A method, device and medium for compensating missed insects for insect radar

Abstract

The present application provides a method, device and medium for compensating for missed insects in insect radar, which belongs to the field of insect radar technology. The method includes: step 1: constructing a migrating insect target characteristic model based on preset migration characteristic data of actual insect migration; step 2: constructing an insect detection quantity loss model based on the migrating insect target characteristic model, preset pulse cancellation technology and preset constant false alarm detection algorithm; step 3: establishing a cost function corresponding to the estimated value of insect flight speed according to the migrating insect target characteristic model and the insect detection quantity loss model, so as to compensate for the number of missed insects based on the cost function; step 4: performing compensation simulation verification on the insect detection quantity loss model based on preset simulation parameters and cost function, and after the corresponding quantity compensation error is less than the preset threshold, deploying the corresponding cost function and insect detection quantity loss model to the user terminal to perform missed insect compensation operation.

Description

Technical Field

[0001] The present application relates to the technical field of insect radars, and in particular to a method, device and medium for compensating for missed insect detection in insect radars. Background Art

[0002] As a key tool for monitoring insect migration, insect radar plays an irreplaceable role in the study of insect migration and early warning of pests and diseases. In the study of the dynamics of migratory insect populations, insect radar can track the migration paths, gathering areas and other information of insects in real time, providing data support for understanding the living habits and reproduction patterns of insects; in the field of early warning of pests and diseases, it can detect the large-scale migration trends of pests in advance, so that agricultural producers have enough time to take preventive measures, greatly reduce the losses caused by pests and diseases, and ensure the yield and quality of crops.

[0003] However, existing scanning insect radars have certain limitations. On the one hand, their power is relatively high, which helps to expand the monitoring range, but also makes the clutter background received by the radar more complex. Clutter comes from a wide range of sources, such as ground clutter reflected by ground objects such as hills, woods, farmland, deserts, and urban buildings. The clutter signal intensity is often very strong, even exceeding the echo signal of the insect target, which seriously interferes with the accurate detection of the insect target.

[0004] In order to deal with the problem of clutter interference, pulse cancellation technology is usually used to suppress clutter during radar signal processing. This technology is based on the difference in Doppler frequency characteristics between clutter and target echoes. It can effectively suppress clutter by subtracting the echo signals of adjacent pulses. However, due to the characteristics of migrating insects flying with the wind, when the flight direction of the insects tends to be orthogonal to the direction of the radar line of sight, their Doppler speed will approach 0. The pulse cancellation technology has a significant echo suppression effect on low-Doppler speed targets, resulting in a significant reduction in the echo power of low-Doppler speed insects in the orthogonal flight direction, causing these insects to be seriously missed during radar detection, which in turn affects the accurate quantification of the number of migrating insects.

[0005] Based on this, in order to achieve accurate statistics on the number of migratory insects, there is an urgent need for a technical solution that can compensate for the number of low-Doppler speed missed insects caused by pulse cancellation. Summary of the invention

[0006] To solve the above problems, the embodiments of the present application provide a method, device and medium for compensating for missed insect detection of an insect radar.

[0007] On the one hand, an embodiment of the present application provides a method for compensating for missed insect detection of an insect radar, the method comprising:

[0008] Step 1: Modeling the target characteristics of migrating insects based on the preset migration characteristic data of the actual migration of insects to construct a target characteristic model of migrating insects; wherein the preset migration characteristic data at least includes the following characteristic data: spatial distribution data, flight direction and speed data, radar cross section RCS data;

[0009] Step 2: constructing an insect detection quantity loss model based on the migratory insect target characteristic model, the preset pulse cancellation technology and the preset constant false alarm detection algorithm;

[0010] Step 3: establishing a cost function corresponding to the estimated value of the insect flight speed according to the migratory insect target characteristic model and the insect detection quantity loss model, so as to compensate for the number of missed insects based on the cost function;

[0011] Step 4: Perform compensation simulation verification on the insect detection quantity loss model based on preset simulation parameters and the cost function, and after the corresponding quantity compensation error is less than a preset threshold, deploy the corresponding cost function and the insect detection quantity loss model to the user terminal to perform missed insect detection compensation operations.

[0012] In one implementation of the present application, based on preset migration characteristic data of actual insect migration, target characteristics of migrating insects are modeled to construct a target characteristic model of migrating insects, specifically including:

[0013] According to the preset migration characteristic data of the actual migration of the insects, the spatial distribution analysis of the insect position information in the spatial distribution data is performed to generate the spatial distribution variables of the migratory insect target characteristic model after determining that the number of insects in different directions at the same height level is uniformly distributed; the spatial distribution variables include the number of insects in different directions and at the same height level;

[0014] Determine whether the flight direction and speed data and the environmental wind information corresponding to the insect position information are associated, and if so, record the corresponding flight characteristic parameters into the flight direction and speed variables of the migratory insect target characteristic model; wherein the association means that the flight direction and speed data correspond to the environmental wind information at the same time and in the same area; the flight characteristic parameters at least include the flight direction and speed data at the same time and in the same area;

[0015] According to a number of RCS data within a preset time, a corresponding RCS distribution probability density function is generated, and the RCS distribution probability density function is used as an RCS distribution variable of the migratory insect target characteristic model.

[0016] In one implementation of the present application, based on the migratory insect target characteristic model, the preset pulse cancellation technology and the preset constant false alarm detection algorithm, an insect detection quantity loss model is constructed, which specifically includes:

[0017] Establish a spatial rectangular coordinate system for the insect radar detection area, and assume that the insect migration speed is ;in, , , are the velocity components along different axial directions in the spatial rectangular coordinate system;

[0018] Determine the insect Doppler velocity based on the insect migration velocity and the unit direction vector of the radar line of sight ; Wherein, the unit direction vector of the radar line of sight is expressed as , is the azimuth of the insect radar’s beam pointing, is the elevation angle of the insect radar’s beam pointing;

[0019] Determine a signal power gain formula after pulse cancellation according to the frequency response function of the preset pulse cancellation technology and the insect Doppler velocity;

[0020] According to the preset constant false alarm detection algorithm and the RCS distribution variable, an insect quantity detection formula is established; wherein the insect quantity detection formula represents the corresponding relationship between the number of detected insects detected by the insect radar and the number of actual migratory insects;

[0021] Based on the signal power gain formula, a signal-to-noise ratio constraint condition is established, so as to construct the insect detection quantity loss model according to the insect quantity detection formula and the signal-to-noise ratio constraint condition.

[0022] In one implementation of the present application, based on the signal power gain formula, a signal-to-noise ratio constraint condition is established, specifically including:

[0023] According to the signal power gain formula, the pulse cancellation insect signal-to-noise ratio of the corresponding insect signal-to-noise ratio after the power loss of pulse cancellation is determined, which is greater than the preset minimum detectable signal-to-noise ratio as the constraint of the integral domain of the insect quantity detection formula, so that the constraint of the integral domain is used as the signal-to-noise ratio constraint condition.

[0024] In one implementation of the present application, a cost function corresponding to the estimated value of the insect flight speed is established according to the migratory insect target characteristic model and the insect detection quantity loss model, so as to compensate for the number of missed insects based on the cost function, specifically including:

[0025] A cost function is established based on the preset vectors of insect quantity detected at different orientations and the vectors of detection quantity loss coefficients at different orientations; wherein the detection quantity loss coefficient is the integral term in the insect quantity detection formula;

[0026] Determining the estimated value of the insect flight speed by constraining the cost function to be minimum;

[0027] The estimated value of the insect flight speed is substituted into the insect detection number loss model to determine the true value of the number of migrating insects after compensation, thereby completing the compensation for the number of missed insects.

[0028] In one implementation of the present application, the cost function uses the vector of the number of insects detected at different orientations as the numerator and the vector of the loss coefficient of the number of detections at different orientations as the denominator, calculates the ratio of the corresponding terms of the vectors, and uses the standard deviation obtained by performing standard deviation operations on each ratio as the function value of the cost function.

[0029] In one implementation of the present application, a compensation simulation verification is performed on the insect detection quantity loss model based on preset simulation parameters and the cost function, specifically including:

[0030] Inputting the preset simulation parameters into the cost function to determine the corresponding estimated value of the simulated insect flight speed;

[0031] Inputting the estimated value of the simulated insect flight speed into the insect detection population loss model to calculate the true value of the simulated migrating insect population;

[0032] The difference between the true value of the simulated number of migrating insects and the actual number of insects in the preset simulation parameters is calculated, and the difference is divided by the actual number of insects to determine the number compensation error, so as to perform compensation simulation verification based on the comparison result of the number compensation error and the preset threshold value.

[0033] In an implementation of the present application, the preset pulse cancellation technology adopts a double pulse cancellation technology; the preset constant false alarm detection algorithm adopts a unit average constant false alarm detection algorithm.

[0034] On the other hand, an embodiment of the present application provides a missed insect detection compensation device for an insect radar, characterized in that the device comprises:

[0035] 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 a missed insect detection compensation method for an insect radar as described above.

[0036] On the other hand, an embodiment of the present application provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions can execute a missed insect detection compensation method for an insect radar as described above.

[0037] Compared with the prior art, the present invention has the following significant effects:

[0038] Through the above technical solution, the present application accurately models the target characteristics of migratory insects and scientifically constructs a model for the number of insect detection losses, which can accurately estimate the number of low-Doppler speed insects missed due to radar pulse cancellation technology, and achieve high-precision compensation for the number of missed insects. And after actual simulation verification, after adopting the method of the present application, the radar detection error of the number of insects can be greatly reduced, significantly improving the accuracy of quantifying the number of migratory insects in the air.

[0039] In addition, accurate insect population monitoring is of vital importance for early warning of pests and diseases. In agricultural production, timely and accurate knowledge of the population dynamics of migratory pests can provide farmers and agricultural departments with sufficient early warning time, allowing them to formulate targeted prevention and control strategies in advance and rationally allocate prevention and control resources, such as precise pesticide application and the deployment of insect-proof nets, effectively avoiding large-scale outbreaks of pests and diseases, reducing agricultural production losses, ensuring food security and the quality of agricultural products, and playing an immeasurable role in promoting the sustainable development of agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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:

[0041] Figure 1 A schematic diagram of a flow chart of a method for compensating for missed insect detection of an insect radar in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of the probability density distribution of insect RCS in a method for compensating for missed insects used in an insect radar in an embodiment of the present application;

[0043] Figure 3 A simulated target spatial distribution diagram in a missed insect detection compensation method for an insect radar in an embodiment of the present application;

[0044] Figure 4 A schematic diagram of the relationship between the number of insects during simulation in a method for compensating for missed insects used in an insect radar in an embodiment of the present application;

[0045] Figure 5 It is a schematic diagram of the structure of a missed insect detection compensation device for an insect radar in an embodiment of the present application. DETAILED DESCRIPTION

[0046] 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.

[0047] The embodiments of the present application provide a method, device and medium for compensating for missed insects in an insect radar, which are used to solve the technical problem that pulse cancellation currently causes the number of missed insects at low Doppler speeds, thus affecting the accurate quantification of the number of migratory insects.

[0048] The following describes in detail various embodiments of the present application in conjunction with the accompanying drawings.

[0049] The present application embodiment provides a method for compensating for missed insect detection by an insect radar, such as Figure 1 As shown, the method may include steps 1 to 4:

[0050] Step 1: Based on the preset migration characteristic data of the actual migration of insects, the target characteristics of migratory insects are modeled to construct a target characteristic model of migratory insects.

[0051] The preset migration characteristic data includes at least the following characteristic data: spatial distribution data, flight direction and speed data, and radar cross section (RCS) data. The insects targeted by this application may be agricultural pests. The preset migration characteristic data is obtained by setting an insect radar to collect data in advance by taking an agricultural production area as the monitoring area of ​​the embodiment.

[0052] It should be noted that the executor of the present application may be a server. The server is only an executor of the missed insect detection compensation method for insect radar for exemplary purposes. The executor is not limited to servers, but may also be a personal computer, a laptop computer, etc. The present application does not make any specific limitation on this.

[0053] Target modeling of migratory insects is the basis for establishing a population loss model for insect detection. When modeling the target, the spatial distribution, flight direction / speed, and RCS distribution of migratory insects must be considered to make the established target model as consistent as possible with the actual situation, to ensure that the subsequent population loss model is consistent with the actual situation, and thus ensure that the biological compensation algorithm proposed for the loss model is effective for actual radar measurements.

[0054] In the embodiment of the present application, based on the preset migration characteristic data of the actual migration of insects, the target characteristics of migrating insects are modeled to construct the target characteristic model of migrating insects, which specifically includes:

[0055] According to the preset migration characteristic data of the actual migration of insects, the spatial distribution analysis of the insect position information in the spatial distribution data is performed to generate the spatial distribution variables of the migratory insect target characteristic model after determining that the number of insects in different directions at the same height level is uniformly distributed. The spatial distribution variables include the number of insects in different directions and at the same height level. Determine whether the flight direction and speed data and the environmental wind information corresponding to the insect position information are associated. If so, record the corresponding flight characteristic parameters to the flight direction and speed variables of the migratory insect target characteristic model. Among them, the association is that the flight direction and speed data correspond to the environmental wind information at the same time and in the same area. The flight characteristic parameters at least include the flight direction and speed data at the same time and in the same area. According to a number of RCS data within the preset time, the corresponding RCS distribution probability density function is generated, and the RCS distribution probability density function is used as the RCS distribution variable of the migratory insect target characteristic model.

[0056] That is to say, the present application models the target characteristics of migratory insects in three dimensions: spatial distribution data, flight direction and speed data, and RCS data, and ultimately obtains a uniform distribution, that is, the number of insects in different directions and at the same height level is the same, and this distribution form of the number of insects constitutes a spatial distribution variable; the flight direction and speed are consistent with the wind direction and wind speed of the environment, that is, at the same time and in the same area, the flight direction and speed data of all insects are the same, and these flight direction and speed data constitute the above-mentioned flight direction and speed variables, and the RCS distribution of migratory insects in the monitoring area that has been monitored for a long time (preset time) is statistically analyzed, that is, the RCS distribution probability density function is the RCS distribution variable of the migratory insect target characteristic model.

[0057] Among them, the spatial distribution analysis can use a clustering algorithm, or it can be used, for example, to divide the monitoring area into multiple small areas of equal area at the same height plane (for example, based on longitude and latitude or radar beam coverage). Then, count the number of insects detected in each small area. Then, calculate the standard deviation of these numbers. If the standard deviation is less than a certain set threshold (for example, a smaller value determined based on historical data or simulation experiments), it can be considered that the insects are evenly distributed in this height plane. The specific setting can be made according to actual use and is not specifically limited here.

[0058] For the correlation analysis of flight direction and speed data, the velocity vector of the insect flight, the environmental wind direction and wind speed vector can be generated, and the cosine similarity or the inverse of the Euclidean distance can be calculated. If the calculation result is within the preset range, it means that the two are correlated. In this regard, the present application can also use algorithms such as Kalman filtering for correlation analysis, which is not specifically limited here.

[0059] When calculating the probability density function of the RCS distribution, the insect radar echo data must first be preprocessed. This may include removing noise (for example, using wavelet denoising or mean filtering), and then extracting RCS-related parameters from information such as echo intensity based on radar equations and signal processing theory. Next, the RCS value is divided into multiple intervals (for example, based on the radar resolution and the actual monitored RCS range), and the frequency of RCS occurrence in each interval is counted. Finally, the probability density function is calculated by frequency, such as using a histogram statistical method or a kernel density estimation method to construct the RCS distribution probability density function.

[0060] The modeling process of the target characteristics of migrating insects is explained, and the modeling analysis process and results are as follows: This application can analyze the actual insect migration data. After the spatial distribution analysis, the spatial distribution pattern of insects can be obtained. After the actual spatial distribution analysis, the insects are evenly distributed. At this time, it can be assumed that the insects are evenly distributed in the same altitude plane during migration, that is, the number of insects in different directions and the same altitude layer is the same. The flight direction and speed data are modeled and analyzed. Since migrating insects have a common directional flight mechanism with the wind, their flight direction and flight speed are close to the wind direction and wind speed of the environment. Therefore, it can be assumed that the flight direction and speed of migrating insects in the same time and the same area are consistent. The RCS distribution data is modeled and analyzed. For the high-resolution insect radar used in this application, the migrating insect targets can be regarded as a large number of independent scatterers. Based on the long-term monitoring results of the insect radar, the RCS distribution of migrating insects in the monitoring area can be statistically analyzed to obtain the RCS distribution probability density function. Figure 2 The figure shows a schematic diagram of the probability density distribution of insect RCS obtained through long-term monitoring statistics of a high-resolution scanning insect radar. The horizontal axis is RCS, in decibel square meter dBsm, and the vertical axis is probability density.

[0061] The above-mentioned judgment of whether the number of insects in different directions at the same altitude level is evenly distributed, judgment of the flight direction and speed data, whether the environmental wind information corresponding to the insect position information is related, and generation of the corresponding RCS distribution probability density function according to a number of RCS data within a preset time can all be pre-set in the server as modeling conditions. The present application can also set other modeling conditions, such as other spatial distribution laws parallel to the uniform distribution, so as to obtain a uniformly distributed spatial distribution variable based on the analysis of the preset migration characteristic data, that is, the server determines whether the preset migration characteristic data belongs to a uniform distribution or other spatial distribution laws. The server finally determines that it belongs to a uniform distribution and obtains the corresponding spatial distribution variable, thereby constructing a migratory insect target characteristic model.

[0062] Through the above scheme, from the perspective of spatial distribution, understanding whether insects are evenly distributed in the same height plane determines the basic assumptions when conducting radar monitoring range coverage and data statistical analysis. If the insects are evenly distributed, a relatively consistent method can be used to process monitoring data at the same height and different directions; if they are unevenly distributed, it is necessary to further explore their distribution patterns and influencing factors, which is crucial for accurately evaluating the overall number and dynamic changes of insects in the monitoring area. In terms of flight direction and speed, its characteristics are directly related to the relative motion of insects in the radar beam. Due to the Doppler effect, the flight direction and speed of insects will affect the frequency and intensity of echoes received by the radar. The assumption of flight direction and speed close to the ambient wind direction and wind speed is consistent with the actual habits of many migratory insects, and provides a key motion parameter basis for subsequent accurate calculation of Doppler speed, analysis of radar signals, and construction of detection models. The RCS distribution characteristics are extremely critical for radar detection. As an indicator of the scattering ability of insects to radar waves, the differences in the RCS distribution of different insect individuals or groups will result in different display effects on the radar screen. The RCS distribution obtained through long-term monitoring statistics can help determine the detectability of insects under different radar parameter settings, as well as how to distinguish insect signals from background noise, clutter, etc. based on RCS characteristics during signal processing, thereby improving the accuracy and reliability of insect detection.

[0063] Step 2: Based on the migratory insect target characteristic model, the preset pulse cancellation technology and the preset constant false alarm detection algorithm, an insect detection quantity loss model is constructed.

[0064] In the embodiment of the present application, based on the migratory insect target characteristic model, the preset pulse cancellation technology and the preset constant false alarm detection algorithm, an insect detection quantity loss model is constructed, which specifically includes:

[0065] First, the spatial rectangular coordinate system of the insect radar detection area is established, and the insect migration speed is assumed to be .in, , , They are the velocity components along different axial directions in the spatial rectangular coordinate system. For example, the due north direction is the positive direction of the x-axis, the due east direction is the positive direction of the y-axis, and the vertical horizontal plane upward is the positive direction of the z-axis to establish a spatial rectangular coordinate system.

[0066] Then, the insect Doppler velocity is determined based on the insect migration speed and the unit direction vector of the radar line of sight. Among them, the unit direction vector of the radar line of sight is expressed as , is the azimuth of the insect radar’s beam pointing, is the elevation angle of the insect radar beam. According to the frequency response function of the preset pulse cancellation technology and the insect Doppler velocity, the signal power gain formula after pulse cancellation is determined. Among them, the azimuth angle represents the horizontal position of the insect relative to the radar, such as due east, due south, due west, due north and various angles in between, and the elevation angle refers to the upward and downward tilt angle of the radar beam relative to the horizontal plane.

[0067] Then, according to the preset constant false alarm detection algorithm and RCS distribution variables, an insect quantity detection formula is established, wherein the insect quantity detection formula represents the corresponding relationship between the number of detected insects detected by the insect radar and the number of actual migratory insects.

[0068] Finally, based on the signal power gain formula, the signal-to-noise ratio constraint condition is established to construct the insect detection quantity loss model according to the insect quantity detection formula and the signal-to-noise ratio constraint condition.

[0069] Specifically, in step 1, it is assumed that the flight direction and speed data of migrating insects are consistent at the same time and in the same area, so it is assumed that the insect migration speed is , when the azimuth and elevation angles of the insect radar’s beam are and When the radar line of sight has a unit direction vector, it is expressed as . Then we get the Doppler velocity .

[0070] Among them, the preset pulse cancellation technology of the present application can adopt the double pulse cancellation technology, or other multi-pulse cancellation technology, adaptive pulse cancellation technology. The present application takes the double pulse cancellation technology as an example, and the frequency response function is as follows:

[0071]

[0072] in, represents the frequency response function, is an imaginary unit, is the Doppler frequency, is the pulse repetition time, for example, the value range is [100μs, 500μs]. The relationship between it and the Doppler velocity is: , is the radar wavelength. The signal power gain formula after pulse cancellation is as follows:

[0073]

[0074] in, It should be noted that according to the signal power gain formula after pulse cancellation, when the Doppler speed When it is 0, S is also 0. That is, after the pulse cancellation of the low Doppler speed target, the echo power will be greatly reduced. It shows that when the insect flight direction tends to be orthogonal to the radar line of sight, the Doppler velocity tends to 0. Therefore, pulse cancellation will greatly reduce the echo power of low-Doppler-speed insects in the orthogonal migration direction.

[0075] Furthermore, this application assumes that the RCS of insects is , the height is H , and is located at the center of the beam, the signal-to-noise ratio (SNR) of the insect is expressed by the radar equation as:

[0076]

[0077] in, is a constant related to the radar system and can be obtained by measuring the radar echo of a standard calibration target. The preset constant false alarm detection algorithm of this application can adopt a unit average constant false alarm detection algorithm, or a detection algorithm such as a two-side average selection constant false alarm, an ordered constant false alarm, etc., which is not specifically limited in this application. Taking the unit average constant false alarm detection algorithm as an example, the corresponding detection probability is expressed as:

[0078]

[0079] in, is the detection probability, is the false alarm probability, is the total number of reference units. It should be noted here that the higher the SNR of an insect, the greater the probability of being detected, and pulse cancellation greatly reduces the echo power of low-Doppler-speed insects in orthogonal migration directions, so the SNR decreases and the detection probability decreases accordingly, which will cause serious missed detection of these insects.

[0080] According to the RCS distribution variable, the corresponding probability distribution is recorded as , then the azimuth and elevation angles of the radar beam can be obtained as and When, the height is H The relationship between the number of detected insects detected by the radar and the number of actual migratory insects, that is, the insect number detection formula:

[0081]

[0082] in, Indicates the number of detected insects detected by the insect radar. Represents the actual number of migratory insects, the integral term To detect the number loss coefficient, represents the integration domain.

[0083] There is a minimum detectable signal-to-noise ratio for radar, which is denoted as , where the signal-to-noise ratio constraint conditions are established based on the signal-to-noise ratio and signal power gain formula, including:

[0084] According to the signal-to-noise ratio and signal power gain formula, the pulse cancellation insect signal-to-noise ratio of the corresponding insect signal-to-noise ratio after the power loss of pulse cancellation is determined, which is greater than the preset minimum detectable signal-to-noise ratio as the constraint of the integral domain of the insect quantity detection formula, so that the constraint of the integral domain is used as the signal-to-noise ratio constraint condition.

[0085] That is to say, The insect signal-to-noise ratio is After the power loss of pulse cancellation, it must be greater than or equal to To be detected by insect radar, therefore, this application will integrate the domain The constraints are expressed as:

[0086]

[0087] in, Represents the signal-to-noise ratio of the pulse cancellation insect after the power loss of the pulse cancellation.

[0088] In the embodiments of the present application:

[0089] Above and A model for detecting population loss of insects was constructed.

[0090] Step 3: According to the migratory insect target characteristic model and the insect detection quantity loss model, a cost function corresponding to the estimated value of the insect flight speed is established to compensate for the number of missed insects based on the cost function.

[0091] In the embodiment of the present application, a cost function corresponding to the estimated value of the insect flight speed is established according to the migratory insect target characteristic model and the insect detection quantity loss model, so as to compensate for the number of missed insects based on the cost function, specifically including:

[0092] Based on the preset vectors of insect numbers detected at different orientations and the vectors of detection number loss coefficients at different orientations, a cost function is established. Among them, the detection number loss coefficient is the integral term in the insect number detection formula. By constraining the cost function to be minimum, the estimated value of insect flight speed is determined. The estimated value of insect flight speed is substituted into the insect detection number loss model to determine the true value of the number of migrating insects after compensation, and the number of missed insects is compensated.

[0093] The above cost function uses the vector of insect quantity detected at different directions as the numerator and the vector of detection quantity loss coefficient at different directions as the denominator, calculates the ratio of the corresponding terms of the vectors, and uses the standard deviation obtained by performing standard deviation operation on each ratio as the function value of the cost function.

[0094] Specifically, this application Indicates the pitch angle ,high When , the number vector of insects detected in different directions is:

[0095]

[0096] in, Indicates the number of azimuth beams of the insect scanning radar.

[0097] make Indicates the pitch angle ,high The detection quantity loss coefficient vector of different directions at :

[0098]

[0099] In step 1, it is assumed that the migrating insects are evenly distributed in the same altitude plane, that is, the number of insects in the same altitude layer at different directions is the same. ,high The detection results at this time can establish the following cost function:

[0100]

[0101] in, is the function value of the cost function, Indicates the standard deviation operation; . / indicates calculating the ratio of corresponding items of the vector.

[0102] The physical meaning of the above cost function is that the radar beam elevation angle is When, altitude level HThe standard deviation of the true number of insects in different directions. is the known number of detected insects vector, The parameters included are: and H is a known quantity, is the unknown speed of migrating insects, which needs to be solved.

[0103] In step 1, this application assumes that the number of insects in different directions is the same, so the constraint Minimum to calculate an estimate of the insect's flight speed:

[0104]

[0105] in, are the velocity components along different axial directions, The above formula can be solved using a genetic algorithm, or other solving tools, such as a particle swarm optimization algorithm, an ant colony optimization algorithm, a gradient descent method, etc., which is not specifically limited in this application.

[0106] This application will Substitute the insect number detection formula of the above insect detection number loss model into the formula to calculate the true value of the number of migrating insects after compensation, and complete the compensation for the number of missed insects.

[0107] Step 4: Perform compensation simulation verification on the insect detection quantity loss model based on preset simulation parameters and cost function, and after the corresponding quantity compensation error is less than the preset threshold, deploy the corresponding cost function and insect detection quantity loss model to the user terminal to perform missed insect detection compensation operations.

[0108] In the embodiment of the present application, a compensation simulation verification of the insect detection quantity loss model is performed based on preset simulation parameters and cost functions, specifically including:

[0109] The preset simulation parameters are input into the cost function to determine the corresponding simulated insect flight speed estimate. The simulated insect flight speed estimate is input into the insect detection population loss model to calculate the true value of the number of simulated migrating insects. The difference between the true value of the number of simulated migrating insects and the real number of insects in the preset simulation parameters is calculated, and the difference is divided by the real number of insects to determine the number compensation error, so as to perform compensation simulation verification based on the comparison result of the number compensation error and the preset threshold.

[0110] The preset simulation parameters are shown in Table 1:

[0111] Table 1 Preset simulation parameters

[0112]

[0113] The simulation target space distribution diagram is as follows: Figure 3 As shown; Figure 4 This is a schematic diagram of the number of real insects in different directions corresponding to the simulation, the number of insects detected by radar, and the number of insects after algorithm compensation. Figure 4 0 degrees is due east, 90 degrees is due north, 180 degrees is due west, and 270 degrees is due south. The true value represents the actual number of insects, the detection represents the number of insects detected by the radar, and the compensation represents the number of insects after algorithm compensation.

[0114] This application inputs the above preset simulation parameters into the above cost function to calculate the estimated value of the simulated insect flight speed, and further calculates the true value of the number of simulated migrating insects through the insect detection number loss model. Subsequently, the number compensation error is calculated using the following formula:

[0115]

[0116] in, is the quantity compensation error, represents the true value of the number of simulated migratory insects, Indicates the actual number of insects.

[0117] When the quantity compensation error is less than the preset threshold, it means that the insect detection quantity loss model has passed the compensation simulation verification. The simulation shows the effectiveness of the above-mentioned missed insect detection compensation method for insect radar. The preset threshold can be set during actual use, and this application does not make specific restrictions on this.

[0118] Furthermore, the cost function and the insect detection quantity loss model are deployed to the user terminal so that the user can perform missed insect detection compensation in actual use. The user terminal can be understood as a computing device used by the user, such as a server, a computer, a mobile phone, etc., and this application does not make specific limitations on this.

[0119] Through the above technical solution, the present application accurately models the target characteristics of migratory insects and scientifically constructs a model for the number of insect detection losses, which can accurately estimate the number of low-Doppler speed insects missed due to radar pulse cancellation technology, and achieve high-precision compensation for the number of missed insects. And after actual simulation verification, after adopting the method of the present application, the radar detection error of the number of insects can be greatly reduced, significantly improving the accuracy of quantifying the number of migratory insects in the air.

[0120] In addition, accurate insect population monitoring is of vital importance for early warning of pests and diseases. In agricultural production, timely and accurate knowledge of the population dynamics of migratory pests can provide farmers and agricultural departments with sufficient early warning time, allowing them to formulate targeted prevention and control strategies in advance and rationally allocate prevention and control resources, such as precise pesticide application and the deployment of insect-proof nets, effectively avoiding large-scale outbreaks of pests and diseases, reducing agricultural production losses, ensuring food security and the quality of agricultural products, and playing an immeasurable role in promoting the sustainable development of agriculture.

[0121] Figure 5 A schematic diagram of the structure of a missed insect detection compensation device for an insect radar provided in an embodiment of the present application, such as Figure 5 As shown, the device includes:

[0122] 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 execute:

[0123] Step 1: Based on the preset migration characteristic data of the actual migration of insects, model the target characteristics of migrating insects to construct a target characteristic model of migrating insects. The preset migration characteristic data includes at least the following characteristic data: spatial distribution data, flight direction and speed data, and radar cross section RCS data.

[0124] Step 2: Based on the migratory insect target characteristic model, the preset pulse cancellation technology and the preset constant false alarm detection algorithm, an insect detection quantity loss model is constructed.

[0125] Step 3: According to the migratory insect target characteristic model and the insect detection quantity loss model, a cost function corresponding to the estimated value of the insect flight speed is established to compensate for the number of missed insects based on the cost function.

[0126] Step 4: Perform compensation simulation verification on the insect detection quantity loss model based on preset simulation parameters and cost function, and after the corresponding quantity compensation error is less than the preset threshold, deploy the corresponding cost function and insect detection quantity loss model to the user terminal to perform missed insect detection compensation operations.

[0127] 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:

[0128] Step 1: Based on the preset migration characteristic data of the actual migration of insects, model the target characteristics of migrating insects to construct a target characteristic model of migrating insects. The preset migration characteristic data includes at least the following characteristic data: spatial distribution data, flight direction and speed data, and radar cross section RCS data.

[0129] Step 2: Based on the migratory insect target characteristic model, the preset pulse cancellation technology and the preset constant false alarm detection algorithm, an insect detection quantity loss model is constructed.

[0130] Step 3: According to the migratory insect target characteristic model and the insect detection quantity loss model, a cost function corresponding to the estimated value of the insect flight speed is established to compensate for the number of missed insects based on the cost function.

[0131] Step 4: Perform compensation simulation verification on the insect detection quantity loss model based on preset simulation parameters and cost function, and after the corresponding quantity compensation error is less than the preset threshold, deploy the corresponding cost function and insect detection quantity loss model to the user terminal to perform missed insect detection compensation operations.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 compensating for missed insect detection by an insect radar, characterized in that: The method comprises: Step 1: Modeling the target characteristics of migrating insects based on the preset migration characteristic data of the actual migration of insects to construct a target characteristic model of migrating insects; wherein the preset migration characteristic data at least includes the following characteristic data: spatial distribution data, flight direction and speed data, radar cross section RCS data; Step 2: constructing an insect detection quantity loss model based on the migratory insect target characteristic model, the preset pulse cancellation technology and the preset constant false alarm detection algorithm; Step 3: establishing a cost function corresponding to the estimated value of the insect flight speed according to the migratory insect target characteristic model and the insect detection quantity loss model, so as to compensate for the number of missed insects based on the cost function; Step 4: Perform compensation simulation verification on the insect detection quantity loss model based on preset simulation parameters and the cost function, and after the corresponding quantity compensation error is less than a preset threshold, deploy the corresponding cost function and the insect detection quantity loss model to the user terminal to perform missed insect detection compensation operation; Among them, based on the preset migration characteristic data of the actual migration of insects, the target characteristics of migratory insects are modeled to construct the target characteristic model of migratory insects, which specifically includes: According to the preset migration characteristic data of the actual migration of the insects, the spatial distribution analysis of the insect position information in the spatial distribution data is performed to generate the spatial distribution variables of the migratory insect target characteristic model after determining that the number of insects in different directions at the same height level is uniformly distributed; the spatial distribution variables include the number of insects in different directions and at the same height level; Determine whether the flight direction and speed data and the environmental wind information corresponding to the insect position information are associated, and if so, record the corresponding flight characteristic parameters into the flight direction and speed variables of the migratory insect target characteristic model; wherein the association means that the flight direction and speed data correspond to the environmental wind information at the same time and in the same area; the flight characteristic parameters at least include the flight direction and speed data at the same time and in the same area; Generate a corresponding RCS distribution probability density function according to a number of RCS data within a preset time, and use the RCS distribution probability density function as an RCS distribution variable of the migratory insect target characteristic model; Among them, based on the migratory insect target characteristic model, the preset pulse cancellation technology and the preset constant false alarm detection algorithm, an insect detection quantity loss model is constructed, which specifically includes: Establish a spatial rectangular coordinate system for the insect radar detection area, and assume that the insect migration speed is ;in, , , are the velocity components along different axial directions in the spatial rectangular coordinate system; Determine the insect Doppler velocity based on the insect migration velocity and the unit direction vector of the radar line of sight ; Wherein, the unit direction vector of the radar line of sight is expressed as , is the azimuth of the insect radar’s beam pointing, is the elevation angle of the insect radar’s beam pointing; Determine a signal power gain formula after pulse cancellation according to the frequency response function of the preset pulse cancellation technology and the insect Doppler velocity; According to the preset constant false alarm detection algorithm and the RCS distribution variable, an insect quantity detection formula is established; wherein the insect quantity detection formula represents the corresponding relationship between the number of detected insects detected by the insect radar and the number of actual migratory insects; Based on the signal power gain formula, a signal-to-noise ratio constraint condition is established, so as to construct the insect detection quantity loss model according to the insect quantity detection formula and the signal-to-noise ratio constraint condition; According to the migratory insect target characteristic model and the insect detection quantity loss model, a cost function corresponding to the estimated value of the insect flight speed is established to compensate for the number of missed insects based on the cost function, specifically including: A cost function is established based on a preset vector of insect numbers detected at different orientations and a vector of detection number loss coefficients at different orientations; wherein the detection number loss coefficient is an integral term in the insect number detection formula; the cost function uses the vector of insect numbers detected at different orientations as a numerator and the vector of detection number loss coefficients at different orientations as a denominator, calculates the ratio of the corresponding terms of the vectors, and uses the standard deviation obtained by performing a standard deviation operation on each ratio as the function value of the cost function; Determining the estimated value of the insect flight speed by constraining the cost function to be minimum; The estimated value of the insect flight speed is substituted into the insect detection number loss model to determine the true value of the number of migrating insects after compensation, thereby completing the compensation for the number of missed insects.

2. The method for compensating for missed insect detection of insect radar according to claim 1, characterized in that: Based on the signal power gain formula, a signal-to-noise ratio constraint condition is established, specifically including: According to the signal power gain formula, the pulse cancellation insect signal-to-noise ratio of the corresponding insect signal-to-noise ratio after the power loss of pulse cancellation is determined, which is greater than the preset minimum detectable signal-to-noise ratio as the constraint of the integral domain of the insect quantity detection formula, so that the constraint of the integral domain is used as the signal-to-noise ratio constraint condition.

3. The method for compensating for missed insect detection of insect radar according to claim 1, characterized in that: The compensation simulation verification of the insect detection quantity loss model is performed based on preset simulation parameters and the cost function, specifically including: Inputting the preset simulation parameters into the cost function to determine the corresponding estimated value of the simulated insect flight speed; Inputting the estimated value of the simulated insect flight speed into the insect detection population loss model to calculate the true value of the simulated migrating insect population; The difference between the true value of the simulated number of migrating insects and the actual number of insects in the preset simulation parameters is calculated, and the difference is divided by the actual number of insects to determine the number compensation error, so as to perform compensation simulation verification based on the comparison result of the number compensation error and the preset threshold value.

4. A method for compensating for missed insect detection of an insect radar according to any one of claims 1 to 3, characterized in that: The preset pulse cancellation technology adopts a double pulse cancellation technology; the preset constant false alarm detection algorithm adopts a unit average constant false alarm detection algorithm.

5. A missed insect detection compensation device for insect radar, 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 a missed insect detection compensation method for an insect radar as described in any one of claims 1 to 4 above.

6. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions can execute a missed insect detection compensation method for an insect radar as described in any one of claims 1 to 4 above.

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