Accurate traceability system and method for atmospheric pollution source

Abstract

The invention relates to the technical field of pollution traceability, in particular to a precise traceability system and method for an atmospheric pollution source. The method comprises the following steps: collecting the concentration value, the wind speed and the wind direction of pollutants; carrying out statistics on common pollutants of different monitoring points, and determining a change characteristic value and concentration variability based on a concentration change condition so as to obtain a pollution approximation degree between the monitoring points; determining a pollutant propagation factor based on the concentration change direction and the wind direction of the monitoring point and the wind speed; therefore, optimization factors are determined to optimize the pollution approximation degree; clustering the optimized pollution approximation degree, and determining a pollution characteristic value of the characteristic point in a cluster based on the pollution approximation degree between the monitoring points and the distance at the wind direction convergence position; and completing source tracing of the pollution source based on the pollution characteristic values of the characteristic points. According to the invention, the traceability precision and efficiency in a multi-pollution source complex scene are improved.

Description

Technical Field

[0001] This application relates to the field of pollution source tracing technology, specifically to a precise source tracing system and method for air pollution sources. Background Technology

[0002] With the rapid development of industrialization, air pollution has become increasingly serious, making its control an urgent matter. Monitoring and controlling air pollution sources is currently the most effective and fundamental measure to prevent various types of air pollution. Understanding the sources of air pollutants is essential for effective control, hence source localization methods are attracting increasing attention from researchers.

[0003] However, in the face of complex scenarios where multiple pollution sources coexist and pollutants diffuse and decay in the atmosphere with meteorological conditions, existing methods still face challenges in distinguishing the impact of different pollution sources, overcoming the interference of decay effects, and achieving precise spatial positioning of pollution sources. More efficient and accurate source tracing technologies are urgently needed. Summary of the Invention

[0004] To address the technical problem of low accuracy in pollutant source tracing, this application provides a precise source tracing system and method for air pollution sources, the specific technical solution of which is as follows: Firstly, this application proposes a precise method for tracing the sources of air pollution, which includes the following steps: Collect the concentration values ​​of each type of pollutant, as well as wind speed and direction at each monitoring point; The effective pollutants are determined by comparing the concentration values ​​with the preset concentrations; for any two monitoring points, the overlapping categories of effective pollutants are counted and recorded as common pollutants; the change characteristic values ​​are determined by comparing the changes in the concentration values ​​of common pollutants at the two monitoring points within a preset time interval; the concentration variability is determined by the differences in concentration changes between the two monitoring points at all adjacent times; and the pollution approximation between the monitoring points is obtained based on the quantity of common pollutants at the two monitoring points, the change characteristic values, and the concentration variability of all common pollutants. The source of pollution is determined by the ratio of all pollutant concentrations at monitoring points to preset concentrations, and the characteristic direction vector between monitoring points is determined based on the source of pollution. The pollutant propagation factor is determined by the similarity between the characteristic direction vectors of all monitoring points and the wind direction vector, as well as the wind speed. The optimization factor is determined based on the difference between the pollutant propagation factor and the source of pollution between two monitoring points, and the pollution approximation is optimized based on the optimization factor to obtain the optimized pollution approximation. Based on the optimized pollution approximation, all monitoring points are clustered to obtain multiple clusters; for any cluster, a feature line is drawn based on the wind direction of each monitoring point, intersecting at feature points in the cluster; pollution similarity and influence weight are obtained based on the pollution approximation between the monitoring point corresponding to the feature line and other monitoring points, as well as the distance to the feature point; pollution feature value of each feature point is obtained based on the number of feature lines intersecting at feature points, the pollution similarity of the feature lines, and the influence weight. Pollution source tracing is completed based on the pollution feature values ​​of feature points.

[0005] In the aforementioned scheme, this application proposes a "pollution approximation" calculation model based on the overlap of effective pollutant categories and concentration change characteristics by comprehensively analyzing pollutant concentration data and meteorological data from monitoring points. Furthermore, by combining pollutant propagation attenuation characteristics and historical wind direction and speed data, a "propagation factor" and an "optimization factor" are constructed to dynamically correct the pollution approximation, significantly improving the accuracy of monitoring point association identification under the influence of the same pollution source. Adaptive clustering is performed using the optimized pollution approximation to effectively distinguish groups of monitoring points affected by different pollution sources. Finally, based on the principle of intersection of characteristic wind direction lines of monitoring points within the cluster and the calculation of pollution characteristic values, adaptive and high-precision positioning of multiple pollution sources is achieved. This method effectively overcomes challenges such as pollutant attenuation and multi-source mixing interference, significantly improving the accuracy and efficiency of source tracing in complex multi-pollution source scenarios, and providing reliable technical support for precise air pollution control.

[0006] In one embodiment, the method for determining the change characteristic value by comparing the changes in the concentration values ​​of common pollutants at two monitoring points within a preset time interval is as follows: For each type of common pollutant, compare the concentration values ​​of the common pollutants at two monitoring points at each time point. As time goes by, if the relationship changes each time, increment the characteristic value by 1.

[0007] In one embodiment, the method for determining concentration variability by the difference in concentration changes between two monitoring points at all adjacent time points is as follows: The ratio of the absolute value of the difference in concentration values ​​of the same type of common pollutant at two monitoring points at each time point to the minimum value of the two concentration values ​​is taken as the concentration change ratio of each type of common pollutant at each time point; For each type of common pollutant, the difference in the concentration change ratio between two adjacent time points is recorded as the concentration change ratio difference; the mean of all concentration change ratio differences within each preset interval is taken as the concentration variability of the preset interval.

[0008] In one embodiment, the pollution approximation is positively correlated with the quantity of common pollutants and negatively correlated with the change characteristic value and concentration variability, respectively.

[0009] In one embodiment, the method for determining the source of pollution based on the ratio of all pollutant concentrations at monitoring points to a preset concentration, and for determining the characteristic direction vector between monitoring points based on the source of pollution, is as follows: The normalized result of the sum of the ratios of the concentration values ​​of each type of pollutant at the monitoring point to the corresponding preset concentration is taken as the source of pollution at the monitoring point. When the root cause of monitoring point A is greater than that of monitoring point B, the direction vector from position A to position B is denoted as the characteristic direction vector of the two monitoring points.

[0010] In one embodiment, the pollutant propagation factor is positively correlated with the similarity between the wind direction vector and the characteristic direction vector, and with the wind speed.

[0011] In one embodiment, the method for determining an optimization factor based on the differences in pollutant transmission factors and root causes between two monitoring points, and then optimizing the pollution approximation based on the optimization factor to obtain the optimized pollution approximation, is as follows: , This indicates the pollutant transmission factor at two monitoring points. This represents the absolute value of the root cause difference between two monitoring points. This represents an exponential function with the natural constant as its base. This represents the optimization factor for two monitoring points; , Indicates the degree of similarity in pollution levels between two monitoring points. This represents the optimization factor between two monitoring points. This indicates the optimized pollution approximation between two monitoring points.

[0012] In one embodiment, the method for obtaining the pollution similarity and influence weight based on the pollution similarity between the monitoring point corresponding to the feature line and other monitoring points, and the distance to the feature point, is as follows: For each characteristic line, calculate the mean of the pollution similarity between the monitoring point corresponding to the characteristic line and the other monitoring points in the cluster, and record the mean as the pollution similarity. Calculate the spatial distance between the feature point and the monitoring point corresponding to the feature line, and record the spatial distance as the influence weight.

[0013] In one embodiment, the pollution feature value is positively correlated with the number of feature lines and the degree of pollution similarity of the feature lines, and negatively correlated with the influence weight.

[0014] Secondly, embodiments of this application also provide a precise source tracing system for air pollution sources, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described precise source tracing methods for air pollution sources.

[0015] The beneficial effects of this application are as follows: This application proposes a "pollution approximation" calculation model based on the overlap of effective pollutant categories and concentration change characteristics by comprehensively analyzing pollutant concentration data and meteorological data from monitoring points. Furthermore, by combining pollutant propagation attenuation characteristics and historical wind direction and speed data, a "propagation factor" and an "optimization factor" are constructed to dynamically correct the pollution approximation, significantly improving the accuracy of identifying the association of monitoring points under the influence of the same pollution source. Adaptive clustering is performed using the optimized pollution approximation to effectively distinguish groups of monitoring points affected by different pollution sources. Finally, based on the principle of intersection of characteristic wind direction lines within the cluster and the calculation of pollution characteristic values, adaptive and high-precision location of multiple pollution sources is achieved. This method effectively overcomes challenges such as pollutant attenuation and multi-source mixing interference, significantly improving the accuracy and efficiency of source tracing in complex multi-pollution source scenarios, and providing reliable technical support for precise air pollution control. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a precise source tracing method for air pollution sources, provided as an embodiment of this application. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a precise source tracing system and method for air pollution sources proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] An example of a precise source tracing system and method for air pollution sources: The following description, in conjunction with the accompanying drawings, details the specific scheme of the precise source tracing system and method for air pollution sources provided in this application.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a precise source tracing method for air pollution sources according to an embodiment of this application. The method includes the following steps: Step S001: Collect the concentration values ​​of each type of pollutant, as well as wind speed and wind direction at each monitoring point.

[0022] Air pollutant concentration data are acquired by uniformly distributing pollutant monitoring stations within the monitored area. The air pollutants involved in this application include PM2.5, PM10, SO2, NO2, CO, O3, and H2S, which can be adjusted by the implementer. Each monitoring point can acquire pollutant concentration data for each type of pollutant over time, as well as meteorological data at the monitoring point, including wind direction and wind speed. The sampling frequency for different data at each monitoring point remains consistent; in this embodiment, the data sampling interval is 1 minute.

[0023] At this point, meteorological data and concentration values ​​of each type of pollutant were obtained for each monitoring point.

[0024] Step S002: Collect common pollutants at different monitoring points, and determine the characteristic values ​​and concentration variability based on the concentration changes, so as to obtain the pollution approximation between monitoring points.

[0025] Following the steps outlined above, the concentration values ​​of each type of pollutant and meteorological data were collected at various monitoring points within the monitored area. This application utilizes publicly available geographic information datasets (such as those obtained from the Ministry of Natural Resources' Geographic Information Public Service Platform, the National Geomatics Center of China, and UAV aerial tilt measurement) and the coordinate information of the monitoring points to obtain a two-dimensional digital map of the monitored area. The digital map contains multiple monitoring stations; in this application, 100 monitoring stations are evenly distributed throughout the monitored area.

[0026] In areas with multiple air pollution sources, the air pollutants from different sources are often different. For example, the air pollutants produced by combustion power plants are mainly sulfur dioxide, while those from chemical plants are mainly volatile organic compounds (VOCs). These pollutants continuously spread in the atmosphere, meaning their concentration varies at different locations. If the pollutant concentration collected at a monitoring station is lower than a preset concentration, it can be considered that the pollutant is not present at that monitoring point. Each type of pollutant has a preset concentration, which is the maximum allowable emission concentration of the air pollutant and can be obtained from the "Ambient Air Quality Standards." Therefore, the concentration values ​​of various pollutants collected in real time at each monitoring point are judged. If the pollutant concentration is greater than or equal to the preset concentration, the pollutant is considered a valid pollutant; otherwise, it is considered an invalid pollutant.

[0027] For any two monitoring points, count the overlapping categories of effective pollutants at the two points, and record the overlapping effective pollutants as common pollutants. The more common pollutants there are, the greater the likelihood that the two monitoring points originate from the same pollution source. If there are no common pollutants, the pollution approximation is 0.

[0028] For each type of common pollutant, the concentration values ​​of the common pollutant at two monitoring points are compared at each time point. As time progresses, if the relative concentration changes, the change characteristic value is incremented by 1, where the initial value of the change characteristic value is 1. For example, at the first time point, the concentration value at the first monitoring point is greater than that at the second monitoring point; at the second time point, the concentration value at the first monitoring point is less than that at the second monitoring point. At this time, the relative concentration has changed, and the change characteristic value is incremented by 1. The change characteristic value within a preset time interval prior to the current time is obtained. In this embodiment, the preset time interval is the time of the day before the current time.

[0029] The larger the effective pollutant concentration change characteristic value of the monitoring point, the more likely it is that the pollutant concentration between the two monitoring points does not meet the characteristic of attenuation of each type of effective pollutant. In this case, the possibility of being affected by different pollution sources is greater, and the possibility of the air pollutants collected from the two monitoring points coming from the same pollution source is smaller.

[0030] The ratio of the absolute value of the difference in concentration values ​​of the same common pollutant at two monitoring points at each time point to the minimum value of the two concentration values ​​is taken as the concentration change ratio of each common pollutant at each time point. In order to prevent the problem of large changes in results due to detection error caused by a small minimum value, the maximum value of the pollution change ratio is set to 10, that is, the difference between the two concentrations cannot exceed 10 times.

[0031] For each type of common pollutant, the difference in the concentration change ratio between two adjacent time points is recorded as the concentration change ratio difference; the mean of all concentration change ratio differences within each preset interval is taken as the concentration variability of the preset interval. This indicates that the larger the effective pollutant concentration change ratio difference, the greater the possibility of being affected by different pollution sources.

[0032] Therefore, the pollution approximation between any two monitoring points can be obtained based on the quantity and change characteristics of common pollutants, as well as the concentration changes of all common pollutants.

[0033] The pollution approximation is positively correlated with the quantity of common pollutants, and negatively correlated with the change characteristic value and concentration variability, respectively.

[0034] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the two variables change in the same direction. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large. The specific relationship is determined by the actual application, and this application does not impose any special restrictions.

[0035] It should be noted that negative correlation means that when one variable increases, the other variable decreases accordingly, and the two variables change in opposite directions. When one variable changes from large to small or from small to large, the other variable also changes from small to large or from large to small. The specific relationship is determined by practical application, and this application does not impose any special restrictions.

[0036] Preferably, in this embodiment, the expression for the degree of contamination approximation is: , Indicates the total amount of pollutants. Indicates the characteristic value of change. This indicates the concentration variation of common pollutants in category a. This indicates the degree of similarity in pollution levels between monitoring points.

[0037] The greater the similarity in pollution levels, the more likely the air pollutants collected from the two monitoring points originate from the same pollution source. Conversely, the smaller the similarity in pollution levels, the less likely the air pollutants collected from the two monitoring points originate from the same pollution source.

[0038] Thus, the pollution approximation between any two monitoring points was obtained.

[0039] Step S003: Determine the pollutant transmission factor based on the direction of concentration change at the monitoring point, wind direction, and wind speed; thereby determine the optimization factor to optimize the pollution approximation.

[0040] The pollution approximation between any two monitoring points can be obtained based on the pollutant types and concentration changes at the monitoring points. However, since pollutant concentrations decrease with distance, the number of effective pollutant categories detected at monitoring points affected by the same pollution source may differ. For example, a pollutant 'a' detected at monitoring point A may have decreased to below a preset concentration by the time it reaches monitoring point B, making pollutant 'a' not an effective pollutant at point B. Consequently, the pollution approximation between the two monitoring points is low. Therefore, this application further constructs an optimization factor for the pollution approximation based on the attenuation characteristics of the monitoring points. This optimizes the pollution approximation to better characterize the attenuation features under the influence of the same pollution source, thereby improving the accuracy and efficiency of subsequent pollution source tracing.

[0041] For any two monitoring points at any given time, the source nature of pollutants at the two monitoring points is first determined. The source nature of pollutants is the normalized result of the sum of the ratios of the concentration values ​​of each type of pollutant at the monitoring point to the corresponding preset concentrations. The greater the source nature, the more likely the monitoring point is to be close to a pollution source. The direction of pollutant propagation is determined based on the source nature. If the source nature of monitoring point A is greater than that of monitoring point B, it can be considered that the pollutant is propagating from monitoring point A to monitoring point B. The positional direction vector from monitoring point A to monitoring point B is obtained and denoted as the characteristic direction vector. The wind direction at each time point is used to construct the wind direction vector. The pollutant propagation factor is obtained by comparing the similarity between the wind direction vector and the characteristic direction vector at all times within the time interval, as well as the wind speed. In this embodiment, the normalization result used is the maximum-minimum value normalization.

[0042] The pollutant propagation factors are positively correlated with the similarity of the wind direction vector and the characteristic direction vector, as well as the wind speed.

[0043] Preferably, the expression for the pollutant transmission factor is: , Let represent the cosine similarity between the wind direction vector and the feature direction vector at time s. This indicates the number of moments within a preset time interval. This represents the wind speed at time s. Indicates the pollutant transmission factor, This represents the normalization function; in this embodiment, it is the maximum and minimum value normalization function.

[0044] A higher pollutant transmission factor indicates a stronger tendency for pollutants to spread between two monitoring points, meaning a greater decrease in pollutant concentration. Conversely, a lower pollutant transmission factor indicates a weaker tendency for pollutants to spread between two monitoring points, meaning a smaller decrease in pollutant concentration.

[0045] The above steps construct a pollutant propagation factor between any two monitoring points. A matching degree is then constructed based on the pollutant propagation factor and the attenuation of pollutant concentrations between monitoring points, serving as an optimization factor for pollution approximation. A higher matching degree indicates that the pollutant concentration values ​​collected between the two monitoring points better match the attenuation characteristics under the influence of wind speed and direction, thus resulting in a higher pollution approximation.

[0046] Therefore, the optimization factors were first determined based on the differences in pollutant transmission factors and root causes between the two monitoring points.

[0047] Preferably, the expression for the optimization factor is: , This indicates the pollutant transmission factor at two monitoring points. This represents the absolute value of the root cause difference between two monitoring points. This represents an exponential function with the natural constant as its base. This represents the optimization factor for two monitoring points; when the absolute value of the root cause difference between the two monitoring points is 0, the optimization factor is also 0.

[0048] The smaller the difference between the pollutant transmission factor and the root cause difference ratio and 1, the more the pollutant concentration decay between monitoring points conforms to the influence of wind speed and direction. This indicates a better match between the transmission factor and the pollutant concentration decay, resulting in a higher degree of matching and a larger optimization factor. A larger optimization factor indicates a greater approximation of the actual pollution between the two monitoring points. Conversely, a smaller optimization factor indicates a smaller approximation of the actual pollution between the two monitoring points.

[0049] The pollution approximation is optimized based on the optimization factor to obtain the optimized pollution approximation, and its expression is: , Indicates the degree of similarity in pollution levels between two monitoring points. This represents the optimization factor between two monitoring points. This indicates the optimized pollution approximation between two monitoring points.

[0050] Thus, the optimized pollution approximation between the two monitoring points was obtained.

[0051] Step S004: Cluster the optimized pollution approximation. Within each cluster, determine the pollution characteristic value of the feature point based on the pollution approximation between monitoring points and the distance at the convergence point of wind direction.

[0052] The optimized pollution approximation between any two monitoring points was obtained based on the above steps. The monitoring points were then clustered using this optimized pollution approximation. In this application, a monitoring point was arbitrarily selected as the initial seed point, and its nearest neighbor monitoring point was obtained. The optimized pollution approximation between the monitoring point and its nearest neighbor was calculated and normalized. If the optimized pollution approximation was greater than or equal to a preset approximation threshold, it indicated that the two monitoring points were affected by the same pollution source, and the two monitoring points were clustered into one class. A new monitoring point was then used as the seed point to obtain the nearest neighbor monitoring point. If the optimized pollution approximation was less than the preset approximation threshold, it indicated that one of the two monitoring points might be affected by multiple pollution sources or by different pollution sources. In this case, another monitoring point was selected as the initial seed point for the above analysis. This process was repeated until the number of monitoring points in each category of the two-dimensional digital map was fixed. In this embodiment, the preset approximation threshold was 0.8.

[0053] The above clustering steps are used to classify all monitoring points, with each category corresponding to a pollution source. The location of pollution sources is adaptively completed by analyzing the pollutant concentration distribution of each monitoring point within the cluster.

[0054] Specifically, for any cluster, for each monitoring point within the cluster, the characteristic wind direction at the monitoring point is obtained. The characteristic wind direction is the direction vector corresponding to the wind direction with the highest frequency in the historical wind direction data of the monitoring point. Since pollutants at the pollution source will spread along the wind direction, the straight line corresponding to the characteristic wind direction is recorded as the characteristic straight line corresponding to the monitoring point. The pollution source may be located at a point on the characteristic straight line. Thus, each monitoring point in the cluster can obtain a corresponding characteristic straight line. Different characteristic straight lines intersect on the two-dimensional digital map, and there may be multiple intersection points. Here, the intersection points are recorded as characteristic points, and each characteristic point may be the location of the pollution source.

[0055] Based on the pollutant distribution on the characteristic lines intersecting the feature points, a pollution feature value is constructed for each feature point. For each characteristic line, the mean of the pollution similarity between the monitoring point corresponding to the characteristic line and the other monitoring points in the cluster is calculated. This mean represents the pollutant similarity between the monitoring points corresponding to the characteristic line; the larger the value, the more likely the intersecting feature point is to be a pollution source. This mean is recorded as the pollution similarity degree. Then, the spatial distance between the feature point and the monitoring point corresponding to the characteristic line is calculated. A larger distance indicates a smaller influence, and this distance is used as the influence weight. In this embodiment, the spatial distance is calculated using Euclidean distance.

[0056] Therefore, the pollution feature value of each feature point is obtained based on the number of feature lines intersecting at the feature point, the pollution similarity of the feature lines, and the influence weight.

[0057] The pollution characteristic values ​​are positively correlated with the number of characteristic lines and the degree of pollution similarity of the characteristic lines, and negatively correlated with the influence weight.

[0058] Preferably, in this embodiment, the expression for the pollution characteristic value is: , This represents the number of characteristic lines intersecting at the characteristic points. This indicates the degree of contamination similarity of the x-th characteristic line. This represents the influence weight of the x-th characteristic line. This represents the pollution characteristic value of a feature point. The larger the pollution characteristic value, the more likely the feature point is to be a pollution source. The smaller the pollution characteristic value, the less likely the feature point is to be a pollution source.

[0059] At this point, the pollution characteristic value of each feature point has been obtained.

[0060] Step S005: Complete the source tracing of pollution based on the pollution characteristic values ​​of the feature points.

[0061] The above steps obtain multiple feature points for each cluster, as well as the corresponding pollution feature values. In this application, the feature point with the largest pollution characteristic is selected as the pollution source corresponding to that cluster. This enables adaptive and accurate source tracing of multiple pollution sources, improving the accuracy and efficiency of source tracing.

[0062] Based on the same inventive concept as the above method, this embodiment of the invention also provides a precise source tracing system for air pollution sources, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described precise source tracing methods for air pollution sources.

[0063] It should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

[0064] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A precise source tracing method for air pollution sources, characterized in that, The method includes the following steps: Collect the concentration values ​​of each type of pollutant, as well as wind speed and direction at each monitoring point; The effective pollutants are determined by comparing the concentration values ​​with the preset concentrations; for any two monitoring points, the overlapping categories of effective pollutants are counted and recorded as common pollutants; the change characteristic values ​​are determined by comparing the changes in the concentration values ​​of common pollutants at the two monitoring points within a preset time interval; the concentration variability is determined by the differences in concentration changes between the two monitoring points at all adjacent times; and the pollution approximation between the monitoring points is obtained based on the quantity of common pollutants at the two monitoring points, the change characteristic values, and the concentration variability of all common pollutants. The source of pollution is determined by the ratio of all pollutant concentrations at monitoring points to preset concentrations, and the characteristic direction vector between monitoring points is determined based on the source of pollution. The pollutant propagation factor is determined by the similarity between the characteristic direction vectors of all monitoring points and the wind direction vector, as well as the wind speed. The optimization factor is determined based on the difference between the pollutant propagation factor and the source of pollution between two monitoring points, and the pollution approximation is optimized based on the optimization factor to obtain the optimized pollution approximation. Based on the optimized pollution approximation, all monitoring points are clustered to obtain multiple clusters; for any cluster, a feature line is drawn based on the wind direction of each monitoring point, intersecting at feature points in the cluster; pollution similarity and influence weight are obtained based on the pollution approximation between the monitoring point corresponding to the feature line and other monitoring points, as well as the distance to the feature point; pollution feature value of each feature point is obtained based on the number of feature lines intersecting at feature points, the pollution similarity of the feature lines, and the influence weight. Pollution source tracing is completed based on the pollution feature values ​​of feature points.

2. The method for precise source tracing of air pollution sources as described in claim 1, characterized in that, The method for determining the change characteristic value by comparing the changes in the concentration values ​​of common pollutants at two monitoring points within a preset time interval is as follows: For each type of common pollutant, compare the concentration values ​​of the common pollutants at two monitoring points at each time point. As time goes by, if the relationship changes each time, increment the characteristic value by 1.

3. The method for precise source tracing of air pollution sources as described in claim 1, characterized in that, The method for determining concentration variability by the difference in concentration changes between two monitoring points at all adjacent time points is as follows: The ratio of the absolute value of the difference in concentration values ​​of the same type of common pollutant at two monitoring points at each time point to the minimum value of the two concentration values ​​is taken as the concentration change ratio of each type of common pollutant at each time point; For each type of common pollutant, the difference in the concentration change ratio between two adjacent time points is recorded as the concentration change ratio difference; the mean of all concentration change ratio differences within each preset interval is taken as the concentration variability of the preset interval.

4. The method for precise source tracing of air pollution sources as described in claim 1, characterized in that, The pollution approximation is positively correlated with the quantity of common pollutants, and negatively correlated with the change characteristic value and concentration variability, respectively.

5. The method for precise source tracing of air pollution sources as described in claim 1, characterized in that, The method for determining the source of pollution based on the ratio of all pollutant concentrations at monitoring points to a preset concentration, and for determining the characteristic direction vector between monitoring points based on the source of pollution, is as follows: The normalized result of the sum of the ratios of the concentration values ​​of each type of pollutant at the monitoring point to the corresponding preset concentration is taken as the source of pollution at the monitoring point. When the root cause of monitoring point A is greater than that of monitoring point B, the direction vector from position A to position B is denoted as the characteristic direction vector of the two monitoring points.

6. The method for precise source tracing of air pollution sources as described in claim 1, characterized in that, The pollutant propagation factors are positively correlated with the similarity of the wind direction vector and the characteristic direction vector, as well as the wind speed.

7. The method for precise source tracing of air pollution sources as described in claim 1, characterized in that, The method for determining optimization factors based on the differences in pollutant transmission factors and root causes between two monitoring points, and then optimizing the pollution approximation based on these optimization factors to obtain the optimized pollution approximation, is as follows: , This indicates the pollutant transmission factor at two monitoring points. This represents the absolute value of the root cause difference between two monitoring points. This represents an exponential function with the natural constant as its base. This represents the optimization factor for two monitoring points; , Indicates the degree of similarity in pollution levels between two monitoring points. This represents the optimization factor between two monitoring points. This indicates the optimized pollution approximation between two monitoring points.

8. The method for precise source tracing of air pollution sources as described in claim 1, characterized in that, The method for obtaining the pollution similarity and influence weight based on the pollution similarity between the monitoring point corresponding to the feature line and other monitoring points, and the distance to the feature point, is as follows: For each characteristic line, calculate the mean of the pollution similarity between the monitoring point corresponding to the characteristic line and the other monitoring points in the cluster, and record the mean as the pollution similarity. Calculate the spatial distance between the feature point and the monitoring point corresponding to the feature line, and record the spatial distance as the influence weight.

9. The method for precise source tracing of air pollution sources as described in claim 1, characterized in that, The pollution characteristic values ​​are positively correlated with the number of characteristic lines and the degree of pollution similarity of the characteristic lines, and negatively correlated with the influence weight.

10. A precise source tracing system for air pollution sources, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the precise source tracing method for air pollution sources as described in any one of claims 1-9.

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