CN117110214A - Water quality analysis system and method based on hyperspectral imaging of unmanned aerial vehicle - Google Patents
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Abstract
The invention provides a water quality analysis system and a water quality analysis method based on hyperspectral imaging of an unmanned aerial vehicle, wherein the system comprises a hyperspectral camera module for shooting a water quality environment image; the mobile positioning module group is used for carrying the hyperspectral camera modules to be respectively moved to a preset position for shooting, and is also used for assisting in mobile positioning among the mobile modules; the data acquisition module is used for acquiring various water parameter data required by water quality analysis; the early warning module is used for sending out early warning and reminding when abnormal water quality conditions occur; the monitoring server is used for further processing the collected data parameters to obtain an analysis conclusion; and the wireless communication module is used for transmitting data information among the modules. The invention can monitor a large-scale flowing water area in real time based on the unmanned aerial vehicle and the remote sensing technology thereof, improves the timeliness of monitoring, has flexible maneuvering and high degree of automation, coordinates and orders among the unmanned aerial vehicles, and can also sample and salvage abnormal water areas.
Description
Technical Field
The invention relates to the technical field of water quality analysis, in particular to a water quality analysis system and method based on hyperspectral imaging of an unmanned aerial vehicle.
Background
With the development of society, the environmental protection of the watershed is more and more important, and the development of the watershed emergency monitoring technology is gradually paid attention to. Compared with the traditional manual operation, the method has the advantage that the intelligent equipment is explored to realize large-scale, rapid and effective monitoring in sudden river basin environmental accidents. Traditional water body monitoring usually adopts a point monitoring mode to inspect the whole river basin, and the condition of the whole river basin is difficult to truly reflect. With the development of unmanned aerial vehicle technology, the implementation of river basin emergency monitoring by using various devices carried by unmanned aerial vehicles has become reality: the camera is carried to pick up the image of the water area, so that the environment of the large-range river basin can be observed; the multispectral imaging device is carried, and the generated multispectral image can quickly find pollution sources and floating garbage, so that the monitoring of water transparency, greasy dirt, suspended matters and the like is realized. Unmanned aerial vehicle has unmanned, automatic and intelligent advantage, makes quality of water sampling point and monitoring scope rapidly increased, greatly increased monitoring data volume, improved work efficiency, avoided personnel to wade to measure simultaneously, ensured personnel's safety.
Currently, a water quality analysis system and analysis based on unmanned aerial vehicles generally perform image acquisition according to a preset cruising route through a single or a small number of unmanned aerial vehicles, so that timeliness is poor for water quality monitoring of a flowing water area, and cooperation among unmanned aerial vehicles is less studied.
Disclosure of Invention
The invention provides a water quality analysis system and a water quality analysis method based on unmanned aerial vehicle hyperspectral imaging, which are used for overcoming the defects that the conventional water quality analysis system and analysis based on unmanned aerial vehicles are commonly used for image acquisition by a single or a small number of unmanned aerial vehicles according to a preset cruising route, the timeliness is poor and the unmanned aerial vehicles lack of cooperative coordination.
The invention provides the following technical scheme:
a water quality analysis system based on hyperspectral imaging of an unmanned aerial vehicle comprises a hyperspectral camera module, a hyperspectral camera module and a hyperspectral camera module, wherein the hyperspectral camera module is used for shooting a water quality environment image; the mobile positioning module group is used for carrying the hyperspectral camera modules to be respectively moved to a preset position for shooting, and is also used for assisting in mobile positioning among the mobile modules; the data acquisition module is used for acquiring various water parameter data required by water quality analysis; the early warning module is used for sending out early warning and reminding when abnormal water quality conditions occur; the monitoring server is used for further processing the collected data parameters to obtain an analysis conclusion; and the wireless communication module is used for transmitting data information among the modules.
Preferably, the mobile positioning module group is composed of a plurality of unmanned aerial vehicles, at least one unmanned aerial vehicle is set as a main machine, other unmanned aerial vehicles are set as auxiliary machines, the main machine is provided with a cabin capable of storing other auxiliary machines, and the auxiliary machines are provided with a water sampling unit and a pickup unit.
Preferably, the unmanned aerial vehicle adopts solar panel to carry out auxiliary charging.
Preferably, the system further comprises an infrared detection module for eliminating human beings and maneuvering devices in abnormal pixels in the hyperspectral image.
The invention also provides a water quality analysis method based on hyperspectral imaging of the unmanned aerial vehicle, which comprises the following steps:
s1, acquiring coordinate position information of a boundary of a water area to be detected, establishing a space coordinate system of the air of the water area to be detected, determining the number of auxiliary machines to be used according to resolution parameters of a host machine, and generating height coordinates of preset fixed points in each air;
s2, controlling the main machine and the auxiliary machine to move to an air preset point corresponding to the upper side of the water area to be detected according to the frequency of monitoring and sampling, and starting a hyperspectral camera module to perform hyperspectral imaging on the water area to be detected within the maximum resolution range according to the preset frequency;
s3, sending hyperspectral imaging data to a monitoring server for processing, splicing images obtained on each unmanned aerial vehicle according to a preset water quality remote sensing inversion model to generate water quality analysis spectrograms of a plurality of water areas to be detected, and taking a first image as a base reference image;
s4, comparing the currently acquired water quality analysis spectrogram with pixels of a base reference image, marking an abnormal region in the spectrogram, acquiring a position coordinate of the plane center of the abnormal region, acquiring a water body sample from the position point by assisting the auxiliary machine navigation through the main machine and the spectral image thereof, and transmitting the sample to a preset detection point for detection to obtain a water body sample parameter;
s5, analyzing and processing the water sample parameters, and synthesizing the data of each abnormal region to obtain a water quality analysis result.
Preferably, the step S4 of acquiring the position coordinate of the plane center of the abnormal area, and the step of acquiring the water body sample by the auxiliary machine navigation to the position point through the main machine and the spectrum image thereof specifically includes the following steps:
s4.1, establishing a space coordinate system of the upper air of the water area to be detected according to position parameter information of preset points, and determining a horizontal coordinate position of the abnormal area in the space coordinate system by utilizing a map and a proportion;
s4.2, selecting an unmanned aerial vehicle closest to the horizontal coordinate position as a task machine, moving the task machine to the position above the horizontal coordinate position, updating the position coordinates of an abnormal region in a water area in real time, calculating a movement deviation value, and synchronizing the deviation value into the task machine to realize synchronous tracking;
s4.3, lowering the vertical height of the task machine, simultaneously sending the vertical distance between the light wave detection and the abnormal area in real time through hyperspectrum, suspending moving when the distance is smaller than a preset value, shooting and transmitting an image of a target object in the abnormal area to a monitoring server, and waiting for receiving a salvage, acquisition or return instruction;
s4.4, corresponding operations are executed through the sampling unit and the pick-up unit according to the instruction, the moving program is executed reversely after the return instruction is received, and the moving program returns to the initial position according to the original path.
Preferably, the processing includes atmospheric correction, spectral scaling, radiometric scaling, image color correction, image geometry correction, image registration and orthographic image stitching of the data.
Preferably, the distinguishing of the abnormal areas is judged according to the difference rate of preset pixels, and the salvaging, collecting or returning instruction is sent by a staff or an AI model at the monitoring server.
Preferably, when there are a plurality of abnormal regions, the pixel color and the three-dimensional shape are compared for the plurality of abnormal regions, and when the similarity exceeds a preset value, the plurality of abnormal regions are merged.
Preferably, the method further comprises the steps of matching and corresponding the water quality analysis result and the historical operation instruction with the spectrogram parameters, storing the result and the historical operation instruction into a monitoring server, sending out early warning information through an early warning module when the similarity of the spectrogram parameters exceeds a preset proportion next time, and executing corresponding actions in the corresponding historical operation instruction.
Compared with the prior art, the invention has the beneficial effects that:
the invention can monitor a large-scale flowing water area in real time based on the unmanned aerial vehicle and the remote sensing technology thereof, improves the timeliness of monitoring, has flexible maneuvering and high automation degree, orderly coordination among the unmanned aerial vehicles and wide shooting vision, combines the unmanned aerial vehicle with the remote sensing technology, and can make up the defects of the traditional monitoring method. After the water quality of the water area is found to be abnormal, abnormal pollutants can be timely treated according to the instruction sent by the monitoring end, and the workload of environmental protection personnel can be greatly reduced. When an abnormal region appears in a water area, firstly, confirmation of a worker and an AI model is carried out, and then corresponding action instructions are executed, so that misjudgment can be reduced, along with historical accumulation of related parameter data and corresponding instructions of the abnormal region, training optimization based on the AI model can be carried out, high accuracy can be maintained while manual work load is gradually reduced, and further a water quality analysis system based on hyperspectral imaging of an unmanned aerial vehicle is gradually perfected.
Drawings
FIG. 1 is a schematic flow chart of a water quality analysis method based on hyperspectral imaging of an unmanned aerial vehicle.
Detailed Description
The invention is further described below with reference to the drawings and examples.
The water quality analysis system based on the hyperspectral imaging of the unmanned aerial vehicle, which is shown with reference to fig. 1, comprises a hyperspectral camera module for shooting a water quality environment image; the mobile positioning module group is used for carrying the hyperspectral camera modules to be respectively moved to a preset position for shooting, and is also used for assisting in mobile positioning among the mobile modules; the data acquisition module is used for acquiring various water parameter data required by water quality analysis; the early warning module is used for sending out early warning and reminding when abnormal water quality conditions occur; the monitoring server is used for further processing the collected data parameters to obtain an analysis conclusion; and the wireless communication module is used for transmitting data information among the modules. The system also comprises an infrared detection module which is used for eliminating human beings and maneuvering equipment in abnormal pixels in the hyperspectral image. When an abnormal region appears in a detection picture shot by the hyperspectral camera module, an infrared detection module is needed to remove possible human beings and motor-driven equipment in the picture, so that erroneous judgment of water body abnormality is reduced.
Specifically, in this embodiment, the mobile positioning module group is composed of 20 unmanned aerial vehicles, wherein 1 frame is set up as a host, the other 19 unmanned aerial vehicles are set up as auxiliary machines, the host is configured with a cabin capable of storing other auxiliary machines, and the auxiliary machines are configured with a water sampling unit and a pickup unit. The cabin that can deposit other auxiliary machines is disposed on the host computer, can improve the accomodating and dispatch efficiency of unit, needs to notice that the carrying capacity of host computer needs to be far greater than the auxiliary machines, and the water sampling unit and the pick-up unit of configuration on the auxiliary machines then can gather and pick up the target object in the aquatic, reduce environmental protection personnel's work load. Optionally, the unmanned aerial vehicle is charged in an auxiliary mode by adopting a solar panel.
A water quality analysis method based on hyperspectral imaging of an unmanned aerial vehicle comprises the following steps:
s1, acquiring coordinate position information of a boundary of a water area to be detected, establishing a space coordinate system of the air of the water area to be detected, determining the number of auxiliary machines to be used according to resolution parameters of a host machine, and generating height coordinates of preset fixed points in each air; the preset fixed point is a position where all unmanned aerial vehicles can hover in the air by combining a plurality of unmanned aerial vehicles to shoot the water quality picture of the whole water area to be detected.
S2, controlling the main machine and the auxiliary machine to move to an air preset point corresponding to the upper side of the water area to be detected according to the frequency of monitoring and sampling, and starting a hyperspectral camera module to perform hyperspectral imaging on the water area to be detected within the maximum resolution range according to the preset frequency; the preset frequency refers to shooting and sampling once in a few hours or once in a few days, and the specific frequency is determined according to actual needs.
S3, sending hyperspectral imaging data to a monitoring server for processing, splicing images obtained on each unmanned aerial vehicle according to a preset water quality remote sensing inversion model to generate water quality analysis spectrograms of a plurality of water areas to be detected, and taking a first image as a base reference image; the processing includes performing atmospheric correction, spectrum calibration, radiation calibration, image color correction, image geometry correction, image registration and orthographic image stitching on the data, and the technology of generating a water quality analysis spectrogram according to the inversion model is relatively mature and will not be described herein.
S4, comparing the currently acquired water quality analysis spectrogram with pixels of a base reference image, marking an abnormal region in the spectrogram, acquiring a position coordinate of the plane center of the abnormal region, acquiring a water body sample from the position point by assisting the auxiliary machine navigation through the main machine and the spectral image thereof, and transmitting the sample to a preset detection point for detection to obtain a water body sample parameter; the method specifically comprises the following steps:
s4.1, establishing a space coordinate system of the upper air of the water area to be detected according to position parameter information of preset points, and determining a horizontal coordinate position of the abnormal area in the space coordinate system by utilizing a map and a proportion; namely, determining the horizontal one-dimensional coordinates of the abnormal region;
s4.2, selecting an unmanned aerial vehicle closest to the horizontal coordinate position as a mission machine, moving the mission machine to the position above the horizontal coordinate position, updating the position coordinates of an abnormal region in a water area in real time, calculating a moving deviation value, and synchronizing the deviation value into the mission machine to realize synchronous tracking, wherein the deviation value can be determined according to horizontal plane spectrograms shot by a plurality of hosts in a preset time interval and the flight speed of the unmanned aerial vehicle;
s4.3, lowering the vertical height of the task machine, simultaneously sending the vertical distance between the light wave detection and the abnormal area in real time through hyperspectrum, suspending moving when the distance is smaller than a preset value, shooting and transmitting an image of a target object in the abnormal area to a monitoring server, and waiting for receiving a salvage, acquisition or return instruction;
s4.4, corresponding operations are executed through the sampling unit and the pick-up unit according to the instruction, the moving program is reversely executed after the return instruction is received, the moving program returns to the initial position in the original way, for example, after the salvage instruction is received, the foreign matters are salvaged through the pick-up unit such as a manipulator and then sent to a designated place, and as the instruction is confirmed by a worker or AI and then sent out, when an object incapable of being salvaged is encountered, the salvage step can be skipped, and the return instruction is executed.
The distinguishing of the abnormal areas is judged according to the difference rate of preset pixels, the difference rate is set for improving the fault tolerance rate of water condition judgment, when the difference is not large enough, the water condition is not judged to be abnormal, the salvage, collection or return instructions are sent out by a staff or an AI model at a monitoring service end, so that the accuracy of action execution is further improved, when a plurality of abnormal areas exist, the comparison of pixel colors and three-dimensional shapes is carried out on the abnormal areas, when the similarity exceeds a preset value, the preset value is customized according to the condition, the merging processing is carried out on the abnormal areas, and the calculation amount and the workload are reduced through the merging processing;
s5, analyzing and processing the water sample parameters, and synthesizing the data of each abnormal region to obtain a water quality analysis result.
As another preferable scheme, the method also comprises the steps of matching and corresponding the water quality analysis result and the historical operation instruction with the spectrogram parameters, storing the result and the historical operation instruction into a monitoring server, sending out early warning information through an early warning module when the similarity of the spectrogram parameters exceeds a preset proportion next time, and executing corresponding actions in the corresponding historical operation instruction. The optimization scheme has the advantages that the system is optimized step by step, along with the history accumulation of related parameter data and corresponding instructions of an abnormal region, the training optimization based on an AI model can be realized, the manual work load is gradually reduced, and meanwhile, higher accuracy can be maintained.
The foregoing examples have shown only the preferred embodiments of the invention, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that modifications, improvements and substitutions can be made by those skilled in the art without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. The water quality analysis system based on the hyperspectral imaging of the unmanned aerial vehicle is characterized by comprising a hyperspectral camera module, wherein the hyperspectral camera module is used for shooting water quality environment images; the mobile positioning module group is used for carrying the hyperspectral camera modules to be respectively moved to a preset position for shooting, and is also used for assisting in mobile positioning among the mobile modules; the data acquisition module is used for acquiring various water parameter data required by water quality analysis; the early warning module is used for sending out early warning and reminding when abnormal water quality conditions occur; the monitoring server is used for further processing the collected data parameters to obtain an analysis conclusion; and the wireless communication module is used for transmitting data information among the modules.
2. The water quality analysis system based on hyperspectral imaging of unmanned aerial vehicle according to claim 1, wherein the mobile positioning module group is composed of a plurality of unmanned aerial vehicles, wherein at least one unmanned aerial vehicle is set as a main machine, other unmanned aerial vehicles are set as auxiliary machines, the main machine is provided with cabins capable of storing other auxiliary machines, and the auxiliary machines are provided with a water sampling unit and a picking unit.
3. The water quality analysis system based on hyperspectral imaging of unmanned aerial vehicle of claim 2, wherein the unmanned aerial vehicle adopts solar panel to carry out auxiliary charging.
4. The water quality analysis system based on hyperspectral imaging of an unmanned aerial vehicle according to claim 1, further comprising an infrared detection module for eliminating human beings and mobile devices from abnormal pixels in the hyperspectral image.
5. A water quality analysis method based on hyperspectral imaging of an unmanned aerial vehicle comprises the following steps:
s1, acquiring coordinate position information of a boundary of a water area to be detected, establishing a space coordinate system of the air of the water area to be detected, determining the number of auxiliary machines to be used according to resolution parameters of a host machine, and generating height coordinates of preset fixed points in each air;
s2, controlling the main machine and the auxiliary machine to move to an air preset point corresponding to the upper side of the water area to be detected according to the frequency of monitoring and sampling, and starting a hyperspectral camera module to perform hyperspectral imaging on the water area to be detected within the maximum resolution range according to the preset frequency;
s3, sending hyperspectral imaging data to a monitoring server for processing, splicing images obtained on each unmanned aerial vehicle according to a preset water quality remote sensing inversion model to generate water quality analysis spectrograms of a plurality of water areas to be detected, and taking a first image as a base reference image;
s4, comparing the currently acquired water quality analysis spectrogram with pixels of a base reference image, marking an abnormal region in the spectrogram, acquiring a position coordinate of the plane center of the abnormal region, acquiring a water body sample from the position point by assisting the auxiliary machine navigation through the main machine and the spectral image thereof, and transmitting the sample to a preset detection point for detection to obtain a water body sample parameter;
s5, analyzing and processing the water sample parameters, and synthesizing the data of each abnormal region to obtain a water quality analysis result.
6. The water quality analysis method based on hyperspectral imaging of an unmanned aerial vehicle according to claim 5, wherein the step of acquiring the position coordinates of the plane center of the abnormal area in step S4, and navigating to the position point through the main machine and the spectral image auxiliary machine thereof to acquire the water body sample specifically comprises the following steps:
s4.1, establishing a space coordinate system of the upper air of the water area to be detected according to position parameter information of preset points, and determining a horizontal coordinate position of the abnormal area in the space coordinate system by utilizing a map and a proportion;
s4.2, selecting an unmanned aerial vehicle closest to the horizontal coordinate position as a task machine, moving the task machine to the position above the horizontal coordinate position, updating the position coordinates of an abnormal region in a water area in real time, calculating a movement deviation value, and synchronizing the deviation value into the task machine to realize synchronous tracking;
s4.3, lowering the vertical height of the task machine, simultaneously sending the vertical distance between the light wave detection and the abnormal area in real time through hyperspectrum, suspending moving when the distance is smaller than a preset value, shooting and transmitting an image of a target object in the abnormal area to a monitoring server, and waiting for receiving a salvage, acquisition or return instruction;
s4.4, corresponding operations are executed through the sampling unit and the pick-up unit according to the instruction, the moving program is executed reversely after the return instruction is received, and the moving program returns to the initial position according to the original path.
7. The method of claim 5, wherein the processing includes performing atmospheric correction, spectral scaling, radiometric scaling, image color correction, image geometry correction, image registration and orthographic image stitching on the data.
8. The water quality analysis method based on hyperspectral imaging of an unmanned aerial vehicle according to claim 5, wherein the distinguishing of the abnormal areas is judged according to the difference rate of preset pixels, and the salvaging, collecting or returning instruction is sent by a staff or an AI model at a monitoring service end.
9. The water quality analysis method based on hyperspectral imaging of an unmanned aerial vehicle according to claim 5, wherein when a plurality of abnormal areas exist, the abnormal areas are compared in pixel color and three-dimensional shape, and when the similarity exceeds a preset value, the abnormal areas are combined.
10. The method for analyzing water quality based on hyperspectral imaging of unmanned aerial vehicle according to claim 5, further comprising matching and corresponding water quality analysis results, historical operation instructions and spectrogram parameters and storing the results and the historical operation instructions to a monitoring server, sending out early warning information through an early warning module when the similarity of the spectrogram parameters exceeds a preset proportion next time, and executing corresponding actions in the corresponding historical operation instructions.
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