CN112630160A - Unmanned aerial vehicle track planning soil humidity monitoring method and system based on image acquisition and readable storage medium - Google Patents
Unmanned aerial vehicle track planning soil humidity monitoring method and system based on image acquisition and readable storage medium Download PDFInfo
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Abstract
The invention relates to an unmanned aerial vehicle track planning soil humidity monitoring method, a system and a readable storage medium based on image acquisition, wherein the method comprises the following steps: establishing a target area observation point, generating an acquisition mode, and generating unmanned aerial vehicle formation according to the acquisition mode to obtain formation information; acquiring initial state information of the unmanned aerial vehicle, and generating position information of the unmanned aerial vehicle; receiving a control instruction, generating a flight mode of the unmanned aerial vehicle, carrying out formation and aggregation according to the flight mode of the unmanned aerial vehicle, and generating a navigation constraint condition; according to the navigation constraint condition, establishing scheduling information and generating a reference track of the unmanned aerial vehicle; acquiring a remote sensing image of a target area, establishing a soil humidity prediction model, extracting a gray value of the remote sensing image, and acquiring the dielectric property of soil; analyzing the water content of the soil according to the dielectric property of the soil to obtain first humidity information; multiplying the scale, and obtaining the remote sensing image of the target area again to obtain second humidity information.
Description
Technical Field
The invention relates to a soil humidity monitoring method, in particular to an unmanned aerial vehicle flight path planning soil humidity monitoring method and system based on image acquisition and a readable storage medium.
Background
Soil moisture is an important basic parameter for climate, hydrology, ecology and agriculture, which directly controls the transport and balance of water and heat between the landing surface and the atmosphere. The change of soil humidity can cause the change of soil thermal property, earth surface optical property, thereby influencing the change of climate. Regional and large-scale land soil humidity change information is a very key factor for land-air interaction balance and land hydrology research, regional and global climate mode forecast result improvement, waterlogging and early and dry monitoring, crop growth situation assessment, research on natural and ecological environment problems and the like. Therefore, the research of regional or large-scale soil moisture is of particular significance, and the traditional ground observation station network cannot meet the requirement of time and space continuous dynamic change research of large-scale soil moisture. The existing widely applied microwave measurement mode has the defects of heavy equipment, large atmospheric interference on electric measurement wave signals in a short wave range, difficult control of wave bands and the like although the penetration force is strong, so that the development of the microwave measurement mode in the aspect of agricultural automation is limited. At present, a plurality of optical remote sensing means such as visible light near infrared thermal infrared and the like are used for obtaining soil humidity space-time distribution information, and the optical remote sensing has the advantages of small volume, simple imaging, short period, low cost and the like and is beneficial to future agricultural popularization. However, most of the current applications of the method depend on high-altitude aircraft loads, and the wave band of optical remote sensing cannot penetrate through cloud layers, so that the method is limited in practical application. And the presence of drones solves these problems.
In order to realize accurate control on soil humidity monitoring, a system matched with the system is required to be developed for control, a target area observation point is established, an acquisition mode is generated, unmanned aerial vehicle formation is generated according to the acquisition mode, scheduling information is established according to navigation constraint conditions, intelligent scheduling is carried out on the unmanned aerial vehicles, cooperative joint shooting of the unmanned aerial vehicles is realized, a soil humidity prediction model is established, a soil image gray value is extracted, and soil dielectric characteristics are obtained; according to soil dielectric property analysis soil water content, but in the control process, how to realize accurate control, the intelligent monitoring that realizes soil moisture all is the problem that needs a lot of waiting to solve.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a method and a system for monitoring the soil humidity in unmanned aerial vehicle track planning based on image acquisition and a readable storage medium.
In order to achieve the purpose, the invention adopts the technical scheme that: an unmanned aerial vehicle track planning soil humidity monitoring method based on image acquisition comprises the following steps:
establishing a target area observation point, generating an acquisition mode, and generating unmanned aerial vehicle formation according to the acquisition mode to obtain formation information;
acquiring initial state information of the unmanned aerial vehicle, and generating position information of the unmanned aerial vehicle;
receiving a control instruction, generating a flight mode of the unmanned aerial vehicle, carrying out formation and aggregation according to the flight mode of the unmanned aerial vehicle, and generating a navigation constraint condition;
according to the navigation constraint condition, establishing scheduling information and generating a reference track of the unmanned aerial vehicle;
acquiring a remote sensing image of a target area, and preprocessing the remote sensing image to obtain soil image information;
establishing a soil humidity prediction model, extracting a grey value of a remote sensing image, and acquiring the dielectric property of soil;
analyzing the water content of the soil according to the dielectric property of the soil to obtain first humidity information;
multiplying the scale, obtaining the remote sensing image of the target area again, and analyzing the soil water content to obtain second humidity information;
comparing the first humidity information with the second humidity information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
and if so, generating correction information to correct the flight mode of the unmanned aerial vehicle.
In a preferred embodiment of the invention, the initial state information of the unmanned aerial vehicle is obtained, and the position information of the unmanned aerial vehicle is generated;
receiving a control instruction, and generating a flight mode of the unmanned aerial vehicle;
carrying out formation aggregation according to the flight mode of the unmanned aerial vehicle to generate aggregation time information;
comparing the aggregation time information with preset time to obtain a deviation rate;
judging whether the deviation rate is larger than the deviation rate threshold value,
if the number of the unmanned aerial vehicles in the different zone bits is larger than the preset number, generating formation reconstruction information, and re-forming the unmanned aerial vehicles in the different zone bits;
and if the number of the queue holding messages is less than the preset value, generating the queue holding messages and transmitting the queue holding messages to the terminal.
In a preferred embodiment of the present invention, the method further comprises:
establishing a constraint model through big data, and generating a navigation time constraint condition through the constraint model;
acquiring an initial position and a target position of the unmanned aerial vehicle;
calculating standard navigation time from the starting position of the unmanned aerial vehicle to the target position according to the reference flight path;
collecting dynamic track information and calculating and predicting navigation time;
comparing the standard voyage time with the predicted voyage time to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the unmanned aerial vehicle node is larger than the target node, the navigation speed of the unmanned aerial vehicle node is adjusted.
In a preferred embodiment of the present invention, the acquiring a remote sensing image in a target area, and preprocessing the remote sensing image to obtain soil image information specifically includes:
obtaining remote sensing image, compressing and coding the image by integer wavelet transform, extracting the characteristic points of the remote sensing image, calculating the image gray value corresponding to each characteristic point,
carrying out similarity measurement on the remote sensing image characteristic points and the standard image characteristic points, and judging whether the pixel difference value between the image gray value of one characteristic point and the center of a preset template is smaller than a preset threshold value or not;
if the number of the feature points is less than the number of the feature points, the feature points and the central point are considered to be equivalent, the remote sensing image is successfully matched with the standard image, and the feature points with the same value are classified;
and if the error correction value is larger than the preset threshold value, correcting the remote sensing image through the error correction model.
In a preferred embodiment of the present invention, the sampling scale d is set1And d2Respectively obtain d1Remote sensing image x collected under scale1And d2Remote sensing image x collected under scale2，
To remote sensing image x1And remote sensing image x2Classifying the characteristic points with the same value to obtain a characteristic point set Q1And Q2；
From Q1And Q2Extracting 3 feature points from the feature point set respectively, judging whether the corresponding feature points satisfy constraint conditions,
if yes, carrying out remote sensing image x1And remote sensing image x2Matching to generate remote sensing image x1And remote sensing image x2Fusing the remote sensing images;
wherein, the feature points are set to Q1Respectively marking the extracted characteristic points as x11，x12，x13；
From a set of feature points Q2Respectively marking the extracted characteristic points as x21，x22，x23；
The constraint condition is
In the formula of1Denotes a first correction factor, λ2Denotes a second correction coefficient, and1≠λ2。
in a preferred embodiment of the present invention, the acquiring a remote sensing image of a target area, and preprocessing the remote sensing image further includes:
obtaining a remote sensing image, extracting boundary points of the remote sensing image, and filtering a disordered boundary to obtain a remote sensing image outline;
extracting boundary points as feature points, comparing similarity of a plurality of feature points,
judging whether the similarity is smaller than a preset threshold value,
if the difference is smaller than the preset value, the characteristic point registration is carried out,
after the registration of the feature points is finished, chain code scanning is carried out according to the remote sensing image outline, and the adaptability of the remote sensing image is adjusted;
the image adaptability adjustment comprises one or more combinations of image rotation, image light filling, image translation and image zooming;
the similarity is expressed by using the Euclidean distance between the feature points.
The invention also provides an unmanned aerial vehicle track planning soil humidity monitoring system based on image acquisition, which comprises: the system comprises a memory and a processor, wherein the memory comprises an unmanned aerial vehicle track planning soil humidity monitoring method program based on image acquisition, and the unmanned aerial vehicle track planning soil humidity monitoring method program based on image acquisition realizes the following steps when being executed by the processor:
establishing a target area observation point, generating an acquisition mode, and generating unmanned aerial vehicle formation according to the acquisition mode to obtain formation information;
acquiring initial state information of the unmanned aerial vehicle, and generating position information of the unmanned aerial vehicle;
receiving a control instruction, generating a flight mode of the unmanned aerial vehicle, carrying out formation and aggregation according to the flight mode of the unmanned aerial vehicle, and generating a navigation constraint condition;
according to the navigation constraint condition, establishing scheduling information and generating a reference track of the unmanned aerial vehicle;
acquiring a remote sensing image of a target area, and preprocessing the remote sensing image to obtain soil image information;
establishing a soil humidity prediction model, extracting a grey value of a remote sensing image, and acquiring the dielectric property of soil;
analyzing the water content of the soil according to the dielectric property of the soil to obtain first humidity information;
multiplying the scale, obtaining the remote sensing image of the target area again, and analyzing the soil water content to obtain second humidity information;
comparing the first humidity information with the second humidity information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
and if so, generating correction information to correct the flight mode of the unmanned aerial vehicle.
In a preferred embodiment of the present invention, the method further comprises:
establishing a constraint model through big data, and generating a navigation time constraint condition through the constraint model;
acquiring an initial position and a target position of the unmanned aerial vehicle;
calculating standard navigation time from the starting position of the unmanned aerial vehicle to the target position according to the reference flight path;
collecting dynamic track information and calculating and predicting navigation time;
comparing the standard voyage time with the predicted voyage time to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the unmanned aerial vehicle node is larger than the target node, the navigation speed of the unmanned aerial vehicle node is adjusted.
In a preferred embodiment of the present invention, the sampling scale d is set1And d2Respectively obtain d1Remote sensing image x collected under scale1And d2Remote sensing image x collected under scale2，
To remote sensing image x1And remote sensing image x2Classifying the characteristic points with the same value to obtain a characteristic point set Q1And Q2；
From Q1And Q2Extracting 3 feature points from the feature point set respectively, judging whether the corresponding feature points satisfy constraint conditions,
if yes, carrying out remote sensing image x1And remote sensing image x2Matching to generate remote sensing image x1And remote sensing image x2Fusing the remote sensing images;
wherein, the feature points are set to Q1Respectively marking the extracted characteristic points as x11，x12，x13；
From a set of feature points Q2Respectively marking the extracted characteristic points as x21，x22，x23；
The constraint condition is
In the formula of1Denotes a first correction factor, λ2Denotes a second correction coefficient, and1≠λ2。
the third aspect of the invention provides a computer-readable storage medium, wherein the computer-readable storage medium comprises a program of a method for monitoring soil humidity based on unmanned aerial vehicle track planning of image acquisition, and when the program of the method for monitoring soil humidity based on unmanned aerial vehicle track planning of image acquisition is executed by a processor, the steps of any one of the methods for monitoring soil humidity based on unmanned aerial vehicle track planning of image acquisition are realized.
The invention solves the defects in the background technology, and has the following beneficial effects:
(1) the method comprises the steps of extracting boundaries of collected soil images to obtain image outlines, registering the soil images obtained at different collection times to realize multi-source information fusion, monitoring differences of the soil images obtained at different times and under different conditions, aligning or matching different images of the same scene, matching two or more images of the same scene obtained at different weather, different brightness and different shooting angles, expressing the differences of the soil images at different resolutions, different gray attributes or different positions and the like, and realizing accurate analysis of a target area scene by fusing two or more image data.
(2) According to the difference of the dielectric properties of the soil with different water contents, the radar echo signals are also different, the relation between the scattering coefficient and the soil water content can be established, the water content in the soil is further analyzed, and the calculation result is accurate.
(3) The method comprises the steps of establishing a target area observation point, generating an acquisition mode, generating unmanned aerial vehicle formation according to the acquisition mode, establishing scheduling information according to navigation constraint conditions, carrying out intelligent scheduling on the unmanned aerial vehicle, realizing the cooperative joint shooting of the unmanned aerial vehicle, acquiring remote sensing images with different proportional scales by adjusting proportional scales, reversely correcting the flying mode of the unmanned aerial vehicle, and ensuring the clarity of a transmission picture of the unmanned aerial vehicle.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 shows a flow chart of an unmanned aerial vehicle track planning soil humidity monitoring method based on image acquisition according to the present invention;
FIG. 2 shows a flow chart of a method for unmanned aerial vehicle formation reconstruction;
FIG. 3 shows a flow chart of a remote sensing image rectification method;
FIG. 4 shows a flow chart of a method of remote sensing image pre-processing;
fig. 5 shows a flow chart of a method of formation of drones;
FIG. 6 shows a block diagram of an unmanned aerial vehicle track planning soil moisture monitoring system based on image acquisition;
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flow chart of an unmanned aerial vehicle track planning soil humidity monitoring method based on image acquisition.
As shown in fig. 1, a first aspect of the present invention provides a method for monitoring soil humidity in unmanned aerial vehicle track planning based on image acquisition, including:
s102, establishing a target area observation point, generating an acquisition mode, and generating unmanned aerial vehicle formation according to the acquisition mode to obtain formation information;
s104, acquiring initial state information of the unmanned aerial vehicle, and generating position information of the unmanned aerial vehicle;
s106, receiving a control instruction, generating an unmanned aerial vehicle flight mode, carrying out formation and aggregation according to the unmanned aerial vehicle flight mode, and generating a navigation constraint condition; according to the navigation constraint condition, establishing scheduling information and generating a reference track of the unmanned aerial vehicle;
s108, collecting a remote sensing image of the target area, and preprocessing the remote sensing image to obtain soil image information;
s110, establishing a soil humidity prediction model, extracting a gray value of a remote sensing image, obtaining soil dielectric characteristics, and analyzing soil water content according to the soil dielectric characteristics to obtain first humidity information;
s112, multiplying the scale, obtaining the remote sensing image of the target area again, and analyzing the soil moisture content to obtain second humidity information;
s114, comparing the first humidity information with the second humidity information to obtain a deviation rate, and judging whether the deviation rate is greater than a preset deviation rate threshold value;
and S116, if the value is larger than the preset value, generating correction information to correct the flight mode of the unmanned aerial vehicle.
It should be noted that the declination varies at different times, and the data collected at different times of the day varies greatly. As for the data acquisition as fast as possible, because in the acquisition process, because moisture evaporates, soil humidity changes at any time, in order to guarantee that the data acquisition is consistent with the photo, the time that the acquisition process needs is as short as possible. Third, the acquisition time per day should be calculated to ensure that the daily declination is minimal. The reason is that the sunlight irradiates the ground to cause shadows, the smaller the solar declination angle is, the fewer the shadows are, in the subsequent flight process of the unmanned aerial vehicle, the optimal flight time needs to be calculated in advance before each flight, and because the accuracy of the remote sensing image shot by the unmanned aerial vehicle is high, errors can be caused by any shadows on the ground.
It should be noted that, establishing a soil humidity prediction model, extracting a soil image gray value, and obtaining a soil dielectric property specifically includes:
receiving radar wave signal, making noise reduction treatment on the wave signal,
forming an echo signal by the soil reflected wave signal, and establishing a radar wave signal scattering coefficient and soil moisture content curve chart;
calculating the surface roughness of the soil according to the curve graph;
and (5) inverting a soil moisture reflection function according to the soil surface roughness, and obtaining the soil dielectric property.
As shown in fig. 2, the invention discloses a flow chart of a reconstruction method for formation of unmanned aerial vehicles;
in a preferred embodiment of the invention, the initial state information of the unmanned aerial vehicle is obtained, and the position information of the unmanned aerial vehicle is generated; the method specifically comprises the following steps:
s202, receiving a control instruction and generating an unmanned aerial vehicle flight mode;
s204, performing formation aggregation according to the flight mode of the unmanned aerial vehicle to generate aggregation time information;
s206, comparing the aggregation time information with preset time to obtain a deviation rate;
s208, judging whether the deviation rate is larger than the deviation rate threshold value,
s210, if the number of the unmanned aerial vehicles is larger than the preset number of the unmanned aerial vehicles, generating formation reconstruction information, and re-forming the unmanned aerial vehicles in different zone bits;
and S212, if the number is smaller than the preset value, generating formation holding information, and transmitting the formation holding information to the terminal.
In a preferred embodiment of the present invention, the method further comprises:
establishing a constraint model through big data, and generating a navigation time constraint condition through the constraint model;
acquiring an initial position and a target position of the unmanned aerial vehicle;
calculating standard navigation time from the starting position of the unmanned aerial vehicle to the target position according to the reference flight path;
collecting dynamic track information and calculating and predicting navigation time;
comparing the standard voyage time with the predicted voyage time to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the unmanned aerial vehicle node is larger than the target node, the navigation speed of the unmanned aerial vehicle node is adjusted.
As shown in FIG. 3, the invention discloses a flow chart of a remote sensing image rectification method;
in a preferred embodiment of the present invention, the acquiring a remote sensing image in a target area, and preprocessing the remote sensing image to obtain soil image information specifically includes:
s302, obtaining a remote sensing image, carrying out compression coding on the image by adopting integer wavelet transformation, extracting the characteristic points of the remote sensing image, calculating the image gray value corresponding to each characteristic point,
s304, carrying out similarity measurement on the remote sensing image characteristic points and the standard image characteristic points, and judging whether the pixel difference value between the image gray value of one characteristic point and the center of a preset template is smaller than a preset threshold value or not;
s306, if the number of the feature points is smaller than the number of the feature points, the feature points and the central point are considered to be identical, the remote sensing image is successfully matched with the standard image, and the feature points with the identical values are classified;
and S308, if the error correction value is larger than the preset threshold value, correcting the remote sensing image through the error correction model.
It should be noted that typical feature points include corner points, line intersections, discontinuities, points with maximum curvature on the contour, centroids of closed curves, and the like, and if there is no shape or curve feature, points of interest in the region, such as operators, may be selected to select points with local maximum variation. The point features are easy to mark and operate, and can reflect the essential features of the image. An appropriate number of feature points is ensured because the registration operation requires enough feature points, while too many feature points make the registration difficult. The feature registration searches for the corresponding relation of the feature points in the two images, the most commonly adopted method is a loose registration algorithm, and the singularity of registration is eliminated through iteration according to the information of all points in the feature point set and the information of the mutual relation between the points, so that the similarity reaches the maximum.
In a preferred embodiment of the present invention, the sampling scale d is set1And d2Respectively obtain d1Remote sensing image x collected under scale1And d2Remote sensing image x collected under scale2，
To remote sensing image x1And remote sensing image x2Classifying the characteristic points with the same value to obtain a characteristic point set Q1And Q2；
From Q1And Q2Extracting 3 feature points from the feature point set respectively, judging whether the corresponding feature points satisfy constraint conditions,
if yes, carrying out remote sensing image x1And remote sensing image x2Matching to generate remote sensing image x1And remote sensing image x2Fusing the remote sensing images;
wherein, the feature points are set to Q1Respectively marking the extracted characteristic points as x11，x12，x13；
From a set of feature points Q2Respectively marking the extracted characteristic points as x21，x22，x23；
The constraint condition is
In the formula of1Denotes a first correction factor, λ2Denotes a second correction coefficient, and1≠λ2。
as shown in FIG. 4, the present invention discloses a flow chart of a remote sensing image preprocessing method;
in a preferred embodiment of the present invention, the acquiring a remote sensing image of a target area, and preprocessing the remote sensing image further includes:
s402, obtaining a remote sensing image, extracting boundary points of the remote sensing image, and filtering a disordered boundary to obtain a remote sensing image outline;
s404, extracting boundary points as feature points, comparing the similarity of a plurality of feature points,
s406, judging whether the similarity is smaller than a preset threshold value,
s408, if the difference is smaller than the preset value, carrying out feature point registration,
s410, after the registration of the feature points is finished, chain code scanning is carried out according to the remote sensing image outline, and the adaptability of the remote sensing image is adjusted;
the image adaptability adjustment comprises one or more combinations of image rotation, image light filling, image translation and image zooming;
the similarity is expressed by using the Euclidean distance between the feature points.
It should be noted that, by extracting the boundary of the collected soil image to obtain the image contour, and registering the soil images obtained at different collection times, multi-source information fusion is realized, the differences of the soil images obtained at different times and under different conditions can be monitored, image registration is to align or match different images of the same scene, two or more images of the same scene obtained at different weather, different brightness and different shooting angles are matched, the differences of the soil images are represented in different resolutions, different gray attributes or different positions, and the like, and the accurate analysis of the target area scene is realized by fusing the data of the two or more images.
As shown in fig. 5, the present invention discloses a flow chart of a method for formation of unmanned aerial vehicles;
according to the embodiment of the invention, formation and aggregation are carried out according to the flight mode of the unmanned aerial vehicle, and the method further comprises the following steps:
s502, establishing a three-dimensional scene, extracting location information of the virtual unmanned aerial vehicles, establishing an unmanned aerial vehicle formation model, and S504, generating formation holding information of the virtual unmanned aerial vehicles according to the unmanned aerial vehicle formation model;
s506, generating a virtual unmanned aerial vehicle formation mode according to the formation keeping information of the virtual unmanned aerial vehicles;
s508, unmanned aerial vehicle formation is carried out according to the virtual unmanned aerial vehicle formation mode, and result information is obtained;
s510, comparing the result information with the actual detection information; obtaining unmanned aerial vehicle formation deviation information;
s512, judging whether the deviation information is larger than a preset threshold value,
and S514, if the number of the virtual unmanned aerial vehicles is larger than the preset number, generating correction information and correcting the formation mode of the virtual unmanned aerial vehicles.
It should be noted that by creating a three-dimensional scene of a target area, an observation scene and an observation effect of an observation target area can be simulated on line by simulating an unmanned aerial vehicle, problems found in the soil monitoring and remote sensing image acquisition processes can be adjusted on line, occurrence of emergency in the processing process is reduced, and soil humidity monitoring efficiency and accuracy are improved.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring a dynamic track of the unmanned aerial vehicle, and decomposing track points of the dynamic track to obtain three-dimensional coordinate information of the track points; performing dimension reduction processing on the three-dimensional coordinate information to obtain one-dimensional information of the track point;
iterative solution is carried out on the one-dimensional information of the track point to obtain a result value;
judging whether the result value is larger than a preset threshold value or not;
if so, continuing iteration;
and if the number of the track points is smaller than the preset value, generating track point information, and coupling the plurality of track point information to obtain dynamic track constraint information.
As shown in fig. 6, the invention discloses a block diagram of a soil humidity monitoring system for unmanned aerial vehicle track planning based on image acquisition;
the second aspect of the present invention also provides an unmanned aerial vehicle track planning soil humidity monitoring system 6 based on image acquisition, wherein the system 6 comprises: the system comprises a memory 61 and a processor 62, wherein the memory comprises an unmanned aerial vehicle track planning soil humidity monitoring method program based on image acquisition, and the unmanned aerial vehicle track planning soil humidity monitoring method program based on image acquisition realizes the following steps when being executed by the processor:
establishing a target area observation point, generating an acquisition mode, and generating unmanned aerial vehicle formation according to the acquisition mode to obtain formation information;
acquiring initial state information of the unmanned aerial vehicle, and generating position information of the unmanned aerial vehicle;
receiving a control instruction, generating a flight mode of the unmanned aerial vehicle, carrying out formation and aggregation according to the flight mode of the unmanned aerial vehicle, and generating a navigation constraint condition;
according to the navigation constraint condition, establishing scheduling information and generating a reference track of the unmanned aerial vehicle;
acquiring a remote sensing image of a target area, and preprocessing the remote sensing image to obtain soil image information;
establishing a soil humidity prediction model, extracting a grey value of a remote sensing image, and acquiring the dielectric property of soil;
analyzing the water content of the soil according to the dielectric property of the soil to obtain first humidity information;
multiplying the scale, obtaining the remote sensing image of the target area again, and analyzing the soil water content to obtain second humidity information;
comparing the first humidity information with the second humidity information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
and if so, generating correction information to correct the flight mode of the unmanned aerial vehicle.
It should be noted that the declination angle changes at different times, and the data collected at different times of the day are very different. As for the data acquisition as fast as possible, because in the acquisition process, because moisture evaporates, soil humidity changes at any time, in order to guarantee that the data acquisition is consistent with the photo, the time that the acquisition process needs is as short as possible. Third, the acquisition time per day should be calculated to ensure that the daily declination is minimal. The reason is that the sunlight irradiates the ground to cause shadows, the smaller the solar declination angle is, the fewer the shadows are, in the subsequent flight process of the unmanned aerial vehicle, the optimal flight time needs to be calculated in advance before each flight, and because the accuracy of the remote sensing image shot by the unmanned aerial vehicle is high, errors can be caused by any shadows on the ground.
It should be noted that, establishing a soil humidity prediction model, extracting a soil image gray value, and obtaining a soil dielectric property specifically includes:
receiving radar wave signal, making noise reduction treatment on the wave signal,
forming an echo signal by the soil reflected wave signal, and establishing a radar wave signal scattering coefficient and soil moisture content curve chart;
calculating the surface roughness of the soil according to the curve graph;
and (5) inverting a soil moisture reflection function according to the soil surface roughness, and obtaining the soil dielectric property.
In a preferred embodiment of the present invention, the method further comprises:
establishing a constraint model through big data, and generating a navigation time constraint condition through the constraint model;
acquiring an initial position and a target position of the unmanned aerial vehicle;
calculating standard navigation time from the starting position of the unmanned aerial vehicle to the target position according to the reference flight path;
collecting dynamic track information and calculating and predicting navigation time;
comparing the standard voyage time with the predicted voyage time to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the unmanned aerial vehicle node is larger than the target node, the navigation speed of the unmanned aerial vehicle node is adjusted.
In a preferred embodiment of the present invention, the sampling scale d is set1And d2Respectively obtain d1Remote sensing image x collected under scale1And d2Remote sensing image x collected under scale2，
To remote sensing image x1And remote sensing image x2Classifying the characteristic points with the same value to obtain a characteristic point set Q1And Q2；
From Q1And Q2Extracting 3 feature points from the feature point set respectively, judging whether the corresponding feature points satisfy constraint conditions,
if yes, carrying out remote sensing image x1And remote sensing image x2Matching to generate remote sensing image x1And remote sensing image x2Fusing the remote sensing images;
wherein, the feature points are set to Q1Respectively marking the extracted characteristic points as x11，x12，x13；
From a set of feature points Q2Respectively marking the extracted characteristic points as x21，x22，x23；
The constraint condition is
In the formula of1Denotes a first correction factor, λ2Denotes a second correction coefficient, and1≠λ2。
in a preferred embodiment of the invention, the initial state information of the unmanned aerial vehicle is obtained, and the position information of the unmanned aerial vehicle is generated;
receiving a control instruction, and generating a flight mode of the unmanned aerial vehicle;
carrying out formation aggregation according to the flight mode of the unmanned aerial vehicle to generate aggregation time information;
comparing the aggregation time information with preset time to obtain a deviation rate;
judging whether the deviation rate is larger than the deviation rate threshold value,
if the number of the unmanned aerial vehicles in the different zone bits is larger than the preset number, generating formation reconstruction information, and re-forming the unmanned aerial vehicles in the different zone bits;
and if the number of the queue holding messages is less than the preset value, generating the queue holding messages and transmitting the queue holding messages to the terminal.
In a preferred embodiment of the present invention, the acquiring a remote sensing image in a target area, and preprocessing the remote sensing image to obtain soil image information specifically includes:
obtaining remote sensing image, compressing and coding the image by integer wavelet transform, extracting the characteristic points of the remote sensing image, calculating the image gray value corresponding to each characteristic point,
carrying out similarity measurement on the remote sensing image characteristic points and the standard image characteristic points, and judging whether the pixel difference value between the image gray value of one characteristic point and the center of a preset template is smaller than a preset threshold value or not;
if the number of the feature points is less than the number of the feature points, the feature points and the central point are considered to be equivalent, the remote sensing image is successfully matched with the standard image, and the feature points with the same value are classified;
and if the error correction value is larger than the preset threshold value, correcting the remote sensing image through the error correction model.
It should be noted that typical feature points include corner points, line intersections, discontinuities, points with maximum curvature on the contour, centroids of closed curves, and the like, and if there is no shape or curve feature, points of interest in the region, such as operators, may be selected to select points with local maximum variation. The point features are easy to mark and operate, and can reflect the essential features of the image. An appropriate number of feature points is ensured because the registration operation requires enough feature points, while too many feature points make the registration difficult. The feature registration searches for the corresponding relation of the feature points in the two images, the most commonly adopted method is a loose registration algorithm, and the singularity of registration is eliminated through iteration according to the information of all points in the feature point set and the information of the mutual relation between the points, so that the similarity reaches the maximum.
In a preferred embodiment of the present invention, the acquiring a remote sensing image of a target area, and preprocessing the remote sensing image further includes:
obtaining a remote sensing image, extracting boundary points of the remote sensing image, and filtering a disordered boundary to obtain a remote sensing image outline;
extracting boundary points as feature points, comparing similarity of a plurality of feature points,
judging whether the similarity is smaller than a preset threshold value,
if the difference is smaller than the preset value, the characteristic point registration is carried out,
after the registration of the feature points is finished, chain code scanning is carried out according to the remote sensing image outline, and the adaptability of the remote sensing image is adjusted;
the image adaptability adjustment comprises one or more combinations of image rotation, image light filling, image translation and image zooming;
the similarity is expressed by using the Euclidean distance between the feature points.
It should be noted that, by extracting the boundary of the collected soil image to obtain the image contour, and registering the soil images obtained at different collection times, multi-source information fusion is realized, the differences of the soil images obtained at different times and under different conditions can be monitored, image registration is to align or match different images of the same scene, two or more images of the same scene obtained at different weather, different brightness and different shooting angles are matched, the differences of the soil images are represented in different resolutions, different gray attributes or different positions, and the like, and the accurate analysis of the target area scene is realized by fusing the data of the two or more images.
According to the embodiment of the invention, formation and aggregation are carried out according to the flight mode of the unmanned aerial vehicle, and the method further comprises the following steps:
establishing a three-dimensional scene, extracting the location information of the virtual unmanned aerial vehicle, establishing an unmanned aerial vehicle formation model,
generating virtual unmanned aerial vehicle formation keeping information according to the unmanned aerial vehicle formation model;
generating a virtual unmanned aerial vehicle formation mode according to the formation keeping information of the virtual unmanned aerial vehicles;
forming unmanned aerial vehicles according to the virtual unmanned aerial vehicle forming mode to obtain result information;
comparing the result information with actual detection information; obtaining unmanned aerial vehicle formation deviation information;
judging whether the deviation information is larger than a preset threshold value,
and if so, generating correction information to correct the formation mode of the virtual unmanned aerial vehicles.
It should be noted that by creating a three-dimensional scene of a target area, an observation scene and an observation effect of an observation target area can be simulated on line by simulating an unmanned aerial vehicle, problems found in the soil monitoring and remote sensing image acquisition processes can be adjusted on line, occurrence of emergency in the processing process is reduced, and soil humidity monitoring efficiency and accuracy are improved.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring a dynamic track of the unmanned aerial vehicle, and decomposing track points of the dynamic track to obtain three-dimensional coordinate information of the track points; performing dimension reduction processing on the three-dimensional coordinate information to obtain one-dimensional information of the track point;
iterative solution is carried out on the one-dimensional information of the track point to obtain a result value;
judging whether the result value is larger than a preset threshold value or not;
if so, continuing iteration;
and if the number of the track points is smaller than the preset value, generating track point information, and coupling the plurality of track point information to obtain dynamic track constraint information.
The third aspect of the invention provides a computer-readable storage medium, wherein the computer-readable storage medium comprises a program of a method for monitoring soil humidity based on unmanned aerial vehicle track planning of image acquisition, and when the program of the method for monitoring soil humidity based on unmanned aerial vehicle track planning of image acquisition is executed by a processor, the steps of any one of the methods for monitoring soil humidity based on unmanned aerial vehicle track planning of image acquisition are realized.
In summary, the acquired soil images are subjected to boundary extraction to obtain image profiles, and the soil images acquired at different acquisition times are registered to realize multi-source information fusion, so that the differences of the soil images acquired at different times and under different conditions can be monitored, the image registration is to align or match different images of the same scene, two or more images acquired at different weather, different brightness and different shooting angles of the same scene are matched, the differences of the soil images are shown in different resolutions, different gray attributes or different positions, and the like, and the target area scene is accurately analyzed by fusing the data of the two or more images.
According to the difference of the dielectric properties of the soil with different water contents, the radar echo signals are also different, the relation between the scattering coefficient and the soil water content can be established, the water content in the soil is further analyzed, and the calculation result is accurate.
The method comprises the steps of establishing a target area observation point, generating an acquisition mode, generating unmanned aerial vehicle formation according to the acquisition mode, establishing scheduling information according to navigation constraint conditions, carrying out intelligent scheduling on the unmanned aerial vehicle, realizing the cooperative joint shooting of the unmanned aerial vehicle, acquiring remote sensing images with different proportional scales by adjusting proportional scales, reversely correcting the flying mode of the unmanned aerial vehicle, and ensuring the clarity of a transmission picture of the unmanned aerial vehicle.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of a unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An unmanned aerial vehicle track planning soil humidity monitoring method based on image acquisition is characterized by comprising the following steps:
establishing a target area observation point, generating an acquisition mode, and generating unmanned aerial vehicle formation according to the acquisition mode to obtain formation information;
acquiring initial state information of the unmanned aerial vehicle, and generating position information of the unmanned aerial vehicle;
receiving a control instruction, generating a flight mode of the unmanned aerial vehicle, carrying out formation and aggregation according to the flight mode of the unmanned aerial vehicle, and generating a navigation constraint condition;
according to the navigation constraint condition, establishing scheduling information and generating a reference track of the unmanned aerial vehicle;
acquiring a remote sensing image of a target area, and preprocessing the remote sensing image to obtain soil image information;
establishing a soil humidity prediction model, extracting a grey value of a remote sensing image, and acquiring the dielectric property of soil;
analyzing the water content of the soil according to the dielectric property of the soil to obtain first humidity information;
multiplying the scale, obtaining the remote sensing image of the target area again, and analyzing the soil water content to obtain second humidity information;
comparing the first humidity information with the second humidity information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
and if so, generating correction information to correct the flight mode of the unmanned aerial vehicle.
2. The method for monitoring the soil humidity based on the image acquisition for unmanned aerial vehicle track planning of claim 1, wherein the method comprises the steps of obtaining the initial state information of the unmanned aerial vehicle, and generating the position information of the unmanned aerial vehicle; the method specifically comprises the following steps:
receiving a control instruction, and generating a flight mode of the unmanned aerial vehicle;
carrying out formation aggregation according to the flight mode of the unmanned aerial vehicle to generate aggregation time information;
comparing the aggregation time information with preset time to obtain a deviation rate;
judging whether the deviation rate is larger than the deviation rate threshold value,
if the number of the unmanned aerial vehicles in the different zone bits is larger than the preset number, generating formation reconstruction information, and re-forming the unmanned aerial vehicles in the different zone bits;
and if the number of the queue holding messages is less than the preset value, generating the queue holding messages and transmitting the queue holding messages to the terminal.
3. The method for monitoring soil humidity based on image acquisition for unmanned aerial vehicle track planning according to claim 2, further comprising:
establishing a constraint model through big data, and generating a navigation time constraint condition through the constraint model;
acquiring an initial position and a target position of the unmanned aerial vehicle;
calculating standard navigation time from the starting position of the unmanned aerial vehicle to the target position according to the reference flight path;
collecting dynamic track information and calculating and predicting navigation time;
comparing the standard voyage time with the predicted voyage time to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the unmanned aerial vehicle node is larger than the target node, the navigation speed of the unmanned aerial vehicle node is adjusted.
4. The method for monitoring the soil humidity in the unmanned aerial vehicle track planning based on image acquisition as claimed in claim 2, wherein the method comprises the steps of acquiring a remote sensing image in a target area, preprocessing the remote sensing image to obtain soil image information, and specifically comprises:
obtaining remote sensing image, compressing and coding the image by integer wavelet transform, extracting the characteristic points of the remote sensing image, calculating the image gray value corresponding to each characteristic point,
carrying out similarity measurement on the remote sensing image characteristic points and the standard image characteristic points, and judging whether the pixel difference value between the image gray value of one characteristic point and the center of a preset template is smaller than a preset threshold value or not;
if the number of the feature points is less than the number of the feature points, the feature points and the central point are considered to be equivalent, the remote sensing image is successfully matched with the standard image, and the feature points with the same value are classified;
and if the error correction value is larger than the preset threshold value, correcting the remote sensing image through the error correction model.
5. According to claim4 the unmanned aerial vehicle track planning soil humidity monitoring method based on image acquisition is characterized in that a sampling proportion scale d is set1And d2Respectively obtain d1Remote sensing image x collected under scale1And d2Remote sensing image x collected under scale2，
To remote sensing image x1And remote sensing image x2Classifying the characteristic points with the same value to obtain a characteristic point set Q1And Q2；
From Q1And Q2Extracting 3 feature points from the feature point set respectively, judging whether the corresponding feature points satisfy constraint conditions,
if yes, carrying out remote sensing image x1And remote sensing image x2Matching to generate remote sensing image x1And remote sensing image x2Fusing the remote sensing images;
wherein, the feature points are set to Q1Respectively marking the extracted characteristic points as x11，x12，x13；
From a set of feature points Q2Respectively marking the extracted characteristic points as x21，x22，x23；
The constraint condition is
In the formula of1Denotes a first correction factor, λ2Denotes a second correction coefficient, and1≠λ2。
6. the method for monitoring the soil humidity in the unmanned aerial vehicle track planning based on the image acquisition as claimed in claim 1, wherein a remote sensing image of a target area is acquired, and the remote sensing image is preprocessed, further comprising:
obtaining a remote sensing image, extracting boundary points of the remote sensing image, and filtering a disordered boundary to obtain a remote sensing image outline;
extracting boundary points as feature points, comparing similarity of a plurality of feature points,
judging whether the similarity is smaller than a preset threshold value,
if the difference is smaller than the preset value, the characteristic point registration is carried out,
after the registration of the feature points is finished, chain code scanning is carried out according to the remote sensing image outline, and the adaptability of the remote sensing image is adjusted;
the image adaptability adjustment comprises one or more combinations of image rotation, image light filling, image translation and image zooming;
the similarity is expressed by using the Euclidean distance between the feature points.
7. The utility model provides an unmanned aerial vehicle flight path planning soil moisture monitoring system based on image acquisition which characterized in that, this system includes: the system comprises a memory and a processor, wherein the memory comprises an unmanned aerial vehicle track planning soil humidity monitoring method program based on image acquisition, and the unmanned aerial vehicle track planning soil humidity monitoring method program based on image acquisition realizes the following steps when being executed by the processor:
establishing a target area observation point, generating an acquisition mode, and generating unmanned aerial vehicle formation according to the acquisition mode to obtain formation information;
acquiring initial state information of the unmanned aerial vehicle, and generating position information of the unmanned aerial vehicle;
receiving a control instruction, generating a flight mode of the unmanned aerial vehicle, carrying out formation and aggregation according to the flight mode of the unmanned aerial vehicle, and generating a navigation constraint condition;
according to the navigation constraint condition, establishing scheduling information and generating a reference track of the unmanned aerial vehicle;
acquiring a remote sensing image of a target area, and preprocessing the remote sensing image to obtain soil image information;
establishing a soil humidity prediction model, extracting a grey value of a remote sensing image, and acquiring the dielectric property of soil;
analyzing the water content of the soil according to the dielectric property of the soil to obtain first humidity information;
multiplying the scale, obtaining the remote sensing image of the target area again, and analyzing the soil water content to obtain second humidity information;
comparing the first humidity information with the second humidity information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
and if so, generating correction information to correct the flight mode of the unmanned aerial vehicle.
8. The system of claim 7, further comprising:
establishing a constraint model through big data, and generating a navigation time constraint condition through the constraint model;
acquiring an initial position and a target position of the unmanned aerial vehicle;
calculating standard navigation time from the starting position of the unmanned aerial vehicle to the target position according to the reference flight path;
collecting dynamic track information and calculating and predicting navigation time;
comparing the standard voyage time with the predicted voyage time to obtain a deviation ratio;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if the unmanned aerial vehicle node is larger than the target node, the navigation speed of the unmanned aerial vehicle node is adjusted.
9. The system of claim 7, wherein a sampling scale d is set for the unmanned aerial vehicle track planning soil moisture monitoring system based on image acquisition1And d2Respectively obtain d1Remote sensing image x collected under scale1And d2Remote sensing image x collected under scale2，
To remote sensing image x1And remote sensing image x2Classifying the characteristic points with the same value to obtain a characteristic point set Q1And Q2；
From Q1And Q2Extracting 3 feature points from the feature point set respectively, judging whether the corresponding feature points satisfy constraint conditions,
if yes, carrying out remote sensing image x1And remote sensing image x2Match, generate remoteImage sensing x1And remote sensing image x2Fusing the remote sensing images;
wherein, the feature points are set to Q1Respectively marking the extracted characteristic points as x11，x12，x13；
From a set of feature points Q2Respectively marking the extracted characteristic points as x21，x22，x23；
The constraint condition is
In the formula of1Denotes a first correction factor, λ2Denotes a second correction coefficient, and1≠λ2。
10. a computer-readable storage medium, wherein the computer-readable storage medium includes a program for monitoring soil humidity based on unmanned aerial vehicle track planning of image acquisition, and when the program is executed by a processor, the steps of the method for monitoring soil humidity based on unmanned aerial vehicle track planning of image acquisition according to any one of claims 1 to 6 are implemented.
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2020
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