Solid fuel environmental protection project online intelligent early warning system based on big data

Abstract

The invention discloses a solid fuel environmental protection project online intelligent early warning system based on big data, relates to the technical field of data processing, and solves the problem that it is difficult to establish a solid fuel environmental protection project database and store monitoring data; a temperature pollutant diffusion correlation model is difficult to establish, and the pollutant discharge rate and the pollutant diffusion influence range are analyzed; the influence degree of the solid fuel on environment sensitive areas, soil and underground water is difficult to evaluate; and an environmental protection risk assessment index is difficult to comprehensively assess and three-level early warning is difficult to establish according to the above technical problems. According to the invention, the monitoring and early warning of the solid fuel environmental protection project are realized by comprehensively evaluating the environmental protection risk evaluation indexes of the solid fuel to atmosphere, soil, underground water and environmental sensitive areas in each link.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular is an online intelligent early warning system for solid fuel environmental protection projects based on big data. Background Art

[0002] With the development of big data technology, the multifaceted environmental impacts of solid fuel production, storage, transportation, and use, coupled with increasingly stringent environmental protection requirements, are becoming increasingly apparent. By collecting real-time monitoring data from all aspects of solid fuel production, including environmental monitoring data on the atmosphere, soil, and groundwater, as well as relevant parameters during production, storage, and transportation, and combining multi-source data such as remote sensing and infrared data with the use of technologies like geographic information systems and computational fluid dynamics, data analysis and evaluation are conducted to achieve a comprehensive assessment and intelligent early warning of the environmental impacts of solid fuels at all stages.

[0003] The existing online intelligent early warning system for solid fuel environmental protection projects has the following problems: it is difficult to establish a solid fuel environmental protection project database to store monitoring data; it is difficult to establish a temperature pollutant diffusion correlation model to analyze the pollutant emission rate and the impact range of pollutant diffusion; it is difficult to assess the impact of solid fuels on environmentally sensitive areas, soil and groundwater; it is difficult to comprehensively evaluate the environmental risk assessment index and establish a three-level early warning based on it. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the first aspect of the present invention provides an online intelligent early warning system for solid fuel environmental protection projects based on big data, comprising the following modules: Data acquisition module: collects monitoring data of solid fuel in various links; Data processing module: establish a solid fuel environmental protection project database to store pre-processed monitoring data; Data Analysis Module: By fusing infrared data with remote sensing data, a temperature pollutant diffusion correlation model is established to analyze pollutant emission rates. The impact range of pollutant diffusion is calculated by combining real-time meteorological data and emission source locations. The impact of solid fuels on environmentally sensitive areas is assessed by overlaying map layers of solid fuels at various stages. The impact index of solid fuels on soil and groundwater is analyzed and assessed. Risk assessment module: Comprehensively evaluate the environmental risk assessment index of the atmosphere, soil, groundwater and environmentally sensitive areas in each link; Early warning module: Use environmental risk assessment index to establish a three-level early warning system.

[0005] In a further solution, the data acquisition module includes the following steps: real-time monitoring and collection of remote sensing data, infrared data and environmental monitoring data of solid fuel in the four links of production, storage, transportation and use; the environmental monitoring data includes: atmospheric monitoring data, soil monitoring data, groundwater monitoring data and environmentally sensitive area monitoring data.

[0006] A further solution is that the data processing module includes the following steps: establishing a solid fuel environmental protection project database, classifying the pre-processed real-time monitoring data according to the four links of production, storage, transportation, and use, establishing a multi-dimensional database index, and integrating and storing it with historical monitoring data.

[0007] A further approach is to build a temperature pollutant diffusion correlation model by fusing infrared data with remote sensing data and analyzing pollutant emission rates, including the following steps: The pre-processed infrared data is superimposed and fused with the remote sensing data, and geo-registration is performed based on the geographic information system platform. Each infrared pixel point is matched with the corresponding pixel-level position in the remote sensing image to obtain a fused data set containing both temperature information and spatial distribution information. Computational fluid dynamics is used to simulate pollutant diffusion paths and establish a temperature-pollutant diffusion correlation model; model diffusion parameters are dynamically adjusted based on the calorific value of solid fuels; Based on the use of solid fuels and monitoring data, the geographic information system platform is used to determine the specific location and type of pollutant emission sources; real-time meteorological data in the area where the emission sources are located is collected and pre-processed; According to the diffusion equation in the temperature pollutant diffusion correlation model, combined with meteorological data, the specific location of the pollutant emission source, and diffusion parameters, the source strength information of the pollutants is reversely calculated; according to the principle of conservation of mass, the emission rate of pollutants is equal to the mass of pollutants emitted per unit time and is dynamically corrected in combination with meteorological data.

[0008] A further approach, combining real-time meteorological data with emission source locations to calculate the impact range of pollutant diffusion, includes the following steps: The temperature data and pollutant concentration data are converted into raster format. The raster calculator of the geographic information system platform is used to calculate the temperature or concentration difference between each raster cell and its adjacent raster cells to obtain the magnitude and direction of the temperature gradient or concentration gradient, and generate the temperature gradient field and pollutant concentration gradient field respectively. Extract the gradient vector of each grid cell from the temperature gradient field and pollutant concentration gradient field grid data, use the vector dot product formula in the grid calculator to calculate the angle between the temperature gradient vector and the pollutant concentration gradient vector, and generate an angle distribution map; With the solid fuel high-temperature source as the center, a multi-level buffer zone of 500m, 1km, and 2km was established on the geographic information system platform, and the average pollutant concentration in each buffer zone was calculated. Combined with wind direction data, the cost-distance weighted function was used to simulate the diffusion path of pollutants from the high-temperature source to the low-temperature zone. Real-time meteorological data, emission source location information, pollutant emission rate, and the generated temperature gradient field, concentration gradient field, angle distribution map, and diffusion path are used as input parameters and substituted into the temperature pollutant diffusion correlation model; the diffusion process of pollutants in the atmosphere is simulated, the concentration distribution of pollutants in time and space is calculated, and the impact range of pollutant diffusion is obtained.

[0009] A further approach, by overlaying map layers of solid fuels at various stages, can assess the impact of solid fuels on environmentally sensitive areas. This approach includes the following steps: Collect map layer data including: map layers of solid fuels in four links and distribution maps of environmentally sensitive areas, and perform pre-processing; Import the processed map layer data into the geographic information system platform, use the overlay analysis tool to overlay all the map layer data to generate the first new layer; Combined with the 500m, 1km, and 2km multi-level buffer zones established on the geographic information system platform, the buffer zone layer was overlaid with the environmentally sensitive area layer, and the areas within the buffer zone that overlapped with the environmentally sensitive areas were identified to generate a second new layer; Conduct statistical analysis on the first new layer to calculate the area and number of environmentally sensitive areas within or near solid fuel activity areas; conduct statistical analysis on the second new layer to calculate the area of environmentally sensitive areas within each buffer zone and the proportion of environmentally sensitive areas within the buffer zone; Comprehensively evaluate the impact of solid fuels on environmentally sensitive areas. The calculation formula is: Among them, H is the comprehensive impact index of solid fuels on environmentally sensitive areas, is the impact range of pollutant diffusion, is the mean of the impact range of pollutant diffusion, is the pollutant emission rate, is the pollutant emission rate limit, is the area of the environmentally sensitive area within the i-th buffer zone, The total area of environmentally sensitive areas, is the radius of the i-th buffer zone; is the weight coefficient of the i-th buffer zone, 、 、 are the correction coefficients respectively.

[0010] A further approach to analyzing and evaluating the impact of solid fuels on soil and groundwater includes the following steps: Real-time monitoring and collection of soil microbial respiration rate, soil enzyme activity and microbial biomass, and evaluation of soil bacterial activity index by weighted summation; Real-time monitoring and collection of microbial metabolic activity and microbial community abundance in groundwater, and evaluation of the groundwater bacterial community activity index by weighted summation; By monitoring the concentrations of pollutants related to solid fuel residues in soil and groundwater in real time, the Pearson correlation coefficients between the bacterial activity index and the pollutant concentrations in soil and groundwater were calculated respectively; The soil impact index was calculated by multiplying the soil microbial activity index with the absolute value of the pollutant concentration and the Pearson correlation coefficient; similarly, the groundwater impact index was obtained.

[0011] In a further embodiment, the risk assessment module comprises the following steps: By calculating the environmental risk assessment index formula: in, is the environmental risk assessment index, is the influence range of pollutant diffusion in the kth link, is the pollutant emission rate of the kth link, t is the pollutant exposure time, is the comprehensive impact index of solid fuel on environmentally sensitive areas in the kth link, is the soil impact index of the kth link, is the groundwater impact index of the kth link; is the distance from the pollution emission source in the kth link to the environmentally sensitive area; k=1, 2, 3, 4 correspond to the four links of solid fuel production, storage, transportation and use respectively.

[0012] In a further embodiment, the early warning module comprises the following steps: According to the calculation results of the environmental risk assessment index, the average value of the historical environmental risk assessment index is calculated. and standard deviation ; when When the risk level is low, a level 1 warning is triggered. When the risk level is medium, it triggers a level 2 warning. When the risk level is high, a level 3 warning is triggered.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention integrates infrared data and remote sensing data, utilizes geographic information system platform to process data and establish temperature pollutant diffusion correlation model, and realizes analysis and simulation of solid fuel pollutant diffusion.

[0014] The present invention comprehensively evaluates the impact of solid fuels on environmentally sensitive areas by superimposing map layers of each link of solid fuels with the distribution map of environmentally sensitive areas and combining it with buffer zone analysis. It also evaluates the impact of solid fuels on soil and groundwater by introducing the bacterial community activity index, real-time monitoring and combining it with correlation analysis.

[0015] The present invention establishes an environmental risk assessment index formula by comprehensively considering multiple factors and formulates a three-level early warning mechanism based on historical data. Compared with traditional single risk factor early warning, it can more accurately reflect the overall risk situation, issue early warning signals in a timely manner, and provide strong support for environmental management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 It is a system module diagram of the present invention; Figure 2 This is a flow chart of the temperature pollutant diffusion correlation model analysis method in the present invention. DETAILED DESCRIPTION

[0018] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] See also Figure 1-2 The first embodiment of the present invention provides an online intelligent early warning system for solid fuel environmental protection projects based on big data, including the following modules: Data acquisition module: collects monitoring data of solid fuel in various links; Data processing module: establish a solid fuel environmental protection project database to store pre-processed monitoring data; Data Analysis Module: By fusing infrared data with remote sensing data, a temperature pollutant diffusion correlation model is established to analyze pollutant emission rates. The impact range of pollutant diffusion is calculated by combining real-time meteorological data and emission source locations. The impact of solid fuels on environmentally sensitive areas is assessed by overlaying map layers of solid fuels at various stages. The impact index of solid fuels on soil and groundwater is analyzed and assessed. Risk assessment module: Comprehensively evaluate the environmental risk assessment index of the atmosphere, soil, groundwater and environmentally sensitive areas in each link; Early warning module: Use environmental risk assessment index to establish a three-level early warning system.

[0020] Specifically, the data acquisition module monitors and collects real-time remote sensing and infrared data from the production, storage, transportation, and use of solid fuels, as well as environmental monitoring data from the atmosphere, soil, groundwater, and environmentally sensitive areas. The data processing module establishes a database for solid fuel environmental protection projects, categorizes the preprocessed real-time monitoring data by process, creates a multidimensional database index, and integrates and stores it with historical monitoring data. The data analysis module first overlays and fuses the preprocessed infrared data with the remote sensing data, georeferencing it using a geographic information system (GIS) platform to produce a fused dataset containing temperature and spatial distribution information. Computational fluid dynamics (CFD) is then used to simulate pollutant diffusion paths, establish a temperature-pollutant diffusion correlation model, and dynamically adjust the model's diffusion parameters based on the calorific value of the solid fuel. Simultaneously, real-time meteorological data is collected from the emission source area to determine the specific location and type of the pollutant emission source, and reverse-calculate the source intensity information, i.e., the emission rate. Combining the real-time meteorological data with the emission source location, the impact range of the pollutant diffusion is calculated through spatial distribution analysis and raster calculations on the GIS platform. Then, by superimposing the map layers of solid fuels at each link with the distribution map of environmentally sensitive areas and combining them with buffer zone analysis, the impact of solid fuels on environmentally sensitive areas is assessed. Finally, the respiration rate, soil enzyme activity and microbial biomass of microorganisms in the soil, as well as the metabolic activity and abundance of microbial communities in groundwater are monitored and collected in real time. The bacterial community activity index in the soil and groundwater is evaluated by weighted summation, and the Pearson correlation coefficient between the bacterial community activity index in the soil and groundwater and the pollutant concentration is calculated to obtain the soil and groundwater impact index. The risk assessment module is used to comprehensively evaluate the environmental risk assessment index of the atmosphere, soil, groundwater and environmentally sensitive areas in each link by calculating the environmental risk assessment index formula. The early warning module is used to calculate the mean and standard deviation of the historical environmental risk assessment index based on the calculation results of the environmental risk assessment index, and establish a three-level early warning mechanism. When the risk level is low, medium and high, the first, second and third level warnings are triggered respectively. Through the collaborative work of various modules, the present invention realizes online intelligent early warning for solid fuel environmental protection projects, which helps to timely discover and respond to environmental risks.

[0021] In this embodiment, the data acquisition module includes the following steps: real-time monitoring and acquisition of remote sensing data, infrared data and environmental monitoring data of solid fuel in the four links of production, storage, transportation and use; the environmental monitoring data includes: atmospheric monitoring data, soil monitoring data, groundwater monitoring data and environmentally sensitive area monitoring data.

[0022] Specifically, real-time monitoring and collection of various data from the production, storage, transportation, and use of solid fuels are carried out. Remote sensing data, acquired through satellite or drone-mounted remote sensing equipment, is used to understand the macroscopic distribution and environmental conditions of solid fuels at each stage. Infrared data, collected using thermal imaging cameras, can monitor temperature changes in each stage of solid fuel production, helping to identify abnormally hot areas, such as incomplete combustion during production or potential spontaneous combustion during storage. Environmental monitoring data covers the atmosphere, soil, groundwater, and environmentally sensitive areas. Atmospheric monitoring data include the concentrations of pollutants such as sulfur dioxide, nitrogen oxides, and particulate matter, and are monitored in real time by deploying automatic atmospheric monitoring stations and mobile monitoring equipment around various links of solid fuel. Soil monitoring data involves the content of pollutants such as heavy metals and organic matter in the soil, and is collected regularly at soil sampling points set up in areas such as solid fuel storage sites, transportation routes, and used land. Groundwater monitoring data focuses on indicators such as total coliform bacteria, ammonia nitrogen, and volatile organic compounds in groundwater, and is obtained through regular sampling and testing of groundwater wells or monitoring holes in areas surrounding solid fuels. Environmentally sensitive area monitoring data focuses on the ecological environment quality status of environmentally sensitive areas such as nature reserves, residential areas, and water sources around various links of solid fuels, and uses corresponding ecological monitoring methods to collect data.

[0023] In this embodiment, the data processing module includes the following steps: establishing a solid fuel environmental protection project database, classifying the pre-processed real-time monitoring data according to the four links of production, storage, transportation, and use, establishing a multi-dimensional database index, and integrating and storing it with historical monitoring data.

[0024] Specifically, first, a database for the solid fuel environmental protection project is established. Next, the collected real-time monitoring data is preprocessed to remove noise, perform data cleaning, and perform normalization to ensure data accuracy. The preprocessed real-time monitoring data is then categorized according to the four stages of solid fuel production: production, storage, transportation, and use. Monitoring data for the production stage can be stored in the database's production data table, data for the storage stage in the storage data table, data for the transportation stage in the transportation data table, and data for the use stage in the use data table. Furthermore, multi-dimensional database indexes are established for each data table. Indexes can be created based on the time dimension to facilitate queries for monitoring data within a specific time period; indexes can be created based on the spatial dimension to facilitate locating monitoring data for a specific area; and indexes can be created based on data type, such as atmospheric monitoring data or soil monitoring data, to improve data query and retrieval efficiency. Finally, the preprocessed real-time monitoring data is integrated and stored with historical monitoring data. For example, new real-time monitoring data can be appended to the historical data table for the corresponding stage to ensure complete and continuous data in the database.

[0025] In this embodiment, by fusing infrared data with remote sensing data, a temperature pollutant diffusion correlation model is established to analyze the pollutant emission rate, including the following steps: The pre-processed infrared data is superimposed and fused with the remote sensing data, and geo-registration is performed based on the geographic information system platform. Each infrared pixel point is matched with the corresponding pixel-level position in the remote sensing image to obtain a fused data set containing both temperature information and spatial distribution information. Computational fluid dynamics is used to simulate pollutant diffusion paths and establish a temperature-pollutant diffusion correlation model; model diffusion parameters are dynamically adjusted based on the calorific value of solid fuels; Based on the use of solid fuels and monitoring data, the geographic information system platform is used to determine the specific location and type of pollutant emission sources; real-time meteorological data in the area where the emission sources are located is collected and pre-processed; According to the diffusion equation in the temperature pollutant diffusion correlation model, combined with meteorological data, the specific location of the pollutant emission source, and diffusion parameters, the source strength information of the pollutants is reversely calculated; according to the principle of conservation of mass, the emission rate of pollutants is equal to the mass of pollutants emitted per unit time and is dynamically corrected in combination with meteorological data.

[0026] Specifically, the collected infrared data is preprocessed, including: removing noise and outliers, filtering, smoothing, etc. The remote sensing data is preprocessed by radiation correction, geometric correction, etc. Using the geographic information system platform, the preprocessed infrared thermal image is geo-referenced with the satellite remote sensing image. Ensure that each infrared pixel point matches the corresponding pixel-level position in the remote sensing image. Through geo-reference, a fused data set containing both temperature information and spatial distribution information is generated. The fusion method can adopt pixel-level fusion, feature-level fusion and other technologies. Based on the fused data set, computational fluid dynamics CFD is used to simulate the diffusion path of pollutants. CFD simulation can take into account physical processes such as fluid flow, heat transfer, and mass transfer. The specific mathematical expression of the diffusion equation is: Where C is the pollutant concentration, t is time, and u is the fluid velocity vector, which can be expressed as ,in 、 、 are the velocity components of the fluid in the x, y, and z directions, respectively, and D is the diffusion coefficient; is the gradient of pollutant concentration, , points to the direction where the concentration increases fastest, and its magnitude is the maximum rate of concentration change; Represents the dot product of two vectors; This describes the diffusion process of pollutants in a stationary fluid, namely molecular diffusion or turbulent diffusion caused by concentration differences. D is the diffusion coefficient, and S represents the source-sink strength, which represents the change in pollutant concentration per unit time per unit volume due to source emission or sink absorption. Based on the calorific value of the solid fuel, the diffusion parameters of the model, including the diffusion coefficient and boundary conditions, are dynamically adjusted. The specific mathematical expression is: , where D is the adjusted diffusion coefficient, is the initial diffusion coefficient, Q is the calorific value of the solid fuel, Is the benchmark calorific value. Among them, by real-time collection and pre-processing of solid fuel calorific value data, through the control variable experiment, the functional relationship between calorific value and diffusion coefficient is fitted, the corrected diffusion coefficient is input into the computational fluid dynamics model in real time, and the coefficient term in the diffusion equation is updated. According to the usage of solid fuels and monitoring data, the GIS platform is used to determine the specific location and type of pollutant emission sources. Emission sources can include chimneys, storage tanks, transport vehicles, etc. Collect real-time meteorological data in the area where the emission source is located, including wind speed, wind direction, temperature, humidity, etc., and pre-process the meteorological data for data cleaning and standardization; meteorological data can be obtained through meteorological stations or numerical weather forecast models. According to the diffusion equation in the temperature pollutant diffusion correlation model, combined with meteorological data, the specific location of the pollutant emission source, and diffusion parameters, the source strength information of the pollutant is reversely calculated, that is, the mass of pollutants emitted per unit time. The source strength formula is: ,in, It's Yuanqiang. is the initial source strength, is the actual pollutant concentration at the i-th monitoring point, is the pollutant concentration calculated by the model at the i-th monitoring point, is the total number of monitoring points; The initial source strength can be set based on historical emission data or expert experience; is the fitting coefficient. The initial fitting coefficient is set to 0.05 and adjusted through cross-validation, L curve method or empirical selection. According to the principle of conservation of mass, the emission rate of pollutants is equal to the mass of pollutants emitted per unit time, and it is dynamically corrected in combination with meteorological data. The formula is: ,in, It's Yuanqiang. is the pollutant emission rate after dynamic correction, is the real-time temperature, is the reference temperature; is the real-time humidity, is the reference humidity; F is the real-time wind speed, is the reference wind speed, 、 and are correction factors, which are 0.5, 0.3, and 0.2, respectively. These correction factors can be adjusted based on historical data or actual conditions. Dynamic correction methods can account for the impact of meteorological factors such as temperature, wind speed, and wind direction on pollutant dispersion. Continuously collect infrared and remote sensing data, and update the fused dataset and model parameters in real time to reflect the latest pollutant emissions and dispersion. Based on real-time monitoring data, the temperature-pollutant-diffusion correlation model is dynamically updated to ensure its accuracy and timeliness.

[0027] This also involves building a temperature-pollutant diffusion correlation model using a neural network model based on machine learning. The input layer receives fused infrared data and remote sensing data, including temperature, pollutant concentrations, and meteorological parameters, while the output layer outputs predicted pollutant diffusion results. The model is trained using a large amount of fused data as input samples and the corresponding actual pollutant diffusion patterns as output samples. By adjusting parameters such as the neural network weights, the error between the model output and the actual results is minimized, thereby learning the linear mapping relationship between temperature and pollutant diffusion. A portion of the data is used as a validation set to verify the trained model and assess its accuracy and generalization ability. Based on the validation results, the model is optimized, such as adjusting the network structure and optimizing algorithm parameters, to improve its performance.

[0028] In this embodiment, the pollutant diffusion impact range is calculated by combining real-time meteorological data and emission source locations, including the following steps: The temperature data and pollutant concentration data are converted into raster format. The raster calculator of the geographic information system platform is used to calculate the temperature or concentration difference between each raster cell and its adjacent raster cells to obtain the magnitude and direction of the temperature gradient or concentration gradient, and generate the temperature gradient field and pollutant concentration gradient field respectively. Extract the gradient vector of each grid cell from the temperature gradient field and pollutant concentration gradient field grid data, use the vector dot product formula in the grid calculator to calculate the angle between the temperature gradient vector and the pollutant concentration gradient vector, and generate an angle distribution map; With the solid fuel high-temperature source as the center, a multi-level buffer zone of 500m, 1km, and 2km was established on the geographic information system platform, and the average pollutant concentration in each buffer zone was calculated. Combined with wind direction data, the cost-distance weighted function was used to simulate the diffusion path of pollutants from the high-temperature source to the low-temperature zone. Real-time meteorological data, emission source location information, pollutant emission rate, and the generated temperature gradient field, concentration gradient field, angle distribution map, and diffusion path are used as input parameters and substituted into the temperature pollutant diffusion correlation model; the diffusion process of pollutants in the atmosphere is simulated, the concentration distribution of pollutants in time and space is calculated, and the impact range of pollutant diffusion is obtained.

[0029] Specifically, a geographic information system (GIS) platform was used to analyze the spatial distribution of monitoring data and create spatial distribution maps of the atmosphere, soil, groundwater, and environmentally sensitive areas. Temperature and pollutant concentration data were converted into a raster format, with each grid cell representing a temperature or pollutant concentration value. The platform's raster calculator was used to calculate the temperature or concentration difference between each grid cell and its adjacent cells, generating temperature and pollutant concentration gradient fields that reflect the spatial rate and direction of change in temperature and pollutant concentration. The gradient vector for each grid cell, containing information on the magnitude and direction of the gradient, was extracted from the temperature and pollutant concentration gradient fields. The vector dot product formula was used to calculate the angle between the temperature and pollutant concentration gradient vectors, generating an angle distribution map to help understand the relationship between the temperature and pollutant concentration fields. Multi-level buffer zones of 500m, 1km, and 2km were established on the GIS platform, centered around a high-temperature solid fuel source. The mean pollutant concentration within each buffer zone was calculated to assess the extent of pollutant diffusion within different distance ranges. Combined with wind direction data, a cost-distance weighted function was used to simulate the diffusion path of pollutants from high-temperature sources to low-temperature areas, accounting for the influence of factors such as wind speed and direction on pollutant diffusion. Real-time meteorological data, including wind speed, wind direction, temperature, humidity, emission source location information, pollutant emission rates, and generated temperature gradient fields, concentration gradient fields, angle distribution maps, and diffusion paths, are used as input parameters and substituted into the temperature-pollutant diffusion correlation model. The model is run to simulate the diffusion of pollutants in the atmosphere and calculate the concentration distribution of pollutants over time and space. By simulating the diffusion of pollutants in the atmosphere and calculating their concentration distribution over time and space, the impact range of pollutant diffusion can be determined, allowing the affected areas and degree to be identified.

[0030] In this embodiment, by superimposing map layers of solid fuels at various stages, the impact of solid fuels on environmentally sensitive areas is assessed, including the following steps: Collect map layer data including: map layers of solid fuels in four links and distribution maps of environmentally sensitive areas, and perform pre-processing; Import the processed map layer data into the geographic information system platform, use the overlay analysis tool to overlay all the map layer data to generate the first new layer; Combined with the 500m, 1km, and 2km multi-level buffer zones established on the geographic information system platform, the buffer zone layer was overlaid with the environmentally sensitive area layer, and the areas within the buffer zone that overlapped with the environmentally sensitive areas were identified to generate a second new layer; Conduct statistical analysis on the first new layer to calculate the area and number of environmentally sensitive areas within or near solid fuel activity areas; conduct statistical analysis on the second new layer to calculate the area of environmentally sensitive areas within each buffer zone and the proportion of environmentally sensitive areas within the buffer zone; Comprehensively evaluate the impact of solid fuels on environmentally sensitive areas. The calculation formula is: Among them, H is the comprehensive impact index of solid fuels on environmentally sensitive areas, is the impact range of pollutant diffusion, is the mean of the impact range of pollutant diffusion, is the pollutant emission rate, is the pollutant emission rate limit, is the area of the environmentally sensitive area within the i-th buffer zone, The total area of environmentally sensitive areas, is the radius of the i-th buffer zone; is the weight coefficient of the i-th buffer zone, 、 、 are the correction coefficients respectively.

[0031] Specifically, collect map layer data for solid fuel production distribution maps, storage maps, transportation route maps, usage maps, and environmentally sensitive area distribution maps. Convert the collected map layer data into a format recognizable by the GIS platform, such as Shapefile or GeoJSON. Ensure that all layers use the same coordinate system, typically using WGS-84 or a projected coordinate system such as UTM. Import the processed map layer data into the GIS platform. Use the overlay analysis tool to overlay the solid fuel production, storage, transportation, and usage layers with the environmentally sensitive areas layer to generate a first new layer. This layer shows the overlap between solid fuel activity areas and environmentally sensitive areas. Create multiple buffer zones of 500m, 1km, and 2km on the GIS platform, centered around the solid fuel high-temperature source. Overlay the buffer zone layer with the environmentally sensitive areas layer to identify areas within the buffer zones that overlap with environmentally sensitive areas, generating a second new layer. Perform statistical analysis on the first new layer to calculate the area and number of environmentally sensitive areas within or near solid fuel activity areas. Perform statistical analysis on the second new layer to calculate the area of environmentally sensitive areas within each buffer zone and the proportion of environmentally sensitive areas within the buffer zone. The formula for the impact of solid fuels on environmentally sensitive areas is used, where: The weight coefficient for the first 500m buffer zone is 0.5. The weight coefficient for the second 1km buffer zone is 0.3. The weight coefficient for the third 2km buffer zone is 0.2. 、 、 The correction coefficients are 0.4, 0.3, and 0.3, respectively. The buffer zone weight coefficient and correction coefficient are dynamically adjusted based on historical data or experimental results. The calculated comprehensive impact index is used to assess the impact of solid fuels on environmentally sensitive areas. Generally, a larger H value indicates a greater impact.

[0032] In this embodiment, analyzing and evaluating the impact index of solid fuels on soil and groundwater includes the following steps: Real-time monitoring and collection of soil microbial respiration rate, soil enzyme activity and microbial biomass, and evaluation of soil bacterial activity index by weighted summation; Real-time monitoring and collection of microbial metabolic activity and microbial community abundance in groundwater, and evaluation of the groundwater bacterial community activity index by weighted summation; By monitoring the concentrations of pollutants related to solid fuel residues in soil and groundwater in real time, the Pearson correlation coefficients between the bacterial activity index and the pollutant concentrations in soil and groundwater were calculated respectively; The soil impact index was calculated by multiplying the soil microbial activity index with the absolute value of the pollutant concentration and the Pearson correlation coefficient; similarly, the groundwater impact index was obtained.

[0033] Specifically, through sensor networks and laboratory analysis, real-time monitoring is performed to collect data such as soil microbial respiration rate, soil enzyme activity, and microbial biomass, as well as groundwater microbial metabolic activity and microbial community abundance. Based on relevant research and practical experience, the weights for microbial respiration rate, soil enzyme activity, and microbial biomass can be set to 0.3, 0.4, and 0.3, respectively. For example, if the microbial respiration rate is R, the soil enzyme activity is E, and the microbial biomass is B, then the soil microbial activity index is 0.3R + 0.4E + 0.3B. Based on research and actual conditions, the weights for microbial metabolic activity and microbial community abundance can be set to 0.6 and 0.4, respectively. Assuming microbial metabolic activity is M and microbial community abundance is A, then the groundwater microbial activity index is 0.6M + 0.4A. These weighting coefficients are dynamically adjusted based on historical data or experimental results. Pollutant concentrations related to solid fuel residues in soil and groundwater are monitored in real time, and statistical software is used to calculate the Pearson correlation coefficient between the soil and groundwater microbial activity index and pollutant concentrations. The soil microbial activity index was multiplied by the absolute value of the pollutant concentration and the Pearson correlation coefficient. Similarly, the groundwater microbial activity index was multiplied by the absolute value of the pollutant concentration and the Pearson correlation coefficient to obtain the corresponding soil impact index and groundwater impact index, respectively.

[0034] In this embodiment, the risk assessment module includes the following steps: By calculating the environmental risk assessment index formula: in, is the environmental risk assessment index, is the influence range of pollutant diffusion in the kth link, is the pollutant emission rate of the kth link, t is the pollutant exposure time, is the comprehensive impact index of solid fuel on environmentally sensitive areas in the kth link, is the soil impact index of the kth link, is the groundwater impact index of the kth link; is the distance from the pollution emission source in the kth link to the environmentally sensitive area; k=1, 2, 3, 4 correspond to the four links of solid fuel production, storage, transportation and use respectively.

[0035] Specifically, based on the fused infrared and remote sensing data, a temperature-pollutant diffusion correlation model was established to simulate the atmospheric diffusion of pollutants, calculate the temporal and spatial distribution of pollutant concentrations, and determine the impact range of pollutant diffusion. By inversely calculating pollutant source intensity information (i.e., the mass of pollutants emitted per unit time) and dynamically correcting it with meteorological data, the pollutant emission rate for each link was determined. By overlaying a map of solid fuels at each link with a distribution map of environmentally sensitive areas and combining it with a buffer zone analysis, the area and number of environmentally sensitive areas within or near the solid fuel activity zone, as well as the area and proportion of environmentally sensitive areas within each buffer zone, were calculated. A comprehensive assessment was conducted using a formula for the degree of impact of solid fuels on environmentally sensitive areas to determine the comprehensive impact index of solid fuels on environmentally sensitive areas for each link. Real-time monitoring and collection of relevant soil and groundwater data were used to evaluate the bacterial activity index in soil and groundwater using a weighted summation method. The Pearson correlation coefficient between the bacterial activity index and pollutant concentration was then calculated to determine the soil impact index and groundwater impact index. Using a geographic information system platform, the specific location of pollution emission sources and the distribution of environmentally sensitive areas were determined, and the distance between them was calculated to determine the distance from the pollution emission sources to environmentally sensitive areas for each link. Substituting the various parameters determined above into the environmental risk assessment index formula, the environmental risk assessment index is calculated. This allows for a comprehensive assessment of the parameters in the four stages of solid fuel production, storage, transportation, and use, and then a comprehensive calculation of the environmental risk assessment index provides a basis for risk warning.

[0036] In this embodiment, the early warning module includes the following steps: According to the calculation results of the environmental risk assessment index, the average value of the historical environmental risk assessment index is calculated. and standard deviation ; when When the risk level is low, a level 1 warning is triggered. When the risk level is medium, it triggers a level 2 warning. When the risk level is high, a level 3 warning is triggered.

[0037] Specifically, historical environmental risk assessment index data is collected to ensure the integrity and accuracy of the data. The mean and standard deviation of historical data are statistically calculated. The mean represents the average level of risk, and the standard deviation measures the degree of risk fluctuation. When the environmental risk assessment index is lower than the historical mean and lower than the mean minus one standard deviation, a level one warning is triggered, which means that the current risk is low, but attention should still be paid to possible changes. When the index is between the mean minus one standard deviation and the mean plus one standard deviation, a level two warning is triggered. At this time, the risk is at a medium level, and vigilance and preventive measures need to be taken. When the index is higher than the mean plus one standard deviation, a level three warning is triggered, which means that the current risk is high and may have a significant impact on the environment, and immediate countermeasures need to be taken. During the real-time monitoring process, the environmental risk assessment index calculated in real time is compared with the set warning threshold to trigger the corresponding level of warning, thereby realizing a multi-level warning mechanism.

[0038] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An online intelligent early warning system for solid fuel environmental protection projects based on big data, characterized by: Includes the following modules: Data acquisition module: collects monitoring data of solid fuel in various links; Data processing module: establish a solid fuel environmental protection project database to store pre-processed monitoring data; Data Analysis Module: By fusing infrared data with remote sensing data, a temperature pollutant diffusion correlation model is established to analyze pollutant emission rates. The impact range of pollutant diffusion is calculated by combining real-time meteorological data and emission source locations. The impact of solid fuels on environmentally sensitive areas is assessed by overlaying map layers of solid fuels at various stages. The impact index of solid fuels on soil and groundwater is analyzed and assessed. Risk assessment module: Comprehensively evaluate the environmental risk assessment index of the atmosphere, soil, groundwater and environmentally sensitive areas in each link; Early warning module: Use environmental risk assessment index to establish a three-level early warning system.

2. The online intelligent early warning system for solid fuel environmental protection projects based on big data according to claim 1 is characterized in that: The data acquisition module includes the following steps: real-time monitoring and acquisition of remote sensing data, infrared data and environmental monitoring data of solid fuel in the four links of production, storage, transportation and use; the environmental monitoring data includes: atmospheric monitoring data, soil monitoring data, groundwater monitoring data and environmentally sensitive area monitoring data.

3. The online intelligent early warning system for solid fuel environmental protection projects based on big data according to claim 1 is characterized in that: The data processing module includes the following steps: establishing a solid fuel environmental protection project database, classifying the pre-processed real-time monitoring data according to the four links of production, storage, transportation, and use, establishing a multi-dimensional database index, and integrating and storing it with historical monitoring data.

4. The online intelligent early warning system for solid fuel environmental protection projects based on big data according to claim 1 is characterized in that: By fusing infrared data with remote sensing data, a temperature pollutant diffusion correlation model is established to analyze pollutant emission rates, including the following steps: The pre-processed infrared data is superimposed and fused with the remote sensing data, and geo-registration is performed based on the geographic information system platform. Each infrared pixel point is matched with the corresponding pixel-level position in the remote sensing image to obtain a fused data set containing both temperature information and spatial distribution information. Computational fluid dynamics is used to simulate pollutant diffusion paths and establish a temperature-pollutant diffusion correlation model; model diffusion parameters are dynamically adjusted based on the calorific value of solid fuels; Based on the use of solid fuels and monitoring data, the geographic information system platform is used to determine the specific location and type of pollutant emission sources; real-time meteorological data in the area where the emission sources are located is collected and pre-processed; According to the diffusion equation in the temperature pollutant diffusion correlation model, combined with meteorological data, the specific location of the pollutant emission source, and diffusion parameters, the source strength information of the pollutants is reversely calculated; according to the principle of conservation of mass, the emission rate of pollutants is equal to the mass of pollutants emitted per unit time and is dynamically corrected in combination with meteorological data.

5. The online intelligent early warning system for solid fuel environmental protection projects based on big data according to claim 4 is characterized in that: Combining real-time meteorological data and emission source locations to calculate the impact range of pollutant diffusion includes the following steps: The temperature data and pollutant concentration data are converted into raster format. The raster calculator of the geographic information system platform is used to calculate the temperature or concentration difference between each raster cell and its adjacent raster cells to obtain the magnitude and direction of the temperature gradient or concentration gradient, and generate the temperature gradient field and pollutant concentration gradient field respectively. Extract the gradient vector of each grid cell from the temperature gradient field and pollutant concentration gradient field grid data, use the vector dot product formula in the grid calculator to calculate the angle between the temperature gradient vector and the pollutant concentration gradient vector, and generate an angle distribution map; With the solid fuel high-temperature source as the center, a multi-level buffer zone of 500m, 1km, and 2km was established on the geographic information system platform, and the average pollutant concentration in each buffer zone was calculated. Combined with wind direction data, the cost-distance weighted function was used to simulate the diffusion path of pollutants from the high-temperature source to the low-temperature zone. Real-time meteorological data, emission source location information, pollutant emission rate, and the generated temperature gradient field, concentration gradient field, angle distribution map, and diffusion path are used as input parameters and substituted into the temperature pollutant diffusion correlation model; the diffusion process of pollutants in the atmosphere is simulated, the concentration distribution of pollutants in time and space is calculated, and the impact range of pollutant diffusion is obtained.

6. The online intelligent early warning system for solid fuel environmental protection projects based on big data according to claim 5 is characterized in that: By overlaying map layers of solid fuels at various stages, the impact of solid fuels on environmentally sensitive areas can be assessed, including the following steps: Collect map layer data including: map layers of solid fuels in four links and distribution maps of environmentally sensitive areas, and perform pre-processing; Import the processed map layer data into the geographic information system platform, use the overlay analysis tool to overlay all the map layer data to generate the first new layer; Combined with the 500m, 1km, and 2km multi-level buffer zones established on the geographic information system platform, the buffer zone layer was overlaid with the environmentally sensitive area layer, and the areas within the buffer zone that overlapped with the environmentally sensitive areas were identified to generate a second new layer; Conduct statistical analysis on the first new layer to calculate the area and number of environmentally sensitive areas within or near solid fuel activity areas; conduct statistical analysis on the second new layer to calculate the area of environmentally sensitive areas within each buffer zone and the proportion of environmentally sensitive areas within the buffer zone; Comprehensively evaluate the impact of solid fuels on environmentally sensitive areas. The calculation formula is: Among them, H is the comprehensive impact index of solid fuels on environmentally sensitive areas, is the impact range of pollutant diffusion, is the mean of the impact range of pollutant diffusion, is the pollutant emission rate, is the pollutant emission rate limit, is the area of the environmentally sensitive area within the i-th buffer zone, The total area of environmentally sensitive areas, is the radius of the i-th buffer zone; is the weight coefficient of the i-th buffer zone, 、 、 are the correction coefficients respectively.

7. The online intelligent early warning system for solid fuel environmental protection projects based on big data according to claim 1 is characterized in that: The analysis and evaluation of the impact index of solid fuels on soil and groundwater includes the following steps: Real-time monitoring and collection of soil microbial respiration rate, soil enzyme activity and microbial biomass, and evaluation of soil bacterial activity index by weighted summation; Real-time monitoring and collection of microbial metabolic activity and microbial community abundance in groundwater, and evaluation of the groundwater bacterial community activity index by weighted summation; By monitoring the concentrations of pollutants related to solid fuel residues in soil and groundwater in real time, the Pearson correlation coefficients between the bacterial activity index and the pollutant concentrations in soil and groundwater were calculated respectively; The soil impact index was calculated by multiplying the soil microbial activity index with the absolute value of the pollutant concentration and the Pearson correlation coefficient; similarly, the groundwater impact index was obtained.

8. The online intelligent early warning system for solid fuel environmental protection projects based on big data according to claim 1 is characterized in that: The risk assessment module includes the following steps: By calculating the environmental risk assessment index formula: in, is the environmental risk assessment index, is the influence range of pollutant diffusion in the kth link, is the pollutant emission rate of the kth link, t is the pollutant exposure time, is the comprehensive impact index of solid fuel on environmentally sensitive areas in the kth link, is the soil impact index of the kth link, is the groundwater impact index of the kth link; is the distance from the pollution emission source in the kth link to the environmentally sensitive area; k=1, 2, 3, 4 correspond to the four links of solid fuel production, storage, transportation and use respectively.

9. The online intelligent early warning system for solid fuel environmental protection projects based on big data according to claim 8 is characterized in that: The early warning module includes the following steps: According to the calculation results of the environmental risk assessment index, the average value of the historical environmental risk assessment index is calculated. and standard deviation ; when When the risk level is low, a level 1 warning is triggered. When the risk level is medium, it triggers a level 2 warning. When the risk level is high, a level 3 warning is triggered.

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