A method for simulating migration trajectories based on insect flight parameters

Abstract

The present invention provides a method for simulating the migration trajectory based on insect flight parameters, including extracting the radar measurement parameters of the target insect, and further comprising the following steps: calculating the flight direction of the target insect; constructing a quantitative relationship model for the flight altitude, flight speed and flight direction of the migrating insect; calculating the migration background field of the insect; and calculating the migration trajectory of the insect. The present invention proposes a method for simulating the migration trajectory based on insect flight parameters. Based on the biological parameters measured by insect radar, the flight altitude, speed and direction of the target insect are identified and extracted, and the changes in the self-flight ability of the target insect under different conditions are fully considered during the trajectory calculation process, so that the migration path of the target insect can be simulated more accurately, and the take-off and landing areas of the insect can be predicted, thereby guiding insect early warning and prevention accurately and efficiently.

Description

Technical Field

[0001] The present invention relates to the technical field of pest monitoring, and particularly to a method for simulating the migration trajectory based on insect flight parameters. Background Art

[0002] The simulation of insect migration trajectories is an important method for tracking the migration paths of insects, predicting the source areas, migration times, and landing areas of insect takeoffs, and is a key issue for achieving precise monitoring and early warning of migrating insects. The simulation of insect migration trajectories has gone through three stages. The first stage is the two-dimensional trajectory simulation method driven by meteorology; the second stage is the three-dimensional trajectory simulation method driven by meteorology; both of these methods use wind as the driving force and do not consider the flight behavior of insects themselves, resulting in large simulation errors; the third stage is the trajectory simulation method with fixed insect flight parameters, which uses the instantaneous flight parameters of insects detected by insect radar, combines meteorological data to calculate the self-speed and head orientation of insects, and uses this speed and head orientation as the constant speed and orientation of insects. Existing research has shown that different species of insects have differences in multiple aspects such as flight behavior and flight ability, and therefore, their migration trajectories also have significant differences; under different meteorological conditions, the flight behavior and flight ability of the same insect also have differences, and therefore, its migration trajectory will also have significant differences. However, the current trajectory simulation methods do not fully consider the differences in the flight behavior of insects themselves under different meteorological conditions and the flight behavior differences of different species of pests, and there are still certain errors in the simulated trajectories.

[0003] The Chinese patent application with the publication number CN114814818A discloses a method for simulating the migration path of pests based on insect radar monitoring, including obtaining the data collected by insect radar, and further including the following steps: judging the migration insect peak of the data collected by the insect radar, and extracting the relevant data of a single target; calculating the high-resolution WRF meteorological background field; decomposing the autonomous flight speed and direction of the target, and re-synthesizing the new speed direction; determining the forward and backward inference durations, performing trajectory calculation, and finally determining the complete migration path. The disadvantage of this method is that it uses the instantaneous migration direction and speed monitored by insect radar, combines the wind direction and wind speed to solve the head orientation and self-flight speed of the target insect above the radar as the constant self-migration speed and direction of the insect, without considering the changes in the self-migration speed and direction of insects under different environmental conditions.

[0004] The Chinese patent application with the publication number CN109116348A discloses a long-distance trajectory simulation method for the takeoff and cruise of insect migration. Based on the background atmospheric field, reasonable conditions are set to determine the takeoff area of insects. Then, based on the proven orientation flight strategies existing in insects and the migration trajectory speed and direction of insects observed by radar, the magnitude of the self-speed and the head orientation of insects during migration are determined, and the Lagrangian particle diffusion model is used to simulate the migration trajectory of insects in the background atmospheric field. The disadvantage of this method is that the self-flight speed and head orientation of insects are obtained based on the insect trajectory speed and direction observed by insect radar, as well as the seasonal preference direction and quantitative strategy of insects, and the trajectory is calculated with this speed and direction as the constant self-flight speed and direction of insects, without considering the changes in the self-migration speed and direction of insects under different environmental conditions. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes a migration trajectory simulation method based on insect flight parameters. Based on the biological parameters measured by insect radar, the flight height, speed, and direction of the target insect are identified and extracted. During the trajectory calculation process, the changes in the self-flight ability of the target insect under different conditions are fully considered, and the migration path of the target insect can be simulated more accurately, predicting the takeoff and landing areas of insects, so as to accurately and efficiently guide insect early warning and prevention and control.

[0006] The present invention provides a migration trajectory simulation method based on insect flight parameters, including extracting the radar measurement parameters of the target insect, and further including the following steps:

[0007] Step 1: Calculate the flight direction of the target insect;

[0008] Step 2: Construct a quantitative relationship model for the flight height, flight speed, and flight direction of migrating insects;

[0009] Step 3: Calculate the migration background field of insects;

[0010] Step 4: Calculate the migration trajectory of insects.

[0011] Preferably, the extraction of the radar measurement parameters of the target insect includes the following sub-steps:

[0012] Step 01: Based on the parameter data of common migrating insects, a random forest classification algorithm is used to establish a migrating insect classification model;

[0013] Step 02: Determine the type of target insect to be extracted. Based on the parameter data of migrating insects detected by insect radar, the random forest classification model of migrating insects is used to extract the information of the target insect detected by radar, and the flight height, flight speed V -flight and head orientation D -insect of each individual target insect detected by radar are obtained.

[0014] Preferably, in any of the above solutions, the parameter data of the migratory insects includes the body length, body width, body weight, and wing flapping frequency of the insects.

[0015] Preferably, in any of the above solutions, step 1 includes the following sub-steps:

[0016] Step 11: Obtain ERA5 meteorological data, perform interpolation operation on the ERA5 meteorological data using the cubic spline interpolation algorithm, and calculate the high-precision wind direction D within the range of 100 - 1500 meters above the radar site -wind and wind speed V -wind data;

[0017] Step 12: Make the time and space correspondence between the high-precision wind direction D -wind , the wind speed V -wind data, the head orientation D -insect and the flight speed V -flight , calculate the included angle α between the wind direction and the head orientation of the insect target, and solve the self-speed V -insect of the target insect, the included angle β between the flight direction and the wind direction, and the flight direction D -flight .

[0018] Preferably, in any of the above solutions, the calculation formula for the included angle α is

[0019] α = min(|D -insect - D -wind |, 360° - |D -insect - D -wind |).

[0020] Preferably, in any of the above solutions, the calculation formula for V -insect is

[0021]

[0022] Preferably, in any of the above solutions, the calculation formula for the wind direction included angle β is

[0023]

[0024] Preferably, in any of the above solutions, the calculation formula for the flight direction D -flight is

[0025] When D -wind - D -insect ≥270°, D -flight = D -wind + β, if D -flight >360°, D -flight = D -flight-360°;

[0026] When D -wind -D -insect < -270°, D -flight = D -wind -β, if D -flight < 0°, D -flight = D -flight +360°;

[0027] When 0° ≤ D -wind -D -insect < 270°, D -flight = D -wind -β;

[0028] When -270° ≤ D -wind -D -insect < 0°, D -flight = D -wind +β.

[0029] Preferably, in any of the above solutions, step 2 includes the following sub-steps:

[0030] Step 21: Correlate the ERA5 meteorological data with the target insect flight altitude, its own speed, and flight direction, and use correlation analysis to screen out the meteorological parameters that affect insect migration behavior;

[0031] Step 22: Use the screened meteorological parameters as model inputs, and use the target insect flight altitude, flight direction, and flight speed monitored by radar as outputs, and establish a quantitative relationship model for the flight altitude, flight speed, and flight direction of migrating insects using a BP neural network.

[0032] Preferably, in any of the above solutions, step 3 includes the following sub-steps:

[0033] Step 31: Extract the hourly pressure level data and hourly single-level data in the ERA5 meteorological data and store them as grib format meteorological data;

[0034] Step 32: Batch convert the grib format meteorological data into hourly ARL meteorological data with a resolution of 0.25 * 0.25, and calculate the meteorological background field for insect migration.

[0035] Preferably, in any of the above solutions, step 4 includes the following sub-steps:

[0036] Step 41: Set the model parameters of the quantitative relationship model for the flight altitude, flight speed, and flight direction of migrating insects;

[0037] Step 42: Calculate the flight trajectory of migratory insects based on the Lagrangian particle diffusion model according to the model parameters, and feedback the position and altitude of the insect target at the current time every hour.

[0038] Step 43: Call the quantitative relationship model of the flight altitude, flight speed and flight direction of the migratory insects to update the current flight speed and flight direction of the insect target.

[0039] Step 44: Determine whether the trajectory operation is completed. If not, re-execute Step 42.

[0040] Step 45: Output the trajectory point file and trajectory map of the migratory insects.

[0041] Preferably, in any of the above solutions, Step 41 includes determining whether the starting position of the Lagrangian particle diffusion model operation is the position where the insect radar is located.

[0042] Preferably, when the starting position of the model operation is the position where the insect radar is located, set the initial time, initial altitude, initial speed and initial direction of the model to the detection time, flight altitude, flight speed and flight direction of the target insect monitored by the insect radar respectively, and set the operation time of the model according to the migration rhythm and detection time of the target insect.

[0043] Preferably, when the starting position of the model operation is not the position where the insect radar is located, set the initial position, initial time and initial altitude layer of the model, call the quantitative relationship model of the flight altitude, flight speed and flight direction of the migratory insects to calculate the initial flight speed and initial flight direction, and set the operation time of the model according to the migration rhythm and detection time of the target insect.

[0044] The present invention proposes a method for simulating the migration trajectory based on insect flight parameters. For different species of insects, a quantitative relationship model between their flight parameters and meteorological factors is established respectively, and the predicted values of the flight parameter models of different species of insects are used as the migration driving force of the model, which can realize the specific simulation of the migration trajectory of the target insect, and the simulation accuracy is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flowchart of a preferred embodiment of the method for simulating the migration trajectory based on insect flight parameters according to the present invention.

[0046] Figure 2 It is a flowchart of another preferred embodiment of the method for simulating the migration trajectory based on insect flight parameters according to the present invention.

[0047] Figure 3Schematic diagram of the solution mode of the migration insect's own speed in an embodiment of the migration trajectory simulation method based on insect flight parameters according to the present invention.

[0048] Figure 4 Schematic diagram of the solution mode of the migration direction of the migration insect in an embodiment of the migration trajectory simulation method based on insect flight parameters according to the present invention. Detailed implementation manners

[0049] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0050] Embodiment 1

[0051] The present invention fully considers the flight behavior characteristics of insects of different species and different meteorological conditions, can establish a quantitative relationship model of their flight parameters for different types of pests, and add a flight parameter prediction model according to the species of target insects during the migration trajectory simulation process, so as to realize the specific and precise migration trajectory simulation and prediction of different types of migrating insects.

[0052] As Figure 1 shown, perform step 100 to extract the radar measurement parameters of the target insect, including the following sub-steps:

[0053] Perform step 101, and establish a migration insect classification model by using a random forest classification algorithm based on the parameter data of common migrating insects. The parameter data of the migrating insects includes insect body length, body width, body weight, and wing flapping frequency.

[0054] Perform step 102, determine the type of target insect to be extracted, and based on the parameter data of the migrating insect detected by the insect radar, use the random forest classification model of the migrating insect to extract the information of the target insect detected by the radar, and obtain the flight altitude and flight speed V -flight and the head orientation D -insect .

[0055] Perform step 110 to calculate the flight direction of the target insect, including the following sub-steps:

[0056] Perform step 111 to obtain ERA5 meteorological data, perform interpolation operation on the ERA5 meteorological data by using a cubic spline interpolation algorithm, and calculate the high-precision wind direction D -wind and wind speed V -wind data in the range of 100 - 1500 meters above the radar station.

[0057] Perform step 112, and combine the high-precision wind direction D -wind , the wind speed V -wind data with the head orientation D -insect and the flight speed V-flight Perform time and space correspondence, calculate the included angle α between the wind direction and the head orientation of the insect target, and solve the self-speed V of the target insect -insect , the included angle β between the flight direction and the wind direction, and the flight direction D -flight , the calculation formula for the included angle α is

[0058] α = min(|D -insect - D -wind |, 360° - |D -insect - D -wind |)

[0059] The calculation formula for the said V -insect is

[0060]

[0061] The calculation formula for the wind direction included angle β is

[0062]

[0063] The calculation formula for the said flight direction D -flight is

[0064] When D -wind - D -insect ≥270°, D -flight = D -wind + β, if D -flight >360°, D -flight = D -flight - 360°;

[0065] When D -wind - D -insect < - 270°, D -flight = D -wind - β, if D -flight <0°, D -flight = D -flight + 360°;

[0066] When 0°≤D -wind - D -insect <270°, D -flight = D -wind - β;

[0067] When - 270°≤D -wind - D -insect <0°, D -flight = D -wind + β.

[0068] Execute step 120 to construct a quantitative relationship model for the flight height, flight speed, and flight direction of migratory insects, including the following sub - steps:

[0069] Execute step 121, correspond the ERA5 meteorological data with the target insect flight altitude, its own speed and flight direction, and use correlation analysis to screen the meteorological parameters affecting insect migration behavior.

[0070] Execute step 122, use the screened meteorological parameters as model inputs, and take the target insect flight altitude, flight direction and flight speed monitored by radar as outputs respectively, and establish a quantitative relationship model for the flight altitude, flight speed and flight direction of migratory insects by using a BP neural network.

[0071] Execute step 130 to calculate the insect migration background field, including the following sub-steps:

[0072] Execute step 131, extract the hourly pressure level data and hourly single-level data in the ERA5 meteorological data and store them as grib format meteorological data.

[0073] Execute step 132, batch convert the grib format meteorological data into hourly ARL meteorological data with a resolution of 0.25*0.25, and calculate the insect migration meteorological background field.

[0074] Execute step 140 to calculate the insect migration trajectory, including the following sub-steps:

[0075] Execute step 141, set the model parameters of the quantitative relationship model for the flight altitude, flight speed and flight direction of the migratory insects, and judge whether the starting position of the Lagrangian particle diffusion model is the position where the insect radar is located;

[0076] 1) When the starting position of the model is the position where the insect radar is located, set the initial time, initial altitude, initial speed and initial direction of the model to the detection time, flight altitude, flight speed and flight direction of the target insect monitored by the insect radar respectively, and set the running time of the model according to the migration rhythm and detection time of the target insect;

[0077] 2) When the starting position of the model is not the position where the insect radar is located, set the initial position, initial time and initial altitude layer of the model, call the quantitative relationship model for the flight altitude, flight speed and flight direction of the migratory insects to calculate the initial flight speed and initial flight direction, and set the running time of the model according to the migration rhythm and detection time of the target insect.

[0078] Execute step 142, calculate the flight trajectory of the migratory insects based on the Lagrangian particle diffusion model according to the model parameters, and feedback the position and altitude of the insect target at the current time every hour.

[0079] Execute step 143, and call the quantitative relationship model of the flight altitude, flight speed, and flight direction of the migratory insects to update the current flight speed and flight direction of the insect target.

[0080] Execute step 144, and determine whether the trajectory operation is completed. If not, re-execute step 142.

[0081] Execute step 145, and output the trajectory point file and trajectory map of the migratory insects.

[0082] The present invention fully considers the differences in the self-flight behaviors and flight abilities of different species of insects and the influence of meteorological factors on the self-flight behaviors and flight abilities of insects. By establishing a quantitative relationship model between the flight parameters of different species of insects and meteorological factors, the accurate prediction of the flight parameters of insects under different meteorological conditions is realized, and a specific migratory trajectory model of different species of insects is established, thereby improving the accuracy of the trajectory simulation method.

[0083] Embodiment 2

[0084] The present invention provides a migratory trajectory simulation method based on insect flight parameters. This method is based on the biological parameters measured by an insect radar, identifies and extracts the flight altitude, speed, and direction of the target insect, and fully considers the change in the self-flight ability of the target insect under different conditions during the trajectory operation, and can more accurately simulate the migratory path of the target insect, predict the take-off and landing areas of the insect, so as to accurately and efficiently guide the early warning and prevention and control of insects.

[0085] The present invention realizes the dynamic prediction of the flight parameters of the target species of insects by establishing a quantitative relationship model between meteorological parameters and the self-flight parameters of insects, combines the traditional Lagrangian particle diffusion model with the self-flight parameters of insects, and provides a more accurate insect migratory trajectory simulation method. As Figure 2 shown, the method steps are as follows:

[0086] The first step, extraction of radar measurement parameters of the target insect

[0087] 1.1 Based on the data of the body length, body width, body weight, and wing flapping frequency parameters of common migratory insects measured in the laboratory for a long time, a random forest classification algorithm is used to establish a migratory insect classification model.

[0088] 1.2 Determine the species of the target insect to be extracted. Based on the data of the body length, body width, body weight, and wing flapping frequency parameters of the migratory insects detected by the insect radar, the random forest classification model of the migratory insects is used to extract the information of the target insects detected by the radar, so as to obtain the flight altitude, flight speed, and head orientation parameters of each individual target insect detected by the radar.

[0089] The second step, calculation of the flight direction of the target insect

[0090] 2.1 Download ERA5 meteorological data from the European Centre for Medium-Range Weather Forecasts, and use the cubic spline interpolation algorithm to interpolate the ERA5 data to calculate the high-precision wind direction (D -wind ) and wind speed (V -wind ) data in the range of 100 - 1500 meters (gradient of 50 meters) above the radar site.

[0091] 2.2 Correlate the wind direction (D -wind ) and wind speed (V -wind ) data with the headings (D -insect ) and flight speeds (V -flight ) of insect targets measured by the insect radar in terms of time and space, calculate the angle (α) between the wind direction and the headings of the insect targets, and use the triangular vector formulas (Formulas 1 and 2) to solve for the self-speed (V -insect ) of the target pests, the angle (β) between the flight direction and the wind direction, and the flight direction (D -flight ), as shown in Figure 3 , 4 .

[0092]

[0093] When D -wind - D -insect ≥ 270°, D -flight = D -wind + β. If D -flight > 360°, D -flight = D -flight - 360°;

[0094] When D -wind - D -insect < -270°, D -flight = D -wind - β. If D -flight < 0°, D -flight = D -flight + 360°;

[0095] When 0° ≤ D -wind - D -insect < 270°, D -flight = D -wind - β;

[0096] When -270° ≤ D -wind - D -insect < 0°, D -flight = D -wind + β.

[0097] Step 3: Construct a quantitative relationship model for the flight altitude, flight speed, and flight direction of migratory insects

[0098] Correlate the ERA5 meteorological data with the flight altitude, speed, and direction of target insects detected and calculated by the insect radar. Use correlation analysis to screen the meteorological parameters that affect the migration behavior of pests. Then, take the screened meteorological parameters as the model inputs, and take the flight altitude, flight direction, and flight speed of the target insects monitored by the radar as the outputs. Use the BP neural network to establish a quantitative relationship model for the flight altitude, flight speed, and flight direction of migrating insects.

[0099] Step 4: Calculate the background field of insect migration

[0100] Download the ERA5 hourly pressure level data and ERA5 hourly single-level data respectively, store them in the grib format, and use Python to batch convert the grib format meteorological data of ERA5 into hourly ARL meteorological data with a resolution of 0.25*0.25, and calculate the meteorological background field of insect migration.

[0101] Step 5: Calculate the migration trajectory of insects

[0102] 5.1 Model parameter settings

[0103] When the starting position of the model operation is the location of the insect radar, set the initial time, initial altitude, initial speed, and initial direction of the model to the detection time, flight altitude, flight speed, and flight direction of the target insects monitored by the insect radar respectively; set the running time of the model according to the migration rhythm and detection time of the target insects. For example, when the time when the radar detects the target insects is 22:00, assuming that the insects are nocturnal insects and the migration rhythm is 19:00 - 7:00, then set the forward running time of the model to 9 hours and the backward running time of the model to 3 hours.

[0104] When the starting position of the model operation is not the location of the insect radar, set the initial position, initial time, and initial altitude layer of the model, call the quantitative model of flight speed and flight direction in Step 3 to calculate the initial flight speed and initial flight direction, and set the running time of the model according to the flight rhythm of the target insects. Assuming that the insects are nocturnal insects and the migration rhythm is 19:00 - 7:00, then set the forward running time of the model to 12 hours and the backward running time of the model to 12 hours.

[0105] 5.2 Model operation

[0106] According to the model parameters in Step 5.1, calculate the flight trajectory of migrating insects based on the Lagrangian particle diffusion model. The model feeds back the current position and altitude of the insect target every hour, and calls the quantitative model of flight speed and flight direction in Step 3 to update the current flight speed and flight direction of the insect target. After the parameters are updated, continue to execute the Lagrangian particle diffusion model until the trajectory operation is completed, and output the trajectory point file and trajectory map of the migrating insects.

[0107] The present invention can simulate and predict the migration trajectories of different types of migratory insects in a specific and accurate manner. The method of the present invention uses the instantaneous flight parameters of target pests detected by insect radar as the initial parameters of the model, establishes quantitative relationship models between flight parameters and meteorological factors for different types of insects, and uses the predicted values of the flight parameter models of different types of insects as the migration driving force of the model, which can achieve specific simulation of the migration trajectories of target insects with higher simulation accuracy.

[0108] Embodiment 3

[0109] Southern Yunnan is located at the intersection of the East Asian monsoon and the South Asian monsoon. It is an important migration channel for migratory pests in the Indochina Peninsula to migrate northward across the border into China. According to the capture records of high-altitude detection lights, in southern Yunnan, cotton bollworms can migrate all year round. Therefore, this embodiment takes the migration of cotton bollworms in southern Yunnan as a case, and selects two migration events on April 16, 2023 (spring) and October 24, 2023 (autumn) for research. The migration trajectory simulation method based on insect flight parameters is used to simulate the migration trajectory of cotton bollworms in southern Yunnan, and the Hysplit trajectory model is used to simulate its migration trajectory as a comparison of model effects.

[0110] The first step is to extract radar measurement parameters of cotton bollworm

[0111] 1.1 Based on the long-term biological parameter measurement data in the laboratory, the insect radar target classification model was constructed using the random forest classification algorithm, with the four parameters of insect wingbeat frequency, body length, body width and weight as the model input and the insect species as the model output.

[0112] 1.2 Based on the insect biological parameter and flight parameter dataset collected over a long period of time by the new high-resolution fully polarized insect radar, the insect radar target classification model established in step 1.1 is used to extract the radar target dataset of cotton bollworm.

[0113] Step 2: Calculate the flight direction of cotton bollworm

[0114] Download ERA5 meteorological data and use cubic spline interpolation algorithm to calculate wind direction (D-wind) and wind speed (V-wind) above the radar site. Figure 3 Schematic diagram, according to formula 1, 2 solve the cotton bollworm's own speed (V-insect) and flight direction (D-flight).

[0115] The third step is to build a quantitative relationship model between the flight height, flight speed and flight direction of migratory insects

[0116] Correlate the interpolated ERA5 meteorological data with the flight altitude, speed, and direction of Helicoverpa armigera. Use correlation analysis to screen out 13 meteorological parameters (divergence, fraction_of_cloud_cover, geopotential, ozone_mass_mixing_ratio, potential_vorticity, relative_humidity, vertical_velocity, specific_humidity, specific_rain_water_content, temperature, wind_direction, wind_velocity, vorticity, specific_cloud_liquid_water_content) that affect the flight altitude, flight speed, and flight direction of Helicoverpa armigera. With the 13 meteorological parameters as inputs and the flight altitude, flight speed, and flight direction of Helicoverpa armigera as outputs respectively, construct a quantitative relationship model for the flight altitude, flight speed, and flight direction of Helicoverpa armigera.

[0117] Step 4: Calculate the background field of insect migration

[0118] Download the ERA5 hourly pressure level data and ERA5 hourly single-level data separately, and download them in grib format. Based on the grib2arl.exe program, use Python to develop a data automatic conversion program package arl_toolkit to convert high-precision grib format meteorological data into ARL format, and calculate the background field of insect migration.

[0119] Step 5: Calculate the migration trajectory of Helicoverpa armigera

[0120] Set the operating parameters of the migration trajectory of Helicoverpa armigera according to the parameters in Table 1. Use the migration trajectory simulation method based on insect flight parameters to simulate the migration trajectory of Helicoverpa armigera. The former has a longer migration distance, and the migration trajectory has the characteristics of the insect's own flight, showing the correction of crosswind drift during the insect migration process. The comparison results show that the method of the present invention can more accurately simulate the flight trajectory of migrating insects in the atmosphere.

[0121] Table 1 Parameter settings for the trajectory simulation of Helicoverpa armigera

[0122]

[0123] For a better understanding of the present invention, the above has been described in detail in conjunction with specific embodiments of the present invention, but it is not a limitation of the present invention. Any simple modification made to the above embodiments based on the technical essence of the present invention still belongs to the scope of the technical solution of the present invention. Each embodiment in this specification focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

Claims

1. A method for simulating the migration trajectory based on insect flight parameters, including extracting the radar measurement parameters of the target insect, characterized in that, It also includes the following steps: Step 1: Calculate the flight direction of the target insect, including the following sub-steps: Step 11: Obtain ERA5 meteorological data, perform interpolation operation on the ERA5 meteorological data using the cubic spline interpolation algorithm, and calculate the high-precision wind direction D within the range of 100 - 1500 meters above the radar site -wind and wind speed V -wind data; Step 12: Correlate the high-precision wind direction D -wind , the wind speed V -wind data with the head orientation D -insect and the flight speed V -flight in terms of time and space, calculate the angle α between the wind direction and the head orientation of the insect target, and resolve the self-speed V -insect of the target insect, the angle β between the flight direction and the wind direction, and the flight direction D -flight . The calculation formula for the angle α is α = min(|D -insect - D -wind |, 360° - |D -insect - D -wind |) The said V -insect The calculation formula is The calculation formula of β is Step 2: Construct a quantitative relationship model for the flight height, flight speed and flight direction of migratory insects; Step 3: Calculate the background field of insect migration; Step 4: Calculate the migration trajectory of insects.

2. The migratory trajectory simulation method based on insect flight parameters according to claim 1, characterized in that The extraction of the radar measurement parameters of the target insect includes the following sub-steps: Step 01: Based on the parameter data of common migratory insects, establish a classification model for migratory insects using the random forest classification algorithm; Step 02: Determine the target insect species to be extracted. Based on the parameter data of migratory insects detected by the insect radar, use the random forest classification model for migratory insects to extract the target insect information detected by the radar, and obtain the flight altitude, flight speed V -flight and the head orientation D -insect .

3. The migratory trajectory simulation method based on insect flight parameters according to claim 2, wherein, The parameter data of the migratory insects include insect body length, body width, body weight and wing vibration frequency.

4. The method for simulating the migration trajectory based on insect flight parameters according to claim 3, wherein, The flight direction D -flight The calculation formula is When D -wind -D -insect ≥ 270°, D -flight = D -wind + β. If D -flight > 360°, D -flight = D -flight - 360°; When D -wind -D -insect < -270°, D -flight = D -wind - β, if D -flight < 0°, D -flight = D -flight + 360°; When 0° ≤ D -wind -D -insect <270°, D -flight = D -wind - β; When -270° ≤ D -wind -D -insect <0°, D -flight = D -wind + β.

5. The migratory trajectory simulation method based on insect flight parameters according to claim 4, characterized in that Step 2 includes the following sub-steps: Step 21: Correlate the ERA5 meteorological data with the flight height, self-speed and flight direction of the target insect, and use correlation analysis to screen out the meteorological parameters that affect the migratory behavior of insects; Step 22: Use the selected meteorological parameters as the model input, and use the BP neural network to establish a quantitative relationship model for the flight height, flight speed and flight direction of migratory insects, with the flight height, flight direction and flight speed of the target insect monitored by radar as the output respectively.

6. The migratory trajectory simulation method based on insect flight parameters according to claim 5, wherein Step 3 includes the following sub-steps: Step 31: Extract the hourly pressure level data and hourly single-level data in the ERA5 meteorological data and store them as grib format meteorological data; Step 32: Batch convert the grib format meteorological data into hourly ARL meteorological data with a resolution of 0.25*0.25, and calculate the meteorological background field of insect migration.

7. The migratory trajectory simulation method based on insect flight parameters according to claim 6, characterized in that, Step 4 includes the following sub-steps: Step 41: Set the model parameters of the quantitative relationship model for the flight height, flight speed and flight direction of migratory insects; Step 42: Calculate the flight trajectory of migratory insects based on the Lagrangian particle diffusion model according to the model parameters, and feedback the current position and height of the insect target at each hour; Step 43: Call the quantitative relationship model for the flight height, flight speed and flight direction of migratory insects to update the current flight speed and flight direction of the insect target; Step 44: Judge whether the trajectory operation is completed. If not, re-execute Step 42; Step 45: Output the trajectory point file and trajectory of migratory insects.

8. The migratory trajectory simulation method based on insect flight parameters according to claim 7, characterized in that Step 41 includes judging whether the starting position of the Lagrangian particle diffusion model is the position where the insect radar is located.

9. The migratory trajectory simulation method based on insect flight parameters according to claim 8, wherein, When the starting position of the model operation is the position where the insect radar is located, set the initial time, initial height, initial speed and initial direction of the model to the detection time, flight height, flight speed and flight direction of the target insect monitored by the insect radar respectively, and set the operation time of the model according to the migration rhythm and detection time of the target insect.

10. The migratory trajectory simulation method based on insect flight parameters according to claim 9, characterized in that When the starting position of the model operation is not the position where the insect radar is located, set the initial position, initial time and initial height layer of the model, call the quantitative relationship model for the flight height, flight speed and flight direction of migratory insects to calculate the initial flight speed and initial flight direction, and set the operation time of the model according to the migration rhythm and detection time of the target insect.

Images