Unmanned aerial vehicle adaptive stratified sampling control method facing atmospheric boundary layer

Abstract

The invention relates to the technical field of atmospheric boundary layer sampling, in particular to an unmanned aerial vehicle adaptive stratified sampling control method oriented to an atmospheric boundary layer. A sample data set is constructed based on laser radar signal data of an atmospheric boundary layer, and the height of the atmospheric boundary layer is estimated in combination with category features; performing layered labeling on the sampling space based on the vertical height to obtain a sampling labeling layered section; acquiring atmosphere profile data of the sampling and labeling layered section by integrating a micro meteorological station and an ultrasonic anemograph, and analyzing and processing the atmosphere profile data of the sampling and labeling layered section to obtain an atmosphere parameter characterization value and a flight parameter characterization value of the sampling and labeling layered section; according to the method, the height of the atmospheric boundary layer can be accurately estimated, so that accurate layered labeling is performed on the sampling space, accurate layered sampling of the atmospheric boundary layer is realized, and the accuracy and reliability of sampling data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric boundary layer sampling, and particularly to an unmanned aerial vehicle (UAV) adaptive hierarchical sampling control method for the atmospheric boundary layer. Background Art

[0002] As a transitional region between the Earth's surface and the free atmosphere, the atmospheric boundary layer is a key area for the exchange of matter, energy, and momentum between the atmosphere and the surface. The distribution and variation of meteorological elements (such as temperature, humidity, wind speed, etc.) and atmospheric components (such as aerosols, pollutants, etc.) within it have crucial impacts on many fields such as weather forecasting, climate change research, air quality assessment, and ecological environment protection.

[0003] Traditional atmospheric boundary layer sampling methods have many limitations. Although existing UAV sampling technologies have overcome the deficiencies of traditional methods to a certain extent, there are still obvious deficiencies in the dynamic adjustment of sampling strategies and precise hierarchical sampling. Currently, there is a lack of a UAV control method that can adaptively adjust the sampling strategy based on real-time obtained atmospheric parameters and flight state information to achieve precise hierarchical sampling of the atmospheric boundary layer.

[0004] Therefore, there is an urgent need for a UAV adaptive hierarchical sampling control method for the atmospheric boundary layer to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a UAV adaptive hierarchical sampling control method for the atmospheric boundary layer: to solve the technical problems of inaccurate hierarchical sampling and low efficiency in precise hierarchical sampling existing in the existing atmospheric boundary layer sampling methods.

[0006] The UAV adaptive hierarchical sampling control method for the atmospheric boundary layer includes the following steps: Construct a sample data set based on the lidar signal data of the atmospheric boundary layer, determine the sample attribute weights, cluster the sample data set into k classes through the sample attribute weights, and estimate the height of the atmospheric boundary layer in combination with the class characteristics; Obtain the vertical height of the sampling space according to the preset sampling initial height and the height of the atmospheric boundary layer, and perform hierarchical annotation on the sampling space based on the vertical height to obtain the sampling annotation hierarchical segments; Obtain the atmospheric profile data of the sampling annotation hierarchical segments by integrating a micro meteorological station and an ultrasonic anemometer, analyze and process the atmospheric profile data of the sampling annotation hierarchical segments to obtain the atmospheric parameter characterization values and flight parameter characterization values of the sampling annotation hierarchical segments; Adjust the flight state and sampling strategy of the UAV based on the atmospheric parameter characterization values and flight parameter characterization values.

[0007] Furthermore, a sample data set is constructed based on the lidar signal data of the atmospheric boundary layer, the sample attribute weights are determined, the sample data set is clustered into k classes through the sample attribute weights, and the atmospheric boundary layer height is estimated by combining the class features, which specifically includes the following processes: Step 1: Construct a sample data set, and obtain the lidar backscatter signal, variance signal, and absolute gradient value based on the lidar signal as class attributes; Step 2: Determine the sample attribute weights based on multi-attribute optimization; Step 3: Determine the number of clustering classes k and the initial center C; Step 4: Calculate the distance from the sample to the center and perform clustering; Step 5: Update the clustering center, return to the previous step to recalculate the distance from each sample to the center, and perform clustering until the center update stops or the maximum number of iterations is reached; Step 6: Determine the atmospheric boundary layer height, which is located at the class boundary where the clustering intensity decreases for the first time from bottom to top.

[0008] Furthermore, determining the sample attribute weights based on multi-attribute optimization specifically includes the following processes: Construct a judgment matrix: Determine the number of relevant influencing factors at each level , construct a sample attribute set , , where is the lidar backscatter signal subset, is the variance signal subset, is the absolute gradient value subset. Take two subsets at the same level from the set for comparison, and use to represent the importance ratio, and assign the corresponding importance according to a preset ratio. Combine the importance at each level to form a judgment matrix; calculate the maximum eigenvalue of the judgment matrix: ; where is the matrix obtained by normalizing each column vector of the judgment matrix, takes values of 1, 2...m, is the matrix obtained by adding the elements of the matrix row by row, normalizing the resulting vector, is the matrix obtained by adding the elements of the matrix column by column, normalizing the resulting vector; calculate the sample attribute weights : ; where represents the order of the judgment matrix, represents the base value of the i-th sample attribute, where the base value is user-defined.

[0009] Further, calculating the distance from the sample to the center and performing clustering specifically includes the following process: When the quantity is less than k, let be the sample data set, and be the initial centroid; Based on the objective function calculate the distance D(x) from each data point in the data set to the existing initial centroid, where ; Take the data point corresponding to the maximum value in D(x) as the next initial centroid;

[0010] Further, obtain the vertical height of the sampling space according to the preset sampling initial height and the atmospheric boundary layer height, and perform hierarchical annotation on the sampling space based on the vertical height to obtain the sampling annotation hierarchical segments, which specifically includes the following process: Calculate the height difference between the preset sampling initial height and the atmospheric boundary layer height, record this difference as the vertical height, equally divide the vertical height into several height segments, collect the aerosol particle extinction coefficient, backscattering coefficient, and total pollutant concentration of each height segment, add the aerosol particle extinction coefficient, backscattering coefficient, and total pollutant concentration to obtain the hierarchical annotation reference coefficient within each height segment, calculate the hierarchical annotation reference coefficient difference between the hierarchical annotation reference coefficients of adjacent height segments, set the hierarchical annotation reference coefficient threshold, and determine whether the hierarchical annotation reference coefficient difference between two adjacent height segments exceeds the hierarchical annotation reference coefficient threshold. If not, record the vertical heights corresponding to these two adjacent height segments as one sampling annotation hierarchical segment until i annotated segmented clouds are obtained.

[0011] Further, analyze and process the atmospheric profile data of the sampling annotation hierarchical segments to obtain the atmospheric parameter characterization values of the sampling annotation hierarchical segments, which specifically includes the following process: Generate G management time periods, obtain the atmospheric profile data within each management time period, where the atmospheric profile data includes temperature, humidity, aerosol concentration, and ozone concentration, and obtain the atmospheric characterization index within each management time period based on the atmospheric profile data; Taking the atmospheric characterization index of each management time period as the Y-axis and the execution time of the management time period as the X-axis, establish a rectangular coordinate system, generate an atmospheric characterization curve by plotting points, and then draw perpendicular lines from the two endpoints of the atmospheric characterization curve to the X-axis to obtain two perpendicular line segments. A figure is formed by the atmospheric characterization curve, the two perpendicular line segments, and the X-axis. Calculate the area of the figure and record the area of the figure as the atmospheric parameter characterization value.

[0012] Further, obtaining the atmospheric characterization index specifically includes the following process: The atmospheric characterization index represents the product of the aerosol concentration multiplier value, the ozone concentration multiplier value, the temperature mean, and the humidity mean after data normalization. The aerosol concentration multiplier value is the area enclosed by the line segment of the aerosol concentration curve located above the preset aerosol concentration curve during the management period and the preset aerosol concentration curve. The ozone concentration multiplier value is the area enclosed by the line segment of the ozone concentration curve located above the preset ozone concentration curve during the management period and the preset ozone concentration curve, multiplied by the first area. The first area is the graphical area of the area enclosed by the ozone concentration curve and the X-axis. The temperature mean is the average temperature during the management period, and the humidity mean is the average humidity during the management period.

[0013] Furthermore, the atmospheric profile data of the sampled and annotated layered segments are analyzed and processed to obtain the flight parameter representation values of the sampled and annotated layered segments, which specifically includes the following process: The turbulence pulsation signal of the sampling and marking layer segment is obtained based on the atmospheric profile data. When the UAV flies to the sampling and marking layer segment, the turbulence wind resistance of the preset standard sampling and marking layer segment is , the turbulent wind resistance is obtained by collecting the sampled and labeled stratified segments. , then according to The turbulent drag coefficient is calculated as , the flight coefficient is ; If the waveform of the turbulent pulsation signal of the sampled and marked stratified segment only changes in position with time but not in phase, and the turbulent drag coefficient is is 0, and the flight coefficient is If is 1, the flight parameter representation value of the sampling and marking layer segment is 1. If the turbulence pulsation signal waveform of the sampling and marking layer segment changes in phase, and / Greater than 1 or / When it is less than 1, the flight parameter representation value of the sampling and annotation layer segment is 0.

[0014] Furthermore, adjusting the flight state and sampling strategy of the UAV based on the atmospheric parameter characterization value and the flight parameter characterization value specifically includes the following process: Adjusting the sampling strategy based on the atmospheric parameter characterization value includes: sampling the sampling and labeling layer segments in order from low to high according to the height of the sampling and labeling layer segments; when the atmospheric parameter characterization value of the sampling and labeling layer segment exceeds the preset atmospheric parameter characterization threshold, skipping the sampling and labeling layer segment and sampling other sampling and labeling layer segments; Adjusting the flight state of the drone based on the flight parameter representation value includes: when the flight parameter representation value of the sampling and marking layer segment is 0, skipping the sampling and marking layer segment and collecting other sampling and marking layer segments.

[0015] Beneficial effects achieved by the present invention compared with existing solutions: The present invention constructs a sample data set based on lidar signal data of the atmospheric boundary layer, determines the weights of sample attributes, clusters the sample data set into k categories through the weights of sample attributes, and estimates the height of the atmospheric boundary layer in combination with category features; obtains the vertical height of the sampling space according to the preset sampling initial height and the height of the atmospheric boundary layer, and performs hierarchical annotation on the sampling space based on the vertical height to obtain a sampled annotated hierarchical segment; obtains the atmospheric profile data of the sampled annotated hierarchical segment by integrating a micro meteorological station and an ultrasonic anemometer, analyzes and processes the atmospheric profile data of the sampled annotated hierarchical segment to obtain the atmospheric parameter characterization value and flight parameter characterization value of the sampled annotated hierarchical segment; adjusts the flight state and sampling strategy of the unmanned aerial vehicle (UAV) based on the atmospheric parameter characterization value and the flight parameter characterization value, can accurately estimate the height of the atmospheric boundary layer and then perform accurate hierarchical annotation on the sampling space, realizes precise hierarchical sampling of the atmospheric boundary layer, improves the accuracy and reliability of sampling data, and dynamically adjusts the flight state and sampling strategy of the UAV according to the atmospheric parameter characterization value and the flight parameter characterization value, enabling the UAV to perform adaptive adjustment according to real-time atmospheric conditions and its own state. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0017] Figure 1 is the flowchart of the first method for controlling the adaptive hierarchical sampling of a UAV facing the atmospheric boundary layer according to an embodiment of the present invention; Figure 2 is the flowchart of the second method for controlling the adaptive hierarchical sampling of a UAV facing the atmospheric boundary layer according to an embodiment of the present invention; Figure 3 is the flowchart of the third method for controlling the adaptive hierarchical sampling of a UAV facing the atmospheric boundary layer according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0019] In addition, the described features, structures, or characteristics may be combined in any suitable manner in one or more example embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the example embodiments of the present disclosure. However, those skilled in the art will recognize that one or more of the specific details may be omitted in practicing the technical solutions of the present disclosure, or other methods, components, steps, etc. may be employed. In other cases, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0020] This embodiment provides a method for adaptive hierarchical sampling control of an unmanned aerial vehicle (UAV) for the atmospheric boundary layer. Figure 1 It is a flowchart of the first method for adaptive hierarchical sampling control of an unmanned aerial vehicle for the atmospheric boundary layer in an embodiment of the present invention, as Figure 1 shown, and the method includes the following steps: Construct a sample data set based on the lidar signal data of the atmospheric boundary layer, determine the sample attribute weights, cluster the sample data set into k classes through the sample attribute weights, and estimate the height of the atmospheric boundary layer in combination with the class characteristics; Construct a sample data set based on the lidar signal data of the atmospheric boundary layer, determine the sample attribute weights, cluster the sample data set into k classes through the sample attribute weights, and estimate the height of the atmospheric boundary layer in combination with the class characteristics; Obtain the vertical height of the sampling space according to the preset sampling initial height and the height of the atmospheric boundary layer, and perform hierarchical annotation on the sampling space based on the vertical height to obtain the sampling annotation hierarchical segments; Obtain the atmospheric profile data of the sampling annotation hierarchical segments by integrating a micro meteorological station and an ultrasonic anemometer, analyze and process the atmospheric profile data of the sampling annotation hierarchical segments to obtain the atmospheric parameter characterization values and flight parameter characterization values of the sampling annotation hierarchical segments; Adjust the flight state and sampling strategy of the UAV based on the atmospheric parameter characterization values and flight parameter characterization values.

[0021] In summary, the present invention constructs a sample data set based on lidar signal data of the atmospheric boundary layer, determines the weights of sample attributes, clusters the sample data set into k categories through the weights of sample attributes, and estimates the height of the atmospheric boundary layer in combination with category features; obtains the vertical height of the sampling space according to the preset sampling initial height and the height of the atmospheric boundary layer, and performs hierarchical annotation on the sampling space based on the vertical height to obtain the sampling annotation hierarchical segment; obtains the atmospheric profile data of the sampling annotation hierarchical segment by integrating a micro meteorological station and an ultrasonic anemometer, analyzes and processes the atmospheric profile data of the sampling annotation hierarchical segment to obtain the atmospheric parameter characterization value and the flight parameter characterization value of the sampling annotation hierarchical segment; adjusts the flight state and sampling strategy of the unmanned aerial vehicle based on the atmospheric parameter characterization value and the flight parameter characterization value, can accurately estimate the height of the atmospheric boundary layer and then perform accurate hierarchical annotation on the sampling space, realizes the precise hierarchical sampling of the atmospheric boundary layer, improves the accuracy and reliability of the sampling data, and dynamically adjusts the flight state and sampling strategy of the unmanned aerial vehicle according to the atmospheric parameter characterization value and the flight parameter characterization value, so that the unmanned aerial vehicle can perform adaptive adjustment according to the real-time atmospheric conditions and its own state.

[0022] In some embodiments, Figure 2 is the flowchart of the second method for controlling the adaptive hierarchical sampling of an unmanned aerial vehicle for the atmospheric boundary layer according to an embodiment of the present invention. As Figure 2 shown, constructing a sample data set based on lidar signal data of the atmospheric boundary layer, determining the weights of sample attributes, clustering the sample data set into k categories through the weights of sample attributes, and estimating the height of the atmospheric boundary layer in combination with category features specifically include the following processes: Step 1, construct a sample data set, and obtain the lidar backscattering signal, variance signal, and absolute value of the gradient as category attributes based on the lidar signal; Step 2, determine the weights of sample attributes based on multi-attribute optimization; Step 3, determine the number of clustering categories k and the initial center C; Step 4, calculate the distance from the sample to the center and perform clustering; Step 5, update the clustering center, return to the previous step to recalculate the distance from each sample to the center, and perform clustering until the center update stops or the maximum number of iterations is reached; Step 6, determine the height of the atmospheric boundary layer, and the height of the atmospheric boundary layer is located at the category boundary where the clustering intensity drops for the first time from bottom to top.

[0023] It should be noted that the lidar backscatter signal is the part of the scattered signal generated when the lidar beam interacts with molecules, aerosol particles, etc. in the atmosphere during lidar detection of the atmosphere, which returns along the original path and is received by the lidar receiver. When the lidar emits laser pulses into the atmosphere, the laser beam will interact with various components in the atmosphere (such as atmospheric molecules, aerosol particles, etc.) to produce a scattering phenomenon. Among them, backscattering refers to the part of the scattered light that propagates in the direction opposite to the laser incident direction. These backscatter signals are received by the lidar receiver, and through the analysis and processing of these signals, physical parameters related to the atmosphere can be obtained. The lidar variance signal plays an important role in the fields of lidar data processing, calibration, signal characteristic analysis, etc.

[0024] In summary, by constructing a sample set through multi-attribute fusion (backscatter signal, variance signal, and absolute value of gradient), the problem that a single signal source is vulnerable to noise interference is overcome, and the reliability of the atmospheric boundary layer height detection is significantly improved. Experiments show that compared with the traditional single-parameter method, the accuracy of the boundary layer height identification of this method is increased by about 40%.

[0025] By adopting a dynamic attribute weight assignment mechanism, the contribution weights of each signal feature can be automatically adjusted according to different meteorological conditions. For example, the weight of the gradient feature is automatically enhanced in haze weather, and the backscatter signal is emphasized in sunny weather, so that the clustering result always remains optimal.

[0026] In some embodiments, determining the sample attribute weights based on multi-attribute optimization specifically includes the following process: Construct a judgment matrix: Determine the number of relevant influencing factors at the level , construct a sample attribute set , , where is a subset of the lidar backscatter signal, is a subset of the variance signal, is a subset of the absolute value of the gradient. Take two subsets at the same level from the set for comparison, and use to represent the importance ratio, and assign the corresponding importance according to a preset ratio. Combine the importance of each layer to form a judgment matrix; calculate the maximum eigenvalue of the judgment matrix: ; where is the matrix obtained by normalizing each column vector of the judgment matrix, has values of 1, 2...m, is the matrix obtained by adding the elements of the matrix row by row, normalizing the resulting vector, is the matrix The elements are added column by column to obtain a vector, and then the matrix is normalized; calculate the sample attribute weights : ; Among them, represents the order of the judgment matrix, represents the base value of the i-th sample attribute, where the base value is set by the user.

[0027] In some embodiments, calculating the distance from the sample to the center and performing clustering specifically includes the following process: When the quantity is less than k, let be the sample data set, be the initial centroid; Based on the objective function calculate the distance D(x) of each data point in the data set from the existing initial centroid, where, ; Take the data point corresponding to the maximum value in D(x) as the next initial centroid; Successively obtain k initial centroids and cluster the data set based on the k initial centroids.

[0028] Obtain the vertical height of the sampling space according to the preset sampling initial height and the atmospheric boundary layer height, and perform hierarchical annotation on the sampling space based on the vertical height to obtain the sampling annotation hierarchical segments, which specifically includes the following process: In some embodiments, calculate the height difference between the preset sampling initial height and the atmospheric boundary layer height, record the difference as the vertical height, equally divide the vertical height into several height segments, collect the aerosol particle extinction coefficient, backscattering coefficient and total pollutant concentration of each height segment, add the aerosol particle extinction coefficient, backscattering coefficient and total pollutant concentration to obtain the hierarchical annotation reference coefficient within each height segment, calculate the hierarchical annotation reference coefficient difference between the hierarchical annotation reference coefficients of adjacent height segments, set the hierarchical annotation reference coefficient threshold, and determine whether the hierarchical annotation reference coefficient difference between adjacent two height segments exceeds the hierarchical annotation reference coefficient threshold. If not, record the vertical heights corresponding to the adjacent two height segments as one sampling annotation hierarchical segment until i annotated segmented clouds are obtained.

[0029] In some embodiments, Figure 3 is the flowchart of the third method for controlling the adaptive hierarchical sampling of an unmanned aerial vehicle facing the atmospheric boundary layer in the embodiments of the present invention. As Figure 3 shown, analyzing and processing the atmospheric profile data of the sampling annotation hierarchical segments to obtain the atmospheric parameter characterization values of the sampling annotation hierarchical segments specifically includes the following process: Step S301: Generate G management time periods, and obtain the atmospheric profile data within each management time period. The atmospheric profile data includes temperature, humidity, aerosol concentration, and ozone concentration. Based on the atmospheric profile data, obtain the atmospheric characterization index within each management time period. Step S302: Establish a rectangular coordinate system with the atmospheric characterization index of each management time period as the Y-axis and the execution time of the management time period as the X-axis. Generate an atmospheric characterization curve by plotting points. Then, draw perpendicular lines from the two endpoints of the atmospheric characterization curve to the X-axis to obtain two perpendicular line segments. A figure is formed by the atmospheric characterization curve, the two perpendicular line segments, and the X-axis. Calculate the area of the figure and record the area of the figure as the atmospheric parameter characterization value.

[0030] Further, obtaining the atmospheric characterization index specifically includes the following process: The atmospheric characterization index represents the product value obtained by normalizing the aerosol concentration magnification value, the ozone concentration magnification value, the average temperature, and the average humidity. The aerosol concentration magnification value is the area enclosed by the upper segment of the aerosol concentration curve within the management time period above the preset aerosol concentration curve and the preset aerosol concentration curve. The ozone concentration magnification value is the product value obtained by multiplying the area enclosed by the upper segment of the ozone concentration curve within the management time period above the preset ozone concentration curve and the preset ozone concentration curve by the first area. The first area is the area of the figure enclosed by the ozone concentration curve and the X-axis. The average temperature is the average value of the temperature within the management time period, and the average humidity is the average value of the humidity within the management time period.

[0031] In some embodiments, analyzing and processing the atmospheric profile data of the sampling annotation stratified segment to obtain the flight parameter characterization value of the sampling annotation stratified segment specifically includes the following process: Based on the atmospheric profile data, obtain the turbulent pulsation signal of the sampling annotation stratified segment. When the unmanned aerial vehicle flies to the sampling annotation stratified segment, the preset standard turbulent wind resistance of the sampling annotation stratified segment is Collect the sampling annotation stratified segment to obtain the turbulent wind resistance as Then, according to Calculate the turbulent wind resistance coefficient as The flight coefficient is ; If the waveform of the turbulent pulsation signal of the sampling annotation stratified segment only changes in position with time and does not change in phase, and the turbulent wind resistance coefficient is is 0, and the flight coefficient is is 1, then the flight parameter characterization value of the sampling annotation stratified segment is 1. If the waveform of the turbulent pulsation signal of the sampling annotation stratified segment changes in phase, and / is greater than 1 or / is less than 1, then the flight parameter characterization value of the sampling annotation stratified segment is 0.

[0032] In some embodiments, adjusting the flight state and sampling strategy of the drone based on the characterization values of atmospheric parameters and flight parameters specifically includes the following processes: Adjusting the sampling strategy based on the characterization value of the atmospheric parameter includes: sampling the stratified segments of the sampling annotation in ascending order of height from low to high. When the characterization value of the atmospheric parameter of a stratified segment of the sampling annotation exceeds the preset atmospheric parameter characterization threshold, skip this stratified segment of the sampling annotation and collect other stratified segments of the sampling annotation; Adjusting the flight state of the drone based on the characterization value of the flight parameter includes: when the characterization value of the flight parameter of a stratified segment of the sampling annotation is 0, skip this stratified segment of the sampling annotation and collect other stratified segments of the sampling annotation.

[0033] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0034] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0035] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0036] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only for some logical function divisions, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms.

[0037] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0038] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An adaptive hierarchical sampling control method for an unmanned aerial vehicle (UAV) facing the atmospheric boundary layer, characterized in that, The method includes: Constructing a sample data set based on lidar signal data of the atmospheric boundary layer, determining sample attribute weights, clustering the sample data set into k classes through the sample attribute weights, and estimating the atmospheric boundary layer height in combination with class characteristics; Obtaining the vertical height of the sampling space according to the preset sampling initial height and the atmospheric boundary layer height, and performing hierarchical annotation on the sampling space based on the vertical height to obtain the sampling annotation hierarchical segments; Obtaining the atmospheric profile data of the sampling annotation hierarchical segments by integrating a micro meteorological station and an ultrasonic anemometer, and analyzing and processing the atmospheric profile data of the sampling annotation hierarchical segments to obtain the atmospheric parameter characterization values and flight parameter characterization values of the sampling annotation hierarchical segments; Adjusting the flight state and sampling strategy of the unmanned aerial vehicle based on the atmospheric parameter characterization values and the flight parameter characterization values.

2. The method for adaptively hierarchical sampling control of an unmanned aerial vehicle for the atmospheric boundary layer according to claim 1, wherein Constructing a sample data set based on lidar signal data of the atmospheric boundary layer, determining sample attribute weights, clustering the sample data set into k classes through the sample attribute weights, and estimating the atmospheric boundary layer height in combination with class characteristics specifically includes the following processes: Step 1, constructing a sample data set, and obtaining the lidar backscatter signal, variance signal, and absolute gradient value based on the lidar signal as class attributes; Step 2, determining sample attribute weights based on multi-attribute optimization; Step 3, determining the number of clustering classes k and the initial center C; Step 4, calculating the distance from the sample to the center and performing clustering; Step 5, updating the clustering center, returning to the previous step to recalculate the distance from each sample to the center, and performing clustering until the center update stops or the maximum number of iterations is reached; Step 6, determining the atmospheric boundary layer height, where the atmospheric boundary layer height is located at the class boundary where the clustering intensity decreases for the first time from bottom to top.

3. The method for adaptively stratified sampling control of an unmanned aerial vehicle facing the atmospheric boundary layer according to claim 2, wherein Determining sample attribute weights based on multi-attribute optimization specifically includes the following processes: Construct a judgment matrix: Determine the number of relevant influencing factors at each level , construct a set of sample attributes , , where is a subset of the lidar backscatter signal, is a subset of the variance signal, is a subset of the absolute value of the gradient. Take two subsets at the same level from the set for comparison. Use to represent the ratio of importance, and assign the corresponding importance values according to a preset ratio. Combine the importance values of each layer to form a judgment matrix; Calculate the maximum eigenvalue of the judgment matrix: ; where is the matrix obtained by normalizing each column vector of the judgment matrix, takes values of 1, 2...m, is the matrix obtained by adding the elements of matrix row by row, normalizing the resulting vector, is the matrix obtained by adding the elements of matrix column by column, normalizing the resulting vector; Calculate the weights of sample attributes : ; where represents the order of the judgment matrix, represents the base value of the i-th sample attribute, where the base value is user-defined.

4. The method for adaptively hierarchical sampling control of an unmanned aerial vehicle for the atmospheric boundary layer according to claim 1, characterized in that, Calculating the distance from the sample to the center and performing clustering specifically includes the following processes: When the quantity is less than k, let be the sample data set, and be the initial centroid; Based on the objective function Calculate the distance D(x) between each data point in the dataset and the existing initial centroids, where ; Taking the data point corresponding to the maximum value in D(x) as the next initial centroid; Successively obtaining k initial centroids and clustering the data set based on the k initial centroids.

5. The adaptive hierarchical sampling control method for an unmanned aerial vehicle facing the atmospheric boundary layer according to claim 1, wherein Obtaining the vertical height of the sampling space according to the preset sampling initial height and the atmospheric boundary layer height, and performing hierarchical annotation on the sampling space based on the vertical height to obtain the sampling annotation hierarchical segments specifically includes the following processes: Calculating the height difference between the preset sampling initial height and the atmospheric boundary layer height, recording the difference as the vertical height, equally dividing the vertical height into several height segments, collecting the aerosol particle extinction coefficient, backscatter coefficient, and total pollutant concentration of each height segment, adding the aerosol particle extinction coefficient, backscatter coefficient, and total pollutant concentration to obtain the hierarchical annotation reference coefficient within each height segment, calculating the hierarchical annotation reference coefficient difference between the hierarchical annotation reference coefficients of adjacent height segments, setting the hierarchical annotation reference coefficient threshold, and determining whether the hierarchical annotation reference coefficient difference between two adjacent height segments exceeds the hierarchical annotation reference coefficient threshold. If not, recording the vertical heights corresponding to the two adjacent height segments as a sampling annotation hierarchical segment until i annotated segmented clouds are obtained.

6. The method for controlling the adaptive hierarchical sampling of an unmanned aerial vehicle for the atmospheric boundary layer according to claim 1, wherein Analyzing and processing the atmospheric profile data of the sampling annotation hierarchical segments to obtain the atmospheric parameter characterization values of the sampling annotation hierarchical segments specifically includes the following processes: Generate G management time periods, and obtain the atmospheric profile data within each management time period. The atmospheric profile data includes temperature, humidity, aerosol concentration, and ozone concentration. Based on the atmospheric profile data, obtain the atmospheric characterization index within each management time period; Taking the atmospheric characterization index of each management time period as the Y-axis and the execution time of the management time period as the X-axis, establish a rectangular coordinate system. Generate an atmospheric characterization curve by plotting points. Then, draw perpendicular lines from the two endpoints of the atmospheric characterization curve to the X-axis to obtain two perpendicular line segments. A figure is formed by the atmospheric characterization curve, the two perpendicular line segments, and the X-axis. Calculate the area of the figure and record the area of the figure as the atmospheric parameter characterization value.

7. The method for adaptive hierarchical sampling control of an unmanned aerial vehicle for the atmospheric boundary layer according to claim 6, characterized in that, Obtaining the atmospheric characterization index specifically includes the following process: The atmospheric characterization index represents the product value obtained after data normalization of the aerosol concentration magnification value, the ozone concentration magnification value, the average temperature, and the average humidity. The aerosol concentration magnification value is the area enclosed by the upper segment of the aerosol concentration curve within the management time period above the preset aerosol concentration curve and the preset aerosol concentration curve. The ozone concentration magnification value is the area enclosed by the upper segment of the ozone concentration curve within the management time period above the preset ozone concentration curve and the preset ozone concentration curve, multiplied by the first area. The first area is the area of the figure enclosed by the ozone concentration curve and the X-axis. The average temperature is the average temperature within the management time period, and the average humidity is the average humidity within the management time period.

8. The adaptive hierarchical sampling control method for an unmanned aerial vehicle facing the atmospheric boundary layer according to claim 1, wherein Analyze and process the atmospheric profile data of the sampled annotation stratified segments to obtain the flight parameter characterization value of the sampled annotation stratified segments, which specifically includes the following process: Obtain the turbulent pulsation signal of the sampling annotation stratified section based on the atmospheric profile data. When the unmanned aerial vehicle flies to the sampling annotation stratified section, the preset turbulent wind resistance of the standard sampling annotation stratified section is , collect the sampling annotation stratified section to obtain the turbulent wind resistance as , then according to calculate the turbulent wind resistance coefficient as , and the flight coefficient is ; If the waveform of the turbulent pulsation signal of the sampled and marked stratified section only changes in position with the change of time but does not change in phase, and the turbulent wind resistance coefficient is 0, and the flight coefficient is 1, then the flight parameter characterization value of the sampled and marked stratified section is 1. If the waveform of the turbulent pulsation signal of the sampled and marked stratified section changes in phase, and / is greater than 1 or / is less than 1, then the flight parameter characterization value of the sampled and marked stratified section is 0.

9. The method for adaptively hierarchical sampling control of an unmanned aerial vehicle for the atmospheric boundary layer according to claim 1, wherein Adjust the flight state and sampling strategy of the drone based on the atmospheric parameter characterization value and the flight parameter characterization value, which specifically includes the following process: Adjusting the sampling strategy based on the atmospheric parameter characterization value includes: sampling the sampled annotation stratified segments in ascending order of the height of the sampled annotation stratified segments. When the atmospheric parameter characterization value of a sampled annotation stratified segment exceeds the preset atmospheric parameter threshold, skip this sampled annotation stratified segment and collect other sampled annotation stratified segments; Adjusting the flight state of the drone based on the flight parameter characterization value includes: when the flight parameter characterization value of a sampled annotation stratified segment is 0, skip this sampled annotation stratified segment and collect other sampled annotation stratified segments.

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