Method for Determining Air Pollution Monitoring Locations and Index Parameters Based on Unmanned Aerial Vehicles

Abstract

The present invention discloses a method for determining the air pollution monitoring positions and index parameters based on unmanned aerial vehicles. 1) Use a ground mobile monitoring platform to monitor along the road to be measured to obtain planar data; 2) Match and correct the monitoring data; 3) After matching the corrected monitoring data with the electronic map, obtain the complete ground two-dimensional distribution data through interpolation, and then use hotspot analysis to obtain the air pollution hotspot positions in the area to be measured; 4) Use the unmanned aerial vehicle pollution monitoring platform to conduct vertical monitoring on the hotspot positions; 5) Use cluster analysis to classify each hotspot position according to the height layer, and then obtain the optimal values of various category indicators and screen the positions that need to be monitored for a long time; 6) Use analytic hierarchy process - grey relational analysis to quantify the results after clustering, and determine the height with the largest relational weight and relational degree for each index; 7) Combine the monitoring situation to determine the height layer with the largest relational degree as the position that needs to be monitored for a long time, and the index with a large weight is used as an auxiliary monitoring index.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental science monitoring, and particularly to a method for determining air pollution monitoring positions and index parameters based on unmanned aerial vehicles (UAVs). Background Art

[0002] Currently, although the regional air pollution problem has been significantly improved, the local pollution problem remains very serious. And direct exposure of residents to highly polluted positions can cause various acute and chronic diseases, which is extremely harmful to the human body. Therefore, it is imperative to improve the air quality within the city. Especially in high-emission areas such as construction sites, urban roads, and industrial parks, people will face a greater exposure risk due to the existence of specific pollution emission sources. However, at present, urban air monitoring is mainly completed by sparse urban air monitoring stations, and their small number and low resolution (1h) cannot effectively characterize the air quality of each high-emission area within the city with a wide pollution area and large fluctuations in pollution concentration. This means that it is difficult to accurately reflect the pollution exposure risks suffered by residents scattered throughout the city. Even the emerging mobile monitoring cannot achieve long-term monitoring of local positions due to its high cost, that is, it cannot react in real time to the pollution changes at specific positions. With the increase in the height of residents' activities, it is obviously insufficient to evaluate the pollution exposure risk only on a two-dimensional plane. Therefore, it is crucial to conduct long-term monitoring of these areas from a three-dimensional perspective. However, randomly selecting long-term monitoring points in these areas will not only cause waste of resources but also cannot accurately reflect the changes in air pollution in this area. This leads to inaccuracies in the assessment of the pollution exposure of the population in the measured area, and thus the control measures in this area cannot be adjusted according to the actual situation. Especially during construction periods, peak traffic hours, and adverse weather conditions, precise regulation of air pollution at specific positions requires real-time and accurate data support. At the same time, in order to reduce the monitoring cost, it is particularly important to select indicators that can fully reflect the changes in air quality in high-emission areas. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to propose a method for determining air pollution monitoring positions and index parameters based on UAVs. This solution selects long-term monitoring points that can effectively reflect the characteristics of air pollution changes in high-emission areas from a three-dimensional level according to the real-time change characteristics of high-emission areas, providing a scientific basis for long-term monitoring of the complex and changeable air pollution in high-emission areas and characterizing the change characteristics of air pollution in high-emission areas.

[0004] In order to achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0005] A method for determining air pollution monitoring positions and index parameters based on UAVs, which includes:

[0006] S01. Monitor the area to be measured, obtain the planar distribution data of each preset monitoring parameter on the ground, and generate monitoring data;

[0007] S02. Preprocess and correct the monitoring data in a preset manner;

[0008] S03. Match the corrected monitoring data with the electronic map, then obtain the two-dimensional ground data with a preset integrity through interpolation processing, and then obtain the positions of multiple hot spots corresponding to air pollution in the area to be measured through hot spot analysis;

[0009] S04. Vertically monitor the positions of the hot spots through a drone to obtain each monitoring parameter again;

[0010] S05. Classify each hot spot area according to the height layer through cluster analysis to generate a classification result, and then obtain various category indicators and their optimal values, and screen the positions that need to be monitored for a long time;

[0011] S06. Use the analytic hierarchy process to determine the weights of each index corresponding to the monitoring data, then calculate the correlation values between the index data corresponding to each height layer and the optimal values of the indexes through the grey relational analysis method, and then obtain the final correlation degree according to the weights of each index calculated by the analytic hierarchy process, and then sort the correlation degrees of each height layer to obtain the height position with the largest correlation degree;

[0012] S07. Combine the monitoring data situation of the area to be measured to determine the height layer with the largest correlation degree as the position to be monitored for a long time, and the index with the weight meeting the preset requirements as the auxiliary monitoring index.

[0013] As a possible implementation manner, further, in S01 of this solution, the monitoring data includes: one or more of air pollutant concentration data, local meteorological data, longitude and latitude coordinate data, sampling time data, moving speed data, and sampling hovering height data; among them, the index parameters corresponding to the air pollutant concentration data include: one or more of VOCs index, SO2 index, PM 2.5 、PM 10 、PM 1.0 、PM4 index; the index parameters corresponding to the local meteorological data include: one or more of wind speed, wind direction, temperature, humidity, air pressure, and dew point temperature.

[0014] As a preferred implementation method, preferably, in this solution S01, a monitoring device is carried by a vehicle to perform mobile monitoring on the area to be measured, obtaining two-dimensional plane data along the vehicle's movement route and the plane distribution data of each preset monitoring parameter corresponding to the ground in the two-dimensional plane data along the line, and generating monitoring data; in this step, the monitoring route can be planned according to the scope of the area to be measured and should cover the area to be measured as comprehensively as possible, and a high spatio-temporal resolution ground mobile monitoring platform (a vehicle carrying a monitoring device (such as a vehicle, an unmanned aerial vehicle, etc.)) is used to synchronously monitor the high-emission area along the line to obtain real-time data such as the original pollutant concentration, meteorological data, longitude and latitude, etc., and at the same time store the data for subsequent use.

[0015] As a preferred implementation method, preferably, in this solution S02, the pretreatment and correction of the monitoring data are carried out according to a preset method, including: for the obtained original monitoring data, according to the characteristics of various pollutants and the principle of the monitoring instrument, the original monitoring results of the pollutant concentration are corrected using the corresponding quantitative empirical formula combined with environmental parameters; specifically, it includes:

[0016] Obtain the monitoring data, and correct the air pollutant concentration data through the preset quantitative empirical formula corresponding to each monitoring parameter combined with environmental parameters.

[0017] Among them, the air pollutant concentration data includes PM 2.5 When it is, its pretreatment and correction method includes:

[0018] Perform humidity rejection on the collected PM 2.5 raw data, and the rejection formula is:

[0019] CF = a + b*RH 2 / (1 - RH)

[0020] Among them, CF represents the humidity calibration factor, RH represents the relative humidity, and its value ranges from 0 to 1. a and b are empirical parameters respectively, and a and b are set to 1 and 0.25 respectively;

[0021] Y = X / CF

[0022] Among them, Y represents the true value of the calibrated PM concentration, and its unit is μg / m 3 , X represents the original measured value of the PM concentration, and its unit is μg / m 3 , RH represents the relative humidity (%).

[0023] As a preferred implementation method, preferably, in this solution S03, according to the longitude and latitude coordinate data of the area to be measured, the monitoring data processed in S02 is imported into the ArcGIS software to be matched with the electronic map of the preset area. Subsequently, in ArcGIS, the Kriging interpolation method is used to obtain the two-dimensional ground data with the preset integrity and the continuous pollutant concentration data along the vehicle movement road. Then, a hot spot analysis tool is used to obtain the positions of multiple hot spot areas along the vehicle movement road. At the same time, the centroid coordinates of each hot spot area are calculated in the attribute table, and the longitude and latitude of the centroid are output, that is, the longitude and latitude of the hot spot position.

[0024] As a preferred implementation method, preferably, in this solution S04, the positions of the hot spot areas are vertically monitored by an unmanned aerial vehicle (UAV) to obtain again the monitoring parameters including:

[0025] According to the flight requirements of the UAV for the road corresponding to the position of the hot spot area and the building and terrain conditions around the road, the monitoring height range of the hot spot area is determined. Then, under the height range, the centroid position of the hot spot area is vertically monitored by the UAV to obtain again the monitoring parameters in real time;

[0026] Among them, the monitoring parameters include PM 2.5 concentration data, meteorological data, flight height and speed.

[0027] As a preferred implementation method, preferably, this solution S05 includes: performing cluster analysis on the monitoring data processed in S02 according to the height layer corresponding to the hot spot area to obtain a clustering result, and then obtaining various category indicators and their optimal values, and screening the positions that need to be monitored for a long time;

[0028] Among them, the monitoring data for performing cluster analysis includes PM 2.5 concentration data and meteorological data.

[0029] In S05, cluster analysis is performed on the vertical monitoring data of the centroid positions of each hot spot area after correction according to the height layer. When the pollutant categories at different height layers of multiple hot spot positions are clustered into one category, a representative and appropriately high monitoring position or interval can be screened according to the difficulty of installing monitoring instruments around, and at the same time, the most representative position height interval and the optimal values of each indicator (the mean value of the indicators in the clustering result) in each category of the cluster analysis are output.

[0030] As a preferred implementation method, preferably, this solution S06 includes:

[0031] Using the analytic hierarchy process to determine PM 2.5The weights of concentration data and meteorological data are then calculated, and the optimal values corresponding to each height layer in different hot spots are correlated through the grey relational analysis method. Subsequently, the final correlation degree is obtained based on the weights of each index calculated by the analytic hierarchy process method. Then, the correlation degrees of each height layer are sorted to obtain the height position with the largest correlation degree.

[0032] As an example, in S06, when using the analytic hierarchy process - grey relational analysis method to quantify the clustering results and determine the correlation weights of each index and the height layer with the largest correlation degree with the clustering results, it includes: First, use the analytic hierarchy process method to determine the weights of multiple monitoring indicators (pollutant indicators and meteorological indicators), and discard the indicators with smaller weights; Subsequently, use the grey relational analysis method to calculate the correlation values between the data of each height index and the optimal values of the indicators, and obtain the final correlation degree based on the weights calculated by the analytic hierarchy process method; Finally, sort the correlation degrees of each height layer after clustering and screening to obtain the height position with the largest correlation degree.

[0033] As an example, in S07, the location with the largest correlation degree is monitored again to verify the feasibility of the selected location, and finally the verified points are determined as long - term monitoring points. Subsequently, according to the influence weights of different influencing factors obtained by the analytic hierarchy process method, the factors with larger weights are found and monitored together with air pollutants to assist in pollution control.

[0034] As a preferred implementation method, preferably, this solution S07 also includes:

[0035] The location with the largest correlation degree is monitored again to verify the feasibility of the selected location. Finally, the verified points are determined as long - term monitoring points. Subsequently, according to the weights of different meteorological data obtained by the analytic hierarchy process method, the data with larger weights are found and monitored together with PM 2.5 to assist in pollution control.

[0036] Based on the above, the present invention also provides an air pollution monitoring method, which includes the method for determining the air pollution monitoring location and index parameters based on an unmanned aerial vehicle as described above.

[0037] Adopting the above - mentioned technical solution, compared with the prior art, the present invention has the following beneficial effects:

[0038] 1) In this solution, considering the three - dimensional distribution of air pollutants, an unmanned aerial vehicle is used to conduct three - dimensional monitoring of the monitoring area, and the obtained results can determine the best monitoring points from a three - dimensional perspective.

[0039] 2) In this solution, based on the fact that the distribution of pollutants in high - emission areas is not uniform and is easily affected by factors such as prevailing winds, pollution hot spots are selected through measured data, and then an unmanned aerial vehicle is used to find the best monitoring height, which is not only convenient to implement but also flexible in application.

[0040] 3) Based on the fact that the change in air pollutant concentration is the result of the coupling of multiple factors and the pollution degree will change with factors such as emission sources and climate, long-term monitoring points are selected through analytic hierarchy process - grey relational analysis, and the monitoring points can effectively characterize the air pollution change characteristics of this high-emission area, making them more suitable for long-term monitoring layout.

[0041] 4) This solution can conduct three-dimensional stereo monitoring of the high-emission area according to requirements, and then determine the points suitable for long-term key monitoring, which can effectively reduce the monitoring cost and improve the monitoring efficiency. At the same time, it can accurately and effectively reflect the pollution situation in this area, providing a scientific basis for relevant departments to select effective monitoring positions in the high-emission area. Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0043] Figure 1 It is a brief implementation flowchart of the solution of the present invention.

[0044] Figure 2 It is a schematic diagram of the result of applying hotspot analysis to the road as an example in the solution of the present invention.

[0045] Figure 3 It is a schematic diagram of the result of applying cluster analysis to the road as an example in the solution of the present invention. Detailed Implementation Manner

[0046] The following will further describe the present invention in detail in conjunction with the drawings and embodiments. It should be particularly noted that the following embodiments are only used to illustrate the present invention, but do not limit the scope of the present invention. Similarly, the following embodiments are only partial embodiments of the present invention rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0047] As Figure 1 shown, this embodiment includes:

[0048] 1) Use a ground mobile monitoring platform (a vehicle equipped with monitoring equipment) to monitor along the road to be measured and obtain plane data;

[0049] 2) Match and correct the monitoring data;

[0050] 3) Match the corrected monitoring data with the electronic map, and then obtain the complete two-dimensional ground distribution data through interpolation. Then, use hot spot analysis to obtain the air pollution hot spot positions in the area to be measured;

[0051] 4) Use the drone pollution monitoring platform to conduct vertical monitoring of the hot spot positions;

[0052] 5) Use cluster analysis to classify each hot spot position according to the height layer, and then obtain the optimal values of various category indicators and screen the positions that need to be monitored for a long time;

[0053] 6) Use analytic hierarchy process - grey relational analysis to quantify the results after clustering, and determine the associated weights of each indicator and the height with the largest degree of association;

[0054] 7) Combine the monitoring situation to determine the height layer with the largest degree of association as the position that needs to be monitored for a long time, and the indicators with large weights are used as auxiliary monitoring indicators.

[0055] In step 1) of the solution of this embodiment, according to the length of the road, a vehicle or a non-motor vehicle can be used to carry a ground mobile monitoring platform to conduct mobile monitoring on the roads with monitoring requirements, and two-dimensional plane data along the line can be obtained. According to the typical urban air pollutants: PM 2.5 , PM 10 and pollutant indicators such as SO2, select monitoring instruments, and execute the monitoring task according to the preset route. Synchronously monitor the real-time data such as the original pollutant concentration, meteorological data, longitude and latitude of the road along the line, and store the data for subsequent use. In this case, taking the collection of PM 2.5 in the air as an example, select long-term monitoring points for an urban expressway with a length of 2 km.

[0056] For step 2) of matching and correcting the obtained data, in this step, for the obtained original monitoring data, according to the characteristics of various pollutants and the principle of the monitoring instrument, use the corresponding quantitative empirical formula combined with environmental parameters to correct the pollutant concentration. For example, for the original data of the collected PM 2.5 , perform humidity rejection, and the rejection formula is:

[0057] CF = a + b * RH 2 / (1 - RH)

[0058] Among them, CF represents the humidity calibration factor, RH represents the relative humidity (the value range is 0 - 1), and a and b are empirical parameters (a and b are set to 1 and 0.25 respectively).

[0059] Y = X / CF

[0060] Among them, Y represents the true value of the calibrated PM concentration (μg / m 3 ), and X represents the original measured value of the PM concentration (μg / m 3), RH represents relative humidity (%).

[0061] In step 3) of this solution, the corrected data is matched with the electronic map and then the complete ground two-dimensional data is obtained through interpolation, and then the hot spot analysis is used to obtain the air pollution hot spot positions in the area to be measured. In this step, the corrected monitoring data is imported into ArcGIS according to the longitude and latitude and matched with the electronic map of the expressway area. Subsequently, the Kriging interpolation method is used in ArcGIS to obtain the continuous pollutant concentration data along the road, and then the hot spot analysis tool is used to obtain the positions of multiple hot spot areas along the expressway. The hot spot positions are as Figure 2 shown. At the same time, the centroid coordinates of each hot spot area are calculated in the attribute table, and the longitude and latitude of the centroid (hot spot position) are output.

[0062] In the electronic map matching of step S03, as an implementation example, the steps of this solution can use the map matching algorithm of the Hidden Markov Model (HMM) to match the corrected monitoring data into the preset route, then obtain the ground two-dimensional data through interpolation, use the hot spot analysis to obtain multiple hot spot areas on the preset route, and output the longitude and latitude of the centroid (hot spot position) of each hot spot area.

[0063] In step 4) of this solution, a drone pollution monitoring platform is used to conduct vertical monitoring of each hot spot position. In this step, the monitoring height range is determined according to the flight requirements of civilian drones in the area where the road is located and the environmental conditions such as surrounding buildings and terrain of the road. Then, within this height range, the drone is used to conduct vertical monitoring at the centroid position, and real-time PM 2.5 concentration data, corresponding meteorological data, flight height, speed and other indicators are obtained again, and the pollutant concentration, flight height, meteorological data, etc. are matched.

[0064] In step 4), the monitoring range can be 5 - 500 m, which can be determined according to the air traffic control requirements and the actual situation on the ground. Then, the drone is used to conduct vertical monitoring at the hot spot positions to obtain air pollutant indicators and meteorological indicators, and the indicators obtained at each position are matched and corrected. As an example, in this step, the vertical monitoring height range can be determined according to the flight control height and the surrounding building conditions. For example, it can fly to the height level with the roadside buildings within the legal range, and then use the building as the 0 m height, with each 5 m as a monitoring point, up to 500 m for vertical monitoring. Or the vertical height can also be selected according to the specific situation. When conducting vertical monitoring, the drone rises slowly so that sufficient data can be obtained in each height interval. Then, the obtained data is matched and corrected.

[0065] Based on this step, it can also include:

[0066] 4.1) The PM 2.5Humidity rejection is performed using the calibration method in step 2) of data usage.

[0067] In step 5) of this solution, cluster analysis is used to classify each hot spot location according to height, and then the optimal values of various category indicators are obtained and the locations that need to be monitored for a long time are screened. In this step, according to the height layer, the PM 2.5 concentration and corresponding meteorological data vertically monitored at the centroid of each corrected hot spot area are subjected to cluster analysis, and the clustering results are as Figure 2 shown. When hot spot 1 (10 - 15m) and hot spot 3 (20m) in category 1 are clustered into one category, hot spot 1 (10 - 15m) with a lower height will be selected as the monitoring height range. At the same time, the data of hot spot 2 (20 - 25m), hot spot 2 (15 - 20m), and hot spot 1 (25m) in the other three categories of cluster analysis and the optimal values of these four category indicators (i.e., the mean values of each indicator in these four clustering results) will also be output.

[0068] In step 6) of this solution, analytic hierarchy process - grey relational analysis is used to quantify the results after clustering, and then the height with the largest correlation weight and correlation degree of each indicator is determined. Combining Figure 3 as shown, in this step, the analytic hierarchy process is used to determine the weights of PM 2.5 concentration and meteorological data respectively, and then grey relational analysis is used to calculate the relational values of hot spot 1 (10 - 15m), hot spot 2 (20 - 25m), hot spot 2 (15 - 20m), and hot spot 1 (25m) with the optimal values of category 1, category 2, category 3, and category 4 respectively. Then, according to the weights calculated by the analytic hierarchy process, the final correlation degree is obtained. Finally, the correlation degrees of each height layer are sorted to obtain the height position with the largest correlation degree.

[0069] In step 7) of this solution, the height layer with the largest correlation degree is determined as the location that needs to be monitored for a long time in combination with the monitoring situation, and the indicators with large weights are auxiliary monitoring indicators. In this step, the location with the largest correlation degree is monitored again to verify the feasibility of the selected location, and finally the verified point is determined as the long - term monitoring location. Subsequently, according to the weights of different meteorological data obtained by the analytic hierarchy process, the data with larger weights is found and monitored together with PM 2.5 to assist in pollution control.

[0070] The above are only some embodiments of the present invention, and thus do not limit the protection scope of the present invention. Any equivalent device or equivalent process transformation made using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for determining the air pollution monitoring location and index parameters based on an unmanned aerial vehicle, characterized in that, It includes: S01. Mount a monitoring device on a vehicle to conduct mobile monitoring on the area to be measured, obtain the two-dimensional plane data along the vehicle's movement route, and the plane distribution data of each preset monitoring parameter on the ground corresponding to the two-dimensional plane data along the route, and generate monitoring data; S02. Preprocess and correct the monitoring data in a preset manner; S03. Import the monitoring data processed in S02 into ArcGIS software according to the longitude and latitude coordinate data of the area to be measured and match it with the electronic map of the preset area. Then, use the Kriging interpolation method in ArcGIS to obtain the two-dimensional ground data with a preset integrity and the continuous pollutant concentration data along the vehicle's movement road. Then, use the hot spot analysis tool to obtain the positions of multiple hot spot areas along the vehicle's movement road. At the same time, calculate the centroid coordinates of each hot spot area in the attribute table and output the longitude and latitude of the centroid, that is, the longitude and latitude of the hot spot position; S04. Conduct vertical monitoring on the hot spot area positions through a drone to obtain each monitoring parameter again; S05. Conduct cluster analysis on the monitoring data processed in S02 according to the height layer corresponding to the hot spot area to obtain a cluster result, and then obtain various category indicators and their optimal values, and screen the positions that need to be monitored for a long time; S06. Use the analytic hierarchy process to determine the weights of each index corresponding to the monitoring data. Then, use the grey relational analysis method to calculate the relational values between the index data corresponding to each height layer and the optimal values of the indexes. Then, obtain the final relational degree according to the weights of each index calculated by the analytic hierarchy process. Then, sort the relational degrees of each height layer to obtain the height position with the largest relational degree; S07. Determine the height layer with the largest relational degree as the position that needs to be monitored for a long time in combination with the monitoring data situation of the area to be measured, and the indexes with weights meeting the preset requirements are auxiliary monitoring indexes.

2. The method for determining the air pollution monitoring location and index parameters based on an unmanned aerial vehicle according to claim 1, wherein In S01, the monitoring data includes one or more of: air pollutant concentration data, local meteorological data, longitude and latitude coordinate data, sampling time data, moving speed data, and sampling hovering height data; Among them, the index parameters corresponding to the air pollutant concentration data include: VOCs index, SO2 index, PM 2.5 , PM 10 , PM 1.0 , and more than one of the PM4 index; The index parameters corresponding to the local meteorological data include one or more of: wind speed, wind direction, temperature, humidity, air pressure, dew point temperature.

3. The method for determining the air pollution monitoring location and index parameters based on an unmanned aerial vehicle according to claim 2, wherein S02. Preprocessing and correcting the monitoring data in a preset manner includes: Obtain the monitoring data, and correct the air pollutant concentration data through the preset quantitative empirical formula corresponding to each monitoring parameter in combination with the environmental parameters; Among them, the air pollutant concentration data includes PM 2.5 When, its preprocessing and calibration methods include: For the collected PM 2.5 raw data, humidity rejection is performed, and the rejection formula is: CF = a + b * RH 2 / (1 - RH) Among them, CF represents the humidity calibration factor, RH represents the relative humidity, and its value ranges from 0 to 1. a and b are empirical parameters, and a and b are set to 1 and 0.25 respectively; Y = X / CF Among them, Y represents the true value of the calibrated PM concentration, and its unit is μg / m 3 , X represents the original measured value of the PM concentration, and its unit is μg / m 3 , and RH represents the relative humidity (%).

4. The method for determining the air pollution monitoring location and index parameters based on an unmanned aerial vehicle according to claim 2, wherein S04. Conduct vertical monitoring on the hot spot area positions through a drone to obtain each monitoring parameter again includes: Determine the monitoring height range of the hot spot area according to the flight requirements of the drone for the road corresponding to the hot spot area position and the surrounding buildings and terrain conditions of the road. Then, under the height range, conduct vertical monitoring on the centroid position of the hot spot area through the drone to obtain each monitoring parameter in real time again; Among them, the monitoring parameters include PM 2.5 concentration data, meteorological data, flight altitude, and speed.

5. The method for determining the air pollution monitoring location and index parameters based on an unmanned aerial vehicle according to claim 4, wherein In S05, the monitoring data for cluster analysis includes PM 2.5 concentration data and meteorological data.

6. The method for determining the air pollution monitoring location and index parameters based on an unmanned aerial vehicle according to claim 5, wherein S06 includes: The analytic hierarchy process is used to determine the weights of PM 2.5 concentration data and meteorological data respectively. Then, through the grey relational analysis method, the correlation values of the optimal values corresponding to each height layer in different hot spots are calculated. Subsequently, the final correlation degree is obtained according to the weights of each index calculated by the analytic hierarchy process. Finally, the correlation degrees of each height layer are sorted to obtain the height position with the largest correlation degree.

7. The method for determining the air pollution monitoring location and index parameters based on an unmanned aerial vehicle according to any one of claims 1 to 6, characterized in that, S07 also includes: Monitor the position with the highest correlation again to verify the feasibility of the selected position. Finally, determine the verified points as long-term monitoring points. Then, according to the weights of different meteorological data obtained by the analytic hierarchy process, find the data with relatively large weights and monitor them together with PM 2.5 to assist in pollution control.

8. An air pollution monitoring method, characterized in that, It includes the method for determining the air pollution monitoring position and index parameters based on a drone according to any one of claims 1 to 7.

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