Unmanned aerial vehicle control management method and system

Abstract

The invention relates to the technical field of unmanned aerial vehicle control, and particularly provides an unmanned aerial vehicle control management method. The method comprises the following steps: firstly, constructing a farmland grid map containing three-dimensional position coordinates, farmland health degree and crop coverage by using high-precision surveying and mapping and multispectral remote sensing technologies; a target grid needing pesticide spraying is screened out according to the map; the real-time position of the unmanned aerial vehicle and the pesticide stock are obtained; an optimal operation path is determined through an optimization algorithm by integrating multi-dimensional factors, and the unmanned aerial vehicle is driven to execute spraying and update a map state; and finally, circulating the steps until all the target grids are sprayed. According to the scheme, through multi-source data fusion and intelligent decision making, safe operation of the unmanned aerial vehicle is guaranteed, meanwhile, precise planning and efficient management of a pesticide spraying operation path are achieved, and the intelligent level and resource utilization efficiency of farmland operation are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of drone control technology, and in particular to a drone control and management method and system. Background Art

[0002] With the rapid development of modern precision agriculture, plant protection drones, as efficient agricultural production tools, are increasingly being used in crop pest control due to their significant advantages, such as high efficiency, strong maneuverability, and ability to reduce pesticide usage. These advantages have brought revolutionary changes to agricultural production. However, current drones have numerous significant deficiencies in path planning for pesticide spraying, which significantly restrict further improvement in their application effectiveness. Many crop protection drones currently on the market suffer from significant deficiencies in their path planning algorithms. Some simple algorithms only achieve basic, straight-line, round-trip flight, completely ignoring the actual topographical characteristics of farmland. In real-world agricultural scenarios, farmland features complex and diverse terrain, such as the undulating terraces of mountainous areas and the presence of ditches and earthen mounds in plains. Paths planned by these algorithms can cause drones to fly too close to obstacles, increasing flight risks and potentially causing collisions and equipment damage. Alternatively, in areas with significant terrain undulations, precise control of pesticide spraying height and dosage cannot be achieved, resulting in uneven application. For example, in terraced fields, high plots may receive insufficient pesticide spraying, resulting in ineffective pest control, while low plots may receive excessive amounts of pesticide, wasting pesticide and potentially harming the environment and crops. Path planning becomes even more challenging in multi-drone collaborative operations. Existing technologies lack efficient coordination mechanisms when multiple drones are simultaneously spraying pesticides. Conflicts often arise between the flight paths of individual drones. For example, in intersecting areas, repeated spraying or inconsistent spraying dosages may occur, wasting pesticide resources and potentially causing crop damage due to excessive amounts of pesticide. In remote areas, drones may excessively avoid each other, resulting in unmanned operations and missed pesticide spraying areas. This chaotic collaborative operation situation means that multi-drone operations offer limited efficiency gains compared to single-drone operations, preventing the full realization of the advantages of multi-drone collaboration. Summary of the Invention

[0003] One purpose of the present application is to provide a method for controlling and managing a drone, at least to solve the problem of how to accurately plan the working route of a drone while ensuring the safe operation of the drone.

[0004] To achieve the above objectives, some embodiments of the present application provide the following aspects: In a first aspect, some embodiments of the present application further provide a method for controlling and managing a drone, the method comprising: Step S100: obtaining a grid map of the farmland currently to be sprayed with pesticides in real time; wherein the farmland grid map includes a plurality of grids of equal area, each grid including preset three-dimensional position coordinates, farmland health, and crop coverage; Step S102: determining a plurality of grids to be sprayed with pesticides based on the farmland grid map to be sprayed with pesticides; Step S104: obtaining the current three-dimensional position coordinates of the drone and the current volume of pesticides in the drone; Step S106: Based on the current three-dimensional position coordinates of the drone, the current volume of pesticide in the drone, and the multiple grids to be sprayed, the next three-dimensional position coordinates of the drone are determined, and the drone is controlled to move to the next three-dimensional position coordinates. After marking the grids to be sprayed as grids where pesticide spraying has been completed, the grid map of the farmland to be sprayed is updated. Step S108: cyclically executing steps S102 to S106 until the grid for spraying pesticides does not exist in the current farmland grid map for spraying pesticides.

[0005] In a second aspect, some embodiments of the present application further provide a drone control and management system, the system comprising: The first acquisition module is configured to perform step S100: obtaining in real time a grid map of the farmland currently to be sprayed with pesticides; wherein the farmland grid map includes a plurality of grids of equal area, each grid including preset three-dimensional position coordinates, farmland health, and crop coverage; The first data processing module is configured to: determine a plurality of grids to be sprayed with pesticides based on the farmland grid map to be sprayed with pesticides; The second acquisition module is configured to perform step S104: acquiring the current three-dimensional position coordinates of the UAV and the current volume of pesticides in the UAV; The second data processing module is configured to perform step S106: based on the current three-dimensional position coordinates of the drone, the current volume of pesticide in the drone, and the multiple grids to be sprayed with pesticides, determine the next three-dimensional position coordinates of the drone, control the drone to move to the next three-dimensional position coordinates, mark the grids to be sprayed with pesticides as grids where spraying has been completed, and then update the current grid map of the farmland to be sprayed with pesticides; The third data processing module is configured to perform step S108: looping through steps S102 to S106 until no grid to be sprayed with pesticides exists in the current farmland grid map to be sprayed with pesticides.

[0006] In a third aspect, some embodiments of the present application further provide an electronic device comprising: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to perform the steps of the method described above.

[0007] In a fourth aspect, some embodiments of the present application further provide a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method described above.

[0008] Compared to related technologies, the solution provided in the embodiments of this application, a drone control and management method, achieves precise path planning and efficient management of drone pesticide spraying by building an intelligent operation system. First, in step S100, high-precision lidar mapping technology and multispectral remote sensing equipment are used to perform detailed scans of farmland, constructing a three-dimensional grid map in real time. Each grid is assigned precise spatial coordinates. Advanced image recognition algorithms and spectral analysis techniques are used to deeply mine farmland health scores and crop coverage data, comprehensively understanding crop growth trends and potential pest and disease risks, and laying a solid data foundation for subsequent operational decisions. Proceeding to step S102, multiple grids to be sprayed are determined based on the grid map of the farmland currently to be sprayed, avoiding resource waste caused by blind operations. In step S104, the drone's onboard GNSS positioning module and inertial navigation system work together to obtain the drone's three-dimensional spatial coordinates in real time, ensuring the accuracy and real-time nature of its position information. Simultaneously, a high-precision liquid level sensor dynamically monitors the remaining volume of pesticide within the drone, providing key equipment status parameters for path planning. Step S106 is the core decision-making step. Based on the drone's current location, pesticide inventory, and the specific conditions of the target grid, the optimal operation path is dynamically calculated by comprehensively weighing factors such as operational efficiency, resource utilization, and equipment endurance. The drone follows the planned path to the target location to perform the spraying task. Upon completion, the present invention automatically updates the farmland grid map status and marks the sprayed areas as completed. Finally, by looping through steps S102 to S106, the present invention continuously scans, assesses, and executes operations on the farmland until all target grids have completed the pesticide spraying task. In this way, the drone's operating route is accurately planned while ensuring its safe operation. Through the deep integration of multi-source data and the precise decision-making of intelligent algorithms, this solution significantly improves the accuracy and efficiency of pesticide spraying operations while ensuring the safe and stable operation of the drone, effectively reducing production costs and providing reliable technical support for the intelligent development of modern agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] One or more embodiments are exemplarily illustrated by pictures in the corresponding drawings. These exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements. Unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0010] Figure 1 A flowchart of a drone control and management method provided in an embodiment of the present application; Figure 2 This is a schematic diagram of the structure of a drone control and management system according to an embodiment of the present application; Figure 3 is a schematic diagram of an exemplary structure of a processor and memory according to the present application; Figure 4 Schematic diagram of an exemplary structure of an electronic device according to the present application. DETAILED DESCRIPTION

[0011] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0012] See attached Figure 1 , Figure 1 This is a flow chart of a drone control and management method provided in an embodiment of the present application. Figure 1 As shown, the drone control and management method in the embodiment of the present application mainly includes: Step S100: Acquire a grid map of the farmland currently to be sprayed with pesticides in real time; wherein the farmland grid map includes multiple grids of equal area, each grid including preset three-dimensional position coordinates, farmland health, and crop coverage; Step S102: determining a plurality of grids to be sprayed with pesticides based on a grid map of the farmland to be sprayed with pesticides; Step S104: obtaining the current three-dimensional position coordinates of the drone and the current volume of pesticides in the drone; Step S106: Based on the current 3D position coordinates of the drone, the current volume of pesticide in the drone, and the multiple grids to be sprayed, the next 3D position coordinates of the drone are determined, and the drone is controlled to move to the next 3D position coordinates. The grid to be sprayed is marked as a grid where spraying has been completed, and the current grid map of the farmland to be sprayed is updated; Step S108: cyclically executing steps S102 to S106 until there is no grid to be sprayed with pesticides in the current farmland grid map to be sprayed with pesticides.

[0013] In this embodiment, the present invention achieves precise path planning and efficient management for drone pesticide spraying by building an intelligent operation system. First, in step S100, high-precision LiDAR mapping technology and multispectral remote sensing equipment are used to perform detailed scans of farmland, constructing a three-dimensional grid map in real time. Each grid is assigned precise spatial coordinates. Advanced image recognition algorithms and spectral analysis techniques are used to deeply mine farmland health scores and crop coverage data, comprehensively understanding crop growth trends and potential pest and disease risks, and laying a solid data foundation for subsequent operational decisions. Proceeding to step S102, multiple grids to be sprayed are determined based on the grid map of the farmland currently being sprayed, avoiding resource waste caused by blind operations. In step S104, the drone's onboard GNSS positioning module and inertial navigation work together to obtain the drone's three-dimensional spatial coordinates in real time, ensuring the accuracy and real-time nature of its position information. Simultaneously, a high-precision liquid level sensor dynamically monitors the remaining volume of pesticide within the drone, providing key equipment status parameters for path planning. Step S106 is the core decision-making step. Based on the drone's current location, pesticide inventory, and the specific conditions of the target grid, the optimal operation path is dynamically calculated by comprehensively weighing factors such as operational efficiency, resource utilization, and equipment endurance. The drone follows the planned path to the target location to perform the spraying task. Upon completion, the present invention automatically updates the farmland grid map status and marks the sprayed areas as completed. Finally, by looping through steps S102 to S106, the present invention continuously scans, assesses, and executes the operation until all target grids have completed the pesticide spraying task. In this way, the drone's operating route is accurately planned while ensuring its safe operation. Through the deep integration of multi-source data and the precise decision-making of intelligent algorithms, this solution significantly improves the accuracy and efficiency of pesticide spraying operations while ensuring the safe and stable operation of the drone, effectively reducing production costs and providing reliable technical support for the intelligent development of modern agriculture.

[0014] In one embodiment, based on a grid map of farmland currently to be sprayed with pesticides, determining a plurality of grids to be sprayed with pesticides includes: Determine the health coefficient of each grid in the farmland grid map based on the farmland health and crop coverage in the farmland grid map to be sprayed with pesticides; Grids whose health coefficients are less than a first preset threshold are marked as grids to be sprayed with pesticides.

[0015] In this embodiment, the present invention utilizes an intelligent decision-making mechanism to accurately identify and select target grids for pesticide spraying. Specifically, based on a real-time farmland grid map, the present invention first applies a multi-dimensional data fusion algorithm to deeply analyze the farmland health and crop cover data for each grid cell. Farmland health is quantitatively assessed using multispectral image analysis techniques to obtain indicators such as vegetation index and leaf spectral characteristics. Crop cover is accurately calculated using a high-resolution image recognition algorithm to calculate the percentage of crop planted area. Based on this, the present invention uses a pre-defined health coefficient assessment model to weight and fuse key parameters such as farmland health and crop cover to construct a comprehensive health coefficient for each grid cell. This model, based on a machine learning algorithm, dynamically adjusts the weights of various parameters based on crop type, growth cycle, and historical pest and disease data, ensuring the accuracy and adaptability of the health coefficient assessment. After calculating the health coefficient, the present invention compares the health coefficient of each grid cell with a first preset threshold. This threshold is not fixed but dynamically adjusted through an adaptive algorithm, combined with statistical analysis of the overall farmland health. When a grid's health factor falls below this threshold, the system automatically marks it as a target for spraying, thereby precisely demarcating the spraying area. This mechanism effectively avoids the waste of pesticides associated with traditional "wide-area spraying" practices, significantly improving pesticide efficiency and pest control effectiveness.

[0016] In one embodiment, determining the next three-dimensional position coordinates of the drone based on the current three-dimensional position coordinates of the drone, the current volume of pesticide in the drone, and a plurality of grids where the pesticide is to be sprayed includes: Based on the current 3D position coordinates of the UAV, the volume of pesticides currently in the UAV, and the grids of pesticides to be sprayed, the moving cost of each grid of pesticides to be sprayed is determined; Sort the movement cost of each grid to be sprayed with pesticides from small to large, and determine the movement cost sorting table; The preset three-dimensional position coordinates corresponding to the grid to be sprayed with the pesticide having the smallest movement cost are selected from the movement cost sorting table as the next three-dimensional position coordinates of the UAV.

[0017] In this embodiment, first, based on the real-time three-dimensional position coordinates of the drone, the pesticide inventory on board, and the information of the grids to be sprayed, a quantitative model including spatial distance, pesticide supply and demand matching, and flight energy consumption is used to calculate the movement cost of each grid to be sprayed, where the weight of each evaluation indicator is dynamically adjusted according to the operation stage; then, the movement costs of all grids are arranged in ascending order to form a priority queue; finally, the three-dimensional position coordinates corresponding to the grid with the smallest movement cost are selected as the next operation target of the drone, thereby achieving efficient utilization of pesticide resources and optimal configuration of flight paths while ensuring operation safety.

[0018] In one embodiment, the cost of moving the grid to spray pesticide is determined by the following formula: ; ; ; ; ; ;

[0019] Where, is the moving cost of the i-th grid, is the distance weight, is the time and space distance cost, is the first preset nonlinear transformation index, is the pesticide weight, is the pesticide demand of the i-th grid, is the volume of pesticide in the current machine, is the second preset nonlinear transformation index, is the energy consumption weight, is the energy consumption cost corresponding to the i-th grid, is the total power of the drone battery, is the third preset nonlinear transformation index, ( , , ) is the three-dimensional space coordinate of the i-th grid, ( , , ) is the current three-dimensional position coordinate of the UAV, is the spraying time conversion coefficient, is the spraying speed of the drone, is the pesticide spraying coefficient, is the area of ​​a single grid, is the health weight, For pest and disease weights, is the gradient weight, is the farmland health of the i-th grid, is the maximum value of the health coefficient, is the predicted value of pest and disease probability of the i-th grid, is the health gradient of the i-th grid, is the path energy density function, is the terrain influence coefficient, is the terrain complexity index of the i-th grid area, is the indicator sensitivity parameter, is the real-time queue length of the i-th index at time t, is the terrain height function, is the adjustment coefficient, is the health measure variance, is the prediction variance of pests and diseases, is the wind speed measurement variance, is the obstacle density in the i-th grid area, is the wind speed in the i-th grid area.

[0020] In this embodiment, traditional path planning often only considers distance factors, while this set of equations incorporates multiple key dimensions such as time and space distance, pesticide demand, energy consumption, and environment into a unified framework. For example, the calculation integrates factors such as health coefficient and pest probability to determine the amount of pesticide required, making pesticide spraying more precise. The energy cost is calculated by combining the path energy consumption and terrain complexity to fit the actual flight energy consumption and achieve multi-objective collaborative optimization. Dynamically adjust the weight of each cost indicator based on the urgency of the task. When the pesticide residue is insufficient, increase , give priority to areas with high demand for pesticides; when the power is low, increase , giving priority to paths with low energy consumption, and enhancing the adaptability of the algorithm to different operation scenarios, is the first preset nonlinear transformation index, is the second preset nonlinear transformation index, The third preset nonlinear transformation index, and other exponential parameters can perform nonlinear transformation on the cost function, flexibly adjust the sensitivity of the cost calculation to the changes of various factors, and make the path planning more consistent with the actual operation logic. Quantify the uncertainty of data such as health, pest and disease prediction, and wind speed. When planning the route, take the uncertainty into consideration. Adjust the comprehensive movement cost to make the planned path more robust and reduce the operation risk caused by inaccurate data. In the calculation of time and space distance cost, the three-dimensional space distance formula is combined with the spraying time conversion, and the integral calculation is performed. Path energy consumption, accurately depicts the time and space consumption and energy consumption of UAVs under complex terrain and different operation requirements. The terrain height gradient analysis in the calculation optimizes the flight trajectory in complex terrain from the perspective of differential geometry, improving the scientificity and accuracy of path planning. Most of the parameters in the equations have clear physical meanings and measurability, such as , , , which facilitates parameter setting and data collection in actual UAV operations in this invention, provides convenience for the engineering application of the algorithm, and effectively builds a bridge between theoretical models and actual operations.

[0021] In one embodiment, the health factor of the grid is determined by the following formula: ; Where, is the health coefficient of the i-th grid, is the first preset weight coefficient, To score the severity of crop diseases based on multispectral analysis, is the second preset weight coefficient, is the crop coverage, is the third preset weight coefficient, is the normalized difference vegetation index, is the fourth preset weight coefficient, is the historical probability of occurrence of pests and diseases.

[0022] In one embodiment, when the smallest movement cost in the movement cost ranking table is greater than a second preset threshold, the spraying task is terminated and the UAV is controlled to return along the original route.

[0023] To ensure operational safety and resource efficiency, this embodiment innovatively introduces a dynamic task termination mechanism. After calculating and generating a ranked mobility cost table based on a comprehensive mobility cost equation system, the present invention automatically extracts the minimum mobility cost value from the table and compares it in real time with a second preset threshold. This threshold is not a fixed parameter but is dynamically adjusted and generated using an adaptive algorithm based on multiple factors, including the drone's remaining pesticide volume, battery life, and the complexity of the current operating area. This ensures the scientific and adaptable nature of the threshold setting.

[0024] If the minimum movement cost exceeds a second preset threshold, it indicates that the resources required to continue the remaining mission (e.g., pesticides, electricity) or the risks involved (e.g., long-distance flight, traversing complex terrain) are beyond reasonable limits. At this point, the present invention immediately triggers the mission termination procedure, halts the path planning calculation, and automatically generates an optimal return path using a reverse path tracing algorithm based on the coordinates of the drone's current position and initial takeoff point. Simultaneously, the present invention sends a control signal containing return instructions and path data to the drone, directing it to return along the established route. During the return process, the drone transmits real-time status data such as its position, speed, and attitude. The ground control system dynamically monitors the drone and fine-tunes its path according to preset safety rules to ensure its safe return. This minimizes resource waste while ensuring equipment safety, improving the reliability and cost-effectiveness of the overall operation.

[0025] In one embodiment, when there are multiple grids with the smallest movement costs to be sprayed with pesticides, the three-dimensional spatial position coordinates of the grid closest to the current three-dimensional position coordinates of the drone are used as the next three-dimensional position coordinates of the drone.

[0026] In this embodiment, when multiple pesticide-spraying grids with the same minimum movement cost are encountered, the present invention employs a priority supplementation strategy to ensure unique and efficient path planning. Specifically, the present invention initiates a secondary screening mechanism. For these "cost-equivalent" grids, the linear distance between each grid and the drone is calculated using the Euclidean distance formula. Ultimately, the grid with the smallest distance is selected as the target, and its three-dimensional spatial coordinates are determined as the drone's next operation point. This mechanism effectively avoids the decision-making ambiguity caused by identical cost function values. While ensuring pesticide spraying priority, it further shortens the drone's flight path, reduces ineffective flight time and energy consumption, and ensures the continuity and efficiency of the operation process, thereby optimizing overall operational efficiency and resource utilization.

[0027] See attached Figure 2 , Figure 2 This is a schematic diagram of the structure of a drone control and management system implemented in this application. Figure 2 As shown, the drone control and management system in the embodiment of the present invention mainly includes: The first acquisition module 200 is configured to perform step S100: obtaining in real time a grid map of the farmland currently to be sprayed with pesticides; wherein the farmland grid map includes a plurality of grids of equal area, each grid including preset three-dimensional position coordinates, farmland health, and crop coverage; The first data processing module 202 is configured to perform step S102: determining a plurality of grids to be sprayed with pesticides based on the farmland grid map to be sprayed with pesticides; The second acquisition module 204 is configured to perform step S104: acquiring the current three-dimensional position coordinates of the UAV and the current volume of pesticides in the UAV; The second data processing module 206 is configured to perform step S106: based on the current three-dimensional position coordinates of the drone, the current volume of pesticide in the drone, and the multiple grids to be sprayed with pesticides, determine the next three-dimensional position coordinates of the drone, control the drone to move to the next three-dimensional position coordinates, mark the grids to be sprayed with pesticides as grids where spraying has been completed, and then update the current farmland grid map to be sprayed with pesticides; The third data processing module 208 is configured to perform step S108: looping through steps S102 to S106 until no grid to be sprayed with pesticides exists in the current farmland grid map to be sprayed with pesticides.

[0028] In one embodiment of the present invention, the first data processing module 202 is configured to perform the following operations: Determining a health coefficient of each grid in the farmland grid map based on the farmland health and crop coverage in the farmland grid map currently to be sprayed with pesticides; The grids whose health coefficients are less than a first preset threshold are marked as the grids to be sprayed with pesticides.

[0029] In one embodiment of the present invention, the second data processing module 206 is configured to perform the following operations: Determining a movement cost of each grid of pesticide to be sprayed based on the current three-dimensional position coordinates of the drone, the current volume of pesticide in the drone, and the grid of pesticide to be sprayed; Sort the movement cost of each grid to be sprayed with pesticide from small to large to determine a movement cost sorting table; The preset three-dimensional position coordinates corresponding to the grid to be sprayed with the pesticide having the smallest movement cost are selected from the movement cost sorting table as the next three-dimensional position coordinates of the UAV.

[0030] In one embodiment of the present invention, the grid movement cost of the pesticide to be sprayed is determined by the following formula: ; ; ; ; ; ;

[0031] Where, is the moving cost of the i-th grid, is the distance weight, is the time and space distance cost, is the first preset nonlinear transformation index, is the pesticide weight, is the pesticide demand of the i-th grid, is the volume of pesticide in the current machine, is the second preset nonlinear transformation index, is the energy consumption weight, is the energy consumption cost corresponding to the i-th grid, is the total power of the drone battery, is the third preset nonlinear transformation index, ( , , ) is the three-dimensional space coordinate of the i-th grid, ( , , ) is the current three-dimensional position coordinate of the UAV, is the spraying time conversion coefficient, is the spraying speed of the drone, is the pesticide spraying coefficient, is the area of ​​a single grid, is the health weight, For pest and disease weights, is the gradient weight, is the farmland health of the i-th grid, is the maximum value of the health coefficient, is the predicted value of pest and disease probability of the i-th grid, is the health gradient of the i-th grid, is the path energy density function, is the terrain influence coefficient, is the terrain complexity index of the i-th grid area, is the indicator sensitivity parameter, is the real-time queue length of the i-th index at time t, is the terrain height function, is the adjustment coefficient, is the health measure variance, is the prediction variance of pests and diseases, is the wind speed measurement variance, is the obstacle density in the i-th grid area, is the wind speed in the i-th grid area.

[0032] In one embodiment of the present invention, the health factor of the grid is determined by the following formula: ; Where, is the health coefficient of the i-th grid, is the first preset weight coefficient, To score the severity of crop diseases based on multispectral analysis, is the second preset weight coefficient, is the crop coverage, is the third preset weight coefficient, is the normalized difference vegetation index, is the fourth preset weight coefficient, is the historical probability of occurrence of pests and diseases.

[0033] In one embodiment of the present invention, the system further includes a fourth data processing module, wherein the fourth data processing module is configured to perform the following operations: When the smallest movement cost in the movement cost sorting table is greater than a second preset threshold, the spraying task is ended, and the UAV is controlled to return along the original route.

[0034] In one embodiment of the present invention, the system further includes a fifth data processing module, and the fifth data processing module is configured to perform the following operations: When there are multiple grids with the smallest movement cost to be sprayed with pesticides, the three-dimensional spatial position coordinates of the grid closest to the current three-dimensional position coordinates of the drone are used as the next three-dimensional position coordinates of the drone.

[0035] The steps of the various methods above are divided only for the purpose of clear description. During implementation, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0036] It is not difficult to find that this embodiment is a method embodiment corresponding to the system embodiment, and this embodiment can be implemented in conjunction with the system embodiment. The relevant technical details mentioned in the system embodiment are still valid in this embodiment, and to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the system embodiment.

[0037] It is worth mentioning that all modules involved in this embodiment are logical modules. In actual applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovation of this application, this embodiment does not include units that are not closely related to solving the technical problems proposed by this application. However, this does not mean that other units do not exist in this embodiment.

[0038] In addition, some embodiments of the present application further provide an electronic device. The electronic device may be various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The electronic device may also be various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0039] The electronic device includes: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, enable the processor to perform the steps of the method provided in any one or more of the above embodiments. Figure 4 An exemplary structural diagram of the electronic device is disclosed. Figure 4 As shown, the electronic device includes: one or more processors 1101, memory 1102, and interfaces for connecting various components, including high-speed and low-speed interfaces. The various components are interconnected using different buses and can be mounted on a common motherboard or in other ways as needed. The processor can process instructions executed within the electronic device, including instructions stored in or on the memory for displaying graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if desired, multiple processors and / or multiple buses can be used with multiple memories and multiple storage devices. Similarly, multiple electronic devices can be connected, with each device providing some of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0040] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected via a bus or other means. Figure 4 The bus connection is taken as an example.

[0041] Input device 1103 can receive input digital or character information and generate key signal input related to user settings and function control of the electronic device. Examples include a touch screen, keypad, mouse, trackpad, touchpad, pointing stick, one or more mouse buttons, trackball, joystick, and other input devices. Output device 1104 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibration motors). Display devices may include, but are not limited to, liquid crystal displays (LCDs), light-emitting diode (LED) displays, and plasma displays. In some embodiments, the display device may be a touch screen.

[0042] To provide user interaction, the electronic device may be a computer. The computer includes a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user, as well as a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices may also be used to provide user interaction; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user may be received in any form, including acoustic input, voice input, or tactile input.

[0043] In the embodiments of the present application, a computer program / instruction is stored on a computer-readable medium. When executed by a processor, the computer program / instruction implements the steps of the method provided in any one or more of the above embodiments. The computer-readable medium may be included in the electronic device described in the above embodiments, or it may exist independently and not be incorporated into the device. The computer-readable medium carries one or more computer-readable instructions.

[0044] The memory 1102 can be used as a non-transitory computer-readable storage medium to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 1101 executes the non-transitory software programs, instructions, and modules stored in the memory 1102 to execute various functional applications and data processing of the server, thereby implementing the program instructions / modules corresponding to the method provided in any one or more of the above embodiments of the present application.

[0045] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1102 may optionally include a memory remotely located relative to the processor 1101, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0046] It should be noted that the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component.

[0047] Computer-readable media include both permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc-read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0048] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0049] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. For example, implementation may be achieved using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of the present application may be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including related data structures) may be stored in a computer-readable recording medium, such as a RAM memory, a magnetic or optical drive, a floppy disk, or the like. In addition, some steps or functions of the present application may be implemented using hardware, for example, as a circuit that cooperates with a processor to perform the various steps or functions.

[0050] The computer program product provided in the embodiments of the present application includes one or more computer programs / instructions that, when executed by a processor, fully or partially produce the processes or functions described in the embodiments of the present application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0051] The flowcharts or block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations of the devices, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-specific system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0052] The scope of this application is defined by the appended claims rather than the foregoing description and is therefore intended to encompass within this application all changes that come within the meaning and range of equivalents of the claims. Any reference signs in the claims should not be construed as limiting the claims to which they relate. In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in a device claim may also be implemented by one unit or device through software or hardware. Words such as "first" and "second" are only used to distinguish the description and do not indicate any particular order, nor should they be understood as indicating or implying relative importance.

[0053] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art may easily propose variations or substitutions within the technical scope disclosed in the present application, and such variations or substitutions shall be encompassed within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the scope of protection of the claims, and the above embodiments shall be regarded as exemplary and non-limiting.

Claims

1. A method for controlling and managing a drone, characterized in that: The method comprises: Step S100: obtaining a grid map of the farmland currently to be sprayed with pesticides in real time; wherein the farmland grid map includes a plurality of grids of equal area, each grid including preset three-dimensional position coordinates, farmland health, and crop coverage; Step S102: determining a plurality of grids to be sprayed with pesticides based on the farmland grid map to be sprayed with pesticides; Step S104: obtaining the current three-dimensional position coordinates of the drone and the current volume of pesticides in the drone; Step S106: Based on the current three-dimensional position coordinates of the drone, the current volume of pesticide in the drone, and the multiple grids to be sprayed, the next three-dimensional position coordinates of the drone are determined, and the drone is controlled to move to the next three-dimensional position coordinates. After marking the grids to be sprayed as grids where pesticide spraying has been completed, the grid map of the farmland to be sprayed is updated. Step S108: cyclically executing steps S102 to S106 until the grid for spraying pesticides does not exist in the current farmland grid map for spraying pesticides.

2. The method according to claim 1, characterized in that The determining of a plurality of grids to be sprayed with pesticides based on the current farmland grid map to be sprayed with pesticides comprises: Determining a health coefficient of each grid in the farmland grid map based on the farmland health and crop coverage in the farmland grid map currently to be sprayed with pesticides; The grids whose health coefficients are less than a first preset threshold are marked as the grids to be sprayed with pesticides.

3. The method according to claim 2, characterized in that The determining of the next three-dimensional position coordinates of the drone based on the current three-dimensional position coordinates of the drone, the current volume of pesticide in the drone, and the plurality of grids of pesticides to be sprayed includes: Determining a movement cost of each grid of pesticide to be sprayed based on the current three-dimensional position coordinates of the drone, the current volume of pesticide in the drone, and the grid of pesticide to be sprayed; Sort the movement cost of each grid to be sprayed with pesticide from small to large to determine a movement cost sorting table; The preset three-dimensional position coordinates corresponding to the grid to be sprayed with the pesticide having the smallest movement cost are selected from the movement cost sorting table as the next three-dimensional position coordinates of the UAV.

4. The method according to claim 3, characterized in that The grid movement cost of the pesticide to be sprayed is determined by the following formula: ； ； ； ； ； ； ； Where, is the moving cost of the i-th grid, is the distance weight, is the time and space distance cost, is the first preset nonlinear transformation index, is the pesticide weight, is the pesticide demand of the i-th grid, is the volume of pesticide in the current machine, is the second preset nonlinear transformation index, is the energy consumption weight, is the energy consumption cost corresponding to the i-th grid, is the total power of the drone battery, is the third preset nonlinear transformation index, ( , , ) is the three-dimensional space coordinate of the i-th grid, ( , , ) is the current three-dimensional position coordinate of the UAV, is the spraying time conversion coefficient, is the spraying speed of the drone, is the pesticide spraying coefficient, is the area of ​​a single grid, is the health weight, For pest and disease weights, is the gradient weight, is the farmland health of the i-th grid, is the maximum value of the health coefficient, is the predicted value of pest and disease probability of the i-th grid, is the health gradient of the i-th grid, is the path energy density function, is the terrain influence coefficient, is the terrain complexity index of the i-th grid area, is the indicator sensitivity parameter, is the real-time queue length of the i-th index at time t, is the terrain height function, is the adjustment coefficient, is the health measure variance, is the prediction variance of pests and diseases, is the wind speed measurement variance, is the obstacle density in the i-th grid area, is the wind speed in the i-th grid area.

5. The method according to claim 2, characterized in that The health factor of the grid is determined by the following formula: ； Where, is the health coefficient of the i-th grid, is the first preset weight coefficient, To score the severity of crop diseases based on multispectral analysis, is the second preset weight coefficient, is the crop coverage, is the third preset weight coefficient, is the normalized difference vegetation index, is the fourth preset weight coefficient, is the historical probability of occurrence of pests and diseases.

6. The method according to claim 2, characterized in that Also includes: When the smallest movement cost in the movement cost sorting table is greater than a second preset threshold, the spraying task is ended, and the UAV is controlled to return along the original route.

7. The method according to claim 2, characterized in that Also includes: When there are multiple grids with the smallest movement cost to be sprayed with pesticides, the three-dimensional spatial position coordinates of the grid closest to the current three-dimensional position coordinates of the drone are used as the next three-dimensional position coordinates of the drone.

8. A drone control and management system, characterized in that: Applied to the method according to any one of claims 1 to 7, the system comprising: The first acquisition module is configured to perform step S100: obtaining in real time a grid map of the farmland currently to be sprayed with pesticides; wherein the farmland grid map includes a plurality of grids of equal area, each grid including preset three-dimensional position coordinates, farmland health, and crop coverage; The first data processing module is configured to: determine a plurality of grids to be sprayed with pesticides based on the farmland grid map to be sprayed with pesticides; The second acquisition module is configured to perform step S104: acquiring the current three-dimensional position coordinates of the UAV and the current volume of pesticides in the UAV; The second data processing module is configured to perform step S106: based on the current three-dimensional position coordinates of the drone, the current volume of pesticide in the drone, and the multiple grids to be sprayed with pesticides, determine the next three-dimensional position coordinates of the drone, control the drone to move to the next three-dimensional position coordinates, mark the grids to be sprayed with pesticides as grids where spraying has been completed, and then update the current grid map of the farmland to be sprayed with pesticides; The third data processing module is configured to perform step S108: looping through steps S102 to S106 until no grid to be sprayed with pesticides exists in the current farmland grid map to be sprayed with pesticides.

9. An electronic device, characterized in that: The device comprises: one or more processors; and A memory storing computer program instructions, wherein when the computer program instructions are executed, the processor is caused to perform the method according to any one of claims 1 to 7.

10. A computer-readable medium, characterized in that Computer program instructions are stored thereon, and the computer program instructions can be executed by a processor to implement the method according to any one of claims 1 to 7.

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