A method and apparatus for insect migration forecasting

Abstract

The application provides an insect migration forecasting method and device, and relates to the field of biological control. The method comprises the following steps: acquiring historical occurrence position data of insects, migration behavior data, meteorological data of each sub-region contained in a target region, atmospheric trajectory data of the insects in each flight period, and global phenology data; determining at least one migration starting point of the insects in the target region according to the historical occurrence position data and the meteorological data of each sub-region; predicting a next migration landing point of the insects when the insects migrate from each migration starting point according to the migration behavior data and the atmospheric trajectory data; and screening at least one migration landing point corresponding to each migration starting point according to the global phenology data to obtain screened migration landing points corresponding to each migration starting point. The application can predict the migration landing points of the insects in combination with the meteorological data and the global phenology data, and improves the reliability and accuracy of the predicted migration landing points.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biological control, in particular to a method and device for predicting insect migration. BACKGROUND

[0002] Insect migration occurs at a specific stage of its life cycle, in a certain season, and in groups or dispersedly and regularly from one occurrence to another, to ensure the continuation of its life cycle and the proliferation of the species. However, large-scale migration of insects can cause huge losses to agricultural production. For example, Spodoptera frugiperda adult can migrate up to 100 kilometers in one night, and is estimated to migrate up to 500 kilometers in one generation. Since its introduction into Africa in 2016, it has spread rapidly and invaded dozens of countries in Africa, Asia and Australia, feeding on more than 350 plant species in the invaded areas, causing huge losses to global food production and leading to food shortages in affected areas, threatening the livelihoods of millions of small farmers.

[0003] It is necessary to make timely warnings on the migration dynamics of insects and take preventive measures in advance. Currently, only meteorological factors are considered in the research on insect migration prediction, and the vegetation conditions for the survival of insects at the migration destination are not considered, resulting in low reliability and accuracy of the predicted migration destination. SUMMARY

[0004] Therefore, the present application provides a method and device for predicting insect migration to solve the problem of low reliability and accuracy of the predicted migration destination in the prior art. The technical solution is as follows:

[0005] A method for predicting insect migration, comprising:

[0006] obtaining historical occurrence location data of insects, migration behavior data, historical meteorological data of each sub-region contained in a target region, atmospheric trajectory data of insects in an initial flight period, and global phenological data, wherein the target region refers to a region where the migration starting point of the insects is located, and the initial flight period refers to the period when the insects migrate for the first time in a generation;

[0007] determining at least one migration starting point of the insects in the target region according to the historical occurrence location data and the historical meteorological data of each sub-region;

[0008] predicting the next migration destination of the insects when migrating from each migration starting point according to the migration behavior data and the atmospheric trajectory data, to obtain at least one migration destination corresponding to each migration starting point predicted;

[0009] According to the global phenology data, at least one migration landing point corresponding to each migration starting point is screened to exclude the migration landing points not in the host plant growth period among the at least one migration landing point corresponding to each migration starting point, so as to obtain the screened migration landing point corresponding to each migration starting point.

[0010] Optionally, according to the historical occurrence position data and the meteorological data of each sub-region, at least one migration starting point of the insect in the target region is determined, including:

[0011] According to the historical occurrence position data and the meteorological data of each sub-region, potential habitat distribution data of the insect in the target region is determined.

[0012] According to the potential habitat distribution data of the insect in the target region, a seasonal migration area corresponding to the insect is determined from the target region.

[0013] At least one migration starting point of the insect in the seasonal migration area is determined.

[0014] Optionally, according to the historical occurrence position data and the meteorological data of each sub-region, potential habitat distribution data of the insect in the target region is determined, including:

[0015] According to the historical occurrence position data and the meteorological data of each sub-region, annual reproduction area suitability data and seasonal migration area suitability data of each preset station included in the target region are determined.

[0016] According to the annual reproduction area suitability data and the seasonal migration area suitability data of each preset station, spatial interpolation is performed on each sub-region included in the target region to obtain potential habitat distribution data composed of the annual reproduction area suitability data and the seasonal migration area suitability data of each sub-region included in the target region.

[0017] Optionally, according to the potential habitat distribution data of the insect in the target region, a seasonal migration area corresponding to the insect is determined from the target region, including:

[0018] The sub-region in the target region with annual reproduction area suitability data greater than a first preset threshold is determined as the seasonal migration area corresponding to the insect.

[0019] And / or,

[0020] The sub-region in the target region with annual reproduction area suitability data less than or equal to the first preset threshold and seasonal migration area suitability data greater than a second preset threshold is determined as the seasonal migration area corresponding to the insect.

[0021] Optionally, at least one migration starting point of the insect in the seasonal migration area is determined, including:

[0022] The seasonal migration area is divided into a plurality of target regions with the same size.

[0023] selecting a point from each target area as a migration starting point.

[0024] Optionally, at least one migration ending point corresponding to each migration starting point is screened according to global phenology data to remove migration ending points not in the host plant growth period, comprising:

[0025] extracting the host plant growth period at the at least one migration ending point from the global phenology data;

[0026] determining the date when the insect migrates to the at least one migration ending point corresponding to each migration starting point as the migration ending date corresponding to the at least one migration ending point corresponding to each migration starting point respectively;

[0027] for each migration ending point in the at least one migration ending point corresponding to each migration starting point, if the migration ending date corresponding to the migration ending point is not in the host plant growth period at the migration ending point, the migration ending point is removed.

[0028] Optionally, the host plant growth period comprises host plant growth period start data and host plant growth period end data;

[0029] extracting the host plant growth period at the at least one migration ending point from the global phenology data, comprising:

[0030] preprocessing the global phenology data to obtain global host plant growth period start data and global host plant growth period end data;

[0031] extracting the host plant growth period start data at the at least one migration ending point from the global host plant growth period start data, and extracting the host plant growth period end data at the at least one migration ending point from the global host plant growth period end data.

[0032] Optionally, determining the date when the insect migrates to the at least one migration ending point corresponding to each migration starting point, comprises:

[0033] calculating the year cumulative day corresponding to the date when the insect migrates to the at least one migration ending point corresponding to each migration starting point.

[0034] Optionally, further comprising:

[0035] taking the screened migration ending point as a new migration starting point, and obtaining atmospheric trajectory data in the next flight period of the insect as new atmospheric trajectory data, and returning to perform the prediction of the next migration ending point when the insect migrates from each migration starting point according to the migration behavior data and the atmospheric trajectory data, to obtain the predicted at least one migration ending point corresponding to each migration starting point, until the migration of one generation of the insect ends.

[0036] An insect migration forecasting device comprises:

[0037] a data acquisition module configured to acquire historical occurrence location data of the insects, migration behavior data of the insects, historical meteorological data of each sub-region included in a target region, atmospheric trajectory data of the insects in an initial flight period, and global phenology data, wherein the target region refers to a region where the insects start migration, and the initial flight period refers to a period when the insects migrate for the first time in a generation;

[0038] a migration starting point determination module configured to determine at least one migration starting point of the insects in the target region according to the historical occurrence location data and the historical meteorological data of each sub-region;

[0039] a migration landing point prediction module configured to predict a next migration landing point of the insects when migrating from each migration starting point according to the migration behavior data and the atmospheric trajectory data, to obtain at least one predicted migration landing point corresponding to each migration starting point;

[0040] a migration landing point screening module configured to screen the at least one migration landing point corresponding to each migration starting point according to the global phenology data, to exclude the migration landing points not in a host plant growth period from the at least one migration landing point corresponding to each migration starting point, to obtain screened migration landing points corresponding to each migration starting point.

[0041] According to the technical solution, the insect migration forecasting method provided by the application first acquires historical occurrence location data of the insects, migration behavior data of the insects, meteorological data of each sub-region included in a target region, atmospheric trajectory data of the insects in each flight period, and global phenology data, then determines at least one migration starting point of the insects in the target region according to the historical occurrence location data and the meteorological data of each sub-region, then predicts a next migration landing point of the insects when migrating from each migration starting point according to the migration behavior data and the atmospheric trajectory data, to obtain at least one predicted migration landing point corresponding to each migration starting point, and finally screens the at least one migration landing point corresponding to each migration starting point according to the global phenology data, to exclude the migration landing points not in a host plant growth period from the at least one migration landing point corresponding to each migration starting point, to obtain screened migration landing points corresponding to each migration starting point. The application can combine meteorological data and global phenology data to predict migration landing points of the insects, and improve the reliability and accuracy of the predicted migration landing points. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute a part of the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of the provided drawings.

[0043] Figure 1 A flowchart of an insect migration prediction method provided by an embodiment of the present application is shown in

[0044] Figure 2 A structural diagram of an insect migration prediction device provided by an embodiment of the present application is shown in

[0045] Figure 3 A hardware structure block diagram of an insect migration prediction device provided by an embodiment of the present application is shown in DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0047] The present application provides an insect migration prediction method, which is a kind of insect migration prediction method considering the whole process before, during and after migration, comprehensively considers the influence of meteorological and phenological factors on insect migration behavior, predicts the future migration dynamics of insects, and improves the reliability and accuracy of the prediction results. In order to make those skilled in the art understand the present application better, the insect migration prediction method provided by the present application will be described in detail in the following embodiments.

[0048] Please refer to Figure 1 , a flowchart of an insect migration prediction method provided by an embodiment of the present application is shown, which can include:

[0049] Step S101, obtaining historical occurrence position data of insects, migration behavior data, historical meteorological data of each sub-region contained in the target region, atmospheric trajectory data of insects in the initial flight period and global phenological data.

[0050] Optionally, the historical occurrence position data includes specific positions and occurrence times of the insects in the target area in a preset historical time period. In this embodiment, the historical time period can be determined according to actual conditions, for example, it can be from 1961 to 2021. For example, taking the fall armyworm as an example, the historical occurrence position data includes specific positions and occurrence times of the fall armyworm in the target area from 1961 to 2021.

[0051] The target area mentioned above refers to the area where the migration of the insects starts. For example, since the fall armyworm invaded Africa in 2016, it has invaded dozens of countries in Africa, Asia and Australia in a few years. It is of great significance to predict the migration of the fall armyworm from Africa as the starting point of migration.

[0052] The migration behavior data mentioned above refers to data reflecting the migration behavior of the insects. For example, the fall armyworm is a nocturnal moth with the habit of multi-stop migration. It usually takes off in the evening, migrates long distances at night, and stops flying in the early morning of the next day, and takes off again in the evening (in this embodiment, the period from the start of migration at night to the stop of flying activity in the early morning of the next day is regarded as a flight period). If the temperature conditions are suitable and there is no precipitation, the fall armyworm will fly for three consecutive nights in one generation, taking off at 8 p.m. every night and stopping at 6 a.m. the next morning, and resting during the day. Each night, the fall armyworm flies for 10 hours. However, when encountering adverse weather conditions, the migration of the fall armyworm will be forced to stop. When the air temperature is lower than 13.1℃ or the rainfall is greater than 1mm / h, the fall armyworm will stop migrating. In addition, if the fall armyworm migrates long distances across the sea, the original 10-hour migration activity is not enough to migrate to another piece of land. At this time, the fall armyworm will extend the single flight time, and the maximum single flight time is set to 36 hours. The flight speed of the moth insect similar in size to the fall armyworm is about 2.5-4.0m / s, so the flight speed of the fall armyworm can be set to 3m / s, and the direction is consistent with the wind direction.

[0053] Table 1: Migration behavior data of the fall armyworm

[0054]

[0055] Optionally, the migration behavior data of the fall armyworm is shown in Table 1, wherein the flight height is a parameter that has not been studied so far. In this embodiment, 8 groups of flight heights are set every 250 meters from 500 meters to 2250 meters. Of course, other flight heights can also be set, which are not limited in this embodiment.

[0056] It should be further pointed out that the parameter values in Table 1 above are only examples and do not limit the present application.

[0057] Optionally, the historical meteorological data of the sub-region CliMond refers to the average value of the meteorological data in the historical time period of the sub-region; optionally, the meteorological data includes but is not limited to the following data: temperature data, humidity data and precipitation data. For example, the historical time period is from 1961 to 2021, and the historical meteorological data of a sub-region can include: the average value of the temperature data of the sub-region from 1961 to 2021, the average value of the humidity data, etc.

[0058] The initial flight period refers to the period when a generation of insects migrates for the first time, for example, a generation of fall armyworms will fly for three consecutive nights, and the initial flight period refers to the first night of flight of a generation of fall armyworms.

[0059] Optionally, the above-mentioned atmospheric trajectory data can be obtained from the Global Data Assimilation System (GDAS), wherein the GDAS system puts balloon data, wind profiler data, buoy observations, radar observations, etc. into a grid-based three-dimensional model space to generate global atmospheric trajectory data.

[0060] Optionally, the above-mentioned global phenology data can be stored in h5 format, and the global phenology data in this step can be VNP22Q2.

[0061] Step S102, determining at least one migration starting point of the insect in the target region according to the historical occurrence position data and the historical meteorological data of each sub-region.

[0062] It can be understood that the migration starting point needs to be a place suitable for the survival of insects, that is, the migration starting point needs to be a place where insects will appear with a certain probability, and it has practical significance to make migration prediction based on such migration starting point. The above-mentioned historical occurrence position data can reflect which sub-regions in the target region have ever appeared the insects to be studied, and these sub-regions are probably suitable for the survival of insects, but whether they are suitable for the survival of insects needs to be comprehensively judged in combination with the historical meteorological data of the sub-region, therefore, this step can determine at least one migration starting point of the insect in the target region according to the historical occurrence position data and the historical meteorological data of each sub-region.

[0063] Step S103, predicting the next migration landing point of the insect when migrating from each migration starting point according to the migration behavior data and the atmospheric trajectory data, to obtain at least one migration landing point corresponding to each predicted migration starting point.

[0064] Optionally, this step can find all possible migration paths based on the aerodynamic model HYSPLIT, with the wind of the flight period as the medium, according to the migration behavior data and the atmospheric trajectory data, to obtain at least one migration landing point corresponding to each predicted migration starting point.

[0065] It should be noted that some parameters will be included in the air dynamics model HYSPLIT, such as the maximum calculation height of the model is 10000m AGL, the vertical movement option is isobaric, etc.

[0066] Step S104: screening at least one migration landing point corresponding to each migration starting point according to global phenology data, so as to exclude the migration landing point not in the host plant growth period from the at least one migration landing point corresponding to each migration starting point, and obtain the screened migration landing point corresponding to each migration starting point.

[0067] The host plant refers to a plant capable of providing food for insects, for example, for the fall armyworm, the host plant can be corn, wheat, rice, cotton, peanut, soybean, ryegrass, sugar beet, tobacco, tomato, etc.

[0068] Specifically, the at least one migration landing point corresponding to each migration starting point can be screened based on the global phenology data, so as to exclude those migration landing points not suitable for the survival of insects.

[0069] Optionally, after obtaining the screened migration landing point corresponding to each migration starting point, the application can also perform kernel density analysis on all screened migration landing points in ArcGIS, and obtain an insect migration prediction risk map, wherein in the risk map, the more the number of landing points in a certain range of spatial units, the higher the risk.

[0070] The insect migration prediction method provided by the application first acquires historical occurrence position data of insects, migration behavior data, meteorological data of each sub-region contained in a target region, atmospheric trajectory data of each flight period of the insects, and global phenology data, then determines at least one migration starting point of the insects in the target region according to the historical occurrence position data and the meteorological data of each sub-region, then predicts the next migration landing point of the insects when migrating from each migration starting point according to the migration behavior data and the atmospheric trajectory data, obtains at least one predicted migration landing point corresponding to each migration starting point, and finally screens the at least one migration landing point corresponding to each migration starting point according to the global phenology data, so as to exclude the migration landing point not in the host plant growth period from the at least one migration landing point corresponding to each migration starting point, and obtain the screened migration landing point corresponding to each migration starting point. The application can predict the migration landing point of the insects in combination with the meteorological data and the global phenology data, and improve the reliability and accuracy of the predicted migration landing point.

[0071] The embodiment can comprehensively consider three stages of before, during and after the occurrence of insect migration behavior, propose an insect migration prediction method combining meteorological and phenology factors, and realize intercontinental insect migration prediction.

[0072] In one embodiment of the present application, the process of "Step S102, determining at least one migration starting point of the insects in the target area according to the historical occurrence location data and the historical meteorological data of each sub-region" is introduced.

[0073] The process of "Step S102, determining at least one migration starting point of the insects in the target area according to the historical occurrence location data and the historical meteorological data of each sub-region" can include:

[0074] A1, determining potential habitat distribution data of the insects in the target area according to the historical occurrence location data and the meteorological data of each sub-region.

[0075] The potential habitat distribution data can reflect the survival suitability of the insects in each sub-region included in the target area.

[0076] Optionally, the specific implementation process of the present step can include the following two steps:

[0077] A11, determining annual breeding area suitability data and seasonal migration area suitability data of each preset station included in the target area according to the historical occurrence location data and the meteorological data of each sub-region.

[0078] The present step divides the survival suitability of the insects into annual breeding area suitability and seasonal migration area suitability, wherein the annual breeding area suitability is an index for measuring whether a sub-region is suitable for the survival of the insects throughout the year, and the seasonal migration area suitability is an index for measuring whether a sub-region is suitable for the seasonal survival of the insects.

[0079] In one possible implementation manner, the present step can be implemented through a pre-trained CLIMEX model, in which the location comparison function can be used to estimate the climate conditions required for the survival of the insects by analyzing the known annual breeding area and seasonal migration area distribution of the fall armyworm, and the biological data required for the growth and development of the fall armyworm is combined to iteratively update the parameters in the model so that the predicted habitat distribution is fitted with the known actual distribution area to the maximum extent. The parameters of the trained CLIMEX model are shown in Table 2.

[0080] Table 2 Parameters of the trained CLIMEX model

[0081]

[0082]

[0083] It should be noted that the parameter values of all the humidity indices, all the temperature indices, the temperature threshold at which cold inhibition starts to accumulate, the temperature threshold at which heat inhibition starts to accumulate, the temperature threshold at which dry inhibition starts to accumulate, and the temperature threshold at which wet inhibition starts to accumulate are existing data obtained by consulting relevant information at the beginning of the CLIMEX model construction, and the parameter values of the cold inhibition accumulation rate, the heat inhibition accumulation rate, the dry inhibition accumulation rate, the wet inhibition accumulation rate, and the effective accumulated temperature of the species to complete one generation are obtained by training the model after the CLIMEX model is constructed.

[0084] Taking the fall armyworm as an example, 565,801 stations are preset in the target area, and the historical occurrence position data and the meteorological data of each sub-region are input into the trained CLIMEX model to obtain the annual reproduction area suitability data and the seasonal migration area suitability data of each of the 565,801 stations.

[0085] For the convenience of subsequent description, the annual reproduction area suitability data is defined as EI (Eco-climatic index) data, and the seasonal migration area suitability data is defined as GI (Growth index) data.

[0086] In this embodiment, the value ranges of EI and GI are both 0-100, and the greater the value, the higher the suitability.

[0087] A12. Spatial interpolation is performed on each sub-region contained in the target area according to the annual reproduction area suitability data and the seasonal migration area suitability data of each preset station to obtain potential habitat distribution data composed of the annual reproduction area suitability data and the seasonal migration area suitability data of each sub-region contained in the target area.

[0088] For example, the annual reproduction area suitability data and the seasonal migration area suitability data (discrete data) of each preset station can be further processed in the ArcGIS software by the inverse distance weighted spatial interpolation method to be processed into the annual reproduction area suitability data and the seasonal migration area suitability data (planar continuous data) of each sub-region contained in the target area.

[0089] Based on the existing grading method, the EI data can be divided into four categories in this embodiment: EI = 0 indicates that the sub-region is not suitable for the long-term survival of insects; 0 < EI ≤ 10 indicates that it is generally suitable, and the conditions for meeting its long-term survival are limited; 10 < EI ≤ 30 indicates that it is moderately suitable, and the sub-region can accommodate a large number of individuals of the species; and EI > 30 indicates that it is very suitable, and the sub-region has very favorable conditions for the survival of insect populations.

[0090] For the sub-regions with EI=0, the GI is used to evaluate the suitability of seasonal migration, and these sub-regions do not have suitable climate conditions for overwintering and breeding of insects, but some periods of the year may be suitable for the survival of insects, thus forming a seasonal damage situation. For the sub-regions with EI=0 and GI>0, they can also be divided into multiple categories: EI=0, 0<GI≤10, indicating general suitability; EI=0, 10<GI≤30, indicating moderate suitability; and EI=0, GI>30, indicating very suitable. Finally, the potential distribution data of the suitable regions of the insects in the target region are obtained.

[0091] A2. From the potential distribution data of the suitable regions of the insects in the target region, the seasonal migration region of the insects is determined from the target region.

[0092] Optionally, this step can determine the sub-regions in the target region with the overwintering and breeding region suitability data greater than a first preset threshold as the seasonal migration region of the insects. Optionally, this step can also determine the sub-regions in the target region with the overwintering and breeding region suitability data less than or equal to the first preset threshold and the seasonal migration region suitability data greater than a second preset threshold as the seasonal migration region of the insects.

[0093] Preferably, this step can determine the sub-regions in the target region with the overwintering and breeding region suitability data greater than the first preset threshold, and the sub-regions in the target region with the overwintering and breeding region suitability data less than or equal to the first preset threshold and the seasonal migration region suitability data greater than the second preset threshold as the seasonal migration region of the insects.

[0094] The first preset threshold and the second preset threshold can be determined according to actual conditions, for example, the first preset threshold and the second preset threshold can both be 0.

[0095] A3. At least one migration starting point of the insects in the seasonal migration region is determined.

[0096] Optionally, this step can randomly select at least one point from the seasonal migration region as the at least one migration starting point; preferably, this step can divide the seasonal migration region into multiple target regions with the same size, and select one point from each target region as a migration starting point to obtain multiple migration starting points. For example, this step can divide the seasonal migration region into multiple 1°*1° target regions, and then select one point from each target region as the starting point of the migration prediction, for example, this step divides 67 migration starting points.

[0097] This embodiment can determine the suitable region distribution of the insects, and then determine the seasonal migration region from the suitable region distribution, and then determine the migration starting point from the seasonal migration region, thereby improving the reliability of the migration starting point.

[0098] The following embodiment illustrates the process of the aforementioned "Step S104, filtering at least one migration destination corresponding to each migration starting point according to global phenology data, to filter out migration destinations not in the host plant growth period corresponding to each migration starting point".

[0099] The embodiment includes the following steps:

[0100] B1. Extracting the host plant growth period at the at least one migration destination from the global phenology data.

[0101] In this step, the host plant growth period includes host plant growth period start data and host plant growth period end data. For ease of subsequent description, the host plant growth period start data is denoted as SOS data, and the host plant growth period end data is denoted as EOS data.

[0102] Optionally, the process of the step "extracting the host plant growth period at the at least one migration destination from the global phenology data" includes:

[0103] B11. Preprocessing the global phenology data to obtain global host plant growth period start data and global host plant growth period end data.

[0104] Optionally, this step can preprocess the global phenology data VNP22Q2 stored in h5 format using Python to extract global host plant growth period start SOS data and global host plant growth period end EOS data.

[0105] Optionally, this step can convert the extracted SOS and EOS data into tiff format to facilitate subsequent processing.

[0106] B12. Extracting host plant growth period start data at the at least one migration destination from the global host plant growth period start data, and extracting host plant growth period end data at the at least one migration destination from the global host plant growth period end data.

[0107] It should be noted that the host plant growth period start data and the host plant growth period end data at the at least one migration destination extracted in this step are both day of year (DOY), which represents the number of days accumulated from January 1 as the first day (DOY = 1), i.e., it can represent the day of the year for EOS or SOS data.

[0108] B2. Determining the date when the insect migrates to the at least one migration destination corresponding to each migration starting point as the migration end date corresponding to each migration starting point respectively.

[0109] Optionally, in the case that the SOS and EOS data determined in the foregoing step is the accumulated day, the process of the present step can include: calculating the accumulated day DOY corresponding to the date when the insect migrates to each migration destination corresponding to each migration starting point.

[0110] B3. For each migration destination corresponding to each migration starting point, if the migration ending date corresponding to the migration destination is not within the host plant growth period at the migration destination, the migration destination is excluded.

[0111] Specifically, if the migration ending date corresponding to the migration destination is not within the host plant growth period at the migration destination, i.e., the migration ending date corresponding to the migration destination is not between the SOS and EOS data, it indicates that the time when the insect lands at the migration destination is not within the host plant growth period, i.e., the insect has no food to survive when it lands at the migration destination, so the insect is unlikely to land at the migration destination. Therefore, the migration destination can be excluded. Conversely, if the migration ending date corresponding to the migration destination is within the host plant growth period at the migration destination, it indicates that the migration destination has host plants in the growth period to support its survival, so the insect can land at the migration destination. In this case, the predicted migration destination is considered to be a reasonable migration destination.

[0112] The present embodiment can exclude unreasonable migration destinations among the initially predicted migration destinations based on the phenological factor, thereby improving the reliability of the determined migration destinations.

[0113] In an optional embodiment, considering that the insect can continue to migrate after migrating to the migration destination predicted in step S104, for example, one generation of the fall armyworm can fly for three consecutive nights, the present embodiment can perform iterative simulation based on the number of flight periods of one generation of the insect.

[0114] Specifically, the screened migration destination can be used as a new migration starting point, and the atmospheric trajectory data within the next flight period of the insect can be obtained as new atmospheric trajectory data, and the process returns to step S103 until the migration of one generation of the insect is completed.

[0115] For example, for the fall armyworm, the migration landing point after the first night flight of a generation is predicted based on steps S101-S104 as the first migration landing point; the first migration landing point is taken as a new migration starting point, and the atmospheric trajectory data of the flight period corresponding to the second night is obtained, and then the migration landing point after the second night flight of the current generation is predicted based on steps S103 (prediction using the first migration landing point and the atmospheric trajectory data of the flight period corresponding to the second night) -S104 as the second migration landing point; the second migration landing point is taken as a new migration starting point, and the atmospheric trajectory data of the flight period corresponding to the third night is obtained, and then the migration landing point after the third night flight of the current generation is predicted based on steps S103 (prediction using the second migration landing point and the atmospheric trajectory data of the flight period corresponding to the third night) -S104 as the third migration landing point; After that, the generation may not continue to migrate, but take the third migration landing point as the last landing point before the end of life, and then end this prediction.

[0116] The embodiment can continuously predict the migration landing point (migration path) of a generation of insects, and the reliability and accuracy of the prediction result are higher.

[0117] The embodiment of the present application also provides an insect migration prediction device, which will be described below. The insect migration prediction device described below can be correspondingly referred to the insect migration prediction method described above.

[0118] Please refer to Figure 2 , which shows the structure diagram of the insect migration prediction device provided by the embodiment of the present application. As shown in Figure 2 , the insect migration prediction device can include a data acquisition module 201, a migration starting point determination module 202, a migration landing point prediction module 203 and a migration landing point screening module 204.

[0119] The data acquisition module 201 is configured to acquire historical occurrence position data of the insects, migration behavior data, historical meteorological data of each sub-region contained in a target region, atmospheric trajectory data of the insects within an initial flight period, and global phenological data, wherein the target region refers to a region where the migration starting point of the insects is located, and the initial flight period refers to a period of initial migration of a generation of the insects.

[0120] The migration starting point determination module 202 is configured to determine at least one migration starting point of the insects in the target region according to the historical occurrence position data and the historical meteorological data of each sub-region.

[0121] The migration landing point prediction module 203 is configured to predict a next migration landing point of the insects when the insects migrate from each migration starting point according to the migration behavior data and the atmospheric trajectory data, and obtain at least one predicted migration landing point corresponding to each migration starting point.

[0122] The migration landing point screening module 204 is configured to screen the at least one migration landing point corresponding to each migration starting point according to the global phenology data, to exclude the migration landing points not in the host plant growth period from the at least one migration landing point corresponding to each migration starting point, and obtain the screened migration landing point corresponding to each migration starting point.

[0123] The insect migration prediction device provided in the present application firstly acquires the historical occurrence position data of the insects, the migration behavior data, the meteorological data of each sub-region included in the target region, the atmospheric trajectory data of each flight period of the insects and the global phenology data, then determines at least one migration starting point of the insects in the target region according to the historical occurrence position data and the meteorological data of each sub-region, then predicts a next migration landing point of the insects when the insects migrate from each migration starting point according to the migration behavior data and the atmospheric trajectory data, and obtains at least one predicted migration landing point corresponding to each migration starting point, and finally screens the at least one migration landing point corresponding to each migration starting point according to the global phenology data, to exclude the migration landing points not in the host plant growth period from the at least one migration landing point corresponding to each migration starting point, and obtain the screened migration landing point corresponding to each migration starting point. The present application can predict the migration landing point of the insects in combination with the meteorological data and the global phenology data, and improve the reliability and accuracy of the predicted migration landing point.

[0124] In a possible implementation, the migration starting point determination module 202 can include a suitable area distribution determination sub-module, a seasonal migration area determination sub-module and a migration starting point determination sub-module.

[0125] The suitable area distribution determination sub-module is configured to determine potential suitable area distribution data of the insects in the target region according to the historical occurrence position data and the meteorological data of each sub-region.

[0126] The seasonal migration area determination sub-module is configured to determine a seasonal migration area corresponding to the insects from the target region according to the potential suitable area distribution data of the insects in the target region.

[0127] The migration starting point determination sub-module is configured to determine at least one migration starting point of the insects in the seasonal migration area.

[0128] In a possible implementation, the suitable area distribution determining submodule can be specifically configured to determine annual breeding area suitability data and seasonal migration area suitability data of each preset site contained in the target area according to the historical occurrence location data and the meteorological data of each sub-area, and perform spatial interpolation in each sub-area contained in the target area according to the annual breeding area suitability data and the seasonal migration area suitability data of each preset site, to obtain potential suitable area distribution data composed of the annual breeding area suitability data and the seasonal migration area suitability data of each sub-area contained in the target area.

[0129] In a possible implementation, the seasonal migration area determining submodule can be specifically configured to determine, as the seasonal migration area of the insect, a sub-area in the target area in which the annual breeding area suitability data is greater than a first preset threshold, and / or a sub-area in the target area in which the annual breeding area suitability data is less than or equal to the first preset threshold and the seasonal migration area suitability data is greater than a second preset threshold.

[0130] In a possible implementation, the migration starting point determining submodule can be specifically configured to divide the seasonal migration area into a plurality of target areas of the same size, and select one point in each target area as a migration starting point.

[0131] In a possible implementation, the migration ending point screening module 204 can include a growth period extracting submodule, a migration ending date determining submodule, and a migration ending point excluding submodule.

[0132] The growth period extracting submodule is configured to extract, from the global phenology data, a growth period of a host plant at at least one migration ending point.

[0133] The migration ending date determining submodule is configured to determine a date when the insect migrates to the at least one migration ending point corresponding to each migration starting point, as a migration ending date corresponding to each migration ending point.

[0134] The migration ending point excluding submodule is configured to, for each migration ending point of the at least one migration ending point corresponding to each migration starting point, exclude the migration ending point if a migration ending date corresponding to the migration ending point is not within a growth period of a host plant at the migration ending point.

[0135] In a possible implementation, the host plant growth period includes host plant growth period start data and host plant growth period end data, and the growth period extraction submodule can be specifically configured to preprocess the global phenology data to obtain global host plant growth period start data and global host plant growth period end data, extract host plant growth period start data at at least one migration landing point from the global host plant growth period start data, and extract host plant growth period end data at the at least one migration landing point from the global host plant growth period end data.

[0136] In a possible implementation, the migration end date determination submodule can be specifically configured to calculate an accumulated day corresponding to a date when the insect migrates to at least one migration landing point corresponding to each migration starting point.

[0137] In a possible implementation, the insect migration prediction device provided by the embodiment of the application can further include a cycle iteration module.

[0138] The cycle iteration module is configured to take the screened migration landing point as a new migration starting point, obtain atmospheric trajectory data in a next flight period of the insect as new atmospheric trajectory data, and return to the migration landing point prediction module 203 until migration of one generation of the insect ends.

[0139] The embodiment of the application further provides an insect migration prediction device. Optionally, Figure 3 A hardware structure block diagram of the insect migration prediction device is shown, and the hardware structure of the insect migration prediction device can include at least one processor 301, at least one communication interface 302, at least one memory 303, and at least one communication bus 304. Figure 3

[0140] In the embodiment of the application, the number of the processor 301, the communication interface 302, the memory 303, and the communication bus 304 is at least one, and the processor 301, the communication interface 302, and the memory 303 complete communication with each other through the communication bus 304.

[0141] The processor 301 can be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiment of the application, etc.

[0142] The memory 303 can include a high-speed RAM memory, and can also include a non-volatile memory, for example, at least one disk memory.

[0143] ​The memory 303 stores a program, and the processor 301 can invoke the program stored in the memory 303, and the program is used for:

[0144] obtaining historical occurrence position data of the insects, migration behavior data, historical meteorological data of each sub-region included in a target region, atmospheric trajectory data of the insects in an initial flight period, and global phenology data, wherein the target region refers to a region where the migration starting point of the insects is located, and the initial flight period refers to a period when the insects initially migrate in one generation;

[0145] determining at least one migration starting point of the insects in the target region according to the historical occurrence position data and the historical meteorological data of each sub-region;

[0146] predicting a next migration landing point of the insects when migrating from each migration starting point according to the migration behavior data and the atmospheric trajectory data, to obtain at least one predicted migration landing point corresponding to each migration starting point;

[0147] screening the at least one migration landing point corresponding to each migration starting point according to the global phenology data, to exclude the migration landing points not in the growth period of the host plants from the at least one migration landing point corresponding to each migration starting point, to obtain screened migration landing points corresponding to each migration starting point.

[0148] Optionally, the detailed functions and extended functions of the program can refer to the description above.

[0149] The embodiments of the present application also provide a readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the insect migration prediction method.

[0150] Optionally, the detailed functions and extended functions of the program can refer to the description above.

[0151] Finally, it should be noted that, in this document, the relationship terms such as and the second and the like 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 the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the processes, methods, articles or equipment including a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such processes, methods, articles or equipment. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or equipment including the element.

[0152] The various embodiments described in this specification are intended to be illustrative only and in no way limit the scope of the application. Changes and modifications can be made to these embodiments without departing from the spirit or scope of the application. Accordingly, the specification is to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of the present application.

[0153] The above description of disclosed embodiments is intended to be illustrative only and not limiting of the scope of the application. Numerous modifications to these embodiments can be apparent to those skilled in the art without departing from the spirit or scope of the application. Accordingly, the specification is to be regarded as illustrative rather than restrictive, and all such modifications are intended to be included within the scope of the present application.

Claims

1. A method of insect migration forecasting, characterized by, The method comprises the following steps: acquiring historical occurrence location data of insects, migration behavior data, historical meteorological data of each sub-region included in a target region, atmospheric trajectory data within an initial flight period of the insects, and global phenology data, wherein the target region refers to a region where the insects migrate from, and the initial flight period refers to a period when the insects migrate for the first time in a generation; determining at least one migration starting point of the insects in the target region according to the historical occurrence location data and the historical meteorological data of each sub-region; predicting a next migration landing point of the insects when migrating from each migration starting point according to the migration behavior data and the atmospheric trajectory data, to obtain at least one predicted migration landing point corresponding to each migration starting point; screening the at least one migration landing point corresponding to each migration starting point according to the global phenology data, to remove a migration landing point not in a host plant growth period from the at least one migration landing point corresponding to each migration starting point, to obtain a screened migration landing point corresponding to each migration starting point; the screening the at least one migration landing point corresponding to each migration starting point according to the global phenology data, to remove a migration landing point not in a host plant growth period from the at least one migration landing point corresponding to each migration starting point, comprises: extracting a host plant growth period at the at least one migration landing point from the global phenology data; determining a date when the insects migrate to the at least one migration landing point corresponding to each migration starting point as a migration ending date corresponding to the at least one migration landing point corresponding to each migration starting point respectively; for each migration landing point in the at least one migration landing point corresponding to each migration starting point, if the migration ending date corresponding to the migration landing point is not in the host plant growth period at the migration landing point, the migration landing point is removed; the host plant growth period comprises host plant growth period start data and host plant growth period end data; the extracting the host plant growth period at the at least one migration landing point from the global phenology data comprises: preprocessing the global phenology data to obtain global host plant growth period start data and global host plant growth period end data; extracting the host plant growth period start data at the at least one migration landing point from the global host plant growth period start data, and extracting the host plant growth period end data at the at least one migration landing point from the global host plant growth period end data.

2. The insect flight prediction method according to claim 1, characterized by, the determining the at least one migration starting point of the insects in the target region according to the historical occurrence location data and the meteorological data of each sub-region comprises: determining potential suitable area distribution data of the insects in the target region according to the historical occurrence location data and the meteorological data of each sub-region; determining a seasonal migration area corresponding to the insects from the target region according to the potential suitable area distribution data of the insects in the target region; determining at least one migration starting point of the insects in the seasonal migration area.

3. The insect flight prediction method according to claim 2, characterized in that, The method further comprises: determining the annual breeding area suitability data and the seasonal migration area suitability data of each preset site included in the target area according to the historical occurrence location data and the meteorological data of each sub-region; spatially interpolating the annual breeding area suitability data and the seasonal migration area suitability data of each preset site in each sub-region included in the target area to obtain the potential habitat distribution data composed of the annual breeding area suitability data and the seasonal migration area suitability data of each sub-region included in the target area.

4. The insect flight prediction method according to claim 3, characterized in that, The method further comprises: determining the seasonal migration area of the insect corresponding to the insect in the target area according to the potential habitat distribution data of the insect in the target area, comprising: determining the sub-region in the target area where the annual breeding area suitability data is greater than a first preset threshold as the seasonal migration area of the insect corresponding to the insect; and / or, 5. The insect flight prediction method according to claim 2, characterized by, determining the sub-region in the target area where the annual breeding area suitability data is less than or equal to the first preset threshold and the seasonal migration area suitability data is greater than a second preset threshold as the seasonal migration area of the insect corresponding to the insect. The method further comprises: dividing the seasonal migration area into a plurality of target areas of the same size; 6. The insect flight prediction method of claim 1, wherein, selecting a point in each of the target areas as a migration starting point. The method further comprises:

7. The insect flight prediction method of claim 1, wherein, calculating the accumulated day corresponding to the date when the insect migrates to at least one migration landing point corresponding to each migration starting point. The method further comprises:

8. A device for performing the method of any one of claims 1 to 7, characterized in that taking the screened migration landing point as a new migration starting point, obtaining the atmospheric trajectory data in the next flight period of the insect as new atmospheric trajectory data, and returning to perform the step of predicting the next migration landing point of the insect starting from each migration starting point according to the migration behavior data and the atmospheric trajectory data until the migration of one generation of the insect is completed. The method further comprises: a data acquisition module configured to acquire historical occurrence location data, migration behavior data, historical meteorological data of each sub-region included in a target area, atmospheric trajectory data in an initial flight period of the insect, and global phenological data, wherein the target area refers to an area where a migration starting point of the insect is located, and the initial flight period refers to a period of initial migration of one generation of the insect; a migration starting point determination module configured to determine at least one migration starting point of the insect in the target area according to the historical occurrence location data and the historical meteorological data of each sub-region; and a migration landing point prediction module configured to predict a migration landing point of the insect starting from each migration starting point according to migration behavior data and atmospheric trajectory data. a migration landing point prediction module configured to predict a next migration landing point of the insects when migrating from each of the migration starting points according to the migration behavior data and the atmospheric trajectory data, and obtain at least one predicted migration landing point corresponding to each of the migration starting points; a migration landing point screening module configured to screen the at least one migration landing point corresponding to each of the migration starting points according to the global phenology data, and exclude the migration landing points not in the host plant growth period from the at least one migration landing point corresponding to each of the migration starting points, and obtain screened migration landing points corresponding to each of the migration starting points.

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