An ambient air quality automatic monitoring method, system, product and medium

Abstract

The application discloses an environmental air quality automatic monitoring method, system, product and medium, and relates to the field of environmental monitoring.The method comprises the following steps: obtaining concentration data of malodorous chemical substances based on monitoring sensors in multiple monitoring sites; selecting a target monitoring point with the highest concentration data as a reference point, marking multiple potential pollution sources based on current wind direction and current wind speed data of the target monitoring point at a current monitoring time point; obtaining reference distances of each potential pollution source from the reference point, consistency degrees of each potential pollution source with the current wind direction and emission historical data of each potential pollution source; calculating pollution scores of each potential pollution source based on the reference distances, the consistency degrees and the emission historical data; determining a potential pollution source with the highest pollution score as a pollution starting point; and sending position coordinates of the pollution starting point to a target client. By implementing the method, the accuracy of pollution source speculation can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of environmental monitoring, and in particular to an environmental air quality automatic monitoring method, system, product and medium. BACKGROUND

[0002] With the rapid development of modern industry and the acceleration of urbanization process, environmental air quality problems have been increasingly concerned by people. It is an important problem to find the pollution source when monitoring the surrounding environment at the monitoring site.

[0003] In the related art, various types of pollutant concentration data can be obtained by setting monitoring sensors at fixed monitoring sites. For the determination of the pollution source, the possible pollution area is circled according to the pollutant concentration, and then the direction of the pollution source is speculated by means of the wind direction.

[0004] However, when there are multiple areas with high pollutant concentration in the related art, the concentration data alone cannot effectively narrow down the range of the pollution source. When the direction of the pollution source is speculated according to the wind direction, the instability of the wind direction will lead to inaccurate speculation results. SUMMARY

[0005] The present application provides an environmental air quality automatic monitoring method, system, product and medium, which is used to improve the accuracy of pollution source speculation.

[0006] In a first aspect, the present application provides an environmental air quality automatic monitoring method applied to an environmental air quality automatic monitoring system. The method comprises: obtaining concentration data of malodorous chemicals based on monitoring sensors in multiple monitoring sites; selecting a target monitoring point with the highest concentration data as a reference point, and marking multiple potential pollution sources based on current wind direction and current wind speed data of the target monitoring point at a current monitoring time point; obtaining reference distance of each potential pollution source from the reference point, consistency degree with the current wind direction and emission history data; calculating pollution score of each potential pollution source based on the reference distance, the consistency degree and the emission history data; determining the potential pollution source with the highest pollution score as a pollution starting point; and sending the position coordinates of the pollution starting point to a target client.

[0007] By adopting the technical scheme, the concentration data of the malodorous chemical substance is acquired based on the monitoring sensors in the plurality of monitoring sites, the target monitoring point with the highest concentration data is selected as the reference point, and the plurality of potential pollution sources are marked in combination with the current wind direction and wind speed data, the range of the pollution source can be narrowed more specifically, the reference distance, consistency degree and emission history data of the potential pollution source are acquired, the possibility of each potential pollution source can be evaluated more scientifically, the potential pollution source with the highest pollution score is finally determined as the pollution starting point, and the position coordinates are sent to the target client, so that the relevant personnel can quickly and accurately acquire the pollution source information, which is helpful for taking measures for treatment in time, thereby improving the accuracy of environmental air quality monitoring.

[0008] With reference to the embodiments of the first aspect, in some embodiments, the target monitoring point with the highest concentration data is selected as the reference point, and the plurality of potential pollution sources are marked based on the current wind direction and current wind speed data of the target monitoring point at the current monitoring time point, specifically including: acquiring the historical concentration data of all monitoring points within a preset distance range around the target monitoring point, and calculating the concentration change rate of each monitoring point based on the historical concentration data; the monitoring point with a concentration change rate exceeding a preset change threshold is marked as an abnormal monitoring point; based on the current wind direction of the target monitoring point, a predicted pollution source within a preset distance range of the abnormal monitoring point is searched in the upwind direction area of the target monitoring point; the historical emission data of the predicted pollution source is acquired, and the pollutant diffusion range of each predicted pollution source is calculated based on the historical emission data and the current wind speed data, and the predicted pollution source with a position coincidence degree of the pollutant diffusion range and the abnormal monitoring point exceeding a preset percentage is marked as a potential pollution source.

[0009] By adopting the technical scheme, the historical concentration data of all monitoring points within a preset distance range around the target monitoring point is acquired, and the concentration change rate is calculated, the monitoring point with a concentration change rate exceeding a preset change threshold is marked as an abnormal monitoring point, based on the current wind direction of the target monitoring point, a predicted pollution source within a preset distance range of the abnormal monitoring point is searched in the upwind direction area, the pollutant diffusion range is calculated in combination with the historical emission data and the current wind speed data, and the predicted pollution source with a position coincidence degree exceeding a preset percentage is further screened out and marked as a potential pollution source, which can more accurately find the possible pollution source and reduce the possibility of misjudgment.

[0010] In some embodiments of the first aspect, in some embodiments, the step of calculating the pollution score of each of the potential pollution sources based on the reference distance, the consistency degree, and the emission history data specifically comprises: obtaining an actual distance between each of the potential pollution sources and the reference point; calculating a theoretical concentration attenuation value based on the actual distance and a pre-established distance-concentration attenuation model; comparing the theoretical concentration attenuation value with actual concentration data of the reference point to obtain a concentration coincidence degree; calculating an included angle between an emission direction of the potential pollution source and the current wind direction to obtain a direction consistency degree; and calculating the pollution score of each of the potential pollution sources based on a preset weight coefficient by weighting the concentration coincidence degree, the direction consistency degree, and the emission history data.

[0011] By using the above technical solution, the actual distance between each potential pollution source and the reference point is obtained, the theoretical concentration attenuation value is calculated based on the actual distance and the pre-established distance-concentration attenuation model, the concentration coincidence degree is obtained by comparing the theoretical concentration attenuation value with the actual concentration data of the reference point, the influence degree of the potential pollution source on the reference point can be more accurately evaluated, the included angle between the emission direction of the potential pollution source and the current wind direction is calculated to obtain the direction consistency degree, and the pollution score of each of the potential pollution sources is calculated based on the preset weight coefficient by weighting the concentration coincidence degree, the direction consistency degree, and the emission history data, the pollution possibility of the potential pollution source can be more comprehensively and accurately evaluated.

[0012] In some embodiments of the first aspect, in some embodiments, after the step of determining the potential pollution source with the highest pollution score as the pollution starting point, the method further comprises: inputting the position coordinates of the pollution starting point, current meteorological data of the pollution starting point, and concentration data of the monitoring point closest to the pollution starting point into an atmospheric diffusion model to calculate an estimated concentration value of each monitoring point; calculating a concentration error between the estimated concentration value and the concentration data; and if the concentration error is within a preset error range, sending the position coordinates of the pollution starting point to a target client.

[0013] By using the above technical solution, the position coordinates of the pollution starting point, the current meteorological data, and the concentration data of the monitoring point closest to the pollution starting point are input into the atmospheric diffusion model, the diffusion of the pollutant in the atmosphere can be simulated, and the estimated concentration value of each monitoring point can be more accurately predicted. The concentration error between the estimated concentration value and the concentration data is calculated, the accuracy of the model prediction can be evaluated. If the concentration error is within the preset error range, the position coordinates of the pollution starting point are sent to the target client, so that relevant personnel can obtain more reliable pollution starting point information.

[0014] In some embodiments of the first aspect, after the step of sending the position coordinates of the pollution starting point to the target client, the method further comprises: obtaining pollution monitoring data and enterprise electricity consumption data of the pollution starting point; and generating an early warning information and sending it to a preset client if the pollution monitoring data exceeds a preset pollution threshold and the enterprise electricity consumption data exceeds a preset electricity consumption threshold.

[0015] By obtaining the pollution monitoring data and enterprise electricity consumption data of the pollution starting point, the pollution situation of the pollution starting point and the production activity situation of the related enterprise can be comprehensively understood. If the pollution monitoring data exceeds a preset pollution threshold and the enterprise electricity consumption data exceeds a preset electricity consumption threshold, an early warning information is generated and sent to a preset client, which enables the relevant departments to timely discover potential serious pollution situations and quickly take countermeasures.

[0016] In some embodiments of the first aspect, after the step of sending the position coordinates of the pollution starting point to the target client, the method further comprises: obtaining historical monitoring data of the pollution starting point, analyzing the historical monitoring data; and generating a data falsification early warning based on the pollution starting point and sending it to the target client if the historical monitoring data contains pollution monitoring data that remains unchanged or the pollution monitoring data of each pollutant changes proportionally within a preset time period.

[0017] By obtaining the historical monitoring data of the pollution starting point and analyzing it, abnormal situations in the data can be discovered in a timely manner. If the historical monitoring data contains pollution monitoring data that remains unchanged or the pollution monitoring data of each pollutant changes proportionally within a preset time period, a data falsification early warning is generated based on the pollution starting point and sent to the target client, which helps to improve the authenticity and reliability of the monitoring data and ensures the accuracy of environmental air quality monitoring.

[0018] In some embodiments of the first aspect, after the step of sending the position coordinates of the pollution starting point to the target client, the method further comprises: labeling the concentration data on a monitoring site map based on the position information of the plurality of monitoring sites, generating a concentration data schematic diagram; and sending the concentration data schematic diagram to the target client.

[0019] By labeling the concentration data on the monitoring site map based on the position information of the plurality of monitoring sites, the pollutant concentration distribution of different monitoring sites can be intuitively displayed, and a concentration data schematic diagram is generated, so that relevant personnel can understand the air quality status in the entire monitoring area at a glance.

[0020] In a second aspect, the embodiments of the present application provide an environmental air quality automatic monitoring system, comprising: one or more processors and a memory; the memory is coupled with the one or more processors, and the memory is configured to store computer program codes, the computer program codes comprising computer instructions, and the one or more processors are configured to invoke the computer instructions to enable the environmental air quality automatic monitoring system to perform the method described in the first aspect and any possible implementation manner of the first aspect.

[0021] In a third aspect, the embodiments of the present application provide a computer program product comprising instructions, which, when executed on an environmental air quality automatic monitoring system, enable the environmental air quality automatic monitoring system to perform the method described in the first aspect and any possible implementation manner of the first aspect.

[0022] In a fourth aspect, the embodiments of the present application provide a computer-readable storage medium comprising instructions, which, when executed on an environmental air quality automatic monitoring system, enable the environmental air quality automatic monitoring system to perform the method described in the first aspect and any possible implementation manner of the first aspect.

[0023] It can be understood that the environmental air quality automatic monitoring system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved are referred to the beneficial effects in the corresponding method, which will not be described here.

[0024] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. In the present application, the concentration data of malodorous chemical substances is obtained based on the monitoring sensors in multiple monitoring sites, the target monitoring point with the highest concentration data is selected as the reference point, and the multiple potential pollution sources are marked in combination with the current wind direction and wind speed data, which can more specifically narrow down the investigation range of the pollution source, obtain the reference distance, consistency degree and emission history data of the potential pollution source, more scientifically evaluate the possibility of each potential pollution source, finally determine the potential pollution source with the highest pollution score as the pollution starting point, and send the position coordinates of the potential pollution source to the target client, so that the relevant personnel can quickly and accurately obtain the pollution source information, which is helpful to timely take measures for governance, thereby improving the accuracy of environmental air quality monitoring.

[0026] 2、The application can more accurately find possible pollution sources and reduce the possibility of misjudgment by acquiring historical concentration data of all monitoring points within a preset distance range around the target monitoring point, calculating a concentration change rate, marking monitoring points with a concentration change rate exceeding a preset change threshold as abnormal monitoring points, searching for a predicted pollution source within a preset distance range of the abnormal monitoring points in an upwind area based on a current wind direction of the target monitoring point, calculating a pollutant diffusion range based on historical emission data and current wind speed data, and further screening a predicted pollution source with a position coincidence degree exceeding a preset percentage from the abnormal monitoring point as a potential pollution source.

[0027] 3、The application can more accurately evaluate the influence degree of the potential pollution source on the reference point by acquiring an actual distance between each potential pollution source and the reference point, calculating a theoretical concentration attenuation value based on the actual distance and a pre-established distance-concentration attenuation model, and comparing the theoretical concentration attenuation value with actual concentration data of the reference point to obtain a concentration coincidence degree. The application can more accurately evaluate the influence degree of the potential pollution source on the reference point by calculating an included angle between an emission direction of the potential pollution source and a current wind direction to obtain a direction consistency degree, and calculating a pollution score of each potential pollution source based on a preset weight coefficient by weighting the concentration coincidence degree, the direction consistency degree, and emission history data. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 is a flowchart of an environmental air quality automatic monitoring method in an embodiment of the application;

[0029] Figure 2 is another flowchart of an environmental air quality automatic monitoring method in an embodiment of the application;

[0030] Figure 3 is a schematic diagram of an entity device structure of an environmental air quality automatic monitoring system in an embodiment of the application. DETAILED DESCRIPTION

[0031] The terms used in the following embodiments of the application are only for the purpose of describing specific embodiments and are not intended to be limiting of the application. As used in the specification, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "and / or," as used herein, refers to any or all possible combinations of one or more of the associated listed items.

[0032] Hereinafter, the terms "first", "second", "third", "fourth", "fifth", "sixth", "seventh" and "eighth" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implying a specific number of the technical features indicated. Therefore, the features defined with "first", "second", "third", "fourth", "fifth", "sixth", "seventh" and "eighth" can explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified.

[0033] The following describes the flow of the method provided by the present embodiment. Please refer to Figure 1 , which is a flowchart of the automatic environmental air quality monitoring method in the embodiments of the present application.

[0034] S101, obtaining concentration data of malodorous chemical substances based on monitoring sensors in a plurality of monitoring sites.

[0035] Among them, the monitoring site represents a fixed place for environmental monitoring distributed in a specific geographical area. The monitoring sensor refers to a device installed in the monitoring site for detecting the concentration of a specific chemical substance. The malodorous chemical substance refers to a chemical substance with irritating odor and adverse effects on human health and the environment, such as harmful substances such as nitrogen oxides (NOx), ozone (O3), particulate matter (PM), etc. The concentration data is used to represent the content of malodorous chemical substances in unit volume of air.

[0036] Specifically, data is obtained from a plurality of monitoring sites distributed in the monitoring area. Through network connection or other communication methods, real-time or periodic concentration data of various malodorous chemical substances is obtained from the sensors, and the data is integrated into the central database.

[0037] In some embodiments, obtaining concentration data of malodorous chemical substances based on monitoring sensors in a plurality of monitoring sites can be achieved in various ways: optionally, a timing task can be set to automatically send a data request to all monitoring sites every fixed time interval (such as every 5 minutes), and the monitoring site returns the current concentration data immediately after receiving the request; optionally, the monitoring site can be configured in an active push mode, and when a significant change in concentration data is detected or exceeds a preset threshold, the data is automatically pushed to the central system. It can be understood that other ways can also be used, which are not limited here.

[0038] S102, selecting a target monitoring point with the highest concentration data as a reference point, and marking a plurality of potential pollution sources based on the current wind direction and current wind speed data of the target monitoring point at the current monitoring time point.

[0039] The reference point refers to a specific position used as a basis for subsequent analysis and calculation. The current wind direction refers to the wind direction at the location of the target monitoring point at the time point of analysis. The current wind speed data represents the wind speed at the target monitoring point at the same time point. The potential pollution source refers to a place or facility that emits malodor chemicals.

[0040] Specifically, first, the concentration data of all monitoring points obtained is compared to find the monitoring point with the highest concentration value, which is set as the target monitoring point. The wind direction and wind speed data of the target monitoring point at the current time are obtained. In the upwind direction area of the target monitoring point, possible pollution sources are searched. Based on factors such as wind direction, wind speed, and terrain, and in combination with a pre-established potential pollution source database, multiple potential pollution sources that may cause high concentration of malodor substances are marked.

[0041] In some embodiments, selecting the target monitoring point and marking the potential pollution source can be achieved in various ways. Optionally, a sliding time window method can be used to compare the average concentration data of all monitoring points in the last hour, select the point with the highest average concentration as the target monitoring point, and obtain the real-time wind direction and wind speed data of the point. A sector search area is drawn in the GIS system, with the vertex of the sector being the target monitoring point and the opening direction being opposite to the upwind direction. The angle and radius are determined according to the wind speed. In this search area, all potential pollution sources located in the area are filtered out from the pre-established pollution source database. Optionally, a multi-point comparison method can be used to select multiple monitoring points with high concentration, analyze the concentration trend and relative position relationship of the monitoring points, combine the wind direction data, and infer the most likely pollution source location range through triangulation or other spatial analysis methods, and then mark the potential pollution sources in this range. It can be understood that other methods can also be used, which are not limited here.

[0042] S103, obtaining a reference distance of each potential pollution source from the reference point, a consistency degree with the current wind direction, and emission history data.

[0043] The reference distance represents the straight-line distance from the potential pollution source to the target monitoring point (reference point). The consistency degree refers to the angular relationship between the location of the potential pollution source and the current wind direction.

[0044] Specifically, the straight-line distance of each marked potential pollution source from the target monitoring point is first calculated. The included angle between the azimuth of each potential pollution source relative to the target monitoring point and the current wind direction is compared to calculate the consistency degree. The historical emission data of each potential pollution source is retrieved from the database.

[0045] In some embodiments, the relevant data of the potential pollution sources can be obtained in various ways: optionally, using GIS (Geographic Information System), importing the coordinates of the target monitoring point and all potential pollution sources into the system, automatically calculating the straight-line distance between them by spatial analysis tools, using vector calculation method to perform dot product operation between the direction vector of the pollution source to the monitoring point and the current wind direction vector, obtaining the included angle between them, thereby calculating the consistency degree, accessing the connected environmental regulation database to extract the daily emission data of each potential pollution source in the recent period (such as the past 30 days), including the emission amount and over-standard situation of various pollutants. Optionally, sending a data request to the online monitoring system of the enterprise or institution where each potential pollution source is located to obtain their real-time position, current emission status and other information. It can be understood that other ways can also be used, which are not limited here.

[0046] S104, calculating a pollution score of each of the potential pollution sources based on the reference distance, the consistency degree and the emission history data.

[0047] The pollution score represents a comprehensive evaluation value of the possibility of each potential pollution source. Specifically, a calculation model is established, which is based on the reference distance, the consistency degree and the emission history data. For the reference distance, an inverse proportional relationship is used, the closer the distance, the higher the score. For the consistency degree, a best angle range is set, the pollution source within this range has the highest score, and the farther the deviation from this range, the lower the score. For the emission history data, the recent emission amount and frequency are analyzed, the pollution source with large emission amount and high frequency has a higher score. The scores of the three factors are weighted according to the preset weight to obtain the final pollution score of each potential pollution source.

[0048] In some embodiments, a linear weighting model is used. First, the reference distance is normalized to convert it to a value between 0 and 1, the closer the distance, the closer the value to 1; then the consistency degree is calculated, the included angle between the direction of the pollution source and the wind direction is converted to a cosine value, the closer the cosine value to 1, the higher the consistency; then the emission history data is analyzed, the average emission amount in the past 30 days is calculated and compared with the legal limit value, and the three factors are respectively assigned with weights (such as distance 0.4, consistency 0.3, and historical emission 0.3), and the final score is obtained by weighted summation.

[0049] S105, determining the potential pollution source with the highest pollution score as the pollution starting point.

[0050] The pollution starting point refers to the location of the most likely source of pollution determined by the system. Specifically, after calculating the pollution scores of all potential pollution sources, the pollution scores of all potential pollution sources are first sorted to find the highest scoring potential pollution source. This highest scoring potential pollution source is considered to be the most likely source of the current high concentration of pollutants being monitored. A series of verification steps are performed, including checking whether the score of the pollution source is significantly higher than that of other pollution sources, whether it exceeds a certain preset confidence threshold, etc. If it passes the verification, the highest scoring potential pollution source is marked as the pollution starting point.

[0051] In some embodiments, the highest scoring potential pollution source can be determined as the pollution starting point in various ways: optionally, a simple maximum selection method can be used to select the highest scoring potential pollution source as the pollution starting point, and check whether the highest score exceeds a preset threshold (e.g. 80 points out of a total of 100 points) to ensure that the selected pollution source has a high enough confidence level. If the difference between the highest score and the second highest score is greater than a certain preset significance level (e.g. 20 points), the reliability of the selection is further confirmed, and the historical records of the potential pollution source are reviewed. Optionally, a more complex integrated decision method is used, first selecting the top three potential pollution sources in terms of score, running more detailed pollution diffusion simulations for the three pollution sources, considering more factors such as terrain, buildings, weather conditions, etc., comparing the simulation results with the actual monitoring data, calculating the degree of agreement of each simulation result, and finally considering the initial score, simulation agreement and additional information, using weighted voting or other decision algorithms to select the final pollution starting point. It can be understood that other methods can also be used, which are not limited here.

[0052] S106, sending the position coordinates of the pollution starting point to the target client.

[0053] The target client refers to a terminal device or software system that receives this information, including computers, mobile device applications or other related systems of environmental regulatory departments.

[0054] Specifically, after the system determines the pollution starting point, it needs to convey this important information to relevant personnel or departments in a timely manner. The precise position coordinates of the pollution starting point will be extracted, which usually include longitude, latitude, and sometimes altitude. The coordinate data is packaged into a standard format message, which can also contain other related information such as the type of pollutant, concentration level, discovery time, etc. Next, a pre-set target client list is identified, including the central system of the environmental regulatory department, the mobile device of the on-site law enforcement personnel, the environmental protection person in charge of the relevant enterprise, etc. Finally, the message containing the position coordinates of the pollution starting point is safely sent to the target client through a pre-set communication channel, such as the Internet, a dedicated network or a wireless communication network.

[0055] In some embodiments, the position coordinates of the pollution starting point can be sent to the target client in various ways: optionally, a real-time push mechanism is adopted, the position coordinates of the pollution starting point are first converted into a standard geographic information format, including longitude and latitude information and possible additional attributes such as pollutant type, detection time, etc., a pre-configured target client list is queried, the communication protocol and address of each client are determined, a customized message is generated for each target client, including different levels of detailed information, and the message will be pushed to each target client in real time using the appropriate communication protocol. Optionally, a hierarchical notification strategy is used, the position coordinate information is first stored in a central database, and a unique event identifier is generated, the notification priority and manner are determined according to the preset emergency level, the highest level of receiver (such as the environmental emergency command center) is immediately sent an alarm message containing complete position coordinates and detailed information, and the secondary receiver is only sent a reminder notification prompting that there is a new pollution event and they need to log in to the system to check the details. The sending status and receiving confirmation of all notifications are recorded to ensure effective communication of information. It can be understood that other ways of sending position coordinates can also be used, such as using blockchain technology to ensure data tamper resistance, or through edge computing devices to achieve faster local notification to improve information transmission efficiency and security, which are not limited here.

[0056] The method provided by the present embodiment is further described in more detail below. Please refer to Figure 2 , another flowchart of the environmental air quality automatic monitoring method in the embodiment of the present application.

[0057] S201, obtaining concentration data of malodorous chemical substances based on monitoring sensors in multiple monitoring sites.

[0058] It can be understood that this step is similar to step S101, which will not be described here.

[0059] S202, obtaining historical concentration data of all monitoring points within a preset distance range around the target monitoring point, and calculating a concentration change rate of each monitoring point based on the historical concentration data.

[0060] The target monitoring point refers to the monitoring site with the highest concentration data selected in step S101. The preset distance range refers to a fixed radius pre-set by the system to limit the range of monitoring points to be analyzed. The historical concentration data refers to the concentration data recorded by the monitoring point in the past period of time (such as the past 24 hours). The concentration change rate refers to the change amplitude of the concentration value per unit time, usually expressed in percentage.

[0061] In implementation, the geographic coordinates of the target monitoring point are determined, and then all monitoring points within a preset distance range (e.g. 5 kilometers in radius) are screened out with the target monitoring point as the center. For each screened monitoring point, the system extracts its concentration data records in the past 24 hours from the database. The system then calculates the concentration change rate of each monitoring point using historical data. The calculation method can use simple linear regression analysis, or use more complex time series analysis methods. For example, the percentage change between the average concentration of the last 1 hour and the average concentration of the previous 23 hours can be calculated. If the average concentration of the last 1 hour is 100 ppb and the average concentration of the previous 23 hours is 80 ppb, the concentration change rate is (100-80) / 80 * 100% = 25%.

[0062] S203, marking the monitoring point with a concentration change rate exceeding the preset change threshold as an abnormal monitoring point.

[0063] The concentration change rate refers to the concentration change amplitude of each monitoring point calculated in step S202. The preset change threshold is a judgment criterion preset by the system to determine whether the concentration change is abnormal. The abnormal monitoring point refers to the monitoring site with abnormal concentration change.

[0064] In implementation, a reasonable preset change threshold needs to be set. The setting of this threshold can be based on historical data analysis, expert experience or relevant environmental standards. For example, the threshold can be set to 30%, and the concentration change rate of each monitoring point calculated in step S202 is compared with the preset threshold. If the change rate of a monitoring point exceeds 30%, the system will mark it as an abnormal monitoring point. This process can be realized through a simple conditional judgment, that is, if the change rate of monitoring point A is greater than 30%, monitoring point A is added to the list of abnormal monitoring points. All monitoring points are traversed to complete the marking process.

[0065] S204, searching for a predicted pollution source within a preset distance range of the abnormal monitoring point in the upwind area of the target monitoring point based on the current wind direction of the target monitoring point.

[0066] The current wind direction refers to the wind direction data of the location of the target monitoring point, which is usually provided by a weather station. The upwind area refers to the direction of the wind, which is the area where the potential pollution source may exist. The abnormal monitoring point is the monitoring site with abnormal concentration change marked in step S203. The preset distance range is a search radius preset by the system. The predicted pollution source refers to a factory, facility or other emission source that may produce pollution.

[0067] When implementing this step, real-time wind direction data of the target monitoring point needs to be obtained. This can be achieved by connecting to the API of a local weather station or using a weather sensor provided by the monitoring point. After obtaining the wind direction, search in the upwind direction of the target monitoring point. The search range can be set as a sector, for example, a 10-kilometer sector with the target monitoring point as the center and extending in the upwind direction, with an angle of 60 degrees. In this area, query the pre-established pollution source database to screen all possible pollution sources. At the same time, the system also needs to consider the location of the abnormal monitoring points marked in S203. For each screened possible pollution source, calculate its distance to each abnormal monitoring point. If the distance is within the preset range (for example, 3 kilometers), mark this pollution source as a predicted pollution source.

[0068] S205, obtain the historical emission data of the predicted pollution source, and based on the historical emission data and the current wind speed data, calculate the pollutant diffusion range of each predicted pollution source.

[0069] A predicted pollution source refers to a factory, facility or other emission source that may cause pollution determined in S204. Historical emission data refers to the pollutant emission records of the pollution source in the past period of time, including emission amount, emission time, etc. Current wind speed data refers to the real-time wind speed information at the location of the target monitoring point, usually provided by a weather station. The pollutant diffusion range refers to the area where the pollutant spreads in the air, its size and shape are affected by factors such as emission amount, wind speed, terrain, etc.

[0070] When implementing this step, the historical emission data of each predicted pollution source needs to be obtained from the database of the environmental regulation department or the self-reported emission data of the enterprise. The historical emission data should include the daily average emission amount in the past period of time (such as the last 30 days). At the same time, the system also needs to obtain the current wind speed data of the target monitoring point. After obtaining the data, the system will use an atmospheric diffusion model to calculate the pollutant diffusion range of each predicted pollution source. A commonly used model is the Gaussian plume model, which takes into account factors such as emission source strength, wind speed, atmospheric stability, etc. For example, assuming that the average daily emission amount of a predicted pollution source is 100 kg / day, the current wind speed is 3 m / s, and the atmospheric conditions are stable, using the Gaussian plume model to calculate that at a distance of 2 km downwind, the pollutant concentration is reduced to 10% of the initial concentration. The system will take this 2 km distance as the diffusion radius of the pollution source to determine the diffusion range. For each predicted pollution source, the system will perform similar calculations to obtain a series of pollutant diffusion ranges.

[0071] S206, mark the predicted pollution source whose pollutant diffusion range overlaps with the location of the abnormal monitoring point by more than a preset percentage as a potential pollution source.

[0072] The pollutant diffusion range is the pollution impact area of each predicted pollution source calculated in step S205. The abnormal monitoring point is the monitoring site with abnormal concentration change marked in step S203. The position coincidence degree refers to the degree that the abnormal monitoring point falls within the pollutant diffusion range. The preset percentage is a judgment criterion preset by the system to determine whether the coincidence degree is significant enough. The potential pollution source refers to the predicted pollution source that is more likely to be the actual pollution source after this step of screening.

[0073] In the implementation process, a reasonable preset percentage threshold needs to be set, for example, it can be set to 60%, and each predicted pollution source is evaluated. The evaluation method is to compare the pollutant diffusion range (usually a circular or elliptical area) of the predicted pollution source with the positions of all abnormal monitoring points. The system calculates the number of abnormal monitoring points falling within the diffusion range, and divides it by the total number of abnormal monitoring points to obtain a percentage. If the percentage exceeds the preset 60%, the predicted pollution source is marked as a potential pollution source. For example, if there are 10 abnormal monitoring points, 7 of which fall within the diffusion range of a predicted pollution source, the coincidence degree is 70%, which exceeds the threshold of 60%, so the predicted pollution source is marked as a potential pollution source. All predicted pollution sources are evaluated in this way, and finally a list of potential pollution sources is obtained.

[0074] S207, obtaining the actual distance between each of the potential pollution sources and the reference point.

[0075] The potential pollution source is the predicted pollution source determined in step S206 that is more likely to be the actual pollution source. The reference point refers to the target monitoring point with the highest concentration data selected in step S101. The actual distance refers to the straight-line distance or the shortest path distance along the ground between two geographical positions.

[0076] In this step, the distance between each potential pollution source and the reference point (i.e., the target monitoring point) is calculated. The specific method to achieve this step is as follows: First, obtain the precise geographic coordinates (latitude and longitude) of each potential pollution source and the reference point. The coordinates can be obtained from the previous step or extracted from the system's geographic information database. The system uses a geographic distance calculation formula to calculate the distance between the two points. The most commonly used method is to use the Haversine formula, which takes into account the curvature of the Earth and can provide more accurate distance calculation results. For example, assuming that the coordinates of a potential pollution source are (39.9042°N, 116.4074°E) and the coordinates of the reference point are (39.9522°N, 116.4268°E), the distance calculated using the Haversine formula is approximately 5.42 kilometers. Perform such distance calculations for each potential pollution source and store the results. The distance data will be used for further analysis in subsequent steps, such as evaluating the impact of pollution sources, calculating the decay of pollutant concentrations, etc.

[0077] S208, based on the actual distance and the pre-established distance-concentration decay model, calculate the theoretical concentration decay value.

[0078] The actual distance refers to the distance between each potential pollution source and the reference point calculated in step S207. The distance-concentration decay model is a pre-established mathematical model that describes the law of pollutant concentration decay with increasing distance. The theoretical concentration decay value refers to the degree of reduction of pollutant concentration at a given distance relative to the concentration at the emission source calculated according to the model.

[0079] In this step, the system will use the known actual distance and the pre-established distance-concentration decay model to calculate the theoretical concentration decay value of each potential pollution source to the reference point. In implementation, a suitable decay model needs to be selected first. Commonly used models include inverse proportion model, exponential decay model and Gaussian diffusion model, etc. Taking the exponential decay model as an example, its formula can be expressed as C = C0 * e^(-kx), where C is the concentration at a distance x from the pollution source, C0 is the initial concentration at the pollution source, and k is the decay coefficient. Substitute the actual distance calculated in S207 into this formula to calculate the theoretical concentration decay value. For example, assuming that a potential pollution source is 5 kilometers away from the reference point, using the exponential decay model with a decay coefficient k = 0.2, the theoretical concentration decay value is e^(-0.2*5) ≈ 0.368, i.e., the theoretical concentration at the reference point is about 36.8% of the concentration at the pollution source. Perform such calculations for each potential pollution source to obtain a series of theoretical concentration decay values.

[0080] S209, compare the theoretical concentration decay value with the actual concentration data of the reference point to obtain the concentration consistency.

[0081] Theoretical concentration decay value is the value calculated in S208 based on the distance-concentration decay model. Actual concentration data of the reference point refers to the pollutant concentration actually observed at the target monitoring point (i.e. the reference point). Concentration fit degree refers to the matching degree between the theoretically calculated value and the actually observed value, which is used to evaluate the possibility of potential pollution sources.

[0082] In this step, the theoretical concentration decay value of each potential pollution source is compared with the actual concentration data of the reference point to evaluate their fit degree. The specific implementation method is as follows: First, the actual concentration data of the reference point is obtained, which is usually collected in real time by monitoring equipment, and the system will use the theoretical concentration decay value calculated in S208 to estimate the theoretical concentration of each potential pollution source at the reference point. This requires knowing or estimating the initial concentration at the pollution source, which can be estimated using the emission data of the pollution source and a simple diffusion model. Next, the system will calculate the difference between the theoretical concentration and the actual concentration. A commonly used method is to calculate the relative error: (|theoretical concentration - actual concentration| / actual concentration) * 100%. For example, if the theoretical concentration of a potential pollution source is 80 ppb, and the actual concentration of the reference point is 75 ppb, the relative error is (|80-75| / 75) * 100% ≈ 6.67%. Convert this relative error to fit degree, such as using the formula: fit degree = 100% - relative error. In this example, the fit degree is about 93.33%. Do this calculation for each potential pollution source to get a series of concentration fit degrees.

[0083] S210, calculate the included angle between the emission direction of the potential pollution source and the current wind direction, to get the direction consistency degree.

[0084] The emission direction of the potential pollution source refers to the direction of pollutant emission from the source, which is usually related to the design and layout of the pollution source. The current wind direction refers to the real-time wind direction data at the reference point (target monitoring point). The included angle refers to the angle difference between the two directions. The direction consistency degree refers to the matching degree between the emission direction and the wind direction, which is used to evaluate the possibility of the potential pollution source affecting the reference point.

[0085] In this step, the angle between the emission direction of each potential pollution source and the current wind direction is calculated and converted into a degree of direction consistency. The specific implementation method is as follows: First, obtain the emission direction data of each potential pollution source from the design documents of the pollution source or field investigation data, usually expressed in degrees (e.g., 0° for due north, increasing clockwise). At the same time, the system also needs to obtain the current wind direction data of the reference point, which is usually obtained from a weather station or on-site weather sensor. The system calculates the angle between the two directions. The calculation formula is: angle = |emission direction - wind direction|, if the result is greater than 180°, subtract 360° from it. For example, if the emission direction of a certain potential pollution source is 45° (northeast) and the current wind direction is 30° (north by east 30°), the angle is |45° - 30°| = 15°. Next, convert this angle into a degree of direction consistency. A simple method is to use a linear conversion: consistency degree = (180° - angle) / 180° * 100%. In this example, the consistency degree is (180° - 15°) / 180° * 100% ≈ 91.67%. Perform such calculations for each potential pollution source to obtain a series of direction consistency degrees.

[0086] S211、Based on the preset weight coefficient, the concentration fitting degree, the direction consistency degree, and the emission history data are weighted and calculated to obtain a pollution score of each potential pollution source.

[0087] It can be understood that this step is similar to step S104, which will not be described here.

[0088] S212, the potential pollution source with the highest pollution score is determined as the pollution starting point.

[0089] It can be understood that this step is similar to step S105, which will not be described here.

[0090] S213, the position coordinates of the pollution starting point are sent to the target client.

[0091] It can be understood that this step is similar to step S106, which will not be described here.

[0092] S214, obtain the pollution monitoring data and enterprise electricity consumption data of the pollution starting point.

[0093] The pollution starting point refers to the most likely pollution source location determined in the previous steps. The pollution monitoring data refers to various pollutant concentration data collected at the pollution starting point or nearby monitoring stations, such as PM2.5, sulfur dioxide, nitrogen oxides, etc. The enterprise electricity consumption data refers to the power consumption records of the enterprise corresponding to the pollution starting point, usually recorded in units of electricity consumption at certain time intervals (e.g., every hour or every day).

[0094] In this step, two types of data are acquired simultaneously: pollution monitoring data and enterprise electricity consumption data. For the acquisition of pollution monitoring data, connect to the environmental monitoring network at the location of the pollution source. This network usually includes multiple fixed monitoring stations and mobile monitoring equipment, which can record the concentration of various pollutants in real time. For example, acquire the average concentration of PM2.5, SO2, NOx, etc. pollutants in the last 24 hours per hour. For the acquisition of enterprise electricity consumption data, connect to the database of the power company to acquire the enterprise electricity consumption data in the same time period as the pollution monitoring data, such as the electricity consumption per hour in the last 24 hours.

[0095] S215, if the pollution monitoring data exceeds the preset pollution threshold and the enterprise electricity consumption data exceeds the preset electricity consumption threshold, generate a warning message and send it to the preset client.

[0096] The preset pollution threshold refers to the upper limit of the concentration of pollutants set in advance, which is considered to be a potential environmental risk if it exceeds this value. The preset electricity consumption threshold refers to the upper limit of the enterprise electricity consumption set in advance, which may mean that the production activity is abnormally increased. The warning message is a warning message generated by the system to remind relevant personnel of potential environmental risks. The preset client refers to the terminal device or software application that receives the warning message, which may include the monitoring center of the environmental protection department, the mobile application of the enterprise management personnel, etc.

[0097] In this step, the data acquired in S214 is judged and processed. First, compare the pollution monitoring data with the preset pollution threshold. This threshold may be based on the national or local environmental quality standard, such as the 24-hour average concentration of PM2.5 exceeding 75 μg / m³. At the same time, the system will also compare the enterprise electricity consumption data with the preset electricity consumption threshold. The electricity consumption threshold is set based on the historical electricity consumption pattern of the enterprise, such as exceeding 120% of the average electricity consumption in the same period in the past three months. If the pollution monitoring data exceeds the pollution threshold, and the enterprise electricity consumption data also exceeds the electricity consumption threshold, the system will trigger the warning mechanism. The generation process of the warning message includes: creating a structured message containing the types of pollutants exceeding the standard, the concentration value, the time of exceeding the standard, the enterprise electricity consumption, etc.

[0098] S216, acquire the historical monitoring data of the pollution source, and analyze the historical monitoring data.

[0099] The historical monitoring data refers to the pollution concentration data collected at a certain pollution source or nearby monitoring site in the past period, which usually includes various pollution concentration records at different time scales (such as hours, days, months). Data analysis refers to statistical processing, pattern recognition, trend analysis and other operations on historical data to extract valuable information.

[0100] In this step, historical monitoring data of pollution starting points needs to be extracted from the environmental monitoring database. These data usually cover a long time span, such as the past few months or years. During data extraction, missing values or outliers may be processed based on data integrity and quality. For example, extract the concentration data of major pollutants such as PM2.5, SO2, NOx, etc. every hour in the past two years. After obtaining the data, conduct multi-faceted analysis. First, basic statistical analysis, including calculating the mean, median, maximum, minimum, standard deviation, etc. of each pollutant, to understand the overall distribution of pollutant concentrations. Second, time series analysis, drawing trend charts of each pollutant concentration over time, identifying whether there are long-term trends (such as rising or falling year by year) or periodic patterns (such as seasonal changes).

[0101] S217, if the pollution monitoring data in the historical monitoring data remains unchanged or each pollution monitoring data changes proportionally within a preset time period, a data falsification warning is generated based on the pollution starting point and sent to the target client.

[0102] In this step, the historical monitoring data obtained in S216 needs to be detected for abnormalities. Set a preset time period, such as 12 consecutive hours or 24 hours, and then check if there are abnormal patterns in this period. Abnormal patterns mainly include two cases: one is that the data remains completely unchanged, and the other is that the data changes proportionally. For the first case, check if the concentration value of each pollutant remains completely unchanged throughout the entire preset time period. For example, if the concentration of PM2.5 is 35.6 μg / m³ for 12 consecutive hours, mark this group of data as suspicious. For the second case, calculate the change proportion of different pollutant concentration values. If it is found that the concentration values of all pollutants increase or decrease by the same proportion, it will also be marked as suspicious. For example, if the concentration values of PM2.5, SO2 and NOx increase by 10% in a certain hour compared to the previous hour, it will be marked as abnormal. When these abnormal patterns are detected, a data falsification warning is generated, which contains the time period of abnormal data, the types of pollutants involved, the description of abnormal patterns (such as "data unchanged" or "proportional change"), and the information of pollution starting point. Send this warning information to the preset target client.

[0103] S218, based on the location information of multiple monitoring sites, mark the concentration data on the monitoring site map to generate a concentration data diagram.

[0104] Monitoring sites refer to fixed or mobile devices distributed in a specific area for collecting environmental data. Location information includes the latitude and longitude coordinates of each monitoring site. Concentration data refers to the concentration values of different pollutants measured at each monitoring site. The monitoring site map is a geographical image showing the distribution of monitoring sites. The concentration data diagram is a visual representation of concentration data on the map, usually using different colors or symbols to represent different concentration levels.

[0105] In this step, the pollutant concentration data is combined with geographical information to create an intuitive visual representation. First, load a base map containing the locations of all relevant monitoring sites, which can be a vector map created using GIS (Geographic Information System) software or a raster map obtained from an online map service such as Google Maps API or OpenStreetMap. Read the latest concentration data for each monitoring site, including multiple pollutants such as PM2.5, SO2, NOx, etc. For each monitoring site, mark a symbol or color block at the corresponding location on the map. The color or size of the symbol will change according to the concentration of the pollutant. For example, use green for low concentration, yellow for medium concentration, and red for high concentration. For multiple pollutant data, use pie charts or multi-layer symbols to represent.

[0106] S219, send the concentration data diagram to the target client.

[0107] In this step, the concentration data diagram generated in S218 is effectively transmitted to the pre-set target client. First, determine the type and receiving capacity of the target client, different types of clients require different formats or resolutions of images. For example, if the target client is a mobile application, compress the image to a size and resolution suitable for mobile device display, and push the image file to the client through HTTP or HTTPS protocol.

[0108] In some embodiments, step 106 or step S213 can further include:

[0109] Input the location coordinates of the pollution source, the current weather data of the pollution source, and the concentration data of the nearest monitoring point to the pollution source into the atmospheric diffusion model, and calculate the estimated concentration value of each monitoring point;

[0110] The pollution starting point is the initial location where the pollutant is released or generated. The location coordinates are expressed in longitude and latitude to locate the pollution source. Current meteorological data include wind speed, wind direction, temperature, humidity, atmospheric pressure, and other environmental factors that affect the diffusion of pollutants. The nearest monitoring point is the air quality monitoring station closest to the geographical location of the pollution starting point. Concentration data are the pollutant concentration values measured at the monitoring points, expressed in micrograms per cubic meter of air (μg / m³). The atmospheric diffusion model is a mathematical model used to simulate and predict the propagation and dilution of pollutants in the atmosphere. The estimated concentration value is the theoretical pollutant concentration at each monitoring point calculated by the model based on the input parameters.

[0111] In this step, the GPS coordinate data of the pollution starting point is obtained. Then, real-time meteorological data, including wind speed, wind direction, temperature, humidity, and atmospheric pressure, are obtained from the weather station. The system simultaneously extracts real-time concentration data from the environmental monitoring network for the monitoring point closest to the pollution starting point. The system selects a suitable atmospheric diffusion model, such as the Gaussian plume model, Lagrangian particle model, or Eulerian grid model. The selection criteria are based on the type of pollution source, terrain features, and computing resources. The system inputs all collected data into the selected model, performs data format conversion and unit unification. The model also requires input of additional parameters such as pollutant emission rate, emission height, etc., which are obtained from historical data or calibrated estimates. The model is run to simulate the diffusion process of pollutants in the atmosphere from the pollution starting point to the current time. The model takes into account changes in wind direction and speed to calculate the transmission path and concentration changes of pollutants in the atmosphere.

[0112] Calculate the concentration error between the estimated concentration value and the concentration data;

[0113] In this step, the estimated concentration value is the theoretical pollutant concentration calculated by the atmospheric diffusion model. The concentration data are the actual pollutant concentrations measured at the monitoring points. The concentration error is the difference between the estimated value and the measured value, which is used to evaluate the accuracy of the model prediction.

[0114] In this step, the measured concentration data for each monitoring point are extracted from the database, ensuring that these data correspond to the time points predicted by the model. The system compares the estimated concentration value and the measured concentration value for each monitoring point one by one. The system uses multiple error calculation methods: absolute error: calculates the absolute value of the difference between the estimated value and the measured value. Relative error: calculates the percentage of the error relative to the measured value. Root mean square error (RMSE): for multiple monitoring points, calculate the average of the squares of the differences between the estimated value and the measured value, then take the square root. The system records the results of these three error calculation methods for each monitoring point separately.

[0115] If the concentration error is within the preset error range, the location coordinates of the pollution starting point are sent to the target client.

[0116] In this step, the preset error range is the acceptable error range set by the system in advance to judge the accuracy of the model prediction. The target client is the terminal device or application program that receives the pollution starting point position coordinates.

[0117] In this step, the preset error range is read. This range is set based on historical data analysis and relevant specifications. The system sets different error ranges for each pollutant and different concentration levels. For example, a ±5 μg / m³ error is allowed when the PM2.5 concentration is below 35 μg / m³, and a ±15% relative error is allowed when the PM2.5 concentration is above 35 μg / m³. The system compares the concentration error calculated in the previous step with the preset error range. The comparison process is carried out individually for each monitoring point, while considering the average error of all monitoring points. The system requires that the results of all error calculation methods be within the preset range. When the system confirms that the error is within the preset range, it prepares to send the position coordinates of the pollution starting point. The data sent includes longitude, latitude, and altitude (if applicable).

[0118] The environmental air quality automatic monitoring system in the embodiment of the present application will be described from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic diagram of an entity device structure of the environmental air quality automatic monitoring system in the embodiment of the present application.

[0119] It should be noted that, Figure 3 The structure of the environmental air quality automatic monitoring system shown is only an example and should not limit the functions and use range of the embodiment of the present application.

[0120] As Figure 3 shown, the environmental air quality automatic monitoring system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 302 or loaded from a storage portion 308 to a random access memory (RAM) 303, such as performing the method described in the above embodiment. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0121] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a push button switch, and the like; an output section 307 including a Liquid Crystal Display (LCD), and an audio output device, a lamp, and the like; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, and the like. The communication section 309 performs a communication process via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as necessary. A removable recording medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like, is attached to the drive 310 as necessary so that a computer program read therefrom can be installed into the storage section 308 as necessary.

[0122] In particular, the processes described above with reference to the flow charts can be implemented as a computer software program in accordance with embodiments of the present application. For example, an embodiment of the present application includes a computer program product comprising a computer program carried on a computer readable medium, the computer program containing computer programs for executing the methods shown in the flow charts. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 309, and / or installed from the removable recording medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present application are performed.

[0123] Note that specific examples of the computer readable storage medium can include but are not limited to one or more of a conduit with one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read only memory (ROM), an erasable programmable read only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium can be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0124] The flow diagrams and the block diagrams in the drawings are schematic and specific embodiments of the present application will be described with reference to these drawings, but these depictions should not be interpreted in a limiting sense. It will be understood that the system, method, and computer program product of the present application can be embodied in many other forms including an operational art implementation, OBI. In addition, each of the components of the system, method, and computer program product of the present application can be implemented alone or in combination with at least one other component. Furthermore, specific embodiments of the present application can incorporate any of the features described herein.

[0125] In particular, the ambient air quality automatic monitoring system of the embodiment includes a processor and a memory, and the memory stores a computer program. When the computer program is executed by the processor, the ambient air quality automatic monitoring method provided in the above embodiment is implemented.

[0126] As another aspect, the present application also provides a computer readable storage medium. The storage medium can be included in the ambient air quality automatic monitoring system described in the above embodiments, or can exist independently without being assembled into the ambient air quality automatic monitoring system. The storage medium carries one or more computer programs. When the one or more computer programs are executed by a processor of the ambient air quality automatic monitoring system, the ambient air quality automatic monitoring system implements the ambient air quality automatic monitoring method provided in the above embodiments.

[0127] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application; even though the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

[0128] In the above embodiments, according to the context, the term "when" can be interpreted as meaning "if" or "after" or "in response to determining" or "in response to detecting". Similarly, according to the context, the phrase "on determining" or "if detecting (the stated condition or event)" can be interpreted as meaning "if determining" or "in response to determining" or "on detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)".

[0129] Those skilled in the art can understand that all or part of the processes in the above-mentioned method embodiments can be implemented by a computer program instructing relevant hardware to complete, the program can be stored in a computer readable storage medium, and the program can include the processes of the above-mentioned method embodiments when executed. The aforementioned storage medium includes ROM or random storage memory RAM, magnetic disc or optical disc and various storage code medium.

Claims

1. A method for automatic monitoring of ambient air quality, characterized in that, The method is applied to an automatic environmental air quality monitoring system, and comprises the following steps: Obtaining concentration data of malodorous chemicals based on monitoring sensors in multiple monitoring sites; Selecting a target monitoring site with the highest concentration data as a reference point, and marking multiple potential pollution sources based on current wind direction and current wind speed data of the target monitoring site at a current monitoring time point; Obtaining reference distance of each potential pollution source from the reference point, consistency degree with the current wind direction, and emission history data; Calculating pollution scores of each potential pollution source based on the reference distance, consistency degree, and emission history data; Determining a potential pollution source with the highest pollution score as a pollution starting point; Sending position coordinates of the pollution starting point to a target client; The step of selecting a target monitoring site with the highest concentration data as a reference point, and marking multiple potential pollution sources based on current wind direction and current wind speed data of the target monitoring site at a current monitoring time point, specifically comprises the following steps: Obtaining historical concentration data of all monitoring sites within a preset distance range around the target monitoring site, and calculating concentration change rates of each monitoring site based on the historical concentration data; Marking monitoring sites with concentration change rates exceeding a preset change threshold as abnormal monitoring sites; Searching for predicted pollution sources within a preset distance range of the abnormal monitoring sites in an upwind area of the target monitoring site based on the current wind direction of the target monitoring site; Obtaining historical emission data of the predicted pollution sources, and calculating pollutant diffusion ranges of each predicted pollution source based on the historical emission data and the current wind speed data Marking predicted pollution sources with a position coincidence degree of the pollutant diffusion range and the abnormal monitoring site position exceeding a preset percentage as potential pollution sources; The step of calculating pollution scores of each potential pollution source based on the reference distance, consistency degree, and emission history data, specifically comprises the following steps: Obtaining actual distances between each potential pollution source and the reference point; Calculating theoretical concentration attenuation values based on the actual distances and a pre-established distance-concentration attenuation model; Comparing the theoretical concentration attenuation values with actual concentration data of the reference point to obtain concentration coincidence degrees; Calculating an included angle between an emission direction of the potential pollution source and the current wind direction to obtain a direction consistency degree; Based on a preset weight coefficient, the concentration coincidence degrees, the direction consistency degree, and the emission history data are weighted and calculated to obtain pollution scores of each potential pollution source.

2. The method for automatic monitoring of ambient air quality according to claim 1, characterized in that, After the step of determining a potential pollution source with the highest pollution score as a pollution starting point, the method further comprises the following steps: Inputting position coordinates of the pollution starting point, current meteorological data of the pollution starting point, and concentration data of a monitoring site closest to the pollution starting point into an atmospheric diffusion model to calculate estimated concentration values of each monitoring site; Calculating concentration errors between the estimated concentration values and concentration data; If the concentration errors are within a preset error range, the position coordinates of the pollution starting point are sent to a target client.

3. The method for automatic monitoring of ambient air quality according to claim 2, characterized in that, The method further comprises, after the step of sending the position coordinate of the pollution starting point to the target client: obtaining pollution monitoring data and enterprise electricity consumption data of the pollution starting point; if the pollution monitoring data exceeds a preset pollution threshold and the enterprise electricity consumption data exceeds a preset electricity consumption threshold, generating an early warning information and sending it to a preset client.

4. The method for automatic monitoring of ambient air quality according to claim 1, wherein, The method further comprises, after the step of sending the position coordinate of the pollution starting point to the target client: obtaining historical monitoring data of the pollution starting point, and analyzing the historical monitoring data; if the historical monitoring data contains pollution monitoring data that remains unchanged or the pollution monitoring data of each pollutant changes proportionally within a preset time period, generating a data falsification early warning based on the pollution starting point and sending it to the target client. The method further comprises, after the step of sending the position coordinate of the pollution starting point to the target client:

5. The method for automatic monitoring of ambient air quality according to claim 1, wherein, annotating the concentration data on a monitoring station map based on the position information of the plurality of monitoring stations, and generating a concentration data schematic diagram; sending the concentration data schematic diagram to the target client. The environmental air quality automatic monitoring system comprises one or more processors and a memory; the memory is coupled with the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors invoke the computer instructions to enable the environmental air quality automatic monitoring system to perform the method according to any one of claims 1-5.

6. An automatic monitoring system of ambient air quality, characterized in that, When the instructions run on the environmental air quality automatic monitoring system, the environmental air quality automatic monitoring system performs the method according to any one of claims 1-5.

7. A computer-readable storage medium comprising instructions, wherein: When the computer program product runs on the environmental air quality automatic monitoring system, the environmental air quality automatic monitoring system performs the method according to any one of claims 1-5.

8. A computer program product, characterised in that, ​

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