynamic schematic diagrams of the diffusion of different pollution sources using the diffusion model of the pollution simulation unit, and mark the diffusion trend with different colors;

[0062] (4) By selecting the time interval and setting the emission scenario or parameters, the dynamic schematic diagram of the diffusion of the source tracing analysis unit using the diffusion model of the pollution simulation unit is displayed, and the diffusion trend is marked with different colors, as well as the dynamic schematic diagram of the diffusion of different pollution sources can be displayed.

[0063] The emission intensity of each pollution source is simulated using the dynamic intensity that varies over time, and the dynamic pollution contribution ratio of each pollution source is analyzed to identify the main contributing sources at different times.

[0064] IV. Source tracing analysis unit, used to analyze the pollution contribution weight of each pollution source, and can set emission parameters based on the enterprise's atmospheric pollutant emission source strength data by simulating arbitrary time intervals, and use the pollution simulation unit to analyze and predict the enterprise's pollution contribution weight.

[0065] The source tracing analysis unit is used to analyze the pollution contribution weight of each pollution source, including the following steps:

[0066] S11: Statistical analysis of emission intensity data of various air pollutants in the region within a specified time period;

[0067] S12: Calculate the hourly dynamic air pollutant emission source intensity data for each enterprise / pollution source within a set time period;

[0068] S13: Through pollution diffusion model simulation, calculate and obtain grid concentration field data of different pollutants for each emission source within a set time period;

[0069] S14: Calculate the pollution contribution weight of each enterprise (or each pollution source).

[0070] The source tracing analysis unit simulates arbitrary time intervals and different atmospheric pollutant emission scenarios and parameters. Using a pollution simulation unit, it analyzes and predicts the pollution contribution weight of enterprises. The pollution simulation unit, according to its diffusion model, inputs relevant parameters into the model. It can input emission source strength for any time interval; a variable time interval of one hour (3600 seconds) can be selected. The pollutant concentration contribution weight can then be calculated, including the following steps:

[0071] S21: Setting emission scenarios and emission parameters within a set time interval means directly obtaining emission source strength data under the actual operating conditions of the enterprise, or setting pollution load according to the scenario for all or part of the enterprise's production processes, thereby obtaining emission source strength data under that scenario.

[0072] S22: Statistical data on dynamic atmospheric pollutant emission source strength over a specified time interval;

[0073] S23: Calculate the source intensity data of air pollutants at each emission source at a set time interval;

[0074] S24: Through pollution diffusion model simulation, the grid concentration field data of different pollutants at each pollution source at a set time interval are calculated and obtained respectively;

[0075] S25: Calculate the pollution contribution weight of each enterprise (or each pollution source).

[0076] Under different emission scenarios or parameter conditions, the source tracing analysis unit obtains multiple sets of simulation results through the pollution simulation unit, and analyzes the dynamic pollution contribution ratio of each pollution source under different simulation results.

[0077] Furthermore, based on the company's production plan, historical emissions, and previous dynamic source strength, predictions can be made regarding the dynamic pollution contribution to the next production cycle.

[0078] The source analysis unit uses the pollution simulation unit to analyze and predict the pollution contribution weight of enterprises. The pollution simulation unit uses the CALPUFF model for processing. Adjusting the emission parameters also adjusts the input parameters of the CALPUFF model. The user inputs initial operating parameters that are consistent with the control parameters of the generated NetCDF file according to the simulation parameters and calculation requirements of the CALPUFF model. The CALPUFF model performs simulation based on the input initial operating parameters and obtains the simulation results.

[0079] The source tracing analysis unit analyzes the simulation results of the pollution simulation unit and compares the simulation results under different emission scenarios and emission parameter conditions to obtain the dynamic pollution contribution ratio of each enterprise or pollution source in the preset area, and provides corresponding time and location operating condition data, emission data, and meteorological condition data.

[0080] The source tracing analysis unit obtains simulation results under multiple sets of different emission parameters from the pollution simulation unit, analyzes the contribution ratio of each pollution source emission intensity under different simulation results, sorts the parameters according to the order of pollutant emission changes and meteorological field data changes, and obtains the parameter, contribution ratio, and time series corresponding change array. Based on the change array, the dynamic pollution contribution ratio of each pollution source emission intensity is determined, and corresponding time and location operating condition data, emission data, and meteorological condition data are provided for the contribution ratio.

[0081] The present invention has the following advantages:

[0082] (1) The present invention can automatically acquire the enterprise's operating data and emission data, and establish a correlation mathematical model to form a dynamic pollution emission source inventory. Compared with the current atmospheric pollution simulation and prediction using CALPUFF, which uses a static pollution emission source inventory and the simulation results only represent the annual average or quarterly average level, the present invention can simulate through a dynamic source inventory and has timeliness.

[0083] (2) This invention establishes the relationship between source strength and environmental quality impact, and dynamically tracks the relationship between emissions and environmental quality: Based on pollution source monitoring data, environmental monitoring data, meteorological data, etc., an atmospheric pollution diffusion model is used to simulate the concentration contribution of pollution sources to sensitive points around the environment, identify the main contributing sources, analyze the contribution of different pollution sources to national control stations and sensitive points at different times, and assess the impact of pollution sources in the region on ambient air quality.

[0084] (3) Based on the simulation results of different preset emission scenarios or parameters, this invention provides comprehensive analysis and suggestions for air pollution prevention and control. In particular, it uses models to accurately trace the source of pollution during periods of time, providing scientific tools for clarifying key contributing sources and targeted governance, and ultimately providing services for managers' decision-making.

Claims

1. A big data system for dynamic simulation of environmental air quality impacts and pollution source tracing, characterized in that: It includes a data acquisition and preprocessing unit, a pollution simulation unit, a dynamic demonstration unit, and a source tracing and analysis unit; the data acquisition unit is used to acquire the atmospheric pollutant emission source strength data of the designated enterprise in real time; the pollution simulation unit uses the atmospheric pollutant emission source strength data through a diffusion model to simulate the pollutant diffusion process of several pollution sources in a preset time period. The dynamic demonstration unit provides a dynamic demonstration of the simulation results from the scenario simulation unit; the source analysis unit is used to analyze the pollution contribution weight of each pollution source, and can analyze and predict the pollution contribution weight of enterprises by simulating arbitrary time intervals and different atmospheric pollutant emission scenarios and parameters using the pollution simulation unit.

2. The big data system for dynamic simulation of ambient air quality impact and pollution source tracing according to claim 1, characterized in that: The data acquisition unit is used to acquire the atmospheric pollutant emission source intensity data of the designated enterprise in real time, including the following steps: S1: For emission outlets where continuous pollution source monitoring equipment is installed, the emission source strength of air pollutants is obtained through the continuous monitoring equipment, including the emission concentration, flue gas flow rate, emission rate, flue gas temperature, and flue gas humidity of different air pollutants; S2: For emission outlets that do not have continuous pollution source monitoring equipment installed and use variable frequency fans, the concentration of atmospheric pollutant emissions is obtained through regular manual monitoring. The flue gas flow rate under different operating conditions is obtained by fitting the variable frequency data of the fan with the flue gas flow rate, and then the source strength of atmospheric pollutant emissions is obtained. S3: For emission outlets that do not have continuous pollution source monitoring equipment installed and use fixed-frequency fans, the concentration of atmospheric pollutant emissions is obtained through periodic manual monitoring, and the flue gas flow rate is obtained through actual measurement, thereby obtaining the intensity of atmospheric pollutant emission sources.

3. The big data system for dynamic simulation of ambient air quality impact and pollution source tracing according to claim 2, characterized in that: The enterprise uses variable frequency fans for exhausting air pollutants. The operating frequency obtained is the variable frequency data of the variable frequency fan. By collecting air pollutant emission data at 10Hz, 20Hz, 30Hz and 40Hz and performing linear regression processing, the emission parameters of the variable frequency fan at different operating frequencies can be obtained by monitoring the exhaust flow rate at different frequencies. For the exhaust outlet using a fixed frequency fan, the corresponding pollutant emission parameters are constant.

4. The big data system for dynamic simulation of ambient air quality impact and pollution source tracing according to claim 1, characterized in that: It also includes an environmental data unit connected to the pollution simulation unit, which is used to collect meteorological and preset terrain data of a set area in real time.

5. The big data system for dynamic simulation of ambient air quality impact and pollution source tracing according to claim 1, characterized in that: The source tracing analysis unit is used to analyze the pollution contribution weight of each pollution source, including the following steps: S11: Statistical analysis of emission intensity data of various air pollutants in the region within a specified time period; S12: Calculate the hourly dynamic atmospheric pollutant emission source intensity data for each enterprise / pollution source within a set time period; S13: Through pollution diffusion model simulation, calculate and obtain grid concentration field data of different pollutants for each emission source in a set time period; S14: Calculate the pollution contribution weight of each enterprise or each emission source.

6. The big data system for dynamic simulation of ambient air quality impact and pollution source tracing according to claim 1, characterized in that: The source tracing analysis unit simulates arbitrary time intervals and different atmospheric pollutant emission scenarios and parameters, and uses the pollution simulation unit to analyze and predict the pollution contribution ratio of enterprises, including the following steps: S21: Setting emission scenarios and emission parameters within a set time interval means directly obtaining emission source strength data under the actual operating conditions of the enterprise, or setting pollution loads for all or part of the enterprise's production processes according to the scenario, thereby obtaining emission source strength data under that scenario. S22: Statistical data on dynamic atmospheric pollutant emission source strength over a specified time interval; S23: Calculate the source intensity data of air pollutants at each emission source at a set time interval; S24: Through pollution diffusion model simulation, calculate and obtain grid concentration field data of different pollutants for each emission source at a set time interval; S25: Calculate the pollution contribution weight of each enterprise or emission source.

7. The big data system for dynamic simulation of ambient air quality impact and pollution source tracing according to claim 6, characterized in that: Under different emission scenarios or parameter conditions, the source tracing analysis unit obtains multiple sets of simulation results through the pollution simulation unit, and analyzes the dynamic pollution contribution ratio of each pollution source under different simulation results.

8. The big data system for dynamic simulation of ambient air quality impact and pollution source tracing according to claim 2, characterized in that: When the emission characteristics of an enterprise change, it is necessary to collect the emission concentration, exhaust flow rate, emission ratio, and operating frequency of exhaust fans of each emission source after the change, so as to obtain the updated first emission parameters. By simulating the updated emission parameters and analyzing the changes in their pollution contribution, an assessment can be made on whether the enterprise's internal work processes have been optimized.

9. The big data system for dynamic simulation of ambient air quality impact and pollution source tracing according to claim 1, characterized in that, The dynamic demonstration unit displays the following: (1) The display data acquisition unit acquires the atmospheric pollutant emission source strength data of the set enterprise in real time. The atmospheric pollutant emission source strength data includes the overall atmospheric pollutant emission source strength data, as well as the atmospheric pollutant emission source strength data of each production process and each pollution source (emission concentration, exhaust flow rate, emission temperature, emission velocity, operating frequency of the exhaust fan, etc.). (2) Display a dynamic schematic diagram of the diffusion of enterprise pollution sources using the diffusion model of the pollution simulation unit, and mark the diffusion trend with different colors; (3) By selecting different processes or different pollution sources, display the dynamic schematic diagram of diffusion using the diffusion model of the pollution simulation unit, and mark the diffusion trend with different colors; (4) By selecting the time interval and setting the emission scenario or parameters, the dynamic schematic diagram of the diffusion of the source tracing analysis unit using the diffusion model of the pollution simulation unit is displayed, and the diffusion trend is marked with different colors, as well as the dynamic schematic diagram of the diffusion of different pollution sources can be displayed.

10. The big data system for dynamic simulation of ambient air quality impact and pollution source tracing according to claim 9, characterized in that, The emission intensity of each pollution source is simulated using the dynamic intensity that varies over time, and the dynamic pollution contribution ratio of each pollution source is analyzed to identify the main contributing sources at different times.

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