Pollution prevention and control management method and system based on GIS
By deploying sensors in the target area to collect wind and pollutant data, and combining this with GIS topographic data and a source tracing model, the problem of inaccurate source tracing in complex scenarios was solved, and the precise location of pollution sources was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHAANXI TRANSPORTATION VOCATIONAL & TECH COLLEGE
- Filing Date
- 2026-03-24
- Publication Date
- 2026-05-19
AI Technical Summary
Existing technologies struggle to accurately trace pollution sources in large-scale monitoring scenarios and complex terrain environments, especially in densely populated industrial areas where multiple pollution points lead to inaccurate analysis.
By deploying sensors in the target area to collect wind information and pollutant concentration values, a sequence of pollutant concentration and wind information is constructed. Pollution correlation analysis is then performed in conjunction with GIS topographic data, and source tracing models such as Gaussian diffusion model and Lagrange particle model are used to accurately locate the pollution source area.
It enables accurate source tracing of pollution in complex scenarios, solves the problem of inaccurate analysis caused by interference between different pollution sources, and can quickly locate the geographical area of pollution source.
Smart Images
Figure CN122067643A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of pollution management technology, specifically relating to a GIS-based pollution prevention and control management method and system. Background Technology
[0002] Traditional air pollution management methods typically involve collecting pollutant concentration data from multiple monitoring points and then using algorithms such as Gaussian diffusion models or Lagrange particle models to infer the location of pollution occurrence, thereby tracing the source of pollution. However, traditional technologies do not take into account factors such as complex terrain and environmental obstructions, resulting in inaccurate source tracing.
[0003] To address the aforementioned problems, existing technologies have proposed the following solutions. For example, Chinese patent application CN202111168382.5 discloses an atmospheric pollution trend analysis method based on GIS technology. This method, when identifying pollution sources, combines the main pollutants and pollution distribution data of a monitoring station over the past 12 hours with the main exhaust emissions of surrounding enterprises to conduct a correlation analysis, thereby identifying and confirming pollution sources. Simultaneously, by utilizing changes in wind force and direction, it performs overlay analysis on the main pollutants and spatial distribution of pollution over a larger surrounding area to determine the contribution ratio of external and internal pollution sources. Another example is Chinese patent application CN202011436704.5, which discloses an atmospheric pollution source tracing and diffusion analysis system and method based on a Gaussian diffusion model. This method combines topographic and meteorological data to obtain a topographically corrected wind field distribution. Based on monitoring data and the Gaussian diffusion model formula, it obtains the concentration impact of each pollution source on the center point of the grid in the assessment area, calculates the cumulative concentration value, and visualizes the calculated pollutant concentration value at the center point of the grid on a GIS map.
[0004] However, for large-scale monitoring scenarios, pollution detected at different monitoring points may originate from different pollution points. If these pollution points are treated as originating from a single pollution point for analysis, it may be impossible to accurately trace the source of pollution. Although the existing technologies mentioned above introduce the possibility of correlation analysis with waste gas emitting enterprises around the monitoring point during source tracing, they do not provide specific instructions on how to conduct the correlation analysis. Furthermore, in situations where industrial enterprises are densely concentrated, the same pollution source may involve multiple enterprises, thus limiting the effectiveness of correlation analysis. Summary of the Invention
[0005] To address the aforementioned problems, this invention provides a GIS-based pollution prevention and control management method and system, thereby resolving the issues present in the background art.
[0006] To achieve the aforementioned objectives, this invention proposes a GIS-based pollution prevention and control management method, comprising: Multiple measurement points are determined in the target area, and detection sensors are deployed at the measurement points. Based on the detection sensors, wind information and actual concentration values of pollutants are collected at multiple time points. The wind information includes wind speed angle and wind force. The measurement point where the actual concentration value exceeds the first threshold is determined and defined as the pollution point, and the corresponding time point is defined as the first time point. Starting from the first time point, a first sequence of the actual concentration values of the pollutants and a second sequence of the wind information are collected and constructed. The pollution points where the same pollutant is detected are defined as first associated points, and first terrain data between the first associated points are obtained based on GIS data; Based on the first terrain data, the first sequence, and the second sequence, a pollution correlation analysis is performed on the first associated point to extract a second associated point that detects the same pollution source. The source area of pollution is determined by using a source tracing model and conducting source tracing analysis based on the wind information and actual concentration value of the second correlation point.
[0007] Further, extracting the second association point includes the following steps: The first sequence is constructed at a second time point after the first time point. The peak time point when the actual concentration value in each first sequence reaches the peak value is located. A time window is generated with the peak time point as the center. Based on the time window, a third sequence and a fourth sequence are extracted in the first sequence and the second sequence respectively. The similarity between the third sequences is calculated. A digital elevation model is established based on GIS terrain data. Based on the digital elevation model and the fourth sequence, the path accessibility score between the first associated points and the delay reasonableness score of the peak time point are calculated. A topology map is constructed based on the digital elevation model. The first associated point in the topology map is used as a node. The edge distance between nodes is determined based on similarity, path reachability score and delay reasonableness score. The first associated point is clustered based on the DBSCAN algorithm and edge distance, and the first associated point located in the same clustering result is defined as the second associated point.
[0008] Furthermore, the source tracing model includes the Gaussian diffusion model and the Lagrange particle model.
[0009] Further, identifying the source area of pollution includes the following steps: Based on the Gaussian diffusion model and the first and second sequences of the second correlation points, a first source region is determined. Based on GIS data, a second source region in the first source region is determined to generate the pollutant. One of the second source regions is selected as the first pollution source. Based on GIS data, second terrain data of the second correlation points and the first pollution source is obtained. The first pollution source is simulated by combining the second terrain data and the Lagrange particle model to obtain the simulated concentration values of all the second correlation points. The second source area, which is circumferentially adjacent to the first pollution source, is located and designated as the second pollution source. A diffusion simulation is performed on the second pollution source to obtain the simulated concentration values of each of the second associated points. Based on the actual concentration values and simulated concentration values of each of the second associated points, a first reasonable value for the first pollution source and a second reasonable value for the second pollution source are calculated. If there is a second reasonable value that is greater than the first reasonable value, the second pollution source is defined as the third pollution source. The search is then performed in the direction of the corresponding third pollution source until the pollution source area is determined.
[0010] Further, searching in the direction of the second pollution source until the pollution source area is determined includes the following steps: A first connecting line is obtained by connecting the first pollution source and the third pollution source. A fan-shaped region is generated with the first connecting line as the central axis and the first pollution source as the center. Each of the second source regions included in the fan-shaped region is taken as the fourth pollution source, and a third reasonable value corresponding to the fourth pollution source is calculated. The fourth pollution source corresponding to the largest third reasonable value is taken as the fifth pollution source. The fifth pollution source is connected to the third pollution source to obtain a second connecting line. The fan-shaped region is generated based on the fifth pollution source and the second connecting line. This step is repeated until the generated fan-shaped region no longer includes the second source region. The pollution source corresponding to the calculated maximum reasonable value is taken as the pollution source region.
[0011] Further, determining the primary source region includes the following steps: The Gaussian diffusion model determines hypothetical source regions with different pollution intensities based on the first sequence and the second sequence, generates a predicted distribution map based on the hypothetical source regions, compares the predicted distribution maps of different second correlation points, and determines the first source region. The first source region is the overlapping part between hypothetical source regions with the same pollution intensity in different predicted distribution maps.
[0012] Further, calculating the first reasonable value of the first pollution source and the second reasonable value of the second pollution source includes the following steps: For each pollution source, the first number of second associated points whose difference between the actual concentration value and the simulated concentration value is less than a second threshold after statistical simulation, and the second number of all second associated points, are counted. The ratio of the first number to the second number is used as the first evaluation value of the corresponding pollution source. The ratio of the simulated concentration value to the actual concentration value of each second associated point is used as the base value. The base values of each second associated point during pollution source simulation are constructed as a third sequence. The variance of the base values is calculated based on the third sequence, and the variance is used as the second evaluation value of each pollution source. The first and second evaluation values of each pollution source are normalized to obtain the corresponding third and fourth evaluation values. The difference between the third and fourth evaluation values of the first and second pollution sources is taken as the corresponding first reasonable value or second reasonable value.
[0013] The present invention also provides a GIS-based pollution prevention and control management system, which is used to implement the above-described method, and the system includes: The data acquisition module determines multiple measurement points in the target area, deploys detection sensors at the measurement points, and collects wind information and actual pollutant concentration values at multiple time points based on the detection sensors. The wind information includes wind speed angle and wind force. The extraction module determines the measurement point where the actual concentration value exceeds the first threshold and defines it as a pollution point. The corresponding time point is defined as the first time point. Starting from the first time point, it collects and constructs a first sequence of the actual concentration values of the pollutants and a second sequence of the wind information. The preprocessing module defines pollution points that detect the same pollutant as first associated points, acquires first terrain data between the first associated points based on GIS data, performs pollution correlation analysis on the first associated points based on the first terrain data, the first sequence, and the second sequence, and extracts second associated points that detect the same pollution source from them. The source tracing module uses a source tracing model and performs source tracing analysis based on the wind information and actual concentration value of the second associated point to determine the pollution source area.
[0014] The beneficial effects of this invention are as follows: This invention collects wind information and pollutant concentration values by deploying sensors in the target area. When the concentration exceeds a threshold, it constructs a first sequence of pollutant concentration and a second sequence of wind information starting from that time point, thus achieving precise capture of pollution events and time-series construction of key data. Subsequently, by defining pollution points detecting the same pollutant as first correlation points, and combining GIS topographic data, the first sequence, and the second sequence for pollution correlation analysis, it accurately extracts second correlation points that truly originate from the same pollution source, solving the problem of inaccurate analysis caused by interference between different pollution sources in complex scenarios. Then, by using a source tracing model to perform source tracing analysis on this set of highly correlated second correlation points and their wind and concentration information, it is possible to accurately trace back and determine the geographical area of the pollution source. Of particular note is that this invention solves the problem in existing technologies that, when facing large-scale monitoring and multiple pollution sources, cannot effectively distinguish pollution signals from different sources, leading to a general analysis of all pollution points and ultimately inaccurate source tracing. Attached Figure Description
[0015] Figure 1 This is a flowchart illustrating the steps of a GIS-based pollution prevention and control management method according to the present invention. Figure 2 This is a schematic diagram illustrating the principle of determining the pollution source area in this invention; Figure 3 This is a schematic diagram illustrating the principle of determining the first source region in this invention; Figure 4 This is a schematic diagram of the structure of a GIS-based pollution prevention and control management system according to the present invention. Detailed Implementation
[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0017] It is understood that the terms "first," "second," etc., used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are used only to distinguish one element from another. For example, without departing from the scope of this application, a first script may be referred to as a second script, and similarly, a second script may be referred to as a first script.
[0018] like Figure 1 As shown, a GIS-based pollution prevention and control management method includes: S1: Determine multiple measurement points in the target area, deploy detection sensors at the measurement points, and collect wind information and actual pollutant concentration values at multiple time points based on the detection sensors. The wind information includes wind speed angle and wind force.
[0019] S2: Determine the measurement point where the actual concentration value exceeds the first threshold and define it as the pollution point. The corresponding time point is defined as the first time point. Starting from the first time point, collect and construct the first sequence of actual pollutant concentration values and the second sequence of wind information.
[0020] Specifically, the location of measurement points can be determined based on GIS data. For example, the number of measurement points can be increased in areas with dense industrial enterprises or concentrated construction areas, while the number of measurement points can be reduced in densely populated urban areas or other areas with a lower probability of pollution. The detection sensors deployed at the measurement points can detect the concentration of various pollutants. In this embodiment, the collected pollutant concentrations are all concentrations minus background values, where the background value is the concentration of various pollutants in the air when no pollution occurs. Pollutants include particulate matter, sulfur dioxide, carbon dioxide, etc. The detection sensors collect the concentration values of the above-mentioned pollutants every 3 minutes and wind information every 30 seconds. The wind information includes wind speed angle and wind force. Then, based on the wind information collected within 3 minutes, the comprehensive wind information within 3 minutes is determined. For example, if 6 wind speed angles and wind forces are collected within 3 minutes, the angle that appears most frequently is taken as the final determined wind speed angle, and the average of the 6 wind force values is taken as the determined wind speed.
[0021] The first threshold is determined based on ambient air quality standards, such as a sulfur dioxide concentration of less than 150 mg / L. g / m 3 When pollutants exceeding the first threshold are detected, the current time point is determined as the first time point, and a first sequence is constructed based on the concentration of sulfur dioxide after the first time point. For example, if the first sequence is constructed based on the actual concentration value one hour after the first time point, the first sequence includes 20 sulfur dioxide concentrations. The construction process of the second sequence is similar.
[0022] S3: Define the pollution points where the same pollutant is detected as the first associated points, and obtain the first terrain data between the first associated points based on GIS data.
[0023] S4: Based on the first terrain data, the first sequence, and the second sequence, perform pollution correlation analysis on the first associated point and extract the second associated point that was detected to be the same pollution source.
[0024] S5: Use the source tracing model and perform source tracing analysis based on the wind information and actual concentration values of the second correlation point to determine the pollution source area.
[0025] In this embodiment, the source tracing model includes the Gaussian diffusion model and the Lagrange particle model.
[0026] First, pollution points where the same pollutant is detected are designated as first correlation points. For example, measurement points where sulfur dioxide concentrations all exceed a first threshold are designated as first correlation points. This is a prerequisite for subsequent pollutant source tracing. Then, first topographic data is acquired between the first correlation points. By acquiring this topographic data and combining it with first and second sequences among the first correlation points, correlation analysis can be performed. For instance, in urban scenarios, due to the obstruction of tall buildings, wind direction changes with the terrain. Therefore, by combining wind and topographic data, the wind direction can be determined, thereby determining the pollutant diffusion direction. Based on this, the concentration changes of pollutants at each first correlation point are determined to match the pollutant diffusion direction and diffusion rate, allowing for correlation analysis of the first correlation points. Finally, second correlation points that detect the same pollution source are extracted.
[0027] Finally, by combining the source tracing model with the analysis of the actual concentration value and wind information of the second correlation point, the pollution source area can be located, helping relevant personnel to quickly find the pollution source.
[0028] This invention begins by constructing a first sequence of actual pollutant concentrations and a second sequence of wind information, starting from the time point when the actual pollutant concentration exceeds a first threshold, thus defining the time range for data collection and analysis. Then, pollution points where the same pollutant is detected are defined as first correlation points, and their topographic data is acquired. By establishing time series of pollution concentrations and wind information sequences, combined with topographic data, pollution correlation analysis is performed to identify the pollution propagation path and correlation points. This allows for a more accurate assessment of the correlation between first correlation points, and further extraction of second correlation points that detect the same pollution source. Finally, a source tracing model is used, combined with the wind and concentration information of the second correlation points, to perform source tracing analysis, enabling rapid location of the pollution source area and helping relevant personnel take timely measures.
[0029] In this embodiment, extracting the second association point from the first association point includes the following steps: At the second time point after the first time point, a first sequence is constructed. The peak time point when the actual concentration value in each first sequence reaches the peak value is located. A time window is generated with the peak time point as the center. Based on the time window, the third and fourth sequences are extracted from the first and second sequences respectively. The similarity between the third sequences is calculated. A digital elevation model is established based on GIS terrain data. Based on the digital elevation model and the fourth sequence, the path accessibility score between the first associated points and the delay reasonableness score of the peak time point are calculated.
[0030] A topology map is constructed based on a digital elevation model. The first associated point in the topology map is used as a node. The edge distance between nodes is determined based on similarity, path reachability score and delay reasonableness score. The first associated point is clustered based on the DBSCAN algorithm and edge distance, and the first associated point located in the same cluster result is defined as the second associated point.
[0031] The interval between the second time point and the first time point is determined according to the detection frequency of the detection sensor. In this embodiment, the interval between the second time point and the first time point is 1 hour. The following example illustrates the above steps. There are currently first correlation points 1 to 4, and the peak time points appear sequentially in each of the first correlation points. For example, the peak time point of the first correlation point 1 appears earliest, and the peak time point of the first correlation point 4 appears latest. The time window is preferably set to an odd number. For example, if the time window is set to 5, the first sequence of the first correlation point 1 is [160 165 175 180 182 190 188 189 187 188]. The value corresponding to the peak time point is 190. Then, a time window is generated with the value 190 as the center. During the generation, two values are extracted from the left and right sides of 190 as the third sequence. The third sequence is then [180 182 190 188 189]. The process of generating the fourth sequence based on the third sequence is similar.
[0032] After generating the third sequences for the first association points 1-4, the similarity between each pair of third sequences is compared. Euclidean distance can be used to measure the similarity between two third sequences; the smaller the Euclidean distance, the better the similarity. For ease of calculation, after obtaining the Euclidean distance between all third sequences, its reciprocal is calculated and normalized to the range of 0-1, making the calculated similarity intuitive. Higher similarity between two third sequences indicates more similar changes in pollutant concentrations, making them more likely to be caused by the same pollution source.
[0033] Next, a digital elevation model (DEM) is built based on GIS data. The DEM reflects the changes in terrain elevation, which can then be used to infer the direction of wind flow. For example, to calculate the path accessibility score between the first associated point 1 and 2, the heights of the first associated points 1 and 2 themselves are obtained based on the DEM, as well as the degree of obstruction between them. The more obstacles and the higher the obstacles, the lower the path accessibility score. Based on this idea, various calculation methods can be set, such as assigning scores to obstacles of different heights, with higher obstacles receiving higher scores. Then, an initial score, such as 1 point, is set, and the scores of obstacles between the first associated points 1 and 2 are accumulated to obtain the total score. The initial score is then subtracted from the total score to obtain the path accessibility score. The higher the path accessibility score, the more likely pollutants are to spread from the first associated point 1 to the first associated point 2.
[0034] For the reasonableness scoring of the delay, firstly, the time difference of the third sequence in the first correlation point 1 and 2 is calculated. Since both first correlation points 1 and 2 collect data every 3 minutes, all elements between the sequences have the same time difference, such as 9 minutes. Based on the digital elevation model, multiple possible propagation paths between the first correlation point 1 and the first correlation point 2 are determined, and the path length of each propagation path is calculated. Then, the fourth sequence of the first correlation points 1 and 2 is obtained, and the first element is selected from each fourth sequence. Each element in the fourth sequence includes the wind speed angle and wind force. Therefore, based on the wind speed angle of the first correlation point 1, some propagation paths with unreasonable propagation directions are first eliminated. For example, if the wind speed angle is southwest and there is a propagation path starting from the northeast, then that propagation path is eliminated.
[0035] Then, the wind speed change rate is determined based on the wind speed and time difference between the first correlation points 1 and 2. For example, if the wind speed at the first correlation point 1 is 10 m / s and the wind speed at the first correlation point 2 is 8 m / s, with a time difference of 9 min, then the wind speed change rate is -0.22 m / min. Finally, the ideal path value is calculated by combining the displacement formula, the wind speed at the first correlation point 1, the wind speed change rate, and the time difference. The propagation path with the shortest path length to the ideal path value is located, and the difference between the two is calculated. For ease of statistical calculation, the reciprocal of the absolute value of the difference is used as the propagation score. This step can be repeated for each element in the fourth sequence to obtain 5 propagation scores. After normalizing the 5 propagation scores, the average value is calculated and used as the delay reasonableness score. The higher the delay reasonableness score, the more likely the pollutant is to pass through the first correlation point 1 first and then the first correlation point 2.
[0036] Next, a topology map is constructed based on the geographical location of the first associated point. The first associated point is represented as a node in the topology map, and nodes are connected by edges. The length of the edge, i.e., the distance between nodes, is determined based on similarity, path reachability scores, and delay reasonableness scores. The lower the scores of these three scores, the larger the edge distance. Finally, the first associated points in the topology map are clustered using the DBSCAN algorithm. The DBSCAN algorithm is a density-based clustering algorithm; the closer the first associated points are in the topology map, the higher their density, and the more likely they are to be clustered together. Finally, the first associated points located in the same cluster are defined as second associated points. The second associated points selected using this method not only have a high probability that their detected concentration data originates from the same pollution source, but their geographical distribution is also in the same direction as wind flow.
[0037] In this embodiment, the source tracing model is used and source tracing analysis is performed based on the wind force information and concentration information of the second correlation point to obtain the pollution source area, including the following steps.
[0038] The first source region is determined based on the first sequence and the second sequence of the second correlation point using the Gaussian diffusion model. The second source region that will generate pollutants is determined based on GIS data. One of the second source regions is selected as the first pollution source. The second topographic data of the second correlation point and the first pollution source is obtained based on GIS data. The diffusion simulation of the first pollution source is carried out by combining the second topographic data and the Lagrange particle model to obtain the simulated concentration values of all second correlation points.
[0039] The second source area adjacent to the first pollution source in the periphery is located and designated as the second pollution source. A diffusion simulation is performed on the second pollution source to obtain the simulated concentration values of each second associated point. Based on the actual concentration values and simulated concentration values of each second associated point, the first reasonable value of the first pollution source and the second reasonable value of the second pollution source are calculated. If there is a second reasonable value that is greater than the first reasonable value, the second pollution source is defined as the third pollution source. The search is performed in the direction of the corresponding third pollution source until the pollution source area is determined.
[0040] Traditional methods rely on a single detection point for source tracing, resulting in reduced accuracy. To improve accuracy, this invention proposes the following method. First, a source region is determined by combining a source tracing model with a second associated point, aiming to quickly narrow down the detection range. The source tracing model used here is a Gaussian diffusion model. Specifically, the process of determining the pollution source region of a single second associated point using a Gaussian diffusion model, combined with pollutant concentration data and wind data, and by setting up a virtual region is existing technology and will not be described here. While the Gaussian diffusion model is fast, its accuracy is low, and the closer the measurement point is to the pollution source, the smaller the calculated first source region becomes. Therefore, predictions from multiple second associated points may deviate from each other. Thus, it is necessary to combine the pollution source regions of multiple second associated points to determine the first source region; the specific determination method will be described later.
[0041] After identifying the primary source area of potential pollution, GIS data is used to determine the factories within it, and then the pollutants that each factory might produce are identified. The area containing the factories that could produce the target pollutants is designated as the secondary source area. However, for industrial clusters, the secondary source areas may be adjacent, or even if a secondary source area is selected, it may be a large area comprised of multiple factories. In this case, a secondary source area is selected as the primary pollution source, and secondary topographic data is acquired, including the topographic distribution between the primary pollution source and the secondary associated points. Then, using the primary pollution source as the pollution origin point, a forward diffusion simulation is performed using the Lagrange particle model, which offers high computational accuracy and takes topographic factors into account. Based on the simulation results, the simulated concentration values at peak times for each secondary associated point are determined.
[0042] For example, such as Figure 2 As shown, secondary source regions B through D exist around secondary source region A. After simulating secondary source region A, diffusion simulations are then performed on secondary source regions B through D using the Lagrange particle model to obtain the simulated concentration value at each secondary associated point. Finally, to compare with the actual concentration value at the peak time point, the simulated concentration value at the corresponding peak time point needs to be selected from the simulation results. The difference between the simulated concentration value and the previously detected value is used to determine the first reasonable value for secondary source region A, and the second reasonable values for secondary source regions B through D. The larger the reasonable value, the more reasonable the secondary source region is as a source of infection. If the second reasonable value for secondary source region D is greater than the first reasonable value for secondary source region A, it indicates that the location of secondary source region D as a diffusion source is more reasonable. Therefore, a search is performed in the direction of secondary source region D to find the most suitable diffusion source. The search is conducted in the following manner.
[0043] A first connecting line is obtained by connecting the first pollution source and the third pollution source. A fan-shaped region is generated with the first connecting line as the central axis and the first pollution source as the center. Each second source region included in the fan-shaped region is taken as the fourth pollution source, and the third reasonable value corresponding to the fourth pollution source is calculated. The fourth pollution source corresponding to the largest third reasonable value is taken as the fifth pollution source. The fifth pollution source is connected to the third pollution source to obtain a second connecting line. The fan-shaped region is generated based on the fifth pollution source and the second connecting line. This step is repeated until the generated fan-shaped region no longer includes the second source region. The pollution source corresponding to the calculated largest reasonable value is taken as the pollution source region.
[0044] like Figure 2 In the middle, connecting the first pollution source ( Figure 2 Point A) and the third pollution source ( Figure 2 Starting from point D, obtain the first connecting line. The specific steps for generating a fan-shaped region with the first connecting line as the central axis are as follows: Extend the first connecting line. The extension value is determined empirically; here, the first connecting line is extended by a factor of 2. Then, using the extended first connecting line as the central axis and the third pollution source as the center, generate a fan-shaped region with a central angle of 90 degrees. The fan-shaped region includes three second source regions, namely... Figure 2 Points D, E, and F in the model are used. Since point D has already been simulated, points E and F are taken as pollution sources. The Lagrange particle model is used to simulate diffusion and obtain the third reasonable value for points E and F. Here, the reasonable values of points D, E, and F are all the third reasonable value. If the reasonable value of point D is the largest, then point D is taken as a potential pollution source. If the reasonable value of point E is the largest, then a fan-shaped region is generated based on points D and E and searched until the potential pollution source is found.
[0045] The source tracing model determines the first source region based on the first and second sequences of each second association point, including the following steps: The Gaussian diffusion model determines hypothetical source regions with different pollution intensities based on the first and second sequences. Based on the hypothetical source regions, a predicted distribution map is generated. The predicted distribution maps of different second correlation points are compared to determine the first source region. The first source region is the overlapping part between hypothetical source regions with the same pollution intensity in different predicted distribution maps.
[0046] Generally, the higher the concentration of pollutants emitted by a pollution source, the farther the pollution will spread. When the initial pollution intensity is unknown, source tracing models are needed to simulate and determine the possible distribution area of the pollution source under different initial pollution concentrations, i.e., the hypothetical source area. The following uses... Figure 3 To explain, for point X, the hypothetical pollution sources are determined using the source tracing model to be x1 and x2. This means that the pollution detected at point X may originate from region x1 or region x2. If the pollution source is located in x2, the pollution intensity emitted from that source will be higher than that from region x1 because region x2 is farther from point X. Therefore, the predicted distribution map of point X includes regions x1 and x2. Similarly, the hypothetical source regions for point Y can be determined as y1 and y2. When determining the first source region, the predicted distribution maps of different second associated points are superimposed, and then... Figure 3 It can be seen that regions x1 and y1 have the same pollution intensity, so the overlapping part (shown in shaded areas in the figure) is taken as the hypothetical pollution source, and the overlapping part of regions x2 and y2 is also taken as the hypothetical pollution source.
[0047] In this embodiment, calculating the first reasonable value of the first pollution source and the second reasonable value of the second pollution source includes the following steps: For each pollution source, the first number of second correlation points whose difference between the actual concentration value and the simulated concentration value is less than the second threshold after statistical simulation, and the second number of all second correlation points are counted. The ratio of the first number to the second number is used as the first evaluation value of the corresponding pollution source. The ratio of the simulated concentration value to the actual concentration value of each second correlation point is used as the base value. The base values of each second correlation point during pollution source simulation are used to construct a third sequence. The variance of the base values is calculated based on the third sequence, and the variance is used as the second evaluation value of each pollution source.
[0048] The first and second evaluation values of each pollution source are normalized to obtain the corresponding third and fourth evaluation values. The difference between the third and fourth evaluation values of the first and second pollution sources is taken as the corresponding first or second reasonable value.
[0049] The following example illustrates the above steps. For the first pollution source, after simulation, the first number of second correlation points where the difference between the actual and simulated concentration values is less than the second threshold is determined to be 4. The smaller the difference between the actual and simulated concentration values, the more consistent the pollutant diffusion results are with the monitoring results of each measurement point when simulating diffusion using the first pollution source. For example, if the total number of second measurement points, i.e., the second number, is 5, then the first evaluation value is 4 / 5 = 0.8.
[0050] For a certain second correlation point, the measured actual concentration is 180, and the simulated concentration is 170. Therefore, the baseline value is 170 / 180 = 0.944. After calculation, the baseline values for the five second correlation points are 0.944, 0.969, 0.769, 0.9, and 0.944, respectively. The variance of these five values is 0.00513, which is then used as the second evaluation value for the first pollution source. A smaller variance indicates a better match between the pollutant diffusion results and the monitoring results at each measurement point. Similarly, the first and second evaluation values for the second pollution source are calculated. To eliminate the influence of dimensions, the calculated first and second evaluation values are normalized using the minimum-maximum normalization method. After normalization, the first pollution source obtains third and fourth evaluation values corresponding to the first and second evaluation values, respectively. Since the fourth evaluation value is subtracted from the third evaluation value, a larger variance results in a larger fourth evaluation value, a smaller result, and a smaller calculated first reasonable value. Similarly, the second reasonable value can be calculated.
[0051] like Figure 4 As shown, the present invention also provides a GIS-based pollution prevention and control management system for implementing the above-described method. The system includes: The data acquisition module identifies multiple measurement points in the target area, deploys detection sensors at these points, and collects wind information and actual pollutant concentrations at multiple time points based on the detection sensors. The wind information includes wind speed angle and wind force.
[0052] The extraction module identifies measurement points where the actual concentration value exceeds the first threshold and defines them as pollution points. The corresponding time point is defined as the first time point. Starting from the first time point, it collects and constructs a first sequence of actual pollutant concentration values and a second sequence of wind information.
[0053] The preprocessing module defines pollution points that detect the same pollutant as first associated points, acquires first topographic data between the first associated points based on GIS data, and performs pollution correlation analysis on the first associated points based on the first topographic data, the first sequence, and the second sequence to extract second associated points that detect the same pollution source.
[0054] The source tracing module uses a source tracing model and performs source tracing analysis based on wind information and actual concentration values from the second correlation point to determine the pollution source area.
[0055] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0056] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.
[0057] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A GIS-based pollution prevention and control management method, characterized in that, include: Multiple measurement points are determined in the target area, and detection sensors are deployed at the measurement points. Based on the detection sensors, wind information and actual concentration values of pollutants are collected at multiple time points. The wind information includes wind speed angle and wind force. The measurement point where the actual concentration value exceeds the first threshold is determined and defined as the pollution point, and the corresponding time point is defined as the first time point. Starting from the first time point, a first sequence of the actual concentration values of the pollutants and a second sequence of the wind information are collected and constructed. The pollution points where the same pollutant is detected are defined as first associated points, and first terrain data between the first associated points are obtained based on GIS data; Based on the first terrain data, the first sequence, and the second sequence, a pollution correlation analysis is performed on the first associated point to extract a second associated point that detects the same pollution source. The source area of pollution is determined by using a source tracing model and conducting source tracing analysis based on the wind information and actual concentration value of the second correlation point.
2. The method according to claim 1, characterized in that, Extracting the second correlation point includes the following steps: The first sequence is constructed at a second time point after the first time point. The peak time point when the actual concentration value in each first sequence reaches the peak value is located. A time window is generated with the peak time point as the center. Based on the time window, a third sequence and a fourth sequence are extracted in the first sequence and the second sequence respectively. The similarity between the third sequences is calculated. A digital elevation model is established based on GIS terrain data. Based on the digital elevation model and the fourth sequence, the path accessibility score between the first associated points and the delay reasonableness score of the peak time point are calculated. A topology map is constructed based on the digital elevation model. The first associated point in the topology map is used as a node. The edge distance between nodes is determined based on similarity, path reachability score and delay reasonableness score. The first associated point is clustered based on the DBSCAN algorithm and edge distance, and the first associated point located in the same clustering result is defined as the second associated point.
3. The method according to claim 1, characterized in that, The source tracing models include the Gaussian diffusion model and the Lagrange particle model.
4. The method according to claim 3, characterized in that, Identifying the source area of pollution includes the following steps: Based on the Gaussian diffusion model and the first and second sequences of the second correlation points, a first source region is determined. Based on GIS data, a second source region in the first source region is determined to generate the pollutant. One of the second source regions is selected as the first pollution source. Based on GIS data, second terrain data of the second correlation points and the first pollution source is obtained. The first pollution source is simulated by combining the second terrain data and the Lagrange particle model to obtain the simulated concentration values of all the second correlation points. The second source area, which is circumferentially adjacent to the first pollution source, is located and designated as the second pollution source. A diffusion simulation is performed on the second pollution source to obtain the simulated concentration values of each of the second associated points. Based on the actual concentration values and simulated concentration values of each of the second associated points, a first reasonable value for the first pollution source and a second reasonable value for the second pollution source are calculated. If there is a second reasonable value that is greater than the first reasonable value, the second pollution source is defined as the third pollution source. The search is then performed in the direction of the corresponding third pollution source until the pollution source area is determined.
5. The method according to claim 4, characterized in that, Searching in the direction of the second pollution source until the pollution source area is determined includes the following steps: A first connecting line is obtained by connecting the first pollution source and the third pollution source. A fan-shaped region is generated with the first connecting line as the central axis and the first pollution source as the center. Each of the second source regions included in the fan-shaped region is taken as the fourth pollution source, and a third reasonable value corresponding to the fourth pollution source is calculated. The fourth pollution source corresponding to the largest third reasonable value is taken as the fifth pollution source. The fifth pollution source is connected to the third pollution source to obtain a second connecting line. The fan-shaped region is generated based on the fifth pollution source and the second connecting line. This step is repeated until the generated fan-shaped region no longer includes the second source region. The pollution source corresponding to the calculated maximum reasonable value is taken as the pollution source region.
6. The method according to claim 4, characterized in that, Determining the primary source region includes the following steps: The Gaussian diffusion model determines hypothetical source regions with different pollution intensities based on the first sequence and the second sequence, generates a predicted distribution map based on the hypothetical source regions, compares the predicted distribution maps of different second correlation points, and determines the first source region. The first source region is the overlapping part between hypothetical source regions with the same pollution intensity in different predicted distribution maps.
7. The method according to claim 4, characterized in that, Calculating the first reasonable value for the first pollution source and the second reasonable value for the second pollution source includes the following steps: For each pollution source, the first number of second associated points whose difference between the actual concentration value and the simulated concentration value is less than a second threshold after statistical simulation, and the second number of all second associated points, are counted. The ratio of the first number to the second number is used as the first evaluation value of the corresponding pollution source. The ratio of the simulated concentration value to the actual concentration value of each second associated point is used as the base value. The base values of each second associated point during pollution source simulation are constructed as a third sequence. The variance of the base values is calculated based on the third sequence, and the variance is used as the second evaluation value of each pollution source. The first and second evaluation values of each pollution source are normalized to obtain the corresponding third and fourth evaluation values. The difference between the third and fourth evaluation values of the first and second pollution sources is taken as the corresponding first reasonable value or second reasonable value.
8. A GIS-based pollution prevention and control management system, used to implement the method as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition module determines multiple measurement points in the target area, deploys detection sensors at the measurement points, and collects wind information and actual pollutant concentration values at multiple time points based on the detection sensors. The wind information includes wind speed angle and wind force. The extraction module determines the measurement point where the actual concentration value exceeds the first threshold and defines it as a pollution point. The corresponding time point is defined as the first time point. Starting from the first time point, it collects and constructs a first sequence of the actual concentration values of the pollutants and a second sequence of the wind information. The preprocessing module defines pollution points that detect the same pollutant as first associated points, acquires first terrain data between the first associated points based on GIS data, performs pollution correlation analysis on the first associated points based on the first terrain data, the first sequence, and the second sequence, and extracts second associated points that detect the same pollution source from them. The source tracing module uses a source tracing model and performs source tracing analysis based on the wind information and actual concentration value of the second associated point to determine the pollution source area.