Intelligent agricultural unmanned aerial vehicle pesticide spraying trajectory control method and system

Abstract

The application discloses a smart agricultural unmanned aerial vehicle pesticide spraying track control method and system, relates to the technical field of data processing, and evaluates crop growth conditions at each position of a farmland based on crop growth state data, obtains crop estimated pesticide demand data at each position of the farmland according to the evaluation results of the crop growth conditions at each position of the farmland, evaluates pesticide spraying stability at each position of the farmland in each unmanned aerial vehicle flight scheme based on the crop estimated pesticide demand data at each position of the farmland and unmanned aerial vehicle operation state data, judges whether the pesticide spraying operation time at each position of the farmland in each unmanned aerial vehicle flight scheme needs to be prolonged according to the evaluation results, obtains an unmanned aerial vehicle flight operation scheme through the evaluation results of the pesticide spraying stability at each position of the farmland in each unmanned aerial vehicle flight scheme, performs operation according to the obtained scheme, and evaluates the spraying stability of each position point under different schemes in a forward-looking manner in a flight scheme design stage, so that the spraying time is appropriately prolonged, and the uniformity of regional pesticide application is improved.

Description

Technical Field

[0001] This application belongs to the field of data processing, specifically a method and system for controlling the spraying trajectory of a smart agricultural drone. Background Art

[0002] With the rapid development of drone technology, its application in agricultural plant protection is becoming increasingly widespread. Drone spraying, with its advantages such as high efficiency, flexibility, and adaptability to complex terrain, is gradually replacing traditional manual operations and large-scale mechanical spraying. Precise and efficient spraying operations are the core goals of drone plant protection, and flight trajectory planning directly determines spray coverage uniformity, drug utilization rate, and ultimate control effectiveness.

[0003] However, the current mainstream UAV spraying trajectory planning technology still has the following significant defects:

[0004] Static planning and uniform spraying: Most commercial drone crop protection systems use regular routes generated based on farmland boundaries, such as "well" and "U" patterns, for flight and spraying. This planning approach typically assumes that crop conditions and pesticide requirements are uniform across the entire field, using fixed spray rates and flight speeds. The core problem is that it ignores the objective spatial heterogeneity within farmland. Due to uneven distribution of soil fertility, moisture, and pests and diseases, crop growth, density, and actual pesticide requirements often vary significantly across the field. Uniform spraying inevitably leads to under-application in some areas, impacting control effectiveness, while over-application in others causes pesticide waste, environmental pollution, and even pesticide damage.

[0005] Ignoring dynamic interference during the spraying process: Existing technologies typically plan routes based on ideal flight conditions, such as constant altitude, speed, and a windless environment. However, in actual operations, drones are inevitably affected by dynamic factors such as wind speed and direction, and changes in their own posture, such as acceleration, deceleration, and turns. These factors can significantly change the droplet deposition pattern and coverage of the spray solution, causing the actual spray volume to deviate from the preset value. This is especially true at turning points in the route or in areas with sudden environmental changes, where missed spraying, repeated spraying, or drifting are likely to occur. Existing methods lack real-time assessment and compensation mechanisms for the spraying stability of drones under actual operating conditions.

[0006] Isolated decisions and suboptimal solutions: Existing methods typically treat route planning, spray rate setting, and flight control as relatively independent modules. Decisions on whether to extend the spraying time to compensate for spray rate loss caused by unstable factors are often based on simple rules or post-hoc statistics. There is a lack of forward-looking evaluation of the spraying stability of each location under different plans during the flight plan design phase to select the flight operation plan with the best overall performance.

[0007] In order to solve the problems raised by this background technology, this application designs a smart agricultural drone spraying trajectory control system and method. Summary of the Invention

[0008] In response to the above-mentioned technical deficiencies, this application proposes a smart agricultural drone spraying trajectory control system and method.

[0009] To solve the above technical problems, the present invention adopts the following technical solution: This application provides a method for controlling the spraying trajectory of a smart agricultural drone, which includes the following specific steps:

[0010] S1. Obtain reference farmland crop drug demand data, reference spraying extension time data, drone operation status data, and crop growth status data;

[0011] S2. Evaluate the growth status of crops at various locations in the farmland based on the crop growth status data, and obtain estimated drug demand data for crops at various locations in the farmland based on the evaluation results of the crop growth status at various locations in the farmland;

[0012] S3. Evaluate the stability of pesticide spraying at each location in each flight plan of the drone based on the estimated pesticide demand data of crops at each location in the farmland and the drone's operating status data. Determine whether the pesticide spraying operation at each location in each flight plan of the drone needs to be extended based on the evaluation results.

[0013] S4. Obtain a UAV flight operation plan based on the stability evaluation results of pesticide spraying at each location in the farmland in each UAV flight plan, and perform UAV pesticide spraying flight operations according to the obtained UAV flight operation plan.

[0014] It should be noted that, as a preferred technical solution for a smart agricultural drone spraying trajectory control method, the specific steps of S1 are:

[0015] S11. Obtain reference farmland crop drug demand data and reference spraying extension time data through a database;

[0016] S12. Obtaining drone operation status data from a database, wherein the drone operation status data includes flight swing angle data, pesticide spraying coverage data for each location of the farmland in each flight plan of the drone, and wind speed data;

[0017] S13, obtaining crop growth status data through light sensors and crop leaf image processing, wherein the crop growth status data includes light intensity data of the bottom of the crops at each position in the farmland and total area data of the crop leaves;

[0018] S14. Storing the collected data in a storage component for use in the analysis process.

[0019] It should be noted that, as an optimal technical solution for a smart agricultural drone spraying trajectory control method, the specific steps of S2 are:

[0020] S21. Evaluate the crop growth condition at each location in the farmland based on the crop bottom light intensity data and the crop leaf total area data at each location in the farmland. The calculation formula for evaluating the crop growth condition at the i-th location in the farmland is: , where i is the number corresponding to each location of the farmland, and i is any item from 1 to N. is the actual light intensity at the bottom of the crop at the i-th position in the farmland, is the light intensity on the top of the crop field, k is the extinction coefficient, is the total covered area of ​​crop leaves at the i-th position in the farmland, is the total area of ​​the i-th farmland. It should be noted that in this formula Its function is to reflect the growth density of crops at various locations in the field by comparing the light intensity at the bottom of the crop with the theoretical attenuation. The function of k is to describe the attenuation of light when passing through the crop leaves, reflecting the absorption and scattering of light by the crop leaves. To describe the degree of light attenuation when passing through crop leaves; is the light intensity at the top of the crop, The shading area of ​​crops in the farmland is used to quantify the surface covering capacity of crops at various locations in the farmland. By the light attenuation coefficient Multiplying them together gives the theoretical light intensity at the bottom of the crops at each location in the farmland. In this formula, The first part compares the theoretical light intensity at the bottom with the actual light intensity, quantifies the light penetration intensity of crops in the farmland, and indirectly reflects the amount of pesticide spraying required when the drone sprays pesticides at various locations in the farmland; The results are positively correlated with the amount of pesticide spraying, that is, the greater the crop growth assessment value at a certain location in the farmland, the greater the amount of pesticide spraying required at that location;

[0021] S22. Obtain the crop growth assessment results at each location of the farmland, and obtain the estimated drug demand for crops at each location of the farmland based on the crop growth assessment values ​​at each location of the farmland and the drug demand data of reference farmland crops. It should be noted that the accuracy of the estimated drug demand for crops at each location of the farmland is improved by comprehensively analyzing the drug spraying amount required for each location of the farmland through the crop growth assessment values ​​at each location of the farmland and the drug demand data of reference farmland crops.

[0022] It should be noted that, as a preferred technical solution for a method for controlling the spraying trajectory of a smart agricultural drone, S3 includes the following specific steps:

[0023] S31, evaluating the flight balance of each UAV flight plan based on the estimated drug carrying amount data and flight swing angle data at each location in the farmland in each UAV flight plan;

[0024] S32, evaluating the pesticide spraying coverage capacity at each location of the farmland in each flight plan of the drone based on the pesticide spraying coverage area data and wind speed data at each location of the farmland in each flight plan of the drone;

[0025] S33, obtaining the calculated flight balance evaluation results of each UAV flight plan and the evaluation results of the pesticide spraying coverage capacity at each location of the farmland in each UAV flight plan;

[0026] S34. Perform negative exponential operation on the flight balance evaluation results of each UAV flight plan, and then add them to the weighted evaluation results of the drug spraying coverage ability of each farmland location in each UAV flight plan to obtain the drug spraying stability evaluation value of each farmland location in each UAV flight plan. It should be noted that the purpose of performing negative exponential operation on the flight balance evaluation results of each UAV flight plan is to positively correlate the flight balance evaluation results with the drug spraying stability evaluation value results. By comprehensively evaluating the flight balance evaluation results of each UAV flight plan and the drug spraying coverage ability evaluation results of each farmland location in each UAV flight plan, the drug spraying stability evaluation value of each farmland location in each UAV flight plan is evaluated, thereby improving the accuracy of the drug spraying stability evaluation value of each farmland location in each UAV flight plan.

[0027] S35. Determine whether the spraying time at each location in each flight plan of the drone needs to be extended based on the spraying stability evaluation value at each location in the farmland. Mark each location in each flight plan of the drone where the spraying time needs to be extended as a location for extending the spraying time.

[0028] S36. The pesticide spraying stability evaluation value at each farmland location in each flight plan of the drone that is judged to need to be extended and the reference spraying extension time data are used to obtain the required extension time for each spraying time extension location, and the spraying time extension processing is performed on each spraying time extension location according to the required extension time for each spraying time extension location.

[0029] It should be noted that, as a preferred technical solution for a smart agricultural drone spraying trajectory control method, the specific steps of S31 are:

[0030] S311. Obtaining estimated pesticide requirements for crops at each location in the farmland and data on each flight plan for the drone, and obtaining an estimated pesticide carrying amount for each location in the farmland in each flight plan based on the estimated pesticide requirements for crops at each location in the farmland and the data on each flight plan for the drone;

[0031] S312. Based on the estimated drug carrying amount data and flight swing angle data at each location in the farmland in each flight plan of the UAV, the flight balance of each flight plan of the UAV is evaluated. The flight balance evaluation calculation formula of the j-th flight plan of the UAV is: , where j is the number corresponding to each unmanned flight plan, and j is any one of 1 to M. Estimated drug carrying data for the i-th position of the farmland in the j-th flight plan of the UAV, For reference, the average amount of drugs carried during the drone operation cycle, is the UAV’s flight swing angle at the i-th position of the farmland in the j-th UAV flight plan, For reference to the permissible value of the swing angle, it should be noted that in this formula It directly reflects the dynamic changes of the load during the UAV spraying operation in each flight plan of the UAV and is the core variable affecting the stability of the UAV flight operation; To measure the degree of deviation of the drone at each location of the farmland in each flight plan of the drone when the amount of medicine carried changes; The reason for setting is that the UAVs in different flight plans operate at different times at different locations in the farmland, and the weather changes are also different. Weather changes will affect the stability of the UAV's flight attitude during operation. The reason for setting it is to measure the degree of deviation of the flight attitude stability of the UAV when it is operating at various locations in the farmland in different flight plans; The part is the relative deviation term, which is used to quantify the instantaneous fluctuation intensity of the amount of medicine carried by the drone. The smaller the result, the more stable the load. The degree of fluctuation of the UAV flight angle is quantified by the ratio of the UAV flight swing angle to the reference swing angle value; in this formula By integrating the full-cycle flight performance of the UAV in different flight plans, we can avoid local outliers interfering with the overall evaluation. The smaller the result, the better the stability of the UAV during flight operations.

[0032] It should be noted that, as a preferred technical solution for the spraying trajectory control method of a smart agricultural drone, the specific steps of S32 are: based on the drug spraying coverage area data and wind speed data of each farmland position in each flight plan of the drone, the drug spraying coverage capacity of each farmland position in each flight plan of the drone is evaluated, wherein the calculation formula for evaluating the drug spraying coverage capacity of the farmland position i in the jth flight plan of the drone is: , where T is the UAV operation cycle, is the pesticide spraying coverage area of ​​the i-th position of the farmland in the j-th flight plan of the UAV at time t within the operation cycle, is the reference drug spraying area coverage value, is the wind speed at the i-th position of the farmland in the j-th flight plan of the UAV at time t during the operation cycle, The reference allowable wind speed is set. It should be noted that the T setting in this formula means the upper limit of the formula integration time, which represents the complete operation cycle of the UAV. is the actual coverage area, indicating the actual coverage capacity of drone pesticide spraying; To refer to the area coverage of drug spraying, the drone drug coverage capacity was standardized; The function is to reflect the degree of adaptation between the wind speed at time t during the UAV operation cycle and the ideal situation, and to quantify the influence of the wind speed deviation during the UAV flight operation on the drug spraying coverage ability; this formula uses the time integral form to accumulate the dynamic effects of the UAV drug spraying process, avoid the sampling deviation of the instantaneous value, and improve the accuracy of the UAV drug spraying coverage ability evaluation value.

[0033] It should be noted that, as an optimal technical solution for the spraying trajectory control method of a smart agricultural UAV, the specific steps of S35 are: obtaining the drug spraying stability evaluation value of each farmland position in each flight plan of the UAV, and comparing the drug spraying stability evaluation value of each farmland position in each flight plan of the UAV with the set drug spraying stability evaluation value threshold. If the drug spraying stability evaluation value of a farmland position in a certain flight plan of the UAV is greater than the set drug spraying stability evaluation value threshold, then it is judged that the spraying time of the farmland position in the flight plan does not need to be extended; if the drug spraying stability evaluation value of a farmland position in a certain flight plan of the UAV is less than or equal to the set drug spraying stability evaluation value threshold, then it is judged that the spraying time of the farmland position in the flight plan needs to be extended, and the farmland position in the flight plan corresponding to this drug spraying stability evaluation value is marked as the spraying time extension position, and in this way, each spraying time extension position is obtained.

[0034] It should be noted that, as a preferred technical solution for a smart agricultural drone spraying trajectory control method, the specific steps of S36 are: obtaining the drug spraying stability evaluation value corresponding to each spraying time extension position, and obtaining the required extension time for each spraying time extension position based on the drug spraying stability evaluation value corresponding to each spraying time extension position and the reference spraying extension time data, wherein the calculation formula for the extension time required for the yth spraying time extension position is: , where y is the number corresponding to each spraying time extension position, and y is any one of 1 to b. is the threshold value for drug spraying stability assessment, is the drug spraying stability evaluation value corresponding to the y-th spraying time extension position, For the reference extension time set, it should be noted that Determine the minimum extension time to avoid When it is 0, the spray coverage is insufficient; in this formula This partly reflects the nonlinear relationship that the larger the drug spraying stability assessment value is, the smaller the extension time is.

[0035] It should be noted that, as an optimal technical solution for the spraying trajectory control method of a smart agricultural UAV, the specific steps of S4 are: obtaining the drug spraying stability evaluation value of each farmland position in each UAV flight plan, summing up the drug spraying stability evaluation values ​​of each farmland position in each UAV flight plan and averaging them to obtain the drug spraying stability evaluation value corresponding to each UAV flight plan, arranging the drug spraying stability evaluation values ​​corresponding to each UAV flight plan in descending order, and taking the one ranked first as the UAV flight operation plan; It should be noted that, the drug spraying stability of each UAV flight plan is analyzed by comprehensively summing up the drug spraying stability evaluation values ​​of each farmland position in each UAV flight plan and averaging them, thereby improving the accuracy of the drug spraying stability evaluation value.

[0036] A smart agricultural drone spraying trajectory control system, which is implemented based on the above-mentioned smart agricultural drone spraying trajectory control method, specifically includes a trajectory control data acquisition module, a crop growth assessment module, a plan stability assessment module, and a flight plan output module. The trajectory control data acquisition module is used to obtain reference farmland crop drug demand data, reference spraying extension time data, drone operation status data, and crop growth status data;

[0037] The crop growth assessment module is used to assess the growth of crops at various locations in the farmland based on the crop growth status data, and to obtain estimated drug demand data for crops at various locations in the farmland based on the crop growth assessment results.

[0038] The program stability assessment module is used to evaluate the stability of drug spraying at each location of the farmland in each flight plan of the drone based on the estimated drug demand data of crops at each location of the farmland and the operating status data of the drone, and to determine whether the drug spraying operation at each location of the farmland in each flight plan of the drone needs to be extended according to the assessment results;

[0039] The flight plan output module is used to obtain a UAV flight operation plan based on the pesticide spraying stability evaluation results at each farmland location in each UAV flight plan, and perform UAV spraying flight operations according to the obtained UAV flight operation plan.

[0040] A computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the above-mentioned method for controlling the spraying trajectory of a smart agricultural drone.

[0041] Compared with the prior art, the beneficial effects of the present invention are: the present invention obtains reference farmland crop drug demand data, reference spraying extension time data, UAV operation status data and crop growth status data; the crop growth status of each position in the farmland is evaluated based on the crop growth status data, and the estimated drug demand data of the crops at each position in the farmland is obtained according to the crop growth status evaluation results; the drug spraying stability of each position in the farmland in each flight plan of the UAV is evaluated based on the estimated drug demand data of the crops at each position in the farmland and the UAV operation status data, and it is judged whether the drug spraying operation of each position in the farmland in each flight plan of the UAV needs to be extended according to the evaluation results; the UAV flight operation plan is obtained through the evaluation results of the drug spraying stability of each position in the farmland in each flight plan of the UAV, and the UAV spraying flight operation is performed according to the obtained UAV flight operation plan, and the spraying stability of each position point under different plans is prospectively evaluated in the flight plan design stage, so that the spraying time is appropriately extended, thereby improving the uniformity of regional pesticide application. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This application provides a schematic diagram of the overall process of a smart agricultural drone spraying trajectory control method.

[0043] Figure 2 This application provides a flow chart of step S3 of a method for controlling the spraying trajectory of a smart agricultural drone.

[0044] Figure 3 This is a schematic diagram of the overall framework of a smart agricultural drone spraying trajectory control system for this application.

[0045] Figure 4 This is a schematic diagram of the process of obtaining the drug spraying stability evaluation value of a smart agricultural drone spraying trajectory control method. DETAILED DESCRIPTION

[0046] In order to better understand the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings.

[0047] In order to solve the technical problems raised in the background technology, this application provides a preferred embodiment:

[0048] The specific contents of this embodiment are:

[0049] like Figure 1 As shown, a method for controlling the spraying trajectory of a smart agricultural drone includes the following specific steps:

[0050] S1. Obtain reference farmland crop drug demand data, reference spraying extension time data, drone operation status data, and crop growth status data;

[0051] In this embodiment, the specific steps of S1 are:

[0052] S11. Obtain reference farmland crop drug demand data and reference spraying extension time data through a database;

[0053] S12. Obtaining drone operation status data from a database, wherein the drone operation status data includes flight swing angle data, pesticide spraying coverage data for each location of the farmland in each flight plan of the drone, and wind speed data;

[0054] S13, obtaining crop growth status data through light sensors and crop leaf image processing, wherein the crop growth status data includes light intensity data of the bottom of the crops at each position in the farmland and total area data of the crop leaves;

[0055] S14. Storing the collected data in a storage component for use in the analysis process.

[0056] In one implementation of the present invention, reference farmland crop drug demand data and reference spraying extension time data are obtained through a database to analyze the drug spraying amount and extension time required when a drone sprays drugs at various locations in the farmland to prevent insufficient spraying coverage; crop growth status data are obtained through light sensors and crop leaf image processing, and the crop growth status data include crop bottom light intensity data and crop leaf total area data at various locations in the farmland to analyze the drug spraying amount required at various locations in the farmland; drone operation status data are obtained through a database, and the drone operation status data include flight swing angle data, drug spraying coverage area data and wind speed data at various locations in the farmland in various flight plans of the drone to analyze the stability of drug spraying at various locations in the farmland in various flight plans of the drone. When the drug spraying stability is not high, it will affect the drug spraying efficiency.

[0057] S2. Evaluate the growth status of crops at various locations in the farmland based on the crop growth status data, and obtain estimated drug demand data for crops at various locations in the farmland based on the evaluation results of the crop growth status at various locations in the farmland;

[0058] In this embodiment, S2 includes the following specific steps:

[0059] S21. Evaluate the crop growth condition at each location in the farmland based on the crop bottom light intensity data and the crop leaf total area data at each location in the farmland. The calculation formula for evaluating the crop growth condition at the i-th location in the farmland is: , where i is the number corresponding to each location of the farmland, and i is any item from 1 to N. is the actual light intensity at the bottom of the crop at the i-th position in the farmland, is the light intensity on the top of the crop field, k is the extinction coefficient, is the total covered area of ​​crop leaves at the i-th position in the farmland, is the total area of ​​the i-th farmland. It should be noted that in this formula Its function is to reflect the growth density of crops at various locations in the field by comparing the light intensity at the bottom of the crop with the theoretical attenuation. The function of k is to describe the attenuation of light when passing through the crop leaves, reflecting the absorption and scattering of light by the crop leaves. To describe the degree of light attenuation when passing through crop leaves; is the light intensity at the top of the crop, The shading area of ​​crops in the farmland is used to quantify the surface covering capacity of crops at various locations in the farmland. By the light attenuation coefficient Multiplying them together gives the theoretical light intensity at the bottom of the crops at each location in the farmland. In this formula, The first part compares the theoretical light intensity at the bottom with the actual light intensity, quantifies the light penetration intensity of crops in the farmland, and indirectly reflects the amount of pesticide spraying required when the drone sprays pesticides at various locations in the farmland; The results are positively correlated with the amount of pesticide sprayed, that is, the greater the crop growth assessment value at a certain location in the farmland, the greater the amount of pesticide spraying required at that location. An example is given to illustrate the design benefits and basis of this formula: this formula combines the extinction coefficient k to calculate the theoretical attenuation of the light intensity at the bottom of the crop after being blocked by leaves in the farmland, and compares it with the measured light intensity at the bottom of the crop, thereby improving the accuracy of the crop growth assessment value.

[0060] S22. Obtain the crop growth assessment results at each location of the farmland, and obtain the estimated drug demand for crops at each location of the farmland based on the crop growth assessment values ​​at each location of the farmland and the drug demand data of reference farmland crops. It should be noted that the accuracy of the estimated drug demand for crops at each location of the farmland is improved by comprehensively analyzing the drug spraying amount required for each location of the farmland through the crop growth assessment values ​​at each location of the farmland and the drug demand data of reference farmland crops.

[0061] S3. Evaluate the stability of pesticide spraying at each location in each flight plan of the drone based on the estimated pesticide demand data of crops at each location in the farmland and the drone's operating status data. Determine whether the pesticide spraying operation at each location in each flight plan of the drone needs to be extended based on the evaluation results.

[0062] like Figure 2 As shown, in this embodiment, the specific steps of S3 are:

[0063] S31, evaluating the flight balance of each UAV flight plan based on the estimated drug carrying amount data and flight swing angle data at each location in the farmland in each UAV flight plan;

[0064] In this embodiment, the specific steps of S31 are:

[0065] S311. Obtaining estimated pesticide requirements for crops at each location in the farmland and data on each flight plan for the drone, and obtaining an estimated pesticide carrying amount for each location in the farmland in each flight plan based on the estimated pesticide requirements for crops at each location in the farmland and the data on each flight plan for the drone;

[0066] S312. Based on the estimated drug carrying amount data and flight swing angle data at each location in the farmland in each flight plan of the UAV, the flight balance of each flight plan of the UAV is evaluated. The flight balance evaluation calculation formula of the j-th flight plan of the UAV is: , where j is the number corresponding to each unmanned flight plan, and j is any one of 1 to M. Estimated drug carrying data for the i-th position of the farmland in the j-th flight plan of the UAV, For reference, the average amount of drugs carried during the drone operation cycle, is the UAV’s flight swing angle at the i-th position of the farmland in the j-th UAV flight plan, For reference to the permissible value of the swing angle, it should be noted that in this formula It directly reflects the dynamic changes of the load during the UAV spraying operation in each flight plan of the UAV and is the core variable affecting the stability of the UAV flight operation; To measure the degree of deviation of the drone at each location of the farmland in each flight plan of the drone when the amount of medicine carried changes; The reason for setting is that the UAVs in different flight plans operate at different times at different locations in the farmland, and the weather changes are also different. Weather changes will affect the stability of the UAV's flight attitude during operation. The reason for setting it is to measure the degree of deviation of the flight attitude stability of the UAV when it is operating at various locations in the farmland in different flight plans; The part is the relative deviation term, which is used to quantify the instantaneous fluctuation intensity of the amount of medicine carried by the drone. The smaller the result, the more stable the load. The degree of fluctuation of the UAV flight angle is quantified by the ratio of the UAV flight swing angle to the reference swing angle value; in this formula By comprehensively analyzing the full-cycle flight performance of the UAV in different flight plans, local outliers are avoided from interfering with the overall evaluation. The smaller the result, the better the stability of the UAV during flight operation. For example, the benefits and basis of setting this formula are given as examples: This formula comprehensively evaluates the balance of UAV flight operations in different flight plans. By comprehensively considering the amount of medicine carried and the degree of deviation of the swing angle at the same position of the UAV in different flight plans, the flight balance of the UAV is comprehensively evaluated to ensure the stable operation of the UAV, and the difference in the flight performance of the UAV in different plans is quantified, making the evaluation results more accurate, which helps to optimize the flight path of the UAV operation, improve the uniformity and efficiency of drug spraying, reduce drug waste, and enhance the overall effect of the operation.

[0067] S32, evaluating the pesticide spraying coverage capacity at each location of the farmland in each flight plan of the drone based on the pesticide spraying coverage area data and wind speed data at each location of the farmland in each flight plan of the drone;

[0068] In this embodiment, the specific step of S32 is: based on the pesticide spraying coverage area data and wind speed data of each farmland location in each flight plan of the drone, the pesticide spraying coverage capacity of each farmland location in each flight plan of the drone is evaluated, wherein the calculation formula for evaluating the pesticide spraying coverage capacity of the farmland location i in the jth flight plan of the drone is: , where T is the UAV operation cycle, is the pesticide spraying coverage area of ​​the i-th position of the farmland in the j-th flight plan of the UAV at time t within the operation cycle, is the reference drug spraying area coverage value, is the wind speed at the i-th position of the farmland in the j-th flight plan of the UAV at time t during the operation cycle, The reference allowable wind speed is set. It should be noted that the T setting in this formula means the upper limit of the formula integration time, which represents the complete operation cycle of the UAV. is the actual coverage area, indicating the actual coverage capacity of drone pesticide spraying; To refer to the area coverage of drug spraying, the drone drug coverage capacity was standardized; The function is to reflect the degree of adaptation between the wind speed at time t during the UAV operation cycle and the ideal situation, and to quantify the impact of the wind speed deviation during the UAV flight operation on the drug spraying coverage ability. This formula uses the time integral form to accumulate the dynamic effects of the UAV drug spraying process, avoid the sampling bias of the instantaneous value, and improve the accuracy of the UAV drug spraying coverage ability assessment value. For example, the benefits and basis of the design of this formula are illustrated: T in this formula eliminates the influence of short-term fluctuations and reflects the continuous operation capability of the UAV. The dynamic UAV pesticide spraying coverage capability reflects the changes in the UAV operation capability in the time dimension; Make the evaluation results of drone pesticide spraying coverage capability dimensionless; By quantifying the degree of deviation of wind speed during drone operation, its impact on the spraying coverage ability of the drug is evaluated. When the actual wind speed is higher than the reference wind speed, the spraying coverage ability evaluation value will decrease accordingly, and vice versa.

[0069] S33, obtaining the calculated flight balance evaluation results of each UAV flight plan and the evaluation results of the pesticide spraying coverage capacity at each location of the farmland in each UAV flight plan;

[0070] S34, such as Figure 4 As shown in the figure, the flight balance evaluation results of each UAV flight plan are subjected to negative exponential operation and weighted with the evaluation results of the drug spraying coverage ability of each farmland position in each UAV flight plan, and then the weighted sum is obtained to obtain the drug spraying stability evaluation value of each farmland position in each UAV flight plan; it should be noted that the purpose of performing negative exponential operation on the flight balance evaluation results of each UAV flight plan is to positively correlate the flight balance evaluation results with the drug spraying stability evaluation value results. By comprehensively evaluating the flight balance evaluation results of each UAV flight plan and the drug spraying coverage ability evaluation results of each farmland position in each UAV flight plan, the drug spraying stability evaluation value of each farmland position in each UAV flight plan is evaluated, thereby improving the accuracy of the drug spraying stability evaluation value of each farmland position in each UAV flight plan;

[0071] S35. Determine whether the spraying time at each location in each flight plan of the drone needs to be extended based on the spraying stability evaluation value at each location in the farmland. Mark each location in each flight plan of the drone where the spraying time needs to be extended as a location for extending the spraying time.

[0072] In this embodiment, the specific steps of S35 are: obtaining the drug spraying stability evaluation value of each position of the farmland in each flight plan of the drone, and comparing the drug spraying stability evaluation value of each position of the farmland in each flight plan of the drone with the set drug spraying stability evaluation value threshold. If the drug spraying stability evaluation value of a certain position of the farmland in a certain flight plan of the drone is greater than the set drug spraying stability evaluation value threshold, then it is judged that the spraying time of the farmland at that position in the flight plan does not need to be extended; if the drug spraying stability evaluation value of a certain position of the farmland in a certain flight plan of the drone is less than or equal to the set drug spraying stability evaluation value threshold, then it is judged that the spraying time of the farmland at that position in the flight plan needs to be extended, and the farmland position in the flight plan corresponding to this drug spraying stability evaluation value is marked as a spraying time extension position, and in this way, each spraying time extension position is obtained.

[0073] S36. The pesticide spraying stability evaluation value at each farmland location in each flight plan of the drone that is judged to need to be extended and the reference spraying extension time data are used to obtain the required extension time for each spraying time extension location, and the spraying time extension processing is performed on each spraying time extension location according to the required extension time for each spraying time extension location.

[0074] In this embodiment, the specific steps of S36 are: obtaining the drug spraying stability evaluation value corresponding to each spraying time extension position, and obtaining the required extension time for each spraying time extension position based on the drug spraying stability evaluation value corresponding to each spraying time extension position and the reference spraying extension time data, wherein the calculation formula for the required extension time for the y-th spraying time extension position is: , where y is the number corresponding to each spraying time extension position, and y is any one of 1 to b. is the threshold value for drug spraying stability assessment, is the drug spraying stability evaluation value corresponding to the y-th spraying time extension position, For the reference extension time set, it should be noted that Determine the minimum extension time to avoid When it is 0, the spray coverage is insufficient; in this formula Part of the nonlinear relationship reflects that the larger the drug spraying stability evaluation value is, the smaller the extension time is; for example, The purpose of setting it is to ensure the minimum extension time when the drug spraying stability assessment value is equal to the drug spraying stability assessment value threshold to prevent insufficient spraying coverage. It can be set to the shortest time required for drug diffusion.

[0075] S4. Obtain a UAV flight operation plan based on the stability evaluation results of pesticide spraying at each location in the farmland in each UAV flight plan, and perform UAV pesticide spraying flight operations according to the obtained UAV flight operation plan.

[0076] In this embodiment, the specific steps of S4 are: obtaining the drug spraying stability evaluation value of each farmland location in each flight plan of the UAV, summing up the drug spraying stability evaluation values ​​of each farmland location in each flight plan of the UAV and averaging them to obtain the drug spraying stability evaluation value corresponding to each flight plan of the UAV, arranging the drug spraying stability evaluation values ​​corresponding to each flight plan of the UAV in descending order, and taking the one ranked first as the UAV flight operation plan; it should be noted that, the drug spraying stability of each flight plan of the UAV is analyzed by summing up the drug spraying stability evaluation values ​​of each farmland location in each flight plan of the UAV and averaging them, thereby improving the accuracy of the drug spraying stability evaluation value.

[0077] It should be noted here that the setting parameters (such as weights and thresholds, etc.) in this embodiment need to be set by technical personnel in this field based on relevant experiments. The specific experimental method is: obtain reference farmland crop drug demand data, reference spraying extension time data, UAV operation status data and crop growth status data, and bring them into the various steps in this embodiment to calculate the drug spraying stability evaluation value of each flight plan, obtain the descending order results of the flight plan drug spraying stability evaluation value and the implementation results of each UAV flight plan, import the descending order results of the flight plan drug spraying stability evaluation value and the implementation results of each UAV flight plan into the fitting software for continuous fitting, and output the drug spraying stability evaluation value that meets the setting parameters (such as weights and thresholds, etc.) of the optimal UAV flight plan.

[0078] According to the above implementation content, this embodiment has the following advantages over the existing technology: this embodiment obtains reference farmland crop drug demand data, reference spraying extension time data, UAV operation status data and crop growth status data; based on the crop growth status data, the crop growth situation at each location of the farmland is evaluated, and the estimated drug demand data of the crops at each location of the farmland is obtained according to the crop growth situation evaluation results at each location of the farmland; based on the estimated drug demand data of the crops at each location of the farmland and the UAV operation status data, the drug spraying stability at each location of the farmland in each UAV flight plan is evaluated, and according to the evaluation results, it is judged whether the drug spraying operation at each location of the farmland in each UAV flight plan needs to be extended; the UAV flight operation plan is obtained through the evaluation results of the drug spraying stability at each location of the farmland in each UAV flight plan, and the UAV spraying flight operation is performed according to the obtained UAV flight operation plan. In the flight plan design stage, the spraying stability of each position point under different plans is prospectively evaluated, so that the spraying time is appropriately extended, thereby improving the uniformity of regional pesticide application.

[0079] like Figure 3As shown, this embodiment also provides a smart agricultural drone spraying trajectory control system, which is implemented based on the above-mentioned smart agricultural drone spraying trajectory control method, and specifically includes a trajectory control data acquisition module, a crop growth evaluation module, a scheme stability evaluation module and a flight scheme output module. The trajectory control data acquisition module is used to obtain reference farmland crop drug demand data, reference spraying extension time data, drone operation status data and crop growth status data; the crop growth evaluation module is used to evaluate the crop growth conditions at each farmland location based on the crop growth status data, and obtain estimated drug demand data for crops at each farmland location according to the crop growth condition evaluation results; the scheme stability evaluation module is used to evaluate the drug spraying stability at each farmland location in each drone flight plan based on the estimated drug demand data for crops at each farmland location and the drone operation status data, and determine whether the drug spraying operation at each farmland location in each drone flight plan needs to be extended according to the evaluation results; the flight scheme output module is used to obtain a drone flight operation plan based on the drug spraying stability evaluation results at each farmland location in each drone flight plan, and perform drone spraying flight operations according to the obtained drone flight operation plan.

[0080] The specific steps for each unit module in the above-mentioned smart agricultural drone spraying trajectory control system of the present application to realize the corresponding functions can refer to the steps in the above-mentioned embodiment of a smart agricultural drone spraying trajectory control method, and will not be repeated here.

[0081] This embodiment also provides a computer-readable storage medium storing instructions. When the instructions are executed on a computer, the computer executes the above-mentioned method for controlling the spraying trajectory of a smart agricultural drone.

[0082] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0083] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0084] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0085] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application of this application is not limited to technical solutions formed by a specific combination of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned application concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for controlling the spraying trajectory of a smart agricultural drone, characterized in that: include: S1. Obtain reference farmland crop drug demand data, reference spraying extension time data, drone operation status data, and crop growth status data; S2. Evaluate the growth status of crops at various locations in the farmland based on the crop growth status data, and obtain estimated drug demand data for crops at various locations in the farmland based on the evaluation results of the crop growth status at various locations in the farmland; S3. Based on the estimated pesticide demand data of crops at each location in the farmland and the drone operation status data, the stability of the pesticide spraying at each location in the farmland in each flight plan of the drone is evaluated, and based on the evaluation results, it is determined whether the pesticide spraying operation at each location in the farmland in each flight plan of the drone needs to be extended. S3 includes the following specific steps: S31, evaluating the flight balance of each UAV flight plan based on the estimated drug carrying amount data and flight swing angle data at each location in the farmland in each UAV flight plan; The specific steps of S31 are: S311. Obtaining estimated pesticide requirements for crops at each location in the farmland and data on each flight plan for the drone, and obtaining an estimated pesticide carrying amount for each location in the farmland in each flight plan based on the estimated pesticide requirements for crops at each location in the farmland and the data on each flight plan for the drone; S312, obtaining a flight balance evaluation value for each flight plan of the UAV based on the estimated drug carrying amount data and flight swing angle data at each location of the farmland in each flight plan of the UAV; S32, evaluating the pesticide spraying coverage capacity at each location of the farmland in each flight plan of the drone based on the pesticide spraying coverage area data and wind speed data at each location of the farmland in each flight plan of the drone; The specific steps of S32 are: based on the pesticide spraying coverage area data and wind speed data of each farmland location in each flight plan of the drone, the pesticide spraying coverage capacity of each farmland location in each flight plan of the drone is evaluated, wherein the calculation formula for evaluating the pesticide spraying coverage capacity of the farmland location i in the jth flight plan of the drone is: , where T is the UAV operation cycle, is the pesticide spraying coverage area of ​​the i-th position of the farmland in the j-th flight plan of the UAV at time t within the operation cycle, is the reference drug spraying area coverage value, is the wind speed at the i-th position of the farmland in the j-th flight plan of the UAV at time t during the operation cycle, is the set reference allowable wind speed; S33, obtaining the calculated flight balance evaluation results of each UAV flight plan and the evaluation results of the pesticide spraying coverage capacity at each location of the farmland in each UAV flight plan; S34, performing negative exponential operation on the flight balance evaluation results of each UAV flight plan, and then weighting and adding the weighted results of the pesticide spraying coverage ability evaluation results at each farmland location in each UAV flight plan to obtain the pesticide spraying stability evaluation value at each farmland location in each UAV flight plan; S35. Determine whether the spraying time at each location in each flight plan of the drone needs to be extended based on the spraying stability evaluation value at each location in the farmland. Mark each location in each flight plan of the drone where the spraying time needs to be extended as a location for extending the spraying time. The specific steps of S35 are: obtaining the drug spraying stability evaluation value of each position of the farmland in each flight plan of the drone, comparing the drug spraying stability evaluation value of each position of the farmland in each flight plan of the drone with the set drug spraying stability evaluation value threshold, if the drug spraying stability evaluation value of a certain position of the farmland in a certain flight plan of the drone is greater than the set drug spraying stability evaluation value threshold, then judging that the spraying time of the farmland at this position in the flight plan does not need to be extended; if the drug spraying stability evaluation value of a certain position of the farmland in a certain flight plan of the drone is less than or equal to the set drug spraying stability evaluation value threshold, then judging that the spraying time of the farmland at this position in the flight plan needs to be extended, marking the farmland position in the flight plan corresponding to this drug spraying stability evaluation value as a spraying time extension position, and in this way obtaining each spraying time extension position; S36. Determine the required extension time for each spraying time extension location based on the pesticide spraying stability assessment value for each farmland location in each flight plan of the drone that is determined to require extension and the reference spraying extension time data. Extend the spraying time for each spraying time extension location based on the required extension time. The specific steps of S36 are: obtaining the drug spraying stability evaluation value corresponding to each spraying time extension position, and obtaining the required extension time for each spraying time extension position based on the drug spraying stability evaluation value corresponding to each spraying time extension position and the reference spraying extension time data, wherein the calculation formula for the required extension time for the y-th spraying time extension position is: , where y is the number corresponding to each spraying time extension position, and y is any one of 1 to b. is the threshold value for drug spraying stability assessment, is the drug spraying stability evaluation value corresponding to the y-th spraying time extension position, Extend the time for a set reference; S4. Obtain a UAV flight operation plan through the stability evaluation results of drug spraying at each location on the farmland in each UAV flight plan, and perform UAV spraying flight operations according to the obtained UAV flight operation plan; the specific steps of S4 are: obtain the drug spraying stability evaluation value at each location on the farmland in each UAV flight plan, sum up the drug spraying stability evaluation values ​​at each location on the farmland in each UAV flight plan, and then calculate the average value to obtain the drug spraying stability evaluation value corresponding to each UAV flight plan, arrange the drug spraying stability evaluation values ​​corresponding to each UAV flight plan in descending order, and use the one ranked first as the UAV flight operation plan.

2. The method for controlling the spraying trajectory of a smart agricultural drone according to claim 1, wherein: The specific steps of S2 are: S21, obtaining crop growth assessment values ​​at each location in the farmland based on the crop bottom light intensity data and the crop leaf total area data at each location in the farmland; S22. Obtain the crop growth assessment results at each location of the farmland, and obtain the estimated drug demand of the crops at each location of the farmland based on the crop growth assessment values ​​at each location of the farmland and the drug demand data of the reference farmland crops.

3. A smart agricultural drone spraying trajectory control system, which is implemented based on the smart agricultural drone spraying trajectory control method according to any one of claims 1-2, characterized in that: It specifically includes a trajectory control data acquisition module, a crop growth assessment module, a program stability assessment module and a flight program output module. The trajectory control data acquisition module is used to obtain reference farmland crop drug demand data, reference spraying extension time data, UAV operation status data and crop growth status data; The crop growth assessment module is used to assess the growth of crops at various locations in the farmland based on the crop growth status data, and to obtain estimated drug demand data for crops at various locations in the farmland based on the crop growth assessment results. The program stability assessment module is used to evaluate the stability of drug spraying at each location of the farmland in each flight plan of the drone based on the estimated drug demand data of crops at each location of the farmland and the operating status data of the drone, and to determine whether the drug spraying operation at each location of the farmland in each flight plan of the drone needs to be extended according to the assessment results; The flight plan output module is used to obtain a UAV flight operation plan based on the pesticide spraying stability evaluation results at each farmland location in each UAV flight plan, and perform UAV spraying flight operations according to the obtained UAV flight operation plan.

4. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer executes a smart agricultural drone spraying trajectory control method as described in any one of claims 1-2.

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