Green prevention and control method for agricultural bird damage

Abstract

The invention relates to the technical field of agriculture, in particular to an agricultural bird damage green prevention and control method, which comprises the following steps: acquiring the actual flight direction of a target bird at each historical position point in a historical flight path and environmental data in a neighborhood range; setting an initial weight of each piece of environmental data, calculating a predicted flight direction of each historical position point in the historical flight path, and updating the initial weight by taking the minimum difference between the predicted flight direction and the actual flight direction as a target to obtain a target weight of each piece of environmental data; in the flight process of the target birds, the predicted flight direction of the target birds at the real-time position point is calculated according to the target weight, whether natural enemy data increment is applied to the prevention and control area or not is judged according to the predicted flight direction, and bird damage green prevention and control are achieved. According to the technical scheme, the flight path of the birds can be accurately predicted, and then accurate prevention and control of bird damage are achieved.

Description

Technical Field

[0001] The present application relates to the field of agricultural technology, and in particular to a green control method for agricultural bird pests. Background Art

[0002] Bird migration is a complex activity, and many birds have directional movement behaviors during migration. Sparrows will gnaw on crops such as rice, wheat and sorghum; pigeons will attack a variety of crops such as grains or beans. Therefore, the migration of harmful birds such as sparrows, crows or pigeons will threaten the safety of food crop production. Therefore, it is of great significance to clarify the occurrence patterns of bird migration behavior and understand bird habits for the prediction and scientific prevention and control of bird damage.

[0003] At present, the patent application document with authorization announcement number CN116644862B discloses a method and device for predicting bird flight trajectories, the method comprising: starting from the initial moment when a flock of birds is detected until the boundary moment when any individual bird reaches a preset boundary position, based on the position vector and velocity vector of each individual bird in the flock at the boundary moment and the historical moment, determining the transfer entropy of each individual bird, and determining the leader individual in the flock; starting from the boundary moment, based on the position vector and velocity vector of the leader individual tracked in real time at the current moment and the historical moment, predicting the trajectory area of ​​the leader individual in a preset time step in the future, and updating the leader individual in the flock based on the position vector and velocity vector of each individual bird at the current moment and the historical moment; based on the trajectory area and protection area of ​​the leader individual in the preset time step in the future, determining 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 step. However, the prediction process of the flight trajectory does not involve the impact of environmental factors on the flight trajectory, and it is impossible to accurately understand the habits of flying birds, and it is impossible to accurately predict the flight trajectory of flying birds, and thus it is impossible to achieve accurate prevention and control of bird damage. Summary of the invention

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

[0006] The present application provides a green control method for agricultural bird pests, the control method comprising: collecting the actual flight direction of each historical position point in the historical flight trajectory of the target bird and environmental data of multiple angle intervals in the neighborhood range, the environmental data comprising meteorological data, crop data and natural enemy data; calculating the predicted flight direction of each historical position point in the historical flight trajectory according to the initial weight of each environmental data, updating the initial weight with the goal of minimizing the difference between the predicted flight direction and the actual flight direction, and obtaining the target weight 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 weight; in response to the 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 angle interval where the control area is located, applying a natural enemy data increment in the control area to make the predicted flight direction of the real-time position point deviate from the angle interval where the control area is located.

[0007] According to the actual flight direction of the target birds at each historical location in the historical flight trajectory and the environmental data in the neighborhood, the environmental data in the neighborhood will affect the flight direction of the target birds; set the initial weight of each environmental data, and calculate the predicted flight direction of each historical location 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 weight to obtain the target weight of each environmental data. The target weight can accurately reflect the degree of influence of the environmental data on the flight direction of the target birds; during the flight of the target birds, the flight direction of the target birds at the real-time location can be accurately predicted based on the target weight and the environmental data collected in real time, and then the flight trajectory of the birds can be accurately predicted. When the target birds are judged to fly to the prevention and control area based on the flight trajectory, the green prevention of bird damage is achieved by simulating the natural enemy information (pheromone concentration and sound wave intensity).

[0008] In one embodiment, the neighborhood range is a circular area with the historical location point as the center and 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 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 drawing an accuracy curve with the initial distance as the horizontal coordinate and the prediction accuracy as the vertical coordinate, 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.

[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 and the data collection cost is higher. The target distance can balance the data collection cost and prediction accuracy, reducing the data collection cost while ensuring prediction accuracy.

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

[0012] The neighborhood range includes multiple angle intervals, and environmental data is collected in units of angle intervals, so that the environmental data of each angle interval can participate in the subsequent calculation of the predicted flight direction, thereby 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 area occupied by each crop, or the product of the area occupied 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 weight of each environmental data includes: continuously updating the initial weight using an optimization algorithm, and taking the initial weight corresponding to the minimum value of the objective function as the target weight of each environmental data, and the optimization algorithm is a hill climbing algorithm or a simulated annealing algorithm.

[0015] In one embodiment, the historical location point Predicted flight direction for: ; For historical locations The actual flight direction, is the number of angle intervals in the neighborhood, , and Historical location points No. Meteorological data, crop data and natural enemy data for each angle interval, , and are the initial weights of meteorological data, crop data, and natural enemy data, respectively; For historical locations Starting from The unit vector of the angle bisector of the angle interval.

[0016] For any historical position point in the historical flight trajectory, the impact of the environmental data of the historical position point on the actual flight direction is comprehensively considered to accurately predict the predicted flight direction of the next adjacent historical position point of the historical position point.

[0017] In one embodiment, the difference between the predicted flight direction and the actual flight direction for: , is the number of historical position points in the historical flight trajectory, and Historical location points The actual flight direction and predicted flight direction.

[0018] In one embodiment, the natural enemy data increment is manually set based on experience.

[0019] In one embodiment, the method for obtaining the natural enemy data increment includes: initializing the natural enemy data increment; after updating the environmental data according to the natural enemy data increment, recalculating the predicted flight direction of the target bird at the real-time position point; in response to the recalculated predicted flight direction being located in the angle interval where the control area is located, updating the natural enemy data increment within a preset range, and stopping the updating until the recalculated predicted flight direction is not located in the angle interval where the control area is located.

[0020] Within the preset range of the natural enemy data increment that can be applied artificially, the natural enemy information that needs to be applied (pheromone concentration and sound wave intensity, i.e. the natural enemy data increment) is accurately determined to ensure the effectiveness of bird damage prevention.

[0021] The technical solution of this application has the following beneficial technical effects: According to the actual flight direction of the target bird at each historical position point in the historical flight trajectory and the environmental data in the neighborhood, the environmental data in the neighborhood will affect the flight direction of the target bird; the initial weight of each environmental data is set, and the predicted flight direction of each historical position point in the historical flight trajectory is calculated. With the goal of minimizing the difference between the predicted flight direction and the actual flight direction, the initial weight is continuously updated to obtain the target weight of each environmental data. The target weight can accurately reflect the degree of influence of the environmental data on the flight direction of the target bird; during the flight of the target bird, the flight direction of the target bird at the real-time position point can be accurately predicted based on the target weight and the environmental data collected in real time, thereby accurately predicting the flight trajectory of the bird.

[0022] Furthermore, when the target birds are flying towards the control area, the flight direction of the target birds can be interfered with by applying natural enemy data increments in the control area, which can drive the target birds to change their flight direction and deviate from the angle range where the control area is located, thereby achieving the effect of bird damage prevention. Based on the understanding of the living habits of the target birds, green prevention of bird damage can be achieved by simulating natural enemy information (pheromone concentration and sound wave intensity), which will not cause pollution to crops and the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a green control method for agricultural bird pests according to an embodiment of the present application.

[0024] Figure 2 It is a schematic diagram of environmental data of a historical location point according to an embodiment of the present application.

[0025] Figure 3 It is a schematic diagram of the neighborhood range of the control area and the real-time location point according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0027] Figure 1 Flow chart of a green method for preventing and controlling agricultural bird damage according to an embodiment of the present application. Figure 1 As shown, the agricultural bird pest green control method 100 includes steps S101 to S104, which are described in detail below.

[0028] S101, collecting the actual flight direction of the target bird at each historical position point in the historical flight trajectory and environmental data of multiple angle intervals in the neighborhood, wherein the environmental data includes meteorological data, crop data and natural enemy data.

[0029] In one embodiment, the target bird may be a sparrow, a crow or a pigeon, and this application does not limit this. The historical flight trajectory of the target bird is collected, and the historical flight trajectory includes the coordinate information of all historical position points in the historical migration process of the target bird, and each historical position point corresponds to an actual flight direction. Specifically, the actual flight direction of the target bird at each historical position point can be collected by using radar monitoring technology.

[0030] Specifically, see Figure 2, is a schematic diagram of 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 the historical position point can be collected, that is, 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 the historical position point as the center and a preset distance as the radius; the neighborhood range of the historical position point is divided into multiple angle intervals, and the environmental data in each angle interval is collected to obtain the environmental data of the historical position point, and the environmental data includes meteorological data, crop data and natural enemy data; the meteorological data includes wind speed, wind direction, temperature and precipitation in an angle interval; the crop data includes the area occupied by each crop in an angle interval, or the product of the area occupied and the growth stage; the natural enemy data includes the pheromone concentration or sound wave intensity of various natural enemies in an angle area.

[0031] The preset distance can be set manually, and the value of the preset distance is 30 meters. The neighborhood of a historical location point is divided into 8 equal angle areas, each angle interval is a sector area, and the center angle of the sector area is 45 degrees, that is, the center angle of each angle interval is equal, and the implementer can also adjust the center angle of each sector area according to the actual situation.

[0032] In this embodiment, the meteorological data is a vector of 4 rows and 1 column. The crop data is M rows and 1 column, each row corresponds to the area of ​​a crop, or the product of the 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, 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.

[0033] Exemplarily, the growth stages of crops are divided into seedling stage, growth stage and maturity stage; when the growth stage is seedling stage, the value of the growth stage is set to 1; when the growth stage is growth stage, the value of the growth stage is set to 2; when the growth stage is maturity stage, the value of the growth stage is set to 3; when the area of ​​a crop is 100 and the growth stage is maturity stage, the crop data corresponding to the crop is 100×3=300.

[0034] It should be noted that when the natural enemy data include the pheromone concentration of each natural enemy, the gas chromatography-mass spectrometry method can be used to monitor the pheromone concentration of the natural enemies within the angle range; when the natural enemy data include the sound wave intensity of each natural enemy, the acoustic recording equipment can be used to collect the sound wave intensity of the natural enemies within the angle range; both pheromone concentration and sound wave intensity can reflect the number of natural enemies within the angle range.

[0035] In this way, the actual flight direction and environmental data of the target birds 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 at various angle intervals within the neighborhood, providing a data basis for the subsequent realization of green prevention and control of agricultural bird pests.

[0036] S102, calculating the predicted flight direction of each historical position point in the historical flight trajectory according to the initial weight of each environmental data, and updating the initial weight with the goal of minimizing the difference between the predicted flight direction and the actual flight direction to obtain the target weight of each environmental data.

[0037] In one embodiment, meteorological data, crop data, and natural enemy data will directly affect the path of bird migration. For example, during the migration process, birds will fly in a direction that is close to crops, away from natural enemies, and where the meteorological data is suitable for survival. In order to quantify the impact of each environmental data on the flight direction of the target bird, the initial weight of each environmental data is given.

[0038] Specifically, the initial weights of meteorological data, crop data, and natural enemy data are recorded as , and ; Since the meteorological data is a vector of 4 rows and 1 column, the initial weight of the meteorological data is The size of is 1 row and 4 columns. Similarly, the crop data is M rows and 1 column, so the initial weight The size of is 1 row and M columns; the natural enemy data is N rows and 1 column, so the initial weight The size is 1 row and N columns.

[0039] In one embodiment, the initial weights , and The historical flight trajectory includes multiple historical position points in sequence. , and record the next adjacent historical position point as , based on the historical location of the target bird Actual flight direction and historical location points Predict the next adjacent historical location point based on environmental data Predicted flight direction. Historical location points Predicted flight direction for: ; For historical locations The actual flight direction, is the number of angle intervals in the neighborhood, , and Historical location points No. Meteorological data, crop data and natural enemy data for each angle interval, , and are the initial weights of meteorological data, crop data, and natural enemy data, respectively; For historical locations Starting from The unit vector of the angular bisector of the angle interval. The direction vector of the angle interval For historical locations Starting from The unit vector of the angle bisector of the angle interval.

[0040] In this way, for any historical position point in the historical flight trajectory, the impact of the environmental data of the historical position point on the actual flight direction is comprehensively considered to obtain the predicted flight direction of the next adjacent historical position point of the historical position point.

[0041] In one embodiment, the target weight can accurately measure the degree of influence of each environmental data on the flight direction of the target bird. In order to obtain accurate target weight, the target weight acquisition process is converted into an optimization process. The difference between the predicted flight direction and the actual flight direction is used as the objective function. The initial weight is continuously updated using the optimization algorithm, and the initial weight corresponding to the minimum value of the objective function is used as the target weight of each environmental data.

[0042] Among them, the optimization algorithm can be a hill climbing algorithm, a simulated annealing algorithm, etc.

[0043] Among them, the difference between the predicted flight direction and the actual flight direction for: , is the number of historical position points in the historical flight trajectory, and Historical location points The actual flight direction and predicted flight direction.

[0044] It can be understood that the target weight can reflect the influence of various environmental data on the flight direction of the target bird and reflect the life habits of the target bird.

[0045] S103, 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 weight.

[0046] 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 similar to the process of "calculating historical position points" in step S102. Predicted flight direction ” in the same way.

[0047] Specifically, the real-time location point is recorded as , record the previous adjacent position point of the real-time position point as the position point , collect location points Environmental data, based on target weights and location points Calculate the real-time location of target birds using environmental data The predicted flight direction of the target bird is at the real-time location point Predicted flight direction for: , , and Location points No. Meteorological data, crop data and natural enemy data for each angle interval, , and are the target weights of meteorological data, crop data and natural enemy data, is the number of angle intervals, For the The direction vector of the angle interval, For location point The actual flight direction, The target bird is located in real time. The predicted flight direction.

[0048] In this way, during the migration process of the target birds, the predicted flight direction of the target birds is calculated based on the real-time location points of the target birds and the environmental data in the neighborhood of the real-time location points. The future flight direction of the target birds can be predicted, and then the flight trajectory of the target birds can be predicted.

[0049] In an optional embodiment, the neighborhood ranges of the historical location point and the real-time location point are both circular areas. 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 and the data collection cost is higher. Therefore, the method for obtaining the neighborhood range includes: setting an initial distance, constructing a neighborhood range with the historical location 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 drawing an accuracy curve with the initial distance as the horizontal coordinate and the prediction accuracy as the vertical coordinate, 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 location point as the center and the target distance as the radius.

[0050] It can be understood that the target distance balances the data collection cost and prediction accuracy. On the basis of ensuring prediction accuracy, the data collection cost is reduced. The initial distance can be increased gradually within a reasonable distance range by multiple increases. For example, the initial distance is set to 0, and the reasonable distance range is 0 meters to 50 meters. Each time the initial distance is increased, it increases by 5 meters.

[0051] S104, in response to the control area being within the neighborhood of the real-time location point, and the predicted flight direction of the real-time location point being within the angular interval where the control area is located, applying a natural enemy data increment in the control area so that the predicted flight direction of the real-time location point deviates from the angular interval where the control area is located.

[0052] In one embodiment, see Figure 3 , is a schematic diagram of the neighborhood range of the control area and the real-time position point according to the embodiment of the present application. The control area can be a crop planting area such as farmland and cultivated land. When the control area is located in the neighborhood range of the real-time position point, and the predicted flight direction points to the angle interval where the control area is located, it means that the target birds are flying towards the control area, which will cause damage to the crops in the control area. In order to prevent the target birds from eating the crops in the control area, the flight direction of the target birds can be interfered by applying the natural enemy data increment in the control area, and then the target birds can be driven to change the flight direction and deviate from the angle interval where the control area is located, thereby achieving the effect of bird damage prevention. The natural enemy data increment is the pheromone concentration or sound wave intensity, and the natural enemy data can be applied by spraying pheromones or simulating natural enemy sound waves in the control area.

[0053] It can be understood that the natural enemy data increment is consistent with the natural enemy data. When the natural enemy data includes the pheromone concentration of each natural enemy, the natural enemy data increment includes the pheromone concentration increment of each natural enemy; similarly, when the natural enemy data includes the sound wave intensity of each natural enemy, the natural enemy data increment includes the sound wave intensity increment of each natural enemy.

[0054] Specifically, the natural enemy data increment can be directly set manually based on experience.

[0055] In another embodiment, in order to ensure the effectiveness of bird damage prevention, the method for obtaining the natural enemy data increment includes: initializing the natural enemy data increment; after updating the environmental data based on the natural enemy data increment, recalculating the predicted flight direction of the target bird at the real-time position point; in response to the recalculated predicted flight direction being located in the angle interval where the control area is located, updating the natural enemy data increment within a preset range until the recalculated predicted flight direction is not located in the angle interval where the control area is located, and then stopping the updating.

[0056] Among them, the angle interval where the control area is located is recorded as , update the environmental data incrementally according to the natural enemy data, that is, update the location point in step S103 No. The process of natural enemy data in angle intervals is to convert the position points No. The natural enemy data of the angle interval is added to the natural enemy data increment to complete the updating of the environmental data. Among them, the initialized natural enemy data increment is 0.

[0057] The predicted flight direction can be calculated again using the updated environmental data and target weights; in response to the recalculated predicted flight direction being within the angle range where the prevention and control area is located, it means that the incremental natural enemy data at this time cannot effectively interfere with the flight direction of the target birds, and it is necessary to gradually increase the incremental natural enemy data within a preset range so that the incremental natural enemy data can effectively interfere with the flight direction of the target birds.

[0058] Among them, the preset range includes the maximum and minimum values ​​of the natural enemy data increment that can be manually imposed.

[0059] In this way, bird damage prevention can be achieved by simulating natural enemy information (pheromone concentration and sound wave intensity) based on the understanding of the living habits of the target birds.

[0060] It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these modifications and improvements are all within the scope of protection of the present application. Therefore, the scope of protection of the patent of the present application shall be subject to the attached claims.

Claims

1. A green control method for agricultural bird damage, characterized in that: The prevention and control methods include: Collect the actual flight direction of the target bird at each historical position point in the historical flight trajectory and environmental data of multiple angle intervals in the neighborhood, wherein the environmental data includes meteorological data, crop data and natural enemy data; The predicted flight direction of each historical position point in the historical flight trajectory is calculated based on the initial weight of each environmental data. The initial weight is updated to obtain the target weight of each environmental data with the goal of minimizing the difference between the predicted flight direction and the actual flight direction. During the flight of the target bird, the predicted flight direction of the target bird at the real-time position point is calculated based on the target weight; In response to the control area being located within the neighborhood of the real-time location point, and the predicted flight direction of the real-time location point being located within the angle interval where the control area is located, a natural enemy data increment is applied to the control area so that the predicted flight direction of the real-time location point deviates from the angle interval where the control area is located.

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

3. A green control method for agricultural bird damage according to claim 1, characterized in that: The method for obtaining the neighborhood range includes: Set an initial distance, take the historical location point as the center and the initial distance as the radius to construct a neighborhood range, and calculate 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; The initial distance is increased multiple times, and an accuracy curve is drawn with the initial distance as the horizontal coordinate and the prediction accuracy as the vertical coordinate, and the initial distance corresponding to the inflection point of the accuracy curve is used as the target distance; the neighborhood range is a circular area with the location point as the center and the target distance as the radius.

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

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

6. A green control method for agricultural bird damage according to claim 1, characterized in that: The target weights of the environmental data are obtained as follows: The initial weights are continuously updated using an optimization algorithm, and the initial weights corresponding to the minimum value of the objective function are used as the target weights of each environmental data; The optimization algorithm is a hill climbing algorithm or a simulated annealing algorithm.

7. A green control method for agricultural bird damage according to claim 1, characterized in that: Historical location points Predicted flight direction for: ; For historical locations The actual flight direction, is the number of angle intervals in the neighborhood, , and Historical location points No. Meteorological data, crop data and natural enemy data for each angle interval, , and are the initial weights of meteorological data, crop data, and natural enemy data, respectively; For historical locations Starting from The unit vector of the angle bisector of the angle interval.

8. A green method for preventing and controlling agricultural bird damage according to claim 1, characterized in that: Difference between predicted and actual flight direction for: , is the number of historical position points in the historical flight trajectory, and Historical location points The actual flight direction and predicted flight direction.

9. A green control method for agricultural bird damage according to claim 1, characterized in that: The natural enemy data increment is artificially set based on experience.

10. A green control method for agricultural bird damage according to claim 1, characterized in that: The method for obtaining the natural enemy data increment includes: Initializing the natural enemy data increment; after updating the environmental data according to the natural enemy data increment, recalculating the predicted flight direction of the target bird at the real-time position point; In response to the recalculated predicted flight direction being within the angle interval where the control area is located, the natural enemy data increment is updated within a preset range until the recalculated predicted flight direction is no longer within the angle interval where the control area is located, and then the updating is stopped.

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