Insect body axial ratio inversion method and device based on RCS characteristics and medium

Abstract

The invention provides an insect body axial ratio inversion method and device based on RCS features and a medium, and belongs to the technical field of insect radars. The method comprises the following steps: determining an incidence relation between each directional diagram characteristic parameter and an insect body axial ratio based on an analysis result of a preset insect collinear polarization directional diagram, and determining a plurality of insect body axial ratio estimators based on the incidence relation; wherein the association relationship at least comprises the association relationship with the insect body length and the association relationship with the insect body width; the insect body axial ratio estimator is used for representing the quantitative relation between the insect polarization characteristic and the insect body axial ratio; according to each insect body axis ratio estimator and a preset XGBoost regression algorithm, constructing a pre-trained body axis ratio inversion model; wherein the body axial ratio inversion model establishes a mapping relation between each insect body axial ratio estimator and the insect body axial ratio; and deploying the body axial ratio inversion model to a user terminal, so that the user terminal performs insect body axial ratio inversion based on the input insect collinear polarization directional diagram and the body axial ratio inversion model.

Description

Technical Field

[0001] The present application relates to the field of insect radar technology, and in particular to an insect body axis ratio inversion method, device and medium based on RCS characteristics. Background Art

[0002] Insect radar is a tool specifically used to monitor and study migratory insects. It can achieve all-weather and all-day monitoring without interfering with the migratory behavior of insects. Therefore, it is widely used in insect research. The insect echoes measured by insect radar can be used to estimate the biological and behavioral characteristics of insects, such as body length, weight, wing beat frequency, horizontal flight speed and direction. Based on these parameters, migratory insects can be identified and their trajectories analyzed, which is of great significance for building an effective aerial monitoring and interception control system for migratory pests.

[0003] The body axis ratio (i.e., length-to-width ratio) of insects is an important morphological feature in species identification. Traditional methods have established a mapping relationship between RCS estimators and morphological features by finding radar cross-section (RCS) estimators related to specific morphological features of insects (such as weight and body length), thereby achieving the inversion of these features. However, no RCS estimator related to the body axis ratio has been found so far, and traditional methods for measuring insect body axis ratios mainly rely on manual observation and physical measurement, which is not only inefficient, but also extremely difficult to measure for tiny insects or insects in complex environments, and the accuracy is difficult to guarantee. Accurate and efficient inversion of insect body axis ratios remains an unsolved problem.

[0004] Therefore, in order to further improve the accuracy of migratory insect species identification, a technical solution that can accurately invert the body axis ratio of migratory insects is urgently needed. Summary of the invention

[0005] The embodiments of the present application provide a method, device and medium for inverting the body axis ratio of insects based on RCS features, which are used to solve the current technical problem that it is difficult to efficiently and accurately invert the body axis ratio of insects.

[0006] On the one hand, an embodiment of the present application provides an insect body axis ratio inversion method based on RCS features, the method comprising: Based on the analysis results of the preset insect colinear polarization pattern, the correlation relationship between the characteristic parameters of each pattern and the insect body axis ratio is determined, so as to determine a plurality of insect body axis ratio estimators based on the correlation relationship; wherein the correlation relationship at least includes the correlation with the insect body length in the insect body axis ratio and the correlation with the insect body width in the insect body axis ratio determined based on the co-polarization radar scattering cross section RCS; the insect body axis ratio estimator is used to characterize the quantitative relationship between the insect polarization characteristics and the insect body axis ratio; According to each of the insect body axis ratio estimators and the preset XGBoost regression algorithm, a pre-trained body axis ratio inversion model is constructed; wherein the body axis ratio inversion model establishes a mapping relationship between each of the insect body axis ratio estimators and the insect body axis ratio; The body axis ratio inversion model is deployed to a user terminal, so that the user terminal performs insect body axis ratio inversion based on the input insect colinear polarization pattern and the body axis ratio inversion model.

[0007] In one implementation of the present application, based on the analysis results of the preset insect colinear polarization pattern, before determining the correlation between the characteristic parameters of each pattern and the insect body axis ratio, the method further includes: Obtaining co-polarization radar cross sections (RCS) of different polarization directions corresponding to the pre-observed insects; wherein the co-polarization RCS is obtained by observing the pre-observed insects in a linear polarization mode using a polarization insect radar; According to the change of the co-polarization RCS in the polarization direction, the preset insect co-linear polarization pattern is determined; wherein the preset insect co-linear polarization pattern is expressed as:

[0008] in, represents the insect collinear polarization pattern, It is the characteristic parameter of the pattern that is independent of the polarization direction; and is a pattern characteristic parameter related to the shape of the insect colinear polarization pattern; Represents the polarization direction, and its value range is ; Indicates the direction of the insect's body axis.

[0009] In one implementation of the present application, based on the analysis results of the preset insect colinear polarization pattern, the correlation relationship between the characteristic parameters of each pattern and the insect body axis ratio is determined, specifically including: Determine the preset insect collinear polarization pattern Item and When the items respectively reach their maximum values, the angle between the polarization direction and the insect body axis direction; According to the angle side directions corresponding to the angles and the preset insect body length direction, respectively determine the body length direction and body width direction corresponding to the polarization directions; According to the Item and The corresponding relationship between the item and the body length direction and the body width direction is established. and stated The correlations with the body axis ratios of the insects respectively.

[0010] In one implementation of the present application, a plurality of insect body axis ratio estimators are determined based on the association relationship, specifically including: According to the association relationship, when the angle is 0 or When As the RCS in the body length direction; According to the association relationship, when the angle is or When As the RCS in the body width direction; According to the association relationship, the ratio of the RCS in the body length direction to the RCS in the body width direction is As an indicator of RCS body axis ratio; The , , the RCS in the body length direction, the RCS in the body width direction and the RCS body axis ratio index are respectively used as the insect body axis ratio estimators.

[0011] In one implementation of the present application, a pre-trained body axis ratio inversion model is constructed according to each of the insect body axis ratio estimators and the preset XGBoost regression algorithm, specifically including: According to each of the insect body axis ratio estimators and a number of insect collinear polarization pattern samples, a number of multidimensional feature vector samples are constructed; wherein the multidimensional feature vector samples include a multidimensional feature vector composed of each of the insect body axis ratio estimators corresponding to each of the insect collinear polarization pattern samples and an insect body axis ratio label; Each of the multidimensional feature vector samples is input into the preset XGBoost regression algorithm to train the preset XGBoost regression algorithm until a training end condition is met, thereby obtaining the body axis ratio inversion model.

[0012] In one implementation of the present application, the preset XGBoost regression algorithm is trained until the training end condition is met to obtain the body axis ratio inversion model, which specifically includes: Iteratively training the preset XGBoost regression algorithm through each of the multidimensional feature vector samples in a preset training set; Input each of the multidimensional feature vector samples in the preset test set into the preset XGBoost regression algorithm after iterative training to calculate the corresponding average relative error according to the output result; When the average relative error is less than a preset threshold, the preset XGBoost regression algorithm after iterative training is determined as the body axis ratio inversion model.

[0013] In one implementation of the present application, after the body axis ratio inversion model is deployed to the user terminal, the method further includes: Obtaining each of the insect body axis ratio estimators corresponding to the input insect colinear polarization pattern, and constructing the corresponding multidimensional feature vector; The multi-dimensional feature vector is sent to the user terminal so that the user terminal performs insect body axis ratio inversion.

[0014] In one implementation of the present application, the user terminal performs insect body axis ratio inversion based on the input insect colinear polarization pattern and the body axis ratio inversion model, specifically including: The user terminal determines a plurality of corresponding insect body axis ratio estimators according to the input insect colinear polarization pattern, and constructs the multidimensional feature vector; The user terminal inputs the multi-dimensional feature vector into the pre-deployed body axis ratio inversion model to perform insect body axis ratio inversion.

[0015] On the other hand, the embodiment of the present application further provides an insect body axis ratio inversion device based on RCS features, the device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an insect body axis ratio inversion method based on RCS features as described above.

[0016] On the other hand, an embodiment of the present application further provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions can execute an insect body axis ratio inversion method based on RCS features as described above.

[0017] Compared with the prior art, the present invention has the following significant effects: (1) Through the above scheme, the present application deeply analyzes the preset insect collinear polarization pattern, determines the correlation between the characteristic parameters of each pattern and the insect body axis ratio, and then determines multiple insect body axis ratio estimators. These estimators can accurately characterize the quantitative relationship between the insect polarization characteristics and the insect body axis ratio, and provide an accurate data basis for the subsequent construction of the body axis ratio inversion model, thereby greatly improving the accuracy of the insect body axis ratio inversion. The body axis ratio inversion model constructed based on the preset XGBoost regression algorithm can quickly process the input insect polarization data. Compared with traditional methods, it greatly improves the efficiency of insect body axis ratio inversion, making it possible to perform body axis ratio analysis on a large number of insect samples in a short time.

[0018] (2) The body axis ratio inversion model is deployed to the user terminal. The user only needs to input the insect colinear polarization pattern in the terminal to perform insect body axis ratio inversion based on the model. This design gets rid of the dependence of traditional measurement methods on complex equipment and professional environments, allowing insect researchers to conveniently analyze insect body axis ratios in a variety of scenarios such as the field and the laboratory, significantly enhancing the ease of application of the technology. This application effectively solves the technical problem of the difficulty in achieving efficient and accurate inversion of insect body axis ratios, and provides a more accurate, efficient and convenient research method for insect research. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A schematic diagram of a flow chart of an insect body axis ratio inversion method based on RCS features in an embodiment of the present application; Figure 2 A schematic diagram of a simulated collinear polarization pattern in an insect body axis ratio inversion method based on RCS features in an embodiment of the present application; Figure 3 A schematic diagram showing the comparison between the estimated body axis ratio and the true body axis ratio in an insect body axis ratio inversion method based on RCS features in an embodiment of the present application; Figure 4 This is a schematic diagram of the structure of an insect body axis ratio inversion device based on RCS characteristics in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0021] In the field of insect research, accurately obtaining the insect body axis ratio is of great significance for in-depth understanding of the ecological habits, behavioral patterns, and evolutionary characteristics of insects. Traditional methods for measuring insect body axis ratios mainly rely on manual observation and physical measurement, which is not only inefficient, but also extremely difficult to measure for tiny insects or insects in complex environments, and the accuracy is difficult to guarantee.

[0022] Based on this, the embodiments of the present application provide an insect body axis ratio inversion method, device and medium based on RCS features, which are used to solve the current technical problem that it is difficult to achieve efficient and accurate inversion of insect body axis ratio.

[0023] The following describes in detail various embodiments of the present application in conjunction with the accompanying drawings.

[0024] The present application embodiment provides an insect body axis ratio inversion method based on RCS features, such as Figure 1 As shown, the method may include steps S101-S103: S101, the server determines the correlation between the characteristic parameters of each directional pattern and the insect body axis ratio based on the analysis results of the preset insect colinear polarization directional pattern, so as to determine multiple insect body axis ratio estimators based on the correlation.

[0025] The correlation relationship includes at least determining the correlation with the insect body length in the insect body axis ratio and the correlation with the insect body width in the insect body axis ratio based on the co-polarization radar scattering cross section RCS. The insect body axis ratio estimator is used to characterize the quantitative relationship between the insect polarization characteristics and the insect body axis ratio.

[0026] It should be noted that the server, as the executor of the insect body axis ratio inversion method based on RCS features, exists only for exemplary purposes. The executor is not limited to the server, and this application does not make any specific limitations on this.

[0027] In the embodiment of the present application, based on the analysis result of the preset insect colinear polarization pattern, before determining the correlation relationship between the characteristic parameters of each pattern and the insect body axis ratio, it also includes: Obtain the co-polarization radar cross section RCS of different polarization directions corresponding to the pre-observed insects. The co-polarization RCS is obtained by observing the pre-observed insects in a linear polarization mode using a polarization insect radar. According to the change of the co-polarization RCS in the polarization direction, determine the preset insect collinear polarization pattern. The preset insect collinear polarization pattern is expressed as:

[0028] in, represents the insect collinear polarization pattern, It is a characteristic parameter of the directional pattern that is independent of the polarization direction. and is a characteristic parameter of the pattern related to the shape of the insect's collinear polarization pattern and is a dimensionless parameter. Represents the polarization direction, and its value range is . Indicates the direction of the insect's body axis.

[0029] That is to say, the present application analyzes the relationship between the polarization characteristics and the insect body axis ratio in the insect colinear polarization pattern, and calculates the insect body axis ratio estimator. Specifically, when the present application uses the polarization insect radar to observe with linear polarization, the co-polarization RCS of the insect in different polarization directions can be obtained. Under the assumption that the insect is symmetric, the present application expresses the co-polarization RCS as above .

[0030] In one embodiment of the present application, the above-mentioned determination of the correlation between the characteristic parameters of each pattern and the insect body axis ratio based on the analysis result of the preset insect colinear polarization pattern specifically includes: Determine the collinear polarization pattern of the preset insect Item and When the items are maximized, the angle between the polarization direction and the insect body axis direction is determined. According to the angle side direction corresponding to each angle and the preset insect body length direction, the body length direction and body width direction corresponding to the polarization direction are determined respectively. Item and The corresponding relationship between the item and the body length direction and the body width direction is established. and Their respective correlations with the insect body axis ratios.

[0031] In other words, the present application can calculate the collinear polarization pattern. Item and The angle between the polarization direction and the insect body axis when the terms reach their maximum values. The period of the term is , whose maximum value appears at , At this time, the polarization direction is aligned with the preset insect body length direction, and the polarization direction is the body length direction, that is, It is related to the elongation of the collinear polarization pattern along the body length. The period is , whose maximum value appears at , , , , since the above known , Indicates the direction of body length, , It means that the polarization direction is perpendicular to the body axis, and the polarization direction is the body width direction, that is, It is related to the collinear polarization pattern along the length and width of the insect body, which can be more generally explained as being related to the cross shape of the collinear polarization pattern.

[0032] Figure 2The normalized collinear polarization patterns of four simulated insects are shown in Figure 1. The body length of the four insects is 15 mm, and the preset insect body length direction is 0-180°. Figure 2 From (a) to (d), the insect body axis ratios are 2, 3, 4, and 5 respectively. Under the condition of the same body length, for insects with smaller body axis ratios, The more the collinear polarization pattern appears to be a "cross" shape, the more the collinear polarization pattern appears to be a "cross" shape. For insects with a relatively large body axis, Plays a leading role, the longer the collinear polarization pattern is along the preset insect body length direction, based on this, the above application and It can reflect the body axis ratio of insects.

[0033] In an embodiment of the present application, multiple insect body axis ratio estimators are determined based on the association relationship, specifically including: According to the association relationship, when the angle is 0 or When As the RCS in the body length direction. According to the association relationship, when the angle is or When As the RCS in the body width direction. According to the correlation relationship, the ratio of the RCS in the body length direction to the RCS in the body width direction is As an indicator of the RCS body axis ratio. , , RCS in body length direction, RCS in body width direction and RCS body axis ratio indicators are used as insect body axis ratio estimators, respectively.

[0034] By the above Figure 2 It can be seen that the larger the body axis ratio, the greater the RCS difference between the body length direction and the body width direction. At this time, the application obtains the RCS in the body length direction, the RCS in the body width direction, and the ratio of the two (RCS body axis ratio index) according to the above angle setting method. So far, the server has obtained 5 collinear polarization pattern features related to the insect body axis ratio, namely: , , , , , and used them as estimators of insect body axis ratios.

[0035] S102, the server constructs a pre-trained body axis ratio inversion model based on the body axis ratio estimator of each insect and the preset XGBoost regression algorithm.

[0036] Among them, the body axis ratio inversion model establishes a mapping relationship between each insect body axis ratio estimator and the insect body axis ratio.

[0037] In the embodiment of the present application, a pre-trained body axis ratio inversion model is constructed according to each insect body axis ratio estimator and a preset extreme gradient boosting algorithm (eXtremeGradient Boosting, XGBoost) regression algorithm, specifically including: According to each insect body axis ratio estimator and a number of insect collinear polarization pattern samples, a number of multidimensional feature vector samples are constructed. Among them, the multidimensional feature vector samples include multidimensional feature vectors composed of each insect body axis ratio estimator corresponding to each insect collinear polarization pattern sample and insect body axis ratio labels. Each multidimensional feature vector sample is input into a preset XGBoost regression algorithm to train the preset XGBoost regression algorithm until the training end condition is met, and the body axis ratio inversion model is obtained.

[0038] That is to say, the present application combines the advanced XGBoost regression algorithm to realize the insect body axis ratio inversion based on the above-mentioned multiple insect body axis ratio estimators. The present application performs iterative training of the XGBoost regression algorithm through a plurality of pre-set multidimensional feature vector samples. The plurality of multidimensional feature vector samples are extracted from a plurality of insect colinear polarization pattern samples to form a multidimensional feature vector of the insect body axis ratio estimator, and an expert or other device labels each multidimensional feature vector with an insect body axis ratio label. The multidimensional feature vector is as follows [ ].

[0039] Among them, the preset XGBoost regression algorithm is trained until the training end conditions are met to obtain the body axis ratio inversion model, which specifically includes: The preset XGBoost regression algorithm is iteratively trained through each multidimensional feature vector sample in the preset training set. Each multidimensional feature vector sample in the preset test set is input into the preset XGBoost regression algorithm after iterative training to calculate the corresponding average relative error according to the output result. When the average relative error is less than the preset threshold, the preset XGBoost regression algorithm after iterative training is determined to be the body axis ratio inversion model.

[0040] When the preset XGBoost regression algorithm is iteratively trained in the present application, a number of multidimensional feature vector samples may include 75% training sets and 25% test sets. For example, for insects measured at 16.2 GHz in the Ku band, 75% of the 159 insects measured in a darkroom are randomly selected as insects for generating training sets to train the body axis ratio inversion model, and the remaining 25% are used as test sets to evaluate the performance of the trained body axis ratio inversion model.

[0041] This application uses the mean relative error (MRE) as the evaluation indicator of the body axis ratio estimation error:

[0042] in, represents the number of insects involved in the inversion, Indicates the first The insect body axis ratio obtained by inversion of each insect is Indicates the first The true insect body axis ratio of each insect; The larger the value is, the greater the estimation error is. When the value is less than the preset threshold, the body axis ratio inversion model is obtained. The preset threshold is set by the user in actual use, and this application does not make any specific limitation on this.

[0043] In the embodiment of the present application, the evaluation results of the body axis ratio inversion model constructed above are shown in Table 1 (Model Evaluation Results Table). The difference between the MRE of the training set and the test set can be used to determine whether the model is overfitting. Generally speaking, if the MRE of the test set is much larger than that of the training set, it indicates that the model is at risk of overfitting. Table 1 shows that the MRE of the model training set and the test set are 9.31% and 10.57% respectively, indicating that the model has good generalization. Figure 3 The figure is a comparison diagram of the inverted insect body axis ratio (i.e., estimated body axis ratio) and the true body axis ratio of the above 159 insects, where points represent insects and lines represent contour lines of estimated values ​​and true values. The body axis ratio inversion model is trained and verified based on the Ku-band (16.2GHz) data of 159 insects measured in a darkroom, which is only for illustrative purposes. The model training samples can be selected in actual use, such as obtained through the Internet, and this application does not make specific restrictions on this.

[0044] Table 1 Model evaluation results

[0045] S103, the server deploys the body axis ratio inversion model to the user terminal, so that the user terminal performs insect body axis ratio inversion based on the input insect colinear polarization pattern and the body axis ratio inversion model.

[0046] After obtaining the above-mentioned body axis ratio inversion model, the body axis ratio inversion model can be deployed on the user terminal, or directly deployed on the server, or directly deployed locally if the execution subject is the user terminal. The user terminal can be understood as the user's mobile phone, computer and other devices, and this application does not make specific restrictions on this.

[0047] In the embodiment of the present application, after the body axis ratio inversion model is deployed to the user terminal, the method further includes: The body axis ratio estimator of each insect corresponding to the input insect collinear polarization pattern is obtained, and the corresponding multi-dimensional feature vector is constructed. The multi-dimensional feature vector is sent to the user terminal so that the user terminal can perform insect body axis ratio inversion.

[0048] If the execution subject is not a user terminal but a server, the server can accept the input insect co-linear plan direction map from the user terminal or from other devices, and obtain the body axis ratio estimator of each insect according to the above steps S101-S102, and then obtain a multi-dimensional feature vector.

[0049] In another embodiment of the present application, the user terminal performs insect body axis ratio inversion based on the input insect collinear polarization pattern and body axis ratio inversion model, specifically including: The user terminal determines the corresponding multiple insect body axis ratio estimators according to the input insect collinear polarization pattern and constructs a multi-dimensional feature vector. The user terminal inputs the multi-dimensional feature vector into the pre-deployed body axis ratio inversion model to perform insect body axis ratio inversion.

[0050] That is to say, the execution of the above steps S101-S102 can also be arranged in the user terminal, and the user terminal constructs a multi-dimensional feature vector to complete the inversion of the insect body axis ratio.

[0051] Through the above scheme, the present application deeply analyzes the preset insect collinear polarization pattern, determines the correlation between the characteristic parameters of each pattern and the insect body axis ratio, and then determines multiple insect body axis ratio estimators. These estimators can accurately characterize the quantitative relationship between the insect polarization characteristics and the insect body axis ratio, and provide an accurate data basis for the subsequent construction of the body axis ratio inversion model, thereby greatly improving the accuracy of the insect body axis ratio inversion. The body axis ratio inversion model constructed based on the preset XGBoost regression algorithm can quickly process the input insect polarization data, which greatly improves the efficiency of insect body axis ratio inversion compared to traditional methods, making it possible to perform body axis ratio analysis on a large number of insect samples in a short time.

[0052] The body axis ratio inversion model is deployed to the user terminal. The user only needs to input the insect's collinear polarization pattern in the terminal to perform insect body axis ratio inversion based on the model. This design gets rid of the traditional measurement method's dependence on complex equipment and professional environment, allowing insect researchers to conveniently analyze insect body axis ratios in a variety of scenarios such as the field and the laboratory, significantly enhancing the ease of application of the technology. This application effectively solves the technical problem of the difficulty in achieving efficient and accurate inversion of insect body axis ratios, and provides a more accurate, efficient and convenient research method for insect research.

[0053] Figure 4 A schematic diagram of the structure of an insect body axis ratio inversion device based on RCS characteristics provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the device includes: At least one processor; and a memory in communication with the at least one processor. The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Based on the analysis results of the preset insect colinear polarization pattern, the correlation between the characteristic parameters of each pattern and the insect body axis ratio is determined, so as to determine multiple insect body axis ratio estimators based on the correlation. Among them, the correlation relationship at least includes the correlation with the insect body length in the insect body axis ratio and the correlation with the insect body width in the insect body axis ratio based on the co-polarization radar scattering cross section RCS. The insect body axis ratio estimator is used to characterize the quantitative relationship between the insect polarization characteristics and the insect body axis ratio. According to each insect body axis ratio estimator and the preset XGBoost regression algorithm, a pre-trained body axis ratio inversion model is constructed. Among them, the body axis ratio inversion model establishes a mapping relationship between each insect body axis ratio estimator and the insect body axis ratio. The body axis ratio inversion model is deployed to the user terminal so that the user terminal performs insect body axis ratio inversion based on the input insect colinear polarization pattern and the body axis ratio inversion model.

[0054] The embodiment of the present application further provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured as follows: Based on the analysis results of the preset insect colinear polarization pattern, the correlation between the characteristic parameters of each pattern and the insect body axis ratio is determined, so as to determine multiple insect body axis ratio estimators based on the correlation. Among them, the correlation relationship at least includes the correlation with the insect body length in the insect body axis ratio and the correlation with the insect body width in the insect body axis ratio based on the co-polarization radar scattering cross section RCS. The insect body axis ratio estimator is used to characterize the quantitative relationship between the insect polarization characteristics and the insect body axis ratio. According to each insect body axis ratio estimator and the preset XGBoost regression algorithm, a pre-trained body axis ratio inversion model is constructed. Among them, the body axis ratio inversion model establishes a mapping relationship between each insect body axis ratio estimator and the insect body axis ratio. The body axis ratio inversion model is deployed to the user terminal so that the user terminal performs insect body axis ratio inversion based on the input insect colinear polarization pattern and the body axis ratio inversion model.

[0055] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0056] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0057] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0058] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for inverting insect body axis ratio based on RCS features, characterized in that: The method comprises: Based on the analysis results of the preset insect colinear polarization pattern, the correlation relationship between the characteristic parameters of each pattern and the insect body axis ratio is determined, so as to determine a plurality of insect body axis ratio estimators based on the correlation relationship; wherein the correlation relationship at least includes the correlation with the insect body length in the insect body axis ratio and the correlation with the insect body width in the insect body axis ratio determined based on the co-polarization radar scattering cross section RCS; the insect body axis ratio estimator is used to characterize the quantitative relationship between the insect polarization characteristics and the insect body axis ratio; According to each of the insect body axis ratio estimators and the preset XGBoost regression algorithm, a pre-trained body axis ratio inversion model is constructed; wherein the body axis ratio inversion model establishes a mapping relationship between each of the insect body axis ratio estimators and the insect body axis ratio; The body axis ratio inversion model is deployed to a user terminal, so that the user terminal performs insect body axis ratio inversion based on the input insect colinear polarization pattern and the body axis ratio inversion model.

2. The method for inverting insect body axis ratio based on RCS feature according to claim 1, characterized in that: Based on the analysis results of the preset insect colinear polarization patterns, before determining the correlation between the characteristic parameters of each pattern and the insect body axis ratio, the method further includes: Obtaining co-polarization radar cross sections (RCS) of different polarization directions corresponding to the pre-observed insects; wherein the co-polarization RCS is obtained by observing the pre-observed insects in a linear polarization mode using a polarization insect radar; According to the change of the co-polarization RCS in the polarization direction, the preset insect co-linear polarization pattern is determined; wherein the preset insect co-linear polarization pattern is expressed as: in, represents the insect collinear polarization pattern, It is the characteristic parameter of the pattern that is independent of the polarization direction; and is a pattern characteristic parameter related to the shape of the insect colinear polarization pattern; Represents the polarization direction, and its value range is ; Indicates the direction of the insect's body axis.

3. The method for inverting insect body axis ratio based on RCS feature according to claim 2, characterized in that: Based on the analysis results of the preset insect collinear polarization patterns, the correlation between the characteristic parameters of each pattern and the insect body axis ratio is determined, including: Determine the preset insect collinear polarization pattern Item and When the items respectively reach their maximum values, the angle between the polarization direction and the insect body axis direction; According to the angle side directions corresponding to the angles and the preset insect body length direction, respectively determine the body length direction and body width direction corresponding to the polarization directions; According to the Item and The corresponding relationship between the item and the body length direction and the body width direction is established. and stated The correlations with the body axis ratios of the insects respectively.

4. The method for inverting insect body axis ratio based on RCS feature according to claim 3 is characterized in that: Determining a plurality of insect body axis ratio estimators based on the association relationship specifically includes: According to the association relationship, when the angle is 0 or When As the RCS in the body length direction; According to the association relationship, when the angle is or When As the RCS in the body width direction; According to the association relationship, the ratio of the RCS in the body length direction to the RCS in the body width direction is As an indicator of RCS body axis ratio; The , , the RCS in the body length direction, the RCS in the body width direction and the RCS body axis ratio index are respectively used as the insect body axis ratio estimators.

5. The method for inverting insect body axis ratio based on RCS feature according to claim 1, characterized in that: According to each of the insect body axis ratio estimators and the preset XGBoost regression algorithm, a pre-trained body axis ratio inversion model is constructed, specifically including: According to each of the insect body axis ratio estimators and a number of insect collinear polarization pattern samples, a number of multidimensional feature vector samples are constructed; wherein the multidimensional feature vector samples include a multidimensional feature vector composed of each of the insect body axis ratio estimators corresponding to each of the insect collinear polarization pattern samples and an insect body axis ratio label; Each of the multidimensional feature vector samples is input into the preset XGBoost regression algorithm to train the preset XGBoost regression algorithm until a training end condition is met, thereby obtaining the body axis ratio inversion model.

6. The method for inverting insect body axis ratio based on RCS feature according to claim 5, characterized in that: The preset XGBoost regression algorithm is trained until the training end condition is met to obtain the body axis ratio inversion model, which specifically includes: Iteratively training the preset XGBoost regression algorithm through each of the multidimensional feature vector samples in a preset training set; Input each of the multidimensional feature vector samples in the preset test set into the preset XGBoost regression algorithm after iterative training to calculate the corresponding average relative error according to the output result; When the average relative error is less than a preset threshold, the preset XGBoost regression algorithm after iterative training is determined as the body axis ratio inversion model.

7. The method for inverting insect body axis ratio based on RCS feature according to claim 5, characterized in that: After the body axis ratio inversion model is deployed to the user terminal, the method further includes: Obtaining each of the insect body axis ratio estimators corresponding to the input insect colinear polarization pattern, and constructing the corresponding multidimensional feature vector; The multi-dimensional feature vector is sent to the user terminal so that the user terminal performs insect body axis ratio inversion.

8. The method for inverting insect body axis ratio based on RCS feature according to claim 5, characterized in that: The user terminal performs insect body axis ratio inversion based on the input insect colinear polarization pattern and the body axis ratio inversion model, specifically including: The user terminal determines a plurality of corresponding insect body axis ratio estimators according to the input insect colinear polarization pattern, and constructs the multidimensional feature vector; The user terminal inputs the multi-dimensional feature vector into the pre-deployed body axis ratio inversion model to perform insect body axis ratio inversion.

9. An insect body axis ratio inversion device based on RCS characteristics, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an insect body axis ratio inversion method based on RCS features as described in any one of claims 1 to 8.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are capable of executing an insect body axis ratio inversion method based on RCS features as described in any one of claims 1 to 8 above.

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