Low-altitude unmanned aerial vehicle environment monitoring and pollution early warning system

Abstract

The invention discloses a low-altitude unmanned aerial vehicle environment monitoring and pollution early warning system, relates to the technical field of environment monitoring, and solves the problem that the accuracy of original pollution early warning is not accurate enough as an unmanned aerial vehicle cannot search a corresponding abnormal specific area according to the spatial characteristics of a point location. According to the method, related data collected by the low-altitude unmanned aerial vehicle are analyzed, space abnormal points are locked based on an analysis result, and feature points with the highest pollution concentration are confirmed one by one based on the positions of the space abnormal points in a mode of constructing a space circle and a spherical area, so that the pollution concentration of the low-altitude unmanned aerial vehicle is determined. According to the method and the system, correlation confirmation is carried out step by step until the corresponding abnormal circle area is determined, by adopting the confirmation mode, the corresponding area with the most serious pollution concentration can be quickly and effectively locked, pollution early warning is carried out based on the determined area, the specific accuracy of pollution early warning can be guaranteed, and a better pollution concentration early warning effect can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a low-altitude unmanned aerial vehicle environmental monitoring and pollution early warning system. Background Art

[0002] By controlling low-altitude drones, the drones can detect pollutant gases in the atmosphere, including but not limited to sulfur dioxide (SO2), nitrogen oxides (NO x ), carbon monoxide (CO), ozone (O3) and volatile organic compounds (VOCs); the concentration levels of these pollutants are crucial to assessing air quality. For example, in an urban environment, a gas sensor carried by a low-altitude drone can accurately monitor NO around a busy traffic intersection. x Concentration changes.

[0003] The application with publication number CN106643916A discloses a drone environment monitoring system and a monitoring method. The drone environment monitoring system includes a flight control module, a navigation module, a data acquisition module, an environment detection module, an optical camera, a barometric altimeter, a memory, a wireless transmission module, and a ground workstation installed on the drone; the data acquisition module is connected to the environment detection module, and the data acquisition module is also connected to the GPS and the barometric altimeter, and is also connected to the memory; the optical camera stores the image in the memory; the wireless transmission module is connected to the ground workstation through a wireless communication network, and the ground workstation controls the drone, receives environmental data, geographic data, and image data, and performs storage and analysis processing. The monitoring system realizes the rapid, comprehensive, and accurate monitoring function of the environmental monitoring area, and improves the efficiency of monitoring and the real-time and intuitive nature of monitoring.

[0004] In actual application, the UAV environmental monitoring system generally controls the UAV to fly according to a preset flight route, and then analyzes the air pollution situation at relevant spatial points based on the air data collected during the flight, and issues warnings based on the corresponding analysis results. However, the original method can only target the relevant air data of the set collection points. The pollution concentration of such collection points does not belong to the most serious area, and the UAV cannot find the specific area corresponding to the abnormality based on the spatial characteristics of the points. Therefore, the accuracy of the original pollution warning is not accurate enough and cannot achieve better pollution warning effects. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a low-altitude UAV environmental monitoring and pollution warning system, which solves the problem that the UAV is unable to find the specific area corresponding to the abnormality based on the spatial characteristics of the point, resulting in the original pollution warning being not accurate enough.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a low-altitude UAV environment monitoring and pollution early warning system, comprising:

[0007] The drone data real-time analysis terminal performs real-time analysis on the air data collected by low-altitude drones and determines the spatial abnormal points based on the real-time analysis results. The specific methods are as follows:

[0008] Process the air data generated by the collection node in real time: check the corresponding collection items in the air data with the preset standard values, and calibrate the values ​​of the corresponding collection items as S i , calibrate the preset standard value of the corresponding collection item as B i , where i represents different collection items;

[0009] Using S i -B i =GX i Lock the associated value. If GX i ≤0, the currently determined GX i Recorded as 0;

[0010] It is proposed that i = 1, 2, ..., n, where n represents the total number of collection items, and YC i =GX1×C1+GX2×C2+……+GX n ×Cn locks the abnormal value YC of the corresponding acquisition node i , where C1, C2, ..., Cn are preset factors associated with the corresponding collection items;

[0011] The outlier YC i Verify with the preset value Y1, where Y1 is the preset evaluation value. i ≤Y1, no calibration is performed. If YC i >Y1, mark this acquisition node as a spatial abnormal point, and transmit this spatial abnormal point to the abnormal point positioning terminal;

[0012] The abnormal point positioning terminal, based on the confirmed spatial abnormal point, locks the spatial coordinate point of this spatial abnormal point in the preset three-dimensional space. The specific method is as follows:

[0013] Based on the spatial coordinate point of the determined spatial abnormal point, a group of spatial circles is generated, the spatial circle is parallel to the horizontal plane of the three-dimensional space, and the spatial circle is a preset circle, and its internal radius is a preset value. The spatial coordinate point is the center of the spatial circle, and the low-altitude drone is controlled to collect air data of different spatial points in the spatial circle, and the abnormal values ​​corresponding to each different spatial point are confirmed one by one, and the maximum value is selected from the confirmed groups of abnormal values, and the spatial point corresponding to the maximum value is marked as the first feature point;

[0014] Taking the first characteristic point as the center point, a group of spherical areas are determined, and the spherical radius of the spherical area is a preset value, and the low-altitude drone is controlled to collect air data of different spatial points in the spherical area and confirm the abnormal values, and the maximum value is locked from the abnormal values ​​corresponding to several different spatial points, and the second characteristic point is determined;

[0015] Then, the second feature point is used as the center of the circle to construct a space circle again. Similarly, the subsequent feature points are confirmed one by one. When the feature points confirmed subsequently are consistent with the feature points confirmed in the previous set of confirmation processes, the circumference area associated with the space circle where the feature point is located is marked as the abnormal circumference area within the area where the spatial abnormal point is located. After the abnormal circumference area of ​​the spatial abnormal point is confirmed, the drone is made to collect data according to the original flight route, and the subsequent spatial abnormal points and the corresponding abnormal circumference areas are associated and confirmed;

[0016] The abnormal circular areas of several spatial abnormal points confirmed during this flight are transmitted to the different-area integration processing terminal;

[0017] The circumference control analysis end performs circumference confirmation based on the spatial coordinate points of the locked spatial abnormal point, and performs associated confirmation of the characteristic points within the confirmed circumference. Based on the gradually confirmed characteristic points, the abnormal circumference area within the area where the spatial abnormal point is located is locked;

[0018] The heterogeneous area integration processing end associates and integrates the abnormal circular areas with the same spatial characteristics based on the several groups of abnormal circular areas confirmed during the flight of this low-altitude drone, and transmits the integrated set of circular areas with the same characteristics and the corresponding abnormal circular areas to the regional analysis and warning end:

[0019] Based on the confirmed groups of abnormal circular areas, the specific positions of the abnormal circular areas are locked in the three-dimensional space, and the single group of abnormal circular areas are randomly moved vertically up and down to assess whether there are other abnormal circular areas intersecting with the moved abnormal circular areas. If they intersect, the intersecting areas are integrated so that the groups of abnormal circular areas intersecting are merged into a set of circular areas with the same characteristics. If they do not intersect, no processing is performed;

[0020] The regional analysis and warning terminal, based on the confirmed set of circular areas with the same characteristics or a single abnormal circular area, comprehensively evaluates the abnormal values ​​associated with the spatial points in the set of circular areas with the same characteristics or a single abnormal circular area, and determines whether to generate a regional warning signal based on the evaluation results:

[0021] For the confirmed single abnormal circular area, the abnormal values ​​associated with several spatial points in the abnormal circular area are confirmed, and the confirmed several groups of abnormal values ​​are averaged, and the verification mean is locked. If the verification mean is greater than Y2, a regional warning signal for the abnormal circular area is generated and displayed through the display terminal, where Y2 is a preset value. If the verification mean is ≤Y2, no signal is generated, and the abnormal circular area can be directly displayed through the display terminal;

[0022] For a set of circular areas with the same characteristics, the abnormal values ​​associated with several spatial points of several abnormal circular areas in this set are confirmed, and several groups of abnormal values ​​are averaged to determine the verification mean. If the verification mean is greater than Y2, a regional warning signal for this set of circular areas with the same characteristics is generated and displayed through the display end, where Y2 is a preset value. If the verification mean is ≤Y2, no signal is generated, and several abnormal circular areas in this set of circular areas with the same characteristics can be directly displayed through the display end.

[0023] Preferably, the low-altitude UAV performs flight collection according to a preset flight route, and a plurality of equidistant collection nodes are synchronously preset in the flight route.

[0024] Preferably, different abnormal circular areas within the same characteristic circular set all carry the same characteristic mark, and the characteristic mark is a preset mark, and different same characteristic circular sets correspond to different characteristic marks.

[0025] The present invention provides a low-altitude UAV environment monitoring and pollution early warning system. Compared with the prior art, it has the following beneficial effects:

[0026] The present invention analyzes the relevant data collected by the low-altitude drone, and based on the analysis results, locks the spatial abnormal points, and then based on the location of the spatial abnormal points, by constructing spatial circles and spherical areas, confirms the characteristic points with the highest pollution concentration one by one, and gradually performs associated confirmation until the corresponding abnormal circle area is determined. By adopting this confirmation method, the corresponding area with the most serious pollution concentration can be quickly and effectively locked, and pollution warning can be carried out based on the determined area, which can ensure the specific accuracy of the pollution warning and achieve a better pollution concentration warning effect;

[0027] Subsequently, the abnormal circle areas with similar spatial features are integrated to confirm their corresponding sets, and then the numerical characteristics of the relevant sets are comprehensively evaluated and warned. This can avoid duplication of pollution warnings and improve the overall accuracy of pollution warnings, thereby achieving better pollution warning effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic diagram of the principle framework of the present invention;

[0029] Figure 2 It is a schematic diagram for determining the abnormal circumferential area of ​​the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] First embodiment

[0032] See also Figure 1 , the present application provides a low-altitude UAV environmental monitoring and pollution warning system, including a UAV data real-time analysis terminal, an abnormal point positioning terminal, a circumferential control analysis terminal, a different-area integration processing terminal, a regional analysis and warning terminal, and a display terminal, wherein the UAV data real-time analysis terminal is electrically connected to the abnormal point positioning terminal or the circumferential control analysis terminal input node, and the abnormal point positioning terminal, the circumferential control analysis terminal, the different-area integration processing terminal, the regional analysis and warning terminal, and the display terminal are electrically connected from the output node to the input node;

[0033] Among them, the drone data real-time analysis end performs real-time analysis on the air data collected by the low-altitude drone, and the low-altitude drone performs flight collection according to the preset flight route, and a number of equidistant collection nodes are preset in the flight route. Based on the real-time analysis results, the spatial anomaly points are determined, and the determined spatial anomaly points are transmitted to the anomaly point positioning end. Specifically, when the low-altitude drone performs environmental monitoring, it flies based on the preset flight route and stops at each different collection node. The collection nodes are all preset in the flight route. Based on the set collection logic, the air data of the collection nodes are collected and analyzed. The air data includes sulfur dioxide (SO2) content, nitrogen oxides (NO x ) content, volatile organic compounds (VOCs) content, etc. The specific method for determining the spatial abnormal points is:

[0034] Process the air data generated by the collection node in real time: check the corresponding collection items in the air data with the preset standard values, and calibrate the values ​​of the corresponding collection items as S i , calibrate the preset standard value of the corresponding collection item as B i , where i represents different collection items, such as sulfur dioxide content, nitrogen oxide content, etc.;

[0035] Using S i -B i =GX iLock the associated value. If GX i ≤0, the currently determined GX i Recorded as 0, for example: that is, GX i When it is a negative number, in order to affect its subsequent specific confirmation, this negative number is recorded as 0 to prevent it from affecting the subsequent overall numerical determination;

[0036] It is proposed that i = 1, 2, ..., n, where n represents the total number of collection items, and YC i =GX1×C1+GX2×C2+……+GX n ×Cn locks the abnormal value YC of the corresponding acquisition node i , where C1, C2, ..., Cn are all preset factors associated with the corresponding collection items, which are prepared in advance by the operator;

[0037] The outlier YC i Verify with the preset value Y1, where Y1 is the preset evaluation value, which is prepared in advance by the operator. i ≤Y1, no calibration is performed. If YC i >Y1, mark this acquisition node as a spatial abnormal point, and transmit this spatial abnormal point to the abnormal point positioning terminal;

[0038] Specifically, after the drone arrives at the designated collection node, it will stop at the corresponding collection node to complete the air data collection process at the corresponding node. The air data includes several collection items, which correspond to different air pollutant content data. Each content data has a corresponding evaluation standard. Based on the overall numerical analysis results of several air data and several evaluation standards, the corresponding outlier value can be locked, and then the anomaly judgment of the corresponding node can be made based on the determined outlier value.

[0039] Among them, the abnormal point positioning end, based on the confirmed spatial abnormal point, locks the spatial coordinate point of this spatial abnormal point in the preset three-dimensional space, and transmits the determined spatial coordinate point to the circumference control analysis end. Specifically, the three-dimensional space is constructed in advance by the operator, which is consistent with the area where the corresponding low-altitude UAV is flying and detecting. The flight route of the low-altitude UAV can be reflected in this three-dimensional space, and the corresponding spatial coordinate point can be determined based on the confirmed spatial abnormal point, so as to perform relevant calibration on the spatial abnormal point;

[0040] The circumference control analysis end performs circumference confirmation based on the spatial coordinate points of the locked spatial anomaly points, and performs association confirmation of the characteristic points within the confirmed circumference. Based on the gradually confirmed characteristic points, the abnormal circumference area in the area where the spatial anomaly point is located is locked. Specifically, in order to find a more accurate air pollution area, it is necessary to perform step-by-step analysis based on the determined spatial anomaly points. Here, the regional confirmation method is adopted to confirm the air pollution conditions involved in different associated areas. From the confirmation results, a more accurate abnormal circumference area is locked. The specific method of locking the abnormal circumference area is as follows:

[0041] Based on the spatial coordinate point of the determined spatial abnormal point, a group of spatial circles is generated, the spatial circle is parallel to the horizontal plane of the three-dimensional space, and the spatial circle is a preset circle, and its internal radius is a preset value. The spatial coordinate point is the center of the spatial circle, and the low-altitude drone is controlled to collect air data of different spatial points in the spatial circle, and the abnormal values ​​corresponding to each different spatial point are confirmed one by one, and the maximum value is selected from the confirmed groups of abnormal values, and the spatial point corresponding to the maximum value is marked as the first feature point;

[0042] Taking the first characteristic point as the center point, a group of spherical areas are determined, and the spherical radius of the spherical area is a preset value, and the low-altitude drone is controlled to collect air data of different spatial points in the spherical area and confirm the abnormal values, and the maximum value is locked from the abnormal values ​​corresponding to several different spatial points, and the second characteristic point is determined;

[0043] Then, take the second feature point as the center of the circle, and construct the space circle again. Similarly, confirm the subsequent feature points one by one. When the subsequent confirmed feature points are consistent with the feature points confirmed in the previous set of confirmation processes (for example, if feature point A is proposed, the corresponding space circle is constructed through A first, and the maximum value of the abnormal value is locked in the space circle, thereby locking the corresponding feature point. If the feature point selected this time is still A, it means that the confirmed feature point has not changed. Similarly, if the feature point is A, and the spherical area is constructed, the confirmed feature point in this spherical area is also A, then the feature point confirmed this time is consistent with the feature point confirmed last time, that is, no change has occurred, then the corresponding A is the point with the largest abnormal value in the two previous and subsequent confirmation processes, then the currently confirmed area is the area with the largest degree of abnormality), the circumferential area associated with the space circle where this feature point is located is marked as the abnormal circumferential area in the area where this spatial abnormal point is located. After the abnormal circumferential area of ​​this spatial abnormal point is confirmed, the drone is enabled to collect data according to the original flight route, and the subsequent spatial abnormal points and the corresponding abnormal circumferential areas are associated and confirmed. For example: combined with Figure 2, determine a group of corresponding spatial anomaly points in the three-dimensional space, lock the spatial circle about this point based on the confirmed spatial anomaly point, then analyze several spatial points inside the spatial circle, lock the corresponding first feature point, based on the confirmed first feature point, take the first feature point as the center of the circle, and construct a group of corresponding spherical areas, and then confirm the second feature point again in the spherical area and confirm the spatial circle of the second feature point, and stop when the subsequent confirmed feature points are repeated. This processing method can gradually analyze and confirm the associated area with the highest degree of abnormality based on the confirmed spatial anomaly point;

[0044] The abnormal circular areas of several spatial abnormal points confirmed during this flight are transmitted to the different-area integration processing terminal;

[0045] Specifically, each different spatial anomaly point will confirm a group of abnormal circular areas. Therefore, in the actual flight collection and processing process, the low-altitude UAV can confirm the spatial anomaly points one by one. After confirming each group of spatial anomaly points, the abnormal circular area associated with the spatial anomaly point will be confirmed, and then the unfinished flight collection task will be executed, and the subsequent different spatial anomaly points will be confirmed one by one. At the end of the flight, several groups of abnormal circular areas will be obtained.

[0046] Second embodiment

[0047] This embodiment is a further embodiment of the first embodiment, and its main execution end is a different-area integration processing end, which is used to perform specific integration analysis on the confirmed groups of abnormal circumferential areas;

[0048] Among them, the heterogeneous area integration processing end, based on several groups of abnormal circular areas confirmed during the flight of this low-altitude UAV, associates and integrates the abnormal circular areas with the same spatial characteristics, and transmits the integrated set of circular areas with the same characteristics and the corresponding abnormal circular areas to the regional analysis and early warning end. The specific method of association and integration is:

[0049] Based on the confirmed groups of abnormal circular areas, the specific positions of the abnormal circular areas are locked in the three-dimensional space, and the single group of abnormal circular areas are randomly moved vertically up and down to assess whether there are other abnormal circular areas intersecting with the moved abnormal circular areas. If they intersect, the intersecting areas are integrated so that the groups of abnormal circular areas intersecting are merged into a set of circular areas with the same characteristics. If they do not intersect, no processing is performed;

[0050] Different abnormal circumferential areas within the same characteristic circumferential set all have the same characteristic mark, and the characteristic mark is a preset mark, which is prepared in advance by the operator, and different same characteristic circumferential sets correspond to different characteristic marks;

[0051] Specifically, for the confirmed abnormal circular areas with the same characteristics, it means that such areas are basically of different heights, but the spatial coverage is relatively the same, so they are basically produced by the same pollution source. It is only because of the influence of the corresponding wind force that multiple different abnormal circular areas are confirmed, but the spatial characteristics of multiple different abnormal circular areas are relatively consistent.

[0052] Third embodiment

[0053] In the specific implementation process, this embodiment is a further embodiment of the above two groups of embodiments, which mainly focuses on the abnormal analysis process of the abnormal circumference area, and is associated and executed by the regional analysis and warning end;

[0054] Among them, the regional analysis and warning end, based on the confirmed set of circular areas with the same characteristics or a single abnormal circular area, comprehensively evaluates the abnormal values ​​associated with the spatial points in the set of circular areas with the same characteristics or a single abnormal circular area, and determines whether to generate a regional warning signal based on the evaluation result and displays it through the display end. The specific sub-steps of comprehensively evaluating the associated abnormal values ​​are:

[0055] For the confirmed single abnormal circular area, the abnormal values ​​associated with several spatial points in the abnormal circular area are confirmed, and the confirmed groups of abnormal values ​​are averaged, and the verification mean is locked. If the verification mean is greater than Y2, a regional warning signal for the abnormal circular area is generated and displayed through the display terminal, where Y2 is a preset value, and its specific value is determined by the operator based on experience. If the verification mean is ≤Y2, no signal is generated, and the abnormal circular area can be directly displayed through the display terminal;

[0056] For a set of circular areas with the same characteristics, the abnormal values ​​associated with several spatial points of several abnormal circular areas in the set are confirmed, and several groups of abnormal values ​​are averaged to determine the verification mean. If the verification mean is greater than Y2, a regional warning signal for the set of circular areas with the same characteristics is generated and displayed through the display terminal. If the verification mean is ≤Y2, no signal is generated, and several abnormal circular areas in the set of circular areas with the same characteristics can be directly displayed through the display terminal.

[0057] Specifically, when the corresponding abnormal circular area is confirmed, the abnormal values ​​associated with each internal spatial point are all confirmed. Therefore, based on the corresponding confirmation process, the associated verification mean can be locked, and then the abnormality can be confirmed through the corresponding verification mean to complete the corresponding pollution warning process.

[0058] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0059] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. Low-altitude UAV environmental monitoring and pollution warning system, characterized by: include: The drone data real-time analysis terminal performs real-time analysis on the air data collected by low-altitude drones and determines spatial abnormal points based on the real-time analysis results; The abnormal point positioning terminal, based on the confirmed spatial abnormal point, locks the spatial coordinate point of the spatial abnormal point in a preset three-dimensional space; The circumference control analysis end performs circumference confirmation based on the spatial coordinate points of the locked spatial abnormal point, and performs associated confirmation of the characteristic points within the confirmed circumference. Based on the gradually confirmed characteristic points, the abnormal circumference area within the area where the spatial abnormal point is located is locked; The heterogeneous area integration processing end associates and integrates the abnormal circular areas with the same spatial characteristics based on the several groups of abnormal circular areas confirmed during the flight of the low-altitude UAV, and transmits the integrated set of circular areas with the same characteristics and the corresponding abnormal circular areas to the regional analysis and warning end; The regional analysis and warning end, based on the confirmed set of circular areas with the same characteristics or a single abnormal circular area, comprehensively evaluates the abnormal values ​​associated with the spatial points in the set of circular areas with the same characteristics or a single abnormal circular area, and determines whether to generate a regional warning signal based on the evaluation results.

2. The low-altitude UAV environment monitoring and pollution early warning system according to claim 1 is characterized in that: The low-altitude UAV performs flight collection according to a preset flight route, and a plurality of equidistant collection nodes are synchronously preset in the flight route.

3. The low-altitude UAV environment monitoring and pollution early warning system according to claim 2 is characterized in that: The specific method of determining the spatial abnormal point by the drone data real-time analysis terminal is as follows: Process the air data generated by the collection node in real time: check the corresponding collection items in the air data with the preset standard values, and calibrate the values ​​of the corresponding collection items as S i , calibrate the preset standard value of the corresponding collection item as B i , where i represents different collection items; Using S i -B i =GX i Lock the associated value. If GX i ≤0, the currently determined GX i Recorded as 0; It is proposed that i = 1, 2, ..., n, where n represents the total number of collection items, and YC i =GX1×C1+GX2×C2+……+GX n ×Cn locks the abnormal value YC of the corresponding acquisition node i , where C1, C2, ..., Cn are preset factors associated with the corresponding collection items; The outlier YC i Verify with the preset value Y1, where Y1 is the preset evaluation value. i ≤Y1, no calibration is performed. If YC i >Y1, mark this acquisition node as a spatial anomaly point, and transmit this spatial anomaly point to the anomaly point positioning terminal.

4. The low-altitude UAV environment monitoring and pollution early warning system according to claim 3 is characterized in that: The specific method of the abnormal point positioning end to lock the abnormal circumferential area is: Based on the spatial coordinate point of the determined spatial abnormal point, a group of spatial circles is generated, the spatial circle is parallel to the horizontal plane of the three-dimensional space, and the spatial circle is a preset circle, and its internal radius is a preset value. The spatial coordinate point is the center of the spatial circle, and the low-altitude drone is controlled to collect air data of different spatial points in the spatial circle, and the abnormal values ​​corresponding to each different spatial point are confirmed one by one, and the maximum value is selected from the confirmed groups of abnormal values, and the spatial point corresponding to the maximum value is marked as the first feature point; Taking the first characteristic point as the center point, a group of spherical areas are determined, and the spherical radius of the spherical area is a preset value, and the low-altitude drone is controlled to collect air data of different spatial points in the spherical area and confirm the abnormal values, and the maximum value is locked from the abnormal values ​​corresponding to several different spatial points, and the second characteristic point is determined; Then, the second feature point is used as the center of the circle to construct a space circle again. Similarly, the subsequent feature points are confirmed one by one. When the feature points confirmed subsequently are consistent with the feature points confirmed in the previous set of confirmation processes, the circumference area associated with the space circle where the feature point is located is marked as the abnormal circumference area within the area where the spatial abnormal point is located. After the abnormal circumference area of ​​the spatial abnormal point is confirmed, the drone is made to collect data according to the original flight route, and the subsequent spatial abnormal points and the corresponding abnormal circumference areas are associated and confirmed; The abnormal circular areas of several spatial abnormal points confirmed during this flight are transmitted to the different area integration processing terminal.

5. The low-altitude UAV environment monitoring and pollution early warning system according to claim 4 is characterized in that: The specific method of associating and integrating the abnormal circular areas with the same spatial characteristics at the heterogeneous area integration processing end is as follows: Based on the confirmed groups of abnormal circular areas, the specific positions of the abnormal circular areas are locked in three-dimensional space, and a single group of abnormal circular areas is randomly moved vertically up and down to assess whether there are other abnormal circular areas intersecting with the moved abnormal circular areas. If so, the intersecting areas are integrated so that the intersecting groups of abnormal circular areas are merged into a set of circular areas with the same characteristics. If not, no processing is performed.

6. The low-altitude UAV environment monitoring and pollution early warning system according to claim 5 is characterized in that: Different abnormal circular areas within the same characteristic circular set all carry the same characteristic mark, and the characteristic mark is a preset mark, and different same characteristic circular sets correspond to different characteristic marks.

7. The low-altitude UAV environment monitoring and pollution early warning system according to claim 1 is characterized in that: The regional analysis and warning terminal, for the confirmed single abnormal circular area, confirms the abnormal values ​​associated with several spatial points in this abnormal circular area, and averages the confirmed several groups of abnormal values, locks the verification mean, and if the verification mean is greater than Y2, generates a regional warning signal for this abnormal circular area and displays it through the display terminal, where Y2 is a preset value. If the verification mean is ≤Y2, no signal is generated, and the abnormal circular area can be directly displayed through the display terminal.

8. The low-altitude UAV environment monitoring and pollution early warning system according to claim 1 is characterized in that: The regional analysis and warning terminal, for a set of circular areas with the same characteristics, confirms the abnormal values ​​associated with several spatial points of several abnormal circular areas in the set, and performs mean processing on several groups of abnormal values ​​to determine the verification mean. If the verification mean is greater than Y2, a regional warning signal for this set of circular areas with the same characteristics is generated and displayed through the display terminal, where Y2 is a preset value. If the verification mean is ≤Y2, no signal is generated, and several abnormal circular areas in the set of circular areas with the same characteristics can be directly displayed through the display terminal.

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