Migration insect migration prediction method and system and readable storage medium

Abstract

The invention provides a migratory insect migration prediction method and system and a readable storage medium, and relates to the technical field of biological prevention and control prediction simulation. Volatile organic compounds released by a host area and propagation characteristics of the volatile organic compounds are collected, and concentration values, propagation rates and gradient change rates of chemical signals are combined, so that the migration of migratory insects is predicted. According to the method, firstly, a quantitative model of host plant attraction is constructed, secondly, the sensitivity of migratory insects to chemical signals is represented by using an olfactory nerve response model, and the model improves the scientificity and accuracy of chemical signal attraction analysis from the neurophysiology level; and finally, in combination with the migration attraction index and the olfactory nerve response model, dynamic prediction of migration drop points of host plants of various grades is further realized, the prediction precision for small-region targets is improved, and the limitation of the prior art on time and spatial resolution is made up.

Description

Technical Field

[0001] The present invention relates to the field of biological control prediction simulation technology, and in particular to a migratory insect migration prediction method, system and readable storage medium. Background Art

[0002] At present, the research on migration prediction of migratory insects mainly focuses on the analysis of meteorological data and the observation of insect movement trajectories. By using meteorological factors such as wind speed, wind direction, temperature and rainfall, many studies have preliminarily constructed large-scale distribution models of migratory insects to assess the possible migration direction and range of insects. However, these methods mainly rely on macro-environmental conditions and ignore the dynamic effects of migratory insects on host plant distribution, chemical signal gradients and olfactory nerve responses during the landing site selection process. In particular, when insects choose specific landing sites, their behavior is strongly affected by volatile organic compounds (VOCs) released by host plants and their transmission conditions. This microscopic attraction mechanism has not yet been fully quantified and incorporated into migration prediction models.

[0003] In the prior art, the publication number is CN115544808A, and the name is a method and device for predicting insect migration, which relates to the field of biological control. The method includes: obtaining historical occurrence location data of insects, migration behavior data, meteorological data of each sub-area contained in the target area, atmospheric trajectory data of insects in each flight period, and global phenological data; determining at least one migration starting point of insects in the target area according to the historical occurrence location data and the meteorological data of each sub-area; predicting the next migration landing point of insects when they start to migrate from each migration starting point according to the migration behavior data and the atmospheric trajectory data; screening at least one migration landing point corresponding to each migration starting point according to the global phenological data, and obtaining the filtered migration landing point corresponding to each migration starting point. The migration landing point of insects can be predicted in combination with meteorological data and global phenological data, which improves the reliability and accuracy of the predicted migration landing point.

[0004] In the existing technology, although some studies have begun to explore the migratory behavior analysis model based on chemical signals, there are still significant technical bottlenecks: first, the relationship between the multidimensional parameters of chemical signal characteristics (such as concentration, propagation rate, gradient change rate) and insect behavior has not yet established an accurate mathematical model; second, the existing methods lack the refined modeling of the response mechanism of the olfactory nervous system of migratory insects, so that the characterization and quantitative analysis of the attractiveness of chemical signal fields to insects have not been fully carried out; third, the prediction of the behavior of migratory insects in selecting highly attractive host plants mostly remains in the qualitative stage, lacking the dynamic prediction ability of both time and space dimensions; The above information disclosed in the above Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a migratory insect migration prediction method, system and readable storage medium, device, equipment and storage medium to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: The migratory insect migration prediction method comprises the following specific steps: Step S1: in the monitoring area, multiple host plants of the same type associated with migratory insects are determined, and the areas where these host plants are located are defined as host areas to form multiple host areas, and the areas outside each host area are divided into four unit sub-areas; Step S2: collecting the current chemical signal field data of each unit sub-area in the monitoring area, the chemical signal field data including: volatile gas concentration value, gas propagation rate and gas gradient spatial change rate; Step S3: classifying the host plants according to the volatile gas concentration values ​​of the multiple host plants in the host area, wherein the higher the volatile gas concentration value, the higher the level of the host plant; Step S4: obtaining chemical signal field data corresponding to each host region; then, analyzing these chemical signal field data, calculating and generating a migration attraction index for each host region; Step S5: constructing an olfactory neural response model of migratory insects. The olfactory neural response model generates an output result of neural response by analyzing different chemical signal field data in four unit sub-areas corresponding to the host area and combining the neural response delay time of migratory insects. The output result is used to characterize the attraction of different chemical signal field data to migratory insects. Step S6: using the output result of the olfactory nerve response model, the migration attraction index is corrected, and the migration landing point of the migratory insects is predicted by the corrected migration attraction index.

[0007] A migratory insect migration prediction system, the system is used to execute the migratory insect migration prediction method, comprising: Area division module: used to determine multiple host plants of the same type associated with migratory insects within the monitoring area, and define the areas where these host plants are located as host areas, forming multiple host areas, and dividing the areas outside each host area into four unit sub-areas; Data acquisition module: used to collect the current chemical signal field data of each unit sub-area in the monitoring area, and the chemical signal field data includes: volatile gas concentration value, gas propagation rate and gas gradient spatial change rate; Grade classification module: used to classify host plants according to the volatile gas concentration values ​​of multiple host plants in the host area. The higher the volatile gas concentration value, the higher the grade of the host plant; Index generation module: used to obtain the chemical signal field data corresponding to each host area; then, these chemical signal field data are analyzed to calculate and generate the migration attraction index of each host area; Model building module: used to build the olfactory neural response model of migratory insects. The olfactory neural response model analyzes the different chemical signal field data in the four unit sub-areas corresponding to the host area, and combines the neural response delay time of migratory insects to generate the output results of neural response. The output results are used to characterize the attractiveness of different chemical signal field data to migratory insects. Landing point prediction module: used to modify the migration attraction index using the output results of the olfactory nerve response model, and predict the landing point of migratory insects through the modified migration attraction index.

[0008] A readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the migratory insect migration prediction method are implemented.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: by collecting volatile organic compounds released in the host area and their propagation characteristics, combining the concentration value, propagation rate and gradient change rate of chemical signals, a quantitative model of host plant attraction is constructed; secondly, the olfactory nerve response model is used to characterize the sensitivity of migratory insects to chemical signals, and this model improves the scientificity and accuracy of chemical signal attraction analysis from a neurophysiological level; finally, combined with the migration attraction index and the olfactory nerve response model, the dynamic prediction of the migration landing points of host plants of various levels is further realized, which can improve the prediction accuracy for small area targets, make up for the limitations of the prior art in time and space resolution, and provide a technical breakthrough for the ecological prevention and control of migratory insects and agricultural pest management. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a block diagram of the system modules of the present invention. DETAILED DESCRIPTION

[0011] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0012] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0013] Embodiment 1: See also Figure 1 , the present invention provides a technical solution: The migratory insect migration prediction method comprises the following specific steps: Step S1: in the monitoring area, multiple host plants of the same type associated with migratory insects are determined, and the areas where these host plants are located are defined as host areas to form multiple host areas, and the areas outside each host area are divided into four unit sub-areas; Step S2: collecting the current chemical signal field data of each unit sub-area in the monitoring area through the volatile organic compounds released from the host area, and the chemical signal field data includes: volatile gas concentration value, gas propagation rate and gas gradient spatial change rate; Step S3: classifying the host plants according to the volatile gas concentration values ​​of the multiple host plants in the host area, wherein the higher the volatile gas concentration value, the higher the level of the host plant; Step S4: obtaining chemical signal field data corresponding to each host region; then, analyzing these chemical signal field data, calculating and generating a migration attraction index for each host region; Step S5: constructing an olfactory neural response model of migratory insects. The olfactory neural response model generates an output result of neural response by analyzing different chemical signal field data in four unit sub-areas corresponding to the host area and combining the neural response delay time of migratory insects. The output result is used to characterize the attraction of different chemical signal field data to migratory insects. Step S6: using the output result of the olfactory nerve response model, the migration attraction index is corrected, and the migration landing point of the migratory insects is predicted by the corrected migration attraction index.

[0014] Further explanation: The types of host plants and the delineation of host areas are as follows: The steps for screening host plant types are: 1.1) Research on the habits of migratory insects; 1.1.1: Collect ecological data of target migratory insects, including information on their life cycle, activity habits, and feeding preferences.

[0015] 1.1.2: Determine the main host plant species of insects through literature review and field surveys (e.g., refer to the insects' preference for specific tree species such as pine, oak, birch, etc.).

[0016] 1.2) Plant ecology survey in forest areas; 1.2.1: Use high-resolution drones equipped with multispectral sensors to obtain vegetation distribution images of monitored forest areas.

[0017] 1.2.2: Combined with ground field surveys, record the distribution density and health status of different tree species to form a comprehensive plant distribution database.

[0018] 1.3) Plant relevance verification; 1.3.1: Use electrocardiography (EAG) equipment to test the olfactory response of insects to volatiles from different selected plant leaves and screen out plant species with significant attractiveness.

[0019] Electroantennogram Recording (EAG) is a technique commonly used in insect olfactory research to record the electrophysiological response of insect antennae to volatile gas compounds. Through this experiment, the sensitivity of insect antennae to gas stimuli and the functional characteristics of their olfactory receptors can be studied.

[0020] How EAG works: When the gas stimulus contacts the olfactory receptors on the insect's antennae, it triggers electrical activity in the olfactory neurons.

[0021] These activities are recorded through electrodes and appear as electrical signal waveforms.

[0022] Different concentrations or chemical compositions of gaseous stimuli will cause electrical responses of different amplitudes. Researchers can determine the sensitivity of insects to different gas molecules by analyzing these responses.

[0023] 1.3.2: Conduct behavioral experiments (such as wind chamber experiments) to observe the insects’ selection behavior among different plants and further verify the host effect of plants.

[0024] 1.3.3: Ultimately identify a group of host plants from the same family that are highly relevant to migratory insects (e.g. pine and larch in the Pinaceae family).

[0025] Determine that the host plant is selected from the set {P1, P2}, where P1 and P2 are plants of the same family; in this embodiment: P1 represents pine; P2 represents larch; select the largest number of {P1, P2} as the host plant; in this embodiment, the selected host plant type is recorded as P1; The steps for dividing the host area and unit sub-area are as follows: 2.1) Definition and delineation of host areas; The host area refers to a specific geographical area within the monitoring area, which is delineated based on the distribution density of host plants and the host-attraction relationship of target migratory insects; this area is generated by spatial clustering or analysis methods of the distribution points of host plants, and its boundaries reflect the natural distribution concentration of host plants, which is closely related to the prediction of the tracks of target insects.

[0026] Determination of the distribution density of host plants: The number of host plants per unit area is used as a measure of the distribution density; in this embodiment, the unit area is 1 hectare; Set the distribution density threshold per unit area as "Dh plants / hectare"; use clustering methods (such as K-means clustering algorithm or cluster analysis based on geographic average distance) to identify areas exceeding "Dh plants / hectare" as high-density areas; Dh=80 plants / hectare, and areas exceeding 80 plants / hectare are defined as high-density, and then clarify the boundaries of high-density areas; The host attraction relationship for target migratory insects is defined as: Attraction effect of volatile chemical components: Collect VOC samples from host plants in the monitoring area and determine the types and concentrations of VOCs using a chemical analyzer (such as GC-MS equipment).

[0027] The insect electrophysiological experiment (such as EAG) is used to record the response signals of the target migratory insects to different volatiles, and the quantitative standard of the attraction intensity is defined; in this embodiment: The EAG signal intensity for pine (P1) VOCs was 1.2 mV.

[0028] The EAG signal intensity for larch (P2) VOCs was 1.0 mV.

[0029] Behavioral experiments verify the attraction strength: Set up trapping devices (such as pheromone traps or gas attractant devices) in the monitoring area and observe the number of target migratory insects captured per unit time; On the basis of the high-density area, the minimum density standard related to the attraction of target migratory insects is set as "Dk / 24 hours"; in this embodiment: Dk=40 / 24 hours, and the area exceeding "Dk=40 / 24 hours" is used as a preliminary mark of a high-attractive area; In the forest monitoring area, the geographic coordinates of each host plant are recorded using high-precision GPS equipment.

[0030] Using GIS (Geographic Information System) software, we can perform spatial cluster analysis on all the host plant locations and determine the main host plant concentration areas, which are defined as "host areas". For example: Host zone A: Located in the northern part of the forest, it contains areas with dense distribution of pine trees (P1).

[0031] Located in the northern part of the forest monitoring area (the specific coordinates are determined based on the actual regional survey), the area is 10 square kilometers. The density of pine trees (P1) distributed in this area is: Pine trees (P1): 4,000 per square kilometer, a total of 40,000 trees.

[0032] Through UAV remote sensing technology combined with ground sampling survey statistics, it was determined that pine trees (P1) accounted for 40% of the total vegetation area in the area.

[0033] The areas that exceed both "Dh plants / hectare" and "Dk individuals / 24 hours" are taken as preliminary screening areas, and each preliminary screening area is gradually expanded outward until any of the conditions of "Dh plants / hectare" and "Dk individuals / 24 hours" are not met. Then the expansion is stopped, and non-overlapping areas are selected from these expanded preliminary screening areas in a discrete manner as host areas; and the target migratory insects in "Dk individuals / 24 hours" need to stay on the host plants in the high-density area for 20 minutes before being recorded; In this embodiment, the host regions are initially set to four; Establish uniform area standards for each host area; Description of non-overlapping host regions: Set Selection Path: Set a specific selection path or strip area and make sure there is no overlap between these paths; The host region selection criteria are as follows: Within each path in a given direction, select the area closest to the center that meets the criteria.

[0034] Avoid overlapping selection paths to ensure that the selection results in each direction are independent and do not overlap; To determine a unified area standard, select the area box: Establish area standards: Determine a uniform or corresponding area standard for the host area selected in each direction. For example, select the area by setting a radius, specifying the side length of a square, or defining a polygonal boundary; The framed areas were flexibly adjusted according to specific ecological research needs and geographical features to ensure that each area was moderate in size and had sufficient representativeness of environmental characteristics; Use GIS tools to delineate the boundaries of the identified host areas and assign unique numbers A, B, C, and D to the four host areas; It should be noted that the host regions corresponding to A, B, C, and D have no overlapping areas in the sub-regions generated below; Record the specific geographic coordinates and size of each host area; for example: A: The center point is (N34.51234°, E108.12345°); the total area is 4.5 hectares.

[0035] 2.2) Division of unit sub-areas outside the host area: Taking the geometric center of each host region as the reference, the host region is expanded outward to be divided into four nested or continuous unit sub-regions according to the boundary shape characteristics of the host region. The boundary shape of each unit sub-region is appropriately consistent with the overall boundary of the host region, or is generated by equidistant adjustment based on the geometric center, and there is no overlapping part between the unit sub-regions of adjacent host regions; The index of the host region is recorded as i, ; In this embodiment, the numbers A, B, C, and D of the four host regions are uniformly represented as i∈{A,B,C,D}, where i represents any host region in {A,B,C,D}; the number of the four unit sub-regions is at least four; The four unit sub-regions of host region i are recorded as i1, i2, i3, and i4 respectively; the distances between the unit sub-regions corresponding to i1, i2, i3, and i4 and the boundary of host region i gradually increase; In this embodiment, the unit sub-regions corresponding to i1, i2, and i3 are expressed as follows: Unit sub-area i1: a circular area extending outward from the boundary of host area i with a square kilometer; Unit sub-area i2: a circular area extending from the boundary of host area i to an area of ​​1 square kilometer to 2 square kilometers; Unit sub-area i3: a circular area extending from the boundary of host area i to an area of ​​2 to 3 square kilometers; Unit sub-area i4: a circular area extending from the boundary of host area i to an area of ​​3 to 4 square kilometers; Uniquely number each host plant in each host area to ensure data accuracy and traceability; The index of the j1th host plant in host region i is recorded as ;and , is a set of positive integers.

[0036] Further explanation: Sensor selection and layout: Sensor type: Choose a gas sensor array, such as a MOS (metal oxide semiconductor) sensor array or other specific gas sensors (such as a MEMS gas sensor array), which can accurately measure volatile gas concentration values ​​with high spatial resolution.

[0037] Sensor accuracy requirements: The sensor should be able to monitor the concentration changes of volatile organic compounds (such as volatile substances in pine trees) in the air, with a sensitivity of ppb (parts per billion) and a response time of less than 5 seconds.

[0038] Sensor layout: The sensor nodes are arranged in a matrix form in each unit sub-area with a spacing of 2 to 3 meters to ensure high-density sampling data. A small wind speed meter is configured above each sensor node to dynamically adjust the gas propagation model.

[0039] For gas concentration measurements: The concentration of volatile gases in each unit subarea is measured regularly using gas sensors; the sensors are able to distinguish between gases of different chemical compositions, such as the volatile organic compounds of pine and larch.

[0040] Data collection frequency: The frequency of data collection of volatile gas concentration is set to once per minute to ensure that the instantaneous changes of gas are captured. If the volatile gas concentration value changes suddenly, the system will automatically increase the sampling frequency to once per second.

[0041] The volatile gas concentration value refers to the mass or volume concentration of the target volatile organic compound (VOC) contained in a unit volume of air; it is directly measured by a sensor; the calculation formula for the volatile gas concentration value is defined as: ; Wherein, CHF is the concentration value of volatile gases (concentration, in ppm or ppb); the volatile gases in this embodiment refer to VOCs generated by pine and larch; the specific selection of VOCs is determined by the type of migratory insects; When pine and larch are subjected to biological stress such as bark beetles and pine alternatants, resin secretion increases significantly, accompanied by the release of highly volatile monoterpenes (such as α-pinene and β-pinene); S is the voltage signal value of the real-time gas response signal of the sensor; is the background signal value of the sensor, that is, the output value in the absence of target gas, used as a calibration standard.

[0042] It is the sensitivity constant of the sensor, which indicates the response degree of the sensor to the concentration of the target gas (given by the calibration of the sensor equipment, and the unit depends on the specific sensor parameters).

[0043] The volatile gas concentration values ​​of each unit sub-area corresponding to the host area i are recorded as , , and ; For the measurement of gas propagation velocity: Each sensor is equipped with a wind speed sensor (such as a hot wire anemometer) to accurately measure the speed of gas propagation. By combining wind speed with gas concentration, the gas propagation rate (unit: m / s) is estimated by calculating the concentration change of gas at different time points. The gas propagation rate will be calculated along the three axes of X, Y, and Z to form three-dimensional gas propagation data.

[0044] Specific measurement process: For each unit sub-area, the data collected by the sensor in different directions will be combined with the wind speed value output by the wind speed sensor to comprehensively calculate the diffusion rate of the gas in each direction.

[0045] The gas propagation velocity refers to the speed at which volatile organic compounds diffuse along different axes (X, Y, Z) in the environment, and the unit is m / s. The gas propagation velocity is affected by factors such as wind speed, molecular diffusion characteristics, and environmental resistance. The gas propagation velocity is estimated by the following formula: ; in, is the component of the gas propagation velocity on the e-axis (unit: m / s); e∈{X, Y, Z}; It is the convection velocity caused by wind flow during the propagation of volatile gases on the e-axis; it is obtained through a wind speed sensor, unit: m / s.

[0046] It is the diffusion coefficient of volatile gas on the e-axis (unit: m^2 / s, determined by the molecular diffusion characteristics of the target VOC).

[0047] is the distance between adjacent sensors in the direction of volatile gas propagation (unit: m).

[0048] For the absolute rate in space, the modulus of the three-dimensional vector is synthesized by the following formula: ; The gas propagation rates of each unit sub-area corresponding to the host area i are respectively recorded as , , and ; For the calculation of the spatial rate of change of gas gradient: The spatial change rate of gas gradient refers to the change amplitude of gas concentration within a unit distance. By densely deploying the sensor array, the difference in gas concentration between adjacent sensors is calculated, and thus the change rate of gas gradient is inferred. The spatial rate of change of the gas gradient is calculated by the following formula: ; in, and are the volatile gas concentration values ​​measured by adjacent sensors, is the distance between adjacent sensors.

[0049] The spatial change rate of the gas gradient of each unit sub-region corresponding to the host region i is recorded as , , and .

[0050] The collected volatile gas concentration values, odor propagation rate and gas gradient spatial change rate data are transmitted in real time to the central database for storage through the data acquisition system; the database can process a large amount of time series data and provide a real-time access interface.

[0051] The data format is unified into CSV or JSON format to ensure compatibility with GIS system and subsequent data analysis software. Data analysis software includes but is not limited to: MATLAB or Python data analysis library.

[0052] The collected volatile gas concentration values, odor transmission rate and gas gradient spatial change rate data are combined with GIS tools to generate two-dimensional / three-dimensional maps of gas transmission to analyze the gas transmission trend and change law.

[0053] Further explanation: Host plants are classified into levels: Select high-sensitivity VOC analysis equipment for gas collection and concentration monitoring. Specifically use the following equipment: Gas chromatography-mass spectrometry (GC-MS): suitable for accurate analysis of volatile organic compound composition and concentration.

[0054] Equipped with portable PID (photoionization detector) VOC sensor: convenient for real-time monitoring.

[0055] Layout method: Sampling probes were arranged in each unit sub-area (the sampling distance was 0.5 to 1 meter around the host plant).

[0056] At least one VOC sensor is set up for mobile sampling in the unit sub-area to supplement the data from fixed sampling points.

[0057] After the equipment is deployed, samples of volatile gases released by host plants are collected and their concentration values ​​are calculated.

[0058] Based on the calculation formula of volatile gas concentration, the volatile gas concentration value measured for the j1th host plant corresponding to host area i is recorded as ; The collection methods include but are not limited to the following methods: Method 1: Increase spatial isolation to reduce the propagation interference of volatile gases; Methods: Host plants with large spatial intervals were selected for measurement, ensuring that the physical distance between collection points was large enough to reduce the influence of volatiles from other plants.

[0059] If host plants are highly aggregated, measure them by temporary artificial isolation (e.g., using gas barriers) or by selecting plants with distinct individual distributions.

[0060] Application suggestions: Determine a minimum sampling interval threshold (such as 5 meters or above), calculate the diffusion range of volatiles based on the on-site plant distribution density and wind speed and direction, and ensure that adjacent plants have low interference with the volatile concentration measured in the target plant.

[0061] Method 2: Use wind direction analysis, measure along the wind direction and eliminate upwind interference: Reason: The spread of volatile gases is significantly affected by wind direction and wind speed. In the same area, volatiles from adjacent plants may form concentrated areas with the wind, thus affecting the measurement results.

[0062] Method: Monitor wind direction and speed: With the help of a small weather station, anemometer or wind vane, record wind direction and speed in real time during measurement.

[0063] Select the measurement location: When sampling host plants, set up the sensor from its upwind direction to avoid interfering volatiles entering the measurement area.

[0064] Correction effect: The contribution of the concentration of interfering volatiles to the measurement results is estimated using a diffusion model combined with wind speed and the distance between target plants, and then corrected.

[0065] Application suggestions: Deploy a sensor array and dynamically adjust the location of the sampling points according to wind direction, or correct the concentration in combination with wind direction modeling analysis.

[0066] Method 3: Use gas shielding and collection hood to avoid interference from neighboring plants: Reason: Volatile gases are transmitted by diffusion and can gradually mix into the air around the target plants. Therefore, physical isolation technology is used to reduce the impact of gases on non-target plants.

[0067] Method: Use a sealed collection hood (such as a dynamic gas collection system) around the target plant to collect volatiles directly from the surface of the host plant to avoid interference from the surrounding air.

[0068] Clean air (air with VOC removed) is introduced into the collection hood to ensure that the collected volatiles are completely derived from the target plants rather than the surrounding vegetation.

[0069] Keep the system sealed during the entire collection process, and clean and zero-calibrate the measuring equipment to ensure the accuracy of the concentration data.

[0070] Equipment recommendation: dynamic collection device (such as bag sampler, vacuum sampler).

[0071] The collection chamber is made of glass or Teflon to avoid material adsorption or secondary contamination of volatiles.

[0072] Host plants are divided into different levels to distinguish their ability to attract migratory insects: Set the host plant level classification corresponding to The division thresholds are Nd1 and Nd2, Nd1 and Nd2 are positive integers, and Nd1<Nd2; Will Host plants with values ​​exceeding Nd2 are classified as primary host plants L1, which have a strong attraction to migratory insects; Will Host plants with values ​​in the interval [Nd1, Nd2] are classified as secondary host plants L2, and the attraction of secondary host plants L2 to migratory insects is medium attraction; Will Host plants with values ​​less than Nd1 are classified as the third-level host plants L3, and the third-level host plants L3 have weak attraction to migratory insects; In this embodiment, Nd1 and Nd2 are determined according to the source of the chemical signal field data of migratory insect positioning, and Nd1 and Nd2 are respectively and ; Steps to determine Nd1, Nd2: Step 1: Collect data on volatile gas concentrations and attractiveness to migratory insects: Through experiments, the concentration of volatile gases in host plants was collected The relationship curve between the migratory insect behavior response and the specific methods include: Concentration range setting: In multiple groups of experiments, chemical signal fields of different concentrations are simulated respectively, for example, several volatile gas concentration gradient points within the range of 0ppm-1000ppm are set (such as 100ppm, 200ppm, 300ppm, 500ppm, etc.).

[0073] Behavioral data collection: measuring the insect's response to chemical signal fields with different volatile gas concentrations, such as: tropism: whether the insect chooses to approach the target plant.

[0074] Residence time: How long an insect remains in a target area.

[0075] Behavioral frequency: How often an insect visits a target area.

[0076] Step 2: Determine the threshold of attraction ability; According to the experimental data, find out the critical point of volatile gas concentration corresponding to the attraction ability: Nd1 is the lower limit of attraction in the division: the lowest volatile gas concentration at which insect behavior begins to be obviously attracted, for example 100ppm.

[0077] If the concentration of volatile gases is lower than [Nd1], the insects' reaction is not significant, that is, the attraction ability is weak.

[0078] Nd2 is the upper limit for strong attraction: the concentration at which insect behavior is observed to be saturated (i.e., the point at which the attraction does not increase significantly with further increase in volatile gas concentration), for example 500 ppm.

[0079] If the volatile gas concentration is higher than Nd2, the insects' response reaches a clearly strong attraction level.

[0080] The volatile gas concentration range corresponding to L1 level is ; The volatile gas concentration range corresponding to L2 level is ; The volatile gas concentration range corresponding to L3 level is ; PPM is the abbreviation of parts per million, which means "one part per million". It is a commonly used concentration unit used to indicate the content or proportion of a substance in a mixture (usually gas, liquid or solid); The above volatile gas concentration value range is only an example and is not limited. It should be adjusted or replaced according to actual measurement data.

[0081] Further explanation: The calculation formula for the migration attraction index of host area i is defined as: ; in, is the migration attraction index of host region i; is the volatile gas concentration value of the unit sub-area u1 corresponding to the host area i; is the gas propagation rate of the unit sub-area u1 corresponding to the host area i; is the spatial change rate of the gas gradient of the unit sub-area u1 corresponding to the host area i; It is to normalize the same type of collected data to the same scale. represent or or ; , and is the weight factor of the corresponding parameter in the chemical signal field data; , and The value range is in the interval (0,1), and ; It should be noted that the minimum-maximum standardization operation is to select the chemical signal field data of the unit sub-regions corresponding to the four host regions respectively. or or The global minimum and global maximum of , ; ; , Used for normalization, it is the global minimum and maximum value in the chemical signal field data collected for m1 times in history; set 5≥m1≥15, m1 is a positive integer, and m1 in this embodiment is 7; the data range after normalization is [0,1]; This embodiment is set as follows: ; ; ; , and The corresponding weights are determined by entropy weight method and fuzzy analytic hierarchy process (FAHP); when When the value is large: The concentration levels of chemical signals released by host plants in the target host area i are higher and the attraction is usually stronger.

[0082] Migratory insects will find it easier to locate the host plants corresponding to the host area i, and their migratory behavior will tend to the host area i with higher concentration.

[0083] Overall attractiveness is positively correlated.

[0084] when When the value is small: The concentration of chemical signals is diluted, making it more difficult for migratory insects to perceive the host plant, so the attractiveness is greatly reduced.

[0085] When approaching the environmental background value, it is considered that the influence of host plants in host area i on attracting migratory insects is small or non-existent.

[0086] Judgment of critical point: The threshold value of volatile gas concentration that attracts migratory insects to host plants in host area i is determined through experiments (for example, when the volatile gas concentration is higher than 100 ppm and lower than 500 ppm, it has moderate attraction, when it is higher than 500 ppm, it has strong attraction, and when it is lower than 100 ppm, it has weak attraction to insects).

[0087] Gas propagation velocity It measures the diffusion rate of volatile chemical gases released by host plants in host area i in space. The transmission rate is affected by factors such as wind speed and diffusion coefficient.

[0088] when When the value is large: Volatile gases can diffuse quickly to farther places, allowing migratory insects to perceive the chemical signals of host plants within the host area i over a larger range.

[0089] Within the effective diffusion range, the probability of migratory insects finding host plants in host area i and the success rate of migration increase.

[0090] Attractiveness is usually positively correlated.

[0091] when When the value is over hours: Gas diffusion is very slow, and most of the volatile gases are retained near the plants, limiting the diffusion range.

[0092] The range of chemical signals that insects perceive is reduced, and their ability to induce migratory behavior is significantly reduced.

[0093] The law of attraction is: Moderate gas propagation rate It is the strongest attraction; Too small gas propagation velocity is weak attraction; Excessive gas propagation velocity It will reduce attractiveness; Determined by experimental simulation Moderate, too small and too large values ​​of The experimental simulation method is designed to control wind speed and diffusion conditions in the field or laboratory to test different Under the value of , the changing trend of migratory insects' attraction to host plants in host area i is: Experimental variables: Changing wind speed (ensuring value), simulating the actual diffusion process of volatile gases.

[0094] Experimental observation: record the behavioral data of migratory insects (such as quantity, reaction time, efficiency of reaching host plants) to verify whether there is a "moderate transmission rate range" and an "excessively high transmission rate threshold".

[0095] Gas gradient spatial variation rate Indicates the speed of change of the concentration gradient of chemical signals in space (unit: ppm / m). A large gas gradient spatial change rate means that insects can clearly perceive the concentration difference of chemical signals and locate the high concentration source of chemical signals by "gradient climbing".

[0096] When the value is large: The concentration gradients of volatile gases in space vary significantly, and insects can more easily locate host plants in host area i by sensing and climbing to the direction of high concentration.

[0097] A larger gradient change rate directly promotes the enhancement of attraction.

[0098] when When the value is small: The concentration of volatile gases in space is relatively uniform, the concentration gradient is not obvious, it is difficult for insects to perceive direction, and their migration behavior is affected.

[0099] If the gradient approaches zero, it is equivalent to the host plants in host area i having no attraction effect on insects.

[0100] The above-mentioned ranges of larger, smaller, too small, too large and moderate are determined based on simulation experiments on specific migratory insects and corresponding host plants and will not be elaborated on here.

[0101] In this embodiment, a simulation experiment was conducted based on migratory insects and corresponding host plants. The following example policies are given for the values ​​of large, small, too small, too large, and moderate: Too small value: less than or equal to 50ppm; Smaller value: 50ppm to 100ppm, including only the right end point value; Moderate range: 100ppm to 500ppm, including only the right end point value; Maximum value: 500ppm to 600ppm, including only the right end point value; Excessive value: more than 600ppm; for The strategies for selecting larger, smaller, too small, too large, and moderate values ​​of are as follows: Too small value: less than 0.5m / s; Smaller value: 0.5m / s to 1.0m / s, including only the left end value; Moderate range: 1m / s to 2.0m / s, including only the left end value; Larger value: 2.0m / s to 2.5m / s, including only the left endpoint value; Too large value: greater than or equal to 2.5m / s; for The strategies for selecting larger, smaller, too small, too large, and moderate values ​​of are as follows: Too small value: less than 0.1ppm / m; Minimum value: 0.1ppm / m to 0.2ppm / m; Moderate range: 0.2ppm / m to 0.5ppm / m, including only the right end point value; Larger value: 0.5ppm / m to 0.56ppm / m, including only the right end point value; Excessive value: greater than or equal to 0.56ppm / m; For volatile gas concentration The strategies for selecting larger, smaller, too small, too large, and moderate values ​​of are as follows: Too small value is less than or equal to 50ppm; The smaller value is 50ppm-100ppm, including only the right end point value; The moderate range is 100ppm-500ppm, including only the right end point values; The larger value is 500ppm-600ppm, including only the right end point value; An excessive value is greater than 600 ppm.

[0102] set up The valid value range of is (0,1); when The closer it is to 1, the more attractive the host area i is to migratory insects and the greater the impact on the behavior of migratory insects. when The closer it is to 0, the less attractive the host area i is to migratory insects and the smaller the impact on the behavior of migratory insects.

[0103] To avoid Too large: causes the gas propagation rate to be too high (such as in a strong wind environment), and the volatile gas is quickly diluted, resulting in insects being able to sense the gas from a long distance, but the attraction is reduced. is the strongest attraction; "When there is a conflict in technical effects, Too big, ; ; ; This setting can reduce the The impact of "too big".

[0104] To further illustrate, the olfactory nerve response model is presented using the following expression: ; in, is the output value of the olfactory nerve response model corresponding to host region i; is the volatile gas concentration value of the unit sub-area u1 corresponding to the host area i; is the gas propagation rate of the unit sub-area u1 corresponding to the host area i; is the spatial change rate of the gas gradient of the unit sub-area u1 corresponding to the host area i; is the neural response delay time of the migratory insect corresponding to the unit sub-area u1 corresponding to the host area i; g is a nonlinear dynamic function, which represents the mapping relationship from neural stimulation to response; The determination method is as follows: Experimental data collection: The experimental response data of migratory insects to different volatile gas concentration values, gas propagation rates and gas gradient spatial change rates of this embodiment are simulated; through actual measurement, the neural response delay time of insects under different conditions is obtained.

[0105] Subjects: Select target migratory insect species and ensure that the individuals are healthy and uniform (i.e. same species, sex, age class).

[0106] Experimental environment: Establish a controlled laboratory environment and equip the necessary equipment to control the volatile gas concentration value, gas propagation rate and gas gradient spatial change rate.

[0107] A wind tunnel experimental apparatus was used to simulate the real environment in which insects fly towards an odor source.

[0108] Data fitting: Use statistical methods or machine learning models to fit collected experimental data; The relationship between different volatile gas concentration values, gas propagation rate, gas gradient spatial change rate and neural response delay time is set as follows: , where E1 is the correlation function fitted to the data; Parametric model building: Construct a parametric model that Used as input, output by the model .

[0109] E1 is a linear or nonlinear correlation function, and the specific form of E1 depends on the complexity of the experimental results and the fitting effect.

[0110] Model validation and optimization: Use a portion of the data for validation to ensure that the model can accurately predict the new input conditions .

[0111] Directly measure neural signals using electrophysiological monitoring, as follows: Using electrophysiological methods, such as electroantennography (EAG) or single-neuron recording.

[0112] Connect the insect's antennae or olfactory receptors to recording electrodes to monitor changes in electrical signals after it senses odor signals.

[0113] Experimental steps: Set the odor stimulus source and control it through the device The combined environment; Release odor signals to experimental insects and control the stimulation interval; Record the response potential of the antennae or olfactory nerves and observe the time difference between the stimulus and the nerve response. This time difference is .

[0114] Output data: Record as: Input conditions Delay time of neural response Correspondence table.

[0115] set up The high response threshold and low response threshold are and ; when When the chemical signal field data is judged to have a strong attraction to migratory insects, migratory insects are more likely to locate the source; the probability of locating the source more easily in this embodiment is more than 60%; when When the chemical signal field data is judged to have medium attraction to migratory insects, the migratory insects' ability to locate the source is medium; the probability of finding "the ability to locate the source is medium" in this embodiment is in the interval (40%, 60%); when When the chemical signal field data is judged to have a weak attraction to migratory insects, it is not easy for migratory insects to locate the source. The probability of finding the "difficult to locate the source" in this embodiment is below 40%; This embodiment The specific expression is: ; a1, a2, a3 and a4 are weight factors of corresponding parameters, and the values ​​of a1, a2, a3 and a4 are all in the interval (0,1), and the sum of a1, a2, a3 and a4 is 1; a1, a2, a3 and a4 are determined by entropy weight method and fuzzy analytic hierarchy process (FAHP); is the index of the unit sub-region, and ; , , and The collected data of the same type are normalized to the same scale; In this embodiment, The valid value range of is (0,1); define and The values ​​of 0.7 and 0.3 are just examples, and will be determined by the expert group through simulation experiments. The fuzzy analytic hierarchy process (FAHP) is used to and to confirm.

[0116] Further explanation: The output results of the olfactory nerve response model are used to correct the migration attraction index, and the migration landing point of migratory insects is predicted by the corrected migration attraction index, including: set up The judgment interval is ; and initially set The value ranges are ; ; and set ; In this embodiment, The determination is made by using the fuzzy analytic hierarchy process (FAHP), and the steps are as follows: Clear goals: The goal is to determine The threshold interval The migration attraction index is used to predict the migration destination of migratory insects.

[0117] Selection of influencing factors: The evaluation index for determining the division of the impact interval is: the intensity of the neural response of the olfactory neuron corresponding to the output value of the olfactory neural response model due to the stimulation of chemical signals, specifically the neural response delay time of migratory insects; The host region i corresponds to each unit sub-region , and ; Flight path from the source of the chemical signal.

[0118] Building a hierarchical model: First layer (target layer): determine [Nd1,Nd2].

[0119] The second layer (indicator layer): evaluation indicators related to thresholds (such as insect behavior response data, gas concentration thresholds, and environmental factors).

[0120] Construct a fuzzy judgment matrix: Based on expert experience or experimental data, fuzzy linguistic variables are used to evaluate the impact of different indicators on the target.

[0121] Weight calculation: Fuzzy comprehensive evaluation: Use the operation rules of fuzzy numbers to perform weighted calculation on the relative importance of each indicator.

[0122] Defuzzification: Through methods such as the centroid method and the maximum likelihood method, the fuzzy numbers are converted into exact values ​​to obtain the weights of each indicator.

[0123] Determine the specific threshold range: Combining the characteristics of the exponential function and the experimental data of insect behavior, the weights processed by FAHP are used to define the threshold interval; when Since migratory insects are more likely to locate their source, the following corrections are made to the migration attraction index: ; when Since it is difficult to locate the source of migratory insects, the following corrections are made to the migration attraction index: ; in, is the modified migration attraction index; when When the migration attraction index is 1.0, no correction is made; like When , it means that the host plants in the host area i are highly attractive to the corresponding migratory insects, and the primary host plant L1, the secondary host plant L2, or the tertiary host plant L3 is used as the predicted location of the migratory insects’ migration landing point; Selection principles and reasons: Combined high sensitivity and high attraction: This indicates that insects respond particularly strongly to chemical signals in the current environment, combined with high The value indicates that the primary host plant L1, the secondary host plant L2, or the tertiary host plant L3 is the main target of insect migration under the current environmental conditions.

[0124] Accurately locate the migration landing point: Selecting the primary host plant L1, the secondary host plant L2, or the tertiary host plant L3 can more accurately predict the migration direction and landing point of insects and ensure the reliability of the prediction results.

[0125] like Values ​​in the range When , it means that the host plants in the host area i have medium attraction to the corresponding migratory insects, and the primary host plant L1 or the secondary host plant L2 is used as the predicted location of the migratory insects’ migration landing point; Selection principles and reasons: Prioritize plants of highest appeal: The value indicates that the primary host plant L1 or the secondary host plant L2 is more attractive to insects. Therefore, selecting the primary host plant L1 or the secondary host plant L2 as the predicted location of the migration landing point can more effectively identify the migration target of insects.

[0126] Optimize prediction accuracy: By giving priority to primary host plants L1 or secondary host plants L2, the accuracy of migration landing point prediction can be improved and misjudgment can be reduced.

[0127] like When the primary host plant L1 is used as the predicted landing point of migratory insects.

[0128] Selection principles and reasons: Preference for higher-ranking host plants: Despite the current host plant's The value does not meet the high attractiveness standard, but due to It shows that insects respond strongly to chemical signals and tend to choose higher-level primary host plants L1 as migration landing points to meet their survival and reproduction needs.

[0129] Low compensation Insufficient value: By selecting the primary host plant L1, the insufficient attractiveness of the host plants in the current area can be compensated, ensuring the rationality and effectiveness of the prediction of the migration landing point.

[0130] Embodiment 2: See also Figure 2 , a migratory insect migration prediction system, the system is used to execute the migratory insect migration prediction method, comprising: Area division module: used to determine multiple host plants of the same type associated with migratory insects within the monitoring area, and define the areas where these host plants are located as host areas, forming multiple host areas, and dividing the areas outside each host area into four unit sub-areas; Data acquisition module: used to collect the current chemical signal field data of each unit sub-area in the monitoring area for volatile organic compounds released through the host area. The chemical signal field data includes: volatile gas concentration value, gas propagation rate and gas gradient spatial change rate; Grade classification module: used to classify host plants according to the volatile gas concentration values ​​of multiple host plants in the host area. The higher the volatile gas concentration value, the higher the grade of the host plant; Index generation module: used to obtain the chemical signal field data corresponding to each host area; then, these chemical signal field data are analyzed to calculate and generate the migration attraction index of each host area; Model building module: used to build the olfactory neural response model of migratory insects. The olfactory neural response model analyzes the different chemical signal field data in the four unit sub-areas corresponding to the host area, and combines the neural response delay time of migratory insects to generate the output results of neural response. The output results are used to characterize the attractiveness of different chemical signal field data to migratory insects. Landing point prediction module: used to modify the migration attraction index using the output results of the olfactory nerve response model, and predict the landing point of migratory insects through the modified migration attraction index.

[0131] Embodiment three: A readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the migratory insect migration prediction method are implemented.

[0132] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0133] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0134] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A method for predicting the migration of migratory insects, characterized in that: The specific steps include: Step S1: in the monitoring area, multiple host plants of the same type associated with migratory insects are determined, and the areas where these host plants are located are defined as host areas to form multiple host areas, and the areas outside each host area are divided into four unit sub-areas; Step S2: collecting the current chemical signal field data of each unit sub-area in the monitoring area, the chemical signal field data including: volatile gas concentration value, gas propagation rate and gas gradient spatial change rate; Step S3: classifying the host plants according to the volatile gas concentration values ​​of the multiple host plants in the host area, wherein the higher the volatile gas concentration value, the higher the level of the host plant; Step S4: obtaining chemical signal field data corresponding to each host region; then, analyzing these chemical signal field data, calculating and generating a migration attraction index for each host region; Step S5: constructing an olfactory neural response model of migratory insects. The olfactory neural response model generates an output result of neural response by analyzing different chemical signal field data in four unit sub-areas corresponding to the host area and combining the neural response delay time of migratory insects. The output result is used to characterize the attraction of different chemical signal field data to migratory insects. Step S6: using the output result of the olfactory nerve response model, the migration attraction index is corrected, and the migration landing point of the migratory insects is predicted by the corrected migration attraction index.

2. The migratory insect migration prediction method according to claim 1, characterized in that: The steps for dividing the host area and unit sub-area are as follows: 2.1) Definition and delineation of host areas; Host area refers to a specific geographical area within the monitoring area, which is delineated based on the distribution density of host plants and the host attraction relationship of target migratory insects; Determination of the distribution density of host plants: the number of host plants per unit area is used as the measurement value of distribution density; The distribution density threshold per unit area is set as "Dh strains / hectare"; the clustering method is used to identify areas exceeding "Dh strains / hectare" as high-density areas; The host attraction relationship for target migratory insects is defined as: Based on the high-density area, the minimum density standard related to the attraction of target migratory insects is set as "Dk / 24 hours"; both Dk and Dh are positive values; The areas that exceed both "Dh strains / hectare" and "Dk individuals / 24 hours" are taken as preliminary screening areas, and each preliminary screening area is gradually expanded outward until any of the conditions of "Dh strains / hectare" and "Dk individuals / 24 hours" are not met, and the expansion is stopped. From these expanded preliminary screening areas, non-overlapping areas are selected in a discrete manner as host areas; 2.2) Division of unit sub-areas outside the host area: Taking the geometric center of each host region as the reference, the host region is expanded outward to be divided into four nested or continuous unit sub-regions according to the boundary shape characteristics of the host region, and there is no overlapping part between the unit sub-regions of adjacent host regions; The index of the host region is recorded as i, ; The four unit sub-regions of host region i are recorded as i1, i2, i3, and i4 respectively; the distances between the unit sub-regions corresponding to i1, i2, i3, and i4 and the boundary of host region i gradually increase; The index of the j1th host plant in host region i is recorded as ;and , is a set of positive integers.

3. The migratory insect migration prediction method according to claim 2, characterized in that: The volatile gas concentration values ​​of each unit sub-area corresponding to the host area i are recorded as , , and ; The gas propagation rates of each unit sub-area corresponding to the host area i are respectively recorded as , , and ; The spatial change rate of the gas gradient of each unit sub-region corresponding to the host region i is recorded as , , and .

4. The migratory insect migration prediction method according to claim 3, characterized in that: Classification of host plants: The host plant The measured volatile gas concentration value is recorded as ; according to The host plants are divided into different levels according to the value range to distinguish the strength of their ability to attract migratory insects. The higher the level of the host plant, the higher the corresponding The larger the value; Set the host plant level classification corresponding to The division thresholds are Nd1 and Nd2, Nd1 and Nd2 are the minimum threshold and the maximum threshold respectively, and Nd1<Nd2; Will Host plants with values ​​exceeding Nd2 are classified as primary host plants L1, which have a strong attraction to migratory insects; Will Host plants with values ​​in the interval [Nd1, Nd2] are classified as secondary host plants L2, and the attraction of secondary host plants L2 to migratory insects is medium attraction; Will Host plants with values ​​less than Nd1 are classified as tertiary host plants L3, which have weak attraction to migratory insects.

5. The migratory insect migration prediction method according to claim 4, characterized in that: The calculation formula of the migration attraction index of host area i is defined as: ； in, is the migration attraction index of host region i; is the volatile gas concentration value of host region i; is the gas propagation rate in host region i; is the spatial rate of change of the gas gradient in host region i; It is to normalize the same type of collected data to the same scale. represent or or ; , and is the weight factor of the corresponding parameter in the chemical signal field data; , and The value range is in the interval (0,1), and ; set up The valid value range of is (0,1); when The closer it is to 1, the more attractive the host area i is to migratory insects and the greater the impact on the behavior of migratory insects. when The closer it is to 0, the less attractive the host area i is to migratory insects and the smaller the impact on the behavior of migratory insects.

6. The migratory insect migration prediction method according to claim 5, characterized in that: The olfactory nerve response model is expressed as follows: ； in, is the output value of the olfactory neural response model corresponding to host region i, which represents the intensity of the neural response of the olfactory neurons of migratory insects stimulated by chemical signals; a1, a2, a3 and a4 are the weight factors of the corresponding parameters, and the values ​​of a1, a2, a3 and a4 are all in the interval (0,1), a1+a2+a3+a4=1; is the index of the unit sub-region, and ; The larger the value, the greater the intensity of the neural response produced by the olfactory neuron due to the stimulation of chemical signals; is the volatile gas concentration value of the unit sub-area u1 corresponding to the host area i; is the gas propagation rate of the unit sub-area u1 corresponding to the host area i; is the spatial change rate of the gas gradient of the unit sub-area u1 corresponding to the host area i; is the neural response delay time of migratory insects corresponding to the unit sub-area u1 corresponding to the host area i; set up The high response threshold and low response threshold are and ; when When , in host region i, it is judged that the chemical signal field data has a strong attraction to migratory insects, and migratory insects are more likely to locate the source; when When , in host area i, it is judged that the chemical signal field data has a weak attraction to migratory insects, and it is not easy for migratory insects to locate the source.

7. The migratory insect migration prediction method according to claim 6, characterized in that: The output results of the olfactory nerve response model are used to correct the migration attraction index, and the migration landing point of migratory insects is predicted by the corrected migration attraction index, including: when Since migratory insects are more likely to locate their source, the following corrections are made to the migration attraction index: ； when Since it is difficult to locate the source of migratory insects, the following corrections are made to the migration attraction index: ； in, is the modified migration attraction index; when When the migration attraction index is 1.0, no correction is made; set up The judgment interval is ; and They are the lower and upper limits of the judgment interval, and are initially set The value ranges are ; ; and set ; like When , it means that the host plants in the host area i are highly attractive to the corresponding migratory insects, and the primary host plant L1, the secondary host plant L2, or the tertiary host plant L3 is used as the predicted location of the migratory insects’ migration landing point; like Values ​​in the range When , it means that the host plants in the host area i have medium attraction to the corresponding migratory insects, and the primary host plant L1 or the secondary host plant L2 is used as the predicted location of the migratory insects’ migration landing point; like When the primary host plant L1 is used as the predicted landing point of migratory insects.

8. A migratory insect migration prediction system, characterized in that: The system is used to execute the migratory insect migration prediction method according to any one of claims 1 to 7, comprising: Area division module: used to determine multiple host plants of the same type associated with migratory insects within the monitoring area, and define the areas where these host plants are located as host areas, forming multiple host areas, and dividing the areas outside each host area into four unit sub-areas; Data acquisition module: used to collect the current chemical signal field data of each unit sub-area in the monitoring area, and the chemical signal field data includes: volatile gas concentration value, gas propagation rate and gas gradient spatial change rate; Grade classification module: used to classify host plants according to the volatile gas concentration values ​​of multiple host plants in the host area. The higher the volatile gas concentration value, the higher the grade of the host plant; Index generation module: used to obtain the chemical signal field data of each host area; then, these chemical signal field data are analyzed to calculate and generate the migration attraction index of each host area; Model building module: used to build the olfactory neural response model of migratory insects. The olfactory neural response model analyzes the different chemical signal field data in the four unit sub-areas corresponding to the host area, and combines the neural response delay time of migratory insects to generate the output results of neural response. The output results are used to characterize the attractiveness of different chemical signal field data to migratory insects. Landing point prediction module: used to modify the migration attraction index using the output results of the olfactory nerve response model, and predict the landing point of migratory insects through the modified migration attraction index.

9. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the migratory insect migration prediction method according to any one of claims 1 to 7 are implemented.

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