Remote online intelligent analysis method for pollutants

Abstract

This invention provides a remote online intelligent analysis method for pollutants, which utilizes a swarm of sub-UAVs to synchronously track and detect pollutants that have been emitted into the atmospheric environment. This method can obtain data on the diffusion and distribution of pollutants in the atmospheric environment, thereby improving the accuracy and reliability of remote pollutant analysis and ensuring timely early warning of the diffusion coverage of pollutants in the atmospheric environment.

Description

Technical Field

[0001] This invention relates to the technical field of intelligent pollutant analysis, and in particular to a remote online intelligent analysis method for pollutants. Background Technology

[0002] Steel mills and waste incineration plants generate large amounts of polluting exhaust gases during operation. These gases are typically emitted directly into the atmosphere through chimneys, continuously spreading and causing air pollution in the affected areas. Current methods for detecting air pollutants involve installing particulate sensors inside the chimneys to directly detect the composition and concentration of pollutants emitted. However, these methods cannot provide real-time, continuous tracking of pollutants already emitted into the atmosphere, nor can they comprehensively identify and analyze the spread and coverage of pollutants in the atmosphere. This reduces the accuracy and reliability of remote pollutant analysis and hinders timely early warning of the extent of pollutant spread in the atmosphere. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a remote online intelligent analysis method for pollutants. First, a main UAV captures images of pollutants emitted from emission sources, and then analyzes these images via a ground base station to obtain the three-dimensional spatial distribution information of the pollutants. Next, based on this distribution information, all sub-UAVs in a swarm are controlled to perform distributed detection of pollutants. Simultaneously, each sub-UAV collects airflow information about its location to estimate the pollutant diffusion trend. Based on this diffusion trend, the flight status of the sub-UAVs is adjusted accordingly. The ground base station receives and analyzes the pollutant status data collected by all sub-UAVs throughout the entire flight process in real time, thereby obtaining information on the pollutant's changing status over a predetermined time period. By using a swarm of sub-UAVs to synchronously track and detect pollutants already emitted into the atmosphere, this method can obtain data on the diffusion and distribution of pollutants in the atmosphere, thus improving the accuracy and reliability of remote pollutant analysis and ensuring timely early warning of the pollutant diffusion coverage in the atmosphere.

[0004] This invention provides a remote online intelligent analysis method for pollutants, comprising the following steps:

[0005] Step S1: Instruct the main UAV to fly in the vicinity of the pollutant emission source, and collect images of pollutants emitted by the pollutant emission source during the flight, and send the pollutant images to the ground base station; The ground base station analyzes and processes the pollutant images to determine the distribution status information of pollutants emitted by the pollutant emission source.

[0006] Step S2: Based on the pollutant distribution status information, determine the distribution status information of pollutants emitted by the pollutant emission source in three-dimensional space; based on the distribution status information, determine several initial pollutant detection spatial points; based on the location information of all initial pollutant detection spatial points, instruct several sub-drones of the sub-drone swarm to fly to the corresponding initial pollutant detection spatial points to perform pollutant detection operations.

[0007] Step S3: During the pollutant detection process of the sub-drone, the sub-drone is instructed to collect airflow status information at its current location; the airflow status information collected by all sub-drones is analyzed and processed by the ground base station to estimate the diffusion trend of pollutants; based on the diffusion trend, the flight status of each sub-drone is adjusted, and pollutant status data collected by all sub-drones throughout the entire flight process is acquired in real time.

[0008] Step S4: The pollutant status data is analyzed and processed by the ground base station to obtain the pollutant change status information within a future preset time period.

[0009] Furthermore, in step S1, instructing the main UAV to fly in the area near the pollutant emission source, and during the flight to collect images of pollutants emitted by the pollutant emission source, and then sending the pollutant images to the ground base station specifically includes:

[0010] The main UAV is instructed to first hover in an area within a preset distance range from the emission outlet of the pollutant emission source, and scan and photograph the area near the emission outlet during the hovering flight to obtain a corresponding panoramic image of the pollutants; then the panoramic image of the pollutants is sent to the ground base station in real time.

[0011] Furthermore, in step S1, the analysis and processing of the pollutant image by the ground base station to determine the distribution status information of pollutants emitted by the pollutant emission source specifically includes:

[0012] After performing pixel grayscale processing and edge pixel sharpening processing on the panoramic image of pollutants by the ground base station, the boundary contour information of pollutants is identified from the panoramic image of pollutants.

[0013] Based on the boundary contour information, the existence information of pollutants on the boundary surface in three-dimensional space in the area near the emission outlet is determined, and this is used as the pollutant distribution status information.

[0014] Further, in step S2, based on the pollutant distribution status information, the distribution status information of the pollutants emitted by the pollutant emission source in three-dimensional space is determined; based on the distribution status information, determining several initial pollutant detection spatial points specifically includes:

[0015] Based on the information about the existence of pollutants at the boundary surface in three-dimensional space in the vicinity of the emission outlet, determine the location and area of ​​the boundary surface in three-dimensional space for the pollutants at the vicinity of the emission outlet.

[0016] Then, based on the location and area of ​​the pollutants on the boundary surface in three-dimensional space near the emission outlet, several spatial points evenly distributed on each boundary surface are selected as the initial pollutant detection spatial points.

[0017] Furthermore, in step S2, based on the location information of all initial pollutant detection spatial points, instructing several sub-drones of the sub-drone swarm to fly to the corresponding initial pollutant detection spatial points to perform pollutant detection operations specifically includes:

[0018] Using the central location point of the emission outlet as a reference, the relative positional relationship between each initial pollutant monitoring spatial point and the central location point is determined; wherein, the relative positional relationship includes relative distance and relative azimuth angle;

[0019] Based on the relative positional relationship, corresponding flight control commands are generated and sent to each sub-UAV in the sub-UAV swarm. Each sub-UAV then flies to the initial pollutant detection spatial point according to the received flight control commands.

[0020] The ground base station also instructs the sub-UAV to stay at the corresponding initial pollutant detection spatial point for a predetermined time length, and to perform pollutant detection operations during the stay.

[0021] Further, in step S2, generating corresponding flight control commands based on the relative positional relationship and sending the flight control commands to each sub-UAV in the sub-UAV swarm, so that each sub-UAV flies to the initial pollutant detection spatial point according to the received flight control commands, specifically includes:

[0022] Step S201: Using the following formula (1), based on the number of sub-UAVs in the swarm, the total number of initial pollutant monitoring spatial points, and the relative positional relationships, obtain the number of sub-UAVs assigned to each initial pollutant monitoring spatial point.

[0023]

[0024] In the above formula (1), D(a) represents the number of sub-UAVs controlled and allocated by the a-th initial pollutant monitoring spatial point; M represents the number of the sub-UAV swarm; n represents the total number of initial pollutant monitoring spatial points; L(a) represents the relative distance between the a-th initial pollutant monitoring spatial point and the central location point; Indicates rounding down;

[0025] If D(a) = 0, it means that the number of drones in the current drone swarm is too small, and more drones need to be added before repeating the above step S201.

[0026] If D(a)≠0, the control operation is performed as follows: Each UAV will form a set of numbers according to the order of access control. The first UAV to access is numbered 1, the second UAV to access is numbered 2, and so on. Each UAV is allocated according to the order of UAV numbers and the order of numbers of each initial pollutant monitoring space point. The first initial pollutant monitoring space point will allocate D(1) UAVs according to the value of D(1) and the order of UAV numbers. This process continues until the number of UAVs at all initial pollutant monitoring space points has been allocated. If there are any remaining UAVs, the remaining UAVs are instructed to start the backup mode and fly in a ring around the central position point. If there are any other UAV malfunctions, additional UAVs will be automatically added.

[0027] Step S202: Using the formula (2) below, control the predetermined time length of each sub-UAV at each initial pollutant monitoring space point according to the number of sub-UAVs allocated to each initial pollutant monitoring space point.

[0028]

[0029] In the above formula (2), T(a) represents the predetermined time length of the UAV around the a-th initial pollutant monitoring spatial point; T0 represents the shortest detection time for detecting the initial pollutant monitoring spatial point; This means substituting the values ​​of 'a' from 1 to 'n' into the parentheses to obtain the maximum value within the parentheses;

[0030] Step S203: Using the following formula (3), generate flight control commands for each UAV based on the initial pollutant monitoring space point assigned to the UAV and the predetermined time length of the UAV at the corresponding initial pollutant monitoring space point.

[0031]

[0032] In the above formula (3), G(i) represents the flight control command of the i-th UAV; A(i) represents the initial pollutant monitoring spatial point A(i) assigned to the i-th UAV for detection in step S201. If the i-th UAV is assigned to the vicinity of the initial pollutant monitoring spatial point, then... If the i-th drone is not assigned to the vicinity of the initial pollutant monitoring spatial point, then L[A(i)] represents the relative distance between the A(i)th initial pollutant monitoring spatial point and the central position point; θ[A(i)] represents the relative azimuth angle between the A(i)th initial pollutant monitoring spatial point and the central position point; T[A(i)] represents the predetermined time length of the UAVs around the A(i)th initial pollutant monitoring spatial point; Y0 represents the frame header of the flight control command; E0 represents the frame tail of the flight control command; {,,,,} represents the control command composed of data separated by commas in parentheses. When the UAV receives the control command, it will verify the frame header and frame tail. After successful verification, it will fly to the corresponding initial pollutant monitoring spatial point according to the relative distance value and relative azimuth angle to perform a detection time T[A(i)]. If the received control command is {Y0,0,0,0,E0}, the backup mode will be activated, and the UAV will fly in a ring around the central position point. If there is a fault in other UAVs, additional supplementary UAVs will be automatically performed.

[0033] Furthermore, in step S3, during the pollutant detection process of the sub-drone, the sub-drone is instructed to collect airflow status information at its current location; the airflow status information collected by all sub-drones is analyzed and processed by the ground base station to estimate the pollutant diffusion trend, specifically including:

[0034] During the pollutant detection process, the sub-drone is instructed to collect wind speed and wind direction information at its current location and send the wind speed and wind direction information to the ground base station in real time.

[0035] The ground base station analyzes and processes the wind speed and wind direction information collected by all sub-UAVs to determine the wind field vector distribution status information of the current area where the sub-UAV swarm is located.

[0036] Based on the wind field vector distribution information, the diffusion rate and direction of pollutants in the vicinity of the emission outlet are estimated.

[0037] Furthermore, in step S3, adjusting the flight status of each sub-UAV according to the diffusion trend and acquiring pollutant status data collected by all sub-UAVs throughout the entire flight process in real time specifically includes:

[0038] Based on the diffusion speed and direction of pollutants in the vicinity of the emission outlet, the ground base station sends corresponding flight status adjustment commands to each sub-UAV to adjust the flight speed and flight direction of each sub-UAV, so that each sub-UAV can always follow the diffusion movement of pollutants and fly synchronously.

[0039] The system instructs all sub-UAVs to simultaneously transmit the pollutant concentration data and pollutant diffusion rate data collected throughout the flight to the ground base station.

[0040] Furthermore, in step S4, the analysis and processing of the pollutant state data by the ground base station to obtain the pollutant's change state information within a future preset time period specifically includes:

[0041] The ground base station analyzes and processes the pollutant concentration data and pollutant diffusion rate data to estimate the changes in pollutant concentration and diffusion range over a future preset time period.

[0042] Compared to existing technologies, this remote online intelligent analysis method for pollutants first uses a main UAV to capture images of pollutants emitted from the emission source, and then analyzes these images via a ground base station to obtain information on the three-dimensional distribution of pollutants. Based on this distribution information, all sub-UAVs in the swarm are controlled to perform distributed detection of pollutants. Simultaneously, each sub-UAV collects airflow information about its location to estimate the pollutant diffusion trend. Based on this trend, the flight status of the sub-UAVs is adjusted accordingly. The ground base station receives and analyzes the pollutant status data collected by all sub-UAVs throughout the flight in real time, thus obtaining information on the pollutant's changing status over a predetermined time period. By using a swarm of sub-UAVs to synchronously track and detect pollutants already emitted into the atmosphere, this method can obtain data on the diffusion and distribution of pollutants in the atmosphere, thereby improving the accuracy and reliability of remote pollutant analysis and ensuring timely early warning of the pollutant diffusion coverage in the atmosphere.

[0043] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0044] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0045] 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 drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1This is a flowchart illustrating a remote online intelligent analysis method for pollutants provided by the present invention. Detailed Implementation

[0047] 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.

[0048] See Figure 1 This is a schematic flowchart illustrating a remote online intelligent analysis method for pollutants provided in an embodiment of the present invention. The remote online intelligent analysis method for pollutants includes the following steps:

[0049] Step S1: Instruct the main UAV to fly in the vicinity of the pollutant emission source, and collect images of the pollutants emitted by the pollutant emission source during the flight, and send the pollutant images to the ground base station; The ground base station analyzes and processes the pollutant images to determine the distribution status information of the pollutants emitted by the pollutant emission source.

[0050] Step S2: Based on the pollutant distribution status information, determine the three-dimensional spatial distribution status information of the pollutants emitted by the pollutant emission source; based on the distribution status information, determine several initial pollutant detection spatial points; based on the location information of all initial pollutant detection spatial points, instruct several sub-drones of the sub-drone swarm to fly to the corresponding initial pollutant detection spatial points to perform pollutant detection operations.

[0051] Step S3: During the pollutant detection process of the sub-drone, the sub-drone is instructed to collect airflow status information at its current location; the airflow status information collected by all sub-drones is analyzed and processed through the ground base station to estimate the diffusion trend of pollutants; based on the diffusion trend, the flight status of each sub-drone is adjusted, and the pollutant status data collected by all sub-drones throughout the entire flight process is acquired in real time.

[0052] Step S4: The ground base station is used to analyze and process the pollutant status data to obtain information on the changes in the pollutant status over a future preset time period.

[0053] The beneficial effects of the above technical solution are as follows: The remote online intelligent analysis method for pollutants first uses the main UAV to capture images of pollutants emitted from the emission source, and then analyzes the pollutant images through a ground base station to obtain the distribution status information of pollutants in three-dimensional space. Based on this distribution status information, all sub-UAVs in the sub-UAV swarm are controlled to perform distributed detection of pollutants. Simultaneously, the sub-UAVs collect airflow status information at their own locations to estimate the diffusion trend of pollutants. Based on this diffusion trend, the flight status of the sub-UAVs is adjusted accordingly. The ground base station receives and analyzes the pollutant status data collected by all sub-UAVs throughout the entire flight process in real time, thereby obtaining information on the changing status of pollutants within a preset time period. By using a sub-UAV swarm to synchronously track and detect pollutants already emitted into the atmospheric environment, data on the diffusion and distribution of pollutants in the atmospheric environment can be obtained, thereby improving the accuracy and reliability of remote pollutant analysis and ensuring timely early warning of the diffusion coverage of pollutants in the atmospheric environment.

[0054] Preferably, in step S1, instructing the main UAV to fly in the vicinity of the pollutant emission source, and during the flight to collect images of the pollutants emitted by the source, and then sending the pollutant images to the ground base station specifically includes:

[0055] The main UAV is instructed to first hover over an area within a preset distance from the emission outlet of the pollutant source, and during the hovering flight, scan and photograph the area near the emission outlet to obtain a corresponding panoramic image of the pollutants; then the panoramic image of the pollutants is sent to the ground base station in real time.

[0056] The beneficial effects of the above technical solution are as follows: In practical operation, the main UAV equipped with a wide-angle camera is first instructed to hover within a preset distance range from the emission outlet of the pollutant emission source. This allows the wide-angle camera to scan and photograph the area near the emission outlet, thus providing a panoramic view of the pollutants present in the vicinity. The resulting panoramic image of the pollutants is then sent back to the ground base station for analysis. The wide-angle camera can be, but is not limited to, an infrared camera.

[0057] Preferably, in step S1, the analysis and processing of the pollutant image by the ground base station to determine the distribution status information of pollutants emitted by the pollutant emission source specifically includes:

[0058] After performing pixel grayscale processing and edge pixel sharpening processing on the panoramic image of the pollutant using the ground base station, the boundary contour information of the pollutant is identified from the panoramic image of the pollutant.

[0059] Based on the boundary contour information, the existence information of pollutants on the boundary surface in three-dimensional space in the vicinity of the emission outlet is determined, which is used as the distribution status information of the pollutants.

[0060] The beneficial effects of the above technical solution are as follows: the ground base station identifies and analyzes the panoramic image of pollutants, determines the boundary contour of pollutants in the atmospheric environment, and then uses the boundary contour as a reference to determine the existence status information of the boundary surface of pollutants in the three-dimensional space near the emission outlet. In this way, the space occupied by pollutants in the three-dimensional space can be delineated based on the boundary surface of pollutants in the three-dimensional space, which facilitates the subsequent distributed tracking and detection of pollutants by the sub-UAV swarm.

[0061] Preferably, in step S2, based on the pollutant distribution information, the distribution information of pollutants emitted by the pollutant emission source in three-dimensional space is determined; based on the distribution information, determining several initial pollutant detection spatial points specifically includes:

[0062] Based on the information about the existence of pollutants at the boundary surface in three-dimensional space in the vicinity of the emission outlet, determine the location and area of ​​the boundary surface in three-dimensional space for the pollutants at the vicinity of the emission outlet.

[0063] Based on the location and area of ​​the pollutants on the boundary surface in three-dimensional space near the emission outlet, several spatial points evenly distributed on each boundary surface are selected as the initial pollutant detection spatial points.

[0064] The beneficial effects of the above technical solution are as follows: the location and area of ​​the boundary surface of pollutants in three-dimensional space are extracted from the information of the existence of pollutants on the boundary surface in three-dimensional space. This enables the quantitative calibration of the boundary of pollutants in the three-dimensional space of the atmospheric environment, which facilitates the reasonable and uniform determination of a corresponding number of initial pollutant detection spatial points on each boundary surface of the pollutants. This allows different sub-UAVs to be instructed to fly to the corresponding initial pollutant detection spatial points for initial detection of pollutants.

[0065] Preferably, in step S2, instructing several sub-drones of the sub-drone swarm to fly to the corresponding initial pollutant detection spatial points to perform pollutant detection operations, based on the location information of all initial pollutant detection spatial points, specifically includes:

[0066] Using the central location of the emission outlet as a reference, determine the relative positional relationship between each initial pollutant monitoring spatial point and the central location; wherein, the relative positional relationship includes the relative distance and the relative azimuth angle;

[0067] Based on the relative positional relationship, a corresponding flight control command is generated and sent to each sub-drone in the sub-drone swarm. Each sub-drone then flies to the initial pollutant detection spatial point according to the received flight control command.

[0068] The ground base station also instructs the sub-UAV to stay at the corresponding initial pollutant detection spatial point for a predetermined duration, and to perform pollutant detection operations during the stay.

[0069] The beneficial effects of the above technical solution are as follows: In practical operation, the sub-UAV swarm can include several independently operating sub-UAVs. Each sub-UAV is independently controlled by a ground base station. The pollutant data detected by each sub-UAV can be returned to the ground base station in real time. Each sub-UAV is equipped with its own pollutant concentration detection sensor and wind speed / direction sensor. Using the central position point of the emission outlet as a reference, each initial pollutant detection spatial point can be accurately calibrated using the same coordinate system. Based on this relative positional relationship, the ground base station independently sends flight control commands to the corresponding sub-UAVs, ensuring that the sub-UAVs can accurately fly to the corresponding initial pollutant detection spatial point. This facilitates pollutant detection operations at the initial pollutant detection spatial point for a predetermined time period. After the predetermined time period, the ground base station instructs the sub-UAVs to change their current position, enabling continuous pollutant detection operations at different locations.

[0070] Preferably, in step S2, generating corresponding flight control commands based on the relative positional relationship and sending these commands to each sub-drone in the sub-drone swarm, so that each sub-drone flies to the initial pollutant detection spatial point according to the received flight control commands, specifically includes:

[0071] Step S201: Using the formula (1) below, based on the number of sub-UAVs in the swarm, the total number of initial pollutant monitoring spatial points, and the relative positional relationship, obtain the number of sub-UAVs assigned to each initial pollutant monitoring spatial point.

[0072]

[0073] In the above formula (1), D(a) represents the number of sub-UAVs controlled and allocated by the a-th initial pollutant monitoring spatial point; M represents the number of the sub-UAV swarm; n represents the total number of initial pollutant monitoring spatial points; L(a) represents the relative distance between the a-th initial pollutant monitoring spatial point and the central location point. Indicates rounding down;

[0074] If D(a) = 0, it means that the number of drones in the current drone swarm is too small, and more drones need to be added before repeating the above step S201.

[0075] If D(a)≠0, the control operation is performed as follows: each UAV will form a set of numbers according to the order of access control. The first UAV to access is numbered 1, the second UAV to access is numbered 2, and so on. Each UAV is allocated according to the order of UAV numbers and the order of numbers of each initial pollutant monitoring space point. The first initial pollutant monitoring space point will allocate D(1) UAVs according to the value of D(1) and the order of UAV numbers. This process continues until the number of UAVs at all initial pollutant monitoring space points has been allocated. If there are any remaining UAVs, the remaining UAVs are instructed to start the backup mode and fly in a circle around the central position point. If there are any other UAV malfunctions, additional UAVs will be automatically added.

[0076] Step S202: Using the formula (2) below, control the predetermined time length of each sub-UAV at each initial pollutant monitoring space point according to the number of sub-UAVs allocated to each initial pollutant monitoring space point.

[0077]

[0078] In the above formula (2), T(a) represents the predetermined time length of the UAV around the a-th initial pollutant monitoring spatial point; T0 represents the shortest detection time for detecting the initial pollutant monitoring spatial point; This means substituting the values ​​of 'a' from 1 to 'n' into the parentheses to obtain the maximum value within the parentheses;

[0079] Step S203: Using the following formula (3), generate flight control commands for each UAV based on the initial pollutant monitoring space point assigned to the UAV and the predetermined time length of the UAV at the corresponding initial pollutant monitoring space point.

[0080]

[0081] In the above formula (3), G(i) represents the flight control command of the i-th UAV; A(i) represents the initial pollutant monitoring spatial point A(i) assigned to the i-th UAV for detection in step S201. If the i-th UAV is assigned to the vicinity of the initial pollutant monitoring spatial point, then... If the i-th drone is not assigned to the vicinity of the initial pollutant monitoring spatial point, then L[A(i)] represents the relative distance between the A(i)th initial pollutant monitoring spatial point and the central position point; θ[A(i)] represents the relative azimuth angle between the A(i)th initial pollutant monitoring spatial point and the central position point; T[A(i)] represents the predetermined time length of the UAVs around the A(i)th initial pollutant monitoring spatial point; Y0 represents the frame header of the flight control command; E0 represents the frame tail of the flight control command; {,,,,} represents the control command composed of data separated by commas in parentheses. When the UAV receives the control command, it will verify the frame header and frame tail. After successful verification, it will fly to the corresponding initial pollutant monitoring spatial point according to the relative distance value and relative azimuth angle to perform detection for a duration of T[A(i)]. If the received control command is {Y0,0,0,0,E0}, the backup mode will be activated, and the UAV will fly in a ring around the central position point. If there is a fault in other UAVs, additional supplementary UAVs will be automatically performed.

[0082] The beneficial effects of the above technical solution are as follows: Using the above formula (1), based on the number of sub-UAVs, the number of all initial pollutant monitoring space points, and the relative positional relationship, the number of sub-UAVs allocated to each initial pollutant monitoring space point is obtained, thereby allocating more sub-UAVs to initial pollutant monitoring space points that are far away, preventing additional backup time when UAVs fail later; then using the above formula (2), based on the number of sub-UAVs allocated to each initial pollutant monitoring space point, the predetermined time length of each sub-UAV at each initial pollutant monitoring space point is controlled, thereby shortening the detection time of each sub-UAV to improve efficiency when there are many sub-UAVs, and increasing the detection time to improve detection accuracy when there are few UAVs; finally using the above formula (3), based on the initial pollutant monitoring space point to which the UAV is allocated and the predetermined time length of the UAV at the corresponding initial pollutant monitoring space point, the flight control command of each UAV is generated, ensuring the reliable control of each UAV, and that backup UAVs can supplement the system after a UAV fails, ensuring the reliability of the system.

[0083] Preferably, in step S3, during the pollutant detection process of the sub-UAV, the sub-UAV is instructed to collect airflow status information at its current location; the airflow status information collected by all sub-UAVs is analyzed and processed by the ground base station to estimate the pollutant diffusion trend, specifically including:

[0084] During the pollutant detection process, the sub-drone is instructed to collect wind speed and wind direction information at its current location and send the wind speed and wind direction information to the ground base station in real time.

[0085] The ground base station analyzes and processes the wind speed and wind direction information collected by all sub-drones to determine the wind field vector distribution status of the current area where the sub-drone swarm is located.

[0086] Based on the wind field vector distribution information, the diffusion rate and direction of pollutants in the vicinity of the emission outlet are estimated.

[0087] The beneficial effects of the above technical solution are as follows: by using the wind speed and wind direction information collected by the sub-UAVs as a benchmark, the ground base station can easily analyze and determine the wind field vector distribution status information of the area where the sub-UAV group is currently located, thereby accurately estimating the diffusion speed and diffusion direction of pollutants in the vicinity of the emission outlet.

[0088] Preferably, in step S3, adjusting the flight status of each sub-UAV according to the diffusion trend and acquiring pollutant status data collected by all sub-UAVs throughout the flight process in real time specifically includes:

[0089] Based on the diffusion speed and direction of pollutants in the vicinity of the emission outlet, the ground base station sends corresponding flight status adjustment commands to each sub-drone to adjust the flight speed and flight direction of each sub-drone, so that each sub-drone can always fly synchronously with the diffusion movement of pollutants.

[0090] The system instructs all sub-drones to simultaneously transmit the pollutant concentration data and pollutant diffusion rate data collected during the entire flight to the ground base station.

[0091] The beneficial effects of the above technical solution are as follows: By using the diffusion speed and direction of pollutants in the vicinity of the emission outlet as a benchmark, the ground base station sends corresponding flight status adjustment commands to each sub-UAV, so that each sub-UAV changes its own flight speed and flight direction, thereby ensuring that each sub-UAV can always follow the diffusion movement of pollutants and fly synchronously, so that the sub-UAV can continuously detect pollutants in the diffusion state.

[0092] Preferably, in step S4, the analysis and processing of the pollutant state data by the ground base station to obtain the pollutant's change state information within a future preset time period specifically includes:

[0093] By analyzing and processing the pollutant concentration data and pollutant diffusion rate data through the ground base station, information on the changes in pollutant concentration and diffusion range in the future within a preset time period can be estimated.

[0094] The beneficial effects of the above technical solution are as follows: by analyzing and processing the pollutant concentration data and pollutant diffusion rate data, the concentration change information and diffusion range change information of the pollutant in the future preset time period can be estimated, which facilitates the corresponding alarm operation for the location of the pollutant with a high concentration in the diffusion process or the area covered in the diffusion process.

[0095] As can be seen from the above embodiments, the remote online intelligent analysis method for pollutants first uses a main UAV to capture images of pollutants emitted from the emission source, and then analyzes the pollutant images through a ground base station to obtain the distribution information of pollutants in three-dimensional space. Based on this distribution information, all sub-UAVs in the sub-UAV swarm are controlled to perform distributed detection of pollutants. Simultaneously, the sub-UAVs collect airflow information about their own locations to estimate the diffusion trend of pollutants. Based on this diffusion trend, the flight status of the sub-UAVs is adjusted accordingly. The ground base station receives and analyzes the pollutant status data collected by all sub-UAVs throughout the entire flight process in real time, thereby obtaining information on the changing status of pollutants within a preset future time period. By using a sub-UAV swarm to synchronously track and detect pollutants already emitted into the atmosphere, it can obtain data on the diffusion and distribution of pollutants in the atmosphere, thereby improving the accuracy and reliability of remote pollutant analysis and ensuring timely early warning of the diffusion coverage of pollutants in the atmosphere.

[0096] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A remote online intelligent analysis method for pollutants, characterized in that, It includes the following steps: Step S1: Instruct the main UAV to fly in the vicinity of the pollutant emission source, and collect images of pollutants emitted by the pollutant emission source during the flight, and send the pollutant images to the ground base station; The ground base station analyzes and processes the pollutant images to determine the distribution status information of pollutants emitted by the pollutant emission source; Step S2: Based on the pollutant distribution status information, determine the distribution status information of pollutants emitted by the pollutant emission source in three-dimensional space; based on the distribution status information, determine several initial pollutant detection spatial points; based on the location information of all initial pollutant detection spatial points, instruct several sub-drones of the sub-drone swarm to fly to the corresponding initial pollutant detection spatial points to perform pollutant detection operations. Step S3: During the pollutant detection process of the sub-UAV, the sub-UAV is instructed to collect airflow status information at its current location; the airflow status information collected by all sub-UAVs is analyzed and processed by the ground base station to estimate the diffusion trend of pollutants. Based on the diffusion trend, the flight status of each sub-UAV is adjusted, and pollutant status data collected by all sub-UAVs throughout the entire flight process is acquired in real time. Step S4: The pollutant status data is analyzed and processed by the ground base station to obtain the pollutant change status information within a future preset time period; In step S2, based on the location information of all initial pollutant detection spatial points, several sub-drones of the sub-drone swarm are instructed to fly to the corresponding initial pollutant detection spatial points to perform pollutant detection operations, including: Using the central location point of the emission outlet as a reference, the relative positional relationship between each initial pollutant monitoring spatial point and the central location point is determined; wherein, the relative positional relationship includes relative distance and relative azimuth angle; Based on the relative positional relationship, corresponding flight control commands are generated and sent to each sub-UAV in the sub-UAV swarm. Each sub-UAV then flies to the initial pollutant detection spatial point according to the received flight control commands. The ground base station also instructs the sub-UAV to stay at the corresponding initial pollutant detection spatial point for a predetermined time length, and to perform pollutant detection operations during the stay; The step of generating corresponding flight control commands based on the relative positional relationship includes: using the following formula (3), generating flight control commands for each UAV based on the initial pollutant monitoring space point assigned to the UAV and the predetermined time length of the UAV at the corresponding initial pollutant monitoring space point. （3） In the above formula (3), Indicates the first Flight control commands for a drone; Indicates the first The drone was assigned to the first Detection is carried out at the first initial pollutant monitoring spatial point. If the first... The drones were assigned to the vicinity of the initial pollutant monitoring spatial point. If the first If the drone is not assigned to the vicinity of the initial pollutant monitoring point, then... ; Indicates the first The relative distance between each initial pollutant monitoring spatial point and the central location point; Indicates the first The relative azimuth angles between the initial pollutant monitoring spatial points and the central location point; Indicates the first The predetermined time length of the drones around the initial pollutant monitoring spatial point; The frame header representing flight control commands; The frame end indicating flight control commands; This indicates that the data separated by commas within parentheses constitutes the control command. When the UAV receives the control command, it will verify the frame header and frame footer. If the verification is successful, it will fly to the corresponding initial pollutant monitoring spatial point based on the relative distance value and relative azimuth angle to perform detection. Duration, if the received control command is If the backup mode is activated, the drone will fly in a ring around the central location point. If other drones malfunction, additional backup will be automatically provided.

2. The remote online intelligent analysis method for pollutants as described in claim 1, characterized in that: In step S1, instructing the main UAV to fly in the vicinity of the pollutant emission source, and during the flight to collect images of pollutants emitted by the pollutant emission source, and then sending the pollutant images to the ground base station, specifically includes: The main UAV is instructed to first hover in an area within a preset distance from the emission outlet of the pollutant emission source, and scan and photograph the area near the emission outlet during the hovering flight to obtain a corresponding panoramic image of the pollutants; then the panoramic image of the pollutants is sent to the ground base station in real time.

3. The remote online intelligent analysis method for pollutants as described in claim 2, characterized in that: In step S1, the analysis and processing of the pollutant image by the ground base station to determine the distribution status information of pollutants emitted by the pollutant emission source specifically includes: After performing pixel grayscale processing and edge pixel sharpening processing on the panoramic image of pollutants by the ground base station, the boundary contour information of pollutants is identified from the panoramic image of pollutants. Based on the boundary contour information, the existence information of pollutants on the boundary surface in three-dimensional space in the area near the emission outlet is determined, and this is used as the pollutant distribution status information.

4. The remote online intelligent analysis method for pollutants as described in claim 3, characterized in that: In step S2, based on the pollutant distribution status information, the distribution status information of pollutants emitted by the pollutant emission source in three-dimensional space is determined; based on the distribution status information, determining several initial pollutant detection spatial points specifically includes: Based on the information about the existence of pollutants at the boundary surface in three-dimensional space in the vicinity of the emission outlet, determine the location and area of ​​the boundary surface in three-dimensional space for the pollutants at the vicinity of the emission outlet. Then, based on the location and area of ​​the pollutants on the boundary surface in three-dimensional space near the emission outlet, several spatial points evenly distributed on each boundary surface are selected as the initial pollutant detection spatial points.

5. The remote online intelligent analysis method for pollutants as described in claim 1, characterized in that: In step S2, based on the relative positional relationship, corresponding flight control commands are generated and sent to each sub-UAV in the sub-UAV swarm. Each sub-UAV then flies to the initial pollutant detection spatial point according to the received flight control commands. The process also includes: Step S201: Using the following formula (1), based on the number of sub-UAVs in the swarm, the total number of initial pollutant monitoring spatial points, and the relative positional relationships, obtain the number of sub-UAVs assigned to each initial pollutant monitoring spatial point. （1） In the above formula (1), Indicates the first The number of sub-UAVs allocated for the initial pollutant monitoring spatial point control; This indicates the number of sub-drone swarms; This indicates the total number of initial pollutant monitoring points in the spatial data center. Indicates the first The relative distance between each initial pollutant monitoring spatial point and the central location point; Indicates rounding down; like If the number of drones in the current drone swarm is too small, it means that more drones need to be added before repeating the above step S201. like The control operation will proceed as follows: Each drone will be assigned a number based on the order in which it accesses the control system. The first drone to access the system will be numbered 1, the second drone will be numbered 2, and so on. Each drone will be assigned according to its drone number and the order of its initial pollutant monitoring point. The first initial pollutant monitoring point will be assigned according to... The values ​​are assigned according to the order of the drone numbers. The number of drones is allocated in sequence until all the initial pollutant monitoring space points have been allocated. If there are any remaining drones, they are instructed to activate backup mode and fly in a circle around the central location point. If any other drones malfunction, additional drones are automatically added. Step S202: Using the formula (2) below, control the predetermined time length of each sub-UAV at each initial pollutant monitoring space point according to the number of sub-UAVs allocated to each initial pollutant monitoring space point. （2） In the above formula (2), Indicates the first The predetermined time length of the drones around the initial pollutant monitoring spatial point; This indicates the shortest detection time for detecting the initial pollutant monitoring spatial point; Indicates will The value ranges from 1 to Substitute the value into the parentheses to get the maximum value inside the parentheses.

6. The remote online intelligent analysis method for pollutants as described in claim 1, characterized in that: In step S3, during the pollutant detection process of the sub-UAV, the sub-UAV is instructed to collect airflow status information at its current location; the airflow status information collected by all sub-UAVs is analyzed and processed by the ground base station to estimate the pollutant diffusion trend, specifically including: During the pollutant detection process, the sub-drone is instructed to collect wind speed and wind direction information at its current location and send the wind speed and wind direction information to the ground base station in real time. The ground base station analyzes and processes the wind speed and wind direction information collected by all sub-UAVs to determine the wind field vector distribution status information of the current area where the sub-UAV swarm is located. Based on the wind field vector distribution information, the diffusion rate and direction of pollutants in the vicinity of the emission outlet are estimated.

7. The remote online intelligent analysis method for pollutants as described in claim 6, characterized in that: In step S3, adjusting the flight status of each sub-UAV according to the diffusion trend and acquiring pollutant status data collected by all sub-UAVs throughout the flight process in real time specifically includes: Based on the diffusion speed and direction of pollutants in the vicinity of the emission outlet, the ground base station sends corresponding flight status adjustment commands to each sub-UAV to adjust the flight speed and flight direction of each sub-UAV, so that each sub-UAV can always follow the diffusion movement of pollutants and fly synchronously. The system instructs all sub-UAVs to simultaneously transmit the pollutant concentration data and pollutant diffusion rate data collected throughout the flight to the ground base station.

8. The remote online intelligent analysis method for pollutants as described in claim 7, characterized in that: In step S4, the analysis and processing of the pollutant state data by the ground base station to obtain the pollutant change state information within a future preset time period specifically includes: The ground base station analyzes and processes the pollutant concentration data and pollutant diffusion rate data to estimate the changes in pollutant concentration and diffusion range over a future preset time period.

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