Intelligent planning method and system for unmanned aerial vehicle operation path in sugarcane planting area

Abstract

The invention discloses an intelligent planning method and system for an unmanned aerial vehicle operation path in a sugarcane planting area, and relates to the technical field of sugarcane planting, and the method comprises the steps: obtaining the position information of each sugarcane planting area in a preset area and the priority of each sugarcane planting area; based on a traveling salesman problem mathematical model, in combination with the position information of each sugarcane planting area and the priority of each sugarcane planting area, converting the operation sequence of all the sugarcane planting areas into a total cost function about the total length of the path; the total cost is minimized based on the total cost function, the sequence of unmanned aerial vehicle operation in all the sugarcane planting areas is obtained, the position coordinates of the starting operation point and the position coordinates of the ending operation point of each sugarcane planting area are obtained according to the sequence, and then a path for unmanned aerial vehicle operation in the preset area is generated. Operation efficiency can be effectively improved, and the service life of the unmanned aerial vehicle is prolonged.

Description

Technical Field

[0001] The present invention relates to the field of sugarcane planting technology, and in particular to an intelligent planning method and system for unmanned aerial vehicle (UAV) operation paths in sugarcane planting areas. Background Art

[0002] At present, drone operations (such as pesticide spraying) are often carried out separately in each sugarcane planting area, which requires frequent take-off and landing of drones, affecting work efficiency. When operating in multiple sugarcane planting areas, the order of operations in each sugarcane planting area is often subjectively determined, and then the drones are controlled to perform operations according to the subjectively determined order of operations, which will lead to high energy consumption of drones and low work efficiency. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to address the deficiencies of the existing technology and specifically provide a method and system for intelligent planning of UAV operation paths in sugarcane planting areas, as follows:

[0004] 1) In a first aspect, the present invention provides an intelligent planning method for drone operation paths in sugarcane planting areas. The specific technical solution is as follows:

[0005] Obtain the location information of each sugarcane planting area in the preset area and the priority of each sugarcane planting area;

[0006] Based on the mathematical model of the traveling salesman problem and combining the location information and priority of each sugarcane planting area, the operation sequence of all sugarcane planting areas is converted into a total cost function about the total length of the path;

[0007] Minimizing the total cost based on the total cost function to obtain a sequence of drone operations in all sugarcane planting areas, and obtaining the position coordinates of the starting operation point and the ending operation point of each sugarcane planting area according to the sequence;

[0008] According to the order of drone operations in all sugarcane planting areas and the position coordinates of the starting operation point and the position coordinates of the ending operation point of each sugarcane planting area, a path for drone operations in the preset area is generated.

[0009] The beneficial effects of the intelligent planning method for drone operation paths in sugarcane planting areas provided by the present invention are as follows:

[0010] Minimizing the total cost through the total cost function helps to determine the operation path with the lowest total cost, which can reduce the operation time of the drone, reduce energy consumption, and greatly improve operation efficiency. Moreover, since the operating time of the drone is reduced, it helps to reduce the wear and tear of the drone and extend the service life of the drone.

[0011] Based on the above solution, the intelligent planning method for the operation path of a UAV in a sugarcane planting area of ​​the present invention can also be improved as follows.

[0012] Furthermore, it also includes:

[0013] Obtain a terrain model of a preset area, identify obstacles in the terrain model of the preset area, and generate obstacle distribution data of the preset area.

[0014] Furthermore, it also includes:

[0015] Based on the obstacle distribution data of the preset area, the path for the drone operation in the preset area is optimized.

[0016] Furthermore, obtaining a terrain model of a preset area, identifying obstacles in the terrain model of the preset area, and generating obstacle distribution data of the preset area include:

[0017] Using a data fusion algorithm, the satellite remote sensing data and the aerial photography data of the preset area are fused to generate a preliminary terrain data set;

[0018] generating the terrain model based on the preliminary terrain dataset;

[0019] The trained deep learning model is used to identify obstacles in the terrain model of the preset area and generate obstacle distribution data for the preset area.

[0020] 2) In a second aspect, the present invention further provides an intelligent planning system for drone operation paths in sugarcane planting areas. The specific technical solution is as follows:

[0021] It includes a data acquisition module, a conversion module, a sequence position determination module and a path generation module;

[0022] The data acquisition module is used to: acquire the location information of each sugarcane planting area and the priority of each sugarcane planting area within a preset area;

[0023] The conversion module is used to: based on the mathematical model of the traveling salesman problem and in combination with the location information of each sugarcane planting area and the priority of each sugarcane planting area, convert the operation sequence of all sugarcane planting areas into a total cost function of the total path length;

[0024] The sequential position determination module is used to: minimize the total cost based on the total cost function to obtain the order of drone operations in all sugarcane planting areas, and obtain the position coordinates of the starting operation point and the ending operation point of each sugarcane planting area according to the order;

[0025] The path generation module is used to generate a path for drone operations in the preset area based on the order of drone operations in all sugarcane planting areas and the position coordinates of the starting operation point and the end operation point of each sugarcane planting area.

[0026] Based on the above solution, the intelligent planning system for drone operation paths in sugarcane planting areas of the present invention can also be improved as follows.

[0027] Furthermore, it also includes a terrain model acquisition module and an identification module;

[0028] The terrain model acquisition module is used to: acquire a terrain model of a preset area;

[0029] The recognition module is used to: identify obstacles in the terrain model of the preset area and generate obstacle distribution data of the preset area.

[0030] Furthermore, it also includes an optimization module, which is used to optimize the path of the drone operation in the preset area according to the obstacle distribution data of the preset area.

[0031] Furthermore, the terrain model acquisition module is specifically used to:

[0032] Using a data fusion algorithm, the satellite remote sensing data and the aerial photography data of the preset area are fused to generate a preliminary terrain data set;

[0033] generating the terrain model based on the preliminary terrain dataset;

[0034] The recognition module is specifically used to: use the trained deep learning model to identify obstacles in the terrain model of the preset area and generate obstacle distribution data of the preset area.

[0035] 3) In a third aspect, the present invention further provides an electronic device, comprising a processor coupled to a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor, so that the electronic device implements any of the above-mentioned intelligent planning methods for drone operation paths in sugarcane planting areas.

[0036] 4) In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for intelligently planning the operation path of a drone in a sugarcane planting area.

[0037] It should be noted that the beneficial effects achieved by the technical solutions of the second to fourth aspects of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments of the present invention:

[0039] Figure 1 This is a flow chart of an intelligent planning method for a UAV operation path in a sugarcane planting area according to an embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of the structure of an intelligent planning system for drone operation paths in a sugarcane planting area according to an embodiment of the present invention;

[0041] Figure 3 The figure is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0043] The following describes in detail the technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems using specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The following embodiments of the present invention are described in conjunction with the accompanying drawings.

[0044] like Figure 1 As shown, an intelligent planning method for a UAV operation path in a sugarcane planting area according to an embodiment of the present invention includes the following steps:

[0045] S1. Obtaining the location information of each sugarcane planting area and the priority of each sugarcane planting area within a preset area;

[0046] Among them, the preset area can be set according to actual conditions.

[0047] The location information of the sugarcane planting area includes: the boundary of the sugarcane planting area and a set of coordinates of the boundary of the sugarcane planting area. The location information of the sugarcane planting area may also include the shape and area of ​​the sugarcane planting area. The coordinate set of the boundary of the sugarcane planting area may be a set of longitude and latitude coordinates, or may be the position coordinates of pixel points in an image of a predetermined area.

[0048] The priority of each sugarcane growing area can be determined as follows:

[0049] The multispectral camera carried by the drone is used to obtain the current growth image of the sugarcane in each sugarcane planting area, and the Normalized Vegetation Index (NDVI) and disease characteristics are extracted from the current growth image;

[0050] Obtain soil temperature and humidity in each sugarcane planting area;

[0051] Based on the Normalized Difference Vegetation Index (NDVI), disease characteristics, and soil temperature and humidity of each sugarcane planting area, a priority assessment matrix was constructed. The elements in the priority assessment matrix include: Normalized Difference Vegetation Index (NDVI), disease characteristics, and soil temperature and humidity;

[0052] A Markov decision process (MDP) model is established, with sample sugarcane planting areas as state nodes, the historical priority evaluation matrix of the sample sugarcane planting areas as the behavior set, and maximizing the overall benefit (the benefit can be the output of the sample sugarcane planting areas) as the reward function. The Markov decision process (MDP) model is trained through the Q-learning algorithm based on the historical priority evaluation matrices of multiple sample sugarcane planting areas and the actual priority of each sample sugarcane planting area to obtain a trained Markov decision process (MDP) model.

[0053] The priority evaluation matrix of each sugarcane planting area is input into the trained Markov decision process (MDP) model to obtain the priority of each sugarcane planting area.

[0054] It should be noted that when the Markov decision process (MDP) model is trained through the Q-learning algorithm and based on the historical priority evaluation matrix of multiple sample sugarcane planting areas and the actual priority of each sample sugarcane planting area, the weight ratio of each element in the historical priority evaluation matrix will be dynamically adjusted. Therefore, when the trained Markov decision process (MDP) model processes the priority evaluation matrix of each sugarcane planting area, it will dynamically generate the weight ratio of each element in the priority evaluation matrix based on the priority evaluation matrix of each sugarcane planting area, and then obtain the priority of each sugarcane planting area based on the dynamically generated weight ratio of each element. This can better fit the actual situation of each current sugarcane planting area and can more accurately determine the priority of each sugarcane planting area, so that sugarcane planting areas with high priority can receive spraying and other operations in a timely manner, thereby minimizing the impact on sugarcane yield.

[0055] Among them, the priority of each sugarcane growing area can also be set artificially.

[0056] Among them, when the priorities of all sugarcane planting areas are equal, the total cost is the minimum, that is, the total path is the minimum.

[0057] S2. Based on the mathematical model of the traveling salesman problem and combining the location information and priority of each sugarcane planting area, the operation sequence of all sugarcane planting areas is converted into a total cost function related to the total path length;

[0058] Among them, based on the mathematical model of the traveling salesman problem, the operation sequence of all sugarcane planting areas can be transformed into the problem of minimizing the total path length. The total cost function of the total path length is:

[0059]

[0060] Where N represents the number of sugarcane growing areas, Represents: the permutation function of sugarcane planting areas, Indicates: When the drone is operating, the sugarcane planting area numbered i is arranged. Indicates: When the drone is operating, the arrangement number is the i+1th sugarcane planting area, express: and The Euclidean distance between It represents the priority weight of the i-th sugarcane planting area during the UAV operation. The higher the priority, the greater the weight. α represents the adjustment coefficient, which is used to balance the relationship between the total path length and the priority. Adjusting the size of α can control the impact of the priority weight on the total cost. T represents the total cost. Represents: path length cost, i.e. total path length, Denotes: priority delay penalty, specifically, if The priority weight value is high, but the order position is low, then the penalty item It will increase significantly and have a regulating effect.

[0061] Among them, based on The center point position coordinates and The center point position coordinates are calculated and The Euclidean distance between .

[0062] For example, there are 3 sugarcane planting areas, N=3, the center point coordinates of the first sugarcane planting area are (0,0), the priority weight value is 5, the center point coordinates of the second sugarcane planting area are (3,0), the priority weight value is 2, the center point coordinates of the third sugarcane planting area are (1,4), the priority weight value is 3, and α=0.1 is set. When the order of drone operations in these three sugarcane planting areas is: the first sugarcane planting area, the second sugarcane planting area, and the third sugarcane planting area, the total cost is approximately equal to 9.2. When the order of drone operations in these three sugarcane planting areas is: the third sugarcane planting area, the first sugarcane planting area, and the second sugarcane planting area, the total cost is approximately equal to 9.0. Therefore, "the third sugarcane planting area, the first sugarcane planting area, and the second sugarcane planting area" is used as the final order.

[0063] S3. Minimize the total cost based on the total cost function to obtain the order of drone operations in all sugarcane planting areas, and obtain the position coordinates of the starting operation point and the ending operation point of each sugarcane planting area according to the order.

[0064] The total cost is minimized based on the total cost function to obtain the order of drone operations in all sugarcane planting areas. The specific implementation process is as follows:

[0065] S30. Generate an initial population using a genetic algorithm, calculate the penalty term of the total path length and priority deviation through the fitness function, iteratively perform selection, crossover, and mutation operations until convergence to the optimal global access sequence. The optimal global access sequence is the order in which drone operations are performed in all sugarcane planting areas.

[0066] The initial population is usually composed of the order of randomly arranged sugarcane planting areas, and each individual (chromosome) represents a possible order. For example, for 5 sugarcane planting areas, the initial population contains random arrangements such as [3, 1, 4, 2, 0] and [0, 2, 1, 4, 3].

[0067] The fitness function is the inverse of the total cost function with respect to the total length of the path.

[0068] The position coordinates of the starting operation point and the position coordinates of the ending operation point may be latitude and longitude coordinates, or may be coordinates in a coordinate system set according to a preset area.

[0069] The position coordinates of the starting operation point and the ending operation point of each sugarcane planting area are obtained according to the sequence, and the specific implementation process is as follows:

[0070] S31. Approximate the shape of each sugarcane planting area to a regular shape, such as a rectangle, circle, or polygon. Use the vertices of each regular shape as candidate points. Select a candidate point at intervals of a preset length along each boundary of each regular shape. This yields multiple candidate points corresponding to each sugarcane planting area. This facilitates selecting the starting and ending operation points for each sugarcane planting area from among all the candidate points.

[0071] S32. Establish the objective function: in, It represents the straight-line distance between the candidate point corresponding to any terminating operation point of the k-th sugarcane planting area and the candidate point corresponding to any starting operation point of the k+1-th sugarcane planting area in the order of UAV operations in all sugarcane planting areas. TC represents the total cost of the global transfer path. For the convenience of expression, the candidate point corresponding to the terminating operation point is recorded as the terminating candidate area, and the candidate point corresponding to the starting operation point is recorded as the starting candidate point.

[0072] S33. Establish a dynamic programming state transfer equation. Based on the dynamic programming state transfer equation, use the ant colony algorithm, Viterbi algorithm or simulated annealing algorithm to minimize the objective function to obtain the starting operation point and the ending operation point of each sugarcane planting area.

[0073] Among them, the dynamic programming state transition equation is:

[0074]

[0075] Among them, S (k,p) It means: the minimum cumulative cost when calculating the p-th candidate end point of the k-th sugarcane planting area; S (k-1,q) It means: when calculating the minimum cumulative cost of the qth candidate end point in the k-1th sugarcane planting area, it is the previous cumulative cost. It represents the straight-line distance between the qth candidate end point of the k-1th sugarcane planting area and the starting candidate point of the kth sugarcane planting area.

[0076] S34. Use the Dubins path model to verify whether the starting operation point and the ending operation point of each sugarcane planting area finally determined meet the preset constraints. The preset constraints include: the constraint of the minimum turning radius of the drone is satisfied between the ending operation point of the sugarcane planting area numbered k-1 and the starting operation point of the sugarcane planting area numbered k. If satisfied, the position coordinates of the starting operation point and the position coordinates of the ending operation point of each sugarcane planting area are obtained. If not satisfied, the position coordinates of the starting operation point and the position coordinates of the ending operation point of each sugarcane planting area can be manually corrected.

[0077] S4. Generate a path for the drone operation in the preset area according to the order of the drone operation in all sugarcane planting areas and the position coordinates of the starting operation point and the ending operation point of each sugarcane planting area.

[0078] Among them, the paths for drone operations in the preset area include: the paths between each two adjacent sugarcane planting areas in sequence (the path between the ending operation point of the previous sugarcane planting area and the starting operation point of the next sugarcane planting area) and the paths within each sugarcane planting area.

[0079] Among them, the A* algorithm or Dijkstra algorithm is used to plan the path within any sugarcane planting area in combination with the starting operation point and the ending operation point of the sugarcane planting area, and the path within the sugarcane planting area is obtained, until the path within each sugarcane planting area is obtained.

[0080] Optionally, in the above technical solution, the following is further included:

[0081] Obtain a terrain model of a preset area, identify obstacles in the terrain model of the preset area, and generate obstacle distribution data for the preset area. The specific implementation process is as follows:

[0082] S0100. Using a data fusion algorithm, fuse the satellite remote sensing data and aerial photography data of the preset area to generate a preliminary terrain data set. Specifically:

[0083] Satellite remote sensing data and aerial photography data are subjected to geometric correction, atmospheric correction and radiation correction respectively to eliminate geometric deformation and radiation distortion caused by factors such as terrain, weather, and sensor characteristics during the imaging process, and to improve the consistency and accuracy of the data.

[0084] The corrected satellite remote sensing data and the corrected aerial photography data are registered to the same geographic coordinate system. The data fusion algorithm can be a pixel-level fusion method or a weighted fusion algorithm. The corrected satellite remote sensing data and the corrected aerial photography data are weighted averaged according to a certain weight coefficient to obtain the fused image data, which is the preliminary terrain dataset. The weight coefficient can be adjusted based on factors such as the resolution and information richness of the two data. During the fusion process, it may be necessary to adjust the fusion parameters based on the actual effect, such as the weight coefficient in weighted fusion and the number of principal components in principal component transformation, to obtain the best fusion result.

[0085] S0101. Generate the terrain model based on the preliminary terrain dataset;

[0086] Based on the preliminary terrain dataset, a terrain model is generated using both regular grid-based modeling and irregular triangulated network (TIN)-based modeling. To make the terrain model more realistic, texture mapping can be performed to map real photos or images onto the terrain model, increasing the model's detail and realism.

[0087] The generated terrain model can be used to perform various analyses, such as slope analysis, aspect analysis, terrain profile analysis, etc., to meet different application requirements.

[0088] S0102. Utilize the trained deep learning model to identify obstacles in the terrain model of the preset area and generate obstacle distribution data for the preset area.

[0089] The trained deep learning model can be a semantic segmentation model based on a convolutional neural network (CNN), or other deep learning models suitable for terrain obstacle recognition. The trained deep learning model will classify each pixel or point in the terrain model based on the learned features and identify obstacles.

[0090] Analyze the identified obstacles, including extracting information such as obstacle type, location, size, etc. This can be combined with Geographic Information System (GIS) software for visualization to more intuitively understand the distribution of obstacles in the terrain.

[0091] Optionally, in the above technical solution, the following is further included:

[0092] Based on the obstacle distribution data of the preset area, the path for the drone operation in the preset area is optimized, specifically:

[0093] According to obstacle distribution data. The terrain model can be divided into multiple grids using the grid method, where each grid represents an area in the environment. The grid where the obstacle is located is marked as inaccessible, and the other grids are marked as passable. In this way, complex environmental information can be converted into a discrete data structure that is easy for the computer to process, and then the A* algorithm or the Dijkstra algorithm is used to optimize the path for drone operations in the preset area. When the A* algorithm is used, a heuristic function is defined, which can usually be based on Manhattan distance or Euclidean distance, and is adjusted in combination with obstacle distribution information. During the search process, the algorithm will evaluate the potential cost of each node according to the heuristic function, and give priority to paths that are more likely to approach the end point and avoid obstacles for expansion until the optimal path from the starting point to the end point is found to achieve path optimization.

[0094] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the scope of protection of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0095] like Figure 2 As shown, an intelligent planning system 200 for a UAV operation path in a sugarcane planting area according to an embodiment of the present invention includes a data acquisition module 201, a conversion module 202, a sequence position determination module 203, and a path generation module 204;

[0096] The data acquisition module 201 is used to: acquire the location information of each sugarcane planting area and the priority of each sugarcane planting area within a preset area;

[0097] The conversion module 202 is used to: based on the mathematical model of the traveling salesman problem and in combination with the location information of each sugarcane planting area and the priority of each sugarcane planting area, convert the operation sequence of all sugarcane planting areas into a total cost function of the total path length;

[0098] The sequential position determination module 203 is configured to: minimize the total cost based on the total cost function to obtain the sequence of drone operations in all sugarcane planting areas, and obtain the position coordinates of the starting operation point and the ending operation point of each sugarcane planting area according to the sequence;

[0099] The path generation module 204 is used to generate a path for drone operations in the preset area according to the order of drone operations in all sugarcane planting areas and the position coordinates of the starting operation point and the end operation point of each sugarcane planting area.

[0100] Optionally, in the above technical solution, a terrain model acquisition module and an identification module are further included;

[0101] The terrain model acquisition module is used to: acquire a terrain model of a preset area;

[0102] The recognition module is used to: identify obstacles in the terrain model of the preset area and generate obstacle distribution data of the preset area.

[0103] Optionally, the above technical solution further includes an optimization module, which is used to optimize the path of the drone operation in the preset area according to the obstacle distribution data of the preset area.

[0104] Optionally, in the above technical solution, the terrain model acquisition module is specifically used to:

[0105] Using a data fusion algorithm, the satellite remote sensing data and the aerial photography data of the preset area are fused to generate a preliminary terrain data set;

[0106] generating the terrain model based on the preliminary terrain dataset;

[0107] The recognition module is specifically used to: use the trained deep learning model to identify obstacles in the terrain model of the preset area and generate obstacle distribution data of the preset area.

[0108] It should be noted that the beneficial effects of the intelligent planning system 200 for drone operation paths in sugarcane-growing areas provided in the above embodiment are the same as the beneficial effects of the intelligent planning method for drone operation paths in sugarcane-growing areas provided in the above embodiment, and will not be repeated here. In addition, when implementing its functions, the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0109] Among them, the intelligent planning system for drone operation paths in sugarcane planting areas of the present invention can be a computer program (including program code) running on a computer device. For example, the intelligent planning system for drone operation paths in sugarcane planting areas of the present invention is an application software that can be used to execute the corresponding steps in the intelligent planning method for drone operation paths in sugarcane planting areas of the present invention.

[0110] In some embodiments, the intelligent planning system for the operation path of the drone in the sugarcane planting area of ​​the present invention can be implemented by a combination of software and hardware. As an example, the intelligent planning system for the operation path of the drone in the sugarcane planting area of ​​the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the intelligent planning method for the operation path of the drone in the sugarcane planting area of ​​the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0111] The modules described in the embodiments of the present invention may be implemented in software or hardware, and the name of a module does not necessarily limit the module itself.

[0112] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the above-mentioned methods for intelligently planning the operation path of a drone in a sugarcane-growing area is implemented. That is, an electronic device according to an embodiment of the present invention may include but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the intelligent method for intelligently planning the operation path of a drone in a sugarcane-growing area shown in any embodiment of the present invention by calling the computer program.

[0113] In an alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data exchange between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the number of transceivers 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0114] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the present disclosure. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0115] Bus 4002 may include a path for transmitting information between the above components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 In the figure, only one thick line is used to represent the bus 4002, but this does not mean that there is only one bus or one type of bus.

[0116] The memory 4003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0117] The memory 4003 is used to store application code (computer program) for executing the solution of the present invention, and is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0118] Among them, the electronic device can also be a terminal device, and the terminal device can be any device that can install applications, including at least one of a smartphone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart car device.

[0119] It should be noted that Figure 3 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0120] A computer-readable storage medium according to an embodiment of the present invention stores a computer program, which, when executed by a processor, implements any of the above-mentioned intelligent planning methods for drone operation paths in sugarcane planting areas.

[0121] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, or the like.

[0122] In an exemplary embodiment, a computer program product or computer program is also provided. The computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to implement any of the aforementioned methods for intelligently planning a drone operation path in a sugarcane-growing area.

[0123] Computer program code for performing the operations of the present invention 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 the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through 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., through the Internet using an Internet service provider).

[0124] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a 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 a different order than the order 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, and the combination of the boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0125] The computer-readable storage medium provided in the embodiment of the present invention may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EEPROM or flash memory), an 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 the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or component.

[0126] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0127] The above description is merely a preferred embodiment of the present invention and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the present invention.

[0128] It should be noted that the terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and to define a specific order or precedence. Where appropriate, the order used for similar objects may be interchanged, such that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0129] Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention may be implemented in the form of a computer program product embodied in one or more computer-readable media containing computer-readable program code.

[0130] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An intelligent planning method for drone operation paths in sugarcane planting areas, characterized in that: include: Obtain the location information of each sugarcane planting area in the preset area and the priority of each sugarcane planting area; Based on the mathematical model of the traveling salesman problem and combining the location information and priority of each sugarcane planting area, the operation sequence of all sugarcane planting areas is converted into a total cost function about the total length of the path; Minimizing the total cost based on the total cost function to obtain a sequence of drone operations in all sugarcane planting areas, and obtaining the position coordinates of the starting operation point and the ending operation point of each sugarcane planting area according to the sequence; According to the order of drone operations in all sugarcane planting areas and the position coordinates of the starting operation point and the position coordinates of the ending operation point of each sugarcane planting area, a path for drone operations in the preset area is generated.

2. The intelligent planning method for UAV operation paths in sugarcane planting areas according to claim 1 is characterized in that: Also includes: Obtain a terrain model of a preset area, identify obstacles in the terrain model of the preset area, and generate obstacle distribution data of the preset area.

3. The intelligent planning method for UAV operation paths in sugarcane planting areas according to claim 2 is characterized in that: Also includes: Based on the obstacle distribution data of the preset area, the path for the drone operation in the preset area is optimized.

4. The intelligent planning method for drone operation paths in sugarcane planting areas according to claim 2 or 3, characterized in that: Obtaining a terrain model of a preset area, identifying obstacles in the terrain model of the preset area, and generating obstacle distribution data for the preset area, including: Using a data fusion algorithm, the satellite remote sensing data and the aerial photography data of the preset area are fused to generate a preliminary terrain data set; generating the terrain model based on the preliminary terrain dataset; The trained deep learning model is used to identify obstacles in the terrain model of the preset area and generate obstacle distribution data for the preset area.

5. An intelligent planning system for drone operation paths in sugarcane planting areas, characterized in that: It includes a data acquisition module, a conversion module, a sequence position determination module and a path generation module; The data acquisition module is used to: acquire the location information of each sugarcane planting area and the priority of each sugarcane planting area within a preset area; The conversion module is used to: based on the mathematical model of the traveling salesman problem and in combination with the location information of each sugarcane planting area and the priority of each sugarcane planting area, convert the operation sequence of all sugarcane planting areas into a total cost function of the total path length; The sequential position determination module is used to: minimize the total cost based on the total cost function to obtain the order of drone operations in all sugarcane planting areas, and obtain the position coordinates of the starting operation point and the ending operation point of each sugarcane planting area according to the order; The path generation module is used to generate a path for drone operations in the preset area based on the order of drone operations in all sugarcane planting areas and the position coordinates of the starting operation point and the end operation point of each sugarcane planting area.

6. The intelligent planning system for drone operation paths in sugarcane planting areas according to claim 5, characterized in that: It also includes a terrain model acquisition module and a recognition module; The terrain model acquisition module is used to: acquire a terrain model of a preset area; The recognition module is used to: identify obstacles in the terrain model of the preset area and generate obstacle distribution data of the preset area.

7. The intelligent planning system for drone operation paths in sugarcane planting areas according to claim 6, characterized in that: It also includes an optimization module, which is used to optimize the path of the drone operation in the preset area based on the obstacle distribution data of the preset area.

8. The intelligent planning system for drone operation paths in sugarcane planting areas according to claim 6 or 7, characterized in that: The terrain model acquisition module is specifically used for: Using a data fusion algorithm, the satellite remote sensing data and the aerial photography data of the preset area are fused to generate a preliminary terrain data set; generating the terrain model based on the preliminary terrain dataset; The recognition module is specifically used to: use the trained deep learning model to identify obstacles in the terrain model of the preset area and generate obstacle distribution data of the preset area.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for intelligently planning the operation path of a drone in a sugarcane planting area as described in any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the intelligent planning method for a drone operation path in a sugarcane planting area as described in any one of claims 1 to 4.

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