A method and device for predicting spatial distribution of migratory insects

Abstract

The present application provides a method and device for predicting the spatial distribution of migratory insects, the method comprising: obtaining meteorological data and migratory behavior parameters of target insects, taking a preset location as the migration starting point according to the meteorological data and the migration behavior parameters, continuously predicting the future migration location of the target insect, obtaining multiple migration trajectories consisting of the migration starting point and the predicted migration location, screening out invalid trajectories from the multiple migration trajectories according to whether the target insects are in the growth period of the host plant when flying along the multiple migration trajectories, and whether the climate when the target insects fly along the multiple migration trajectories is suitable for the growth and development of the target insects, and using the migration trajectories after screening out invalid trajectories as target effective migration trajectories, and determining the future spatial distribution of the target insects according to the target effective migration trajectories. The present application takes into account the migration and diffusion mechanism, climate suitability and growth period of the host plant of the target insects, so that the predicted spatial distribution is more accurate and has better spatiotemporal continuity.

Description

Technical Field

[0001] The present application relates to the field of biological control, and in particular to a method and device for predicting the spatial distribution of migratory insects. Background Art

[0002] At present, there are some long-distance migratory insects, such as the fall armyworm, which invade a wide range of areas seasonally and are difficult to control. It is particularly important to quantitatively predict the dynamic spatial distribution of such insects, carry out biological or chemical control in hot spots, deploy monitoring instruments, and assess economic losses.

[0003] Previous studies often used species niche models (SDM), statistical analysis models and machine learning models, relying on the climate suitability summarized from the presence / absence data of species in different periods to predict the spatial range and extent of insect occurrence. In addition, some researchers have taken the impact of meteorological background fields on insect diffusion into consideration. For example, they simulated the atmospheric diffusion process of insects based on the insect trajectory migration model, thereby predicting the migration path of insects.

[0004] However, the above models fail to fully consider the biological characteristics related to insect survival, development, and migration, as well as the impact of external environmental conditions and host plant conditions on insect migration and spread, resulting in poor spatiotemporal continuity and low accuracy in the prediction results of insect spatial distribution. Summary of the invention

[0005] In view of this, the present application provides a method and device for predicting the spatial distribution of migratory insects, which are used to solve the above technical problems. The technical solution is as follows:

[0006] A method for predicting the spatial distribution of migratory insects, comprising:

[0007] Acquire meteorological data and migration behavior parameters of target insects, wherein the meteorological data are temporally and spatially discontinuous meteorological data;

[0008] Based on meteorological data and migration parameters, the preset location is used as the migration starting point, and the future migration location of the target insect is continuously predicted to obtain multiple migration trajectories consisting of the migration starting point and the predicted migration location;

[0009] According to whether the target insects are in the growth period of the host plants when they fly along the multiple migration trajectories, and whether the climate when the target insects fly along the multiple migration trajectories is suitable for the growth and development of the target insects, invalid trajectories in the multiple migration trajectories are screened out to obtain the target effective migration trajectories;

[0010] Determine the future spatial distribution of target insects based on the target effective migration trajectory.

[0011] Optionally, invalid trajectories among the multiple migration trajectories are screened out according to whether the target insects are in the growth period of the host plants when they fly along the multiple migration trajectories and whether the climate when the target insects fly along the multiple migration trajectories is suitable for the growth and development of the target insects, including:

[0012] Determine whether the target insects are in the growth period of the host plants when they fly along multiple migration trajectories, and determine whether the climate when the target insects fly along multiple migration trajectories is suitable for the growth and development of the target insects;

[0013] For each migration track among the multiple migration tracks, if the target insect is not in the growth period of the host plant when flying along the migration track, and / or the climate when the target insect flies along the migration track is not suitable for the growth and development of the target insect, the migration track is screened out from the multiple migration tracks.

[0014] Optionally, determine whether the target insect is in the growing season of the host plant when flying along multiple migration trajectories, including:

[0015] Obtain the leaf area index at each period under the migration sites included in multiple migration tracks;

[0016] The leaf area index of each period is matched to the preset farmland grid to obtain the leaf area index of each period under each farmland grid;

[0017] According to the leaf area index of each period under each farmland grid, the annual time series curve of leaf area index corresponding to each farmland grid is generated;

[0018] For each farmland grid in each farmland grid, the annual time series curve of the leaf area index corresponding to the farmland grid is smoothed by using the smoothing method corresponding to the farmland grid to obtain the annual time series curve of the leaf area index after smoothing corresponding to the farmland grid;

[0019] According to the smoothed annual time series curve of leaf area index corresponding to each farmland grid, the key phenological period of the host plant is extracted to determine whether the target insects are in the growth period of the host plant when flying along multiple migration trajectories.

[0020] Optionally, all farmland grids within the target range of any farmland grid correspond to the same smoothing method;

[0021] The process of determining the smoothing method corresponding to the farmland grid within the target range includes:

[0022] For each smoothing method in the set of preset smoothing methods:

[0023] The smoothing method is used to smooth the annual time series curve of leaf area index corresponding to each farmland grid within the target range, and the smoothed annual time series curve of leaf area index corresponding to each farmland grid under the smoothing method is obtained;

[0024] According to the smoothed leaf area index annual time series curve corresponding to each farmland grid under the smoothing method, the key phenological period of the host plant corresponding to each farmland grid under the smoothing method is determined;

[0025] Calculate the root mean square error between the key phenological period of the host plant corresponding to each farmland grid under the smoothing method and the actual key phenological period of the host plant, and use the obtained root mean square error as the root mean square error corresponding to each farmland grid under the smoothing method;

[0026] Calculate the average value of the root mean square errors corresponding to all farmland grids within the target range under the smoothing method to obtain the mean root mean square error corresponding to the smoothing method;

[0027] To obtain the mean root mean square error corresponding to all smoothing methods in the smoothing method set;

[0028] The smallest mean root mean square error is determined from the mean root mean square errors corresponding to all smoothing methods in the smoothing method set, and the smoothing method corresponding to the smallest mean root mean square error is used as the smoothing method corresponding to the farmland grid within the target range.

[0029] Optionally, the key phenological period of the host plant is extracted according to the smoothed annual time series curve of the leaf area index corresponding to each farmland grid, so as to determine whether the target insect is in the growth period of the host plant when flying along multiple migration trajectories according to the extracted key phenological period of the host plant, including:

[0030] Determine the inflection point where the first-order derivative of the smoothed leaf area index annual time series curve corresponding to each farmland grid increases and the second-order derivative is equal to 0, and take the date corresponding to the inflection point as the first key phenological period of the host plant corresponding to each farmland grid;

[0031] Determine the maximum value in the smoothed annual time series curve of the leaf area index corresponding to each farmland grid, and take the date corresponding to the maximum value as the second key phenological period of the host plant corresponding to each farmland grid;

[0032] Determine the inflection point where the first-order derivative of the smoothed leaf area index annual time series curve corresponding to each farmland grid is negative and has the largest absolute value, and take the date corresponding to the inflection point as the third key phenological period of the host plant corresponding to each farmland grid;

[0033] According to the first key phenological period, the second key phenological period and the third key phenological period of the host plants corresponding to each farmland grid, it is determined whether the target insects are in the growth period of the host plants when flying along multiple migration trajectories.

[0034] Optionally, determining whether the climate when the target insects fly along multiple migration trajectories is suitable for the growth and development of the target insects includes:

[0035] Acquire climate data when target insects fly along multiple migration paths, where the climate data includes temperature data and soil moisture data;

[0036] Obtain temperature development-dependent parameters related to target insects, minimum temperature threshold, maximum temperature threshold, minimum soil moisture threshold, survival rate under minimum temperature cumulative stress within a set time, survival rate under maximum temperature cumulative stress within a set time, and survival rate under water content cumulative stress within a set time;

[0037] For each of multiple flyways:

[0038] According to the temperature data of the target insects flying along the migration trajectory, combined with the temperature development-dependent parameters, the positive growth rate of the target insects under the migration trajectory is determined;

[0039] According to the temperature data and soil moisture data of the target insects when flying along the migration trajectory, combined with the minimum temperature threshold, the maximum temperature threshold, the minimum soil moisture threshold, the survival rate under the minimum temperature cumulative stress within a set time, the survival rate under the maximum temperature cumulative stress within a set time, and the survival rate under the water content cumulative stress within a set time, the negative growth rate of the target insects under the migration trajectory is determined;

[0040] Calculate the difference between the positive growth rate and the negative growth rate of the target insect under the migration trajectory, and use the difference as the growth rate under the migration trajectory;

[0041] According to whether the growth rate under the migration trajectory is greater than a preset growth rate threshold, it is determined whether the climate when the target insect flies along the migration trajectory is suitable for the growth and development of the target insect.

[0042] Optionally, the negative growth rate of the target insect along the migration trajectory is determined based on the temperature data and soil moisture data of the target insect when flying along the migration trajectory, combined with the minimum temperature threshold, the maximum temperature threshold, the minimum soil moisture threshold, the survival rate under the minimum temperature cumulative stress within a set time, the survival rate under the maximum temperature cumulative stress within a set time, and the survival rate under the water content cumulative stress within a set time, including:

[0043] The mortality rate of target insects per unit of cold stress is determined based on the survival rate under the lowest cumulative temperature stress within the set time; the mortality rate of target insects per unit of heat stress is determined based on the survival rate under the highest cumulative temperature stress within the set time; the mortality rate of target insects per unit of drought stress is determined based on the survival rate under the cumulative water content stress within the set time;

[0044] Based on the temperature data and soil moisture data of the target insects when flying along the migration trajectory, combined with the mortality rate of the target insects per unit of cold stress, the mortality rate per unit of heat stress, the mortality rate per unit of drought stress, the minimum temperature threshold, the maximum temperature threshold and the minimum soil moisture threshold, the negative growth rate of the target insects along the migration trajectory is determined.

[0045] Optionally, before determining whether the target insects are in the growth period of the host plant when flying along the multiple migration paths, and determining whether the climate when the target insects fly along the multiple migration paths is suitable for the growth and development of the target insects, the method further includes:

[0046] The multiple migration tracks are initially screened based on whether the migration sites included in the multiple migration tracks are non-water areas and / or whether the migration sites included in the multiple migration tracks are lower than the height of the digital elevation model.

[0047] Optionally, the migration flight parameters include one or more of the following parameters: flight time, flight speed, flight altitude and forced landing factors, wherein the forced landing factors refer to factors that cause the target insect to be unable to fly during normal flight.

[0048] A device for predicting spatial distribution of migratory insects, comprising:

[0049] A first acquisition module is used to acquire meteorological data and migration behavior parameters of target insects, wherein the meteorological data is temporally and spatially discontinuous meteorological data;

[0050] A migration trajectory prediction module is used to continuously predict the future migration locations of target insects based on meteorological data and migration parameters, with a preset location as the migration starting point, to obtain multiple migration trajectories consisting of the migration starting point and the predicted migration location;

[0051] The first trajectory screening module is used to screen out invalid trajectories from the multiple migration trajectories according to whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories and whether the climate when the target insect flies along the multiple migration trajectories is suitable for the growth and development of the target insect, so as to obtain the target effective migration trajectory;

[0052] The spatial distribution prediction module is used to determine the future spatial distribution of target insects based on the target effective migration trajectory.

[0053] It can be known from the above technical scheme that the method for predicting the spatial distribution of migratory insects provided by the present application first obtains meteorological data and the migratory behavior parameters of the target insects, and then, based on the meteorological data and the migratory behavior parameters, takes the preset location as the migration starting point, continuously predicts the future migration location of the target insects, and obtains multiple migration trajectories composed of the migration starting point and the predicted migration location. Since the migration behavior parameters can reflect the migration diffusion mechanism of the target insects, the future migration trajectory of the target insects is dynamically predicted based on the migration diffusion mechanism of the target insects, and the prediction result is more accurate. Considering that when the target insects fly, the environmental climate and food under the migration trajectory need to be suitable for the survival of the target insects, otherwise the target insects will not fly according to the migration trajectory. Therefore, the present application will screen out invalid trajectories in the multiple migration trajectories according to whether the target insects are in the growth period of the host plant when flying along the multiple migration trajectories, and whether the climate when the target insects fly along the multiple migration trajectories is suitable for the growth and development of the target insects, and finally determine the future spatial distribution of the target insects according to the target effective migration trajectory. This application comprehensively considers the migration and diffusion mechanism of the target insects, climate suitability and the growth period of the host plants, so that the predicted spatial distribution is more accurate and the prediction accuracy is higher. The spatial and temporal continuity of the insect spatial distribution prediction results predicted by this application is better. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0055] Figure 1 A schematic diagram of a flow chart of a method for predicting the spatial distribution of migratory insects provided in an embodiment of the present application;

[0056] Figure 2 A comparison chart showing the date of the first field capture of fall armyworm each year and the initial migration date of fall armyworm simulated in this application;

[0057] Figure 3 A schematic diagram of the structure of a device for predicting the spatial distribution of migratory insects provided in an embodiment of the present application;

[0058] Figure 4 This is a hardware structure block diagram of the migratory insect spatial distribution prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. 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 creative work are within the scope of protection of this application.

[0060] The present application provides a method for predicting the spatial distribution of migratory insects. The method for predicting the spatial distribution of migratory insects provided by the present application will be described in detail through the following embodiments.

[0061] See also Figure 1 , shows a schematic flow chart of a method for predicting the spatial distribution of migratory insects provided in an embodiment of the present application, and the method for predicting the spatial distribution of migratory insects may include:

[0062] Step S101, obtaining meteorological data and migration behavior parameters of target insects.

[0063] Among them, the meteorological data is meteorological data that is discontinuous in time and space, and spatial discontinuity means that the meteorological data is discontinuous in both horizontal and vertical directions.

[0064] Optionally, this step may obtain final analysis (FNL) data from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR), and the final analysis data is the meteorological data in this step.

[0065] Optionally, the migration flight parameters include one or more of the following parameters: flight time, flight speed, flight altitude and forced landing factors, wherein the forced landing factors refer to factors that cause the target insect to be unable to fly during normal flight.

[0066] Taking the target insect as fall armyworm as an example, the fall armyworm migration behavior parameters set in this application can be found in Table 1 below. The fall armyworm migration behavior parameters include the following: (1) Flight time. According to the biological characteristics of fall armyworm, fall armyworm can fly continuously for 10 hours every night. Optionally, this application can set the fall armyworm to take off at local dusk (19:00) and stop flying at sunrise the next day (05:00), and can fly continuously for 3 nights under suitable temperature conditions; if the fall armyworm enters the sea area, the single flight time will be extended to a maximum of 36 hours. (2) Flight speed. The autonomous flight speed of noctuid insects similar in size to fall armyworm is 2.5 to 4 m / s. Optionally, this application can set the fall armyworm's own flight speed to 3 m / s. (3) Flight altitude. Noctuids usually migrate at the height of low-altitude jet streams (wind speed greater than 10m / s). Optionally, to ensure that the most likely flight altitude can be found, the altitudes of 500, 750, 1000, 1250, 1500, 1750, 2000 and 2250m above sea level can be used in combination with terrain elevation data DEM to calculate trajectories. (4) Forced landing factors. When the air temperature is lower than its flight low temperature threshold, the migrating insects will stop flapping their wings. Optionally, the present application can set the temperature at the flight altitude of the fall armyworm to be lower than the flight low temperature threshold of 13.8°C, terminating the trajectory calculation for that night, and when the hourly rainfall is greater than 1 mm (moderate rain and above), it is considered that the fall armyworm will be affected by the rainfall and forced to land.

[0067] Table 1 Fall Armyworm Migration Behavior Parameters Setting Table

[0068]

[0069]

[0070] Step S102: Based on the meteorological data and migration parameters, the preset location is used as the migration starting point, and the future migration location of the target insect is continuously predicted to obtain a plurality of migration trajectories consisting of the migration starting point and the predicted migration location.

[0071] In this step, a migration starting point can be preset, and at the migration starting point, the possible landing area (i.e., migration location) of the target insect in the future can be continuously predicted based on meteorological data and migration behavior parameters. After multiple predictions, multiple migration trajectories can be obtained based on the continuously predicted migration locations and migration starting points. Here, the migration location refers to the possible landing area of ​​the target insect, that is, the possible landing point of the target insect in the future.

[0072] Taking the fall armyworm as an example, this step can make continuous predictions using the fall armyworm's annual breeding area, i.e., the area with an average temperature greater than 10°C in January (the coldest month), as the starting point for migration.

[0073] Step S103, according to whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories, and whether the climate when the target insect flies along the multiple migration trajectories is suitable for the growth and development of the target insect, the invalid trajectories in the multiple migration trajectories are screened out, and the migration trajectories after the invalid trajectories are screened out are used as the target effective migration trajectories.

[0074] Here, the growth period refers to the period from the beginning of the growing season of the host plant to the end of the growing season. When the host plant is in the growth period, it can provide food for the target insects. For example, if the target insect is the fall armyworm, the host plant can be corn. The whole growth period of corn is divided into major development periods such as sowing, emergence, three leaves, seven leaves, jointing, heading, flowering, filling, milky maturity, and maturity. The growth period refers to the period from the beginning of the growing season to the end of the growing season, that is, the period from the three-leaf stage to the maturity stage.

[0075] It is understandable that the target insects will land in areas with food when they migrate, that is, the target insects must be within the growth period of the host plant from the time they migrate in to the time they migrate out to ensure that the food can meet the growth and development needs of the target insects.

[0076] Based on this, this step can be used to study whether the target insect is in the growth period of the host food when it lands at the migration location if the target insect flies along the predicted migration trajectory, so as to determine whether the target insect will fly along the predicted migration trajectory. If the target insect is not in the growth period of the host plant when flying along multiple migration trajectories, it is judged that the target insect will not fly along the predicted migration trajectory, which means that the predicted migration trajectory is an invalid trajectory, and the predicted migration trajectory can be screened out at this time.

[0077] At the same time, considering that the target insects have certain requirements for the living environment (that is, the climate in this step), if the target insects fly along the predicted migration trajectory and the migration location does not meet the target insects' requirements for the living environment, the target insects cannot survive in the predicted landing area of ​​the migration trajectory.

[0078] Based on this, this step can be used to study whether the climate when the target insect lands at the migration site is suitable for the growth and development of the target insect if the target insect flies along the predicted migration path, so as to determine whether the target insect will fly along the predicted migration path. If the migration site does not meet the target insect's requirements for the living environment when the target insect flies along the predicted migration path, the target insect cannot survive in the predicted migration path landing area, which means that the predicted migration path is an invalid path, and the predicted migration path can be screened out from multiple migration paths.

[0079] It is worth noting that there may be some migration trajectories corresponding to the entire migration range where the climate is suitable and host plants can be planted all year round. In this case, it is not necessary to perform invalid trajectory screening based on the phenological period of the host plant, that is, there is no need to perform this step to screen out invalid trajectories from multiple migration trajectories based on whether the target insects are in the growth period of the host plant when flying along multiple migration trajectories.

[0080] Step S104: Determine the future spatial distribution of the target insects according to the target effective migration trajectory.

[0081] The target effective migration trajectory is the trajectory in which the host plants and environment remaining after filtering out invalid trajectories are suitable for the survival of the target insects.

[0082] Optionally, this step can perform kernel density analysis (KDA) on the migration sites included in the target effective migration trajectory, estimate the bandwidth using the Silverman empirical rule, and sort them from small to large according to the density quantile to obtain the future spatial distribution of the target insect. Here, the future spatial distribution of the target insect can indicate the spatial range of the target insect invasion and the relative density of the population at the destination.

[0083] The method for predicting the spatial distribution of migratory insects provided by the present application first obtains meteorological data and the migratory behavior parameters of the target insects, and then, based on the meteorological data and the migratory behavior parameters, takes the preset location as the migration starting point, continuously predicts the future migration location of the target insects, and obtains multiple migration trajectories consisting of the migration starting point and the predicted migration location. Since the migration behavior parameters can reflect the migration diffusion mechanism of the target insects, the future migration trajectory of the target insects is dynamically predicted based on the migration diffusion mechanism of the target insects, and the prediction result is more accurate. Considering that when the target insects fly, the environmental climate and food under the migration trajectory need to be suitable for the survival of the target insects, otherwise the target insects will not fly along the migration trajectory. Therefore, the present application will screen out invalid trajectories in the multiple migration trajectories according to whether the target insects are in the growth period of the host plant when flying along the multiple migration trajectories, and / or whether the climate when the target insects fly along the multiple migration trajectories is suitable for the growth and development of the target insects, and obtain the target effective migration trajectory. Finally, according to the target effective migration trajectory, the future spatial distribution of the target insects is determined. This application comprehensively considers the migration and diffusion mechanism of the target insects, climate suitability and the growth period of the host plants, so that the predicted spatial distribution is more accurate and the prediction accuracy is higher. The spatial and temporal continuity of the insect spatial distribution prediction results predicted by this application is better.

[0084] An embodiment of the present application introduces the process of "step S102, based on meteorological data and migration as parameters, taking a preset location as the migration starting point, continuously predicting the future migration location of the target insect, and obtaining multiple migration trajectories consisting of the migration starting point and the predicted migration location".

[0085] In an optional embodiment, this step can process the meteorological data through the Weather Research and Forecasting model (WRF) to obtain processed spatiotemporally continuous meteorological data, and then use the spatiotemporally continuous meteorological data and migration as parameters, with a preset location as the migration starting point, to continuously predict the future migration location of the target insects, and obtain multiple migration trajectories consisting of the migration starting point and the predicted migration location.

[0086] Here, the Weather Research and Forecasting model (WRF) is an advanced mesoscale numerical weather forecast system that can provide hourly meteorological background fields for insect trajectory analysis. That is, by inputting meteorological data into the WRF model, it can output simulated spatiotemporal continuous meteorological data such as horizontal and vertical wind speed and precipitation every hour.

[0087] This embodiment can convert spatiotemporally discontinuous meteorological data into spatiotemporally continuous meteorological data, and then perform continuous prediction based on the spatiotemporally continuous meteorological data and migration behavior parameters, so that the prediction results of this application can maintain spatiotemporal continuity.

[0088] The following embodiment describes the process of "step S103, screening out invalid trajectories from multiple migration trajectories according to whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories, and whether the climate when the target insect flies along the multiple migration trajectories is suitable for the growth and development of the target insect, and using the migration trajectories after screening out the invalid trajectories as the target valid migration trajectories."

[0089] Optionally, the process of "step S103, according to whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories, and whether the climate when the target insect flies along the multiple migration trajectories is suitable for the growth and development of the target insect, filtering out invalid trajectories from the multiple migration trajectories, and using the migration trajectories after filtering out the invalid trajectories as the target valid migration trajectories" may include:

[0090] Step S1, determining whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories, and determining whether the climate when the target insect flies along the multiple migration trajectories is suitable for the growth and development of the target insect. The process of "determining whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories" is described below.

[0091] Considering that the leaf area index of the host plant may be different when it is in different phenological periods, based on this, this embodiment can determine whether the target insect is in the growth period of the host plant when it lands based on the leaf area index of the target insect when it flies along multiple migration trajectories.

[0092] Then, optionally, the process of “determining whether the target insect is in the growth period of the host plant when flying along multiple migration trajectories” may include:

[0093] Step a1, obtaining leaf area indexes at different periods at migration sites included in multiple migration tracks.

[0094] Here, the leaf area index specifically refers to the Global Land Surface Satellite (GLASS) Leaf Area Index (LAI). The Global Land Surface Satellite (GLASS) Leaf Area Index (LAI) product is used to monitor the growth and growth period of crops. Compared with global LAI products such as MODIS and CYCLOPES, the GLASS LAI product has the best accuracy and is more spatially complete and temporally continuous. Optionally, the GLASS LAI product in this step can be obtained by inputting fused LAI data (including MODIS LAI product, CYCLOPES LAI product, and reprocessed MODIS reflectivity of the BELMANIP site) into a regression neural network (GRNN).

[0095] Optionally, the spatial resolution of the GLASS LAI product is 1 km, and the temporal resolution is 8 days. In this step, the leaf area index at the migration site can be obtained every 8 days, thereby obtaining the leaf area index of each period. Here, "each period" can specifically be one year.

[0096] Step a2: correspond the leaf area index of each period to the preset farmland grid to obtain the leaf area index of each period under each farmland grid.

[0097] The farmland grid in this step can be a grid in the 1km National Land Cover Dataset (NLCD). Here, NLCD is the national land cover data (data indicating whether a certain location is a building, water body, or vegetation). It is a kind of raster data that can divide the ground into many grids, and each grid (grid) is defined as a certain category.

[0098] In this step, the leaf area index of each period can be mapped to the grid of the farmland type in NLCD according to the region where it is located, and the leaf area index of each period under each farmland grid can be obtained.

[0099] Step a3: Generate an annual time series curve of the leaf area index corresponding to each farmland grid according to the leaf area index of each period under each farmland grid.

[0100] This step can generate an annual time series curve of the leaf area index corresponding to each farmland grid.

[0101] Step a4: for each farmland grid in each farmland grid, use the smoothing method corresponding to the farmland grid to smooth the annual time series curve of the leaf area index corresponding to the farmland grid, so as to obtain the smoothed annual time series curve of the leaf area index corresponding to the farmland grid.

[0102] In this step, an optimal smoothing method will be determined for each farmland grid in advance, and then the annual time series curve of the leaf area index corresponding to the farmland grid will be smoothed based on the optimal smoothing method.

[0103] In an optional embodiment, this step can pre-divide the ground to be studied into several ranges, and all farmland grids within each range correspond to the same smoothing method. For example, if the ground is divided into provinces, all farmland grids within each province correspond to the same smoothing method. For the convenience of subsequent description, the range where any farmland grid is located is defined as the target range, and all farmland grids within the target range correspond to the same smoothing method.

[0104] Optionally, the process of determining the smoothing method (i.e., the optimal smoothing method) corresponding to the farmland grid within the target range may include:

[0105] Step a41: for each smoothing method in the preset smoothing method set, determine the mean root mean square error corresponding to the smoothing method according to the annual time series curve of the leaf area index corresponding to the farmland grid within the target range, so as to obtain the mean root mean square error corresponding to all the smoothing methods in the smoothing method set.

[0106] Optionally, the smoothing method set includes: a dual logic (DL) method, a Savitzky-Golay (SG) filter method, and a wavelet-based filter (WF) method. Of course, the smoothing method set may also include other smoothing methods, which are not limited in this application.

[0107] Specifically, the process of step a41 "determining the mean value of the root mean square error corresponding to the smoothing method according to the annual time series curve of the leaf area index corresponding to the farmland grid within the target range" may include:

[0108] Step a411, using the smoothing method to smooth the annual time series curve of leaf area index corresponding to each farmland grid within the target range, to obtain the smoothed annual time series curve of leaf area index corresponding to each farmland grid under the smoothing method.

[0109] Step a412: Determine the key phenological period of the host plant corresponding to each farmland grid under the smoothing method according to the smoothed leaf area index annual time series curve corresponding to each farmland grid under the smoothing method.

[0110] Optionally, the key phenological periods of the host plant include the first key phenological period, the second key phenological period and the third key phenological period. Taking corn as an example, the first key phenological period refers to the three-leaf stage, the second key phenological period refers to the heading stage, and the third key phenological period refers to the maturity stage.

[0111] In this step, the date corresponding to the inflection point where the first-order derivative increases and the second-order derivative is equal to 0 in the smoothed leaf area index annual time series curve corresponding to each farmland grid under the smoothing method can be determined as the first key phenological period of the host plant corresponding to each farmland grid under the smoothing method, and the date corresponding to the maximum value in the smoothed leaf area index annual time series curve corresponding to each farmland grid under the smoothing method can be determined as the second key phenological period of the host plant corresponding to each farmland grid under the smoothing method, and the date corresponding to the inflection point where the first-order derivative is negative and the absolute value is the largest in the smoothed leaf area index annual time series curve corresponding to each farmland grid under the smoothing method can be determined as the third key phenological period of the host plant corresponding to each farmland grid under the smoothing method.

[0112] For example, the key phenological stages of corn are identified by the inflection points and thresholds of the LAI annual time series curve. That is, the inflection point when the first-order derivative increases continuously and the second-order derivative equals 0 is defined as the corn three-leaf stage, the date when the LAI reaches the maximum value is defined as the heading stage, and the inflection point when the first-order derivative is negative and has the largest absolute value is defined as the maturity stage.

[0113] Step a413, calculate the root mean square error between the key phenological period of the host plant corresponding to each farmland grid under the smoothing method and the actual key phenological period of the host plant, and use the obtained root mean square error as the root mean square error corresponding to each farmland grid under the smoothing method.

[0114] Optionally, in this step, the actual key phenological period of the host plant corresponding to each farmland grid can be determined through data from the National Meteorological Center (CMA).

[0115] In this step, the root mean square error (RMSE) between the key phenological period of the host plant corresponding to each farmland grid under the smoothing method and the actual key phenological period of the host plant (i.e., the observed phenological date compiled by CMA) can be estimated by comparing. The RMSE value is the root mean square error corresponding to each farmland grid under the smoothing method.

[0116] Step a414, calculating the average value of the root mean square errors corresponding to all farmland grids within the target range under the smoothing method, and obtaining the root mean square error mean value corresponding to the smoothing method.

[0117] The previous steps have calculated the root mean square error corresponding to each farmland grid under the smoothing method. In this step, the root mean square errors corresponding to all farmland grids within the target range can be averaged to obtain the mean root mean square error (i.e., average RMSE value) corresponding to the smoothing method.

[0118] In this step, each smoothing method in the smoothing method set is calculated using the above steps a411 to a414, so that the mean root mean square errors corresponding to all the smoothing methods in the smoothing method set can be obtained.

[0119] Step a42: determine the minimum root mean square error mean from the root mean square error means corresponding to all smoothing methods in the smoothing method set, and use the smoothing method corresponding to the minimum root mean square error mean as the smoothing method corresponding to the farmland grid within the target range.

[0120] For example, in this step, the smoothing method with the smallest average RMSE value can be selected within the provincial scope as the smoothing method corresponding to the farmland grid within the provincial scope.

[0121] Step a5: extract the key phenological period of the host plant according to the smoothed leaf area index annual time series curve corresponding to each farmland grid, so as to determine whether the target insect is in the growth period of the host plant when flying along multiple migration trajectories according to the extracted key phenological period of the host plant.

[0122] This step can use the same method as the above step a412 to determine the key phenological period of the host plant corresponding to each farmland grid, so as to determine whether the target insect is in the growth period of the host plant when flying along multiple migration trajectories.

[0123] It should be noted that, in order to cooperate with the calculation of migration trajectories, this embodiment can use a grid statistical method to calculate the DOY (day of year, a certain day of the year) mean of the key phenological period of the host plant on a 1°×1° scale. If there is missing data, the neighborhood mean interpolation is used.

[0124] That is, the process of this step may include:

[0125] Step a51, determine the inflection point where the first-order derivative increases and the second-order derivative is equal to 0 in the smoothed leaf area index annual time series curve corresponding to each farmland grid, and use the date corresponding to the inflection point as the first key phenological period of the host plant corresponding to each farmland grid.

[0126] Here, the first key phenological period is, for example, the three-leaf stage of corn.

[0127] Step a52, determining the maximum value in the smoothed leaf area index annual time series curve corresponding to each farmland grid, and taking the date corresponding to the maximum value as the second key phenological period of the host plant corresponding to each farmland grid.

[0128] Here, the second key phenological period is, for example, the heading period of corn.

[0129] Step a53, determine the inflection point where the first-order derivative of the smoothed leaf area index annual time series curve corresponding to each farmland grid is negative and has the largest absolute value, and use the date corresponding to the inflection point as the third key phenological period of the host plant corresponding to each farmland grid.

[0130] Here, the third key phenological period is, for example, the maturity period of corn. The process of steps a51 to a53 corresponds to the process of the aforementioned step a412, and the details can be referred to the introduction in the aforementioned steps, which will not be repeated here.

[0131] It is worth noting that due to different climatic conditions and planting habits, the restricted time windows of the three key growth periods of spring corn and summer corn in each province are determined according to the survey data compiled by CMA (that is, the actual key phenological period has a time window limit). The three key phenological periods determined in steps a51 to a53 need to be within the time range of the corresponding restricted time windows.

[0132] Step a54, based on the first key phenological period of the host plant, the second key phenological period of the host plant and the third key phenological period of the host plant corresponding to each farmland grid, determine whether the target insect is in the growth period of the host plant when flying along multiple migration trajectories, that is, the period from the beginning of the growing season (for example, the three-leaf stage of corn) to the end of the growing season (for example, the maturity period of corn).

[0133] In this step, the specific time when the target insects land at the migration site when flying along multiple migration paths can be compared with the first key phenological period of the host plant, the second key phenological period of the host plant, and the third key phenological period of the host plant, so as to determine whether the target insects are in the growth period of the host plant when flying along multiple migration paths.

[0134] This embodiment takes into account the key phenological period of the host plant, so that some invalid trajectories among the predicted multiple migration trajectories can be eliminated later, so that the prediction result of the spatial distribution is more accurate.

[0135] The following is an explanation of the process of "determining whether the climate when the target insects fly along multiple migration trajectories is suitable for the growth and development of the target insects."

[0136] Optionally, the process of “determining whether the climate when the target insects fly along multiple migration trajectories is suitable for the growth and development of the target insects” includes:

[0137] Step b1, obtaining climate data when the target insects fly along multiple migration trajectories.

[0138] Among them, climate data includes temperature data and soil moisture data.

[0139] Optionally, this embodiment can obtain global gridded climate data through the European Centre for Medium-Range Weather Forecasts (ECMWF) and the fifth generation ECMWF Global Climate Atmospheric Reanalysis (ERA5-Land), where the spatial resolution is 0.1 radian×0.1 radian.

[0140] Optionally, the above-mentioned global gridded climate data includes the daily average data of the 2m air temperature "temperature_2m" and the water content in the soil layer (0-7cm) "volumetric_soil_water_layer_1", so as to evaluate the impact of the environment under the migration trajectory on the growth and development of target insects based on the obtained 2m air temperature and soil water content data (i.e. the above-mentioned daily average data).

[0141] Step b2, obtaining the temperature development-dependent parameters related to the target insects, the minimum temperature threshold, the maximum temperature threshold, the minimum soil moisture threshold, the survival rate under the minimum temperature cumulative stress within a set time, the survival rate under the maximum temperature cumulative stress within a set time, and the survival rate under the water content cumulative stress within a set time.

[0142] Optionally, this embodiment may introduce an insect population natural growth rate model to determine the growth rate of the target insect, and then determine some invalid trajectories among the multiple migration trajectories based on the determined growth rate.

[0143] Here, the natural growth rate model of insect population is: In the formula, r represents the intrinsic growth rate of the stable insect population N over time t. According to the influence of environmental factors (i.e. climate data) on the occurrence and development of fall armyworm, r is divided into positive growth rate r p and negative growth rate r n .

[0144] This step can obtain the temperature-dependent development parameters related to the target insect, the minimum temperature threshold, the maximum temperature threshold, the minimum soil moisture threshold, the survival rate under the minimum temperature cumulative stress within a set time, the survival rate under the maximum temperature cumulative stress within a set time, and the survival rate under the water content cumulative stress within a set time, so that the subsequent steps can determine the positive growth rate r through the obtained parameters. p and negative growth rate r n .

[0145] Step b3, for each migration track in the multiple migration tracks: determine the positive growth rate of the target insect under the migration track according to the temperature data when the target insect flies along the migration track, combined with the temperature development dependent parameter; determine the negative growth rate of the target insect under the migration track according to the temperature data and soil moisture data when the target insect flies along the migration track, combined with the minimum temperature threshold, the maximum temperature threshold, the minimum soil moisture threshold, the survival rate under the minimum temperature cumulative stress within a set time, the survival rate under the maximum temperature cumulative stress within a set time, and the survival rate under the moisture cumulative stress within a set time; calculate the difference between the positive growth rate and the negative growth rate of the target insect under the migration track, and use the difference as the growth rate under the migration track; determine whether the climate when the target insect flies along the migration track is suitable for the growth and development of the target insect according to whether the growth rate under the migration track is greater than the preset growth rate threshold.

[0146] Specifically, this step can use the improved Sharpe and DeMichele model to study the impact of temperature data on the development rate of the target insect from egg to adult, and construct a biological temperature development dependence model of the target insect to determine the positive growth rate through the biological temperature development dependence model.

[0147] Here, the formula for the biological temperature development dependence model is as follows:

[0148]

[0149] The following is an introduction to the various parameters in formula (1) (i.e., temperature development dependent parameters) in conjunction with Table 2:

[0150] Table 2 Parameters of the temperature-dependent development model of target insect organisms

[0151]

[0152] Therefore, according to the temperature data T when the target insect flies along the migration trajectory, combined with the temperature development dependent parameters shown in Table 2, the positive growth rate of the target insect under the migration trajectory can be determined.

[0153] For the calculation of negative growth rate, this step comprehensively considers the effects of cold and heat stress and drought stress on the development of target insects, and provides a method for determining negative growth rate based on critical thresholds and mortality parameters of cold and heat and drought stress.

[0154] Specifically, the process of this step "determining the negative growth rate of the target insect along the migration trajectory based on the temperature data and soil moisture data of the target insect when flying along the migration trajectory, combined with the minimum temperature threshold, the maximum temperature threshold, the minimum soil moisture threshold, the survival rate under the minimum temperature cumulative stress within a set time, the survival rate under the maximum temperature cumulative stress within a set time, and the survival rate under the water content cumulative stress within a set time" may include:

[0155] Step b21, determine the mortality rate of the target insect per unit of cold stress according to the survival rate under the lowest temperature cumulative stress within the set time, determine the mortality rate of the target insect per unit of heat stress according to the survival rate under the highest temperature cumulative stress within the set time, and determine the mortality rate of the target insect per unit of drought stress according to the survival rate under the water content cumulative stress within the set time.

[0156] It is understandable that when the environment is not suitable for the growth and development of the target insect, it will cause stress death. This step can be quantified by exceeding the threshold S c (In this embodiment, S c Specifically refers to the minimum temperature threshold S Tmin , Maximum temperature threshold S Tmax and the minimum soil moisture threshold S Wmin The average mortality rate per unit of stress per unit of time m s (In this embodiment, m s Specifically, it refers to the mortality rate per unit of cold stress m Tmin , mortality rate per unit of heat stress m Tmax and the mortality rate per unit of drought stress m Wmin ), using the formula To derive r n , where p is the accumulated stress a in time t s The survival rate under .

[0157] Specifically in this step, for the lowest temperature, p refers to the survival rate under the lowest temperature cumulative stress within the set time t; for the highest temperature, p refers to the survival rate under the highest temperature cumulative stress within the set time t; for the soil moisture content, p refers to the survival rate under the moisture content cumulative stress within the set time t. The set time t is 1 day or 1 hour, which is related to the obtained p. Therefore, in p, t and a s When all are known quantities, m can be obtained based on p s That is, according to the survival rate under the lowest temperature cumulative stress within a set time, the mortality rate m of the target insect per unit of cold stress is determined. Tmin According to the survival rate under the highest temperature cumulative stress within the set time, the mortality rate m of the target insect per unit of heat stress is determined. Tmax, according to the survival rate under the cumulative stress of water content within a set time, determine the mortality rate m of the target insect per unit of drought stress Wmin .

[0158] Through experimental calculation, the m determined in this step is Tmin 0.2 (reference basis: 36% mortality rate at -5℃ within 3 hours), m Tmax 0.02 (the reference is that the daily mortality rate is 10% at 45°C), m Wmin It is 1.05 (the reference is 0.1m3 / m3 daily mortality rate is 10%).

[0159] Step b22, based on the temperature data and soil moisture data of the target insects when flying along the migration trajectory, combined with the mortality rate of the target insects per unit of cold stress, the mortality rate per unit of heat stress, the mortality rate per unit of drought stress, the minimum temperature threshold, the maximum temperature threshold and the minimum soil moisture threshold, determine the negative growth rate of the target insects under the migration trajectory.

[0160] In this step, all adverse conditions that cause the target insects to die can be accumulated and added up to get r n =(|T min -S Tmin |)m Tmin +(|T max -S Tmax |)m Tmin +(|W min -S Wmin |)m Wmin , where the minimum temperature threshold S Tmin It can be 12.97℃, the maximum temperature threshold S Tmax can be 39.8℃, the minimum soil moisture threshold S Wmin It can be 0.1m3 / m3.

[0161] After obtaining the positive growth rate and the negative growth rate as described above, the difference between the positive growth rate and the negative growth rate can be calculated to obtain r, which is the growth rate under the migration trajectory. Then, based on whether r is greater than a preset growth rate threshold (for example, the growth rate threshold is 0.01), it is determined whether the climate when the target insect flies along the migration trajectory is suitable for the growth and development of the target insect.

[0162] For each migration trajectory, if r is greater than the preset growth rate threshold, it is determined that the climate when the target insect flies along the migration trajectory is suitable for the growth and development of the target insect; if r is less than or equal to the growth rate threshold, it is determined that the climate when the target insect flies along the migration trajectory is not suitable for the growth and development of the target insect.

[0163] In summary, this embodiment takes the climate environment factor into consideration, so that some invalid trajectories among the predicted multiple migration trajectories can be eliminated later, thereby making the prediction result of the spatial distribution more accurate.

[0164] Step S2: for each migration track among the multiple migration tracks, if the target insect is not in the growth period of the host plant when flying along the migration track, and / or the climate when the target insect flies along the migration track is not suitable for the growth and development of the target insect, then the migration track is screened out from the multiple migration tracks.

[0165] In summary, this embodiment takes into account the factors of host plant growth period and climate suitability on the growth and development of target insects, and screens out invalid trajectories from the predicted multiple migration trajectories, so that the future spatial distribution of target insects can be accurately predicted later.

[0166] In one possible implementation, for fall armyworms, flight activity accelerates ovarian development, and fall armyworm females usually present a peak of egg-laying after flight. c =∑r is equal to 1, it is considered that the fall armyworm develops from eggs to adults during this period and has the ability to migrate long distances. At the same time, the maximum cumulative development period of the fall armyworm in the environmental suitability does not exceed 90 days, otherwise it is considered that the current habitat in the area is not suitable for the growth and development of the fall armyworm.

[0167] In order to verify the accuracy of the prediction model of the dynamic spatial distribution of fall armyworm, this application uses the dates of the first field capture of fall armyworm in 125 cities since the fall armyworm invaded my country collected from literature, news reports, plant protection station surveys and other materials, and compares them with the initial migration date of fall armyworm simulated in this application, and establishes a linear regression (y=10.01+0.54×x, where y refers to the time when the fall armyworm was first captured in the actual ground survey, and x is the time when the fall armyworm spread to the corresponding area simulated in this application). Figure 2 .like Figure 2 As shown, there is no statistically significant difference between the means of the simulation results of this application and the field observation results, which shows that the prediction results of this application are good and have high accuracy.

[0168] In an optional embodiment, considering that the migration starting point and landing end point of the target insects must be on the ground rather than in water, and the target insects will not fly below the ground, this can be used as a screening basis to screen out some invalid trajectories in the multiple migration trajectories. That is, this embodiment can perform a preliminary screening of the multiple migration trajectories before "step S103, based on whether the target insects are in the growth period of the host plant when flying along the multiple migration trajectories, and whether the climate when the target insects fly along the multiple migration trajectories is suitable for the growth and development of the target insects, the invalid trajectories in the multiple migration trajectories are screened out to obtain the target valid migration trajectories" according to whether the migration locations included in the multiple migration trajectories are non-water areas, and / or whether the migration locations included in the multiple migration trajectories are lower than the height of the digital elevation model DEM (i.e., normal migration trajectories).

[0169] Specifically, if a migration point is a water area, the migration track including the migration point in the multiple migration tracks is screened out as an invalid track; if a migration point is lower than the altitude of the digital elevation model DEM, the migration track including the migration point in the multiple migration tracks is screened out as an invalid track. In this embodiment, the subsequent steps S103 to S104 are performed after the initial screening.

[0170] In summary, this embodiment can comprehensively consider whether the migration site (landing site) is non-water area and whether the migration trajectory is a normal migration trajectory, and eliminate invalid trajectories, so that the future spatial distribution of target insects can be accurately predicted later.

[0171] The embodiment of the present application also provides a migratory insect spatial distribution prediction device. The migratory insect spatial distribution prediction device provided in the embodiment of the present application is described below. The migratory insect spatial distribution prediction device described below and the migratory insect spatial distribution prediction method described above can be referenced to each other.

[0172] See also Figure 3 , shows a schematic diagram of the structure of the migratory insect spatial distribution prediction device provided in an embodiment of the present application, such as Figure 3 As shown, the migratory insect spatial distribution prediction device may include: a first acquisition module 301, a migration trajectory prediction module 302, a first trajectory screening module 303 and a spatial distribution prediction module 304.

[0173] The first acquisition module 301 is used to acquire meteorological data and migration behavior parameters of target insects, wherein the meteorological data is temporally and spatially discontinuous meteorological data.

[0174] The migration trajectory prediction module 302 is used to continuously predict the future migration locations of target insects based on meteorological data and migration parameters, with a preset location as the migration starting point, to obtain multiple migration trajectories consisting of the migration starting point and the predicted migration location.

[0175] The first trajectory screening module 303 is used to screen out invalid trajectories from the multiple migration trajectories according to whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories and whether the climate when the target insect flies along the multiple migration trajectories is suitable for the growth and development of the target insect, so as to obtain the target effective migration trajectory.

[0176] The spatial distribution prediction module 304 is used to determine the future spatial distribution of target insects based on the target effective migration trajectory.

[0177] The migratory insect spatial distribution prediction device provided by the present application first obtains meteorological data and the migratory behavior parameters of the target insects, and then, based on the meteorological data and the migratory behavior parameters, takes the preset location as the migration starting point, continuously predicts the future migration location of the target insects, and obtains multiple migration trajectories composed of the migration starting point and the predicted migration location. Since the migration behavior parameters can reflect the migration diffusion mechanism of the target insects, the future migration trajectory of the target insects is dynamically predicted based on the migration diffusion mechanism of the target insects, and the prediction result is more accurate. Considering that when the target insects fly, the environmental climate and food under the migration trajectory need to be suitable for the survival of the target insects, otherwise the target insects will not fly along the migration trajectory. Therefore, the present application will screen out invalid trajectories in the multiple migration trajectories according to whether the target insects are in the growth period of the host plant when flying along the multiple migration trajectories, and / or whether the climate when the target insects fly along the multiple migration trajectories is suitable for the growth and development of the target insects, and obtain the target effective migration trajectory. Finally, according to the target effective migration trajectory, the future spatial distribution of the target insects is determined. This application comprehensively considers the migration and diffusion mechanism of the target insects, climate suitability and the growth period of the host plants, so that the predicted spatial distribution is more accurate and the prediction accuracy is higher. The spatial and temporal continuity of the insect spatial distribution prediction results predicted by this application is better.

[0178] In a possible implementation, the first track screening module 303 may include: a growth period and climate judgment module and an invalid track screening module.

[0179] The growth period and climate judgment module is used to determine whether the target insect is in the growth period of the host plant when flying along multiple migration paths, and to determine whether the climate when the target insect flies along multiple migration paths is suitable for the growth and development of the target insect.

[0180] The invalid trajectory screening module is used to screen out each migration trajectory from the multiple migration trajectories if the target insect is not in the growth period of the host plant when flying along the migration trajectory, and / or the climate when the target insect flies along the migration trajectory is not suitable for the growth and development of the target insect.

[0181] In one possible implementation, the above-mentioned growth period and climate judgment module may include: a second acquisition module, a grid mapping module, a curve generation module, a curve smoothing module and a smooth curve reference module when determining whether the target insect is in the growth period of the host plant when flying along multiple migration trajectories.

[0182] The second acquisition module is used to obtain the leaf area index of each period at the migration sites included in the multiple migration tracks.

[0183] The grid mapping module is used to correspond the leaf area index of each period to the preset farmland grid, and obtain the leaf area index of each period under each farmland grid.

[0184] The curve generation module is used to generate the annual time series curve of the leaf area index corresponding to each farmland grid according to the leaf area index of each period under each farmland grid.

[0185] The curve smoothing module is used to smooth the leaf area index annual time series curve corresponding to each farmland grid in each farmland grid by using the smoothing method corresponding to the farmland grid to obtain the smoothed leaf area index annual time series curve corresponding to the farmland grid.

[0186] The smooth curve reference module is used to extract the key phenological period of the host plant according to the smoothed annual time series curve of the leaf area index corresponding to each farmland grid, so as to determine whether the target insects are in the growth period of the host plant when flying along multiple migration trajectories based on the extracted key phenological period of the host plant.

[0187] In a possible implementation, all farmland grids within the target range of any farmland grid correspond to the same smoothing method.

[0188] Based on this, the process of determining the smoothing method corresponding to the farmland grid within the target range in the curve smoothing module may include: a root mean square error averaging module and a smoothing method determination module.

[0189] The root mean square error averaging module is used to use each smoothing method in the preset smoothing method set to smooth the annual time series curve of leaf area index corresponding to each farmland grid within the target range using the smoothing method to obtain the smoothed annual time series curve of leaf area index corresponding to each farmland grid under the smoothing method; according to the smoothed annual time series curve of leaf area index corresponding to each farmland grid under the smoothing method, the key phenological period of the host plant corresponding to each farmland grid under the smoothing method is determined; the root mean square error between the key phenological period of the host plant corresponding to each farmland grid under the smoothing method and the actual key phenological period of the host plant is calculated; the obtained root mean square error is used as the root mean square error corresponding to each farmland grid under the smoothing method; the average value of the root mean square errors corresponding to all farmland grids within the target range under the smoothing method is calculated to obtain the mean root mean square error corresponding to the smoothing method; so as to obtain the mean root mean square error corresponding to all smoothing methods in the smoothing method set.

[0190] The smoothing method determination module is used to determine the minimum root mean square error mean from the root mean square error means corresponding to all smoothing methods in the smoothing method set, and the smoothing method corresponding to the minimum root mean square error mean is used as the smoothing method corresponding to the farmland grid within the target range.

[0191] In a possible implementation, the smooth curve reference module may include: a first key phenological period determination module, a second key phenological period determination module, a third key phenological period determination module and a key phenological period reference module.

[0192] The first key phenological period determination module is used to determine the inflection point where the first-order derivative increases and the second-order derivative is equal to 0 in the smoothed leaf area index annual time series curve corresponding to each farmland grid, and the date corresponding to the inflection point is used as the first key phenological period of the host plant corresponding to each farmland grid.

[0193] The second key phenological period determination module is used to determine the maximum value in the smoothed leaf area index annual time series curve corresponding to each farmland grid, and use the date corresponding to the maximum value as the second key phenological period of the host plant corresponding to each farmland grid.

[0194] The third key phenological period determination module is used to determine the inflection point where the first-order derivative of the smoothed leaf area index annual time series curve corresponding to each farmland grid is negative and has the largest absolute value, and the date corresponding to the inflection point is used as the third key phenological period of the host plant corresponding to each farmland grid.

[0195] The key phenological period reference module is used to determine whether the target insects are in the growth period of the host plants when flying along multiple migration trajectories based on the first key phenological period, the second key phenological period and the third key phenological period of the host plants corresponding to each farmland grid.

[0196] In a possible implementation, the growth period and climate judgment module may include: a climate data acquisition module, a third acquisition module and an acquisition parameter reference module when determining whether the climate when the target insects fly along multiple migration trajectories is suitable for the growth and development of the target insects.

[0197] The climate data acquisition module is used to acquire the climate data of the target insects when they fly along the multiple migration paths, wherein the climate data includes temperature data and soil moisture content data.

[0198] The third acquisition module is used to obtain the temperature development-dependent parameters related to the target insects, the minimum temperature threshold, the maximum temperature threshold, the minimum soil moisture threshold, the survival rate under the minimum temperature cumulative stress within a set time, the survival rate under the maximum temperature cumulative stress within a set time, and the survival rate under the water content cumulative stress within a set time.

[0199] A parameter reference acquisition module is used to determine, for each of the multiple migration trajectories, the positive growth rate of the target insect along the migration trajectory according to the temperature data of the target insect when flying along the migration trajectory, combined with the temperature development-dependent parameters, determine the negative growth rate of the target insect along the migration trajectory according to the temperature data and soil moisture data of the target insect when flying along the migration trajectory, combined with the minimum temperature threshold, the maximum temperature threshold, the minimum soil moisture threshold, the survival rate under the minimum temperature cumulative stress within a set time, the survival rate under the maximum temperature cumulative stress within a set time, and the survival rate under the moisture cumulative stress within a set time, calculate the difference between the positive growth rate and the negative growth rate of the target insect along the migration trajectory, use the difference as the growth rate under the migration trajectory, and determine whether the climate when the target insect flies along the migration trajectory is suitable for the growth and development of the target insect according to whether the growth rate under the migration trajectory is greater than the preset growth rate threshold.

[0200] In a possible implementation, the above-mentioned parameter reference module, when determining the negative growth rate of the target insect along the migration trajectory based on the temperature data and soil moisture data of the target insect when flying along the migration trajectory, combined with the minimum temperature threshold, the maximum temperature threshold, the minimum soil moisture threshold, the survival rate under the cumulative stress of the minimum temperature within a set time, the survival rate under the cumulative stress of the maximum temperature within a set time, and the survival rate under the cumulative stress of moisture content within a set time, may include: a mortality determination module and a negative growth rate determination module.

[0201] The mortality determination module is used to determine the mortality rate of the target insect per unit of cold stress according to the survival rate under the lowest temperature cumulative stress within the set time, determine the mortality rate of the target insect per unit of heat stress according to the survival rate under the highest temperature cumulative stress within the set time, and determine the mortality rate of the target insect per unit of drought stress according to the survival rate under the water content cumulative stress within the set time.

[0202] The negative growth rate determination module is used to determine the negative growth rate of the target insect along the migration trajectory based on the temperature data and soil moisture data of the target insect when flying along the migration trajectory, combined with the mortality rate of the target insect per unit of cold stress, the mortality rate per unit of heat stress, the mortality rate per unit of drought stress, the minimum temperature threshold, the maximum temperature threshold and the minimum soil moisture threshold.

[0203] In a possible implementation, the migratory insect spatial distribution prediction device provided in the present application may further include: a second trajectory screening module.

[0204] The second track screening module is used to perform preliminary screening of multiple migration tracks before the growing period and climate judgment module according to whether the migration sites included in the multiple migration tracks are non-water areas and / or whether the migration sites included in the multiple migration tracks are lower than the height of the digital elevation model.

[0205] In a possible implementation, the above-mentioned migration behavior parameters include one or more of the following parameters: flight time, flight speed, flight altitude and forced landing factors, wherein the forced landing factors refer to factors that cause the target insects to be unable to fly during normal flight.

[0206] The present application also provides a device for predicting the spatial distribution of migratory insects. Figure 4 The hardware structure diagram of the migratory insect spatial distribution prediction device is shown in FIG. Figure 4 , the hardware structure of the migratory insect spatial distribution prediction device may include: at least one processor 401, at least one communication interface 402, at least one memory 403 and at least one communication bus 404;

[0207] In the embodiment of the present application, the number of the processor 401, the communication interface 402, the memory 403, and the communication bus 404 is at least one, and the processor 401, the communication interface 402, and the memory 403 communicate with each other through the communication bus 404;

[0208] The processor 401 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention;

[0209] The memory 403 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory;

[0210] The memory 403 stores a program, and the processor 401 can call the program stored in the memory 403, and the program is used to:

[0211] Acquiring meteorological data and migration behavior parameters of target insects, wherein the meteorological data is temporally and spatially discontinuous meteorological data;

[0212] According to the meteorological data and the migration behavior parameters, taking a preset location as the migration starting point, continuously predicting the future migration location of the target insect, and obtaining a plurality of migration trajectories consisting of the migration starting point and the predicted migration location;

[0213] According to whether the target insects are in the growth period of the host plants when they fly along the multiple migration trajectories, and whether the climate when the target insects fly along the multiple migration trajectories is suitable for the growth and development of the target insects, invalid trajectories in the multiple migration trajectories are screened out to obtain the target effective migration trajectories;

[0214] Determine the future spatial distribution of target insects based on the target effective migration trajectory.

[0215] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0216] An embodiment of the present application also provides a readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the above-mentioned method for predicting the spatial distribution of migratory insects is implemented.

[0217] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0218] Finally, it should be noted that, in this article, relational terms such as and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, article or device. In the absence of further restrictions, the elements defined by the statement "comprises a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0219] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0220] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the spatial distribution of migratory insects, characterized in that: include: Acquiring meteorological data and migration behavior parameters of target insects, wherein the meteorological data is temporally and spatially discontinuous meteorological data; According to the meteorological data and the migration behavior parameters, taking a preset location as the migration starting point, continuously predicting the future migration location of the target insect, and obtaining a plurality of migration trajectories consisting of the migration starting point and the predicted migration location; According to whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories, and whether the climate when the target insect flies along the multiple migration trajectories is suitable for the growth and development of the target insect, the invalid trajectories in the multiple migration trajectories are screened out to obtain the target effective migration trajectories; Determining the future spatial distribution of the target insects according to the target effective migration trajectory; The method of screening out invalid trajectories from the multiple migration trajectories according to whether the target insects are in the growth period of the host plant when flying along the multiple migration trajectories and whether the climate when the target insects fly along the multiple migration trajectories is suitable for the growth and development of the target insects comprises: Determining whether the target insect is in the growth period of the host plant when flying along the multiple migration paths, and determining whether the climate when the target insect flies along the multiple migration paths is suitable for the growth and development of the target insect; For each migration track in the multiple migration tracks, if the target insect is not in the growth period of the host plant when flying along the migration track, and / or the climate when the target insect flies along the migration track is not suitable for the growth and development of the target insect, then the migration track is screened out from the multiple migration tracks; The step of determining whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories comprises: Obtaining the leaf area index at each period of the migration sites included in the multiple migration tracks; Corresponding the leaf area index of each period to a preset farmland grid to obtain the leaf area index of each period under each farmland grid; According to the leaf area index of each period under each farmland grid, generate the annual time series curve of the leaf area index corresponding to each farmland grid; For each farmland grid in the farmland grids, the annual time series curve of leaf area index corresponding to the farmland grid is smoothed by using the smoothing method corresponding to the farmland grid to obtain the smoothed annual time series curve of leaf area index corresponding to the farmland grid; wherein the ground to be studied is divided into several ranges according to provinces in advance, and all farmland grids within each range correspond to the same smoothing method; Extracting the key phenological period of the host plant according to the smoothed leaf area index annual time series curves corresponding to each farmland grid, so as to determine whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories according to the extracted key phenological period of the host plant; Determining whether the climate when the target insect flies along the multiple migration trajectories is suitable for the growth and development of the target insect comprises: Acquire climate data when the target insects fly along the multiple migration paths, wherein the climate data includes temperature data and soil moisture data; Obtaining temperature development-dependent parameters, a minimum temperature threshold, a maximum temperature threshold, a minimum soil moisture threshold, a survival rate under the minimum temperature cumulative stress within a set time, a survival rate under the maximum temperature cumulative stress within a set time, and a survival rate under the water content cumulative stress within a set time related to the target insect; For each of the plurality of migration trajectories: Determining the positive growth rate of the target insect along the migration trajectory according to the temperature data of the target insect when it flies along the migration trajectory in combination with the temperature development-dependent parameter; According to the temperature data and soil moisture data of the target insect when flying along the migration trajectory, combined with the minimum temperature threshold, the maximum temperature threshold, the minimum soil moisture threshold, the survival rate under the minimum temperature cumulative stress within the set time, the survival rate under the maximum temperature cumulative stress within the set time, and the survival rate under the water content cumulative stress within the set time, the negative growth rate of the target insect under the migration trajectory is determined; Calculating the difference between the positive growth rate and the negative growth rate of the target insect under the migration trajectory, and using the difference as the growth rate under the migration trajectory; According to whether the growth rate under the migration trajectory is greater than a preset growth rate threshold, it is determined whether the climate when the target insect flies along the migration trajectory is suitable for the growth and development of the target insect.

2. The method for predicting the spatial distribution of migratory insects according to claim 1, characterized in that: All farmland grids within the target range of any farmland grid correspond to the same smoothing method; The process of determining the smoothing method corresponding to the farmland grid within the target range includes: For each smoothing method in the set of preset smoothing methods: The smoothing method is used to smooth the annual time series curve of the leaf area index corresponding to each farmland grid within the target range, so as to obtain the smoothed annual time series curve of the leaf area index corresponding to each farmland grid under the smoothing method; According to the smoothed leaf area index annual time series curve corresponding to each farmland grid under the smoothing method, the key phenological period of the host plant corresponding to each farmland grid under the smoothing method is determined; Calculate the root mean square error between the key phenological period of the host plant corresponding to each farmland grid under the smoothing method and the actual key phenological period of the host plant, and use the obtained root mean square error as the root mean square error corresponding to each farmland grid under the smoothing method; Calculate the average value of the root mean square errors corresponding to all farmland grids within the target range under the smoothing method to obtain the root mean square error mean value corresponding to the smoothing method; To obtain the mean values ​​of the root mean square errors corresponding to all the smoothing methods in the smoothing method set; The minimum root mean square error mean is determined from the root mean square error means corresponding to all the smoothing methods in the smoothing method set, and the smoothing method corresponding to the minimum root mean square error mean is used as the smoothing method corresponding to the farmland grid within the target range.

3. The method for predicting the spatial distribution of migratory insects according to claim 1, characterized in that: The extracting the key phenological period of the host plant according to the smoothed leaf area index annual time series curves corresponding to each farmland grid, and determining whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories according to the extracted key phenological period of the host plant, includes: Determine the inflection point where the first-order derivative increases and the second-order derivative is equal to 0 in the smoothed leaf area index annual time series curve corresponding to each farmland grid, and use the date corresponding to the inflection point as the first key phenological period of the host plant corresponding to each farmland grid; Determine the maximum value in the smoothed leaf area index annual time series curve corresponding to each farmland grid, and use the date corresponding to the maximum value as the second key phenological period of the host plant corresponding to each farmland grid; Determine the inflection point where the first-order derivative of the smoothed leaf area index annual time series curve corresponding to each farmland grid is negative and has the largest absolute value, and use the date corresponding to the inflection point as the third key phenological period of the host plant corresponding to each farmland grid; According to the first key phenological period of the host plant, the second key phenological period of the host plant and the third key phenological period of the host plant corresponding to each farmland grid, it is determined whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories.

4. The method for predicting the spatial distribution of migratory insects according to claim 1, characterized in that: The method of determining the negative growth rate of the target insect along the migration trajectory according to the temperature data and soil moisture data of the target insect when flying along the migration trajectory, in combination with the minimum temperature threshold, the maximum temperature threshold, the minimum soil moisture threshold, the survival rate under the minimum temperature cumulative stress within the set time, the survival rate under the maximum temperature cumulative stress within the set time, and the survival rate under the moisture cumulative stress within the set time, comprises: According to the survival rate under the lowest temperature cumulative stress within the set time, the mortality rate of the target insect per unit of cold stress is determined; according to the survival rate under the highest temperature cumulative stress within the set time, the mortality rate of the target insect per unit of heat stress is determined; according to the survival rate under the water content cumulative stress within the set time, the mortality rate of the target insect per unit of drought stress is determined; According to the temperature data and soil moisture data of the target insect when flying along the migration trajectory, combined with the mortality rate of the target insect per unit of cold stress, the mortality rate per unit of heat stress, the mortality rate per unit of drought stress, the minimum temperature threshold, the maximum temperature threshold and the minimum soil moisture threshold, the negative growth rate of the target insect under the migration trajectory is determined.

5. The method for predicting the spatial distribution of migratory insects according to claim 1, characterized in that: Before filtering out invalid trajectories from the multiple migration trajectories to obtain target valid migration trajectories according to whether the target insects are in the growth period of the host plant when flying along the multiple migration trajectories and whether the climate when the target insects fly along the multiple migration trajectories is suitable for the growth and development of the target insects, the method further includes: The multiple migration trajectories are preliminarily screened according to whether the migration locations included in the multiple migration trajectories are non-water areas and / or whether the migration locations included in the multiple migration trajectories are lower than the height of the digital elevation model.

6. The method for predicting the spatial distribution of migratory insects according to claim 1, characterized in that: The migration flight parameters include one or more of the following parameters: flight time, flight speed, flight altitude and forced landing factors, wherein the forced landing factors refer to factors that cause the target insects to be unable to fly during normal flight.

7. A device for predicting the spatial distribution of migratory insects, characterized in that: include: A first acquisition module is used to acquire meteorological data and migration behavior parameters of target insects, wherein the meteorological data is temporally and spatially discontinuous meteorological data; A migration trajectory prediction module is used to continuously predict the future migration location of the target insect based on the meteorological data and the migration behavior parameters, with a preset location as the migration starting point, to obtain multiple migration trajectories consisting of the migration starting point and the predicted migration location; A first trajectory screening module is used to screen out invalid trajectories from the multiple migration trajectories to obtain target valid migration trajectories according to whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories and whether the climate when the target insect flies along the multiple migration trajectories is suitable for the growth and development of the target insect; A spatial distribution prediction module, used to determine the future spatial distribution of the target insects according to the effective migration trajectory of the target; The method of screening out invalid trajectories from the multiple migration trajectories according to whether the target insects are in the growth period of the host plant when flying along the multiple migration trajectories and whether the climate when the target insects fly along the multiple migration trajectories is suitable for the growth and development of the target insects comprises: Determining whether the target insect is in the growth period of the host plant when flying along the multiple migration paths, and determining whether the climate when the target insect flies along the multiple migration paths is suitable for the growth and development of the target insect; For each migration track in the multiple migration tracks, if the target insect is not in the growth period of the host plant when flying along the migration track, and / or the climate when the target insect flies along the migration track is not suitable for the growth and development of the target insect, then the migration track is screened out from the multiple migration tracks; The step of determining whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories comprises: Obtaining the leaf area index at each period of the migration sites included in the multiple migration tracks; Corresponding the leaf area index of each period to a preset farmland grid to obtain the leaf area index of each period under each farmland grid; According to the leaf area index of each period under each farmland grid, generate the annual time series curve of the leaf area index corresponding to each farmland grid; For each farmland grid in the farmland grids, the annual time series curve of leaf area index corresponding to the farmland grid is smoothed by using the smoothing method corresponding to the farmland grid to obtain the smoothed annual time series curve of leaf area index corresponding to the farmland grid; wherein the ground to be studied is divided into several ranges according to provinces in advance, and all farmland grids within each range correspond to the same smoothing method; Extracting the key phenological period of the host plant according to the smoothed leaf area index annual time series curves corresponding to each farmland grid, so as to determine whether the target insect is in the growth period of the host plant when flying along the multiple migration trajectories according to the extracted key phenological period of the host plant; Determining whether the climate when the target insect flies along the multiple migration trajectories is suitable for the growth and development of the target insect comprises: Acquire climate data when the target insects fly along the multiple migration paths, wherein the climate data includes temperature data and soil moisture data; Obtaining temperature development-dependent parameters, a minimum temperature threshold, a maximum temperature threshold, a minimum soil moisture threshold, a survival rate under the minimum temperature cumulative stress within a set time, a survival rate under the maximum temperature cumulative stress within a set time, and a survival rate under the water content cumulative stress within a set time related to the target insect; For each of the plurality of migration trajectories: Determining the positive growth rate of the target insect along the migration trajectory according to the temperature data of the target insect when it flies along the migration trajectory in combination with the temperature development-dependent parameter; According to the temperature data and soil moisture data of the target insect when flying along the migration trajectory, combined with the minimum temperature threshold, the maximum temperature threshold, the minimum soil moisture threshold, the survival rate under the minimum temperature cumulative stress within the set time, the survival rate under the maximum temperature cumulative stress within the set time, and the survival rate under the water content cumulative stress within the set time, the negative growth rate of the target insect under the migration trajectory is determined; Calculating the difference between the positive growth rate and the negative growth rate of the target insect under the migration trajectory, and using the difference as the growth rate under the migration trajectory; According to whether the growth rate under the migration trajectory is greater than a preset growth rate threshold, it is determined whether the climate when the target insect flies along the migration trajectory is suitable for the growth and development of the target insect.

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