A Method for Identifying Air Pollution Events Caused by Straw Burning Based on Multi-Source Monitoring Data Fusion

Abstract

This invention provides a method for identifying straw burning air pollution events based on multi-source monitoring data fusion, belonging to the field of environmental monitoring technology. The method includes: acquiring air quality data, particulate matter composition data, satellite remote sensing fire point data, and meteorological data at the current time and multiple past times; determining candidate air quality monitoring stations; screening first associated stations among particulate matter composition monitoring stations and second associated stations among meteorological monitoring stations; determining judgment feature values; setting judgment conditions for straw burning pollution events; and determining whether a straw burning pollution event has occurred at the location of the candidate air quality monitoring stations. According to this invention, multi-source monitoring data can be fused to achieve near real-time automated and intelligent identification and accurate early warning of straw burning pollution events, reducing monitoring costs and manpower input, improving the accuracy and timeliness of straw burning pollution event identification, and providing effective decision support for environmental management departments.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a method for identifying air pollution events caused by straw burning based on the fusion of multi-source monitoring data. Background Technology

[0002] Straw burning is a common phenomenon in agricultural production, especially after crop harvesting. Farmers often choose to burn straw directly to clean up the fields and facilitate subsequent cultivation. However, this practice has a serious impact on the environment and human health, as straw burning releases large amounts of fine particulate matter (PM2.5). 2.5 PM2.5, carbon monoxide (CO), and various other toxic and harmful gases contribute to regional air pollution. 2.5 Concentrations rise sharply, even leading to severe pollution. Furthermore, the smoke from burning reduces visibility and impacts traffic safety. Therefore, timely identification and intervention of straw burning incidents are crucial. However, current technologies lack the ability to comprehensively assess straw burning pollution by integrating conventional pollutant concentrations, particulate matter composition, satellite remote sensing fire points, and meteorological data. They typically rely on single data sources (e.g., satellite remote sensing fire point data) or manual interpretation, resulting in low accuracy and poor timeliness. This makes it difficult to integrate multi-source monitoring data to identify straw burning pollution incidents, hindering effective responses to large-scale, sudden straw burning pollution events.

[0003] The information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] This invention provides a method for identifying straw burning air pollution events based on the fusion of multi-source monitoring data, which can solve the technical problem that related technologies have difficulty in fusing multi-source monitoring data to identify straw burning pollution events.

[0005] According to a first aspect of the present invention, a method for identifying straw burning air pollution events based on multi-source monitoring data fusion is provided, comprising:

[0006] Acquire air quality data from multiple air quality monitoring stations at the current time and multiple past times, particulate matter composition data from particulate matter composition monitoring stations, satellite remote sensing fire point data, and meteorological data from multiple meteorological monitoring stations;

[0007] Based on air quality data from multiple air quality monitoring stations, candidate air quality monitoring stations were determined.

[0008] Based on the location information of the candidate air quality monitoring stations, the first associated stations were selected from the particulate matter component monitoring stations, and the second associated stations were selected from the meteorological monitoring stations.

[0009] Based on the air quality data from the candidate air quality monitoring stations, the particulate matter composition data from the first associated station, the meteorological data from the second associated station, and the satellite remote sensing fire point data, the judgment characteristic values ​​are determined.

[0010] Based on the characteristic values, the criteria for judging straw burning pollution events are set.

[0011] Based on the judgment characteristic value and the judgment conditions for straw burning pollution events, determine whether a straw burning pollution event has occurred at the location of the candidate air quality monitoring station.

[0012] According to the present invention, determining candidate air quality monitoring sites includes:

[0013] Obtain the current PM2.5 concentration at each air quality monitoring station. 2.5 Air quality data;

[0014] If the current PM 2.5 If the air quality data is greater than the first preset threshold, the air quality monitoring station will be identified as a candidate air quality monitoring station.

[0015] According to the present invention, screening a first associated site among particulate matter component monitoring sites and screening a second associated site among meteorological monitoring sites includes:

[0016] Use the location information of the candidate air quality monitoring stations as the center and the first preset length as the radius to set the search range;

[0017] The particulate matter monitoring stations within the search range were identified as the first associated stations, and the meteorological monitoring stations within the search range were identified as the second associated stations.

[0018] According to the present invention, determining the judgment feature value includes:

[0019] Obtain the current PM2.5 concentration at the candidate air quality monitoring stations. 2.5 Air quality data and PM 10 The ratio of air quality data is used as the first judgment characteristic value;

[0020] Obtain the particulate matter composition data of potassium ions at the current time of the first associated site, as well as the arithmetic mean of the particulate matter composition data of potassium ions at the current time and multiple past times.

[0021] The ratio of the particulate matter composition data of potassium ions at the current moment to the arithmetic mean of the particulate matter composition data of potassium ions at the current moment and at multiple past moments is used as the second judgment feature value.

[0022] Obtain the particulate matter composition data of organic matter at the current time from the first associated monitoring station, and compare it with the PM2.5 data of the current time from the candidate air quality monitoring stations. 2.5 The ratio of air quality data is used as the third judgment characteristic value;

[0023] The sum of the particulate matter composition data (sulfate, nitrate, and ammonium) at the current time of the first associated monitoring station is obtained, and then compared with the PM2.5 at the current time of the candidate air quality monitoring stations. 2.5 The ratio of air quality data is used as the fourth judgment characteristic value;

[0024] Obtain the arithmetic mean of the humidity data from the current time and multiple past time periods of the meteorological data of the second associated station, and use it as the fifth judgment feature value;

[0025] Based on the second associated site, determine the wind direction of the candidate air quality monitoring site, and determine the wind direction range based on the wind direction;

[0026] Based on satellite remote sensing fire data, the number of satellite remote sensing fire points within the specified wind direction range on the current day and the previous day is determined and used as the sixth judgment feature value.

[0027] According to the present invention, the criteria for judging straw burning pollution events are set, including:

[0028] Multiple criteria for judging straw burning pollution events are set as follows:

[0029] Criterion A for judging straw burning pollution incidents: The first judgment characteristic value is greater than the first proportional threshold;

[0030] Criterion B for judging straw burning pollution incidents: The particulate matter component data of potassium ions at the first associated site at the current moment is greater than the first concentration threshold, and the second judgment characteristic value is greater than the second proportion threshold.

[0031] Criterion for judging straw burning pollution incidents: The fourth judgment characteristic value is less than the fourth proportion threshold;

[0032] Criterion D for judging straw burning pollution incidents: The third judgment characteristic value is greater than the third proportion threshold;

[0033] Criterion E for judging straw burning pollution incidents: The fifth judgment characteristic value is less than the second preset threshold;

[0034] Criterion F for judging straw burning pollution incidents: The sixth criterion characteristic value is greater than 0.

[0035] According to the present invention, determining whether a straw burning pollution event has occurred at the location of a candidate air quality monitoring station includes:

[0036] If at least one of the following conditions for determining a straw burning pollution event is met: A, B, or C, then a straw burning pollution event is determined to have occurred.

[0037] According to the present invention, the method further includes:

[0038] The alarm level is determined based on the number of conditions D, E, and F that are met in the judgment of straw burning pollution events.

[0039] According to the present invention, the method further includes:

[0040] Create a list of straw burning pollution incidents;

[0041] Determine whether the location of the straw burning pollution incident occurred at any of the past times;

[0042] If the location of a straw burning pollution event is consecutive to the location of a past straw burning pollution event, then straw burning pollution events at multiple times will be merged.

[0043] Determine the duration of straw burning pollution incidents after the merger;

[0044] If the location of a straw burning pollution event is not consecutive with the location of a past straw burning pollution event, a new straw burning pollution event is created in the list of straw burning pollution events.

[0045] Output a list of straw burning pollution incidents.

[0046] According to a second aspect of the present invention, a straw burning air pollution event identification system based on multi-source monitoring data fusion is provided, comprising:

[0047] The acquisition module acquires air quality data from multiple air quality monitoring stations, particulate matter composition data from particulate matter composition monitoring stations, satellite remote sensing fire point data, and meteorological data from multiple meteorological monitoring stations at the current time and multiple past times.

[0048] The candidate site module determines candidate air quality monitoring sites based on air quality data from multiple air quality monitoring stations.

[0049] The associated site module filters the first associated site from the particulate matter component monitoring sites and the second associated site from the meteorological monitoring sites based on the location information of the candidate air quality monitoring sites.

[0050] The feature value determination module determines the feature values ​​based on the air quality data of the candidate air quality monitoring stations, the particulate matter composition data of the first associated station, the meteorological data of the second associated station, and the satellite remote sensing fire point data.

[0051] The judgment condition module sets the judgment conditions for straw burning pollution events based on the judgment characteristic values;

[0052] The judgment module determines whether a straw burning pollution event has occurred at the location of the candidate air quality monitoring station based on the judgment characteristic value and the judgment conditions for straw burning pollution events.

[0053] According to a third aspect of the present invention, a computer-readable storage medium is provided having computer program instructions stored thereon, which, when executed by a processor, implement the method for identifying straw burning air pollution events based on multi-source monitoring data fusion.

[0054] By adopting the above technical solution, the present invention can achieve the following technical effects:

[0055] According to this invention, air quality data, particulate matter composition data, satellite remote sensing fire point data, and meteorological data from the current moment and multiple past moments can be acquired to determine judgment characteristic values, thereby setting judgment conditions for straw burning pollution events and determining whether a straw burning pollution event has occurred at the location of a candidate air quality monitoring station. It can integrate multi-source monitoring data to achieve near real-time automated and intelligent identification and accurate early warning of straw burning pollution events, reducing monitoring costs and manpower input, avoiding the subjectivity and lag of manual interpretation, and improving the accuracy and timeliness of straw burning pollution event identification, providing effective decision support for environmental management departments. Furthermore, based on air quality data from multiple air quality monitoring stations, particulate matter composition data from particulate matter composition monitoring stations, satellite remote sensing fire point data, and meteorological data from multiple meteorological monitoring stations, candidate air quality monitoring stations can be determined, and then first and second related stations can be screened, providing basic data for determining judgment characteristic values. When determining whether a straw burning pollution event has occurred at the location of a candidate air quality monitoring station, the system can automatically acquire, process, and identify data. By combining multiple dimensions, including air quality data, particulate matter composition data, satellite remote sensing fire point data, and meteorological data, several criteria for judging straw burning pollution events have been established. This allows for a more accurate distinction between straw burning and other fires, improving the efficiency and accuracy of straw burning pollution event identification. It also reduces reliance on a single data source and the need for extensive manual interpretation, effectively lowering monitoring costs, saving significant human resources, and enhancing the objectivity and timeliness of identification. Furthermore, based on these multiple criteria, different alarm levels can be determined, enabling early warnings and facilitating rapid response and effective pollution control measures. The system also creates a list of straw burning pollution events. Automated identification and continuous monitoring of these events help environmental departments understand the patterns, duration, and impact range of straw burning, improving the accuracy and timeliness of event identification and providing effective decision support for environmental management departments.

[0056] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Other features and aspects of the invention will become clearer from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart illustrating a method for identifying air pollution events caused by straw burning based on multi-source monitoring data fusion according to an embodiment of the present invention is shown.

[0059] Figure 2 An exemplary flowchart for determining the feature value according to an embodiment of the present invention is shown;

[0060] Figure 3 A block diagram of a straw burning air pollution event identification system based on multi-source monitoring data fusion according to an embodiment of the present invention is shown as an example. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0063] Figure 1 An exemplary flowchart illustrates a method for identifying air pollution events caused by straw burning based on multi-source monitoring data fusion according to an embodiment of the present invention. The method includes:

[0064] Step S1: Obtain air quality data from multiple air quality monitoring stations at the current time and at multiple past times, particulate matter composition data from particulate matter composition monitoring stations, satellite remote sensing fire point data, and meteorological data from multiple meteorological monitoring stations.

[0065] Step S2: Based on the air quality data from multiple air quality monitoring stations, determine the candidate air quality monitoring stations;

[0066] Step S3: Based on the location information of the candidate air quality monitoring stations, select the first associated station from the particulate matter component monitoring stations and the second associated station from the meteorological monitoring stations;

[0067] Step S4: Determine the judgment characteristic value based on the air quality data of the candidate air quality monitoring stations, the particulate matter composition data of the first associated station, the meteorological data of the second associated station, and the satellite remote sensing fire point data;

[0068] Step S5: Based on the judgment characteristic values, set the judgment conditions for straw burning pollution events;

[0069] Step S6: Based on the judgment characteristic value and the judgment conditions for straw burning pollution events, determine whether a straw burning pollution event has occurred at the location of the candidate air quality monitoring station.

[0070] The straw burning air pollution event identification method based on multi-source monitoring data fusion according to embodiments of the present invention can acquire air quality data, particulate matter composition data, satellite remote sensing fire point data, and meteorological data at the current time and multiple past times, determine judgment characteristic values, thereby setting judgment conditions for straw burning pollution events, and then determining whether a straw burning pollution event has occurred at the location of a candidate air quality monitoring station. It can fuse multi-source monitoring data to achieve near real-time automated and intelligent identification and accurate early warning of straw burning pollution events, reducing monitoring costs and manpower input, avoiding the subjectivity and lag of manual interpretation, improving the accuracy and timeliness of straw burning pollution event identification, and providing effective decision support for environmental management departments.

[0071] Example 1:

[0072] According to an embodiment of the present invention, in step S1, air quality data from multiple air quality monitoring stations, particulate matter composition data from particulate matter composition monitoring stations, satellite remote sensing fire point data, and meteorological data from multiple meteorological monitoring stations are acquired at the current time and at multiple past times. For example, at the current time and at each of the past six times, the concentration of air quality indicators (e.g., PM2.5) collected by multiple air quality monitoring stations within a specified jurisdiction is acquired once. 2.5 PM 10 The air quality data includes the concentrations of sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone. The particulate matter composition data includes the concentrations of various particulate matter (e.g., potassium ions or potassium element, organic matter, sulfate, nitrate, ammonium salts, etc.) collected from multiple particulate matter monitoring stations at each time point. The meteorological data includes the meteorological information (e.g., wind direction, humidity, wind speed, etc.) collected from multiple meteorological monitoring stations at each time point. The interval between adjacent time points can be 1 hour. The satellite remote sensing fire point data includes the number of fire points detected by various satellites (e.g., MODIS, NOAA-20, NOAA-21, NPP, etc.) within a specified range (e.g., the monitoring range of the aforementioned air quality monitoring stations, particulate matter monitoring stations, and meteorological monitoring stations), limiting the fire point ground cover type to straw fire points.

[0073] Example 2:

[0074] According to an embodiment of the present invention, in step S2, determining candidate air quality monitoring stations based on air quality data from multiple air quality monitoring stations includes: obtaining the PM2.5 concentration at the current time for each air quality monitoring station. 2.5 Air quality data; if the current PM2.5 level is... 2.5 If the air quality data exceeds a first preset threshold, the air quality monitoring station is designated as a candidate air quality monitoring station. The PM2.5 concentration at each air quality monitoring station is automatically acquired at any given time. 2.5 Air quality data (i.e., PM2.5) 2.5 (concentration), if the PM2.5 concentration at the air quality monitoring station at the current time 2.5 The air quality data is greater than a first preset threshold (e.g., PM2.5). 2.5 With a moderate pollution threshold of 115 μg / m³, this air quality monitoring station can be considered a potential high-pollution location (i.e., a location where straw burning pollution may exist nearby), and can be identified as a candidate air quality monitoring station.

[0075] Example 3:

[0076] According to an embodiment of the present invention, in step S3, based on the location information of the candidate air quality monitoring stations, a first associated station is selected from the particulate matter monitoring stations, and a second associated station is selected from the meteorological monitoring stations. This includes: setting a search range with the location information of the candidate air quality monitoring stations as the center and a first preset length as the radius; determining the particulate matter monitoring stations within the search range as the first associated stations, and determining the meteorological monitoring stations within the search range as the second associated stations. The circular range with the location information (e.g., latitude and longitude) of the candidate air quality monitoring stations as the center and a first preset length (e.g., 50 kilometers) as the radius is the search range. The particulate matter monitoring station within the search range that is closest to the candidate air quality monitoring station is determined as the first associated station, and the meteorological monitoring station within the search range that is closest to the candidate air quality monitoring station is determined as the second associated station. Since multiple particulate matter monitoring stations and meteorological monitoring stations may exist within the search area of ​​the candidate air quality monitoring stations, each candidate air quality monitoring station may have multiple first-related stations and second-related stations. In subsequent calculations, the average data collected from multiple first-related stations and the average data collected from multiple second-related stations can be used for calculations, or the data collected from the nearest first-related station and the nearest second-related station can be used for calculations. Based on the same processing method, the first-related stations and second-related stations of each candidate air quality monitoring station can be determined to determine the judgment characteristic value.

[0077] In this way, based on air quality data from multiple air quality monitoring stations, particulate matter composition data from particulate matter composition monitoring stations, satellite remote sensing fire point data, and meteorological data from multiple meteorological monitoring stations, candidate air quality monitoring stations can be identified, and then the first and second associated stations can be selected, providing basic data for determining the judgment characteristic values.

[0078] Example 4:

[0079] Figure 2 An exemplary flowchart for determining the feature value according to an embodiment of the present invention is shown.

[0080] According to an embodiment of the present invention, in step S4, the judgment feature value includes a first judgment feature value, a second judgment feature value, a third judgment feature value, a fourth judgment feature value, a fifth judgment feature value, and a sixth judgment feature value; the judgment feature value is determined based on the air quality data of the candidate air quality monitoring station, the particulate matter composition data of the first associated station, the meteorological data of the second associated station, and the satellite remote sensing fire point data, including: step S41, obtaining the PM2.5 concentration of the candidate air quality monitoring station at the current time. 2.5 Air quality data and PM 10 The ratio of the air quality data of the first associated station to the PM2.5 concentration at the current time is used as the first judgment feature value; Step S42: Obtain the particulate matter composition data of potassium ions at the current time of the first associated station, and the arithmetic mean of the particulate matter composition data of potassium ions at the current time and multiple past times; Step S43: Use the ratio of the particulate matter composition data of potassium ions at the current time to the arithmetic mean of the particulate matter composition data of potassium ions at the current time and multiple past times as the second judgment feature value; Step S44: Obtain the particulate matter composition data of organic matter at the current time of the first associated station, and the ratio of the particulate matter composition data of organic matter at the current time of the candidate air quality monitoring station to the PM2.5 concentration at the current time of the candidate air quality monitoring station. 2.5 The ratio of the air quality data is used as the third judgment feature value; step S45, obtain the sum of the particulate matter composition data of sulfate, nitrate and ammonium salt at the current time of the first associated station, and compare it with the PM2.5 data at the current time of the candidate air quality monitoring station. 2.5 The ratio of air quality data is used as the fourth judgment feature value; Step S46: Obtain the arithmetic mean of humidity data in the meteorological data of the second associated station at the current time and multiple past times, as the fifth judgment feature value; Step S47: Determine the wind direction of the candidate air quality monitoring station according to the second associated station, and determine the wind direction range according to the wind direction; Step S48: Determine the number of satellite remote sensing fire points within the wind direction range on the current day and the previous day according to satellite remote sensing fire point data, as the sixth judgment feature value.

[0081] According to an embodiment of the present invention, in step S41, the PM2.5 concentration at the current time of the candidate air quality monitoring station is... 2.5Air quality data (i.e., PM2.5) 2.5 (concentration) and PM 10 Air quality data (i.e., PM2.5) 10 The ratio of the concentration of particulate matter to the concentration of particulate matter (PM2.5) is used as the first criterion. Since straw burning primarily releases fine particulate matter (PM2.5), the concentration of particulate matter is determined by the ratio of PM2.5 to the concentration of particulate matter (PM3.5). 2.5 Therefore, the first judgment feature value can be used to determine whether a straw burning pollution event has occurred.

[0082] According to an embodiment of the present invention, in step S42, the particulate matter composition data of potassium ions at the current time of the first associated station, and the arithmetic mean of the particulate matter composition data of potassium ions at the current time and at multiple past times are obtained. The particulate matter composition data of potassium ions at the current time of the first associated station (i.e., the concentration of potassium ions or potassium element) and the arithmetic mean of the particulate matter composition data of potassium ions (i.e., the concentration of potassium ions or potassium element) corresponding to the current time and at multiple past times (i.e., the current time and six past times) are obtained. When there are multiple first associated stations, the particulate matter composition data of potassium ions at the current time can be the arithmetic mean of the particulate matter composition data of potassium ions at each of the first associated stations at the current time. Similarly, when there are multiple first associated stations, the arithmetic mean of the particulate matter composition data of potassium ions at each of the first associated stations at the current time and at multiple past times can be calculated, and the arithmetic mean of the arithmetic mean of the particulate matter composition data of potassium ions at the current time and at multiple past times can be averaged to obtain the arithmetic mean of the particulate matter composition data of potassium ions at the current time and at multiple past times. Alternatively, take the particulate matter composition data of potassium ions at the current moment from the first associated station that is closest to the candidate air quality monitoring station, as well as the arithmetic mean of the particulate matter composition data of potassium ions at the current moment and multiple past moments.

[0083] According to an embodiment of the present invention, in step S43, the ratio of the particulate matter composition data of potassium ions at the current moment to the arithmetic mean of the particulate matter composition data of potassium ions at the current moment and at multiple past moments is used as a second judgment feature value. Since straw burning releases potassium ions or potassium elements, the second judgment feature value can be used to determine whether there is an abnormal increase in the concentration of potassium ions or potassium elements, thereby determining whether a straw burning pollution event has occurred.

[0084] According to an embodiment of the present invention, in step S44, the particulate matter composition data of organic matter at the current time of the first associated station is obtained, and compared with the PM2.5 data of the candidate air quality monitoring station at the current time. 2.5The ratio of the air quality data to the PM2.5 concentration at the current moment is used as the third judgment feature value. Similar to obtaining the particulate matter composition data of potassium ions at the current moment, if multiple first associated stations exist, the particulate matter composition data of organic matter at the current moment can be the arithmetic mean of the particulate matter composition data of organic matter (i.e., the concentration of organic matter) of each first associated station at the current moment, or it can be the particulate matter composition data of organic matter collected by the first associated station closest to the candidate air quality monitoring station. The particulate matter composition data of organic matter at the current moment is compared with the PM2.5 concentration at the candidate air quality monitoring station at the current moment. 2.5 Air quality data (i.e., PM2.5) 2.5 The ratio of the concentration of ( ) is the third judgment characteristic value.

[0085] According to an embodiment of the present invention, in step S45, the sum of the particulate matter composition data of sulfate, nitrate, and ammonium salts at the current time of the first associated station is obtained, and then compared with the PM2.5 concentration at the current time of the candidate air quality monitoring station. 2.5 The ratio of the air quality data to the current particulate matter data is used as the fourth judgment feature value. Similar to obtaining the particulate matter composition data of potassium ions at the current moment, if multiple first associated stations exist, the sum of the particulate matter composition data of sulfate, nitrate, and ammonium salts at the current moment can be the sum of the arithmetic mean of the particulate matter composition data of sulfate, nitrate, and ammonium salts at the current moment from each of the first associated stations, or it can be the sum of the particulate matter composition data of sulfate, nitrate, and ammonium salts collected at the current moment from the first associated station closest to the candidate air quality monitoring station. The sum of the particulate matter composition data of sulfate, nitrate, and ammonium salts at the current moment is then compared with the PM2.5 concentration at the candidate air quality monitoring station at the current moment. 2.5 Air quality data (i.e., PM2.5) 2.5 The ratio of the concentration of ( ) is the fourth judgment characteristic value.

[0086] According to an embodiment of the present invention, in step S46, the arithmetic mean of the humidity data from the current time and multiple past time periods of the meteorological data of the second associated station is obtained as the fifth judgment feature value. Similar to obtaining the particulate matter composition data of potassium ions at the current time, when multiple second associated stations exist, the arithmetic mean of the humidity data from the multiple second associated stations at each time period can be obtained, and the average of the arithmetic mean at multiple time periods is obtained as the fifth judgment feature value. Alternatively, the arithmetic mean of the humidity data from the second associated station closest to the candidate air quality monitoring station at the current time and multiple past time periods can be used as the fifth judgment feature value.

[0087] According to an embodiment of the present invention, in step S47, the wind direction of the candidate air quality monitoring station is determined based on the second associated station, and the wind direction range is determined based on the wind direction. The wind direction of the candidate air quality monitoring station is monitored through the second associated station (i.e., the meteorological monitoring station). For example, it is determined which of the eight directions (east, north, south, west, northeast, southwest, southeast, and northwest) the candidate air quality monitoring station belongs to. Then, the corresponding range of the circular range with a radius of 50-100 kilometers, with the candidate air quality monitoring station as the origin, is divided into eight fan-shaped ranges (each fan-shaped range corresponds to one of the above eight directions, and the central angle of each fan-shaped range is 45 degrees) as the wind direction range of the candidate air quality monitoring station.

[0088] According to an embodiment of the present invention, in step S48, the number of satellite remote sensing fire points within the wind direction range on the current day and the previous day is determined based on satellite remote sensing fire point data, and is used as the sixth judgment feature value. The number of fire points detected by multiple satellites within the wind direction range at the current moment on the current day and the previous day is the sixth judgment feature value. This is used to determine whether straw burning exists within the upwind range of the candidate air quality monitoring station, thereby assisting in determining the alarm level of a straw burning pollution event.

[0089] Example 5:

[0090] According to an embodiment of the present invention, in step S5, the judgment conditions for straw burning pollution events are set based on the judgment feature values, including setting multiple judgment conditions for straw burning pollution events in the following manner: judgment condition A for straw burning pollution events: the first judgment feature value is greater than the first proportional threshold; judgment condition B for straw burning pollution events: the particulate matter component data of potassium ions at the current time of the first associated site is greater than the first concentration threshold, and the second judgment feature value is greater than the second proportional threshold; judgment condition C for straw burning pollution events: the fourth judgment feature value is less than the fourth proportional threshold; judgment condition D for straw burning pollution events: the third judgment feature value is greater than the third proportional threshold; judgment condition E for straw burning pollution events: the fifth judgment feature value is less than the second preset threshold; judgment condition F for straw burning pollution events: the sixth judgment feature value is greater than 0.

[0091] According to an embodiment of the present invention, when the first judgment feature value is greater than the first proportional threshold, the PM2.5 detected by the candidate air quality monitoring station is... 2.5 With PM 10 If the concentration ratio is higher than the first proportion threshold (e.g., 0.85), the proportion of fine particulate matter released from combustion is relatively high, which is consistent with the characteristics of straw burning products. Therefore, the first judgment characteristic value is greater than the first proportion threshold, which is the judgment condition A for the straw burning pollution event.

[0092] According to an embodiment of the present invention, when the particulate matter composition data of potassium ions at the current moment of the first associated station is greater than the first concentration threshold and the second judgment characteristic value is greater than the second proportion threshold, the concentration of either potassium ions or potassium element collected by the first associated station of the candidate air quality monitoring station at the current moment is greater than or equal to the first concentration threshold (e.g., 1 μg / m³), and the ratio of the concentration at the current moment to the arithmetic mean of the concentration at the current moment and the concentration at multiple past moments is greater than or equal to the second proportion threshold (e.g., 1.5), indicating that the concentration of potassium ions or potassium element is high and there is an abnormal increase in concentration, which is consistent with the characteristics of straw burning products. Therefore, the particulate matter composition data of potassium ions at the current moment of the first associated station is greater than the first concentration threshold and the second judgment characteristic value is greater than the second proportion threshold, which is the straw burning pollution event judgment condition B.

[0093] According to an embodiment of the present invention, when the fourth judgment characteristic value is less than the fourth proportional threshold, the inorganic salts (i.e., sulfates, nitrates, and ammonium salts) of the first associated station of the candidate air quality monitoring station are related to PM2.5. 2.5 The concentration ratio is less than the fourth proportion threshold (e.g., 0.3), and the proportion of non-inorganic salt components in the particulate matter is small, which is consistent with the characteristics of straw burning products. Therefore, the fourth judgment characteristic value is less than the fourth proportion threshold, which is the judgment condition C for the straw burning pollution event.

[0094] According to an embodiment of the present invention, when the third judgment characteristic value is greater than the third proportional threshold, the organic matter concentration collected by the first associated station of the candidate air quality monitoring station is related to PM2.5 concentration. 2.5 If the concentration ratio is greater than the third proportion threshold (e.g., 0.4), the particulate matter has a high organic carbon content, which is consistent with the characteristics of biomass combustion and can increase the confidence of the occurrence of straw burning events. Therefore, the third judgment characteristic value is greater than the third proportion threshold, which is the judgment condition D of the straw burning pollution event.

[0095] According to an embodiment of the present invention, when the fifth judgment feature value is less than the second preset threshold, the average relative humidity of the second associated site at the current time and at multiple past times is lower than the second preset threshold (e.g., 60%), the air is relatively dry, which is conducive to straw burning and can increase the confidence of straw burning events. Therefore, the fifth judgment feature value being less than the second preset threshold is the straw burning pollution event judgment condition E.

[0096] According to an embodiment of the present invention, when the sixth judgment feature value is greater than 0, if there are satellite remote sensing fire points of the type of fire point being straw fire points within the wind direction range of the candidate air quality monitoring station on the same day and yesterday, the confidence of the occurrence of straw burning events can be increased. Therefore, the sixth judgment feature value being greater than 0 is the judgment condition F for the straw burning pollution event.

[0097] Example 6:

[0098] According to an embodiment of the present invention, in step S6, based on the judgment characteristic value and the judgment condition for straw burning pollution events, it is determined whether a straw burning pollution event has occurred at the location of the candidate air quality monitoring station. This includes: if at least one of the judgment condition A, judgment condition B, and judgment condition C for straw burning pollution events is satisfied, then it is determined that a straw burning pollution event has occurred. When at least one of the judgment conditions A, B, and C for straw burning pollution events is satisfied, there is a situation where the proportion of fine particulate matter in the combustion release products is high, or there is a situation where the concentration of potassium ions or potassium element is high and the concentration is abnormally increased, or there is a situation where the proportion of non-inorganic salt components in the particulate matter is high. These conditions are consistent with the characteristics of straw burning products, and it can be determined that a straw burning pollution event has occurred at the corresponding air quality monitoring station. Of course, there may be situations where the above-mentioned judgment conditions A, B, and C for straw burning pollution events are satisfied simultaneously.

[0099] In this way, data can be automatically acquired, processed, and identified. By combining multiple dimensions such as air quality data, particulate matter composition data, satellite remote sensing fire point data, and meteorological data, multiple judgment conditions for straw burning pollution events are determined, thereby determining whether a straw burning pollution event has occurred. This more accurately distinguishes straw burning from other fires, improves the efficiency and accuracy of straw burning pollution event identification, reduces dependence on a single data source and the need for a large amount of manual interpretation, effectively reduces monitoring costs, saves a lot of human resources, and improves the objectivity and timeliness of identification.

[0100] Example 7:

[0101] According to an embodiment of the present invention, the method further includes: determining the alarm level based on the number of times that the judgment conditions D, E, and F of a straw burning pollution event are met. For example, when all three judgment conditions D, E, and F are met simultaneously, the straw burning pollution event can be considered a high-confidence event, and the alarm level can be set to level one. When any one or any two of the three judgment conditions D, E, and F are met simultaneously, the straw burning pollution event can be considered a medium-confidence event, and the alarm level can be set to level two. When none of the three judgment conditions D, E, and F are met, the straw burning pollution event can be considered a low-confidence event, and the alarm level can be set to level three. Different alarm levels can be addressed with different alarm methods to quickly determine the confidence level of the straw burning pollution event and take appropriate action.

[0102] Example 8:

[0103] According to an embodiment of the present invention, the method further includes: creating a list of straw burning pollution events; determining whether straw burning pollution events occurred at the location of the straw burning pollution event at multiple past times; if the location of the straw burning pollution event is continuous between the current time and the past times of straw burning pollution events, merging the straw burning pollution events at multiple times; determining the duration of the merged straw burning pollution event; if the location of the straw burning pollution event is not continuous between the current time and the past times of straw burning pollution events, creating a new straw burning pollution event in the list of straw burning pollution events; and outputting the list of straw burning pollution events.

[0104] According to an embodiment of the present invention, a list of straw burning pollution events is created to store identified straw burning pollution events. The stored straw burning pollution events may include information such as air quality monitoring stations, event start time, event end time, and various judgment characteristic values. The process involves determining whether the air quality monitoring station where the straw burning pollution event occurred at the current time also experienced straw burning pollution events at multiple past times. If the current time and past straw burning pollution events at the same monitoring station are consecutive (e.g., the interval between the current time and past straw burning pollution events at the same monitoring station is less than or equal to 1 hour), it can be considered a continuous straw burning pollution event. In this case, the straw burning pollution events at the current time and those at multiple past times are merged. Furthermore, the duration between the first time a straw burning pollution event was identified at the monitoring station and the current time is determined as the merged duration of the straw burning pollution event. For example, if the first time a straw burning pollution event was identified at a certain monitoring station was 24:00 two days ago, and the current time is 24:00 today, the merged duration of the straw burning pollution event is 48 hours. Through this process, the merged duration of the straw burning pollution event is updated every hour.

[0105] According to an embodiment of the present invention, if the air quality monitoring station where a straw burning pollution incident has occurred is not consecutive between the current time and the time when a straw burning pollution incident occurred in the past (for example, the time interval between the current time and the time when a straw burning pollution incident occurred in the past is greater than 1 hour, or no straw burning pollution incident has occurred at that location in the past), the straw burning pollution incident at the current time can be considered a newly occurring straw burning pollution incident. A new straw burning pollution incident can be created in the straw burning pollution incident list, with the start time of the straw burning pollution incident being the current hour. Through the above processing method, the straw burning pollution incident list can be updated once every hour, and the straw burning pollution incident list can be output. Furthermore, consecutive straw burning pollution incidents and new straw burning pollution incidents can be alerted in different ways.

[0106] This approach allows for the determination of different alert levels based on multiple criteria for identifying straw burning pollution events, enabling early warnings to be issued at the initial stage of an event. This facilitates rapid response and the implementation of effective pollution control measures. Furthermore, the creation of a list of straw burning pollution events, along with the automated identification and continuous monitoring of these events, helps environmental departments understand the patterns, duration, and scope of straw burning, improving the accuracy and timeliness of event identification and providing effective decision support for environmental management departments.

[0107] The straw burning air pollution event identification method based on multi-source monitoring data fusion according to embodiments of the present invention can acquire air quality data, particulate matter composition data, satellite remote sensing fire point data, and meteorological data at the current time and multiple past times, determine judgment feature values, thereby setting judgment conditions for straw burning pollution events, and then determining whether a straw burning pollution event has occurred at the location of a candidate air quality monitoring station. It can fuse multi-source monitoring data to achieve near real-time automated and intelligent identification and accurate early warning of straw burning pollution events, reducing monitoring costs and manpower input, avoiding the subjectivity and lag of manual interpretation, and improving the accuracy and timeliness of straw burning pollution event identification, providing effective decision support for environmental management departments. Furthermore, based on air quality data from multiple air quality monitoring stations, particulate matter composition data from particulate matter composition monitoring stations, satellite remote sensing fire point data, and meteorological data from multiple meteorological monitoring stations, candidate air quality monitoring stations can be determined, and then first and second associated stations can be screened, providing basic data for determining judgment feature values. When determining whether a straw burning pollution event has occurred at the location of a candidate air quality monitoring station, the system can automatically acquire, process, and identify data. By combining multiple dimensions, including air quality data, particulate matter composition data, satellite remote sensing fire point data, and meteorological data, several criteria for judging straw burning pollution events have been established. This allows for a more accurate distinction between straw burning and other fires, improving the efficiency and accuracy of straw burning pollution event identification. It also reduces reliance on a single data source and the need for extensive manual interpretation, effectively lowering monitoring costs, saving significant human resources, and enhancing the objectivity and timeliness of identification. Furthermore, based on these multiple criteria, different alarm levels can be determined, enabling early warnings and facilitating rapid response and effective pollution control measures. The system also creates a list of straw burning pollution events. Automated identification and continuous monitoring of these events help environmental departments understand the patterns, duration, and impact range of straw burning, improving the accuracy and timeliness of event identification and providing effective decision support for environmental management departments.

[0108] Example 9:

[0109] Figure 3 An exemplary block diagram of a straw burning air pollution event identification system based on multi-source monitoring data fusion according to an embodiment of the present invention is shown, the system comprising:

[0110] The acquisition module acquires air quality data from multiple air quality monitoring stations, particulate matter composition data from particulate matter composition monitoring stations, satellite remote sensing fire point data, and meteorological data from multiple meteorological monitoring stations at the current time and multiple past times.

[0111] The candidate site module determines candidate air quality monitoring sites based on air quality data from multiple air quality monitoring stations.

[0112] The associated site module filters the first associated site from the particulate matter component monitoring sites and the second associated site from the meteorological monitoring sites based on the location information of the candidate air quality monitoring sites.

[0113] The feature value determination module determines the feature values ​​based on the air quality data of the candidate air quality monitoring stations, the particulate matter composition data of the first associated station, the meteorological data of the second associated station, and the satellite remote sensing fire point data.

[0114] The judgment condition module sets the judgment conditions for straw burning pollution events based on the judgment characteristic values;

[0115] The judgment module determines whether a straw burning pollution event has occurred at the location of the candidate air quality monitoring station based on the judgment characteristic value and the judgment conditions for straw burning pollution events.

[0116] According to an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions, when executed by a processor, implement the method for identifying straw burning air pollution events based on multi-source monitoring data fusion.

[0117] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0118] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and any variations or modifications may be made to the implementation of the present invention without departing from the stated principles.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying air pollution events caused by straw burning based on multi-source monitoring data fusion, characterized in that, include: Acquire air quality data from multiple air quality monitoring stations at the current time and multiple past times, particulate matter composition data from particulate matter composition monitoring stations, satellite remote sensing fire point data, and meteorological data from multiple meteorological monitoring stations; Based on air quality data from multiple air quality monitoring stations, candidate air quality monitoring stations were determined. Based on the location information of the candidate air quality monitoring stations, the first associated stations were selected from the particulate matter component monitoring stations, and the second associated stations were selected from the meteorological monitoring stations. Based on the air quality data from the candidate air quality monitoring stations, the particulate matter composition data from the first associated station, the meteorological data from the second associated station, and the satellite remote sensing fire point data, the judgment characteristic values ​​are determined. Based on the characteristic values, the criteria for judging straw burning pollution events are set. Based on the judgment characteristic value and the judgment conditions for straw burning pollution events, determine whether a straw burning pollution event has occurred at the location of the candidate air quality monitoring station; The judgment feature values ​​include the first judgment feature value, the second judgment feature value, the third judgment feature value, the fourth judgment feature value, the fifth judgment feature value, and the sixth judgment feature value; Based on air quality data from candidate air quality monitoring stations, particulate matter composition data from the first associated station, meteorological data from the second associated station, and satellite remote sensing fire point data, the judgment characteristic values ​​are determined, including: Obtain the current PM2.5 concentration at the candidate air quality monitoring stations. 2.5 Air quality data and PM 10 The ratio of air quality data is used as the first judgment characteristic value; Obtain the particulate matter composition data of potassium ions at the current time of the first associated site, as well as the arithmetic mean of the particulate matter composition data of potassium ions at the current time and multiple past times. The ratio of the particulate matter composition data of potassium ions at the current moment to the arithmetic mean of the particulate matter composition data of potassium ions at the current moment and at multiple past moments is used as the second judgment feature value. Obtain the particulate matter composition data of organic matter at the current time from the first associated monitoring station, and compare it with the PM2.5 data of the current time from the candidate air quality monitoring stations. 2.5 The ratio of air quality data is used as the third judgment characteristic value; The sum of the particulate matter composition data (sulfate, nitrate, and ammonium) at the current time of the first associated monitoring station is obtained, and then compared with the PM2.5 at the current time of the candidate air quality monitoring stations. 2.5 The ratio of air quality data is used as the fourth judgment characteristic value; Obtain the arithmetic mean of the humidity data from the current time and multiple past time periods of the meteorological data of the second associated station, and use it as the fifth judgment feature value; Based on the second associated site, determine the wind direction of the candidate air quality monitoring site, and determine the wind direction range based on the wind direction; Based on satellite remote sensing fire data, the number of satellite remote sensing fire points within the specified wind direction range on the current day and the previous day is determined and used as the sixth judgment feature value.

2. The method for identifying straw burning air pollution events based on multi-source monitoring data fusion according to claim 1, characterized in that, Based on air quality data from multiple air quality monitoring stations, candidate air quality monitoring stations were identified, including: Obtain the current PM2.5 concentration at each air quality monitoring station. 2.5 Air quality data; If the current time is PM 2.5 If the air quality data is greater than the first preset threshold, the air quality monitoring station will be identified as a candidate air quality monitoring station.

3. The method for identifying straw burning air pollution events based on multi-source monitoring data fusion according to claim 1, characterized in that, Based on the location information of the candidate air quality monitoring stations, first-related stations were selected from the particulate matter component monitoring stations, and second-related stations were selected from the meteorological monitoring stations, including: Use the location information of the candidate air quality monitoring stations as the center and the first preset length as the radius to set the search range; The particulate matter monitoring stations within the search range were identified as the first associated stations, and the meteorological monitoring stations within the search range were identified as the second associated stations.

4. The method for identifying straw burning air pollution events based on multi-source monitoring data fusion according to claim 1, characterized in that, Based on the characteristic values, the criteria for judging straw burning pollution events are set, including: Multiple criteria for judging straw burning pollution events are set as follows: Criterion A for judging straw burning pollution incidents: The first judgment characteristic value is greater than the first proportional threshold; Criterion B for judging straw burning pollution incidents: The particulate matter component data of potassium ions at the first associated site at the current moment is greater than the first concentration threshold, and the second judgment characteristic value is greater than the second proportion threshold. Criterion for judging straw burning pollution incidents: The fourth judgment characteristic value is less than the fourth proportion threshold; Criterion D for judging straw burning pollution incidents: The third judgment characteristic value is greater than the third proportion threshold; Criterion E for judging straw burning pollution incidents: The fifth judgment characteristic value is less than the second preset threshold; Criterion F for judging straw burning pollution incidents: The sixth criterion characteristic value is greater than 0.

5. The method for identifying straw burning air pollution events based on multi-source monitoring data fusion according to claim 4, characterized in that, Based on the characteristic values ​​and the criteria for judging straw burning pollution events, determine whether a straw burning pollution event has occurred at the location of the candidate air quality monitoring station, including: If at least one of the following conditions for determining a straw burning pollution event is met: A, B, or C, then a straw burning pollution event is determined to have occurred.

6. The method for identifying straw burning air pollution events based on multi-source monitoring data fusion according to claim 5, characterized in that, The method further includes: The alarm level is determined based on the number of conditions D, E, and F that are met in the judgment of straw burning pollution events.

7. The method for identifying straw burning air pollution events based on multi-source monitoring data fusion according to claim 1, characterized in that, The method further includes: Create a list of straw burning pollution incidents; Determine whether the location of the straw burning pollution incident occurred at any of the past times; If the location of a straw burning pollution event is consecutive to the location of a past straw burning pollution event, then straw burning pollution events at multiple times will be merged. Determine the duration of straw burning pollution incidents after the merger; If the location of a straw burning pollution event is not consecutive with the location of a past straw burning pollution event, a new straw burning pollution event is created in the list of straw burning pollution events. Output a list of straw burning pollution incidents.

8. A straw burning air pollution event identification system based on multi-source monitoring data fusion, used to execute the method as described in any one of claims 1-7, characterized in that, include: The acquisition module acquires air quality data from multiple air quality monitoring stations, particulate matter composition data from particulate matter composition monitoring stations, satellite remote sensing fire point data, and meteorological data from multiple meteorological monitoring stations at the current time and multiple past times. The candidate site module determines candidate air quality monitoring sites based on air quality data from multiple air quality monitoring stations. The associated site module filters the first associated site from the particulate matter component monitoring sites and the second associated site from the meteorological monitoring sites based on the location information of the candidate air quality monitoring sites. The feature value determination module determines the feature values ​​based on the air quality data of the candidate air quality monitoring stations, the particulate matter composition data of the first associated station, the meteorological data of the second associated station, and the satellite remote sensing fire point data. The judgment condition module sets the judgment conditions for straw burning pollution events based on the judgment characteristic values; The judgment module determines whether a straw burning pollution event has occurred at the location of the candidate air quality monitoring station based on the judgment characteristic value and the judgment conditions for straw burning pollution events.

9. A computer-readable storage medium, characterized in that, It stores computer program instructions that, when executed by a processor, implement the method of any one of claims 1-7.

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