Unmanned aerial vehicle control system and method for agricultural seeding

Abstract

The invention discloses an unmanned aerial vehicle control system and method for agricultural seeding, relates to the technical field of unmanned aerial vehicle control, and solves the technical problems of seed falling path deviation and seeding precision reduction caused by the fact that route generation depends on preset topographic data and position points but real-time environment change is not considered in the prior art. The first flight height of the unmanned aerial vehicle at the current wind speed is obtained; acquiring the real-time horizontal offset of the unmanned aerial vehicle based on the first flight height; based on the first flight height and the real-time horizontal offset, determining a target position of the seed falling in the to-be-sown area; the actual depth of the seeds embedded into the soil is calculated through the kinetic energy theorem; judging whether the actual depth is within a preset normal embedding depth range or not; the real-time flight speed of the unmanned aerial vehicle in the flight process is obtained, and the unmanned aerial vehicle is controlled to complete sowing according to the real-time flight speed and the real-time flight speed; according to the invention, the deviation of a seed falling path is corrected, and the seeding precision is improved.

Description

Technical Field

[0001] The present invention belongs to the field of agricultural intelligence, relates to unmanned aerial vehicle (UAV) control technology, and specifically provides a UAV control system and method for agricultural seeding. Background Art

[0002] UAVs can quickly cover large areas of land for efficient seeding operations. Compared with traditional manual or mechanical seeding methods, UAV seeding can significantly shorten the operation time and improve seeding efficiency. UAVs can operate under complex terrain conditions. The UAV seeding system can accurately measure the amount of seeds to be dropped, enabling precise seeding. This helps avoid seed waste and over-seeding, and improves the utilization rate of seeds. UAVs can be intelligently adjusted according to soil and crop requirements, such as adjusting parameters like seeding density and depth, to adapt to different agricultural environments and crop needs. The UAV seeding system can also be combined with intelligent irrigation and fertilization systems to achieve precise management of water and fertilizer, further saving water resources and reducing the use of chemical fertilizers.

[0003] The prior art (a patent application with the publication number CN117508588A) discloses a method and device for seeding and fertilizing by an agricultural UAV, including a route setting subsystem, a UAV body, a control subsystem, and a seeding and fertilizing device. First, the seeding and fertilizing device stores seeds and fertilizers. Then, the route setting subsystem generates a flight route. Next, the UAV body drives the seeding and fertilizing device to fly based on the flight route. Finally, when the seeding and fertilizing device reaches each position point, the control subsystem controls the seeding and fertilizing device to perform seeding and fertilizing. The prior art depends on preset terrain data and position points when generating the route, but does not consider real-time environmental changes, resulting in problems such as the deviation of the seed falling path and the decline in seeding accuracy.

[0004] The present invention provides a UAV control system and method for agricultural seeding to solve the above technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. For this purpose, the present invention provides a UAV control system and method for agricultural seeding to solve the technical problems in the prior art that when generating the route, it depends on preset terrain data and position points but does not consider real-time environmental changes, resulting in the deviation of the seed falling path and the decline in seeding accuracy.

[0006] To achieve the above object, the first aspect of the present invention provides a UAV control system for agricultural seeding, including: a data acquisition module, a flight altitude control module, and a flight speed control module;

[0007] Data acquisition module: Obtain the topographic map of the area to be sown, and obtain the real-time environmental conditions and UAV data of the UAV through several sensors;

[0008] Flight altitude control module: Obtain the first flight altitude of the UAV under the current wind speed; obtain the real-time horizontal offset of the UAV based on the first flight altitude; determine the target position where the seeds fall in the area to be sown based on the first flight altitude and the real-time horizontal offset;

[0009] Calculate the actual depth of the seeds embedded in the soil through the kinetic energy theorem; determine whether the actual depth is within the preset normal embedding depth range; if so, use the first flight altitude as the actual flight altitude of the UAV; otherwise, compensate for the depth of the seeds embedded in the soil by adjusting the first flight altitude, and use the maximum value of the adjusted flight altitude as the real-time flight altitude;

[0010] Flight speed control module: Obtain the real-time flight speed of the UAV during flight, and control the UAV to complete sowing according to the real-time flight speed and the real-time flight speed.

[0011] Preferably, the data storage module includes: used to store the real-time environmental conditions, UAV data and the data of the UAV's broadcaster obtained by several sensors; among them, the real-time environmental conditions include: the real-time lateral wind speed relative to the UAV; the UAV data includes: the total motor power of the UAV during operation, the total weight of the UAV and the windward area of the UAV; the data of the UAV's broadcaster includes: the maximum rotation speed, the effective sowing width.

[0012] Preferably, obtaining the first flight altitude of the UAV under the current wind speed includes:

[0013] Record the preset standard altitude under windless conditions as H base , retrieve the real-time lateral wind speed V, the total motor power W of the UAV during operation, the control algorithm efficiency coefficient CKZ of the UAV, the aerodynamic optimization factor SQD, the total weight M of the UAV and the windward area S of the UAV;

[0014] Through the formula Calculate the wind resistance coefficient of the corresponding model of UAV;

[0015] Through the formula H max =H base -k×V 2 Calculate the first flight altitude of the UAV under the current wind speed;

[0016] The acquisition method of the control algorithm efficiency coefficient CKZ includes: the average value of the average value of the sensor accuracy of the corresponding model of UAV, the accuracy of the control algorithm and the increased value of the triple differential gain, and use the average value as the control algorithm efficiency coefficient.

[0017] The present invention dynamically adjusts control parameters through the adaptive control mechanism of the unmanned aerial vehicle (UAV), and increasing the differential gain is beneficial to suppressing the oscillation of the UAV.

[0018] Preferably, the method for obtaining the aerodynamic optimization factor includes:

[0019] Obtain the drag coefficient F at the current wind speed d and the air density ρ k , retrieve the windward area S of the UAV and the real-time lateral wind speed V;

[0020] Calculate the aerodynamic optimization factor through the formula .

[0021] It should be noted that the drag coefficient at the current wind speed is obtained through wind tunnel test experiments.

[0022] Preferably, the method for obtaining the real-time horizontal offset of the UAV based on the first flight altitude includes:

[0023] Retrieve the real-time lateral wind speed V and the first flight altitude H of the UAV max ;

[0024] Calculate the seed horizontal offset through the formula ; where m is the weight of a single seed and g is the acceleration due to gravity;

[0025] Take the opposite direction of the seed horizontal offset as the direction of the real-time horizontal offset of the UAV, and take the seed horizontal offset as the real-time horizontal offset of the UAV.

[0026] The present invention ensures that the seeds fall at the target position in the area to be sown by calculating the real-time horizontal offset of the UAV.

[0027] Preferably, the method for calculating the actual depth of the seed embedded in the soil through the kinetic energy theorem includes:

[0028] Obtain the gravity F during the descent of the seed w and the air resistance F a , and obtain the kinetic energy E of the seed when it lands according to the kinetic energy theorem, E = F w -F a ;

[0029] Obtain the soil penetration resistance F when the seed is embedded in the soil s , and calculate the depth of the seed embedded in the soil without lateral wind force through the formula ;

[0030] Retrieve ΔX and the first flight altitude of the UAV, and calculate the actual depth of the seed embedded in the soil through the formula .

[0031] The present invention calculates the depth at which the seeds are embedded in the soil, and then adjusts the flight altitude of the drone according to the depth at which the seeds are embedded in the soil, so as to avoid the situation that the seeds sink excessively due to soft soil and the seeds bounce back due to hard soil.

[0032] Preferably, adjusting the first flight altitude to compensate for the depth at which the seeds are embedded in the soil includes:

[0033] Presetting the flight altitude adjustment value as H new , then the adjusted vertical speed is Taking the average value of the maximum and minimum values in the preset normal embedding depth range as the most suitable embedding depth d; taking the absolute value of the difference between the most suitable embedding depth and the actual depth at which the seeds are embedded in the soil as the reduction value of the depth at which the seeds are embedded;

[0034] Through the formula Calculate the flight altitude adjustment value;

[0035] Taking the sum of the flight altitude adjustment value and the first flight altitude of the drone as the maximum flight altitude.

[0036] In the present invention, when there is horizontal wind force calculated, the seeds may be subject to resistance in the horizontal direction, but at the same time, due to the change of air resistance in the vertical direction, the vertical falling speed may be reduced, thereby reducing the embedding depth. The flight altitude of the drone will affect the sowing accuracy and the distribution of the seeds.

[0037] Preferably, obtaining the real-time flight speed of the drone during flight includes:

[0038] Obtaining the maximum rotation speed f of the spreader of the drone max , effective sowing width ω, preset target sowing density ρ and real-time lateral wind speed compensation coefficient FB;

[0039] Through the formula Calculate the real-time flight speed.

[0040] By adjusting the flight speed of the drone during sowing in the present invention, it is beneficial to avoid the problem that the seeds cannot be accurately and evenly sown to the predetermined position due to too fast flight speed, and the faster flight speed will cause uneven distribution of the seeds in the air, increasing the risk of missed sowing or overlapping sowing; at the same time, it is also beneficial to avoid the problem that too slow flight speed reduces the sowing efficiency and causes excessive accumulation of seeds in a specific area.

[0041] Preferably, the obtaining method of the real-time lateral wind speed compensation coefficient includes:

[0042] Judging whether the real-time lateral wind speed exceeds the set wind speed threshold;

[0043] Yes, obtain the included angle θ between the direction of the real-time lateral wind and the heading of the UAV, and denote the absolute value of the difference between the real-time lateral wind speed and the set wind speed threshold as Δv;

[0044] Calculate the real-time lateral wind speed compensation coefficient through the formula where k1, k2, and k3 are correction coefficients, which are determined by constructing a correction coefficient table;

[0045] No, then mark the real-time lateral wind speed compensation coefficient as 0.

[0046] To achieve the above object, the second aspect of the present invention provides a UAV control method for agricultural sowing, including:

[0047] Obtain the topographic map of the area to be sown, and obtain the real-time environmental conditions and UAV data of the UAV through several sensors;

[0048] Obtain the first flight altitude of the UAV under the current wind speed; obtain the real-time horizontal offset of the UAV based on the first flight altitude; determine the target position where the seeds fall in the area to be sown based on the first flight altitude and the real-time horizontal offset;

[0049] Calculate the actual depth of the seeds embedded in the soil through the kinetic energy theorem; determine whether the actual depth is within the preset normal embedding depth range; if yes, use the first flight altitude as the actual flight altitude of the UAV; if not, compensate for the depth of the seeds embedded in the soil by adjusting the first flight altitude, and use the maximum value of the adjusted flight altitude as the real-time flight altitude;

[0050] Obtain the real-time flight speed of the UAV during flight, and control the UAV to complete sowing according to the real-time flight speed and the real-time flight speed.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] 1. The present invention obtains the specific topographic information of the area to be sown through the topographic map, which helps the UAV to adjust the flight path and altitude according to the terrain to ensure that the seeds can be evenly and accurately sown in the predetermined area; the data collection of the real-time environmental sensors enables the UAV to immediately adjust the flight attitude and sowing strategy to adapt to the changing environmental conditions; calculating the first flight altitude of the UAV according to the current wind speed and adjusting the real-time horizontal offset of the UAV accordingly can effectively reduce the influence of the wind on the flight trajectory of the UAV and improve flight stability; calculating the actual depth of the seeds embedded in the soil through the kinetic energy theorem and comparing it with the preset normal embedding depth range can accurately control the flight altitude, thereby ensuring that the seeds reach the ideal sowing depth.

[0053] 2. The present invention adjusts the flight speed in real time based on the maximum flight altitude and the actual flight performance of the UAV, ensuring that the UAV can adapt to different operating environments and conditions while maintaining efficient sowing. This method reduces the problem of inaccurate or inefficient sowing due to changes in environmental factors and improves overall operating efficiency; precise sowing depth and uniform sowing distribution can minimize seed waste and ensure that each seed can achieve its maximum growth potential. At the same time, by optimizing the flight path and altitude, the energy consumption and wear of the UAV can be reduced, thereby extending the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 This is a schematic diagram of the relationship between modules included in the present invention;

[0056] Figure 2 This is a schematic diagram of the specific steps of the flight altitude control module of the present invention;

[0057] Figure 3 A schematic diagram of the specific steps for optimizing flight speed according to the present invention;

[0058] Figure 4 The figure is a flow chart of the drone-controlled sowing process of the present invention. DETAILED DESCRIPTION

[0059] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] See also Figure 1 , a first aspect of the present invention provides a UAV control system for agricultural sowing, comprising: a data acquisition module, a flight altitude control module and a flight speed control module;

[0061] Data acquisition module: obtains the topographic map of the area to be sown and obtains the real-time environmental conditions and drone data of the drone through several sensors;

[0062] Flight altitude control module: Obtain the first flight altitude of the drone at the current wind speed; obtain the real-time horizontal offset of the drone based on the first flight altitude; determine the target position where the seeds fall in the area to be sown based on the first flight altitude and the real-time horizontal offset;

[0063] Calculate the actual depth of the seeds embedded in the soil through the kinetic energy theorem; determine whether the actual depth is within the preset normal embedding depth range; if yes, take the first flight altitude as the actual flight altitude of the drone; if not, compensate for the depth of the seeds embedded in the soil by adjusting the first flight altitude, and take the maximum value of the adjusted flight altitude as the real-time flight altitude;

[0064] Flight speed control module: Obtain the real-time flight speed of the drone during flight, and control the drone to complete sowing according to the real-time flight speed and the real-time flight speed.

[0065] Please refer to Figure 2 , for the specific steps of the flight altitude control module, record the preset standard altitude under no-wind conditions as H base , retrieve the real-time lateral wind speed V, the total motor power W of the drone during operation, the control algorithm efficiency coefficient CKZ of the drone, the aerodynamic optimization factor SQD, the total weight M of the drone, and the windward area S of the drone;

[0066] Through the formula Calculate the wind resistance coefficient of the corresponding model drone;

[0067] Through the formula H max =H base -k×V 2 Calculate the first flight altitude of the drone at the current wind speed;

[0068] The acquisition method of the control algorithm efficiency coefficient CKZ is: the average value of the mean value of the sensor accuracy of the corresponding model drone, the accuracy of the control algorithm, and the increased value of the triple differential gain, and take the average value as the control algorithm efficiency coefficient;

[0069] Retrieve the real-time lateral wind speed V and the first flight altitude H of the drone max ;

[0070] Through the formula Calculate the seed horizontal offset; where m is the weight of a single seed and g is the acceleration due to gravity; here, the weight of a single seed can be obtained by a preset method, and this value is the average weight of a single seed.

[0071] Take the opposite direction of the seed horizontal offset as the direction of the real-time horizontal offset of the drone, and take the seed horizontal offset as the real-time horizontal offset of the drone;

[0072] Obtain the gravitational force F during the descent of the seeds w and the air resistance F a , and according to the kinetic energy theorem, obtain the kinetic energy E of the seeds when they land, where E = F w -F a ;

[0073] Among them, the gravitational force F during the descent of the seeds w can be obtained by calculating the weight of a single seed, while the air resistance F during the descent of the seeds a can also be obtained based on experiments for different types of seeds.

[0074] Obtain the soil penetration resistance F when the seeds embed in the soil s , and calculate the depth of the seeds embedding in the soil without lateral wind force through the formula ;

[0075] Retrieve the real-time horizontal offset ΔX of the drone and the first flight height of the drone, and calculate the actual depth of the seeds embedding in the soil through the formula ; Preset the flight height adjustment value as H new , then the adjusted vertical speed is Take the average value of the maximum and minimum values in the preset normal embedding depth range as the most suitable embedding depth d; Take the absolute value of the difference between the most suitable embedding depth and the actual depth of the seeds embedding in the soil as the reduction value of the seeds embedding depth;

[0076] Calculate the flight height adjustment value through the formula ;

[0077] Take the sum of the flight height adjustment value and the first flight height of the drone as the maximum flight height.

[0078] For example, there is currently a drone A operating in the area to be sown. Now, the preset standard height under windless conditions is 12m. The real-time lateral wind speed is obtained through the sensor as 2.8m / s, and the total motor power during the operation of the drone is 3.5kW; Since drone A has a streamlined design, the aerodynamic optimization factor is set to 1.2; The total weight of the drone is 5.8kg and the windward area of the drone is 0.89m 2 ;

[0079] The average value of the accuracy of the drone sensor is 98.2%, the accuracy of the control algorithm is 99.5%, and the increased value of the differential gain is 8%. Calculate the efficiency coefficient of the control algorithm as 73.9% through the formula ;

[0080] Calculate the wind resistance coefficient of the corresponding model of the drone as 0.6 through the formula ;

[0081] Through the formula H max = H base - k×V 2 = 12 - 0.6×2.8 2 The first flight altitude of the drone at the current wind speed is calculated to be 7.3 m;

[0082] Retrieve the real-time lateral wind speed of 2.8 m / s and the first flight altitude of the drone at the current wind speed of 7.3 m. g is the gravitational acceleration of 9.8 m / s 2 , and the weight of a single seed is 1 g;

[0083] Through the formula Take the opposite direction of the horizontal offset of the seed as the direction of the real-time horizontal offset of the drone, and take the horizontal offset of the seed as the real-time horizontal offset of the drone;

[0084] Retrieve the calculation formula of air resistance where C is the air resistance coefficient of 1.293 g / L, S z is the windward area of the seed of 0.018 m 2 , V is the relative motion speed of the object and the air of 2.8 m / s; δ is the air density of 1.225 kg / m 3 ;

[0085] Obtain the gravity F during the descent of the seed w is 9.8×10 -3 N;

[0086] According to the kinetic energy theorem, obtain the kinetic energy E of the seed when it lands. E = F w - F a = 9.8×10 -3 - 2.2×10 -4 = 9.68×10 -3 ;

[0087] Obtain the soil penetration resistance F when the seed embeds in the soil s is 6.3×10 -2 N. Through the formula d = Calculate the depth of the seed embedding in the soil without lateral wind force to be 1.33 cm;

[0088] The preset normal embedding depth range is (1.6 cm, 2.8 cm). Since 1.33 cm is less than 1.8 cm, the preset flight altitude adjustment value is H new , then the adjusted vertical speed is Take the average of the maximum and minimum values in the preset normal embedding depth range as the most suitable embedding depth d = (1.6 + 2.8) / 2 = 2.2 cm;

[0089] The absolute value of the difference between the most suitable embedding depth and the actual depth of the seeds embedded in the soil is taken as the reduction value of the seed embedding depth, which is 0.87 cm;

[0090] Through the formula The calculated flight altitude adjustment value is

[0091]

[0092] Then the seeds still need to be embedded in the soil by 0.87 cm, and the maximum adjusted flight altitude is 7.3 m + 3.72 m = 11.02 m.

[0093] Please refer to Figure 3 , for the specific steps of flight speed optimization, obtain the maximum rotation speed f max of the spreader of the drone, the effective spreading width ω, and the preset target seeding density ρ;

[0094] Determine whether the real-time lateral wind speed exceeds the set wind speed threshold;

[0095] Yes, obtain the included angle θ between the direction of the real-time lateral wind and the drone's heading, and record the absolute value of the difference between the real-time lateral wind speed and the set wind speed threshold as ΔV;

[0096] Through the formula Calculate the real-time lateral wind speed compensation coefficient; where, k1, k2, and k3 are correction coefficients, which are determined by constructing a correction coefficient table;

[0097] No, then mark the real-time lateral wind speed compensation coefficient as 0.

[0098] Through the formula Calculate the real-time flight speed.

[0099] For example, obtain the maximum rotation speed f max of the spreader of the drone as 700 revolutions per minute and the effective spreading width ω as 95%, and the preset target seeding density ρ as 118 kg / hm 2 ; the set wind speed threshold is 2.5 m / s; obtain the included angle between the direction of the real-time lateral wind and the drone's heading as 45°;

[0100] The real-time lateral wind speed of 2.8 m / s exceeds the set wind speed threshold of 2.5 m / s. The absolute value of the difference between the real-time lateral wind speed and the set wind speed threshold is recorded as 0.3 m / s, and the real-time lateral wind speed compensation coefficient is obtained by constructing an aerodynamic correction model. The correction coefficients are determined by constructing a correction coefficient table (Table 1);

[0101] Through the formula FB = 0.23 × 0.3 × cos45° = 0.049;

[0102] Table 1 Correction coefficient table

[0103]

[0104] The real-time flight speed of 5.37 m / s is calculated by the formula .

[0105] Please refer to Figure 4 . An unmanned aerial vehicle (UAV) control method for agricultural seeding provided by an embodiment of the second aspect of the present invention includes:

[0106] Obtain a topographic map of the area to be seeded, and obtain the real-time environmental conditions and UAV data of the UAV through a number of sensors;

[0107] Obtain the first flight altitude of the UAV under the current wind speed; obtain the real-time horizontal offset of the UAV based on the first flight altitude; determine the target position where the seeds fall in the area to be seeded based on the first flight altitude and the real-time horizontal offset;

[0108] Calculate the actual depth of the seeds embedded in the soil by the kinetic energy theorem; determine whether the actual depth is within the preset normal embedding depth range; if so, use the first flight altitude as the actual flight altitude of the UAV; if not, compensate for the depth of the seeds embedded in the soil by adjusting the first flight altitude, and use the maximum value of the adjusted flight altitude as the real-time flight altitude;

[0109] Obtain the real-time flight speed of the UAV during flight, and control the UAV to complete seeding according to the real-time flight speed and the real-time flight speed.

[0110] Some of the data in the above formula are calculated by removing the dimension and taking their numerical values. The formula is the one closest to the real situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained by simulation of a large amount of data.

[0111] The working principle of the present invention: The present invention obtains the first flight altitude of the UAV under the current wind speed; obtains the real-time horizontal offset of the UAV based on the first flight altitude; determines the target position where the seeds fall in the area to be seeded based on the first flight altitude and the real-time horizontal offset; calculates the actual depth of the seeds embedded in the soil by the kinetic energy theorem; determines whether the actual depth is within the preset normal embedding depth range; if so, uses the first flight altitude as the actual flight altitude of the UAV; if not, compensates for the depth of the seeds embedded in the soil by adjusting the first flight altitude, and uses the maximum value of the adjusted flight altitude as the real-time flight altitude; obtains the real-time flight speed of the UAV during flight, and controls the UAV to complete seeding according to the real-time flight speed and the real-time flight speed.

[0112] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An unmanned aerial vehicle control system for agricultural seeding, characterized in that, Comprising: A data acquisition module, a flight altitude control module, and a flight speed control module; Data acquisition module: Obtain the topographic map of the area to be sown, and obtain the real-time environmental conditions and UAV data of the UAV through a number of sensors; Flight altitude control module: Obtain the first flight altitude of the UAV under the current wind speed; Obtain the real-time horizontal offset of the UAV based on the first flight altitude; Determine the target position where the seeds fall in the area to be sown based on the first flight altitude and the real-time horizontal offset; Calculate the actual depth of the seeds embedded in the soil through the kinetic energy theorem; Determine whether the actual depth is within the preset normal embedding depth range; If so, use the first flight altitude as the actual flight altitude of the UAV; Otherwise, compensate for the depth of the seeds embedded in the soil by adjusting the first flight altitude, and use the maximum value of the adjusted flight altitude as the real-time flight altitude; Flight speed control module: Obtain the real-time flight speed of the UAV during flight, and control the UAV to complete sowing according to the real-time flight speed and the real-time flight speed.

2. The drone control system for agricultural seeding according to claim 1, characterized in that, Further comprising: Data storage module: Used to store the real-time environmental conditions, UAV data, and UAV broadcaster data obtained by a number of sensors; Among them, the real-time environmental conditions include: the real-time lateral wind speed relative to the UAV; the UAV data includes: the total motor power during UAV operation, the total weight of the UAV, and the windward area of the UAV; the UAV broadcaster data includes: the maximum rotation speed, the effective sowing width.

3. The unmanned aerial vehicle control system for agricultural seeding according to claim 2, characterized in that, The obtaining of the first flight altitude of the UAV under the current wind speed includes: Set the standard height H under windless conditions base , retrieve the real-time lateral wind speed V, the total motor power W during the operation of the drone, the aerodynamic optimization factor SQD, the total weight M of the drone, and the windward area S of the drone; Mark the average value of the sum of the corresponding UAV sensor accuracy mean value, the accuracy of the control algorithm, and the increase value of the triple differential gain as the control algorithm efficiency coefficient CKZ; Calculate the wind resistance coefficient of the corresponding UAV model through the mapping relationship between the total motor power during UAV operation, the aerodynamic optimization factor, the control algorithm efficiency coefficient, the total weight of the UAV, and the windward area of the UAV; Through the formula H max = H base - k × V 2 Calculate the first flight altitude of the drone at the current wind speed.

4. The unmanned aerial vehicle control system for agricultural seeding according to claim 3, wherein The obtaining method of the aerodynamic optimization factor includes: According to the obtained drag coefficient and air density under the current wind speed, and retrieve the windward area of the UAV and the real-time lateral wind speed; Obtain the aerodynamic optimization factor through the mapping relationship between the drag coefficient, air density, windward area of the UAV, and real-time lateral wind speed under the current wind speed.

5. The unmanned aerial vehicle control system for agricultural seeding according to claim 2, characterized in that, The obtaining of the real-time horizontal offset of the UAV based on the first flight altitude includes: Retrieve the real-time lateral wind speed V and the first flight altitude H of the UAV max ; The seed level offset is calculated through the formula where m is the weight of a single seed and g is the acceleration due to gravity; Take the opposite direction of the seed horizontal offset as the direction of the UAV real-time horizontal offset, and take the seed horizontal offset as the UAV real-time horizontal offset.

6. The unmanned aerial vehicle control system for agricultural seeding according to claim 5, characterized in that The calculation of the actual depth of the seeds embedded in the soil through the kinetic energy theorem includes: Obtain the gravity F during the descent of the seed w and the air resistance F a , and obtain the kinetic energy E of the seed when it lands according to the kinetic energy theorem, E = F w -F a ; Obtain the soil penetration resistance F when the seed is embedded in the soil s , through the formula Calculate the depth of the seed embedded in the soil without lateral wind force; Retrieve ΔX and the first flight altitude of the drone, and calculate the actual depth of the seeds embedded in the soil through the formula ​ 7. The unmanned aerial vehicle control system for agricultural seeding according to claim 6, characterized in that, The compensation for the depth of the seeds embedded in the soil by adjusting the first flight altitude includes: The preset flight altitude adjustment value is H new , then the adjusted vertical speed is Take the average value of the maximum and minimum values in the preset normal embedding depth range as the most suitable embedding depth d; take the absolute value of the difference between the most suitable embedding depth and the actual depth of the seed embedded in the soil as the reduction value of the seed embedding depth; The flight altitude adjustment value is calculated through the formula ; Take the sum of the flight altitude adjustment value and the first flight altitude of the UAV as the maximum flight altitude.

8. The unmanned aerial vehicle control system for agricultural seeding according to claim 2, wherein, The obtaining of the real-time flight speed of the UAV during flight includes: Obtain the maximum rotational speed f of the spreader of the drone max , effective spreading width ω, preset target seeding density ρ, and real-time lateral wind speed compensation coefficient FB; The real-time flight speed is calculated through the formula ​ 9. The unmanned aerial vehicle control system for agricultural seeding according to claim 8, characterized in that, The obtaining method of the real-time lateral wind speed compensation coefficient includes: Determine whether the real-time lateral wind speed exceeds the set wind speed threshold; If so, obtain the included angle θ between the direction of the real-time lateral wind and the UAV heading, and record the absolute value of the difference between the real-time lateral wind speed and the set wind speed threshold as Δv; The real-time lateral wind speed compensation coefficient is calculated through the formula wherein, k1, k2, and k3 are correction coefficients; If not, mark the real-time lateral wind speed compensation coefficient as 0.

10. A method for controlling an agricultural seeding drone, applied to an agricultural seeding drone control system according to any one of claims 1-9, characterized in that, Comprising: Obtain the topographic map of the area to be sown, and obtain the real-time environmental conditions and drone data of the drone through a number of sensors; Obtain the first flight altitude of the drone under the current wind speed; Obtain the real-time horizontal offset of the drone based on the first flight altitude; Determine the target position where the seeds fall in the area to be sown based on the first flight altitude and the real-time horizontal offset; Calculate the actual depth of the seeds embedded in the soil through the kinetic energy theorem; judge whether the actual depth is within the preset normal embedding depth range; If yes, use the first flight altitude as the actual flight altitude of the drone; if not, compensate for the depth of the seeds embedded in the soil by adjusting the first flight altitude, and use the maximum value of the adjusted flight altitude as the real-time flight altitude; Obtain the real-time flight speed of the drone during flight, and control the drone to complete sowing according to the real-time flight speed and the real-time flight speed.

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