Automatic environment air quality monitoring method and system, product and medium

Abstract

The invention discloses an automatic environment air quality monitoring method and system, a product and a medium, and relates to the field of environment monitoring, and the method comprises the following steps: obtaining concentration data of malodorous chemical substances based on monitoring sensors in a plurality of monitoring stations; selecting the 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; obtaining the reference distance between each potential pollution source and the reference point, the consistency degree with the current wind direction and emission historical data; calculating a pollution score of each potential pollution source based on the reference distance, the consistency degree and the emission historical data; determining the potential pollution source with the highest pollution score as a pollution starting point; and sending the position coordinate 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 a method, system, product and medium for automatic monitoring of ambient air quality. Background Art

[0002] With the rapid development of modern industry and the acceleration of urbanization, the issue of environmental air quality has attracted increasing attention. Finding the source of pollution is an important issue when monitoring the surrounding environment at monitoring stations.

[0003] In related technologies, monitoring sensors can be set up at fixed monitoring sites to obtain data on the concentrations of various pollutants. The pollution sources can be determined by delineating possible pollution areas based on the concentrations of pollutants, and the direction of the pollution sources can be inferred using wind direction.

[0004] However, when the solutions in the related art are dealing with multiple areas with high pollutant concentrations, relying solely on concentration data cannot effectively narrow the scope of the pollution source. When inferring the direction of the pollution source based on wind direction, the instability of wind direction will lead to inaccurate inference results. Summary of the invention

[0005] The present application provides a method, system, product and medium for automatically monitoring ambient air quality, which are used to improve the accuracy of pollution source speculation.

[0006] In the first aspect, the present application provides an automatic monitoring method for ambient air quality, which is applied to an automatic monitoring system for ambient air quality, and the method includes: 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 the current wind direction and current wind speed data of the target monitoring point at the current monitoring time point; obtaining a reference distance of each potential pollution source from the reference point, a degree of consistency with the current wind direction, and emission history data; calculating a pollution score for each potential pollution source based on the reference distance, degree of consistency, and emission history data; determining the potential pollution source with the highest pollution score as the pollution starting point; and sending the location coordinates of the pollution starting point to the target client.

[0007] By adopting the above technical solution, the concentration data of odorous chemicals 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 multiple potential pollution sources are marked in combination with the current wind direction and wind speed data. The scope of pollution source investigation can be narrowed down more specifically, and the reference distance, consistency degree and emission history data of potential pollution sources can be obtained. The possibility of each potential pollution source can be evaluated more scientifically, and finally the potential pollution source with the highest pollution score is determined as the pollution starting point, and its location coordinates are sent to the target client, so that relevant personnel can quickly and accurately obtain pollution source information, which helps to take timely measures for governance, thereby improving the accuracy of environmental air quality monitoring.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of selecting the target monitoring point with the highest concentration data as a reference point and marking multiple 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 specifically includes: obtaining 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 of the monitoring points based on the historical concentration data; marking the monitoring point whose concentration change rate exceeds a preset change threshold as an abnormal monitoring point; based on the current wind direction of the target monitoring point, searching for predicted pollution sources within a preset distance range of the abnormal monitoring point in the upwind area of ​​the target monitoring point; obtaining the historical emission data of the predicted pollution source, and calculating the pollutant diffusion range of each of the predicted pollution sources based on the historical emission data and the current wind speed data, and marking the predicted pollution source whose pollutant diffusion range overlaps with the position of the abnormal monitoring point by more than a preset percentage as a potential pollution source.

[0009] By adopting the above technical solution, the historical concentration data of all monitoring points within a preset distance range around the target monitoring point are obtained, and the concentration change rate is calculated. The monitoring points whose concentration change rate exceeds the preset change threshold are marked as abnormal monitoring points. Based on the current wind direction of the target monitoring point, the predicted pollution sources within the preset distance range of the abnormal monitoring point are searched in the upwind area. The pollutant diffusion range is calculated in combination with the historical emission data and the current wind speed data. The predicted pollution sources whose overlap with the abnormal monitoring point exceeds a preset percentage are further screened out and marked as potential pollution sources. This can more accurately identify possible pollution sources and reduce the possibility of misjudgment.

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

[0011] By adopting the above technical solution, the actual distance between each potential pollution source and the reference point is obtained, and the theoretical concentration attenuation value is calculated based on the actual distance and the pre-established distance-concentration attenuation model. The theoretical concentration attenuation value is compared with the actual concentration data of the reference point to obtain the concentration consistency. This can more accurately evaluate the impact of the potential pollution source on the reference point, calculate the angle between the emission direction of the potential pollution source and the current wind direction to obtain the degree of directional consistency, and weightedly calculate the concentration consistency, directional consistency and emission history data based on the preset weight coefficient to obtain the pollution score of each potential pollution source. This can more comprehensively and accurately evaluate the pollution possibility of the potential pollution source.

[0012] In combination with 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 also includes: inputting the location coordinates of the pollution starting point, the current meteorological data of the pollution starting point, and the concentration data of the monitoring point closest to the pollution starting point into the atmospheric diffusion model, and calculating the estimated concentration value of each monitoring point; calculating the concentration error between the estimated concentration value and the concentration data; if the concentration error is within a preset error range, sending the location coordinates of the pollution starting point to the target client.

[0013] By adopting the above technical solution, the location 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, which can simulate the diffusion of pollutants in the atmosphere, thereby more accurately predicting the estimated concentration value of each monitoring point. The concentration error between the estimated concentration value and the concentration data is calculated to evaluate the accuracy of the model prediction. If the concentration error is within the preset error range, the location coordinates of the pollution starting point are sent to the target client, so that relevant personnel can obtain more reliable information on the pollution starting point.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the location coordinates of the pollution starting point to the target client if the concentration error is within a preset error range, the method also includes: 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 message and sending it to a preset client.

[0015] By adopting the above technical solution, the pollution monitoring data and enterprise electricity consumption data of the pollution starting point can be obtained, so as to fully understand the pollution situation at the pollution starting point and the production activities of related enterprises. If the pollution monitoring data exceeds the preset pollution threshold and the enterprise electricity consumption data exceeds the preset electricity consumption threshold, an early warning message will be generated and sent to the preset client, which enables the relevant departments to promptly discover potential serious pollution situations and take prompt countermeasures.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the location coordinates of the pollution starting point to the target client, the method also includes: obtaining historical monitoring data of the pollution starting point, and analyzing the historical monitoring data; if the pollution monitoring data in the historical monitoring data remains unchanged within a preset time period or the pollution monitoring data of each pollutant changes in the same proportion, then generating a data falsification warning based on the pollution starting point and sending it to the target client.

[0017] By adopting the above technical solution, the historical monitoring data of the pollution starting point is obtained and analyzed, and abnormalities in the data can be discovered in time. If there is pollution monitoring data in the historical monitoring data that remains unchanged within a preset time period or the pollution monitoring data of each pollutant changes in the same proportion, a data fraud 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 ensure the accuracy of ambient air quality monitoring.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the location coordinates of the pollution starting point to the target client, the method also includes: marking the concentration data on the monitoring site map based on the location information of multiple monitoring sites, and generating a concentration data schematic diagram; sending the concentration data schematic diagram to the target client.

[0019] By adopting the above technical solution, the concentration data is marked on the monitoring site map based on the location information of multiple monitoring sites, which can intuitively display the pollutant concentration distribution of different monitoring sites and generate a concentration data schematic diagram, so that relevant personnel can understand the air quality status in the entire monitoring area at a glance.

[0020] In a second aspect, an embodiment of the present application provides an automatic ambient air quality monitoring system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the automatic ambient air quality monitoring system to perform the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product runs on an automatic ambient air quality monitoring system, the above-mentioned automatic ambient air quality monitoring system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on an automatic ambient air quality monitoring system, the automatic ambient air quality monitoring system executes the method described in the first aspect and any possible implementation method in the first aspect.

[0023] It is understandable that the automatic ambient air quality 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 embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application obtains concentration data of malodorous chemicals based on monitoring sensors in multiple monitoring sites, selects the target monitoring point with the highest concentration data as the reference point, and marks multiple potential pollution sources in combination with the current wind direction and wind speed data. It can narrow the scope of pollution source investigation in a more targeted manner, obtain the reference distance, consistency level and emission history data of potential pollution sources, and more scientifically evaluate the possibility of each potential pollution source. Ultimately, the potential pollution source with the highest pollution score is determined as the pollution starting point, and its location coordinates are sent to the target client, so that relevant personnel can quickly and accurately obtain pollution source information, which helps to take timely measures for governance, thereby improving the accuracy of ambient air quality monitoring.

[0025] 2. This application obtains the historical concentration data of all monitoring points within a preset distance range around the target monitoring point, calculates the concentration change rate, marks the monitoring points whose concentration change rate exceeds the preset change threshold as abnormal monitoring points, searches for predicted pollution sources within the preset distance range of the abnormal monitoring points in the upwind area based on the current wind direction of the target monitoring point, calculates the pollutant diffusion range based on the historical emission data and the current wind speed data, and further screens out predicted pollution sources whose overlap with the abnormal monitoring point exceeds a preset percentage and marks them as potential pollution sources, which can more accurately identify possible pollution sources and reduce the possibility of misjudgment.

[0026] 3. The present application obtains the actual distance between each potential pollution source and the reference point, calculates the theoretical concentration attenuation value based on the actual distance and the pre-established distance-concentration attenuation model, and compares the theoretical concentration attenuation value with the actual concentration data of the reference point to obtain the concentration consistency. It can more accurately evaluate the impact of the potential pollution source on the reference point, calculate the angle between the emission direction of the potential pollution source and the current wind direction to obtain the degree of directional consistency, and weightedly calculate the concentration consistency, directional consistency and emission history data based on the preset weight coefficient to obtain the pollution score of each potential pollution source, which can more comprehensively and accurately evaluate the pollution possibility of the potential pollution source. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the method for automatically monitoring ambient air quality in an embodiment of the present application; Figure 2 is another flow chart of the method for automatically monitoring ambient air quality in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of the automatic ambient air quality monitoring system in the embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more.

[0030] The following is a description of the process of the method provided by this implementation. Figure 1 , which is a flow chart of the method for automatic monitoring of ambient air quality in an embodiment of the present application.

[0031] S101. Acquire concentration data of malodorous chemicals based on monitoring sensors in multiple monitoring sites.

[0032] Among them, monitoring sites refer to fixed places distributed in specific geographical areas for environmental monitoring. Monitoring sensors refer to equipment installed in monitoring sites for detecting the concentration of specific chemical substances. Malodorous chemicals refer to chemical substances with pungent odors that have adverse effects on human health and the environment, such as nitrogen oxides (NOx), ozone (O3), particulate matter (PM) and other harmful substances. Concentration data is used to indicate the content of malodorous chemicals in unit volume of air.

[0033] Specifically, data is obtained from multiple monitoring sites distributed in the monitoring area, and concentration data of various odorous chemicals are obtained from sensors in real time or periodically through network connection or other communication methods, and the data are integrated into a central database.

[0034] In some embodiments, the concentration data of malodorous chemicals can be obtained based on the monitoring sensors in multiple monitoring sites in a variety of ways: Optionally, a timed task can be set to automatically send data requests to all monitoring sites at fixed time intervals (such as every 5 minutes), and the monitoring site immediately returns the current concentration data after receiving the request; Optionally, the monitoring site can be configured to be in active push mode, and when the concentration data is detected to have changed significantly or exceeds a preset threshold, the data is automatically pushed to the central system. It is understandable that other methods can also be used, which are not limited here.

[0035] 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.

[0036] The reference point refers to a specific location used as a benchmark for subsequent analysis and calculation. The current wind direction refers to the wind direction at the target monitoring point at the time of the analysis. The current wind speed data refers to the wind speed at the target monitoring point at the same time. Potential pollution sources refer to places or facilities that produce odorous chemical emissions.

[0037] Specifically, first compare the concentration data of all the monitoring points obtained, find out the monitoring point with the highest concentration value, set it as the target monitoring point, and obtain the wind direction and wind speed data of the target monitoring point at the current time, and search for possible pollution sources in the upwind area of ​​the target monitoring point. Based on factors such as wind direction, wind speed, terrain, etc., combined with a pre-established potential pollution source database, mark multiple potential pollution sources that may cause high concentrations of odorous substances.

[0038] In some embodiments, the selection of target monitoring points and marking of potential pollution sources can be achieved in a variety of ways: Optionally, by using a sliding time window method, 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, draw a fan-shaped search area in the GIS system, the vertex of the fan is the target monitoring point, the opening direction is opposite to the upwind direction, and the angle and radius are determined according to the wind speed; in this search area, all potential pollution sources located in the area are screened out from the pre-established pollution source database. Optionally, by adopting a multi-point comparison method, multiple monitoring points with higher concentrations are selected, the concentration change trend and relative position relationship of the monitoring points are analyzed, and the most likely pollution source location range is inferred through triangulation or other spatial analysis methods in combination with wind direction data, and then the potential pollution sources are marked within this range. It is understandable that other methods can also be used, which are not limited here.

[0039] S103, obtaining the reference distance of each potential pollution source from the reference point, the degree of consistency with the current wind direction, and emission history data.

[0040] The reference distance is the straight-line distance from the potential pollution source to the target monitoring point (reference point). The consistency level refers to the angular relationship between the location of the potential pollution source and the current wind direction.

[0041] Specifically, the system first calculates the straight-line distance from each marked potential pollution source to the target monitoring point, compares the azimuth of each potential pollution source relative to the target monitoring point and the angle between the current wind direction, calculates the degree of consistency, and retrieves the historical emission data of each potential pollution source from the database.

[0042] In some embodiments, the relevant data of potential pollution sources can be obtained in a variety of ways: Optionally, using GIS (geographic information system), the coordinates of the target monitoring point and all potential pollution sources are imported into the system, and the straight-line distance between them is automatically calculated through spatial analysis tools. Using vector calculation methods, the direction vector from the pollution source to the monitoring point is dot-producted with the current wind direction vector to obtain the angle between them, thereby calculating the degree of consistency. The connected environmental regulatory database can be accessed to extract the recent (such as the past 30 days) daily emission data of each potential pollution source, including the emission and exceeding of various pollutants. Optionally, a data request is sent to the online monitoring system of each enterprise or institution where the potential pollution source is located to obtain their real-time location, current emission status and other information. It is understandable that other methods can also be used, which are not limited here.

[0043] S104: Calculate the pollution score of each potential pollution source based on the reference distance, the consistency level and the emission history data.

[0044] Among them, the pollution score represents a comprehensive assessment of the possibility of each potential pollution source. Specifically, a calculation model is established based on three main factors: reference distance, consistency level, and historical emission data. For the reference distance, by using an inverse proportional relationship, the closer the distance, the higher the score. For the consistency level, an optimal angle range is set. Pollution sources within this range have the highest score, and the farther they deviate from this range, the lower the score. For historical emission data, recent emissions and frequencies are analyzed. Pollution sources with large emissions and high frequencies have higher scores. The scores of these three factors are weighted according to the preset weights to obtain the final pollution score of each potential pollution source.

[0045] In some embodiments, a linear weighted model is used. First, the reference distance is normalized and converted into a numerical value between 0 and 1. The closer the distance, the closer the value is to 1. Then, the degree of consistency is calculated. The angle between the pollution source direction and the wind direction is converted into a cosine value. The closer the cosine value is to 1, the higher the consistency. Then, the historical emission data is analyzed, and the ratio of the average emissions in the past 30 days to the statutory limit is calculated. Weights are assigned to these three factors (such as distance 0.4, consistency 0.3, and historical emissions 0.3), and the weighted sum is performed to obtain the final score.

[0046] S105. Determine the potential pollution source with the highest pollution score as the pollution starting point.

[0047] The pollution starting point refers to the location that is determined by the system to be the most likely source of pollution. Specifically, after completing the pollution score calculation for all potential pollution sources, all pollution scores are first sorted to find the potential pollution source with the highest score. This pollution source with the highest score is considered to be the most likely source of the high concentration of pollutants currently monitored. A series of verification steps are performed, including checking whether the score of the pollution source is significantly higher than other pollution sources, whether it exceeds a preset credibility threshold, etc. If the verification is passed, the potential pollution source with the highest score is marked as the pollution starting point.

[0048] In some embodiments, the potential pollution source with the highest pollution score can be determined as the pollution starting point in a variety of ways: Optionally, a simple maximum selection method can be used to select the potential pollution source with the highest pollution score as the pollution starting point, check whether the highest score exceeds a preset threshold (e.g., 80 points, the full score is 100 points) to ensure that the selected pollution source has a sufficiently high credibility, calculate the difference between the highest score and the second highest score, and if the difference is greater than a preset significance level (e.g., 20 points), further confirm the reliability of this selection and check the historical records of this potential pollution source. Optionally, a more complex integrated decision method is used, first select the top three potential pollution sources, run more detailed pollution diffusion simulations for these three pollution sources, consider more factors such as terrain, buildings, meteorological conditions, etc., compare the simulation results with the actual monitoring data, calculate the degree of fit of each simulation result, and finally comprehensively consider the initial score, simulation fit and additional information, use 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.

[0049] S106: Send the location coordinates of the pollution starting point to the target client.

[0050] The target client refers to the terminal device or software system that receives this information, including computers, mobile device applications or other related systems of environmental regulatory departments.

[0051] Specifically, after the system determines the starting point of the pollution, this important information needs to be conveyed to the relevant personnel or departments in a timely manner. The precise location coordinates of the pollution starting point will be extracted, which usually include longitude, latitude, and sometimes altitude. The coordinate data will be packaged into a message in a standard format, which can also include other relevant information, such as pollutant type, concentration level, discovery time, etc. Next, identify the pre-set list of target clients, including the central system of the environmental regulatory department, the mobile devices of on-site law enforcement personnel, and the environmental protection managers of relevant companies. Finally, the message containing the location coordinates of the pollution starting point is securely sent to the target client through a preset communication channel, such as the Internet, a dedicated network, or a wireless communication network.

[0052] In some embodiments, the location coordinates of the pollution starting point can be sent to the target client in a variety of ways: Optionally, a real-time push mechanism is used. First, the location coordinates of the pollution starting point are converted into a standard geographic information format, including latitude and longitude information and possible additional attributes such as pollutant type, detection time, etc., and the pre-configured target client list is queried to determine the communication protocol and address of each client. A customized message is generated for each target client, including different levels of detailed information, and the message is pushed to each target client in real time using an appropriate communication protocol. Optionally, a hierarchical notification strategy is used. First, the location coordinate information is stored in a central database, and a unique event identifier is generated. The priority and method of notification are determined according to the preset urgency level. For the highest level of recipients (such as the environmental emergency command center), an alarm message containing complete location coordinates and detailed information is immediately sent; for secondary recipients, the system only sends a reminder notification, indicating that there is a new pollution event, and they need to log in to the system to view the details. The sending status and receipt confirmation of all notifications will be recorded to ensure the effective communication of information. It is understandable that other methods can also be used to send location coordinates, such as using blockchain technology to ensure the immutability of data, or achieving faster local notifications through edge computing devices to improve the efficiency and security of information transmission, which is not limited here.

[0053] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the method for automatic monitoring of ambient air quality in an embodiment of the present application.

[0054] S201. Acquire concentration data of malodorous chemicals based on monitoring sensors in multiple monitoring sites.

[0055] It can be understood that this step is similar to step S101 and will not be described in detail here.

[0056] S202, obtaining 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.

[0057] 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 preset 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 in the concentration value per unit time, usually expressed as a percentage.

[0058] In specific implementation, the geographic coordinates of the target monitoring point are determined, and then all monitoring points are screened out within a preset distance range (such as a radius of 5 kilometers) with this as the center. For each screened monitoring point, the system extracts its concentration data records for the past 24 hours from the database. The system then uses historical data to calculate the concentration change rate of each monitoring point. The calculation method can use a simple linear regression analysis or a more complex time series analysis method. For example, the percentage change between the average concentration in the last hour and the average concentration in the previous 23 hours can be calculated. If the average concentration in the last hour is 100ppb and the average concentration in the previous 23 hours is 80ppb, the concentration change rate is (100-80) / 80 * 100% = 25%.

[0059] S203: Mark the monitoring point whose concentration change rate exceeds a preset change threshold as an abnormal monitoring point.

[0060] 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 standard pre-set by the system to determine whether the concentration change is abnormal. An abnormal monitoring point refers to a monitoring site where the concentration change exceeds the normal range.

[0061] In the specific implementation process, it is necessary to set a reasonable preset change threshold. The setting of this threshold can be determined 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 this preset threshold. If the change rate of a certain monitoring point exceeds 30%, the system will mark it as an abnormal monitoring point. This process can be achieved 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 abnormal monitoring point list. Traverse all monitoring points to complete this marking process.

[0062] S204: Based on the current wind direction of the target monitoring point, search in the upwind area of ​​the target monitoring point for predicted pollution sources within a preset distance range of the abnormal monitoring point.

[0063] The current wind direction refers to the wind direction data at the location of the target monitoring point, which is usually provided by a weather station. The upwind area refers to the direction from which the wind blows, and is an area where potential pollution sources may exist. The abnormal monitoring point is the monitoring site marked in step S203 with abnormal concentration changes. The preset distance range is a search radius pre-set by the system. The predicted pollution source refers to a factory, facility or other emission source that may generate pollution.

[0064] When implementing this step, it is necessary to obtain real-time wind direction data of the target monitoring point. This can be achieved by connecting to the API of the local weather station or the meteorological sensor that comes with the monitoring point. After obtaining the wind direction, search in the upwind area of ​​the target monitoring point. The search range can be set as a fan-shaped area, for example, with the target monitoring point as the center, extending 10 kilometers in the upwind direction, and the angle of the fan is 60 degrees. In this area, query the pre-established pollution source database to screen out 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 possible pollution source screened out, calculate its distance to each abnormal monitoring point. If this distance is within a preset range (for example, 3 kilometers), the pollution source is marked as a predicted pollution source.

[0065] S205: Obtain historical emission data of the predicted pollution source, and calculate the pollutant diffusion range of each predicted pollution source based on the historical emission data and the current wind speed data.

[0066] The predicted pollution source refers to the factory, facility or other emission source that may generate pollution determined in step S204. Historical emission data refers to the record of pollutant emissions from the pollution source over a period of time in the past, including information such as emission amount and emission time. Current wind speed data refers to the real-time wind speed information at the location of the target monitoring point, usually provided by a meteorological station. The pollutant diffusion range refers to the area where the pollutant spreads in the air, and its size and shape are affected by factors such as emission amount, wind speed, and terrain.

[0067] When implementing this step, it is necessary to obtain the historical emission data of each predicted pollution source from the database of the environmental regulatory department or the emission data reported by the enterprise. The historical emission data should include the average daily emissions in the past certain 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 the 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 intensity, wind speed, and atmospheric stability. For example, assuming that the average daily emissions of a predicted pollution source are 100kg / day, the current wind speed is 3m / s, and the atmospheric conditions are stable, the Gaussian plume model is used to calculate that at 2km downwind, the pollutant concentration is reduced to 10% of the initial concentration. The system uses this 2km 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.

[0068] S206. Mark the predicted pollution source whose pollutant diffusion range overlaps with the position of the abnormal monitoring point by more than a preset percentage as a potential pollution source.

[0069] 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. Position overlap refers to the degree to which the abnormal monitoring point falls within the pollutant diffusion range. The preset percentage is a judgment standard pre-set by the system to determine whether the overlap is significant enough. Potential pollution sources refer to predicted pollution sources that are more likely to be actual pollution sources after screening in this step.

[0070] In the specific implementation process, it is necessary to set a reasonable preset percentage threshold, for example, it can be set to 60%, to evaluate each predicted pollution source. The evaluation method is to compare the pollutant diffusion range of the predicted pollution source (usually a circular or elliptical area) with the location of all abnormal monitoring points. The system calculates the number of abnormal monitoring points that fall within the diffusion range, divides it by the total number of abnormal monitoring points, and obtains a percentage. If this 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, then the overlap is 70%, which exceeds the threshold of 60%, so this predicted pollution source will be marked as a potential pollution source. Such an evaluation is performed on all predicted pollution sources, and a list of potential pollution sources is finally obtained.

[0071] S207: Obtain the actual distance between each potential pollution source and the reference point.

[0072] 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 between two geographical locations or the shortest path distance along the surface.

[0073] 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 implement 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 the 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 a more accurate distance calculation result. 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. Such distance calculations are performed for each potential pollution source and the results are stored. The distance data will be used for further analysis in subsequent steps, such as assessing the impact of pollution sources and calculating the attenuation of pollutant concentrations.

[0074] S208. Calculate a theoretical concentration decay value based on the actual distance and a pre-established distance-concentration decay model.

[0075] The actual distance refers to the distance from each potential pollution source to the reference point calculated in step S207. The distance-concentration decay model is a pre-established mathematical model used to describe the law of pollutant concentration decay as the distance increases. The theoretical concentration decay value refers to the degree of reduction of the pollutant concentration at a given distance relative to the concentration at the emission source calculated according to the model.

[0076] In this step, the system will use the known actual distance and the pre-established distance-concentration attenuation model to calculate the theoretical concentration attenuation value of each potential pollution source to the reference point. In specific implementation, it is first necessary to select a suitable attenuation model. Commonly used models include inverse proportional model, exponential attenuation model and Gaussian diffusion model. Taking the exponential attenuation 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 attenuation coefficient. Substitute the actual distance calculated in S207 into this formula to calculate the theoretical concentration attenuation value. For example, assuming that a potential pollution source is 5 kilometers away from the reference point, using the exponential attenuation model, the attenuation coefficient k=0.2, then the theoretical concentration attenuation value is e^(-0.2*5) ≈ 0.368, that is, the theoretical concentration at the reference point is approximately 36.8% of the concentration at the pollution source. Such calculations are performed for each potential pollution source to obtain a series of theoretical concentration attenuation values.

[0077] S209, comparing the theoretical concentration attenuation value with the actual concentration data of the reference point to obtain a concentration consistency.

[0078] The theoretical concentration decay value is the value calculated in step S208 based on the distance-concentration decay model. The actual concentration data of the reference point refers to the pollutant concentration actually observed at the target monitoring point (i.e., the reference point). The concentration fit refers to the degree of match between the theoretical calculated value and the actual observed value, which is used to evaluate the possibility of potential pollution sources.

[0079] 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 degree of agreement. 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. 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 common 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 80ppb and the actual concentration of the reference point is 75ppb, the relative error is (|80-75| / 75) * 100% ≈ 6.67%. Convert this relative error to a good fit, such as using the formula: Good fit = 100% - relative error. In this example, the good fit is about 93.33%. This calculation is performed for each potential pollution source, resulting in a series of concentration fits.

[0080] S210: Calculate the angle between the emission direction of the potential pollution source and the current wind direction to obtain a degree of directional consistency.

[0081] The emission direction of a potential pollution source refers to the direction in which pollutants are emitted 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 angle refers to the angular difference between the two directions. The degree of directional consistency refers to the degree of match between the emission direction and the wind direction, which is used to assess the possibility of a potential pollution source affecting the reference point.

[0082] In this step, the angle between the emission direction of each potential pollution source and the current wind direction is calculated and converted into the degree of directional consistency. The specific implementation method is as follows: First, obtain the emission direction data of each potential pollution source, which comes from the design documents or field survey data of the pollution source, usually expressed in angles (such as 0° for due north and increases clockwise). At the same time, the system also needs to obtain the current wind direction data of the reference point, which usually comes from a weather station or on-site meteorological sensor, and 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 it from 360°. For example, if the emission direction of a potential pollution source is 45° (northeast) and the current wind direction is 30° (30° north by east), the angle is |45°-30°|=15°. Next, convert this angle into the degree of directional consistency. A simple method is to use a linear conversion: consistency degree = (180°-angle) / 180°*100%. In this example, the consistency is (180°-15°) / 180°*100%≈91.67%. This calculation is performed for each potential pollution source to obtain a series of directional consistency levels.

[0083] S211. Based on a preset weight coefficient, the concentration consistency, the directional consistency, and the emission history data are weightedly calculated to obtain a pollution score for each potential pollution source.

[0084] It can be understood that this step is similar to step S104 and will not be described in detail here.

[0085] S212. Determine the potential pollution source with the highest pollution score as the pollution starting point.

[0086] It can be understood that this step is similar to step S105 and will not be described in detail here.

[0087] S213: Send the location coordinates of the pollution starting point to the target client.

[0088] It can be understood that this step is similar to step S106 and will not be described in detail here.

[0089] S214: Obtain the pollution monitoring data and enterprise electricity consumption data of the pollution starting point.

[0090] The pollution starting point refers to the most likely pollution source location determined in the previous step. Pollution monitoring data refers to the concentration data of various pollutants collected at or near the pollution starting point, such as PM2.5, sulfur dioxide, nitrogen oxides, etc. Enterprise electricity consumption data refers to the electricity consumption records of the enterprise corresponding to the pollution starting point, usually in units of electricity consumption recorded at a certain time interval (such as every hour or every day).

[0091] In this step, two types of data are obtained at the same time: 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 starting point. 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, obtain the average concentration of PM2.5, SO2, NOx and other pollutants every hour in the last 24 hours. For the acquisition of enterprise electricity consumption data, establish a connection with the power company's database to obtain enterprise electricity consumption data for the same time period as the pollution monitoring data, such as electricity consumption per hour in the last 24 hours.

[0092] S215: If the pollution monitoring data exceeds a preset pollution threshold and the enterprise's electricity consumption data exceeds a preset electricity consumption threshold, an early warning message is generated and sent to a preset client.

[0093] The preset pollution threshold refers to the upper limit of pollutant concentration set in advance, exceeding which is considered to be a possible environmental risk. The preset power consumption threshold refers to the upper limit of the enterprise's power consumption set in advance, exceeding which may mean an abnormal increase in production activities. Early warning information is a warning message generated by the system to alert relevant personnel to potential environmental risks. The preset client refers to the terminal device or software application that receives the early warning information, which may include the monitoring center of the environmental protection department, the mobile phone application of enterprise managers, etc.

[0094] In this step, the data obtained in S214 is judged and processed. First, the pollution monitoring data is compared with the preset pollution threshold. This threshold may be set based on national or local environmental quality standards, such as a 24-hour average concentration of PM2.5 exceeding 75μg / m³. At the same time, the system will also compare the enterprise's electricity consumption data with the preset electricity consumption threshold. The setting of the electricity consumption threshold is based on the company's historical electricity consumption pattern, such as exceeding 120% of the average electricity consumption in the same period of the past three months. If the pollution monitoring data exceeds the pollution threshold and the enterprise's electricity consumption data also exceeds the electricity consumption threshold, the system will trigger an early warning mechanism. The process of generating early warning information includes: creating a structured message containing information such as the type of pollutant exceeding the standard, concentration value, exceeding time, and enterprise electricity consumption.

[0095] S216: Obtain historical monitoring data of the pollution starting point, and analyze the historical monitoring data.

[0096] Historical monitoring data refers to pollutant concentration data collected at a certain pollution starting point or a nearby monitoring station over a period of time. Pollutant concentration data usually includes records of various pollutant concentrations at different time scales (such as hours, days, and months). Data analysis refers to statistical processing, pattern recognition, and trend analysis of historical data to extract valuable information.

[0097] In this step, it is necessary to extract historical monitoring data of the pollution starting point from the environmental monitoring database. These data usually cover a longer time span, which is data from the past few months or years. During the data extraction process, possible missing values ​​or outliers are processed based on the integrity and quality of the data. For example, the hourly concentration data of major pollutants such as PM2.5, SO2, and NOx in the past two years are extracted. After obtaining the data, a variety of analyses are carried out. The first is basic statistical analysis, including calculating the mean, median, maximum, minimum, standard deviation and other statistics of each pollutant to understand the overall distribution of pollutant concentrations. The second is time series analysis, drawing a trend chart of the concentration of each pollutant over time to identify whether there is a long-term trend (such as an increase or decrease year by year) or a cyclical pattern (such as seasonal changes).

[0098] S217. If the pollution monitoring data in the historical monitoring data remains unchanged within a preset time period or the pollution monitoring data of each pollutant changes in the same proportion, a data falsification warning is generated based on the pollution starting point and sent to the target client.

[0099] In this step, it is necessary to perform anomaly detection on the historical monitoring data obtained in S216. Set a preset time period, such as 12 hours or 24 hours, and then check whether there is an abnormal pattern in the data within this time period. The abnormal pattern mainly includes two situations: one is that the data is completely unchanged, and the other is that the data changes in the same proportion. For the first situation, check whether the concentration value of each pollutant remains exactly the same throughout the preset time period. For example, if the concentration of PM2.5 is 35.6μg / m³ for 12 hours in a row, mark this set of data as suspicious. For the second situation, calculate the change ratio of the concentration values ​​of different pollutants. If it is found that the concentration values ​​of all pollutants increase or decrease in the same proportion, they will also be marked as suspicious. For example, if in a certain hour, the concentration values ​​of PM2.5, SO2 and NOx all increase by 10% compared with the previous hour, mark it as abnormal. When these abnormal patterns are detected, a data falsification warning is generated. This warning information contains the time period of the abnormal data, the type of pollutant involved, the description of the abnormal pattern (such as "data unchanged" or "same proportion change") and the information of the pollution starting point, and send this warning information to the preset target client.

[0100] S218. Mark the concentration data on a monitoring site map based on the location information of the plurality of monitoring sites, and generate a concentration data schematic diagram.

[0101] Monitoring stations are fixed or mobile devices distributed in a specific area for collecting environmental data. Location information includes the latitude and longitude coordinates of each monitoring station. Concentration data refers to the concentration values ​​of different pollutants measured at each monitoring station. A monitoring station map is a geographic image showing the distribution of monitoring stations. A concentration data diagram is an image that presents concentration data on a map in a visual way, usually using different colors or symbols to represent different concentration levels.

[0102] In this step, pollutant concentration data is combined with geographic information to create an intuitive visual representation. First, load a base map containing the locations of all relevant monitoring stations. The map is 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 station, including multiple pollutants such as PM2.5, SO2, NOx, etc. For each monitoring station, mark a symbol or color block at the corresponding location on the map. The color or size of the symbol will change depending on the concentration of the pollutant. For example, use green for low concentration, yellow for medium concentration, and red for high concentration. For data on multiple pollutants, use pie charts or multi-layer symbols to represent them.

[0103] S219, sending the concentration data schematic diagram to the target client.

[0104] In this step, the concentration data diagram generated in S218 is effectively transmitted to the preset target client. First, the type and receiving capability of the target client are determined. Different types of clients require images of different formats or resolutions. For example, if the target client is a mobile application, the image is compressed to a size and resolution suitable for display on the mobile device, and the image file is pushed to the client via HTTP or HTTPS protocol.

[0105] In some embodiments, step 106 or step S213 may further include: The location coordinates of the pollution starting point, the current meteorological data of the pollution starting point and the concentration data of the monitoring point closest to the pollution starting point are input into the atmospheric diffusion model to calculate the estimated concentration value of each monitoring point; In this step, the pollution starting point is the initial location where the pollutant is released or generated. The location coordinates are expressed in longitude and latitude and are used to locate the pollution source. Current meteorological data include environmental factors such as wind speed, wind direction, temperature, humidity, and atmospheric pressure that affect the diffusion of pollutants. The nearest monitoring point is the air quality monitoring station that is geographically closest to the pollution starting point. The concentration data is the pollutant concentration value measured at the monitoring point, 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 process 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.

[0106] In this step, the GPS coordinate data of the pollution starting point is obtained. Then the current meteorological data, including wind speed, wind direction, temperature, humidity and atmospheric pressure, are obtained from the weather station in real time. The system also extracts the real-time concentration data of the monitoring point closest to the pollution starting point from the environmental monitoring network. The system selects an applicable atmospheric diffusion model, such as a Gaussian plume model, a Lagrangian particle model or an Euler 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 needs to input additional parameters, such as pollutant emission rate, emission height, etc. These parameters come from historical data or calibrated estimates, run the model, and simulate the pollutant diffusion process during the entire time period from the start of pollution to the current moment. The model calculation takes into account changes in wind direction and wind speed to derive the transmission path and concentration changes of pollutants in the atmosphere.

[0107] Calculate the concentration error between the estimated concentration value and the concentration data; In this step, the estimated concentration value is the theoretical pollutant concentration calculated by the atmospheric diffusion model. The concentration data is the actual pollutant concentration measured at the monitoring point. 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.

[0108] In this step, the measured concentration data for each monitoring point is extracted from the database to ensure 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, and then take the square root. The system records the results of these three error calculation methods for each monitoring point.

[0109] If the concentration error is within the preset error range, the location coordinates of the pollution starting point are sent to the target client.

[0110] In this step, the preset error range is an acceptable error range preset by the system and is used to judge the accuracy of the model prediction. The target client is a terminal device or application that receives the location coordinates of the pollution starting point.

[0111] In this step, the preset error range is read. This range is based on historical data analysis and relevant specification settings. The system sets different error ranges for each pollutant and different concentration levels. For example, an error of ±5μg / m³ is allowed when the PM2.5 concentration is below 35μg / m³, and a relative error of ±15% is allowed when it is above 35μg / m³. The system compares the concentration error calculated in the previous step with the preset error range. The comparison process is performed separately for each monitoring point, while taking into account the average error of all monitoring points. The system requires that the results of all error calculation methods are within the preset range. When the system confirms that the error is within the preset range, it is ready to send the location coordinates of the pollution starting point. The data sent includes longitude, latitude and altitude (if applicable).

[0112] The following describes the automatic monitoring system for ambient air quality in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of an automatic ambient air quality monitoring system in an embodiment of the present application.

[0113] It should be noted that Figure 3 The structure of the automatic ambient air quality monitoring system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0114] like Figure 3 As shown, the automatic monitoring system for ambient air quality includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0115] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0116] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0117] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0119] Specifically, the automatic ambient air quality monitoring system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the automatic ambient air quality monitoring method provided in the above embodiment is implemented.

[0120] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the automatic ambient air quality monitoring system described in the above embodiment; or may exist independently without being assembled into the automatic ambient air quality monitoring system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the automatic ambient air quality monitoring system, the automatic ambient air quality monitoring system implements the automatic ambient air quality monitoring method provided in the above embodiment.

[0121] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0122] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0123] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for automatically monitoring ambient air quality, characterized in that: Applied to an automatic monitoring system for ambient air quality, the method comprises: Acquire concentration data of odorous chemicals based on monitoring sensors in multiple monitoring sites; Select the target monitoring point with the highest concentration data as a reference point, and mark multiple 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; Obtaining the reference distance of each of the potential pollution sources from the reference point, the degree of consistency with the current wind direction, and emission history data; Calculating a pollution score for each of the potential pollution sources based on the reference distance, the consistency level, and the emission history data; Determine the potential pollution source with the highest pollution score as the pollution starting point; The location coordinates of the pollution starting point are sent to the target client.

2. The method according to claim 1, characterized in that The step of selecting the target monitoring point with the highest concentration data as a reference point and marking multiple 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 specifically includes: Obtaining 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 of the monitoring points based on the historical concentration data; Marking the monitoring point whose concentration change rate exceeds a preset change threshold as an abnormal monitoring point; Based on the current wind direction of the target monitoring point, searching the upwind area of ​​the target monitoring point for predicted pollution sources within a preset distance range of the abnormal monitoring point; Obtain historical emission data of the predicted pollution source, and calculate the pollutant diffusion range of each predicted pollution source based on the historical emission data and the current wind speed data The predicted pollution source whose pollutant diffusion range overlaps with the position of the abnormal monitoring point by more than a preset percentage is marked as a potential pollution source.

3. The method according to claim 1, characterized in that 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 includes: Obtaining the actual distance between each of the potential pollution sources and the reference point; Calculating a theoretical concentration decay value based on the actual distance and a pre-established distance-concentration decay model; Comparing the theoretical concentration attenuation value with the actual concentration data of the reference point to obtain a concentration consistency; Calculating the angle between the emission direction of the potential pollution source and the current wind direction to obtain the degree of directional consistency; Based on a preset weight coefficient, the concentration consistency, the directional consistency, and the emission history data are weightedly calculated to obtain a pollution score for each potential pollution source.

4. The method according to claim 1, characterized in that: After the step of determining the potential pollution source with the highest pollution score as the pollution starting point, the method further includes: Input the location coordinates of the pollution starting point, the current meteorological data of the pollution starting point, and the concentration data of the monitoring point closest to the pollution starting point into the atmospheric diffusion model to calculate the estimated concentration value of each monitoring point; Calculating a concentration error between the estimated concentration value and the concentration data; If the concentration error is within a preset error range, the position coordinates of the pollution starting point are sent to the target client.

5. The method according to claim 4, characterized in that After the step of sending the position coordinates of the pollution starting point to the target client if the concentration error is within the preset error range, the method further includes: Obtaining pollution monitoring data and enterprise electricity consumption data at 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, an early warning message is generated and sent to a preset client.

6. The method according to claim 1, characterized in that After the step of sending the location coordinates of the pollution starting point to the target client, the method further includes: Obtaining historical monitoring data of the pollution starting point, and analyzing the historical monitoring data; If there is pollution monitoring data in the historical monitoring data that remains unchanged within a preset time period or the pollution monitoring data of each pollutant changes in the same proportion, a data falsification warning is generated based on the pollution starting point and sent to the target client.

7. The method according to claim 1, characterized in that After the step of sending the location coordinates of the pollution starting point to the target client, the method further includes: Marking the concentration data on a monitoring site map based on the location information of the plurality of monitoring sites to generate a concentration data schematic diagram; The concentration data schematic diagram is sent to the target client.

8. An automatic monitoring system for ambient air quality, characterized in that: The automatic ambient air quality monitoring system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the automatic ambient air quality monitoring system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on an automatic ambient air quality monitoring system, the automatic ambient air quality monitoring system is caused to execute the method as claimed in any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on an automatic ambient air quality monitoring system, the automatic ambient air quality monitoring system is enabled to perform the method according to any one of claims 1 to 7.

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