Disease and pest migratory flight propagation path analysis method and system based on multi-source meteorological data

Abstract

The invention discloses a multi-source meteorological data-based pest migratory flight propagation path analysis method and system. The method comprises the following steps of: 1, establishing a pest monomer migratory flight propagation path model and an airborne disease propagation path model which comprise insect flight conditions and meteorological data; 2, based on existing experiments, environment parameter values influencing insect flight conditions are obtained, and experiments are conducted on migratory pests needing to be simulated and predicted and airborne diseases needing to be simulated and predicted; 3, extracting historical or future (short-term) meteorological data from the multi-source meteorological data; 4, calculating to obtain a disease propagation path and a pest monomer migration propagation path; step 5, in each time step, replicating the pest monomers to enable the pest monomers to become a pest group with a plurality of individuals, defining an ecological strategy for each individual in the group, and performing group collaborative simulation; aiming at migratory insects, refined dynamic simulation of migration behaviors of single insects and groups is realized, and accurate analysis and early warning of regional group pest and disease outbreak can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural pest and disease monitoring and early warning, and in particular to a method and system for analyzing pest and disease migration and propagation paths based on multi-source meteorological data. Background Art

[0002] The simulation of the migration and spread of pests and diseases is able to simulate and predict the origin, migration period and landing area of ​​the migration and spread of pests and diseases, and is the core link of accurate monitoring and early warning. Traditional meteorological trajectory models (such as HYSPLIT) are mostly based on the assumption of passive particle diffusion driven by wind fields. Insects or pathogenic spores are regarded as particles without autonomous movement ability, and the migration trajectory is calculated by linear superposition of wind speed and wind direction. Although such methods can reflect the dominant role of large-scale wind fields, they ignore the dynamic influence of the insects' own flight ability, environmental adaptability and group behavior, resulting in significant deviations between the simulation results and the actual migration path. In the existing technology, the single insect flight trajectory simulation method based on single meteorological data is relatively mature, but these methods mainly consider the impact of meteorological factors such as wind speed and temperature on single individuals, and do not consider the changes in the insects' own migration speed and direction under different environmental conditions, and fail to reflect the complex behavior of interactions between groups. In addition, most existing models do not incorporate energy consumption constraints, which allows insects to migrate indefinitely in the simulation, deviating from the laws of real biology. In complex terrain scenarios, traditional methods also lack the ability to incorporate actual terrain obstacle data to avoid flight paths and prevent blockages on migration paths. Furthermore, due to noise and data discreteness in meteorological data, individual individual motion trajectories can be significantly uneven, affecting simulation accuracy.

[0003] Existing airborne disease models often overlook the modulatory effects of terrain gradients on near-surface wind patterns and the inhibitory effects of economic factors (such as regional agricultural density) on transmission risk, resulting in risk predictions that are inconsistent with actual conditions. Steep terrain may slow disease spread by altering local airflow, while improved prevention and control measures in highly economically developed regions may reduce the probability of disease colonization. The absence of these multidimensional factors limits the models' comprehensive predictive capabilities.

[0004] Meteorological conditions such as temperature, humidity, wind direction, and wind speed significantly impact pests and diseases. With the increasing diversification and refinement of meteorological data, effectively utilizing multi-source, multi-scale meteorological data, combined with the biological characteristics of pests and diseases, to construct a precise system for analyzing their migration and spread has become a key technical challenge in current agricultural pest and disease control. Therefore, an integrated system that uses multi-source, multi-scale meteorological data to accurately analyze the migration and spread of pests and diseases is urgently needed. Summary of the Invention

[0005] In view of the deficiencies of the existing technology, the present invention provides a method and system for analyzing the migration and propagation paths of pests and diseases based on multi-source meteorological data.

[0006] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0007] The method for analyzing the migratory transmission paths of pests and diseases based on multi-source meteorological data includes the following steps:

[0008] Step 1: Establish a pest migration and propagation path model and an airborne disease propagation path model that include insect flight conditions and meteorological data.

[0009] Step 2: Based on the existing experiments, the environmental parameter values ​​that affect the flight conditions of insects and the spread of airborne diseases are obtained, and the migratory pests and airborne diseases experiments that need to be simulated and predicted are carried out.

[0010] Step 3: extract meteorological data from multi-source meteorological data;

[0011] Step 4: Substitute the environmental parameter values ​​of the insect flight conditions and the meteorological data obtained in steps 2 and 3 into the pest single migration propagation path model to calculate the pest single migration propagation path;

[0012] Step 5: In each time step, the pest monomer is replicated to turn it into a pest group with multiple individuals. A strategy is defined for each individual in the pest group. The strategies include cooperation, competition, and following. It is defined that when two cooperative strategy individuals meet, the energy gain increases; when a competitive strategy individual meets a cooperative strategy individual, the competitive strategy individual obtains high benefits, the cooperative strategy individual reduces benefits, and the following strategy individual obtains medium benefits regardless of the strategy individual it interacts with. A flight strategy is established for each strategy individual. Each individual will observe the cumulative energy benefits F of other individuals in its neighborhood within time step t. i (t), and imitate the flight strategy of the individual with the highest cumulative energy gain in its neighborhood in the next time step t+1;

[0013] Step 6: Use a heat map to display the changes in population density based on the movement trajectory of the pest population, and convert the heat map to a map projection for drawing;

[0014] Step 7: Substitute the environmental parameter values ​​and meteorological data of airborne disease transmission obtained in steps 2 and 3 into the airborne disease transmission path model to calculate the airborne disease transmission path.

[0015] Step 8. Convert the airborne disease transmission path to a map projection and draw it.

[0016] To optimize the above technical solutions, specific measures taken also include:

[0017] In step 1, the pest migration and propagation path model is:

[0018]

[0019] where x and y are the solutions to the system of equations:

[0020]

[0021] Among them, (lon1,lat1) is the updated geographic location, are the insect autonomous motion displacement vector, wind field displacement vector, and initial position offset vector, respectively. is the insect motion vector, is the wind field driving vector, Δt is the time step, (lon0, lat0) is the initial longitude and latitude, u and v are the radial and latitudinal horizontal velocities of the wind, θ insect is the initial flight angle of the insect, inertia weight is the motion vector smoothing inertia weight, is the velocity vector of the insect in the previous time step, v insect is the initial flight speed of the insect, α T ,β H ,γ W They are temperature influence factor, humidity influence factor and vertical wind influence factor respectively. If the temperature T is T opt Within the range, α T =0; if the humidity H is at H opt Within the range, β H =0, τ is the attenuation coefficient after the temperature and humidity exceed the optimal flight temperature and humidity range, T is the temperature at that time, T opt is the optimal flight temperature range, H is the humidity at that time, and H opt is the optimal flight humidity range, W vertical is the vertical wind speed, E consumed Energy consumed by insects for flight, E budget It is the initial energy reserve of insects. is the unit vector of the insect's flight direction;

[0022] The transmission path model of airborne diseases is:

[0023]

[0024] Among them, lon t and lat t is the latitude and longitude at time t, and the wind speed correction coefficient at different heights z is the current altitude, ref is the reference height, z0 is the ground roughness, β is the wind direction deviation sensitivity coefficient, Δθ is the angle between the current wind direction and the dominant wind direction, α is the distance attenuation coefficient, d 2D is the two-dimensional plane moving distance, σ is the terrain slope sensitivity coefficient, grad is the terrain slope gradient, α GDPis the economic factor coefficient, U is the GDP benchmark unit, and GDP is the per capita gross domestic product.

[0025] In step 2, based on existing experiments, the environmental parameter values ​​that affect insect flight conditions are obtained, including the insect's flight altitude, speed and direction, the temperature influencing factor, humidity influencing factor and vertical wind influencing factor during insect flight, the insect's initial reserve energy, the optimal flight temperature range, the optimal flight humidity range, the energy recovered per time step under optimal conditions, the energy consumed by insect flight, and the attenuation coefficient after the temperature and humidity exceed the optimal flight temperature and humidity range. The environmental parameter values ​​that affect the spread of airborne diseases include the current altitude, ground roughness, the angle between the current wind direction and the dominant wind direction, and the terrain slope gradient.

[0026] In step 3, meteorological data including radial and latitudinal wind speeds u and v, vertical airflow ω, temperature, and humidity are extracted from multi-source meteorological data.

[0027] In step 4, the specific steps for calculating the migration and propagation path of individual pests include:

[0028] 1) Calculation of insect flight speed and direction:

[0029] Vector of insect flying by Wind Vector and the insect's original position vector The new position vector is obtained by weighted superposition of the three The formula is as follows:

[0030]

[0031] According to the wind direction component (x1, y1), the coordinate component of the insect's own flight velocity vector (x2, y2) and the flight angle θ, the formula Determine the direction of the insect's flight vector (x,y) using the formula Constrain the size of this vector to ensure that the actual flight speed and flight angle of the insect after considering temperature and humidity corrections are consistent, using the formula Perform vector summation on the insect's own displacement and the wind field displacement, calculate the resultant displacement, and obtain the new position (lon1, lat1);

[0032] 2) Insect flight speed correction:

[0033] According to the current temperature T and the insect's optimal flight temperature range T opt The difference between them is calculated by the formula temp factor =1-α T ·|TT opt |Calculate temperature factor temp factor, thereby quantifying the correction amplitude of temperature deviation on insect speed; then, through the formula hum factor =1-β H ·|HH opt |Calculate humidity factor hum factor If the current humidity H exceeds the reference humidity range H opt When the vertical wind speed w vertical Calculate the vertical wind speed influence factor w factor , and finally, the initial flight speed v of the insect insect_speed With temp factor , hum factor and w factor Multiply them separately to get v adj ,

[0034] 3) Terrain obstacle avoidance:

[0035] During the insect migration, if the new location is in an area with terrain obstacles, the flight angle θ is gradually adjusted multiple times until an obstacle-free path is obtained;

[0036] 4) Energy consumption constraints:

[0037] When the insect's reserve energy is lower than the energy consumed by movement, the insect stops moving and recovers energy according to the environment to avoid the model from having an infinite migration path that is out of touch with reality. First, the moving distance dx and dy in the longitude and latitude directions of the input insect are converted into the actual distance d xm and d ym , and then calculate the total moving distance D, the formula is According to the moving distance D, the current ambient temperature T and the energy scaling factor E scale Calculate the energy consumption E of this move cost , whose formula is E cost =D×(1+α×T)×E scale , α is the linear adjustment coefficient of temperature on energy consumption, energy scaling factor E scale From the experiment, it is found that when an insect wants to move, it first calculates the energy consumed by this movement, and then compares it with the energy the insect currently has to determine whether there is enough energy to complete the movement. If the energy is insufficient, the insect's position will not be updated, the insect will stop flying and start to recover energy. If the current energy E is greater than or equal to the energy consumption E cost , then update the insect's position and energy state, and accumulate the cumulative energy consumption, the formula is (x new ,y new ) is the updated insect position, E new For the renewed energy,

[0038] If the current energy E is less than the energy consumption E cost , calculate the energy recovery amount and update the energy state according to the humidity H at that time, the formula is α is the attenuation factor, obtained through experiments, H min and H max are the upper and lower limits of the optimal flight humidity range, and the recovered energy E recovery Added to the current energy E, the formula is E new =min(E+E recovery ,E max_limit ), E max_limit It is the maximum energy limit for insects.

[0039] In order to eliminate the random errors caused by numerical calculations and environmental fluctuations during the simulation process, a custom Kalman filter is introduced to smooth the migration and propagation paths of individual pests calculated in step 4:

[0040] Define the initial state vector as where lon k and lat k They represent the longitude and latitude coordinates of the insect at time k, v_lon k and v_lat k They represent the longitude and latitude speeds of the insect at time k respectively. The Kalman filter process is as follows: (1) Prediction step: Calculate the predicted state vector x obtained at time k based on the state estimation at time k-1 k|k-1 , the state transition formula is x k|k-1 =Fx k-1|k-1 , calculate the predicted covariance matrix P obtained at time k based on the covariance estimate at time k-1 k|k-1 , the covariance prediction formula is P k|k-1 =FP k-1|k-1 F T +Q, where the state transfer matrix F is Δt is the time step, Q is the process noise covariance matrix, x k-1|k-1 represents the optimal estimated state vector at time k-1, P k-1|k-1 represents the optimal estimated covariance matrix at time k-1; (2) Update step: measure the residual covariance matrix Calculate the Kalman gain, the formula is Substitute the above calculation results into the formula x k|k =x k|k-1 +K k (z k -H k x k|k-1 ); Covariance matrix update formula P k|k =(IKk H k )P k|k-1 , where S k is the measurement residual covariance matrix, H k is the observation matrix, K k is the Kalman gain weight, z k is the measurement value vector at time k, x k|k is the optimal estimated state vector at time k, P k|k is the optimal estimated covariance matrix at time k, R k is the measurement noise covariance matrix, and I is the identity matrix.

[0041] In step 5, a flight strategy is established for each strategy individual. Specifically, if it is a cooperative strategy individual, the flight direction θ of the cooperative strategy individual is adjusted. i , so that it tends to the center of the group or maintains the formation; if it is a competitive strategy individual, the individual will actively deviate from the group and fly towards the resource-intensive area, and the direction randomness will increase; if it is a following strategy individual, it will directly copy the flight direction of the highest-profit individual in the neighborhood.

[0042] In step 5, each individual observes the cumulative energy gain F of other individuals in its neighborhood within time step t i (t), and in the next time step t+1, imitate the flight strategy of the individual with the highest cumulative energy gain in its neighborhood as follows:

[0043] Each individual observes the cumulative benefits of other individuals in its neighborhood at time step t in, is the set of other individuals in the neighborhood of individual i at time t, is the strategy adopted by individual i at time t, is the strategy adopted by individual j at time t, Based on and The energy gain when interacting with the strategies of the neighbors. If there is an individual with higher benefits in the neighborhood, the current individual will imitate its flight strategy at the next time step t+1. After the strategy is updated, individuals have a certain probability of random mutation to simulate environmental uncertainty or exploration behavior.

[0044] In step 6, the heat map is converted to a map projection using Cartopy, matplotlib, and the Shp files of the relevant maps and terrain obstacles.

[0045] The system of applying the pest migration and propagation path analysis method based on multi-source meteorological data comprises a data reading module, a data processing module and a user interaction module connected in sequence, wherein the data reading module is used to collect insect flight conditions, parameter values ​​of airborne disease propagation and extract meteorological data from multi-source meteorological data, and send the insect flight conditions, parameter values ​​of airborne disease propagation and meteorological data to the data processing module, wherein the data processing module stores a pest single migration and propagation path model, an airborne disease propagation path model and a group collaborative simulation model, and the data processing module can input the received parameter values ​​of insect flight conditions and meteorological data into the pest single migration and propagation path model, the parameter values ​​of airborne disease propagation and meteorological data input module. The airborne disease transmission path model is input to calculate the pest migration and transmission path of individual pests, as well as the airborne disease transmission path. The data processing module can also replicate the pest individual in each time step through the group collaborative simulation model to turn it into a pest group with multiple individuals. Strategies are defined for each individual in the pest group. The strategies include cooperation, competition, and following. When two cooperative strategy individuals meet, the energy gain increases; when a competitive strategy individual meets a cooperative strategy individual, the competitive strategy individual obtains high benefits, the cooperative strategy individual reduces benefits, and the following strategy individual obtains medium benefits regardless of the strategy individual it interacts with. A flight strategy is established for each strategy individual. Each individual will observe the cumulative energy benefits F of other individuals in its neighborhood within the time step t. i (t), and imitate the flight strategy of the individual with the highest cumulative energy gain in its neighborhood in the next time step t+1 to obtain the movement trajectory of the pest group, display the change of group density with a heat map, convert the heat map to be drawn under map projection, convert the airborne disease transmission path to be drawn under map projection, and send the drawn pest and disease migration and transmission path map to the user interaction module; the user interaction module is used to publish the pest and disease migration and transmission path map on the Internet or local area network.

[0046] The beneficial effects of the present invention are:

[0047] 1. By integrating multi-source meteorological data, insect ecological habits, environmental adaptability, insect energy consumption models, and game theory strategies for group coordination, the system achieves refined dynamic simulation of individual and group insect migration behavior. The system uses meteorological data such as wind speed, vertical velocity, temperature, and humidity to dynamically adjust insect migration speed and direction, while also incorporating energy consumption models and energy replenishment mechanisms based on the influence of distance, temperature, and humidity. Furthermore, each individual is assigned game strategies such as cooperation, competition, and following. Through neighborhood benefit interactions, local strategy imitation, and low-probability mutations, the adaptive evolution of individual strategies is achieved, demonstrating both individual movement characteristics and the coordination and competition effects within the group. The system uses custom Kalman filtering technology to smooth motion trajectories, reduce noise interference, and improve prediction accuracy. By loading terrain obstacle data and combining it with collision detection logic, individuals can automatically adjust their routes and avoid geographical obstacles, significantly enhancing the practical applicability of the model. This system is particularly suitable for monitoring and controlling major migratory pests (such as fall armyworm and locusts) and diseases (such as wheat stripe rust and southern corn rust), and can provide early warning of regional mass pest and disease outbreaks. Its application areas include agricultural pest and disease monitoring and early warning, plant protection decision support, and scientific research.

[0048] 2. This invention innovatively constructs a multi-dimensional, coupled topography-environment-economy propagation model by integrating multi-source meteorological data with the characteristics of airborne disease transmission. Based on digital elevation model (DEM) data, a terrain gradient correction algorithm is employed to accurately analyze the modulating effect of terrain on the near-surface wind field, dynamically capturing the deflection characteristics of spore trajectories caused by airflow shear. Furthermore, a dual mechanism of temperature and humidity threshold determination and economic factor attenuation is simultaneously introduced: when the ambient temperature and humidity meet the conditions for spore germination, the system automatically marks high-risk areas for spread. Simultaneously, the regional risk value is dynamically adjusted based on the impact of regional economic levels (such as GDP) on prevention and control measures. Furthermore, through real-time calculation of three-dimensional slope gradients and surface travel distance, the propagation rate of pathogen spores is dynamically corrected. Flat areas accelerate spore migration, realistically reproducing the dual effects of terrain on both inhibiting and promoting disease transmission. This integrated design enables refined dynamic simulation of airborne disease propagation paths, providing highly timely technical support for early prevention and control decisions and enabling early warning of regional airborne disease outbreaks. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a schematic diagram of the migration trajectory of the American bollworm in Texas, USA from March 20 to March 22, 1995, from the article "An advanced numerical trajectory model tracks the migration of the corn earworm in Texas, USA";

[0050] Figure 2A schematic diagram of the trajectory of the American bollworm migration in Texas, the United States from March 20 to March 22, 1995, according to an embodiment of the method for analyzing pest migration and propagation paths based on multi-source meteorological data of the present invention;

[0051] Figure 3 This is the spread trajectory of southern corn rust at 118.87°E, 32.03°N from 08:00 to 22:00 on August 22, 2015, using the HYSPLIT model;

[0052] Figure 4 This is a propagation trajectory map of southern corn rust at 118.87°E, 32.03°N from 08:00 to 22:00 on August 22, 2015, using the airborne disease propagation model of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of this application.

[0054] The present invention provides a pest migration and propagation path analysis method based on multi-source meteorological data, and obtains a pest migration formula. where x and y are the solutions to the system of equations

[0055] Among them, (lon1,lat1) is the updated geographic location, are the insect autonomous motion displacement vector, wind field displacement vector, and initial position offset vector, respectively. is the insect motion vector, is the wind field driving vector, Δt is the time step, (lon0, lat0) is the initial longitude and latitude, u and v are the radial and latitudinal horizontal velocities of the wind, θ insect is the initial flight angle of the insect, inertia weight is the motion vector smoothing inertia weight, is the velocity vector of the insect in the previous time step, v insect is the initial flight speed of the insect, α T ,β H ,γ W They are temperature influence factor, humidity influence factor and vertical wind influence factor respectively. If the temperature T is T opt Within the range, α T =0; if the humidity H is at H opt Within the range, βH =0, τ is the attenuation coefficient after the temperature and humidity exceed the optimal flight temperature and humidity range, T is the temperature at that time, T opt is the optimal flight temperature range, H is the humidity at that time, and H opt is the optimal flight humidity range, W vertical is the vertical wind speed, E consumed Energy consumed by insects for flight, E budget It is the initial energy reserve of insects. is the unit vector of the insect's flight direction.

[0056] The following is a specific implementation case to illustrate the implementation steps:

[0057] Step 1: Parameter setting

[0058] The flight conditions required for the insects to be simulated and predicted are set, and a series of experimental parameters are initialized: (1) Flight conditions including flight altitude, speed, direction, starting point coordinates, and start time. (2) Temperature and humidity ranges suitable for flight, as well as factors affecting temperature, humidity, and vertical wind. (3) Insect reserve energy, optimal humidity range, energy recovered per step under optimal conditions, and the recovery rate attenuation factor when humidity exceeds the optimal range.

[0059] Extract horizontal velocity (u, v), temperature (t), humidity (r), and vertical airflow (w) from multi-source meteorological data. If the temperature unit is Kelvin, convert the temperature data to Celsius and the timestamp to Beijing time. The temperature conversion formula is: T (℃) = T (K) - 273.15, and the time conversion formula is: t new =t old +3600. Where T(K) is the temperature in Kelvin in the original meteorological data, T(℃) is the temperature in Celsius after conversion, and t old is the original timestamp, t new The time after conversion.

[0060] Step 2: Calculate flight speed and direction

[0061] Based on the actual flight height of the target, a weighted addition method is used to accurately calculate the new position of the insect. Wind Vector (composed of wind speed vector [u,v]), and the original position vector (The longitude and latitude coordinates are converted to metric coordinates), and the three are weighted superposition to obtain the new position vector The formula is as follows:

[0062] According to the wind direction component (x1, y1), the coordinate component of the insect's own flight velocity vector (x2, y2) and the flight angle θ, the formula Determine the direction of the insect's flight vector (x,y) using the formula Constrain the size of this vector to ensure that the actual flight speed and flight angle of the insect after considering temperature and humidity corrections are consistent, using the formula Perform vector summation on the insect's own displacement and the wind field displacement, calculate the resultant displacement, and obtain the new position (lon1, lat1).

[0063] Step 3: Flight speed correction

[0064] Different from the traditional single flight speed assumption, in order to more accurately simulate the flight characteristics of insects in the actual environment, it is necessary to comprehensively consider the multiple effects of temperature, humidity and vertical wind. opt The difference between them is calculated by the formula temp factor =1-α T ·|TT opt |Calculate temperature factor temp factor , thereby quantifying the correction amplitude of temperature deviation on insect speed; then, through the formula hum factor =1-β H ·|HH opt |Calculate humidity factor hum factor If the current humidity H exceeds the reference humidity range H opt When the vertical wind speed w vertical Calculate the vertical wind speed influence factor w factor , and finally, the initial flight speed v of the insect insect_speed With temp factor , hum factor and w factor Multiply them separately to get v adj .

[0065] Step 4: Terrain obstacle avoidance

[0066] During the insect's migration, if the new location is in an obstacle area, the flight angle θ is gradually adjusted multiple times until an obstacle-free path is obtained;

[0067] Step 5: Energy Consumption Constraint

[0068] When the insect's reserve energy is lower than the energy consumed by movement, the insect stops moving and recovers energy according to the environment, avoiding the model from having an infinite migration path that is out of touch with reality. First, according to the input longitude and latitude movement distance dx and dy of the insect, it is first converted into the actual distance dxm and d ym , and then calculate the total moving distance D, the formula is According to the moving distance D, the current ambient temperature T and the energy scaling factor E scale Calculate the energy consumption E of this move cost , whose formula is E cost =D×(1+0.1×T)×E scale , energy scaling factor E scale From the experiment, it is found that when an insect wants to move, it first calculates the energy consumed by this movement, and then compares it with the energy the insect currently has to determine whether there is enough energy to complete the movement. If the energy is insufficient, the insect's position will not be updated, the insect will stop flying and start to recover energy. If the current energy E is greater than or equal to the energy consumption E cost , then update the insect's position and energy state, and accumulate the cumulative energy consumption, the formula is (x new ,y new ) is the updated insect position, E new For the updated energy

[0069] If the current energy E is less than the energy consumption E cost , calculate the energy recovery amount and update the energy state according to the humidity H at that time, the formula is α is the attenuation factor, obtained through experiments, H min and H max are the upper and lower limits of the optimal flight humidity range, and the recovered energy E recovery Added to the current energy E, the formula is E new =min(E+E recovery ,E max_limit ), E max_limit It is the maximum energy limit for insects.

[0070] Step 6: Customize Kalman filter to correct trajectory data

[0071] In order to eliminate the random errors caused by numerical calculations and environmental fluctuations during the simulation process, a custom Kalman filter is introduced to smooth the position information processed above to obtain a more accurate trajectory.

[0072] Define the initial state vector as: where lon k and lat k They represent the longitude and latitude coordinates of the insect at time k, v_lon k and v_lat kThey represent the longitude and latitude speeds of the insect at time k, respectively. The Kalman filter process is as follows: (1) Prediction step: Calculate the predicted state vector x obtained at time k based on the state estimation at time (k-1) k|k-1 , the state transition formula is x k|k-1 =Fx k-1|k-1 , calculate the predicted covariance matrix P obtained at time k based on the covariance estimate at time (k-1) k|k-1 , the covariance prediction formula is P k|k-1 =FP k-1|k-1 F T +Q, where the state transfer matrix F is Δt is the time step, Q is the process noise covariance matrix, x k-1|k-1 represents the optimal estimated state vector at time k-1, P k-1|k-1 represents the optimal estimated covariance matrix at time k-1; (2) Update step: measure the residual covariance matrix Calculate the Kalman gain, the formula is Substitute the above calculation results into the updated state and correct the state estimate. The formula is x k|k =x k|k-1 +K k (z k -H k x k|k-1 ); Covariance matrix update formula P k|k =(IK k H k )P k|k-1 Among them, S k is the measurement residual covariance matrix, H k is the observation matrix, K k is the Kalman gain weight, z k is the measurement value vector at time k, x k|k is the optimal estimated state vector at time k, P k|k is the optimal estimated covariance matrix at time k, R k is the measurement noise covariance matrix, and I is the identity matrix.

[0073] Step 7: Group Collaborative Simulation

[0074] Traditional insect migration simulations are limited to individual behavior simulations. However, this model incorporates group coordination and strategic game mechanisms to dynamically simulate large-scale group movements. This model reflects the interactions between individuals and the evolution of local optimal strategies through a strategic interaction payoff matrix, neighborhood perception rules, and flight direction adjustments.

[0075] Profit is the combined benefit an individual gains from interacting with other individual strategies, specifically expressed as energy gain. Its calculation is based on the strategy interaction profit matrix P(S1, S2), where S1 and S2 represent the current individual strategy and the neighboring individual strategies. This matrix quantifies the gains and losses resulting from interactions between different strategies. The strategy set is defined as {cooperative strategy (C), competitive strategy (D), and follower strategy (F)}. When two cooperating individuals (C, C) meet, their energy gain increases due to reduced energy consumption caused by coordinated flight. When a competitive individual (D) meets a cooperative individual (C), the competitive party gains high gains through resource seizure, while the cooperative party experiences reduced gains. A follower strategy individual (F) achieves lower gains regardless of the strategy it interacts with.

[0076] The flight strategy dynamically adjusts the flight direction. If the flight strategy is cooperation (C), adjust the flight direction θ i They tend to move towards the center of the group or maintain formation, reducing energy through collaboration. If the flight strategy is competition (D), individuals actively deviate from the group and fly towards resource-intensive areas, which increases the randomness of the direction and consumes a lot of energy. If the flight strategy is following (F), they directly copy the flight direction of the highest-benefit individual in the neighborhood to reduce energy consumption.

[0077] Each individual (i or j) observes the cumulative benefits of other individuals in its neighborhood at time step t The neighborhood set N i Filter by Euclidean distance (x i ,y i ) and (x j ,y j ) The perception radius r determines the interaction range of the individual, ensuring that only individuals in the vicinity of the particle participate in the strategic game. If there is an individual with higher benefits in the neighborhood, the current individual will imitate its flight strategy. After the strategy is updated, individuals have a certain probability of random mutation to simulate environmental uncertainty or exploration behavior.

[0078] According to steps 2, 3, 4, 5, and 6, the group movement trajectory is further calculated, and finally the group density change is displayed as a heat map.

[0079] Step 8: Visualize the simulation trajectory

[0080] Using Cartopy, matplotlib and Shp files of related maps and terrain obstacles, the simulated trajectory results are converted to map projection and drawn.

[0081] The following is an example of the present invention:

[0082] The present invention provides an integrated system for analyzing the migration and propagation paths of pests and diseases based on multi-source meteorological data, and a method for simulating the migration of the cotton bollworm (Helicoverpa zea). The implementation steps are described below using a specific implementation case:

[0083] The Lower Rio Grande Valley (LRGV) in southern Texas is a key wintering area for the cotton bollworm (Helicoverpazea). The region's prevailing southerly low-level jet stream in spring fuels the insect's northward migration. This study, based on data from the paper "An Advanced Numerical Trajectory Model Tracks a Corn Earworm Moth Migration Event in Texas, USA," examined three 12-hour migrations of the cotton bollworm (Helicoverpazea) from 7:00 PM on March 20, 1995, to 7:00 PM on March 22, 1995. The model's performance was compared with the shortest distance (SDP) and point-to-point distance (PtP) of cotton bollworms carrying citrus pollen.

[0084] The first step is to set the initial parameters and environment construction according to the literature content

[0085] where x and y are the smaller solutions to the system of equations

[0086]

[0087] The same flight conditions as those in the paper were set, including flight speed, initial flight angle, and simulation time step. The temperature and humidity range suitable for flight, the influencing factors of temperature and humidity, vertical wind, and energy attenuation coefficient were set according to the literature. The ERA5 hourly pressure level data with a resolution of 0.25°*0.25° was read to obtain the meteorological conditions at the specified time (such as wind speed, temperature, humidity, etc.).

[0088] Table 1 Initialization parameters and environment construction of American bollworm:

[0089]

[0090] The second step is to establish individual behavior and energy models

[0091] To truly reflect the movement characteristics of insects during migration, the system introduces an energy consumption and replenishment mechanism. Each individual insect consumes a certain amount of energy during movement, depending on its own state and external environmental conditions. When energy is insufficient, it will automatically restore energy based on environmental factors.

[0092] The third step is to build a strategic game mechanism

[0093] The model assumes that individuals engage in game-playing interactions, each adopting a specific strategy (such as cooperation, competition, or following) during their movement. A payoff rule is set to describe the payoff differences resulting from interactions between individuals with different strategies. Individuals not only update their strategies based on local payoffs at each moment but also evolve their overall strategies locally by imitating higher-payoff neighbors and through random mutations.

[0094] Step 4: Single-unit migration trajectory simulation

[0095] The system constructs a migration relationship model based on the initial positions (lon0, lat0) including (97.40°W, 26.14°N), (97.78°W, 26.18°N), (98.03°W, 26.21°N), (98.23°W, 26.36°N), (98.39°W, 26.33°N), preset movement parameters and dynamically acquired meteorological data, and simulates the continuous movement of insects for 12 hours from 19:00 to 7:00 every day from March 20 to March 22, 1995. Substitute the parameters of the first step and the longitude and latitude of the initial position into p new In the formula, the temperature T, humidity H and vertical wind W at each moment are obtained by reading the meteorological data. vertical , if the temperature T is T opt Within the range, α T =0; if the humidity H is at H opt Within the range, β H = 0. Calculate (lon1, lat1) at each moment and keep Calculate the position at the next moment, and calculate 12 times in a row to get the final position. Each step of movement takes into account the insect's own flight tendency, the influence of the external environment and the interference of terrain obstacles. When encountering obstacles, the model will adjust the direction of movement. Finally, the 12 longitude and latitude points are connected and a custom Kalman filter is applied to smooth the curve to generate a smooth migration trajectory that complies with physical constraints.

[0096] Step 5: Group behavior simulation

[0097] Based on the results of the individual simulation, the system further generates multiple individuals around each pre-set central condition, forming a swarm. Each individual in the swarm not only moves according to its own energy and motion model, but is also influenced by the behavior of its neighbors. Individuals adjust their flight direction based on their local relative position and interactions with other individuals (e.g., repulsion or convergence). Simultaneously, within a certain time step, each individual engages in strategic competition, updating their respective behavioral strategies.

[0098] Step 6: Result Verification and Visualization

[0099]

[0100] like Figure 2 As shown in Figure 2, the fourth step calculates the hourly insect migration paths at 850, 900, 925, 950, and 975 hPa. The trajectory points and paths are compared with the models in the literature.

[0101] Comparison of SDP and PtoP indicators of various models

[0102] HYSPLIT WRF0 WRF1 MY_MODEL SDP (km) 7.1±9.4 4.0±3.1 3.2±3.7 4.61±3.69 PtoP(km) 19.3±16.9 19.2±19.1 10.1±4.0 13.86±8.72

[0103] Judging from the verification results, the method of the present invention not only realizes the dynamic simulation of insect migration paths, but also fully considers the influence of external obstacles, meteorological conditions and strategic interactions between individuals. Compared with traditional models, it can accurately simulate the flight trajectories of migrating insects in the atmosphere.

[0104] The simulation results are shown in a set of graphs. Figure 2 In the middle, the left picture shows the migration trajectory obtained by simulating individual cells under various central conditions, which makes it easier to observe the direction of the overall path. The right picture shows the spatial density distribution of the movement trajectory points of each individual in the group in the form of a heat map.

[0105] The present invention provides a method for analyzing the migratory transmission paths of pests and diseases based on multi-source meteorological data, and derives a formula for the transmission of airborne diseases: Among them, lon t and lat t is the latitude and longitude at time t, u and v are the horizontal wind speeds, and the wind speed correction coefficients at different heights z is the current altitude, ref is the reference height, z0 is the ground roughness, β is the wind direction deviation sensitivity coefficient, Δθ is the angle between the current wind direction and the dominant wind direction, α is the distance attenuation coefficient, d 2D is the two-dimensional plane moving distance, σ is the terrain slope sensitivity coefficient, grad is the terrain slope gradient, α GDP is the economic factor coefficient, U is the GDP benchmark unit, and GDP is the per capita gross domestic product.

[0106] The following is a specific implementation case to illustrate the implementation steps:

[0107] Step 1: Parameter setting

[0108] For the disease to be simulated and predicted, set the propagation conditions required for the experiment and initialize a series of parameters required for the experiment: (1) Propagation conditions including flight altitude, starting point coordinates, and start time. (2) Temperature and humidity ranges suitable for disease propagation. (3) Distance attenuation coefficient, terrain sensitivity coefficient, terrain effect coefficient on wind speed, wind speed height correction coefficient, economic parameter benchmark unit, risk reduction rate, wind field data reference altitude (m), etc.

[0109] Extract wind speed (u, v), vertical airflow (w), temperature (t), and humidity (r) from multi-source meteorological data, and load geographic elevation data DEM (30m). If the temperature unit is Kelvin, convert the temperature data to Celsius and the timestamp to Beijing time. The temperature conversion formula is: T (℃) = T (K) - 273.15, and the time conversion formula is: t new =t old +3600. Where T(K) is the temperature in Kelvin in the original meteorological data, T(℃) is the temperature in Celsius after conversion, and t old is the original timestamp, t new The time after conversion.

[0110] Table 2 Parameter settings Initialization parameters and environment construction of corn southern rust transmission trajectory:

[0111]

[0112] Step 2: Propagation simulation

[0113] The temperature and humidity thresholds are set based on the suitable germination conditions of corn southern rust spores.

[0114] Load the digital elevation model (DEM) data and obtain the horizontal gradient component G of each grid cell x and G y .in, E(x,y) represents the terrain elevation (meters) at longitude x and latitude y, and the gradient is in meters / degree.

[0115] For each time step, the center position and the corresponding pressure layer and time point, extracting the original wind speed component u from the meteorological data set ref 、v ref , using the logarithmic wind profile formula u=u ref ×α z , v=v ref ×α z Make altitude correction. Where λ is longitude, is the latitude, z is the current altitude, z0 is the surface roughness constant, zref is the reference height of wind field data.

[0116] For any geographic coordinate point, the elevation value is obtained by bilinear interpolation and its terrain gradient vector is calculated. move With the terrain gradient direction θ terrain When there is an angle, the terrain obstruction effect is calculated using the sine function, with the formula η = sin(Δθ). For any geographic coordinate point, the elevation value is obtained through bilinear interpolation, and its terrain gradient vector is calculated. move = atan2(v,u),θ terrain =atan2(G y ,G x ), Δθ=|θ move -θ terrain |.

[0117] Adjust the wind speed component according to the terrain sensitivity coefficient β:

[0118] When the temperature T and relative humidity RH meet the appropriate range: T min <T<T max , RH>RH ref , RH ref is the relative humidity reference value, then the wind speed moves along the latitude and longitude according to the corrected wind speed:

[0119] Based on the new coordinate position and the old coordinate position Calculate true 3D slope gradient Among them, λ, h is longitude, latitude and altitude, d is 2D is the great circle surface distance between two points.

[0120] At each step of the sequence, the present invention first uses the benchmark risk value Among them, β GDP is the economic factor coefficient, U is the GDP base unit, and GDP is the GDP per capita.

[0121] Considering the impact of economic level on risk, and then based on the actual moving distance d 2D Apply the decay formula with the gradient grad Among them, α is the distance attenuation coefficient, and β' is the adjusted terrain sensitivity coefficient.

[0122] Step 3: Disseminate visualization

[0123] The elevation data is superimposed on the coordinate axis using a topographic map. The trajectory is dynamically updated every hour. Clicking on a trajectory point will display the latitude and longitude, propagation time, and risk value.

[0124] According to the parameters in Table 2, the initialization parameters and environment of the corn southern rust propagation trajectory are set, and the airborne disease propagation formula is used to simulate the propagation trajectory of corn southern rust, as shown in the following example: Figure 3 Shown is the spread trajectory of southern corn rust at 118.87°E, 32.03°N from 08:00 to 22:00 on August 22, 2015, using the HYSPLIT model; Figure 4 Shown is a map of the spread of southern corn rust from 08:00 to 22:00 on August 22, 2015, at 118.87°E, 32.03°N, using the airborne disease transmission model of the present invention. This method incorporates DEM terrain gradients, showing significant deceleration, reversals, or deflections at mountain ridges, consistent with microscopic wind shear. Furthermore, considering the initial risk reduction effect of economic levels, a reasonable risk reduction is achieved between tortuous terrain and long-distance transmission. Comparative results demonstrate that the present method can more realistically simulate the spread of airborne diseases in the atmosphere.

[0125] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for analyzing the migratory spread of pests and diseases based on multi-source meteorological data, characterized by: The following steps are involved: Step 1: Establish a pest migration and propagation path model and an airborne disease propagation path model that include insect flight conditions and meteorological data. Step 2: Based on existing experiments, obtain the environmental parameter values ​​that affect insect flight conditions and the spread of airborne diseases, and conduct experiments on the migratory pests and airborne diseases that need to be simulated and predicted; Step 3: extract meteorological data from multi-source meteorological data; Step 4: Substitute the environmental parameter values ​​of the insect flight conditions and the meteorological data obtained in steps 2 and 3 into the pest single migration propagation path model to calculate the pest single migration propagation path; Step 5: In each time step, the pest monomer is replicated to turn it into a pest group with multiple individuals. A strategy is defined for each individual in the pest group. The strategies include cooperation, competition, and following. It is defined that when two cooperative strategy individuals meet, the energy gain increases; when a competitive strategy individual meets a cooperative strategy individual, the competitive strategy individual obtains high benefits, the cooperative strategy individual reduces benefits, and the following strategy individual obtains medium benefits regardless of the strategy individual it interacts with. A flight strategy is established for each strategy individual. Each individual will observe the cumulative energy benefits F of other individuals in its neighborhood within time step t. i (t), and imitate the flight strategy of the individual with the highest cumulative energy gain in its neighborhood in the next time step t+1; Step 6: Use a heat map to display the changes in population density based on the movement trajectory of the pest population, and convert the heat map to a map projection for drawing; Step 7: Substitute the environmental parameter values ​​and meteorological data of airborne disease transmission obtained in steps 2 and 3 into the airborne disease transmission path model to calculate the airborne disease transmission path. Step 8. Convert the airborne disease transmission path to a map projection and draw it.

2. The pest migration and propagation path analysis method based on multi-source meteorological data according to claim 1 is characterized by: In step 1, the pest migration and propagation path model is: where x and y are the solutions to the system of equations: Among them, (lon1,lat1) is the updated geographic location, are the insect autonomous motion displacement vector, wind field displacement vector, and initial position offset vector, respectively. is the insect motion vector, is the wind field driving vector, Δt is the time step, (lon0, lat0) is the initial longitude and latitude, u and v are the radial and latitudinal horizontal velocities of the wind, θ insect is the initial flight angle of the insect, inertia weight is the motion vector smoothing inertia weight, is the velocity vector of the insect in the previous time step, v insect is the initial flight speed of the insect, α T ,β H ,γ W They are temperature influence factor, humidity influence factor and vertical wind influence factor respectively. If the temperature T is T opt Within the range, α T =0; if the humidity H is at H opt Within the range, β H =0, τ is the attenuation coefficient after the temperature and humidity exceed the optimal flight temperature and humidity range, T is the temperature at that time, T opt is the optimal flight temperature range, H is the humidity at that time, and H opt is the optimal flight humidity range, W vertical is the vertical wind speed, E consumed Energy consumed by insects for flight, E budget It is the initial energy reserve of insects. is the unit vector of the insect's flight direction; The transmission path model of airborne diseases is: Among them, lon t and lat t is the latitude and longitude at time t, and the wind speed correction coefficient at different heights z is the current altitude, ref is the reference height, z0 is the ground roughness, β is the wind direction deviation sensitivity coefficient, Δθ is the angle between the current wind direction and the dominant wind direction, α is the distance attenuation coefficient, d 2D is the two-dimensional plane moving distance, σ is the terrain slope sensitivity coefficient, grad is the terrain slope gradient, α GDP is the economic factor coefficient, U is the GDP benchmark unit, and GDP is the per capita gross domestic product.

3. The method for analyzing the migration and spread of pests and diseases based on multi-source meteorological data according to claim 1, characterized in that: In step 2, based on existing experiments, the environmental parameter values ​​that affect insect flight conditions are obtained, including the insect's flight altitude, speed and direction, the temperature influencing factor, humidity influencing factor and vertical wind influencing factor during insect flight, the insect's initial reserve energy, the optimal flight temperature range, the optimal flight humidity range, the energy recovered per time step under optimal conditions, the energy consumed by insect flight, and the attenuation coefficient after the temperature and humidity exceed the optimal flight temperature and humidity range. The environmental parameter values ​​that affect the spread of airborne diseases include the current altitude, ground roughness, the angle between the current wind direction and the dominant wind direction, and the terrain slope gradient.

4. The method for analyzing the migration and spread of pests and diseases based on multi-source meteorological data according to claim 1, characterized in that: In step 3, meteorological data including radial and latitudinal wind speeds u and v, vertical airflow ω, temperature, and humidity are extracted from multi-source meteorological data.

5. The method for analyzing pest migration and propagation paths based on multi-source meteorological data according to claim 2, characterized in that: In step 4, the specific steps for calculating the migration and propagation path of individual pests include: 1) Calculation of insect flight speed and direction: Vector of insect flying by Wind Vector and the insect's original position vector The new position vector is obtained by weighted superposition of the three The formula is as follows: According to the wind direction component (x1, y1), the coordinate component of the insect's own flight velocity vector (x2, y2) and the flight angle θ, the formula Determine the direction of the insect's flight vector (x,y) using the formula Constrain the size of this vector to ensure that the actual flight speed and flight angle of the insect after considering temperature and humidity corrections are consistent, using the formula Perform vector summation on the insect's own displacement and the wind field displacement, calculate the resultant displacement, and obtain the new position (lon1, lat1); 2) Insect flight speed correction: According to the current temperature T and the insect's optimal flight temperature range T opt The difference between them is calculated by the formula temp factor =1-α T ·|TT opt |Calculate temperature factor temp factor , thereby quantifying the correction amplitude of temperature deviation on insect speed; then, through the formula hum factor =1-β H ·|HH opt |Calculate humidity factor hum factor If the current humidity H exceeds the reference humidity range H opt When the vertical wind speed w vertical Calculate the vertical wind speed influence factor w factor , and finally, the initial flight speed v of the insect insect_speed With temp factor , hum factor and w factor Multiply them separately to get v adj , 3) Terrain obstacle avoidance: During the insect migration, if the new location is in an area with terrain obstacles, the flight angle θ is gradually adjusted multiple times until an obstacle-free path is obtained; 4) Energy consumption constraints: When the insect's reserve energy is lower than the energy consumed by movement, the insect stops moving and recovers energy according to the environment to avoid the model from having an infinite migration path that is out of touch with reality. First, the moving distance dx and dy in the longitude and latitude directions of the input insect are converted into the actual distance d xm and d ym , and then calculate the total moving distance D, the formula is According to the moving distance D, the current ambient temperature T and the energy scaling factor E scale Calculate the energy consumption E of this move cost , whose formula is E cost =D×(1+α×T)×E scale , α is the linear adjustment coefficient of temperature on energy consumption, energy scaling factor E scale From the experiment, it is found that when an insect wants to move, it first calculates the energy consumed by this movement, and then compares it with the energy the insect currently has to determine whether there is enough energy to complete the movement. If the energy is insufficient, the insect's position will not be updated, the insect will stop flying and start to recover energy. If the current energy E is greater than or equal to the energy consumption E cost , then update the insect's position and energy state, and accumulate the cumulative energy consumption, the formula is (x new ,y new ) is the updated insect position, E new For the renewed energy, If the current energy E is less than the energy consumption E cost , calculate the energy recovery amount and update the energy state according to the humidity H at that time, the formula is α is the attenuation factor, obtained through experiments, H min and h max are the upper and lower limits of the optimal flight humidity range, and the recovered energy E recovery Added to the current energy E, the formula is E new =min(E+E recovery ,E max_limit ), E max_limit It is the maximum energy limit for insects.

6. The method for analyzing pest migration and propagation paths based on multi-source meteorological data according to claim 5, characterized in that: In order to eliminate the random errors caused by numerical calculations and environmental fluctuations during the simulation process, a custom Kalman filter is introduced to smooth the migration and propagation paths of pests and diseases calculated in step 4: Define the initial state vector as where lon k and lat k They represent the longitude and latitude coordinates of the insect at time k, v_lon k and v_lat k They represent the longitude and latitude speeds of the insect at time k respectively. The Kalman filter process is as follows: (1) Prediction step: Calculate the predicted state vector x obtained at time k based on the state estimation at time k-1 k|k-1 , the state transition formula is x k|k-1 =Fx k-1|k-1 , calculate the predicted covariance matrix P obtained at time k based on the covariance estimate at time k-1 k|k-1 , the covariance prediction formula is P k|k-1 =FP k-1|k-1 F T +Q, where the state transfer matrix F is Δt is the time step, Q is the process noise covariance matrix, x k-1|k-1 represents the optimal estimated state vector at time k-1, P k-1|k-1 represents the optimal estimated covariance matrix at time k-1; (2) Update step: measure the residual covariance matrix Calculate the Kalman gain, the formula is Substitute the above calculation results into the formula x k|k =x k|k-1 +K k (z k -H k x k|k-1 ); Covariance matrix update formula P k|k =(IK k H k )P k|k-1 , where S k is the measurement residual covariance matrix, H k is the observation matrix, K k is the Kalman gain weight, z k is the measurement value vector at time k, x k|k is the optimal estimated state vector at time k, P k|k is the optimal estimated covariance matrix at time k, R k is the measurement noise covariance matrix, and I is the identity matrix.

7. The method for analyzing pest migration and propagation paths based on multi-source meteorological data according to claim 5, characterized in that: In step 5, a flight strategy is established for each strategy individual. Specifically, if it is a cooperative strategy individual, the flight direction θ of the cooperative strategy individual is adjusted. i , so that it tends to the center of the group or maintains the formation; if it is a competitive strategy individual, the individual will actively deviate from the group and fly towards the resource-intensive area, and the direction randomness will increase; if it is a following strategy individual, it will directly copy the flight direction of the highest-profit individual in the neighborhood.

8. The method for analyzing pest migration and propagation paths based on multi-source meteorological data according to claim 5, characterized in that: In step 5, each individual observes the cumulative energy gain F of other individuals in its neighborhood within time step t i (t), and in the next time step t+1, imitate the flight strategy of the individual with the highest cumulative energy gain in its neighborhood as follows: Each individual observes the cumulative benefits of other individuals in its neighborhood at time step t in, is the set of other individuals in the neighborhood of individual i at time t, is the strategy adopted by individual i at time t, is the strategy adopted by individual j at time t, Based on and The energy gain when interacting with the strategies of the neighbors. If there is an individual with higher benefits in the neighborhood, the current individual will imitate its flight strategy at the next time step t+1. After the strategy is updated, individuals have a certain probability of random mutation to simulate environmental uncertainty or exploration behavior.

9. The method for analyzing pest migration and propagation paths based on multi-source meteorological data according to claim 5, characterized in that: In step 6, the heat map is converted to a map projection using Cartopy, matplotlib, and the Shp files of the relevant maps and terrain obstacles.

10. A system for analyzing the migratory paths of pests and diseases based on multi-source meteorological data, characterized by: The system comprises a data reading module, a data processing module and a user interaction module connected in sequence. The data reading module is used to collect insect flight conditions, parameter values ​​of airborne disease transmission, and extract meteorological data from multi-source meteorological data, and send the insect flight conditions, parameter values ​​of airborne disease transmission and meteorological data to the data processing module. The data processing module stores a pest single migration and transmission path model, an airborne disease transmission path model and a pest group collaborative simulation model. The data processing module can input the received insect flight condition parameter values ​​and meteorological data into the pest single migration and transmission path model, the airborne disease transmission parameter values ​​and meteorological data into the airborne disease transmission path model. The migration and propagation paths of individual pests and the propagation paths of airborne diseases are calculated. The data processing module can also replicate the pest individual in each time step through the group collaborative simulation model to turn it into a pest group with multiple individuals. Strategies are defined for each individual in the pest group. The strategies include cooperation, competition, and following. When two cooperative strategy individuals meet, the energy gain increases; when a competitive strategy individual meets a cooperative strategy individual, the competitive strategy individual obtains high benefits, the cooperative strategy individual reduces benefits, and the following strategy individual obtains medium benefits regardless of the strategy individual it interacts with. A flight strategy is established for each strategy individual. Each individual will observe the cumulative energy benefits F of other individuals in its neighborhood within the time step t. i (t), and imitate the flight strategy of the individual with the highest cumulative energy gain in its neighborhood in the next time step t+1 to obtain the movement trajectory of the pest group, display the change of group density with a heat map, convert the heat map to be drawn under map projection, convert the airborne disease transmission path to be drawn under map projection, and send the drawn pest and disease migration and transmission path map to the user interaction module; the user interaction module is used to publish the pest and disease migration and transmission path map on the Internet or local area network.

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