Migration prediction method, system and readable storage medium for migratory insects

Abstract

The present invention provides a method, a system and a readable storage medium for predicting the migration of migratory insects, which relates to the technical field of biological control prediction and simulation. By collecting the volatile organic compounds released in the host area and their propagation characteristics, and combining the concentration value, propagation rate and gradient change rate of chemical signals, the present invention constructs a quantitative model of the attractiveness of host plants. Secondly, an olfactory nerve response model is used to characterize the sensitivity of migratory insects to chemical signals, which improves the scientificity and accuracy of chemical signal attractiveness analysis from the neurophysiological level; finally, by combining the migration attraction index and the olfactory nerve response model, the dynamic prediction of the migration landing points of host plants at each level is further realized, the prediction accuracy for small-area targets is improved, and the limitations of the prior art in terms of time and space resolution are compensated for.

Description

Technical Field

[0001] The present invention relates to the technical field of biological control prediction and simulation, and specifically to a method, system and readable storage medium for predicting the migration of migratory insects. Background Art

[0002] Currently, the research on predicting the migration 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, air temperature and rainfall, many studies have initially constructed large-scale distribution models of migratory insects to evaluate the possible migration directions and ranges of insects. However, these methods mainly rely on macroscopic environmental conditions and ignore the dynamic effects of host plant distribution, chemical signal gradients and olfactory nerve responses during the landing point selection process of migratory insects. Especially when insects select specific landing points, their behavior is strongly affected by the volatile organic compounds (VOCs) released by host plants and their transmission conditions, and this microscopic attraction mechanism has not been fully quantified and incorporated into the migration prediction model.

[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, migration behavior data, meteorological data of each sub-region included in the target region, atmospheric trajectory data during each flight period of the insect, and global phenology data; determining at least one migration starting point of the insect in the target region according to the historical occurrence location data and the meteorological data of each sub-region; predicting the next migration landing point when the insect starts 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 phenology data to obtain the screened migration landing points corresponding to each migration starting point. It can predict the migration landing points of insects by combining meteorological data and global phenology data, improving the reliability and accuracy of the predicted migration landing points.

[0004] In the prior art, although some studies have begun to explore the migration behavior analysis model based on chemical signals, there are still significant technical bottlenecks: firstly, an accurate mathematical model has not been established for the relationship between multi-dimensional parameters of chemical signal characteristics (such as concentration, propagation rate, gradient change rate) and insect behavior; secondly, the existing methods lack refined modeling of the response mechanism of the olfactory nervous system of migratory insects, making the characterization and quantitative analysis of the attraction of the chemical signal field to insects not fully carried out; thirdly, the prediction of the behavior of migratory insects selecting host plants with high attractiveness mostly stays in the qualitative stage, lacking the dynamic prediction ability with both time dimension and space dimension;

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The object of the present invention is to provide a method, system, readable storage medium, device, equipment and storage medium for predicting the migration of migratory insects, so as to solve the problems raised in the above background art.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for predicting the migration of migratory insects, the specific steps include:

[0009] Step S1: In the monitoring area, determine multiple host plants associated with migratory insects and belonging to the same type, and define the areas where these host plants are located as host areas, forming multiple host areas, and divide the areas outside each host area into four unit sub-areas;

[0010] Step S2: 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;

[0011] Step S3: According to the volatile gas concentration values of multiple host plants in the host area, classify the host plants, and the higher the volatile gas concentration value, the higher the level of the host plant;

[0012] Step S4: Obtain the chemical signal field data corresponding to each host area; then, analyze these chemical signal field data, calculate and generate the migration attraction index of each host area;

[0013] Step S5: Construct an olfactory nerve response model for migratory insects. The olfactory nerve response model analyzes different chemical signal field data in the four unit sub-areas corresponding to the host area, and combines the nerve reaction delay time of migratory insects to generate an output result of the nerve response, and the output result is used to characterize the attraction of different chemical signal field data to migratory insects;

[0014] Step S6: Use the output result of the olfactory nerve response model to correct the migration attraction index, and predict the migration landing point of migratory insects through the corrected migration attraction index.

[0015] A system for predicting the migration of migratory insects, the system is used to execute the method for predicting the migration of migratory insects, including:

[0016] Region division module: used to determine multiple host plants of the same type associated with migratory insects within the monitoring area, define the areas where these host plants are located as host areas, form multiple host areas, and divide the areas outside each host area into four unit sub-areas;

[0017] Data acquisition module: used to acquire the current chemical signal field data of each unit sub-area within the monitoring area, and the chemical signal field data includes: volatile gas concentration value, gas propagation rate, and gas gradient spatial change rate;

[0018] Grade division module: used to divide the host plants according to the volatile gas concentration values of multiple host plants within the host area. The higher the volatile gas concentration value, the higher the grade of the host plant;

[0019] Index generation module: used to obtain the chemical signal field data corresponding to each host area; then, analyze these chemical signal field data, calculate and generate the migration attraction index of each host area;

[0020] Model construction module: used to construct an olfactory nerve response model of migratory insects. The olfactory nerve response model analyzes different chemical signal field data in the four unit sub-areas corresponding to the host area, and combines the nerve reaction delay time of migratory insects to generate the output result of the nerve response. The output result is used to characterize the attraction of different chemical signal field data to migratory insects;

[0021] Landing point prediction module: used to correct the migration attraction index by using the output result of the olfactory nerve response model, and predict the migration landing point of migratory insects through the corrected migration attraction index.

[0022] A readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the migratory insect migration prediction method as described above.

[0023] Compared with the prior art, the beneficial effects of the present invention are: by collecting the volatile organic compounds released by the host area and their propagation characteristics, combining the concentration value, propagation rate, and gradient change rate of chemical signals, a quantitative model of the attraction of host plants is constructed. Secondly, an olfactory nerve response model is used to characterize the sensitivity of migratory insects to chemical signals, which improves the scientificity and accuracy of chemical signal attraction analysis from the neurophysiological level; finally, by combining the migration attraction index and the olfactory nerve response model, the dynamic prediction of the migration landing points of host plants of each grade 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 the management of agricultural pests. Brief Description of the Drawings

[0024] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0025] Figure 2 This is a block diagram of the system modules of the present invention. Detailed implementation manners

[0026] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to specific embodiments.

[0027] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Terms such as "including" or "comprising" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. Terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Terms such as "upper", "lower", "left" and "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0028] Embodiment 1:

[0029] Please refer to Figure 1 , the present invention provides a technical solution:

[0030] A method for predicting the migration of migratory insects, the specific steps include:

[0031] Step S1: In the monitoring area, determine multiple host plants of the same type associated with migratory insects, and define the areas where these host plants are located as host areas, forming multiple host areas, and divide the areas outside each host area into four unit sub-areas;

[0032] Step S2: Through the volatile organic compounds released by the host area, 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;

[0033] Step S3: According to the volatile gas concentration values of multiple host plants in the host area, classify the host plants, and the higher the volatile gas concentration value, the higher the level of the host plant;

[0034] Step S4: Obtain the chemical signal field data corresponding to each host area; then, analyze these chemical signal field data, calculate and generate the migration attraction index for each host area.

[0035] Step S5: Construct an olfactory nerve response model for migratory insects. The olfactory nerve response model analyzes the different chemical signal field data in the four unit sub-areas corresponding to the host area, and combines the nerve reaction delay time of migratory insects to generate the output result of the nerve response. The output result is used to characterize the attraction of different chemical signal field data to migratory insects.

[0036] Step S6: Use the output result of the olfactory nerve response model to correct the migration attraction index, and predict the migration landing point of migratory insects through the corrected migration attraction index.

[0037] Further explanation: The types of host plants and the delimitation of host areas are specified as follows:

[0038] For the screening steps of host plant types:

[0039] 1.1) Research on the habits of migratory insects;

[0040] 1.1.1: Collect ecological data of the target migratory insects, including life cycle, activity habits, and food preference information.

[0041] 1.1.2: Through literature review and field surveys, determine the main host plant species of the insects (for example, refer to the preference of insects for specific tree species such as pine trees, oak trees, birch trees, etc.).

[0042] 1.2) Plant ecological survey in forest areas;

[0043] 1.2.1: Use a high-resolution drone equipped with a multispectral sensor to obtain vegetation distribution images of the monitored forest area.

[0044] 1.2.2: Combine ground field investigations to record the distribution density and health status of different tree species, and form a comprehensive plant distribution database.

[0045] 1.3) Verification of plant correlation;

[0046] 1.3.1: Use electroantennogram (EAG) equipment to test the olfactory responses of insects to the volatile substances of different selected plant leaves, and screen out the plant species with significant attraction.

[0047] Electroantennogram Recording (EAG) is a technique commonly used in the study of insect olfaction to record the electrophysiological responses 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.

[0048] Principle of EAG:

[0049] When gas stimuli contact the olfactory receptors on the insect antennae, they trigger the electrical activities of olfactory neurons.

[0050] These activities are recorded by electrodes and presented as electrical signal waveforms.

[0051] Different concentrations or chemical compositions of gas stimuli will cause different amplitudes of electrical responses, and researchers can judge the sensitivity of insects to different gas molecules by analyzing these responses.

[0052] 1.3.2: Conduct behavioral experiments (such as wind tunnel experiments) to observe the selection behaviors of insects among different plants and further verify the host effects of plants.

[0053] 1.3.3: Finally determine a group of host plants of the same family that are highly related to migratory insects (such as pine trees and larch in the pine family).

[0054] The host plants are selected from the set {P1, P2}, where P1 and P2 are plants of the same family; in this embodiment: P1 represents pine trees; P2 represents larch; the one with the largest quantity in {P1, P2} is selected as the host plant; in this embodiment, the type of the selected host plant is denoted as P1.

[0055] The steps for dividing the host area and unit sub-areas are as follows:

[0056] 2.1) Definition and delimitation of the host area;

[0057] The host area refers to a specific geographical area delimited within the monitoring area 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 from the distribution points of host plants, and its boundary reflects the natural distribution concentration of host plants and is closely related to the trace prediction of target insects.

[0058] For the 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.

[0059] Set the distribution density threshold per unit area to "Dh plants / hectare"; use a clustering method (such as the K-means clustering algorithm or clustering analysis based on geographical 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 the high-density areas.

[0060] The definition of the host attraction relationship for the target migratory insects is as follows:

[0061] The attracting effect of the chemical components of the volatile substances:

[0062] Collect samples of volatile organic compounds from host plants in the monitoring area, and determine the types and concentrations of volatile organic compounds through a chemical analyzer (such as a GC-MS device).

[0063] Use insect electrophysiological experiments (such as EAG) to record the response signals of the target migratory insects to different volatile substances, and define the quantitative criteria for the attracting intensity; in this embodiment:

[0064] The EAG signal intensity of the volatile organic compounds of pine trees (P1) is 1.2 mV.

[0065] The EAG signal intensity of the volatile organic compounds of larch trees (P2) is 1.0 mV.

[0066] Verify the attracting intensity through behavioral experiments:

[0067] 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.

[0068] Based on the high-density areas, set the minimum density standard related to the attraction of the target migratory insects to be "Dk individuals / 24 hours"; in this embodiment: Dk = 40 individuals / 24 hours, and those exceeding "Dk = 40 individuals / 24 hours" are used as the preliminary signs of high-attraction areas.

[0069] In the forest monitoring area, record the geographical coordinates of each host plant through a high-precision GPS device.

[0070] Use GIS (Geographic Information System) software to perform spatial clustering analysis on the location points of all host plants to determine the main host plant concentration areas, which are defined as "host areas". For example:

[0071] Host area A: Located in the north of the forest, containing a dense distribution area of pine trees (P1).

[0072] Located in the north of the forest monitoring area (specific coordinates are determined according to the actual area mapping), with an area of 10 square kilometers. The density of pine trees (P1) distributed in this area is:

[0073] Pine trees (P1): There are 4,000 trees per square kilometer, with a total of 40,000 trees.

[0074] Through the combination of unmanned aerial vehicle remote sensing technology and ground sampling surveys and statistics, it is determined that pine trees (P1) account for 40% of the total vegetation area in this region.

[0075] Regard the areas that simultaneously exceed "Dh trees / ha" and "Dk individuals / 24 hours" as the preliminary screening areas, and gradually expand each preliminary screening area outward until it no longer meets either the condition of "Dh trees / ha" or "Dk individuals / 24 hours", then stop the expansion. From these expanded preliminary screening areas, discrete and non - overlapping areas are selected 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 recording;

[0076] In this embodiment, the initial setting is that there are four host areas;

[0077] Determine a unified area standard for each host area;

[0078] Regarding non - overlapping host areas:

[0079] Set the selection path: Set a specific selection path or strip area, and ensure that there is no overlap between these paths;

[0080] The clear selection criteria for host areas are as follows:

[0081] Within the path in each established direction, select the area closest to the center position and meeting the criteria.

[0082] Avoid overlapping selection paths to ensure that the selection results in each direction are independent and non - overlapping;

[0083] For determining the unified area standard, conduct area framing:

[0084] Formulate the area standard: For the host areas selected in each direction, determine a unified or corresponding area standard. For example, by setting the radius, specifying the side length of a square, or by determining the polygon boundary to frame the area;

[0085] The framed area is flexibly adjusted according to specific ecological research needs and geographical characteristics to ensure that the area of each region is moderate and has sufficient representativeness of environmental characteristics;

[0086] Use GIS tools to delimit the boundaries of the determined host areas, and assign unique numbers A, B, C, D to these four host areas;

[0087] It should be noted that there is no overlapping area in the sub-regions generated below for the host regions corresponding to A, B, C, and D;

[0088] Record the specific geographical coordinates and area of each host region; for example:

[0089] A: The center point is (N34.51234°, E108.12345°); the total area is 4.5 hectares.

[0090] 2.2) Division of unit sub-regions outside the host region:

[0091] Taking the geometric center of each host region as a reference, according to the boundary shape characteristics of the host region, four nested or continuous unit sub-regions are expanded outward. The boundary shape of each unit sub-region is appropriately consistent with the overall boundary of the host region, or generated by equidistant adjustment based on the geometric center, and there is no overlapping part between the unit sub-regions of adjacent host regions;

[0092] Record the index of the host region 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}; there are at least four unit sub-regions;

[0093] The four unit sub-regions of the host region i are respectively denoted as i1, i2, i3, and i4; the distances from the unit sub-regions corresponding to i1, i2, i3, and i4 to the boundary of the host region i gradually increase;

[0094] In this embodiment, the unit sub-regions corresponding to i1, i2, and i3 are described as follows:

[0095] Unit sub-region i1: A circular range with an outward expansion of 1 square kilometer from the boundary of the host region i;

[0096] Unit sub-region i2: A circular range with an outward expansion of 1 square kilometer to 2 square kilometers from the boundary of the host region i;

[0097] Unit sub-region i3: A circular range with an outward expansion of 2 square kilometers to 3 square kilometers from the boundary of the host region i;

[0098] Unit sub-region i4: A circular range with an outward expansion of 3 square kilometers to 4 square kilometers from the boundary of the host region i;

[0099] Assign a unique number to each host plant in each host region to ensure data accuracy and traceability;

[0100] Record the index of the j1th host plant in the host region i as ; and , is the set of positive integers.

[0101] Further explanation: Sensor selection and layout:

[0102] Sensor type: Select 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 the concentration value of volatile gases and have a high spatial resolution.

[0103] Sensor accuracy requirements: The sensor can monitor the concentration change of volatile organic compounds (such as the volatile substances of pine trees) in the air, with a sensitivity in the ppb (parts per billion) order of magnitude and a response time of less than 5 seconds.

[0104] Sensor layout: Sensor nodes are arranged in a matrix form within each unit sub-region, with a spacing of 2 to 3 meters to ensure high-density sampling data. A small wind speed measuring instrument is configured above each sensor node to dynamically adjust the gas propagation model.

[0105] For gas concentration measurement:

[0106] Use gas sensors to regularly measure the concentration of volatile gases in each unit sub-region; the sensor can distinguish gases with different chemical compositions, such as the volatile organic compounds of pine trees and larch trees.

[0107] Data acquisition frequency: The data acquisition frequency of the volatile gas concentration value is set to once per minute to ensure capturing the instantaneous changes of the gas. If the volatile gas concentration value mutates, the system will automatically increase the sampling frequency to once per second.

[0108] The volatile gas concentration value refers to the mass or volume concentration of the target volatile organic compound (VOC) contained in the air per unit volume; it is directly measured by the sensor; the calculation formula for defining the volatile gas concentration value is:

[0109] ;

[0110] where CHF is the volatile gas concentration value (concentration, unit: ppm or ppb); in this embodiment, the volatile gas refers to the VOC generated by pine trees and larch trees; the specific selection of VOC is determined according to the type of migratory insects;

[0111] When pine trees and larch trees are under biological stress such as bark beetles and Monochamus alternatus, the resin secretion increases significantly, and strong volatile monoterpenoid components (such as α-pinene and β-pinene) are released in cooperation;

[0112] S is the voltage signal value of the real-time gas response signal of the sensor;

[0113] is the background signal value of the sensor, i.e., the output value in the absence of the target gas, which is used as a calibration standard.

[0114] is the sensitivity constant of the sensor, indicating the response degree of the sensor to the concentration of the target gas (given by the calibration of the sensor device, and the unit depends on the specific sensor parameters).

[0115] The concentration values of the volatile gases corresponding to each unit sub-region in the host area i are respectively denoted as , , and ;

[0116] For the measurement of the gas propagation rate:

[0117] Each sensor is equipped with a wind speed sensor (such as a hot wire anemometer) to accurately measure the speed of gas propagation. Combining the wind speed and the gas concentration, the propagation rate of the gas (unit: m / s) is estimated by calculating the concentration change of the gas at different time points. The gas propagation rate will be calculated separately along the three axes of X, Y, and Z to form three-dimensional gas propagation data.

[0118] Specific measurement process: For each unit sub-region, the data acquisition of 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.

[0119] The gas propagation rate refers to the speed at which volatile organic compounds diffuse along different axes (X, Y, Z) in the environment, with the unit of m / s. The gas propagation rate is affected by factors such as wind speed, molecular diffusion characteristics, and environmental resistance. The gas propagation rate is estimated by the following formula:

[0120] ;

[0121] where is the component of the gas propagation rate on the e-axis (unit: m / s); e ∈ {X, Y, Z};

[0122] is the convective velocity brought by the wind flow during the propagation of the volatile gas on the e-axis; obtained through the wind speed sensor, unit: m / s.

[0123] is the diffusion coefficient of the volatile gas on the e-axis (unit: m^2 / s, determined by the molecular diffusion characteristics of the target VOC).

[0124] is the distance between adjacent sensors in the propagation direction of the volatile gas (unit: m).

[0125] For the absolute rate in space, the magnitude of the three-dimensional vector is synthesized through the following formula:

[0126] ;

[0127] The gas propagation rates of each unit sub-region corresponding to the host region i are respectively denoted as , , and ;

[0128] For the calculation of the spatial change rate of the gas gradient:

[0129] The spatial change rate of the gas gradient refers to the change amplitude of the gas concentration within a unit distance; by densely arranging the sensor array, the gas concentration difference between adjacent sensors is calculated, so as to infer the change rate of the gas gradient;

[0130] The spatial change rate of the gas gradient is calculated through the following formula:

[0131] ;

[0132] Among them, and are respectively the volatile gas concentration values measured by adjacent sensors, is the distance between adjacent sensors.

[0133] The spatial change rates of the gas gradient of each unit sub-region corresponding to the host region i are respectively denoted as , , and .

[0134] The data of the collected volatile gas concentration values, taste propagation rates, and spatial change rates of the gas gradient are transmitted to the central database for storage in real time through the data acquisition system; the database can process a large amount of time series data and provide a real-time access interface.

[0135] The data format is uniformly in CSV or JSON format to ensure compatibility with the GIS system and subsequent data analysis software, and the data analysis software includes but is not limited to: data analysis libraries of MATLAB or Python.

[0136] The data of the collected volatile gas concentration values, taste propagation rates, and spatial change rates of the gas gradient are combined with GIS tools to generate two-dimensional / three-dimensional maps of gas propagation, and the gas propagation trends and change laws are analyzed.

[0137] Further explanation: The host plants are classified:

[0138] Select high-sensitivity VOC analysis equipment for gas collection and concentration monitoring. The specific equipment used is as follows:

[0139] Gas Chromatograph-Mass Spectrometer (GC-MS): Suitable for accurately analyzing the components and concentrations of volatile organic compounds.

[0140] Configure a portable PID (Photoionization Detector) VOC sensor: Facilitate real-time monitoring.

[0141] Arrangement method:

[0142] Arrange sampling probes within each unit sub-region (the sampling distance is 0.5 meters to 1 meter around the host plant).

[0143] Set at least 1 VOC sensor to move for sampling within the unit sub-region to supplement the data of fixed sampling points.

[0144] After the equipment is arranged, collect the volatile gas samples released by the host plant and calculate their concentration values.

[0145] Based on the calculation formula of the volatile gas concentration value, record the volatile gas concentration value measured from the j1-th host plant corresponding to the host area i as ; The collection methods of

[0146] Method 1: Increase spatial isolation to reduce the interference of volatile gas transmission;

[0147] Method: Select host plants with a large spatial interval for measurement, ensure that the physical distance between sampling points is large enough to reduce the influence of volatile substances from other plants.

[0148] If the host plants are highly concentrated, measure by artificial temporary isolation (such as using a gas barrier) or select plants with obvious single-plant distribution.

[0149] Application suggestion: Determine a minimum sampling interval threshold (such as 5 meters or more), calculate the diffusion range of volatile substances according to the distribution density of plants on site, wind speed and direction, and ensure that the interference of volatile gas concentration measurement of adjacent plants on the target plant is low.

[0150] Method 2: Use wind direction analysis, measure along the wind direction and exclude the upwind interference:

[0151] Reason: The transmission of volatile gases is significantly affected by wind direction and wind speed. In the same area, volatile substances from adjacent plants may form a concentrated area with the wind, thus affecting the measurement results.

[0152] Method: Monitor wind direction and wind speed: With the help of a small weather station, anemometer or wind vane, record the wind direction and wind speed in real time during measurement.

[0153] Select the measurement location: When sampling the host plant, set up the sensor from the upwind direction to avoid interfering with the volatiles from entering the measurement area.

[0154] Correct the influence: Combine the wind speed and the distance to the target plant, and use the diffusion model to estimate the contribution of the interfering volatile concentration to the measurement result and make corrections.

[0155] Application suggestions: Deploy a sensor array, dynamically adjust the position of the sampling point according to the wind direction, or correct the concentration by combining wind direction modeling analysis.

[0156] Method 3: Use a gas shield and a collection hood to avoid interference from neighboring plants:

[0157] Reason: Volatile gases are transported by diffusion and can gradually mix into the air around the target plant. Therefore, use physical isolation technology to reduce the gas influence of non-target plants.

[0158] Method: Use a sealed collection hood (such as a dynamic gas collection system) around the target plant to directly collect volatiles from the surface of the host plant and avoid interference from the surrounding air.

[0159] Introduce clean air (air with VOCs removed) into the collection hood to ensure that the collected volatiles come entirely from the target plant rather than the surrounding vegetation.

[0160] Maintain the system tightness throughout the collection process, and clean and zero-calibrate the measurement equipment to ensure the accuracy of the concentration data.

[0161] Equipment recommendation: Dynamic collection devices (such as bag samplers, vacuum samplers).

[0162] A collection chamber made of glass or Teflon to avoid material adsorption or secondary pollution of volatiles.

[0163] Classify the host plants into different levels to distinguish their attracting abilities to migratory insects:

[0164] Set the division thresholds corresponding to the classification of host plants as Nd1 and Nd2 respectively, where Nd1 and Nd2 are positive integers and Nd1 < Nd2;

[0165] For host plants with values exceeding Nd2, classify them into first-level host plants L1, and the attracting ability of first-level host plants L1 to migratory insects is strong attraction;

[0166] For host plants with values in the interval [Nd1, Nd2], classify them into second-level host plants L2, and the attracting ability of second-level host plants L2 to migratory insects is medium attraction;

[0167] Divide Host plants with values less than Nd1 into third - level host plants L3. The attracting ability of the third - level host plants L3 to migratory insects is weak attraction.

[0168] In this embodiment, Nd1 and Nd2 are determined according to the source of the chemical signal field data of migratory insects. Nd1 and Nd2 are respectively and ;

[0169] Steps for determining Nd1 and Nd2:

[0170] Step 1: Collect data on volatile gas concentration and the attracting ability of migratory insects:

[0171] Through experiments, collect the relationship curve between the concentration of volatile gases of host plants and the behavioral responses of migratory insects. The specific methods include:

[0172] Concentration range setting: In multiple groups of experiments, simulate chemical signal fields with different concentrations respectively. For example, set several volatile gas concentration gradient points in the range of 0 ppm - 1000 ppm (such as 100 ppm, 200 ppm, 300 ppm, 500 ppm, etc.).

[0173] Ethological data collection: Measure the responses of insects to chemical signal fields with different volatile gas concentrations. For example: Tendency: Whether insects choose to approach the target plant.

[0174] Residence time: The length of time insects stay in the target area.

[0175] Behavioral frequency: The visiting frequency of insects in the target area.

[0176] Step 2: Determine the threshold of attracting ability;

[0177] According to the experimental data, find the critical points of the attracting ability corresponding to the volatile gas concentration: Nd1 is the lower limit of medium attraction: Observe the lowest volatile gas concentration at which the insect behavior starts to be significantly attracted, such as 100 ppm.

[0178] If the volatile gas concentration is lower than [Nd1], the response of the insect is not significant, that is, the attracting ability is weak.

[0179] Nd2 is the upper limit of strong attraction: Observe the concentration when the insect behavior saturates (that is, the point at which the attracting ability does not increase significantly with the increase of the volatile gas concentration), such as 500 ppm.

[0180] If the volatile gas concentration is higher than Nd2, the response of the insect reaches an obvious strong attraction level.

[0181] The range of volatile gas concentration values corresponding to Level L1 is ;

[0182] The range of volatile gas concentration values corresponding to Level L2 is ;

[0183] The range of volatile gas concentration values corresponding to Level L3 is ;

[0184] ppm is the abbreviation of parts per million in English, meaning "one in a million". It is a commonly used concentration unit, used to represent the content or proportion of a certain substance in a mixture (usually gas, liquid or solid);

[0185] The above range of volatile gas concentration values is only an example and is not limited. It is specifically adjusted and replaced according to the actual measurement data.

[0186] Further explanation: The calculation formula for the migration attraction index of the host area i is defined as:

[0187] ;

[0188] Among them, is the migration attraction index of the host area i;

[0189] is the volatile gas concentration value of the unit sub-area u1 corresponding to the host area i;

[0190] is the gas propagation rate of the unit sub-area u1 corresponding to the host area i;

[0191] is the gas gradient spatial change rate of the unit sub-area u1 corresponding to the host area i;

[0192] is to perform normalization processing on the same scale for the collected data of the same type, represents or or ;

[0193] , and are the weight factors of the corresponding parameters in the chemical signal field data;

[0194] , and The value ranges are all within the interval (0,1), and ;

[0195] It should be noted that for the chemical signal field data of the unit sub-regions corresponding to the four host regions, the minimum-maximum normalization operation respectively selects or or the global minimum value and the global maximum value, and takes the global minimum value and the global maximum value as , ;

[0196] ;

[0197] , for normalization. It is the global minimum value and the global maximum value in the chemical signal field data collected m1 times in history; it is set that 5 ≥ m1 ≥ 15, and m1 is a positive integer. In this embodiment, m1 takes 7; the range of the normalized data is [0, 1];

[0198] This embodiment is set as:

[0199] ; ; ;

[0200] , and the corresponding weights are determined by the entropy weight method and the fuzzy analytic hierarchy process (FAHP);

[0201] When the value is larger:

[0202] The concentration level of the chemical signal released by the host plant in the target host region i is higher, and the attraction is usually stronger.

[0203] The migratory insects will be more likely to locate the host plant corresponding to the host region i, and the migratory behavior tends to the host region i with a higher concentration.

[0204] The overall attraction shows a positive correlation.

[0205] When the value is smaller:

[0206] The concentration of the chemical signal is diluted, and it is difficult for migratory insects to perceive the host plant, so the attraction drops significantly.

[0207] When approaching the environmental background value, it is considered that the influence of the host plant in the host region i on attracting migratory insects is small or non-existent.

[0208] Judgment of the critical point:

[0209] Determine the threshold of the concentration value of the volatile gas that attracts migratory insects in the host plant in the host area i through experiments (for example, when the volatile gas concentration value is higher than 100 ppm and lower than 500 ppm, it has medium attraction; when it is higher than 500 ppm, it has strong attraction; and when it is lower than 100 ppm, the attraction to insects is weak).

[0210] Gas transmission rate Measures the diffusion speed of the volatile chemical gas released by the host plant in the host area i in space. The transmission rate is affected by factors such as wind speed and diffusion coefficient.

[0211] When The value is large:

[0212] The volatile gas can quickly diffuse to a farther place, enabling migratory insects to perceive the chemical signal of the host plant in the host area i within a larger range.

[0213] Within the effective diffusion range, the probability of migratory insects finding the host plant in the host area i and the success rate of migration increase.

[0214] The attraction is usually positively correlated.

[0215] When The value is too small:

[0216] The gas diffuses very slowly, and most of the volatile gas remains near the plant, with a limited diffusion range.

[0217] The range for insects to perceive the chemical signal shrinks, and the ability to induce migratory behavior is significantly reduced.

[0218] The attraction rule is:

[0219] Moderate gas transmission rate Is the strongest attraction;

[0220] Too small gas transmission rate Is weak attraction;

[0221] Too large gas transmission rate Will cause the attraction to decline;

[0222] Determine the moderate, too small, and too large values of through the experimental simulation method;

[0223] The experimental simulation method is designed to test the change trend of the attraction of migratory insects to the host plant in the host area i under different values by controlling the wind speed and diffusion conditions in the field or laboratory:

[0224] Experimental variable: Change the wind speed (ensuring The change of value), to simulate the diffusion process of actual volatile gases.

[0225] Experimental observation: Record the behavioral data of migratory insects (such as quantity, reaction time, efficiency of reaching host plants), and verify whether there are "moderate transmission rate range" and "excessive transmission rate threshold".

[0226] Spatial change rate of gas gradient It represents the change speed of the concentration gradient of chemical signals in space (unit: ppm / m). A large spatial change rate of gas gradient means that insects can clearly perceive the concentration difference of chemical signals and can locate to the high-concentration source of chemical signals through "gradient climbing".

[0227] When the value is large:

[0228] The concentration gradient difference of volatile gases in space is significant, and it is easier for insects to perceive and climb in the direction of higher concentration, so as to locate the host plants in host area i.

[0229] A larger gradient change rate directly promotes the enhancement of attraction.

[0230] When When the value is small:

[0231] The concentration of volatile gases in space is relatively uniform, the concentration gradient is not obvious, it is difficult for insects to perceive the directionality, and the migratory behavior is affected.

[0232] If the gradient approaches zero, it is equivalent to that the host plants in host area i have no attraction to insects.

[0233] The above relevant ranges such as larger, smaller, too small, too large and moderate are determined through simulation experiments based on specific migratory insects and corresponding host plants, and will not be elaborated here.

[0234] In this embodiment, based on migratory insects and corresponding host plants for simulation experiments, the following example strategies are given for the larger, smaller, too small, too large and moderate values of :

[0235] Too small value: less than or equal to 50 ppm;

[0236] Smaller value: 50 ppm to 100 ppm, only including the right endpoint value;

[0237] Moderate range: 100 ppm to 500 ppm, only including the right endpoint value;

[0238] Larger value: 500 ppm to 600 ppm, only including the right endpoint value;

[0239] Too large value: exceeding 600 ppm;

[0240] For the following are the strategies for larger, smaller, too small, too large, and moderate value ranges:

[0241] Too small value: less than 0.5 m / s;

[0242] Smaller value: from 0.5 m / s to 1.0 m / s, including only the left endpoint value;

[0243] Moderate range: from 1 m / s to 2.0 m / s, including only the left endpoint value;

[0244] Larger value: from 2.0 m / s to 2.5 m / s, including only the left endpoint value;

[0245] Too large value: greater than or equal to 2.5 m / s;

[0246] For the following are the strategies for larger, smaller, too small, too large, and moderate value ranges:

[0247] Too small value: less than 0.1 ppm / m;

[0248] Smaller value: from 0.1 ppm / m to 0.2 ppm / m;

[0249] Moderate range: from 0.2 ppm / m to 0.5 ppm / m, including only the right endpoint value;

[0250] Larger value: from 0.5 ppm / m to 0.56 ppm / m, including only the right endpoint value;

[0251] Too large value: greater than or equal to 0.56 ppm / m;

[0252] For the concentration of volatile gas the following are the strategies for larger, smaller, too small, too large, and moderate value ranges:

[0253] Too small value is less than or equal to 50 ppm;

[0254] Smaller value is 50 ppm - 100 ppm, including only the right endpoint value;

[0255] Moderate range is 100 ppm - 500 ppm, including only the right endpoint value;

[0256] Larger value is 500 ppm - 600 ppm, including only the right endpoint value;

[0257] Too large value is greater than 600 ppm.

[0258] Set the effective value range of to be (0, 1);

[0259] When As it gets closer to 1, it indicates that the host area i has a greater attraction to migratory insects and a greater impact on the behavior of migratory insects;

[0260] When As it gets closer to 0, it indicates that the host area i has a smaller attraction to migratory insects and a smaller impact on the behavior of migratory insects.

[0261] To avoid the situation where " Too large: resulting in too large a gas transmission rate (such as in a strong wind environment), the volatile gas is rapidly diluted, resulting in a decrease in the attraction of insects even though they can sense the gas over a long distance" conflicts with "moderate gas transmission rate is the strongest attraction;" When " too large", ; ; ; This setting can reduce the impact brought by " too large".

[0262] Furthermore, the olfactory nerve response model is shown by the following expression:

[0263] ;

[0264] Among them, is the output value of the olfactory nerve response model corresponding to the host area i;

[0265] is the concentration value of the volatile gas of the unit sub - area u1 corresponding to the host area i;

[0266] is the gas transmission rate of the unit sub - area u1 corresponding to the host area i;

[0267] is the gas gradient spatial change rate of the unit sub - area u1 corresponding to the host area i;

[0268] is the nerve reaction delay time of the migratory insects corresponding to the unit sub - area u1 of the host area i; g is a non - linear dynamic function, representing the mapping relationship from nerve stimulation to response; The determination method is as follows:

[0269] Experimental data collection:

[0270] Simulate the experimental reaction data of migratory insects to different volatile gas concentration values, gas transmission rates and gas gradient spatial change rates in this embodiment; through actual measurement, obtain the nerve reaction delay time of insects under different conditions.

[0271] Experimental object:

[0272] Select the target migratory insect species, ensuring that the individuals are healthy and consistent (e.g., the same species, gender, age group).

[0273] Experimental environment:

[0274] Establish a controllable laboratory environment and assemble the required equipment to control the concentration value of volatile gases, the gas propagation rate, and the spatial change rate of the gas gradient.

[0275] Use a wind tunnel experimental device to simulate the real environment of insects flying towards the odor source.

[0276] Data fitting:

[0277] Use statistical methods or machine learning models to fit the collected experimental data;

[0278] Set the relationship between different volatile gas concentration values, gas propagation rates, and spatial change rates of gas gradients and the neural response delay time as: , where E1 is the correlation function fitted according to the data;

[0279] Parameterized model construction:

[0280] Construct a parameterized model, using as the input, and output .

[0281] E1 is a linear or non-linear correlation function, and the specific form of E1 depends on the complexity of the experimental results and the fitting effect.

[0282] Model validation and optimization:

[0283] Use a part of the data for validation to ensure that the model can accurately predict .

[0284] Use electrophysiological monitoring to directly measure neural signals, and the method is as follows:

[0285] Utilize electrophysiological methods, such as Electroantennography (EAG) or Single-Neuron Recording.

[0286] Connect the antenna or olfactory receptor of the insect to the recording electrode and monitor the change in the electrical signal after it senses the odor signal.

[0287] Experimental procedure:

[0288] Set up the odor stimulus source and control through the equipment;

[0289] Release odor signals to experimental insects and control the stimulation interval time;

[0290] Record the response potential of the antenna or olfactory nerve, and observe the time difference between the stimulation and the nerve response. This time difference is the .

[0291] Output data:

[0292] Record as: input conditions and the nerve response delay time correspondence table.

[0293] Set The high response threshold and low response threshold of are respectively and ;

[0294] When , it is determined that the chemical signal field data has a strong attraction to migratory insects, and migratory insects are more likely to locate the source; the probability of being more likely to locate the source in this embodiment is more than 60%;

[0295] When , it is determined that the chemical signal field data has a medium attraction to migratory insects, and the ability of migratory insects to locate the source is medium; the finding probability of "the ability to locate the source is medium" in this embodiment is in the interval (40%, 60%);

[0296] When , it is determined that the chemical signal field data has a weak attraction to migratory insects, and migratory insects are not easy to locate the source. The finding probability of "not easy to locate the source" in this embodiment is below the interval of 40%;

[0297] This embodiment The specific expression of is:

[0298] ;

[0299] 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), and the sum of a1, a2, a3, and a4 is 1; a1, a2, a3, and a4 are determined by the entropy weight method and the fuzzy analytic hierarchy process (FAHP); is the index of the unit sub-region, and ;

[0300] , , and are the normalization processes of the same type of data collected on the same scale respectively;

[0301] In this embodiment, The effective value range is (0, 1); it is defined that and are 0.7 and 0.3 respectively; the values of 0.7 and 0.3 are only examples and are specifically determined by the expert group through simulation experiments; specifically, the fuzzy analytic hierarchy process (FAHP) is used to determine and .

[0302] Further explanation: Using the output result of the olfactory nerve response model, the migration attraction index is corrected, and the migration landing point of migratory insects is predicted through the corrected migration attraction index, specifically including:

[0303] Set The judgment interval of is ; and initially set The value ranges of are respectively ; ; and set ;

[0304] In this embodiment, It is determined by using the fuzzy analytic hierarchy process (FAHP), and the determination steps are as follows:

[0305] Clarify the goal:

[0306] The goal is to determine The threshold interval of, and predict the migration landing point of migratory insects through the migration attraction index.

[0307] Selection of influencing factors:

[0308] The evaluation index for determining the influence interval division is: the neural response intensity generated by the olfactory neurons corresponding to the output value of the olfactory nerve response model due to chemical signal stimulation, specifically the neural response delay time of migratory insects;

[0309] The corresponding to each unit sub-region in the host area i, and ;

[0310] The flight path from the chemical signal source.

[0311] Establish a hierarchical model:

[0312] The first layer (goal layer): Determine [Nd1, Nd2].

[0313] The second layer (index layer): Evaluation indexes related to the threshold (such as insect behavior response data, gas concentration threshold, environmental factors).

[0314] Construct a fuzzy judgment matrix:

[0315] According to expert experience or experimental data, use fuzzy language variables to evaluate the impact of different indicators on the target.

[0316] Weight calculation:

[0317] Fuzzy comprehensive evaluation: Use the operation rules of fuzzy numbers to perform weighted calculations on the relative importance of each indicator.

[0318] Defuzzification: Convert the fuzzy number into an exact value through methods such as the centroid method and the maximum possibility method to obtain the weights of each indicator.

[0319] Determine the specific threshold interval:

[0320] Combined with the characteristics of the exponential function and the experimental data of insect behavior, use the weights processed by FAHP to delimit the threshold interval;

[0321] When Since migratory insects are more likely to locate the source, the following corrections are made to the migratory attraction index:

[0322] ;

[0323] When Since migratory insects are not easy to locate the source, the following corrections are made to the migratory attraction index:

[0324] ;

[0325] Among them, is the corrected migratory attraction index;

[0326] When No correction is made to the migratory attraction index;

[0327] If It indicates that the host plants in host area i have high attractiveness to the corresponding migratory insects, and the primary host plant L1 or the secondary host plant L2 or the tertiary host plant L3 is used as the predicted location of the migratory landing point of the migratory insects;

[0328] Principle and reason for selection:

[0329] Comprehensive high sensitivity and high attractiveness: It shows that insects respond particularly strongly to chemical signals in the current environment. Combined with the high value, it indicates that the primary host plant L1 or the secondary host plant L2 or the tertiary host plant L3 is the main target of insect migration under the current environmental conditions.

[0330] Precisely locate the migration landing point: Selecting the primary host plant L1 or the secondary host plant L2 or the tertiary host plant L3 can more accurately predict the migration direction and landing point of insects, ensuring the reliability of the prediction results.

[0331] If takes a value within the interval it indicates that the host plants in host area i have medium attraction to the corresponding migratory insects. The primary host plant L1 or the secondary host plant L2 is used as the predicted position of the migration landing point of the migratory insects.

[0332] Selection principle and reason:

[0333] Prioritize selecting the plant with the highest attraction: The value indicates that the primary host plant L1 or the secondary host plant L2 has a more significant attraction to insects. Therefore, selecting the primary host plant L1 or the secondary host plant L2 as the predicted position of the migration landing point can more effectively identify the migration target of insects.

[0334] Optimize the prediction accuracy: By prioritizing the selection of the primary host plant L1 or the secondary host plant L2, the accuracy of the migration landing point prediction can be improved, reducing misjudgments.

[0335] If then the primary host plant L1 is used as the predicted position of the migration landing point of the migratory insects.

[0336] Selection principle and reason:

[0337] Prioritize selecting the host plant of a higher level: Although the value of the current host plant does not meet the high - attraction standard, due to showing that the insects have a strong response to chemical signals, the insects tend to select the higher - level primary host plant L1 as the migration landing point to meet their survival and reproduction needs.

[0338] Compensate for the deficiency of the low value: By selecting the primary host plant L1, the situation of insufficient attraction of the host plants in the current area can be compensated, ensuring the rationality and effectiveness of the migration landing point prediction.

[0339] Example 2:

[0340] Please refer to Figure 2 , a migratory insect migration prediction system, which is used to execute the migratory insect migration prediction method described above, including:

[0341] Region division module: used to determine multiple host plants of the same type associated with migratory insects within the monitoring region, define the regions where these host plants are located as host regions, form multiple host regions, and divide the regions outside each host region into four unit sub-regions;

[0342] Data acquisition module: used to collect the current chemical signal field data of each unit sub-region in the monitoring region through the volatile organic compounds released by the host region. The chemical signal field data includes: volatile gas concentration value, gas propagation rate, and gas gradient spatial change rate;

[0343] Grade division module: used to divide the host plants according to the volatile gas concentration values of multiple host plants in the host region. The higher the volatile gas concentration value, the higher the grade of the host plant;

[0344] Index generation module: used to obtain the chemical signal field data corresponding to each host region; then, analyze these chemical signal field data, calculate and generate the migration attraction index of each host region;

[0345] Model construction module: used to construct an olfactory nerve response model of migratory insects. The olfactory nerve response model analyzes the different chemical signal field data in the four unit sub-regions corresponding to the host region, and combines the nerve reaction delay time of migratory insects to generate the output result of the nerve response. The output result is used to characterize the attraction of different chemical signal field data to migratory insects;

[0346] Landing point prediction module: used to correct the migration attraction index by using the output result of the olfactory nerve response model, and predict the migration landing point of migratory insects through the corrected migration attraction index.

[0347] Embodiment 3:

[0348] A readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the migratory insect migration prediction method as described above.

[0349] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0350] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0351] 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. They may be located in one place or distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the objectives of the solution of this embodiment.

[0352] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all 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 the 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, and the primary host plants L1 have 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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