CN116680994A - Aerosol tracking and wind field inversion method and system based on laser radar - Google Patents
Aerosol tracking and wind field inversion method and system based on laser radar Download PDFInfo
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- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
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Abstract
The application discloses a laser radar-based aerosol tracking and wind field inversion method and system, wherein the method comprises the steps of selecting a fish shoal with a plurality of targets as a tracking object, calculating the position of the fish shoal at the next moment through a plurality of cross-correlation functions, and carrying out multi-target optimization solution to obtain the overall spatial distribution condition and displacement condition of the fish shoal, wherein the system comprises a laser radar signal transmitting and receiving processing module, a data interpolation and image processing module, a simulated fish shoal generating module, a cross-correlation function calculating module, a fish shoal center point and distance calculating module, a multi-target optimization function solving module, an aerosol and wind vector calculating module and the like.
Description
Technical Field
The application relates to the field of atmosphere detection, in particular to an aerosol tracking and wind field inversion method and system based on a laser radar
Background
Aerosol refers to a dispersion system formed by stably suspending solid or liquid particles in a gaseous medium, and has important influence on the problems of atmospheric pollution, atmospheric radiation, climate change and the like. The monitoring of the aerosol can be used for solving the source and distribution of the aerosol, and provides important basis for preventing and treating the atmospheric pollution. The wind field inversion is to calculate wind field parameters with a certain spatial scale by using the flowing condition of gas or substances in the air through physical, mathematical and other methods. The fields of aerospace, weather forecast, wind power generation and the like are not separated from accurate wind field data.
At present, the laser radar is used as a novel atmosphere detection means and can be used for detecting the spatial-temporal distribution of the optical thickness, the reflectivity and the volume concentration of aerosol. However, the research and application of the laser radar on the aspect of aerosol moving path tracking are less, and the existing observation and tracking are difficult to consider the changes of rotation, deformation, divergence and the like of aerosol lumps in the moving process, so that the accuracy and reliability of the result are reduced, and larger errors are brought to the research. In addition, lidar is also a main means of measuring wind fields, and can measure radial wind speed by using doppler effect, but wide-range horizontal wind field detection is still a difficult problem.
Disclosure of Invention
The application aims to: in order to overcome the defects in the prior art, the application provides a laser radar-based aerosol tracking and wind field inversion method and system for obtaining the overall spatial distribution condition and displacement condition of a fish shoal by selecting the fish shoal with a plurality of targets as a tracking object, calculating a plurality of cross-correlation functions, and performing multi-target optimization solution on the fish shoal position at the next moment.
The technical scheme is as follows: in order to achieve the above purpose, the application adopts the following technical scheme: an aerosol tracking and wind field inversion method based on a laser radar comprises the following steps:
step 1: according to the characteristics of the range, the height, the distance and the azimuth angle of the detected target, determining the scanning elevation angle, the range and the scanning frequency of the laser radar, and continuously scanning the target area for multiple times by using the laser radar;
step 2: acquiring echo signal intensity corresponding to each time through each scanning, acquiring parameters representing spatial distribution information of aerosol concentration data, and acquiring an aerosol concentration PPI image under a Cartesian coordinate system through interpolating the aerosol concentration data to lattice points in the Cartesian coordinate system;
step 3: selecting images corresponding to the first time and the second time from the obtained aerosol concentration PPI images with different times, and respectively marking the images asTime imageTime image, time interval of two times is；
Step 4: at the position ofIn the time images, selecting a target aerosol area to be detected and tracked by a frame, regarding aerosol concentration data corresponding to each grid point in the area as a piece of simulated fish, and recording the position coordinates of each piece of simulated fish, wherein a set formed by all simulated fish in the area is called a simulated fish swarm;
step 5: calculating the fish shoal atThe center point in the time image is positioned;
step 6: at the position ofIn the time images, a square comparison area is constructed by taking any lattice point of the simulated fish as the center;
step 7: will beComparison area of each simulated fish in time imagesPerforming cross-correlation operation on all grid points in the time images to obtain the correspondence of the comparison area of the simulated fishCross-correlation function of all grid points in time image and simulated fish in time imageCoordinates in the time image;
step 8: by simulating the fish in each stripObtaining the coordinates in the time images to obtain the whole fish schoolCalculating the distance from each simulated fish to the center point by the center point in the time image;
step 9: according to the calculated cross-correlation function of each simulated fish and the relationship between each simulated fish and the whole fish shoalThe distance of the center point in the time image is used for constructing the whole fish schoolMulti-objective optimization function of distribution in time-dependent images, whereinThe multi-objective optimization function is a function that solves an optimal solution for a problem with multiple objective functions based on given decision variables and constraint conditions;
step 10: obtaining a solution set corresponding to the maximum value of the multi-objective optimization function by utilizing a multi-objective particle swarm optimization algorithm, and further obtaining the fish swarm in-placeCenter point position in the time image;
step 11: obtaining the wind speed and direction characteristics of a background field through the displacement vector of the target aerosol area in a period of time;
step 12: repeating the steps, selecting different target aerosol areas to construct a simulated fish shoal, and realizing inversion of a horizontal wind field; and selecting more time images to realize the tracking of the moving path of the aerosol mass.
As a preferred embodiment of the present application: the parameters of the spatial distribution information of the characterization aerosol concentration data in the step 2 comprise a carrier-to-noise ratio, a signal-to-noise ratio, a backscattering coefficient, an extinction coefficient and PR2 parameters.
As a preferred embodiment of the present application: in the step 4, the number of lattice points in the aerosol area is，Indicating the total number of rows in the area,representing the total number of columns in the region; consider the area as a single memberFish groups of strips of simulated fish, which are then distributed according to the above-mentioned areaIs sequentially denoted asThe method comprises the steps of carrying out a first treatment on the surface of the Each simulated fishAt the position ofThe position of the time image is recorded as coordinates。
As a preferred embodiment of the present application: in the step 5, the center point is wherein ，。
As a preferred embodiment of the present application: in the step 6, the fish is simulated by using any one of the fishLattice point of (2)Is used as a center of the water tank,is of side lengthConstructing square alignment areas
The number of lattice points in the comparison area isAnd each.
As a preferred embodiment of the present application: in the step 7Each simulated fish in the time imageIs a reference to the alignment region of (a)And (3) withPerforming cross-correlation operation on all grid points in the time image to obtainCorresponding toIn time imagesCross-correlation function of individual grid points，Andto simulate fishAt the position ofCoordinates in the time image;
taking any one of the simulated fishThe aerosol concentration distribution of the alignment area A of (2) is，Aerosol concentration fraction of time-dependent imagesThe cloth is；
Pretreatment of the data, the data willPerforming standardization and zero padding treatment to obtainFor a pair ofStandardized processing is carried out to obtain。
For a pair of，Performing fast Fourier transform to obtain:
；
；
wherein the FFT represents a fast fourier transform;
from the convolution and cross-correlation functions:
；
the convolution theorem has:
；
in the formula Is thatIs used for the fast fourier transform of (a),is thatIs a complex conjugate of (a) and (b). The inverse fast fourier transform is performed with:
；
wherein Respectively isAndis set in the standard deviation of (2),representing the inverse fast fourier transform.
As a preferred embodiment of the present application: in the step 8, the fish school is inThe center point in the time image is, wherein ，，Andrepresenting simulated fishAt the position ofCoordinates in time images, any one of which simulates fishThe distance to the center point is a function of:
。
as a preferred embodiment of the present application: in the step 9, the whole fish school is constructedA multi-objective optimization function of distribution in a time-dependent image, comprising:
；
wherein ,andrepresenting simulated fishAt the position ofThe coordinates in the time-dependent image are,；representation ofShoal in time-of-day imagesThe degree of similarity of fish school in the time images;representing the degree of dispersion of the fish school;、is a weight coefficient representing the degree of importance of the degree of similarity and degree of dispersion in the multi-objective optimization.
As a preferred embodiment of the present application: in the step 10, the maximum value of the multi-objective optimization function FThe corresponding solution set isFurther calculate the fish swarmCenter point in time-dependent imageThe method comprises the steps of carrying out a first treatment on the surface of the In the step 11, a vector is constructedVector quantityI.e. the target aerosol region is inDisplacement in time, thereby obtaining a velocity vectorAnd the wind speed and direction characteristics of the background field are shown.
In another aspect, a lidar-based aerosol tracking and wind field inversion system, the system comprising:
and the laser radar signal transmitting and receiving processing module is used for continuously scanning the target area for multiple times according to the set scanning elevation angle, the range and the scanning frequency. Acquiring echo signal intensities of a plurality of times, and processing the echo signal intensities into spatial distribution information of aerosol concentration data for output;
the input end of the data interpolation and image processing module is connected with the output end of the laser radar signal transmitting and receiving processing module, and is used for interpolating aerosol concentration data in a polar coordinate form into grid points in a Cartesian coordinate system and generating corresponding aerosol concentration PPI images according to the data of different times; the input end of the analog fish school generating module is connected with the output end of the data interpolation and image processing module and is used for outputtingSelecting a target aerosol area from the time images to construct a simulated fish swarm, recording information of each simulated fish, and further obtaining comparison area information of each simulated fish;
the input end of the cross-correlation function calculation module is connected with the output end of the simulated fish swarm generation module and is used for performing cross-correlation operation to obtain the comparison area and the comparison area of each simulated fishCross-correlation function of time images;
the input end of the shoal center point and distance calculation module is connected with the output end of the cross-correlation function calculation module, and is used for calculating the position of the shoal center point and the distance between each simulated fish and the center point in each time;
the input end of the multi-objective optimization function solving module is connected with the center point of the fish school and the output end of the distance calculating module, and the multi-objective optimization function can be solved by utilizing a particle swarm optimization algorithm to obtainOptimal shoal distribution of time;
the input end of the calculation module of aerosol and wind vector is connected with the output end of the multi-objective optimization function solving module, and is used for calculating the displacement vector of the objective aerosol area and the wind vector of the background field;
and the input end of the iterative calculation module for path tracking and wind field inversion is connected with the calculation module for aerosol and wind vectors, and the iterative calculation module is used for carrying out cyclic calculation on data of a plurality of time and a plurality of areas to obtain aerosol paths and background wind field data in the whole scanning time.
Compared with the prior art, the application has the following beneficial effects:
the innovation of the application is that a novel fish-like swarm algorithm is provided, the algorithm obtains the overall space distribution condition and displacement condition of the fish shoal by selecting the fish shoal with a plurality of targets as a tracking object, and carrying out multi-target optimization solving on the fish shoal position at the next moment through multiple cross-correlation function calculation. The algorithm has the advantages that the conditions of rotation, deformation, divergence and the like of the aerosol agglomerate in the motion process can be considered better, and the algorithm is more suitable for representing the complexity of the motion of the atmosphere. The method can not only improve the accuracy of aerosol tracking and wind field inversion results, but also study the development and dissipation processes of aerosol and the microstructure of wind field.
Drawings
FIG. 1 is a schematic diagram of interpolation of radar raw data to a Cartesian coordinate system;
FIG. 2 is a schematic view of the processConstructing a simulated fish swarm schematic diagram time by time;
FIG. 3 is a schematic diagram of an alignment area of a simulated fish;
FIG. 4 is a diagram of an alignment areaPerforming cross-correlation operation schematic diagram on the time images;
FIG. 5 is a schematic diagram of a displacement vector and a wind field vector obtained from a multi-objective optimization function optimal solution;
FIG. 6 is a schematic diagram of an aerosol tracking and wind field inversion system module;
FIG. 7 is a flow chart of an embodiment of the present application.
Detailed Description
The present application is further illustrated in the accompanying drawings and detailed description which are to be understood as being merely illustrative of the application and not limiting of its scope, and various equivalent modifications to the application will fall within the scope of the application as defined in the appended claims after reading the application.
The application provides a laser radar-based aerosol tracking and wind field inversion method and system, wherein the method is shown in fig. 7 and comprises the following steps:
step 1: and determining the scanning elevation angle, the scanning range and the scanning frequency of the laser radar according to the characteristics of the range, the height, the distance, the azimuth angle and the like of the detected target, and continuously scanning the target area for multiple times.
Step 2: as shown in fig. 1, echo signal intensities corresponding to one time are obtained through each scanning, wherein the echo signal intensities comprise various parameters such as carrier-to-noise ratio, signal-to-noise ratio, backscattering coefficient, extinction coefficient, PR2 and the like. By means of one or more of the parameters described above, the spatial distribution information of the aerosol concentration data can be characterized. The signal intensity obtained by the radar is in a polar coordinate form, aerosol concentration data in the polar coordinate form is interpolated onto grid points in a Cartesian coordinate system for data processing, an aerosol concentration PPI (Plain Position Indicator) image in the Cartesian coordinate system is obtained, delta is used for representing grid distance, M is the total number of columns of the grid points of the image, and N is the total number of columns of the grid points of the image.
Step 3: selecting images corresponding to the first time and the second time from the obtained aerosol concentration PPI images with different times, and respectively marking the images asTime imageTime image, time interval of two times is。
Step 4: as shown in FIG. 2, inIn the time images, selecting a small target aerosol area to be detected and tracked by a frame, and setting the number of lattice points in the area as，Indicating the total number of rows in the area,indicating the total number of columns in the area. Consider the area as a groupFish groups of strips of simulated fish, which are then distributed according to the above-mentioned areaIs sequentially denoted as. Each simulated fishAt the position ofThe position of the time image is recorded as coordinates。
Step 5: calculating the fish shoal atCenter point in time-dependent imageAt a position where，。
Step 6: as shown in FIG. 3, inIn the time images, for any one of the simulated fishAt the lattice point ofIs used as a center of the water tank,is of side lengthConstructing square alignment areasThe number of lattice points in the comparison area isAnd each.
Step 7: as shown in fig. 4, willEach simulated fish in the time imageIs a reference to the alignment region of (a)And (3) withPerforming cross-correlation operation on all grid points in the time image to obtainCorresponding toIn time imagesCross-correlation function of individual grid points，Andrepresenting simulated fishAt the position ofCoordinates in the time image.
Wherein, any one of the simulated fish is takenThe aerosol concentration distribution of the alignment area A of (2) is，The aerosol concentration distribution of the time image is。
Pretreatment of the data, the data willPerforming standardization and zero padding treatment to obtainFor a pair ofStandardized processing is carried out to obtain。
For a pair of，Performing fast Fourier transform to obtain:
；
；
wherein the FFT represents the fast fourier transform.
Defined by convolution and cross-correlation functions are:
；
the convolution theorem has:
；
in the formula Is thatIs used for the fast fourier transform of (a),is thatIs a complex conjugate of (a) and (b). The inverse fast fourier transform is performed with:
；
wherein Respectively isAndis set in the standard deviation of (2),representing the inverse fast fourier transform.
Step 8: placing the fish shoal inThe center point in the time image is expressed as, wherein ，，Andrepresenting simulated fishAt the position ofCoordinates in the time image. Thus, any one of the simulated fishThe distance to the center point can be expressed as a function。
Step 9: constructing a multi-objective optimization function:
；
wherein ,andrepresenting simulated fishAt the position ofThe coordinates in the time-dependent image are,；representation ofShoal in time-of-day imagesThe degree of similarity of fish school in the time images;representing the degree of dispersion of the fish school;、is a weight coefficient representing the degree of importance of the degree of similarity and degree of dispersion in the multi-objective optimization.
Step 10: solving the maximum value of the multi-objective optimization function F by utilizing a multi-objective particle swarm optimization algorithmCorresponding solution setFurther calculate that the fish is inCenter point in time-dependent image。
Step 11: as shown in fig. 5, a vector is constructedVector quantityI.e. the target aerosol region is inDisplacement in time, thereby obtaining a velocity vectorAnd the wind speed and direction characteristics of the background field are shown.
Step 12: repeating the steps, selecting different target aerosol areas to construct a simulated fish shoal, and realizing inversion of a horizontal wind field; and images of more times are selected, so that the tracking of the moving path of the aerosol mass can be realized.
The system of the present application is shown in fig. 6, and is mainly divided into the following eight modules:
the laser radar signal transmitting and receiving processing module can scan the target area continuously for multiple times according to the set scanning elevation angle, range and scanning frequency. Acquiring echo signal intensities of a plurality of times, and processing the echo signal intensities into spatial distribution data of aerosol concentration for output;
the input end of the data interpolation and image processing module is connected with the output end of the laser radar signal transmitting and receiving processing module, and is used for interpolating aerosol concentration data in a polar coordinate form to a Cartesian coordinate system and generating corresponding aerosol concentration PPI images according to the data of different times;
the input end of the analog fish school generating module is connected with the output end of the data interpolation and image processing moduleFor at the output ofSelecting a target aerosol area from the time images to construct a simulated fish swarm, recording information of each simulated fish, and further obtaining comparison area information of each simulated fish; the input end of the cross-correlation function calculation module is connected with the output end of the simulated fish swarm generation module and is used for performing cross-correlation operation to obtain the comparison area and the comparison area of each simulated fishCross-correlation function of time images;
the input end of the shoal center point and distance calculation module is connected with the output end of the cross-correlation function calculation module, and is used for calculating the position of the shoal center point and the distance between each simulated fish and the center point in each time;
the input end of the multi-objective optimization function solving module is connected with the center point of the fish school and the output end of the distance calculating module, and the objective function can be maximized by utilizing a particle swarm optimization algorithm to obtainOptimal shoal distribution of time;
the input end of the calculation module of aerosol and wind vector is connected with the output end of the multi-objective optimization function solving module, and is used for calculating the displacement vector of aerosol and the wind vector of the background field;
and the input end of the iterative calculation module for path tracking and wind field inversion is connected with the calculation module for aerosol and wind vectors, and the iterative calculation module is used for carrying out cyclic calculation on data of a plurality of time and a plurality of areas to obtain aerosol paths and background wind field data in the whole scanning time.
According to the application, the fish shoal with a plurality of targets is selected as a tracking object, and the multi-target optimization solution is carried out on the fish shoal position at the next moment through multiple cross-correlation function calculation, so that the overall space distribution condition and displacement condition of the fish shoal are obtained. The algorithm has the advantages that the conditions of rotation, deformation, divergence and the like of the aerosol agglomerate in the motion process can be considered better, and the algorithm is more suitable for representing the complexity of the motion of the atmosphere. The method can not only improve the accuracy of aerosol tracking and wind field inversion results, but also study the development and dissipation processes of aerosol and the microstructure of wind field.
The foregoing is only a preferred embodiment of the application, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present application, and such modifications and adaptations are intended to be comprehended within the scope of the application.
Claims (10)
1. The aerosol tracking and wind field inversion method based on the laser radar is characterized by comprising the following steps of:
step 1: according to the characteristics of the range, the height, the distance and the azimuth angle of the detected target, determining the scanning elevation angle, the range and the scanning frequency of the laser radar, and continuously scanning the target area for multiple times by using the laser radar;
step 2: acquiring echo signal intensity corresponding to each time through each scanning, acquiring parameters representing spatial distribution information of aerosol concentration data, and acquiring an aerosol concentration PPI image under a Cartesian coordinate system through interpolating the aerosol concentration data to lattice points in the Cartesian coordinate system;
step 3: selecting images corresponding to the first time and the second time from the obtained aerosol concentration PPI images with different times, and respectively marking the images asTime image and +.>Time image, time interval of two times is +.>；
Step 4: at the position ofIn the time images, selecting a target aerosol area to be detected and tracked by a frame, regarding aerosol concentration data corresponding to each grid point in the area as a piece of simulated fish, and recording the position coordinates of each piece of simulated fish, wherein a set formed by all simulated fish in the area is called a simulated fish swarm;
step 5: calculating the fish shoal atThe center point in the time image is positioned;
step 6: at the position ofIn the time images, a square comparison area is constructed by taking any lattice point of the simulated fish as the center;
step 7: will beAlignment area and +.>Performing cross-correlation operation on all grid points in the time images to obtain the corresponding +.>Cross-correlation function of all grid points in time images and simulated fish in +.>Coordinates in the time image;
step 8: by simulating the fish in each stripCoordinates in the time images get the whole fish school +.>Calculating the distance from each simulated fish to the center point by the center point in the time image;
step 9: according to the calculated cross-correlation function of each simulated fish and the relationship between each simulated fish and the whole fish shoalDistance of center point in time image, constructing whole fish shoal in +.>A multi-objective optimization function of distribution in the time-dependent image, wherein the multi-objective optimization function is a function for solving an optimal solution of a problem with a plurality of objective functions based on given decision variables and constraint conditions;
step 10: obtaining a solution set corresponding to the maximum value of the multi-objective optimization function by utilizing a multi-objective particle swarm optimization algorithm, and further obtaining the fish swarm in-placeCenter point position in the time image;
step 11: obtaining the wind speed and direction characteristics of a background field through the displacement vector of the target aerosol area in a period of time;
step 12: repeating the steps, selecting different target aerosol areas to construct a simulated fish shoal, and realizing inversion of a horizontal wind field; and selecting more time images to realize the tracking of the moving path of the aerosol mass.
2. The method of claim 1, wherein the parameters characterizing the spatial distribution information of the aerosol concentration data in step 2 include carrier-to-noise ratio, signal-to-noise ratio, backscatter coefficient, extinction coefficient, and PR2 parameters.
3. The method for laser radar-based aerosol tracking and wind field inversion according to claim 1, wherein in said step 4, the method comprises the steps ofThe number of lattice points in the aerosol region is，/>Representing the total number of rows of the area, +.>Representing the total number of columns in the region; consider this region as a single unit +.>Fish group consisting of fish strips, then these fish strips are treated according to the above-mentioned area +.>Is marked in turn as->The method comprises the steps of carrying out a first treatment on the surface of the Each simulated fish->At the position ofThe position in the time image is marked as coordinates +.>。
4. A method for aerosol tracking and wind field inversion based on lidar according to claim 3, wherein in said step 5, the center point is wherein />，/>。
5. The method for aerosol tracking and wind field inversion based on lidar according to claim 4, wherein in said step 6, the fish is simulated by using any one of the above-mentioned methodsLattice of->Is the center (is the->Is +.>Constructing a square alignment region +.>The number of lattice points in the comparison area is +.>And each.
6. The method for laser radar-based aerosol tracking and wind field inversion according to claim 5, wherein in said step 7, the following stepsEach simulated fish in the time images>Is->And->All grid points in the time image are subjected to cross-correlation operation to obtain +.>Corresponding to->Time image +.>Cross-correlation function of individual grid points，/> and />For simulating fish->At->Coordinates in the time image;
taking any one of the simulated fishThe aerosol concentration distribution of the alignment area A of (a) is +.>，/>The aerosol concentration distribution of the time-dependent image is +.>；
Pretreatment of the data, the data willPerforming normalization and zero padding treatment to obtain +.>For->Standardized treatment to obtain ∈>；
For a pair of，/>Performing fast Fourier transform to obtain:
；
；
wherein the FFT represents a fast fourier transform;
from the convolution and cross-correlation functions:
；
the convolution theorem has:
；
in the formula Is->Fast fourier transform of->Is->Complex conjugate of (a); the inverse fast fourier transform is performed with:
；
wherein Respectively-> and />Standard deviation of>Representing the inverse fast fourier transform.
7. The method of claim 6, wherein in step 8, the fish shoal is in the followingThe center point in the time-dependent image is +.>, wherein />，，/> and />Representing a simulated fish->At->Coordinates in time images, any one of which simulates fishDistance to the centre point is a function +.>。
8. The method for aerosol tracking and wind field inversion based on lidar of claim 7, wherein in said step 9, the entire fish school is constructedA multi-objective optimization function of distribution in a time-dependent image, comprising:
；
wherein , and />Representing a simulated fish->At->The coordinates in the time-dependent image are,；/>representation->Fish group and +.>The degree of similarity of fish school in the time images; />Representing the degree of dispersion of the fish school; />、/>Is a weight coefficient representing the degree of importance of the degree of similarity and degree of dispersion in the multi-objective optimization.
9. The method for laser radar based aerosol tracking and wind field inversion according to claim 8, wherein in said step 10, the maximum value of the multi-objective optimization function FThe corresponding solution set is->Further calculate the fish swarm +.>Center point in time-dependent image->The method comprises the steps of carrying out a first treatment on the surface of the In the step 11, a vector is constructedVector->I.e. indicating that the target aerosol region is +.>Displacement in time, whereby a velocity vector is obtained>And the wind speed and direction characteristics of the background field are shown.
10. An aerosol tracking and wind field inversion system based on lidar, the system comprising:
the laser radar signal transmitting and receiving processing module is used for continuously scanning the target area for multiple times according to the set scanning elevation angle, the range and the scanning frequency, acquiring echo signal intensities of multiple times, and processing the echo signal intensities into spatial distribution information of aerosol concentration data for output;
the input end of the data interpolation and image processing module is connected with the output end of the laser radar signal transmitting and receiving processing module, and is used for interpolating aerosol concentration data in a polar coordinate form into grid points in a Cartesian coordinate system and generating corresponding aerosol concentration PPI images according to the data of different times;
the input end of the analog fish school generating module is connected with the output end of the data interpolation and image processing module and is used for outputtingSelecting a target aerosol area from the time images to construct a simulated fish swarm, recording information of each simulated fish, and further obtaining comparison area information of each simulated fish;
the input end of the cross-correlation function calculation module is connected with the output end of the simulated fish swarm generation module and is used for performing cross-correlation operation to obtain the comparison area and the comparison area of each simulated fishCross-correlation function of time images;
the input end of the shoal center point and distance calculation module is connected with the output end of the cross-correlation function calculation module, and is used for calculating the position of the shoal center point and the distance between each simulated fish and the center point in each time;
the input end of the multi-objective optimization function solving module is connected with the center point of the fish school and the output end of the distance calculating module, and the multi-objective optimization function can be solved by utilizing a particle swarm optimization algorithm to obtainOptimal shoal distribution of time;
the input end of the calculation module of aerosol and wind vector is connected with the output end of the multi-objective optimization function solving module, and is used for calculating the displacement vector of the objective aerosol area and the wind vector of the background field;
and the input end of the iterative calculation module for path tracking and wind field inversion is connected with the calculation module for aerosol and wind vectors, and the iterative calculation module is used for carrying out cyclic calculation on data of a plurality of time and a plurality of areas to obtain aerosol paths and background wind field data in the whole scanning time.
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