Unmanned aerial vehicle pesticide spraying distribution method and system for commercial crops in multiple operation areas

Abstract

The invention discloses an unmanned aerial vehicle pesticide spraying distribution method and system for commercial crops in multiple operation areas, and the method comprises the steps: carrying out the modeling of a distribution problem of limited pesticide spraying operation time of a UAV among different commercial crops as a game model, taking the UAV as a leader of the game model, and taking each commercial crop as a follower; for the leader, an objective function of the game model is a utility function for selling limited pesticide spraying operation time resources; for the followers, the objective function of the game model is the earnings obtained by purchasing the pesticide spraying operation time; the utility function of the limited pesticide spraying operation time resource is sold to the maximum degree to obtain the optimal unit resource pricing, and the earnings obtained by purchasing the pesticide spraying operation time to the maximum degree to obtain the optimal operation time distribution resource. The distribution problem of the limited pesticide spraying operation time of the UAV among different commercial crops is modeled as a game model to optimize the distribution of the flight operation time of the UAV on different commercial crops.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) flight control, and particularly relates to a method and system for distributing pesticide spraying by an UAV on economic crops in multiple operating areas. Background Art

[0002] With the rapid development of drone technology, its application in agriculture is becoming increasingly widespread, particularly in plant protection, pest control, and weed control. Drone precision spraying has become a valuable aid. This method uses drones equipped with spraying equipment, which can be remotely controlled or programmed to precisely locate and efficiently spray crops. Drones can operate at specific altitudes, speeds, and spray volumes, overcoming the limitations of traditional manual plant protection while significantly improving control effectiveness and efficiency. Furthermore, drone precision spraying reduces direct contact between plant protection workers and chemical pesticides, reducing the risk of poisoning. Furthermore, drone-based pesticide spraying offers advantages such as adaptability to complex terrain, cost savings, and efficient use of pesticides. The wind generated by the drone's rotors can flip crops, ensuring even spraying on both sides, regardless of geographical factors. However, this method also presents challenges, such as short flight time and wind-induced fluctuations in spraying effectiveness. Given the large cultivated areas and the limited payload and flight time of drones, when multiple cash crops require pesticide spraying, the key issue urgently needs to be addressed: how to distribute the appropriate amount of pesticide sprayed across them. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for allocating pesticide spraying of cash crops by unmanned aerial vehicles (UAVs) in multiple operation areas. The problem of allocating the limited pesticide spraying operation time of UAVs among different cash crops is modeled as a game model to optimize the allocation of UAV flight operation time among different cash crops.

[0004] The technical solutions for achieving the purpose of the present invention are:

[0005] A method for distributing pesticides by drones for economic crops in multiple operating areas comprises the following steps:

[0006] S01: The problem of allocating the limited pesticide spraying time of UAVs among different cash crops is modeled as a game model, with the UAV as the leader of the game model and the cash crops as followers;

[0007] S02: For the leader, the objective function of the game model is the utility function of selling the limited pesticide spraying time resource; for the follower, the objective function of the game model is the profit obtained by purchasing pesticide spraying time;

[0008] S03: Maximize the utility function of selling limited pesticide spraying operation time resources to obtain the optimal unit resource price β* , the optimal operation time allocation resources are obtained by maximizing the benefits of purchasing pesticide spraying operation time

[0009] In the preferred technical solution, step S02 includes modeling the profit of cash crop n after spraying a certain amount of pesticide as

[0010] Among them, α n represents the market price of n units of cash crop output, λ n is the basic yield of cash crop n per unit area, θ n is the yield of cash crop n after spraying pesticides, s n is the planting area of each economic crop, ∈ n The effective area of UAV pesticide spraying for cash crops.

[0011] In the preferred technical solution, in step S02, for the follower, the objective function of the game model is:

[0012]

[0013] Among them, β represents the price per unit of pesticide spraying operation time sold by the leader to cash crop n, v is the flying speed of the drone, H is the width of the spraying operation, and t n Time for drones to spray pesticides for cash crops.

[0014] In the preferred technical solution, in step S02, for the leader, the objective function of the game model is:

[0015]

[0016] Where t is the total time of spraying operation, A collection of cash crops.

[0017] In the preferred technical solution, step S03 includes:

[0018] Collect cash crops Cash crops in Arrange in ascending order from small to large; α n represents the market price of n units of cash crop output, λ n is the basic yield of cash crop n per unit area, θ n is the yield of cash crop n after spraying pesticides, s n is the planting area of each economic crop, v is the flying speed of the UAV, and H is the spraying width;

[0019] 1) If When , the optimal unit resource price is:

[0020]

[0021] Where N is the number of economic crops, and t is the total time of spraying operation;

[0022] The operation time for spraying pesticides on cash crop n is:

[0023]

[0024] 2) If When , where cash crops l∈{1,…,N-1}, the optimal unit resource pricing is:

[0025]

[0026] cash crops The operating hours for spraying pesticides are:

[0027]

[0028] cash crops The operating hours for spraying pesticides are:

[0029]

[0030] 3) If t≤0, there is no game behavior, cash crops The operating hours for spraying pesticides are:

[0031]

[0032] In the preferred technical solution, after step S03, the following steps are further included:

[0033] When all cash crops have obtained their UAV pesticide spraying operation time obtained through game, the area that can be sprayed is calculated as:

[0034]

[0035] like A redistribution game is conducted on the flight time of UAV pesticide spraying to redistribute the operation time that overflowed in the previous round of the game among the set of economic crops whose flight time did not overflow, until no UAV flight operation time overflows after a round of the game ends.

[0036] The preferred technical solution includes: calculating the total time of pesticide spillage after the initial game for all cash crops:

[0037]

[0038] If Δt>0, the set of economic crops with overflow of pesticide flight time is UAV is The flight operation time for cash crops is:

[0039]

[0040] Update the economic crop set to be allocated UAV flight operation time as follows And UAV flight time t = Δt, again for the limited UAV operation time set The cash crops in the game are allocated again, and the optimization problem at the follower is:

[0041]

[0042] in, For cash crops to gamble again The operating hours for spraying pesticides;

[0043] The optimization problem at the leader is:

[0044]

[0045] Get the remaining operating time of the UAV in the collection The optimal value of time allocation for cash crops to play the game again

[0046] The present invention also discloses a drone pesticide spraying and distribution system for economic crops in multiple operating areas, comprising a processor having the drone pesticide spraying and distribution method for economic crops in multiple operating areas built in the processor.

[0047] The present invention further discloses a UAV, comprising the UAV pesticide spraying and distribution system for economic crops in multiple operation areas.

[0048] The present invention further discloses a computer storage medium on which a computer program is stored. When the computer program is executed, the above-mentioned drone pesticide spraying and distribution method for economic crops in multiple operation areas is implemented.

[0049] Compared with the prior art, the present invention has the following significant advantages:

[0050] The problem of allocating limited UAV pesticide spraying time among different cash crops is modeled as a game theory. A closed-form solution is presented for the flight time of each cash crop within different UAV limited pesticide spraying time intervals, optimizing the allocation of UAV flight time among different cash crops. This provides a unique and efficient solution that physically conforms to real-world application scenarios and can be effectively applied in engineering practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a scene diagram of the drone pesticide spraying and distribution method for economic crops in multiple operation areas of this embodiment;

[0052] Figure 2 This is a flow chart of the drone pesticide spraying and distribution method for economic crops in multiple operation areas of this embodiment. DETAILED DESCRIPTION

[0053] Example 1:

[0054] A method for distributing pesticides by drones for economic crops in multiple operating areas comprises the following steps:

[0055] S01: The problem of allocating the limited pesticide spraying time of UAVs among different cash crops is modeled as a game model, with the UAV as the leader of the game model and the cash crops as followers;

[0056] S02: For the leader, the objective function of the game model is the utility function of selling the limited pesticide spraying time resource; for the follower, the objective function of the game model is the profit obtained by purchasing pesticide spraying time;

[0057] S03: Maximize the utility function of selling limited pesticide spraying operation time resources to obtain the optimal unit resource price β * , the optimal operation time allocation resources are obtained by maximizing the benefits of purchasing pesticide spraying operation time

[0058] Combine Figure 1 and Figure 2 As shown, the design of the present invention is further analyzed and described in detail.

[0059] In the network described in the present invention, drones are used to perform aerial pesticide spraying operations over large areas of crop cultivation. The pesticides used for spraying can be either a combination or a single type, depending on the target, crop type, environmental conditions, and the purpose of the application.

[0060] The present invention uses the common ox-plow reciprocating method. This method involves selecting a starting point at the boundary of the area to be sprayed, performing an L-shaped turn at the end of the area, and repeating this process to complete the entire sub-area. During the drone's pesticide spraying mission, no spraying occurs while turning.

[0061] Assume that there are N cash crops in a large area of crop planting, and their set is recorded as In order to reduce management and maintenance costs, each cash crop is concentrated in the same area and not planted in a scattered manner. The planting area of each cash crop is recorded as The UAV's flight speed is v, the total time it takes to perform the spraying operation is t, and the spray width is H. Since the UAV's spraying flow rate is fixed, the total amount of sprayed medicine is directly proportional to the operation time t.

[0062] In this invention, given the large planting areas and the limited payload and flight time of drones, when multiple cash crops require pesticide spraying, the key issue that needs to be addressed is how to rationally distribute the pesticide spraying to these crops. Furthermore, given that each crop desires a share of drone pesticide spraying, and pesticide spraying resources bring significant economic benefits to the crops, different cash crops are in competition for the limited flight time resources of UAVs for pesticide spraying.

[0063] The Steinberg game is a pure strategy non-cooperative sequential game model in economics. The participants in the game can be divided into leaders and followers based on the priority of actions and the completeness of information. Followers only have partial information and act first; while leaders act later because they have all the information of followers. Among them, the leader needs to consider the optimal response of followers when setting the game strategy, and followers give their own optimal resource purchase size based on the leader's optimal decision. In the present invention, the UAV is used as the leader of the game model, and the various economic crops that only have partial information act as followers.

[0064] Drones for cash crops The effective area of pesticide spraying is:

[0065] ∈ n =vt n H

[0066] Among them, t n Time for drones to spray pesticides for cash crops.

[0067] Therefore, the yield of cash crop n is modeled as:

[0068] k n =λ n s n +θ n ∈ n

[0069] Among them, λ nis the basic yield of economic crop n per unit area (basic yield usually refers to the crop yield without any fertilizer or pesticide application, objectively and truly reflecting the ability of the soil to provide nutrients); θ n The yield of economic crop n after spraying pesticides (referring to the yield level that crops can achieve after applying pesticides in terms of preventing and controlling pests and diseases and promoting growth. The use of pesticides can improve the resistance of crops to stress, reduce losses caused by pests and diseases, and thus may increase crop yields.) The yield can be obtained by statistical methods or empirical data.

[0070] like This indicates that the available flight operation time of the UAV is large, and it can carry out pesticide spraying operations in all planting areas of all cash crops. Therefore, the flight operation time for each cash crop is:

[0071]

[0072] Otherwise, the following allocation scheme needs to be implemented.

[0073] Because the use of pesticides can increase crop yields to a certain extent, this increase is based on the base yield. If the base yield is already high, then the effect of pesticides on the economic benefits of agricultural products may be relatively limited. The benefits of cash crop n after spraying a certain amount of pesticide are modeled as:

[0074]

[0075] Among them, α n The ln(1+x) function represents the market price of n units of cash crop output. n s n When it is already very high, the effect of pesticide spraying on increasing the economic benefits of crops decreases, and the number 1 is to ensure that the benefits are not negative.

[0076] In this invention, each economic crop hopes to increase the drone's operating time above it in order to obtain more pesticide spraying and thus obtain higher economic benefits. Therefore, the optimization problem of the follower in each game relationship is modeled as:

[0077]

[0078] Here, β represents the price per unit of pesticide spraying time sold by the leader to cash crop n. As can be seen, as the follower purchases more pesticide spraying time, their revenue increases, but the cost function also increases. For each follower in the game model, their goal is to achieve higher revenue at a lower cost.

[0079] The seller leader in the game relationship sells limited pesticide spraying time to multiple competing cash crops for their pesticide spraying. Therefore, the objective function in the game relationship is defined as the total cost paid by all cash crops for purchasing pesticide spraying time, which is:

[0080]

[0081] Since the UAV's operating time is limited by the battery and pesticide loading capacity, the leader in the game relationship has the following constraints:

[0082]

[0083] In the game, the UAV acts as a leader and aims to gain profits by selling limited pesticide spraying time resources. Therefore, for the leader, there are:

[0084]

[0085] The revenue optimization problem of the leader and multiple followers together constitutes a game model. The final equilibrium can be achieved by the two parties playing the game according to certain rules. That is, the optimal unit resource price β is obtained by maximizing the utility function of the leader selling limited pesticide spraying operation time resources using the traditional convex optimization method. * , while maximizing the benefits followers gain from purchasing pesticide spraying time to obtain the optimal time allocation resources See below for details:

[0086] Assume that Cash crops in Arrange in ascending order from small to large;

[0087] 1) If When , it means that all cash crops can participate in the game, thereby obtaining a certain amount of UAV spraying pesticide operation time. At this time, the optimal price of the game leader is:

[0088]

[0089] Therefore, cash crops The operating hours for spraying pesticides are:

[0090]

[0091] 2) If When , where l∈{1,…,N-1}, it means that only the economic crops from l+1 to N can participate in the game, and the first l economic crops cannot participate in the game to obtain a certain amount of UAV spraying operation time. At this time, the optimal price of the game leader is:

[0092]

[0093] Therefore, cash crops The operating hours for spraying pesticides are:

[0094]

[0095] cash crops The operating hours for spraying pesticides are:

[0096]

[0097] 3) If t≤0, it means that the UAV has no flight time to sell, so there is no game behavior, and the cash crop The operating hours for spraying pesticides are:

[0098]

[0099] When all cash crops have obtained their UAV pesticide spraying operation time obtained through game, the area that can be sprayed is calculated as:

[0100]

[0101] like This indicates that cash crops The time for spraying pesticides overflows, and when too much pesticide is sprayed on cash crops, pesticide residues will appear, thereby affecting their sales. Therefore, in the present invention, it is necessary to allocate the flight time again after the initial game of UAV pesticide spraying flight time.

[0102] The total time of pesticide spillage after the initial game for all cash crops is calculated as:

[0103]

[0104] If Δt = 0, it means that there is no time overflow for all cash crops in the initial pesticide spraying flight time game stage, so the algorithm ends;

[0105] If Δt>0, the set of economic crops with overflow of pesticide flight time is It can be seen that UAV is The flight operation time for cash crops is:

[0106]

[0107] Update the economic crop set to be allocated UAV flight operation time as follows And UAV flight time t = Δt, again for the limited UAV operation time set The cash crops in the game are distributed again, and the optimization problem at the follower is:

[0108]

[0109] in, For cash crops to gamble again The operating hours for spraying pesticides;

[0110] The optimization problem at the leader is:

[0111]

[0112] Similarly, the remaining operating time of the UAV is obtained in the set The optimal value of time allocation for cash crops to play the game again renew

[0113] like This indicates that there is still an overflow of pesticide spraying time for economic crops in the network. Similarly, it is necessary to conduct another flight operation time allocation game until The algorithm ends when .

[0114] The following is an explanation of a better method:

[0115] like Figure 2 As shown, a method for distributing pesticides by drones, which is applicable to large-scale planting and different economic crops in the area to be treated, includes the following steps:

[0116] Step 1: Collect parameters v, t, H, and initialize and

[0117] Step 2, if The method ends; otherwise, go to step 3;

[0118] Step 3, Cash crops in Arrange in ascending order from small to large;

[0119] Step 4, if Go to step 5; if

[0120] When t≤0, go to step 7.

[0121] Step 5, Cash Crops The operation time of spraying pesticides is

[0122] Step 6, Cash Crops The operation time of spraying pesticides is cash crops The operation time of spraying pesticides is

[0123] Step 7,

[0124] Step 8, calculate calculate If Δt=0, the algorithm ends; otherwise, execute step 9;

[0125] Step 9, calculate Update the economic crop set to be allocated UAV flight operation time as follows And the UAV flight time t = Δt, let Go to step 3 again.

[0126] When the pesticide spraying area obtained by some economic crops through game is larger than their planting area, that is, It is necessary to conduct a reallocation game of UAV pesticide spraying flight time to reallocate the operation time that overflowed in the previous round of the game among the set of economic crops whose pesticide spraying flight time did not overflow, until there is no UAV flight operation time overflow after the end of a round of the game.

[0127] In another embodiment, a drone pesticide spraying and distribution system for cash crops in multiple operation areas includes a processor having a built-in drone pesticide spraying and distribution method for cash crops in multiple operation areas as described above.

[0128] In another embodiment, a drone includes the above-mentioned drone pesticide spraying and distribution system for economic crops in multiple operation areas.

[0129] In another embodiment, a computer storage medium stores a computer program, and when the computer program is executed, the drone pesticide spraying and distribution method for economic crops in multiple operation areas described in any one of the above items is implemented.

[0130] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A method for distributing pesticides by drones for economic crops in multiple operating areas, characterized in that: The following steps are involved: S01: The problem of allocating the limited pesticide spraying time of drones among different cash crops is modeled as a game model, with drones as the leaders of the game model and each cash crop as the followers; S02: For the leader, the objective function of the game model is the utility function of selling the limited pesticide spraying time resource; for the follower, the objective function of the game model is the profit obtained by purchasing pesticide spraying time; S03: Maximize the utility function of selling limited pesticide spraying operation time resources to obtain the optimal unit resource price β * , maximize the benefits of purchasing pesticide spraying time to obtain the optimal operation time allocation resources 2. The drone pesticide spraying and distribution method for economic crops in multiple operation areas according to claim 1 is characterized in that: Step S02 includes modeling the profit of cash crop n after spraying a certain amount of pesticide as Among them, α n represents the market price of n units of cash crop output, λ n is the basic yield of cash crop n per unit area, θ n is the yield of cash crop n after spraying pesticides, s n is the planting area of each economic crop, ∈ n The effective area of UAV pesticide spraying for cash crops.

3. The drone pesticide spraying and distribution method for cash crops in multiple operation areas according to claim 2 is characterized in that: In step S02, for the follower, the objective function of the game model is: Among them, β represents the price per unit of pesticide spraying operation time sold by the leader to cash crop n, v is the flying speed of the drone, H is the width of the spraying operation, and t n Time for drones to spray pesticides for cash crops.

4. The method for distributing pesticides by drones for economic crops in multiple operating areas according to claim 2 is characterized in that: In step S02, for the leader, the objective function of the game model is: Where t is the total time of spraying operation, A collection of cash crops.

5. The drone pesticide spraying and distribution method for economic crops in multiple operation areas according to claim 1 is characterized in that: Step S03 includes: Collect cash crops Cash crops in Arrange in ascending order from small to large; α n represents the market price of n units of cash crop output, λ n is the basic yield of cash crop n per unit area, θ n is the yield of cash crop n after spraying pesticides, s n is the planting area of each economic crop, v is the flying speed of the UAV, and H is the spraying width; 1) If When , the optimal unit resource price is: Where N is the number of economic crops, and t is the total time of spraying operation; The operation time for spraying pesticides on cash crop n is: 2) If When , where cash crops l∈{1,…,N-1}, the optimal unit resource pricing is: cash crops The operating hours for spraying pesticides are: cash crops The operating hours for spraying pesticides are: 3) If t≤0, there is no game behavior, cash crops The operating hours for spraying pesticides are:

6. The method for distributing pesticides by drones for economic crops in multiple operating areas according to claim 5, characterized in that: After step S03, the following steps are also included: When all economic crops have obtained their drone pesticide spraying operation time obtained through game, the area that can be sprayed is calculated as: like A redistribution game is conducted on the flight time of drone pesticide spraying to redistribute the operation time that overflowed in the previous round of the game among the set of economic crops whose flight time did not overflow, until there is no drone flight operation time overflow after a round of the game.

7. The method for distributing pesticides by drones for economic crops in multiple operating areas according to claim 6, characterized in that: include: The total time of pesticide spillage after the initial game for all cash crops is calculated as: If Δt>0, the set of economic crops with overflow of pesticide flight time is Drones for The flight operation time for cash crops is: Update the economic crop set to be allocated UAV flight time as follows: As well as the drone overflow flight time t = Δt, the limited drone operation time is again used to collect The cash crops in the game are allocated again, and the optimization problem at the follower is: in, For cash crops to gamble again Pesticide spraying operation time; The optimization problem at the leader is: Get the remaining operating time of the drone in the collection The optimal value of time allocation for cash crops to play the game again 8. A drone pesticide spraying and distribution system for cash crops in multiple operating areas, characterized by: The invention comprises a processor having a built-in drone pesticide spraying and distribution method for economic crops in multiple operation areas according to any one of claims 1 to 7.

9. A drone, characterized in that: A drone pesticide spraying and distribution system for economic crops in multiple operating areas comprising the system described in claim 8.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the drone pesticide spraying and distribution method for economic crops in multiple operation areas according to any one of claims 1 to 7 is implemented.

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