Straw open-air incineration pollutant emission quantification method based on multi-source data fusion

Abstract

The invention relates to the technical field of environmental pollutant detection, in particular to a straw open-air incineration pollutant emission quantitative method based on multi-source data fusion, which comprises the following steps: based on air pollutant monitoring data, calling a sensor network to obtain PM2.5, CO and NOX concentrations, analyzing fire point heat radiation intensity, collecting meteorological data, and calculating the concentration of PM2.5, CO and NOX; and obtaining a pollution source data set by combining the time sequence change of the fire points and the meteorological parameters. By integrating an environment monitoring sensor, satellite remote sensing and ground meteorological data, the comprehensiveness and accuracy of pollution source monitoring are improved, the combination of fire point intensity and pollutant concentration time sequence data is optimized, the evaluation of fire point and pollution hot spot areas and the comprehensive analysis of wind directions and meteorological conditions are optimized, the accuracy of a pollutant diffusion model is improved, and the pollution source diffusion efficiency is improved. The pollutant release amount is dynamically adjusted, and the diffusion model is corrected by using a geographic information system, so that the scientificity of pollution prediction and emission intensity evaluation is enhanced, and more accurate pollution prevention and control decision support is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental pollutant detection, and particularly to a method for quantitatively evaluating pollutant emissions from open burning of straw based on multi-source data fusion. Background Art

[0002] The technical field of environmental pollutant detection includes the monitoring, analysis, and evaluation of pollutants in the environment, especially the detection of pollutants in the atmosphere, water bodies, and soil. The core content of this field mainly involves how to accurately and timely detect harmful substances in the environment and analyze their concentrations, types, and sources through various instruments and methods. These technologies are of great significance in aspects such as environmental protection, public health, and safety, and are widely used in multiple fields such as environmental monitoring, pollution source identification, and environmental quality assessment. Technical means include the use of gas sensors, chemical analysis instruments, remote sensing technology, and geographic information systems, aiming to achieve effective identification and quantitative evaluation of environmental pollution.

[0003] Among them, the method for quantitatively evaluating pollutant emissions from open burning of straw based on multi-source data fusion refers to the quantitative calculation of pollutant emissions generated during the open burning of straw through the combination of multiple data sources. The technical matters targeted by this patent theme include the comprehensive analysis of satellite remote sensing data, ground monitoring data, and meteorological data, and the quantitative evaluation of pollutant emissions by combining numerical models. Specifically, remote sensing images are used to monitor the burning points, meteorological data is used to analyze the impact of atmospheric conditions on pollutant diffusion, and at the same time, real-time pollutant concentration data is obtained through ground monitoring stations. These data from different sources are fused to achieve accurate calculation of pollutant emissions during the open burning of straw.

[0004] In the existing technology, during the process of pollutant concentration monitoring, limited by a single data source, the spatial distribution information of pollutant concentration is incomplete, and there are deviations in the identification of pollution hotspots. The evaluation of fire point emission intensity lacks the correlation analysis of pollutant concentration changes, resulting in large calculation errors in pollutant release amounts. The analysis of pollutant diffusion trends does not fully consider factors such as wind direction, wind speed, humidity, and atmospheric stability, resulting in insufficient accuracy in predicting pollutant transport paths. Concentration change monitoring mainly relies on real-time monitoring data, lacking in-depth mining of concentration time series information, and it is difficult to accurately identify abnormal fluctuation areas of pollution concentration. Pollutant emission assessment lacks corrections for factors such as terrain and vegetation cover, ignoring diffusion resistance and vegetation adsorption capacity, resulting in deviations in predicting the pollutant diffusion range. The identification of abnormal pollution events lacks the linkage analysis of emission intensity and concentration changes, resulting in a lag in screening abnormal rising areas and affecting the response efficiency to sudden pollution. These deficiencies affect the accuracy of pollutant diffusion simulation, reducing the effectiveness of pollution source tracing and pollution control measures. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and a method for quantitatively analyzing pollutants emitted from open burning of straw based on multi-source data fusion is proposed.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A method for quantitatively analyzing pollutants emitted from open burning of straw based on multi-source data fusion, comprising the following steps:

[0007] S1: Based on air pollutant monitoring data, call the sensor network to obtain the concentrations of PM2.5, CO, and NOX, analyze the thermal radiation intensity of the fire point, collect meteorological data, and combine the temporal variations of the fire point and meteorological parameters to obtain a pollution source data set;

[0008] S2: Based on the pollution source data set, screen the areas with abnormal concentration fluctuations, analyze the fire point coordinates and intensity, calculate the fire point activity frequency, extract the time distribution information of pollutant concentration changes, and combine the temporal sequence data to obtain the distribution of pollution hot spots;

[0009] S3: Based on the distribution of pollution hot spots, analyze the pollutant diffusion trend, calculate the pollutant transport rate and sedimentation rate, evaluate the concentration decreasing gradient, calculate the diffusion radius in combination with the pollutant release rate, call the wind speed and wind direction data to analyze the main wind direction transport ratio, and analyze the cross-regional transport impact in combination with meteorological conditions to obtain the pollutant diffusion range;

[0010] S4: Based on the pollutant diffusion range, analyze the current pollutant concentration change amplitude, screen the areas with abnormal increase, calculate the pollutant release amount in combination with the thermal radiation intensity of the fire point, and evaluate the pollutant retention time in combination with meteorological data, and adjust the emission rate to obtain the pollutant emission intensity.

[0011] As a further solution of the present invention, the pollution source data set includes PM2.5 concentration, CO concentration, NOX concentration, fire point thermal radiation intensity, air temperature, humidity, wind speed, and meteorological temporal sequence data. The distribution of pollution hot spots includes areas with abnormal concentration fluctuations, fire point intensity, fire point activity frequency, and pollutant concentration time distribution. The pollutant diffusion range includes pollutant diffusion trend, pollutant transport rate, pollutant sedimentation rate, pollutant concentration decreasing gradient, pollutant diffusion radius, and pollutant transport ratio. The pollutant emission intensity includes pollutant concentration change amplitude, areas with abnormal pollutant increase, pollutant release amount, pollutant retention time, and pollutant emission rate.

[0012] As a further solution of the present invention, the specific steps for obtaining the pollution source data set based on air pollutant monitoring data, calling the sensor network to obtain the concentrations of PM2.5, CO, and NOX, analyzing the thermal radiation intensity of the fire point, and collecting meteorological data, and combining the temporal variations of the fire point and meteorological parameters are as follows:

[0013] S101: Obtain the PM2.5, CO, and NOX concentration data monitored by the environmental monitoring sensor network, call the real-time measurement values of the monitoring points, screen out abnormal data and eliminate over-limit values, sort the pollutant concentration data according to the data timestamp, calculate the mean value of the pollutant concentration within the time series, analyze the change trend, and generate the time series data of the pollutant concentration;

[0014] S102: Based on the time series data of the pollutant concentration, analyze the thermal radiation intensity data of the fire point area of the satellite remote sensing equipment, match the monitoring time period of the fire point area, calculate the correlation between the fire point thermal radiation intensity and the pollutant concentration, and generate the fire point pollution impact coefficient;

[0015] S103: According to the fire point pollution impact coefficient, collect the meteorological parameters of the ground meteorological monitoring station, calculate the influence factor of the wind speed change on the pollutant diffusion, and combine with the fire point pollution impact coefficient to generate the pollution source data set.

[0016] As a further solution of the present invention, based on the pollution source data set, the specific steps for screening the concentration abnormal fluctuation area, analyzing the fire point coordinates and intensity, calculating the fire point activity frequency, extracting the time distribution information of the pollutant concentration change, and combining with the time series data to obtain the distribution of the pollution hot spot area are as follows.

[0017] S201: Based on the pollution source data set, call the pollutant concentration data to screen out the concentration abnormal fluctuation area, calculate the change range of the pollutant concentration within the differential time interval, eliminate the fluctuation area, extract the spatial coordinates of the concentration mutation area, screen out the area where the pollutant concentration change rate exceeds the set threshold, and generate the pollution abnormal fluctuation area;

[0018] S202: According to the pollution abnormal fluctuation area, analyze the fire point coordinate data, match the fire point position and the fire point intensity data, calculate the fire point activity frequency and set the high-frequency threshold, screen out the high-frequency fire point area, and generate the high-frequency fire point area;

[0019] S203: Call the high-frequency fire point area, extract the time distribution information of the pollutant concentration change, match the concentration time series data, calculate the pollutant concentration change trend, and generate the distribution of the pollution hot spot area.

[0020] As a further solution of the present invention, based on the distribution of the pollution hot spot area, analyze the pollutant diffusion trend, calculate the pollutant transmission rate and sedimentation rate, evaluate the concentration decreasing gradient, calculate the diffusion radius in combination with the pollutant release rate, call the wind speed and wind direction data to analyze the main wind direction transport ratio, and analyze the cross-regional transport impact in combination with the meteorological conditions. The specific steps for obtaining the pollutant diffusion range are as follows.

[0021] S301: Based on the distribution of the pollution hotspots, call the wind speed and wind direction data, analyze the diffusion trend of pollutants along the main wind direction, analyze the transmission rate of pollutants under different wind speed conditions, screen the pollutant transmission rate intervals within the wind speed change range, analyze the diffusion trajectories of pollutants under different wind direction conditions, calculate the main wind direction diffusion ratio of pollutants, and generate the wind direction diffusion trend of pollutants.

[0022] S302: According to the wind direction diffusion trend of pollutants, call the humidity and atmospheric stability data, calculate the pollutant sedimentation rate, screen the areas where the sedimentation rate is greater than the threshold, analyze the decreasing relationship of pollutant concentration, and generate the pollutant sedimentation gradient.

[0023] S303: Based on the pollutant sedimentation gradient, call the pollutant release rate at the fire point, combine with the pollutant transmission path, analyze the influence of meteorological conditions on pollution diffusion, calculate the cross-regional transport index of pollutants, and generate the pollution diffusion range.

[0024] As a further solution of the present invention, the specific formula for calculating the pollutant sedimentation rate is:

[0025] ;

[0026] Wherein, represents the pollutant sedimentation rate, represents the pollutant concentration at the starting point of the path, represents the pollutant concentration at the end point of the path, represents the path length, represents the current relative humidity of the area where the path is located, represents the average value of humidity in the area, represents the atmospheric stability level of the area where the path is located, represents the average level value of stability in the area.

[0027] As a further solution of the present invention, based on the pollution diffusion range, analyze the current change range of pollutant concentration, screen the abnormally rising areas, calculate the pollutant release amount in combination with the fire point heat radiation intensity, and evaluate the pollutant retention time in combination with meteorological data and adjust the emission rate. The specific steps for obtaining the pollutant emission intensity are as follows.

[0028] S401: Based on the pollution diffusion range, call the current pollutant concentration monitoring data, analyze the change range of the concentration at the monitoring points, screen the areas where the pollutant concentration increases by more than the set threshold within a short time, eliminate the areas with low amplitude changes, extract the spatial coordinates of the abnormally rising areas, calculate the concentration change rate, and generate the abnormally rising areas of pollutants.

[0029] S402: According to the abnormal rise area of the pollutants, call the thermal radiation intensity data of the fire points, calculate the pollutant release amount, match the pollutant release intensity with the fire point distribution, screen the areas where the pollutant release amount exceeds the set standard, and generate the pollutant release amount distribution;

[0030] S403: Based on the pollutant release amount distribution, combine with meteorological data to evaluate the change of pollutant concentration, analyze the pollutant retention time, adjust the emission rate, calculate the influence coefficient of the concentration change during the pollutant transport process, and generate the pollutant emission intensity.

[0031] As a further solution of the present invention, the specific formula for calculating the total pollutant release amount is:

[0032] ;

[0033] Wherein, represents the total pollutant release amount, represents the thermal radiation intensity of the fire point, represents the actual combustion area corresponding to the fire point, represents the atmospheric temperature during the current observation period, represents the average temperature in the area in the past 24 hours, represents the actual vegetation height in the fire point area, represents the average vegetation height in the area.

[0034] As a further solution of the present invention, the method further includes S5: Based on the pollutant emission intensity, call the terrain data to calculate the diffusion resistance, evaluate the influence of vegetation cover on the pollutant adsorption capacity, analyze the influence of ground elevation and wind field change on the flow path, and combine with meteorological data to adjust the diffusion time scale to obtain the pollutant emission quantification plan;

[0035] The pollutant emission quantification plan includes pollutant diffusion resistance, vegetation adsorption capacity, pollutant flow path and pollutant diffusion time scale.

[0036] As a further solution, S501: Based on the pollutant emission intensity, call the terrain data of the fire point area, analyze the influence of terrain undulation on the pollutant diffusion path, calculate the diffusion resistance of the differential elevation area, screen the areas where diffusion is blocked, calculate the diffusion rate attenuation ratio, and generate the pollutant diffusion resistance distribution;

[0037] S502: According to the pollutant diffusion resistance distribution, call the vegetation cover information, analyze the adsorption capacity of vegetation for pollutants, screen the areas where the adsorption rate exceeds the set threshold, analyze the influence of vegetation on the pollutant flow, and generate the pollutant vegetation adsorption coefficient;

[0038] S503: Based on the pollutant vegetation adsorption coefficient, combined with the ground elevation data, analyze the flow path of pollutants along the terrain, match the meteorological data to adjust the diffusion time scale, calculate the transport adjustment amount of pollutants under different terrain conditions, and generate a quantitative pollutant emission plan.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0040] In the present invention, by integrating environmental monitoring sensors, satellite remote sensing, and ground meteorological data, the comprehensiveness and accuracy of pollution source monitoring are improved. The combination of fire point intensity and pollutant concentration time series data optimizes the assessment of fire points and pollution hot spots. The comprehensive analysis of wind direction and meteorological conditions improves the accuracy of the pollutant diffusion model. Dynamically adjusting the pollutant release amount and correcting the diffusion model using geographic information systems enhances the scientific nature of pollution prediction and emission intensity assessment, and provides more accurate decision-making support for pollution prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 It is a schematic flowchart of the steps of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The following will describe the technical solutions in the present invention with reference to the drawings.

[0044] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0045] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meaning they express is the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meaning they express is the same.

[0046] In the embodiments of the present invention, sometimes the subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0047] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0048] Please refer to Figure 1 , a method for quantitatively analyzing pollutants emitted from open burning of straw based on multi-source data fusion, comprising the following steps:

[0049] S1: Obtain air pollutant concentration data, call the environmental monitoring sensor network to monitor the concentrations of PM2.5, CO, and NOX, analyze the thermal radiation intensity of the fire point area through satellite remote sensing equipment (where the fire point refers to the fire point of open burning of straw), collect the temperature, humidity, and wind speed data collected by the ground meteorological monitoring station, analyze the temporal variation of meteorological parameters, and combine the fire point and meteorological data to obtain a pollution source data set;

[0050] S2: Based on the pollution source data set, call the pollutant concentration data to screen the concentration abnormally fluctuating areas, analyze the fire point intensity through the fire point coordinates, calculate the fire point activity frequency according to the remote sensing monitoring data, extract the time distribution information of the pollutant concentration change, and combine the concentration time series data to obtain the distribution of pollution hot spots;

[0051] S3: Based on the distribution of pollution hot spots, call the wind speed and wind direction data to analyze the diffusion trend of pollutants along the main wind direction, calculate the pollutant transport rate, evaluate the pollutant sedimentation rate through humidity and atmospheric stability, analyze the pollutant concentration decreasing gradient, call the pollutant release rate of the fire point to analyze the diffusion radius, combine the pollutant transport path and transport trend, analyze the influence of differential meteorological conditions on pollution diffusion, calculate the cross-regional transport ratio of pollutants, and obtain the pollutant diffusion range;

[0052] S4: Based on the pollutant diffusion range, call the current pollutant concentration monitoring data to analyze the concentration change amplitude, screen the abnormally rising areas, calculate the pollutant release amount through the thermal radiation intensity of the fire point, combine the meteorological data to evaluate the pollutant concentration change, analyze the pollutant retention time, and adjust the emission rate to obtain the pollutant emission intensity;

[0053] S5: Based on the pollutant emission intensity, call the terrain data of the fire point area, calculate the pollutant diffusion resistance, evaluate the vegetation adsorption capacity through the vegetation coverage information, analyze the pollutant flow path based on the ground elevation, and combine the meteorological data to adjust the diffusion time scale to obtain the pollutant emission quantification scheme.

[0054] The pollution source dataset includes PM2.5 concentration, CO concentration, NOX concentration, fire point heat radiation intensity, air temperature, humidity, wind speed, and meteorological time series data. The distribution of pollution hotspots includes concentration anomaly fluctuation areas, fire point intensity, fire point activity frequency, and the time distribution of pollutant concentrations. The pollutant diffusion range includes pollutant diffusion trends, pollutant transport rates, pollutant sedimentation rates, pollutant concentration decreasing gradients, pollutant diffusion radii, and pollutant transport ratios. The pollutant emission intensity includes the change range of pollutant concentrations, pollutant abnormal rise areas, pollutant release amounts, pollutant retention times, and pollutant emission rates. The pollutant emission quantification scheme includes pollutant diffusion resistance, vegetation adsorption capacity, pollutant flow paths, and pollutant diffusion time scales.

[0055] The specific steps of S1 are as follows:

[0056] S101: Obtain the PM2.5, CO, and NOX concentration data monitored by the environmental monitoring sensor network, call the real-time measurement values at the monitoring points, screen out abnormal data and eliminate over-limit values, sort the pollutant concentration data according to the data timestamps, calculate the mean value of the pollutant concentrations within the time series, analyze the change trends, and generate the pollutant concentration time series data;

[0057] First, it is necessary to clarify the deployment locations of each type of sensor and the types of pollutants to be monitored. For example, Type A sensors deployed in urban industrial areas are used to collect PM2.5 concentrations, and Type B sensors deployed near traffic arteries mainly collect CO and NOX data. Each type of sensor automatically uploads monitoring data at fixed time intervals. Each piece of data contains a timestamp, longitude and latitude, pollutant type, and its corresponding concentration value. When calling the real-time measurement values of the monitoring points, the system will retrieve the corresponding real-time data records according to the unique ID of the monitoring point and summarize them in chronological order of timestamps. Time series are established for all pollutant types respectively. For example, from 10:00 to 12:00 on March 12, 2025, a total of 36 pieces of data were uploaded from three monitoring points in a certain place, and each piece of data was collected every 5 minutes to form a continuous concentration record. During the process of screening abnormal data, first, calculate the average value and fluctuation range of the historical one-week data of the monitoring point, and compare the current concentration value of a certain pollutant with its historical fluctuation level at the location. If the concentration value at a certain moment is too different from the daily average value in the past 7 days and exceeds the fixed fluctuation range, such as exceeding 3 times the fluctuation difference of the daily average value, it is considered abnormal. For example, if the average value of PM2.5 at a certain monitoring point in the past week is 60 μg / m³ and the fluctuation range is about ±10 μg / m³, then the values exceeding 60 ± 30 μg / m³ are determined to be abnormal. At the same time, the system sets the safety concentration threshold for pollutants and directly eliminates the data exceeding the upper limit. For example, the threshold for PM2.5 is set at 150 μg / m³, the threshold for CO is set at 10 mg / m³, and the threshold for NOX is set at 0.5 ppm. These over-limit values are directly marked as invalid and removed from the data set. During the sorting process, the remaining valid data records are sorted in ascending order of timestamps to ensure the continuity of concentration records at the same monitoring point at different times, which is convenient for subsequent time series analysis. When calculating the average value of pollutant concentrations within the time series, the system selects an analysis period, such as every hour, every 6 hours, or daily as the unit, and divides the sum of all valid concentration values within this time interval by the number of records to obtain the average value. For example, during the period from 00:00 to 01:00 on March 12, 2025, 12 PM2.5 data values were recorded, which were [60, 62, 61, 59, 63, 60, 61, 64, 65, 62, 60, 61] μg / m³ respectively. The average value is the sum of the above values divided by 12, and the obtained average value is approximately 61.5 μg / m³. When analyzing the change trend of pollutant concentrations, the system compares the average values within multiple adjacent analysis periods, and compares the difference between the average values of two adjacent time periods. If the difference is positive, it means that the pollutant concentration is rising; if the difference is negative, it means that the concentration is decreasing; if the difference between the two average values is less than 5 μg / m³, it is considered that there is no significant change. The system sets the judgment interval range. For example, the PM2.5 concentration is divided into a low concentration interval of 0 - 35 μg / m³, a medium concentration interval of 36 - 75 μg / m³, and a high concentration interval of 76 μg / m³ and above. For example, PM2.5 in a certain placeThe average concentration at 03:00 is 68 μg / m³, and at 06:00 it is 82 μg / m³. It is judged that it enters the high-concentration range from the medium-concentration range, indicating that the pollution degree has intensified. Finally, the average value and change trend of all time periods are integrated into a time series to obtain continuous pollutant concentration change data.

[0058] S102: Based on the time-series data of pollutant concentrations, analyze the thermal radiation intensity data of the fire point areas of satellite remote sensing equipment, match the monitoring time periods of the fire point areas, calculate the correlation between the fire point thermal radiation intensity and pollutant concentrations, and generate a fire point pollution impact coefficient;

[0059] Extract the time period data corresponding to the fire point information recorded by the satellite remote sensing equipment. The fire point information recorded by the satellite remote sensing includes specific longitude and latitude coordinates, fire point detection time, and thermal radiation intensity values. Among them, the thermal radiation intensity is used to measure the energy release degree of the fire point. The system extends the time stamp of the fire point by 15 minutes forward and backward in time, and extracts the data records of the monitoring points closest to the fire point longitude and latitude within this time period in the pollutant time series. For example, if the recording time of a fire point is 10:45 on March 12, 2025, and the thermal radiation intensity is 65 MW, the system extracts the data of multiple monitoring points within 5 kilometers of the fire point between 10:30 and 11:00. If the extracted PM2.5 concentration is 75 μg / m³, CO is 1.5 mg / m³, and NOX is 0.2 ppm, the system will establish an associated data item corresponding to these data and the fire point. After matching multiple fire point data, the system batches and integrates these records, and compares the pollutant concentration changes and thermal radiation intensity corresponding to different fire points. By comparing the concentration values between the matching records of each fire point one by one, it is judged whether the high-intensity fire point causes a significant change in pollutant concentration in the same time window. For example, when the fire point thermal radiation intensity is 80 MW, the nearby PM2.5 concentration rises from 60 μg / m³ to 90 μg / m³ within 30 minutes, then it is considered that there is an obvious correlation between the two. On this basis, a fire point pollution impact coefficient is generated. The system will normalize the pollutant concentrations according to different weights. The different weights are set according to the main composition ratio of pollutants in the past area. For example, if the proportion of PM2.5 in a certain area accounts for 65% of all pollutants, CO accounts for 25%, and NOX is 10%, then the pollution impact coefficient is the sum of the normalized concentration values of these three multiplied by the corresponding weights. For example, after normalization, the PM2.5 concentration is 0.9, CO is 0.4, and NOX is 0.3, then the pollution impact coefficient is 0.65×0.9 + 0.25×0.4 + 0.10×0.3 = 0.765. The system uses this value as the intensity identifier of the fire point's impact on pollution at a specific time period. The higher this value, the greater the degree of pollution intensification that the fire point may cause at that time.

[0060] S103: According to the fire point pollution influence coefficient, collect the meteorological parameters of ground meteorological monitoring stations, calculate the influence factor of wind speed change on pollutant diffusion, and combine with the fire point pollution influence coefficient to generate a pollution source data set;

[0061] The system will further collect the real-time meteorological data of ground meteorological monitoring stations covering the fire point area, especially four types of parameters: wind speed, wind direction, temperature, and humidity. The meteorological monitoring data is generally uploaded to the central system with a sampling unit of 10 minutes. By performing spatial matching with the fire point coordinates, the meteorological station data points within a distance of 10 kilometers and with a data time within 30 minutes before and after the fire point are selected as valid matches, and the meteorological stations in the upwind direction of the fire point are further identified. When calculating the influence factor of wind speed change on pollutant diffusion, first identify the change amount of pollutant concentration at the monitoring point in the downwind direction of the fire point, and record the average wind speed value during the corresponding time period. The ratio between the two is used as the influence factor of pollutant diffusion. For example, when a certain fire point has a wind direction of southeast by east and a wind speed of 4.0 m / s, the PM2.5 concentration at the downwind monitoring point before the fire point is 68 μg / m³, and it rises to 84 μg / m³ within 30 minutes after the fire point, with a concentration change amount of 16 μg / m³. Divide this value by the wind speed to obtain a diffusion influence factor of 4.0, indicating that the relationship between pollution diffusion and wind speed is close. The greater the wind speed, the wider the pollution range. Combining the previous fire point pollution influence coefficient, for example, the fire point pollution influence coefficient is 0.76 and the wind speed influence factor is 4.0, then the comprehensive pollution source intensity value of this fire point is the product of the two, 0.76×4.0 = 3.04, indicating that this fire point has significant pollution propagation ability during this time period and under this meteorological condition. Finally, integrate this information with the basic attributes of the fire point to generate a pollution source data set, including fields such as fire point number, longitude and latitude, recording time, heat radiation intensity, concentrations of three types of pollutants and their time series means, pollution influence coefficient, wind speed, wind direction, diffusion influence factor, comprehensive pollution source intensity, etc., to form a standardized pollution source data set that can be used for pollution analysis and scheduling.

[0062] The specific steps of S2 are as follows:

[0063] S201: Based on the pollution source data set, call the pollutant concentration data to screen for concentration abnormally fluctuating areas, calculate the change amplitude of pollutant concentration within different time intervals, eliminate the fluctuating areas, extract the spatial coordinates of the concentration mutation areas, and screen the areas where the pollutant concentration change rate exceeds the set threshold to generate pollution abnormally fluctuating areas;

[0064] First, extract field information such as pollutant types, spatial locations, monitoring times, and concentration values from the pollution source dataset, and establish a time series index. Organize the data into a continuous time series according to the monitoring point ID and time order. For example, a monitoring point with ID001 recorded PM2.5 concentration data from 00:00 to 12:00 on March 12, 2025, in groups of 5 minutes, resulting in a total of 144 data points. The system calculates using the concentration difference between every two adjacent time points to determine whether its change exceeds the preset concentration fluctuation judgment standard. This judgment standard is set by statistically analyzing the top 10% of the most frequently changing samples in historical data of the same period and setting a threshold for the amplitude of concentration change. For example, if the PM2.5 fluctuation threshold is set at 10 μg / m³, then when the concentration change between two consecutive points within a certain time period is greater than this value, it is marked as a fluctuation section. If the PM2.5 concentration at a monitoring point suddenly increases from 65 μg / m³ to 78 μg / m³ from 01:30 to 01:35, with a change value of 13 μg / m³, exceeding the threshold of 10 μg / m³, then this interval is marked as an abnormal fluctuation area. When calculating the change amplitude of pollutant concentration within the differential time interval, the system will add one period before and after the abnormal section for comparison, subtract the minimum value from the maximum value of the concentration within three consecutive periods to obtain the maximum fluctuation amplitude, and compare it among multiple monitoring points. By comparing whether the fluctuation amplitude is higher than the 95% quantile of the concentration fluctuation within 7 days at each point, it is determined whether it belongs to an abnormal mutation area. During the process of removing the fluctuation area, the system removes the data of all time periods marked as the fluctuation area from the main data sequence, only retaining the sections with relatively stable continuous changes or concentrated mutations. When extracting the spatial coordinates of the concentration mutation area, the system records the longitude and latitude of the monitoring point corresponding to the start time of the mutation section. If multiple adjacent monitoring points mutate during the same period, the minimum envelope surface constructed by these points is used as the output of the spatial coordinates of the mutation area. When screening areas where the change rate of pollutant concentration exceeds the set threshold, first, calculate the change rate of concentration per unit time for each monitoring point, that is, the concentration value changing every 5 minutes, and then compare it with the rate threshold. For example, if the rate threshold is set at 2 μg / m³ per minute, that is, 10 μg / m³ per 5 minutes, and if the concentration increase rate reaches 12 μg / m³ per 5 minutes during a certain period, it is determined as a rate abnormal area, and the corresponding monitoring point ID and geographical coordinates are extracted and marked as abnormal rate points. Finally, the system combines all the points and time periods that meet the conditions to form a pollution abnormal fluctuation area with dual characteristics of time and space.

[0065] S202: According to the pollution abnormal fluctuation area, analyze the fire point coordinate data, match the fire point position and fire point intensity data, calculate the fire point activity frequency and set a high-frequency threshold to screen the high-frequency fire point area and generate a high-frequency fire point area;

[0066] First, extract the fire point monitoring data for the corresponding time period from the satellite remote sensing database. The extracted fields include the longitude and latitude of the fire points, the recording time, and the heat radiation intensity value, and construct a fire point activity record set. When grouping and statistically analyzing each fire point position by time and matching the fire point position and fire point intensity data, judge the spatial overlap between the spatial envelope coordinates of each abnormal fluctuation area and the longitude and latitude of the fire points. The judgment basis for overlap is whether the center point of the fire point falls within the spatial boundary of the abnormal fluctuation area. If it is satisfied, establish a mapping relationship between the fire point and the fluctuation area. When calculating the fire point activity frequency, perform clustering processing with the fire point longitude and latitude accurate to 0.01 degrees (about 1 km range), record the number of fire points appearing in each clustering unit within one month, and judge the activity level of the fire points. If the number of fire points appearing in a certain clustering unit reaches 20 times within 30 days, its daily average appearance frequency is 0.66 times / day. Set the high-frequency threshold according to the statistical results. This threshold is set with reference to the 85th percentile value of the daily average frequency of fire points in the historical distribution. For example, if the 85th percentile value in the historical monthly data is 0.6 times / day, then set the high-frequency fire point judgment threshold to 0.6 times / day. When screening the high-frequency fire point areas, calculate the daily average frequency for all clustering areas, and classify those higher than the threshold as high-frequency fire point areas. At the same time, retain the records of the average value and maximum value of the fire point intensity in this area for subsequent use. For example, if the longitude and latitude range of a certain area is (104.32°E - 104.34°E, 30.66°N - 30.68°N), and the cumulative number of fire points appearing within the month is 25 times, and the average heat radiation intensity is 68 MW, this area is marked as a high-frequency fire point area.

[0067] S203: Call the high-frequency fire point areas, extract the time distribution information of the pollutant concentration changes, match the concentration time series data, calculate the pollutant concentration change trend, and generate the distribution of pollution hot spots;

[0068] Find the pollutant monitoring points within the corresponding area according to the spatial range of each high-frequency fire point area, and extract the pollutant concentration records of these monitoring points within 1 hour before and after the fire point activity period. For example, during the period from 10:00 to 10:10 on March 10, 2025, a record of a heat radiation intensity of 70 MW appears at the center point of a certain high-frequency fire point area. The system extracts the concentration data of three types of pollutants, namely PM2.5, CO, and NOX, of the monitoring points within 5 kilometers of the fire point range from 09:00 to 11:00. Construct a pollutant concentration time series in minutes for each monitoring point. When matching the concentration time series data, align the time series data according to the fire point occurrence time. Synchronously integrate the concentration change curves of multiple monitoring points before and after the fire point, and perform a smoothing process on the concentration mean in the time dimension. For example, calculate the average PM2.5 before 30 minutes and after 30 minutes of the fire point occurrence respectively and then compare them. If it is found that the concentration mean in the latter 30 minutes increases by more than 15 μg / m³, mark this period as a pollution increase section. During the process of calculating the pollutant concentration change trend, use a 10-minute window to count the rate of change of the concentration increase and decrease, and classify and file the rates. For example, 0 - 0.5 μg / m³ / min is a weak change, 0.5 - 1.5 is a medium change, and greater than 1.5 is a drastic change. Statistically analyze the trend level of each monitoring point under the influence of the fire point and aggregate the distribution in the spatial dimension. Finally, the system divides the area with drastic pollutant concentration changes, continuous increase, and high spatial concentration into pollution hot spot areas, marks their spatial coordinate range, trend direction, and peak period, generates pollution hot spot area distribution data, and forms a key area for the correlation between pollution evolution and fire point activity.

[0069] The specific steps of S3 are as follows:

[0070] S301: Based on the pollution hot spot area distribution, call the wind speed and wind direction data, analyze the diffusion trend of pollutants along the main wind direction, analyze the transmission rate of pollutants under different wind speed conditions, screen the pollutant transmission rate interval within the wind speed change range, analyze the diffusion trajectory of pollutants under different wind direction conditions, calculate the main wind direction diffusion ratio of pollutants, and generate the pollutant wind direction diffusion trend;

[0071] First, determine the central coordinates of each pollution hotspot area and its corresponding time range. On this basis, extract wind speed and wind direction data from the ground meteorological monitoring data that are consistent with the time period of the hotspot area and spatially coincide or are no more than 10 kilometers apart. Each wind speed record corresponds to fields including timestamp, wind speed value (unit: m / s), wind direction angle (unit: degree), and the longitude and latitude of the measuring station. For example, a certain hotspot area is near (104.33°E, 30.67°N) during the period from 10:00 to 12:00 on March 10, 2025. The system matches the wind speed and wind direction data records of three neighboring meteorological stations within this time period. When analyzing the diffusion trend of pollutants along the main wind direction, first conduct a direction statistics on all wind direction records. Divide the wind direction into 16 categories according to the angle, with each category occupying a 22.5° angle interval. The wind direction category with the highest statistical frequency is the main wind direction. For example, the main wind direction is southeast (135°) during the above time period. The system takes this main wind direction as the reference axis and analyzes the change trend of pollutant concentrations at multiple monitoring points in the hotspot area in the downstream direction of the main wind direction. Compare the concentration values of the monitoring points at different distances to determine whether the pollutants show a decreasing trend with the increase of distance. If the PM2.5 concentrations at the sampling points every 2 kilometers in the main wind direction are 95 μg / m³, 83 μg / m³, 74 μg / m³, and 65 μg / m³ in sequence, it is considered that there is an effective diffusion trend. When analyzing the transmission rate of pollutants under different wind speed conditions, divide the wind speed range during the monitoring period into four levels: [0 - 1 m / s], [1 - 3 m / s], [3 - 5 m / s], and [above 5 m / s]. Record the concentration change time of the downstream points of the pollutants under each wind speed level in the main wind direction, and measure the time length it takes for the pollutants to be transmitted from the starting point to the downstream point. For example, at a wind speed of 3 - 5 m / s, the peak delay time for PM2.5 to be transmitted 5 kilometers southeast from the hotspot center is 40 minutes, then the transmission rate is 7.5 km / h. After recording the transmission rates corresponding to each wind speed level, conduct interval screening. When screening the pollutant transmission rate interval within the wind speed change range, sort the distributions of all transmission rates and identify the transmission rates within the range of the interval with the highest frequency. For example, at a medium wind speed of [3 - 5 m / s], the transmission rates are concentrated in the interval of 6 - 8 km / h, then this interval is selected as the effective rate interval. When analyzing the diffusion trajectory of pollutants under different wind direction conditions, sample and compare the pollutant concentrations at the downstream points within different wind direction angles respectively, construct the concentration trajectory sequences corresponding to different wind directions, and compare the difference between the concentration change trajectory in the main wind direction and other directions. When calculating the diffusion ratio of pollutants in the main wind direction, divide the maximum diffusion distance and average concentration amplitude in the downstream direction of the main wind direction by the corresponding values in the downstream direction of the secondary main wind direction. For example, the average concentration reduction value within 5 kilometers in the downstream direction of the main wind direction is 30 μg / m³, and that of the secondary main wind direction is only 15 μg / m³, then the diffusion ratio is 2.0. Finally, combine the wind speed, wind direction, concentration change, diffusion speed, and ratio information within the hotspot area to generate the pollutant wind direction diffusion trend.

[0072] S302: According to the pollutant wind direction diffusion trend, call the humidity and atmospheric stability data, calculate the pollutant sedimentation rate, screen the areas where the sedimentation rate is greater than the threshold, analyze the pollutant concentration decreasing relationship, and generate the pollutant sedimentation gradient;

[0073] The specific formula for calculating the pollutant sedimentation rate is:

[0074] ;

[0075] where, represents the pollutant sedimentation rate, represents the pollutant concentration at the starting point of the path, represents the pollutant concentration at the end point of the path, represents the path length, represents the current relative humidity of the area where the path is located, represents the average value of humidity in the area, represents the atmospheric stability level of the area where the path is located, represents the average level value of stability in the area;

[0076] This formula is used to calculate the sedimentation rate of pollutants;

[0077] To specifically derive this formula, assume the actual monitoring data is as follows: starting point concentration = 150 μg / m3, end point concentration = 100 μg / m3, path length = 5 km, current relative humidity = 75%, regional average relative humidity = 70%, current atmospheric stability level = 3 (grade C), regional average atmospheric stability = 2 (grade B).

[0078] Substitute these values into the formula for calculation:

[0079] ;

[0080] ;

[0081] ;

[0082] This result It indicates that the average decline rate of pollutant concentration per kilometer along a given path is 35.71 μg / m³. This value reflects the influence of the relative humidity and stability of the atmosphere on pollutant diffusion under specific environmental conditions, as well as the reduction amplitude of pollutant concentration over a certain distance. The calculation result shows that environmental conditions and path length have a significant impact on pollutant settlement, thus providing effective data support for guiding environmental protection action plans, especially when formulating air quality improvement measures.

[0083] S303: Based on the pollutant settlement gradient, call the pollutant release rate of the fire point, combine with the pollutant transmission path, analyze the influence of meteorological conditions on pollution diffusion, calculate the cross-regional transport index of pollutants, and generate the pollutant diffusion range.

[0084] According to the fire point numbers and location coordinates recorded in the pollution source data, extract the total mass record of pollutants released per unit time, with the release rate unit of μg / m²·s. Match it spatially with the settlement gradient area. If the release rate of a certain fire point is 12 μg / m²·s and the starting point of the corresponding settlement path is within 1 kilometer of the fire point location, establish an association. Combine with the pollutant transmission path, and the system constructs a main transmission path between the fire point and the end point of the settlement area, records the path length, average wind speed, transmission duration, and concentration change amount. When analyzing the influence of meteorological conditions on pollution diffusion, the system compares the concentration change sequences under different humidity and stability combinations, and identifies phenomena such as slow pollutant diffusion and extended duration of concentration peaks under high humidity (>85%), low wind speed (<1 m / s), and high stability (E, F grades) conditions, and records the concentration delay and accumulation values corresponding to these conditions in the data. When calculating the cross-regional transport index of pollutants, take the distance from the fire point location to the boundary of another downstream area as the spatial span, take the transmission time as the time span, and multiply the attenuation ratio of the pollutant concentration peak on this path by the transmission rate to obtain an index value reflecting the diffusion ability. For example, if it still maintains 80% of the initial concentration 5 kilometers away and the transmission time is 1 hour, the transport index is at a high-intensity level. When generating the pollutant diffusion range, the system outlines the boundaries of the path areas where all transport indexes are higher than the set benchmark value (such as concentration attenuation ratio >0.75 and distance >4 km) to form a specific pollutant diffusion range layer, and records the corresponding fire point numbers, path lengths, start and end times, and spatial ranges.

[0085] The specific steps of S4 are as follows:

[0086] S401: Based on the pollutant diffusion range, call the current pollutant concentration monitoring data, analyze the concentration change amplitude of the monitoring points, screen the areas where the pollutant concentration increases by more than the set threshold within a short time, eliminate the areas with low-amplitude changes, extract the spatial coordinates of the abnormally rising areas, calculate the concentration change rate, and generate the pollutant abnormally rising areas.

[0087] First, extract the pollutant concentration records within the latest hour for all monitoring points within the diffusion range from the real-time monitoring database. The records are sampled every 5 minutes, and each record contains the monitoring time, the coordinates of the monitoring point, the type of pollutant, and the corresponding concentration value. For example, the PM2.5 concentration data at a certain point from 10:00 to 10:30 is [72, 74, 76, 85, 96, 108] μg / m³. During the process of analyzing the amplitude change of the concentration at the monitoring point, the system performs a difference operation on every two adjacent pieces of data, calculates the concentration increase every 5 minutes, and records the maximum change amplitude value. In this example, the maximum increase is 108−96 = 12 μg / m³. When screening for areas where the pollutant concentration increase exceeds the set threshold within a short period of time, the increase threshold set by the system is set with reference to the 90th percentile value of the PM2.5 concentration increase every 5 minutes in the area over the past 30 days. For example, if this value is 10 μg / m³, the data point with a current increase of 12 μg / m³ is determined to be an abnormally rising point. During the process of excluding areas with low amplitude changes, the system excludes all points with an increase amplitude lower than the threshold per unit time for each monitoring point from the current record and does not participate in subsequent analysis. When extracting the spatial coordinates of the abnormally rising area, the longitude and latitude values of the remaining points are extracted, and the minimum circumscribed boundary range is calculated based on their spatial distribution. If 3 or more abnormally rising points appear concentrated within 5 kilometers, they are marked as a continuous abnormally rising area. During the process of calculating the concentration change rate, the system records the concentration change values per unit time for all monitoring points within this area and then calculates the average. If the average concentration increase within 5 minutes is 11.5 μg / m³, the change rate is 2.3 μg / m³ per minute. Finally, all the spatial coordinates, time ranges, and concentration change rate records that meet the conditions are integrated to generate the abnormally rising area of pollutants.

[0088] S402: According to the abnormally rising area of pollutants, call the fire point heat radiation intensity data, calculate the pollutant release amount, match the pollutant release intensity with the fire point distribution, screen for areas where the pollutant release amount exceeds the set standard, and generate the pollutant release amount distribution;

[0089] The specific formula for the total pollutant release amount is:

[0090] ;

[0091] Wherein, represents the total pollutant release amount, represents the heat radiation intensity of the fire point, represents the actual combustion area corresponding to the fire point, represents the atmospheric temperature during the current observation period, represents the average temperature in the area over the past 24 hours, represents the actual vegetation height within the fire point area Indicates the average vegetation height within this area;

[0092] Total amount of pollutant release Indicates the pollutant emission intensity caused by the thermal radiation of the fire point per unit time, which is obtained after being corrected by the fire point thermal radiation data, the actual combustion area, the regional temperature condition, and the vegetation height condition. Fire point thermal radiation intensity Obtained by inverting the thermal intensity in the thermal infrared band of remote sensing. Using VIIRS medium-resolution 375-meter fire point data, the corresponding intensity is 64.8 MW. Actual combustion area Estimated based on the fire point pixel area multiplied by the vegetation combustible load index. The pixel area is 0.14 km2 (140,000 m2), the corresponding vegetation coverage rate is 85%, and the actual distribution area of combustibles is 140,000 × 0.85 = 119,000 m2. Observation period temperature Comes from the ground automatic weather station corresponding to the fire point area, recorded as 31.2 °C, and the average temperature in the area in the past 24 hours Is 28.6 °C, and the numerical source is the average value of the all-day weather records at this station. Vegetation height Is the average vegetation height extracted by lidar or optical remote sensing inversion in the fire point area. The actual value is 8.2 m, and the regional average vegetation height Is 6.5 m, based on the average inversion value of the same type of vegetation within 5 kilometers around this fire point.

[0093] Substitute the above values into the formula for calculation:

[0094] ;

[0095] Calculate the denominator of the first part:

[0096] ;

[0097] ;

[0098] Calculate the numerator of the first part:

[0099] ;

[0100] The result of the first part is:

[0101] ;

[0102] Calculate the adsorption correction coefficient of the second part:

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] Final result:

[0108] ;

[0109] The result shows that under the current heat radiation intensity, temperature difference and vegetation height conditions, the total amount of pollutants released by the fire point per unit time is approximately 4,987,291.63 micrograms. This value is directly used to screen high-release areas. After comparing with the set pollutant release amount threshold, it is determined whether the area constitutes a pollution anomaly point, thus supporting the generation of pollutant release amount distribution data.

[0110] S403: Based on the pollutant release amount distribution, combine meteorological data to evaluate the change of pollutant concentration, analyze the pollutant retention time, adjust the emission rate, calculate the influence coefficient of the concentration change during the pollutant transport process, and generate the pollutant emission intensity;

[0111] Extract the meteorological records of the corresponding area, including information such as wind speed, humidity, and stability, and analyze the concentration change trend of pollutants in each direction during the fire point release period. Statistically analyze the concentration change amplitude for each 10-minute period. During the analysis of the pollutant retention time, the system starts from the point where the pollutant concentration reaches the peak and then begins to decline, and records the time span until the concentration recovers to 50% of the peak value. If the PM2.5 concentration reaches 110 μg / m³ at 10:20 and then drops below 55 μg / m³ at 11:00, the retention time is 40 minutes. When adjusting the emission rate, the system compares the pollutant diffusion degree under different wind speed and stability conditions with the actual concentration change. If the actual retention time is much higher than the diffusion theoretical retention time under low stability and high wind speed conditions, the emission rate per unit time is increased. For example, the original estimated release rate is 10 μg / m²·s, and after considering the correction coefficient, it is adjusted to 12 μg / m²·s. When calculating the influence coefficient of the concentration change during the pollutant transport process, the system compares the actually measured maximum concentration change amplitude with the theoretical release amount and path transport conditions to generate a coefficient value. If the release amount is 9×10 8 μg, the average wind speed of the path is 2.5 m / s, and the measured concentration increase value is 50 μg / m³, then the system calculates that the influence coefficient of this pollutant under the current meteorological conditions is 0.75. Finally, all relevant parameters are integrated, and the corrected emission rate, pollutant retention time, and influence coefficient of each area are recorded to generate the pollutant emission intensity.

[0112] The specific steps of S5 are as follows:

[0113] S501: Based on the pollutant emission intensity, call the terrain data of the fire point area, analyze the influence of the terrain undulation on the pollutant diffusion path, calculate the diffusion resistance of different elevation areas, screen the areas where diffusion is blocked, calculate the diffusion rate attenuation ratio, and generate the pollutant diffusion resistance distribution.

[0114] First, extract the fire point location and its pollutant emission intensity value, and establish a digital elevation model based on the terrain elevation data within a radius of 5 kilometers around each fire point. Each elevation point records the specific longitude, latitude, and altitude. The system samples at 100-meter intervals to form grid-like elevation information. During the process of analyzing the influence of the terrain undulation on the pollutant diffusion path, calculate the elevation difference between any two adjacent grid points and determine its slope direction. Use the cumulative elevation difference value along the diffusion path of the pollutant from the fire point center in the main wind direction as the basis for evaluating the terrain resistance of the diffusion direction. If there are multiple consecutive rising segments with an elevation difference exceeding 50 meters on the main path, it is determined as a diffusion-blocked path. During the process of calculating the diffusion resistance of different elevation areas, set the terrain area with an elevation difference greater than a certain threshold as a high-resistance area. This threshold refers to the upper quartile value of the average elevation difference in the area. For example, if the 75% quantile value of the elevation difference data in the area is 40 meters, then mark the elevation difference area greater than 40 meters as a high-resistance area. When screening the areas where diffusion is blocked, compare the total cumulative elevation rise value on the main diffusion path of the pollutant. If this value exceeds 150 meters or the average slope is greater than 15%, then the entire path is regarded as a diffusion-blocked path segment. During the process of calculating the diffusion rate attenuation ratio, the system compares the time required for the pollutant to reach the downstream monitoring point and the concentration change degree under the same wind speed condition between the flat path segment and the resistance path segment. For example, the 5-kilometer transmission time of the flat path segment is 40 minutes, and the PM2.5 concentration at the end point is 85 μg / m³, while the 5-kilometer transmission of the high-resistance path segment requires 70 minutes, and the concentration at the end point is 52 μg / m³. Then the rate attenuation ratio is 70 divided by 40, that is, 1.75. Finally, integrate the resistance type, elevation difference distribution, attenuation rate, and path coordinates of different path segments to generate the pollutant diffusion resistance distribution.

[0115] S502: According to the pollutant diffusion resistance distribution, call the vegetation coverage information, analyze the adsorption capacity of vegetation for pollutants, screen the areas where the adsorption rate exceeds the set threshold, analyze the influence of vegetation on the pollutant flow, and generate the pollutant vegetation adsorption coefficient.

[0116] Based on the diffusion path, vegetation coverage data is extracted for the area covered by the path. The vegetation data of each plot records the NDVI value ranging from 0 to 1. The higher the NDVI value, the denser the vegetation. During the analysis of the adsorption capacity of vegetation to pollutants, the system divides the NDVI values into four levels: 0.0–0.2 (sparse), 0.2–0.5 (medium), 0.5–0.8 (high density), and above 0.8 (extremely high density). The corresponding pollutant adsorption capacities are set to 0.3 times, 0.6 times, 1.0 times, and 1.3 times of the basic adsorption rate respectively. The basic adsorption rate is set by the regional historical observation data. For example, if the basic adsorption rate of PM2.5 is set to 12%, then the adsorption rate in the medium vegetation coverage area is 12%×0.6 = 7.2%. During the process of screening the areas where the adsorption rate exceeds the set threshold, the system takes 10% as the adsorption rate judgment threshold for PM2.5, identifies all areas where the vegetation adsorption rate is greater than 10%, and records their spatial coordinates and areas. When analyzing the impact of vegetation on the pollutant flow, the system counts the length of the diffusion path passing through the high adsorption rate area and introduces an additional adsorption adjustment factor according to the vegetation type (such as shrubs, trees, herbs). For example, the factor for shrubs is 1.0, for trees is 1.2, and for herbs is 0.8. Finally, parameters such as the path length, NDVI level, and vegetation type are combined to calculate the comprehensive adsorption capacity on the path. If the length of a path is 2 kilometers, the average NDVI value is 0.65, the corresponding adsorption rate is 12%, and the vegetation type is trees, then the final adsorption capacity is 12%×1.2 = 14.4%. Finally, the system records the adsorption rate of each path segment and generates the pollutant-vegetation adsorption coefficient in combination with the spatial information.

[0117] S503: Based on the pollutant-vegetation adsorption coefficient, combined with the ground elevation data, analyze the pollutant flow path along the terrain, match the meteorological data to adjust the diffusion time scale, calculate the transport adjustment amount of pollutants under different terrain conditions, and generate a pollutant emission quantification plan;

[0118] Extract the elevation value and adsorption coefficient value corresponding to every 100 meters on the path according to the main diffusion direction of pollutants. During the process of analyzing the flow path of pollutants along the terrain, the system takes the fire point as the starting point and compares the elevation values in the downhill direction in turn. If the altitude of adjacent points decreases, the pollutants will preferentially flow along this path. If there are multiple paths, the one with the lowest adsorption coefficient is selected as the preferred path to form the main diffusion flow path line. During the process of matching meteorological data to adjust the diffusion time scale, the system calculates the required transmission time of this path by combining the transmission length and real-time wind speed data on the path, and introduces a wind direction deviation correction factor for the transmission time. For example, when the wind speed is 3 m / s and the path length is 6 km, the theoretical transmission time is 2000 seconds. If the current wind direction deviates from the main path direction by more than 45 degrees, the transmission time will be increased by 20% based on the original value, and the adjusted value is 2400 seconds. During the process of calculating the transport adjustment amount of pollutants under different terrain conditions, the system compares the concentration attenuation and time extension caused by three factors: elevation increase, vegetation adsorption, and wind direction deviation in the actual diffusion path. If the original emission intensity is 9×10 8 μg, the adsorption attenuation in the path is 20%, the wind direction delay is 10%, and the elevation impact causes a 15% decrease in the transmission rate. Then the actual transport amount of pollutants is calculated according to the comprehensive adjustment factor of 0.55, and the final effective emission amount is 4.95×10 8 μg. The system records the adjustment amount results of all diffusion paths and integrates and outputs them in combination with the spatial range, path trajectory, meteorological conditions, transport duration, and attenuation amplitude to generate a quantitative pollutant emission plan.

[0119] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A quantitative method for pollutant emissions from open-air straw burning based on multi-source data fusion, characterized in that: The following steps are involved: S1: Based on the air pollutant monitoring data, the sensor network is called to obtain the PM2.5, CO, and NOX concentrations, analyze the thermal radiation intensity of the fire point, collect meteorological data, and combine the time series changes of the fire point and meteorological parameters to obtain the pollution source data set; S2: Based on the pollution source data set, screen the concentration abnormal fluctuation area, analyze the fire point coordinates and intensity, calculate the fire point activity frequency, extract the time distribution information of pollutant concentration changes, and combine the time series data to obtain the distribution of pollution hotspot areas; S3: Based on the distribution of the pollution hotspots, analyze the pollutant diffusion trend, calculate the pollutant transmission rate and sedimentation rate, evaluate the concentration decrease gradient, calculate the diffusion radius in combination with the pollutant release rate, use wind speed and wind direction data to analyze the main wind direction transport ratio, analyze the cross-regional transport impact in combination with meteorological conditions, and obtain the pollutant diffusion range; S4: Based on the diffusion range of the pollutants, analyze the current change range of pollutant concentrations, screen abnormal rising areas, calculate the pollutant release amount in combination with the thermal radiation intensity of the fire point, and evaluate the pollutant retention time in combination with meteorological data, adjust the emission rate, and obtain the pollutant emission intensity.

2. The quantitative method for pollutant emission from open-air straw burning based on multi-source data fusion according to claim 1 is characterized in that: The pollution source data set includes PM2.5 concentration, CO concentration, NOX concentration, fire point thermal radiation intensity, temperature, humidity, wind speed and meteorological time series data; the pollution hotspot area distribution includes concentration abnormal fluctuation area, fire point intensity, fire point activity frequency and pollutant concentration time distribution; the pollutant diffusion range includes pollutant diffusion trend, pollutant transmission rate, pollutant sedimentation rate, pollutant concentration decreasing gradient, pollutant diffusion radius and pollutant transport ratio; the pollutant emission intensity includes pollutant concentration change range, pollutant abnormal rising area, pollutant release amount, pollutant retention time and pollutant emission rate.

3. The quantitative method for pollutant emission from open-air straw burning based on multi-source data fusion according to claim 1 is characterized in that: Based on the air pollutant monitoring data, the sensor network is called to obtain the PM2.5, CO, and NOX concentrations, the thermal radiation intensity of the fire point is analyzed, the meteorological data is collected, and the specific steps to obtain the pollution source data set are combined with the time series changes of the fire point and meteorological parameters. S101: Obtain PM2.5, CO, and NOX concentration data monitored by the environmental monitoring sensor network, call the real-time measurement values ​​of the monitoring points, filter abnormal data and remove over-limit values, sort the pollutant concentration data according to the data timestamp, calculate the mean of the pollutant concentration in the time series, and analyze the change trend to generate pollutant concentration time series data; S102: Based on the pollutant concentration time series data, analyzing the fire point area thermal radiation intensity data of the satellite remote sensing equipment, matching the fire point area monitoring period, calculating the correlation between the fire point thermal radiation intensity and the pollutant concentration, and generating the fire point pollution impact coefficient; S103: According to the fire point pollution impact coefficient, meteorological parameters of ground meteorological monitoring stations are collected, the impact factor of wind speed change on pollutant diffusion is calculated, and the pollution source data set is generated in combination with the fire point pollution impact coefficient.

4. The quantitative method for pollutant emission from open-air straw burning based on multi-source data fusion according to claim 1 is characterized in that: Based on the pollution source data set, the specific steps of screening the concentration abnormal fluctuation area, analyzing the fire point coordinates and intensity, calculating the fire point activity frequency, extracting the time distribution information of the pollutant concentration change, and combining the time series data to obtain the distribution of the pollution hotspot area are as follows: S201: Based on the pollution source data set, call the pollutant concentration data to screen the concentration abnormal fluctuation area, calculate the change range of the pollutant concentration in the differentiated time interval, eliminate the fluctuation area, extract the spatial coordinates of the concentration mutation area, screen the area where the pollutant concentration change rate exceeds the set threshold, and generate the pollution abnormal fluctuation area; S202: according to the pollution abnormal fluctuation area, analyzing the fire point coordinate data, matching the fire point location and fire point intensity data, calculating the fire point activity frequency and setting the high-frequency threshold, screening the high-frequency fire point area, and generating the high-frequency fire point area; S203: Call the high-frequency fire point area, extract the time distribution information of pollutant concentration changes, match the concentration time series data, calculate the pollutant concentration change trend, and generate the pollution hot spot area distribution.

5. The quantitative method for pollutant emission from open-air straw burning based on multi-source data fusion according to claim 1 is characterized in that: Based on the distribution of pollution hot spots, the pollutant diffusion trend is analyzed, the pollutant transmission rate and sedimentation rate are calculated, the concentration decrease gradient is evaluated, the diffusion radius is calculated in combination with the pollutant release rate, the wind speed and wind direction data are used to analyze the main wind direction transport ratio, and the cross-regional transport impact is analyzed in combination with meteorological conditions. The specific steps to obtain the pollutant diffusion range are as follows: S301: Based on the distribution of the pollution hotspot area, call the wind speed and wind direction data, analyze the diffusion trend of pollutants along the main wind direction, analyze the transmission rate of pollutants under the conditions of differentiated wind speeds, screen the pollutant transmission rate interval within the wind speed variation range, analyze the diffusion trajectory of pollutants under the conditions of differentiated wind directions, calculate the main wind direction diffusion ratio of pollutants, and generate the wind direction diffusion trend of pollutants; S302: according to the pollutant wind diffusion trend, call humidity and atmospheric stability data, calculate the pollutant deposition rate, screen the area where the deposition rate is greater than the threshold, analyze the decreasing relationship of pollutant concentration, and generate the pollutant deposition gradient; S303: Based on the pollutant deposition gradient, the pollutant release rate at the fire point is called, combined with the pollutant transmission path, the impact of meteorological conditions on pollution diffusion is analyzed, the pollutant cross-regional transportation index is calculated, and the pollutant diffusion range is generated.

6. The quantitative method for pollutant emission from open-air straw burning based on multi-source data fusion according to claim 5 is characterized in that: The specific calculation formula of the pollutant deposition rate is: ； in, represents the pollutant deposition rate, represents the pollutant concentration at the start of the path, represents the pollutant concentration at the end of the path, represents the path length, Indicates the current relative humidity in the area where the path is located. Represents the average humidity in the area. Indicates the atmospheric stability level in the path area. Indicates the average level of stability in the area.

7. The quantitative method for pollutant emission from open-air straw burning based on multi-source data fusion according to claim 1 is characterized in that: Based on the diffusion range of the pollutants, the current pollutant concentration change range is analyzed, the abnormal rising area is screened, the pollutant release amount is calculated in combination with the thermal radiation intensity of the fire point, and the pollutant retention time is evaluated in combination with meteorological data, and the emission rate is adjusted. The specific steps to obtain the pollutant emission intensity are as follows: S401: Based on the pollutant diffusion range, call the current pollutant concentration monitoring data, analyze the concentration change amplitude of the monitoring point, screen the area where the pollutant concentration increase exceeds the set threshold in a short period of time, eliminate the low amplitude change area, extract the spatial coordinates of the abnormal rising area, calculate the concentration change rate, and generate the pollutant abnormal rising area; S402: According to the abnormal pollutant rising area, call the fire point thermal radiation intensity data, calculate the pollutant release amount, match the pollutant release intensity with the fire point distribution, screen the area where the pollutant release exceeds the set standard, and generate the pollutant release distribution; S403: Based on the distribution of pollutant release, combined with meteorological data, the change of pollutant concentration is evaluated, the pollutant retention time is analyzed, the emission rate is adjusted, the influence coefficient of the concentration change during the pollutant transport process is calculated, and the pollutant emission intensity is generated.

8. The quantitative method for pollutant emission from open-air burning of straw based on multi-source data fusion according to claim 7 is characterized in that: The specific calculation formula for the total amount of pollutant release is: ； in, The total amount of pollutant released. Indicates the thermal radiation intensity of the fire point, Indicates the actual burning area corresponding to the fire point, Indicates the atmospheric temperature during the current observation period. Indicates the average temperature of the area in the past 24 hours. Indicates the actual vegetation height in the fire area. Indicates the average vegetation height in the area.

9. The quantitative method for pollutant emission from open-air straw burning based on multi-source data fusion according to claim 1 is characterized in that: The method also Including, S5: based on the pollutant emission intensity, calling terrain data to calculate diffusion resistance, evaluating the impact of vegetation coverage on pollutant adsorption capacity, analyzing the impact of ground elevation and wind field changes on flow paths, and adjusting the diffusion time scale in combination with meteorological data to obtain a quantitative pollutant emission plan; The pollutant emission quantification scheme includes pollutant diffusion resistance, vegetation adsorption capacity, pollutant flow path and pollutant diffusion time scale.

10. The quantitative method for pollutant emission from open-air straw burning based on multi-source data fusion according to claim 1 is characterized in that: The specific steps are: S501: Based on the pollutant emission intensity, the terrain data of the fire point area is called to analyze the influence of terrain undulation on the pollutant diffusion path, calculate the diffusion resistance of the differentiated elevation area, screen the diffusion-impeded area, calculate the diffusion rate attenuation ratio, and generate the pollutant diffusion resistance distribution; S502: According to the pollutant diffusion resistance distribution, the vegetation coverage information is called, the adsorption capacity of the vegetation on the pollutants is analyzed, the area where the adsorption rate exceeds the set threshold is screened, the influence of the vegetation on the flow of the pollutants is analyzed, and the pollutant vegetation adsorption coefficient is generated; S503: Based on the pollutant vegetation adsorption coefficient and combined with ground elevation data, the flow path of pollutants along the terrain is analyzed, the diffusion time scale is adjusted by matching meteorological data, the transport adjustment amount of pollutants under differentiated terrain conditions is calculated, and a quantitative pollutant emission plan is generated.

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