A green prevention and control method for agricultural bird damage

Abstract

This application relates to the field of agricultural technologies, and particularly to a green prevention and control method for agricultural bird damage. The method includes: collecting the actual flight directions of a target bird at each historical position point in its historical flight trajectory and the environmental data within the neighborhood range; setting initial weights for each environmental data, and calculating the predicted flight directions of each historical position point in the historical flight trajectory. Taking the minimum difference between the predicted flight direction and the actual flight direction as the goal, updating the initial weights to obtain the target weights of each environmental data; during the flight of the target bird, calculating the predicted flight direction of the target bird at the real-time position point according to the target weights, and judging whether to apply an increment of natural enemy data in the prevention and control area according to the predicted flight direction, so as to achieve green prevention and control of bird damage. Through the technical solution of this application, the flight trajectory of birds can be accurately predicted, and thus precise prevention and control of bird damage can be achieved.

Description

Technical Field

[0001] This application relates to the field of agricultural technologies, and in particular, to a green prevention and control method for agricultural bird damage. Background Art

[0002] The migration of birds is a complex activity, and many birds exhibit directed movement during migration. Sparrows will gnaw on crops such as rice, wheat, and sorghum; pigeons will attack various crops such as grains or beans. Therefore, the migration of harmful birds such as sparrows, crows, or pigeons poses a threat to the production safety of food crops. Therefore, clarifying the occurrence pattern of bird migration behavior and understanding bird habits are of great significance for the prediction and scientific prevention and control of bird damage.

[0003] Currently, the patent application document with the authorization announcement number CN116644862B discloses a method and device for predicting the flight trajectory of birds. The method includes: from the initial moment when a bird flock is detected until the boundary moment when any individual bird reaches a preset boundary position, based on the position vectors and velocity vectors of each individual bird in the bird flock at the boundary moment and historical moments, determine the transfer entropy of each individual bird, and determine the leader individual in the bird flock; starting from the boundary moment, based on the position vectors and velocity vectors of the leader individual being tracked in real time at the current moment and historical moments, predict the trajectory area of the leader individual in the future preset time steps, and update the leader individual in the bird flock based on the position vectors and velocity vectors of each individual bird at the current moment and historical moments; based on the trajectory area of the leader individual in the future preset time steps and the protected area, determine whether to trigger a bird repelling intervention operation for the leader individual.

[0004] The above method first determines the leader individual in the bird flock and predicts the flight trajectory of the leader individual to obtain the trajectory area of the leader individual in the future preset time steps. However, the prediction process of the flight trajectory does not involve the influence of environmental factors on the flight trajectory, so it is impossible to accurately understand bird habits, accurately predict the flight trajectory of birds, and thus impossible to achieve precise prevention and control of bird damage. Summary of the Invention

[0005] To solve the technical problem of being unable to accurately predict the flight trajectory of birds, this application provides a green prevention and control method for agricultural bird damage, which can accurately predict the flight trajectory of birds and thus achieve precise prevention and control of bird damage.

[0006] The present application provides a green prevention and control method for agricultural bird damage. The prevention and control method includes: collecting the actual flight directions of a target bird at each historical position point in the historical flight trajectory and environmental data in multiple angular intervals within the neighborhood range, where the environmental data includes meteorological data, crop data, and natural enemy data; calculating the predicted flight directions of each historical position point in the historical flight trajectory according to the initial weights of the environmental data, and updating the initial weights with the goal of minimizing the difference between the predicted flight direction and the actual flight direction, so as to obtain the target weights of the environmental data; during the flight of the target bird, calculating the predicted flight direction of the target bird at the real-time position point according to the target weights; in response to the prevention and control area being within the neighborhood range of the real-time position point and the predicted flight direction of the real-time position point being within the angular interval where the prevention and control area is located, applying an increment of natural enemy data in the prevention and control area so that the predicted flight direction of the real-time position point deviates from the angular interval where the prevention and control area is located.

[0007] According to the actual flight directions of a target bird at each historical position point in the historical flight trajectory and the environmental data within the neighborhood range, the environmental data within the neighborhood range will affect the flight direction of the target bird; set the initial weights of the environmental data, and calculate the predicted flight directions of each historical position point in the historical flight trajectory, with the goal of minimizing the difference between the predicted flight direction and the actual flight direction, continuously update the initial weights, and obtain the target weights of the environmental data. The target weights can accurately reflect the influence degree of the environmental data on the flight direction of the target bird; during the flight of the target bird, according to the target weights and the environmental data collected in real time, the flight direction of the target bird at the real-time position point can be accurately predicted, and then the flight trajectory of the bird can be accurately predicted. When it is judged according to the flight trajectory that the target bird is flying towards the prevention and control area, the green prevention of bird damage is realized by simulating natural enemy information (pheromone concentration and sound wave intensity).

[0008] In one embodiment, the neighborhood range is a circular area centered at the historical position point with a preset distance as the radius.

[0009] In one embodiment, the method for obtaining the neighborhood range includes: setting an initial distance, constructing a neighborhood range centered at the historical position point with the initial distance as the radius, and calculating the minimum value of the difference between the predicted flight direction and the actual flight direction within the neighborhood range to obtain the prediction accuracy of the initial distance; increasing the initial distance multiple times, and plotting an accuracy curve with the initial distance as the abscissa and the prediction accuracy as the ordinate, and taking the initial distance corresponding to the inflection point of the accuracy curve as the target distance; the neighborhood range is a circular area centered at the position point with the target distance as the radius.

[0010] If the neighborhood range is set too small, the predicted flight direction of the target bird cannot be accurately predicted. If the neighborhood range is set too large, more environmental data needs to be collected, resulting in a higher data collection cost. The target distance can balance the data collection cost and the prediction accuracy, reducing the data collection cost while ensuring the prediction accuracy.

[0011] In one embodiment, the central angles of the angular intervals within the neighborhood range are equal.

[0012] The neighborhood range includes multiple angular intervals, and environmental data is collected in units of angular intervals, enabling the environmental data of each angular interval to participate in the subsequent calculation of the predicted flight direction and improving the accuracy of the predicted flight direction.

[0013] In one embodiment, the meteorological data includes wind speed, wind direction, temperature, and precipitation; the crop data includes the floor area of each crop, or the product of the floor area and the growth stage; the natural enemy data includes the pheromone concentration or sound wave intensity of each natural enemy.

[0014] In one embodiment, obtaining the target weights of the environmental data includes: continuously updating the initial weights using an optimization algorithm, and taking the initial weights corresponding to the minimum value of the objective function as the target weights of the environmental data. The optimization algorithm is a hill climbing algorithm or a simulated annealing algorithm.

[0015] In one embodiment, the historical position point Predicted flight direction is:

[0016] ; is the actual flight direction of the historical position point , is the number of angular intervals in the neighborhood range, , and are respectively the meteorological data, crop data, and natural enemy data of the th angular interval of the historical position point , and are respectively the initial weights of the meteorological data, crop data, and natural enemy data; is the unit vector along the angular bisector of the th angular interval with the historical position point

[0017] For any historical position point in the historical flight trajectory, comprehensively considering the influence of the environmental data of this historical position point on the actual flight direction, accurately predict the predicted flight direction of the next adjacent historical position point of this historical position point.

[0018] In one embodiment, the difference between the predicted flight direction and the actual flight direction is:

[0019] , where is the number of historical position points in the historical flight trajectory, and are respectively the actual flight direction and the predicted flight direction of the historical position point .

[0020] In one embodiment, the increment of the natural enemy data is set manually according to experience.

[0021] In one embodiment, the method for obtaining the increment of the natural enemy data includes: initializing the increment of the natural enemy data; after updating the environmental data according to the increment of the natural enemy data, calculating again the predicted flight direction of the target bird at the real-time position point; in response to the predicted flight direction calculated again being within the angular range where the prevention and control area is located, updating the increment of the natural enemy data within a preset range until the predicted flight direction calculated again is not within the angular range where the prevention and control area is located, and then stopping the update.

[0022] Precisely determining the natural enemy information (pheromone concentration and sound wave intensity, i.e., the increment of the natural enemy data) that needs to be applied within the preset range of the increment of the natural enemy data that can be applied manually ensures the effect of bird damage prevention.

[0023] The technical solution of the present application has the following beneficial technical effects:

[0024] According to the actual flight direction of the target bird at each historical position point in the historical flight trajectory and the environmental data within the neighborhood range, the environmental data within the neighborhood range will affect the flight direction of the target bird; setting the initial weights of each environmental data and calculating the predicted flight direction of each historical position point in the historical flight trajectory, with the goal of minimizing the difference between the predicted flight direction and the actual flight direction, continuously updating the initial weights to obtain the target weights of each environmental data, and the target weights can accurately reflect the influence degree of the environmental data on the flight direction of the target bird; during the flight of the target bird, based on the target weights and the environmental data collected in real time, the flight direction of the target bird at the real-time position point can be accurately predicted, and then the flight trajectory of the bird can be accurately predicted.

[0025] Furthermore, when the target bird is flying towards the prevention and control area, by applying the increment of the natural enemy data within the prevention and control area to interfere with the flight direction of the target bird, the flight direction of the target bird can be driven to change and deviate from the angular range where the prevention and control area is located, thereby achieving the effect of bird damage prevention. On the basis of understanding the living habits of the target bird, the green prevention of bird damage is realized by simulating the natural enemy information (pheromone concentration and sound wave intensity), and it will not cause pollution to crops and the environment. Brief Description of the Drawings

[0026] Figure 1 is a flowchart of a green prevention and control method for agricultural bird damage according to an embodiment of the present application.

[0027] Figure 2 is a schematic diagram of environmental data of a historical location point according to an embodiment of the present application.

[0028] Figure 3 is a schematic diagram of the neighborhood range of the prevention and control area and the real-time location point according to an embodiment of the present application. Detailed Description of the Embodiment

[0029] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments.

[0030] Figure 1 is a flowchart of a green prevention and control method for agricultural bird damage according to an embodiment of the present application. As Figure 1 shown, the green prevention and control method 100 for agricultural bird damage includes steps S101 to S104, which will be described in detail below.

[0031] S101, collect the actual flight direction of the target bird at each historical location point in the historical flight trajectory and environmental data in multiple angular intervals within the neighborhood range, where the environmental data includes meteorological data, crop data, and natural enemy data.

[0032] In one embodiment, the target bird can be a sparrow, a crow, or a pigeon, and the present application is not limited thereto. Collect the historical flight trajectory of the target bird, where the historical flight trajectory includes the coordinate information of all historical location points during the historical migration process of the target bird, and each historical location point corresponds to an actual flight direction. Specifically, radar monitoring technology can be used to collect the actual flight direction of the target bird at each historical location point.

[0033] Specifically, please refer to Figure 2, which is a schematic diagram of the environmental data of a historical position point according to an embodiment of the present application. A historical flight trajectory includes multiple historical position points. When the target bird is at any historical position point, the environmental data of this historical position point can be collected. That is to say, each historical position point corresponds to an actual flight direction and environmental data; the neighborhood range of a historical position point is a circular area with this historical position point as the center and a preset distance as the radius; the neighborhood range of the historical position point is equally divided into multiple angular intervals, and the environmental data within each angular interval is collected to obtain the environmental data of the historical position point. The environmental data includes meteorological data, crop data, and natural enemy data; the meteorological data includes wind speed, wind direction, temperature, and precipitation within an angular interval; the crop data includes the floor area of each crop within an angular interval, or the product of the floor area and the growth stage; the natural enemy data includes the pheromone concentration or sound wave intensity of various natural enemies within an angular area.

[0034] Among them, the preset distance can be directly set manually, and the value of the preset distance is 30 meters. The neighborhood range of a historical position point is divided into 8 equal angular regions. Each angular interval is a sector area, and the central angle of the sector area is 45 degrees, that is, the central angles of each angular interval are equal. The implementer can also adjust the central angle of each sector area according to the actual situation.

[0035] In this embodiment, the meteorological data is a vector of 4 rows and 1 column. The crop data is M rows and 1 column, and each row corresponds to the floor area of a crop, or the product of the floor area and the growth stage. M is the type of crop, and the type of crop is related to the target bird. For example, when the target bird is a sparrow, the corresponding crops include rice, wheat, and sorghum; when the target bird is a pigeon, the corresponding crops include grains or beans, etc. The natural enemy data is N rows and 1 column, and each row corresponds to the pheromone concentration or sound wave intensity of a natural enemy. N is the type of natural enemy, and the type of natural enemy is also related to the target bird.

[0036] Exemplarily, the growth stage of the crop is divided into the seedling stage, the growth stage, and the maturity stage; when the growth stage is the seedling stage, the value of the growth stage is set to 1; when the growth stage is the growth stage, the value of the growth stage is set to 2; when the growth stage is the maturity stage, the value of the growth stage is set to 3; when the floor area of a crop is 100 and the growth stage is the maturity stage, the crop data corresponding to this crop is 100×3 = 300.

[0037] It should be noted that when the natural enemy data includes the pheromone concentration of each natural enemy, a gas chromatography-mass spectrometry method can be used to monitor the pheromone concentration of natural enemies in the angular range; when the natural enemy data includes the sound wave intensity of each natural enemy, an acoustic recording device can be used to collect the sound wave intensity of natural enemies in the angular range; both the pheromone concentration and the sound wave intensity can reflect the number of natural enemies in the angular range.

[0038] In this way, the actual flight direction and environmental data of the target bird at each historical position point in the historical flight trajectory are collected, and the environmental data of a historical position point includes meteorological data, crop data, and natural enemy data in each angular range, providing a data basis for the subsequent realization of green prevention and control of agricultural bird damage.

[0039] S102. Calculate the predicted flight direction of each historical position point in the historical flight trajectory according to the initial weights of the environmental data, and update the initial weights with the goal of minimizing the difference between the predicted flight direction and the actual flight direction to obtain the target weights of the environmental data.

[0040] In one embodiment, meteorological data, crop data, and natural enemy data will directly affect the migration path of birds. For example, during migration, birds will fly towards areas with crops, away from natural enemies, and where the meteorological data is suitable for survival. To quantify the influence of each environmental data on the flight direction of the target bird, the initial weights of the environmental data are determined.

[0041] Specifically, the initial weights of meteorological data, crop data, and natural enemy data are respectively denoted as 、 and ; Since the meteorological data is a 4-row and 1-column vector, the initial weight of the meteorological data has a size of 1 row and 4 columns. Similarly, if the crop data is an M-row and 1-column vector, the initial weight has a size of 1 row and M columns; if the natural enemy data is an N-row and 1-column vector, the initial weight has a size of 1 row and N columns.

[0042] In one embodiment, the initial weights 、 and can all be zero vectors. The historical flight trajectory includes multiple historical position points in a sequential order. For the historical position point , the next adjacent historical position point is denoted as . According to the actual flight direction of the target bird at the historical position point , and the environmental data at the historical position point , predict the predicted flight direction of the next adjacent historical position point . The predicted flight direction of the historical position point ​ is:

[0043] ; is the historical position point of the actual flight direction, is the number of angular intervals in the neighborhood range, , and are respectively the meteorological data, crop data, and natural enemy data of the th angular interval of the historical position point, , and are respectively the initial weights of the meteorological data, crop data, and natural enemy data; is the unit vector starting from the historical position point along the angular bisector of the th angular interval. Among them, the direction vector of the th angular interval is the unit vector starting from the historical position point

[0044] In this way, for any historical position point in the historical flight trajectory, considering the influence of the environmental data of this historical position point on the actual flight direction, the predicted flight direction of the next adjacent historical position point of this historical position point is obtained.

[0045] In one embodiment, the target weight can accurately measure the influence degree of each environmental data on the flight direction of the target bird. In order to obtain an accurate target weight, the process of obtaining the target weight is transformed into an optimization process, the difference between the predicted flight direction and the actual flight direction is used as the objective function, and the initial weight is continuously updated using an optimization algorithm. The initial weight corresponding to the minimum value of the objective function is used as the target weight of each environmental data.

[0046] Among them, the optimization algorithm can be a hill climbing algorithm, a simulated annealing algorithm, etc.

[0047] Among them, the difference between the predicted flight direction and the actual flight direction is:

[0048] , is the number of historical position points in the historical flight trajectory, and are respectively the actual flight direction and the predicted flight direction of the historical position point .

[0049] Understandably, the target weights can reflect the influence degree of each environmental data on the flight direction of the target bird, and embody the living habits of the target bird.

[0050] S103. During the flight of the target bird, calculate the predicted flight direction of the target bird at the real-time position point according to the target weights.

[0051] In one embodiment, after determining the target weights of each environmental data, the flight direction of the target bird can be predicted. The process of predicting the flight direction of the target bird is the same as the method of "calculating the predicted flight direction of the historical position point" in step S102. of the predicted flight direction ".

[0052] Specifically, denote the real-time position point as , denote the previous adjacent position point of the real-time position point as position point , collect the environmental data of position point , calculate the predicted flight direction of the target bird at the real-time position point according to the target weights and the environmental data of position point . The predicted flight direction of the target bird at the real-time position point is: where

[0053] , , and are respectively the meteorological data, crop data and natural enemy data of the th angle interval of position point , and are respectively the target weights of the meteorological data, crop data and natural enemy data, is the number of angle intervals, is the direction vector of the th angle interval, is the actual flight direction of position point , is the predicted flight direction of the target bird at the real-time position point .

[0054] In this way, during the migration of the target bird, according to the real-time position point of the target bird and the environmental data within the neighborhood range of the real-time position point, calculate the predicted flight direction of the target bird, so as to predict the future flight direction of the target bird, and further predict the flight trajectory of the target bird.

[0055] In an optional embodiment, the neighborhood ranges of the historical position points and the real-time position points are both circular regions. If the neighborhood range is set too small, the predicted flight direction of the target bird cannot be accurately predicted. If the neighborhood range is set too large, more environmental data needs to be collected, resulting in a higher data collection cost. Therefore, the method for obtaining the neighborhood range includes: setting an initial distance, constructing a neighborhood range with the historical position point as the center and the initial distance as the radius, and calculating the minimum difference between the predicted flight direction and the actual flight direction within the neighborhood range to obtain the prediction accuracy of the initial distance; increasing the initial distance multiple times, and plotting an accuracy curve with the initial distance as the abscissa and the prediction accuracy as the ordinate, and taking the initial distance corresponding to the inflection point of the accuracy curve as the target distance; the neighborhood range is a circular region with the position point as the center and the target distance as the radius.

[0056] It can be understood that the target distance balances the two aspects of data collection cost and prediction accuracy, and reduces the data collection cost on the basis of ensuring the prediction accuracy. The initial distance can be increased gradually within a reasonable distance range. For example, the initial distance is set to 0, the reasonable distance range is from 0 meters to 50 meters, and the initial distance is increased by 5 meters each time.

[0057] S104, in response to the prevention and control area being within the neighborhood range of the real-time position point, and the predicted flight direction of the real-time position point being within the angular interval where the prevention and control area is located, applying an increment of natural enemy data in the prevention and control area so that the predicted flight direction of the real-time position point deviates from the angular interval where the prevention and control area is located.

[0058] In one embodiment, please refer to Figure 3 , which is a schematic diagram of the prevention and control area and the neighborhood range of the real-time position point according to an embodiment of the present application. The prevention and control area can be a crop planting area such as a farmland or cultivated land. When the prevention and control area is within the neighborhood range of the real-time position point, and the predicted flight direction points to the angular interval where the prevention and control area is located, it means that the target bird is flying towards the prevention and control area and will cause damage to the crops in the prevention and control area. In order to prevent the target bird from eating the crops in the prevention and control area, the flight direction of the target bird can be interfered by applying an increment of natural enemy data in the prevention and control area, thereby driving the target bird to change its flight direction and deviate from the angular interval where the prevention and control area is located, and thus achieving the effect of bird damage prevention. The increment of natural enemy data is the concentration of pheromone or the intensity of sound wave, and the increment of natural enemy data can be applied by spraying pheromone or simulating the sound wave of natural enemies in the prevention and control area.

[0059] It can be understood that the increment of natural enemy data is consistent with the natural enemy data. When the natural enemy data includes the concentration of pheromone of each natural enemy, the increment of natural enemy data includes the increment of the concentration of pheromone of each natural enemy; similarly, when the natural enemy data includes the intensity of sound wave of each natural enemy, the increment of natural enemy data includes the increment of the intensity of sound wave of each natural enemy.

[0060] Specifically, the increment of natural enemy data can be directly set manually according to experience.

[0061] In another embodiment, to ensure the effect of bird damage prevention, the method for obtaining the increment of natural enemy data includes: initializing the increment of natural enemy data; after updating the environmental data according to the increment of natural enemy data, calculating the predicted flight direction of the target bird at the real-time position point again; in response to the predicted flight direction calculated again being within the angular interval where the prevention and control area is located, updating the increment of natural enemy data within a preset range until the predicted flight direction calculated again is not within the angular interval where the prevention and control area is located, and then stopping the update.

[0062] Among them, the angular interval where the prevention and control area is located is denoted as , and updating the environmental data according to the increment of natural enemy data means updating the natural enemy data of the position point in the nth angular interval. Adding the natural enemy data of the position point

[0063] in the

[0064] nth

[0065] angular interval to the increment of natural enemy data can complete the update of the environmental data. Among them, the initialized increment of natural enemy data is 0.

[0066] It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A green prevention and control method for agricultural bird damage, characterized in that, The prevention and control method includes: Collecting the actual flight directions of the target birds at each historical position point in the historical flight trajectory and the environmental data in multiple angular intervals within the neighborhood range, where the environmental data includes meteorological data, crop data, and natural enemy data; Calculating the predicted flight directions of the historical position points in the historical flight trajectory according to the initial weights of the environmental data, and updating the initial weights with the goal of minimizing the difference between the predicted flight direction and the actual flight direction to obtain the target weights of the environmental data; During the flight of the target birds, calculating the predicted flight directions of the target birds at the real-time position points according to the target weights; In response to the prevention and control area being within the neighborhood range of the real-time position point and the predicted flight direction of the real-time position point being within the angular interval where the prevention and control area is located, applying an increment of natural enemy data in the prevention and control area so that the predicted flight direction of the real-time position point deviates from the angular interval where the prevention and control area is located.

2. The green prevention and control method for agricultural bird damage according to claim 1, characterized in that, The neighborhood range is a circular area with the historical position point as the center and a preset distance as the radius.

3. The green prevention and control method for agricultural bird damage according to claim 1, characterized in that, The method for obtaining the neighborhood range includes: Setting an initial distance, constructing a neighborhood range with the historical position point as the center and the initial distance as the radius, and calculating the minimum difference value between the predicted flight direction and the actual flight direction within the neighborhood range to obtain the prediction accuracy of the initial distance; Increasing the initial distance multiple times, plotting an accuracy curve with the initial distance as the abscissa and the prediction accuracy as the ordinate, and taking the initial distance corresponding to the inflection point of the accuracy curve as the target distance; the neighborhood range is a circular area with the position point as the center and the target distance as the radius.

4. The green prevention and control method for agricultural bird damage according to claim 2 or 3, characterized in that, The central angles of the angular intervals within the neighborhood range are equal.

5. The green prevention and control method for agricultural bird damage according to claim 1, characterized in that, The meteorological data includes wind speed, wind direction, temperature, and precipitation; the crop data includes the occupied area of each crop, or the product of the occupied area and the growth stage; the natural enemy data includes the pheromone concentration or sound wave intensity of each natural enemy.

6. The green prevention and control method for agricultural bird damage according to claim 1, characterized in that, The obtaining of the target weights of the environmental data includes: Continuously updating the initial weights using an optimization algorithm, and taking the initial weights corresponding to the minimum value of the objective function as the target weights of the environmental data; The optimization algorithm is a hill climbing algorithm or a simulated annealing algorithm.

7. A green prevention and control method for agricultural bird damage according to claim 1, characterized in that Historical position point Predicted flight direction is: ; is the historical position point of the actual flight direction, is the number of angular intervals in the neighborhood range, , and are respectively the meteorological data, crop data, and natural enemy data of the th angular interval of the historical position point , and are respectively the initial weights of the meteorological data, crop data, and natural enemy data; is the unit vector starting from the historical position point along the angular bisector of the th angular interval.

8. The green prevention and control method for agricultural bird damage according to claim 1, characterized in that, Difference between predicted flight direction and actual flight direction is: , is the number of historical position points in the historical flight trajectory, and are respectively the actual flight direction and the predicted flight direction of the historical position point .

9. The green prevention and control method for agricultural bird damage according to claim 1, wherein, The increment of natural enemy data is set manually according to experience.

10. A green prevention and control method for agricultural bird damage according to claim 1, characterized in that, The method for obtaining the increment of natural enemy data includes: Initializing the increment of natural enemy data; after updating the environmental data according to the increment of natural enemy data, calculating the predicted flight direction of the target birds at the real-time position point again; In response to the predicted flight direction calculated again being within the angular interval where the prevention and control area is located, updating the increment of natural enemy data within a preset range until the predicted flight direction calculated again is not within the angular interval where the prevention and control area is located, and then stopping the update.

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