Unmanned aerial vehicle sowing uniformity evaluation and optimization method, device, equipment and medium

Abstract

The invention relates to the technical field of computer testing, in particular to an unmanned aerial vehicle sowing uniformity judgment and optimization method, device and equipment and a medium. The unmanned aerial vehicle sowing uniformity evaluation and optimization method comprises the following steps: establishing an unmanned aerial vehicle sowing model; presetting a plurality of groups of influence parameter combinations, and carrying out sowing tests in sequence; obtaining corresponding particle distribution data under each influence parameter combination through multiple sowing tests; according to the particle distribution data corresponding to each influence parameter combination, calculating a variable coefficient to judge the uniformity; and selecting the influence parameter combination with the minimum variable coefficient as the optimal influence parameter combination. The accurate and efficient evaluation and optimization method is provided for the sowing uniformity of the unmanned aerial vehicle, the test cost is reduced, and the test period is shortened.

Description

Technical Field

[0001] The present invention relates to the field of computer testing technology, and in particular to a method, device, equipment and medium for judging and optimizing the uniformity of unmanned aerial vehicle spreading. Background Art

[0002] During the R&D and testing phase, agricultural drone airborne spreaders need to undergo a large number of material spreading tests to calibrate and debug the spreader's spreading performance. Currently, most of them are obtained by referring to the current agricultural aviation quality technical standards and analyzing the measured data of flight spreading. However, due to the influence of gusts and the accuracy of ground fertilizer reception measurements, the test results usually have large errors, which leads to insufficient design or design transition during design. In addition, it is very time-consuming, material-consuming and costly to iterate the spreader design through flight measured data analysis, and it lags behind in the verification process. It is difficult to meet the design requirements of airborne spreader equipment at the beginning of aircraft design. This application is proposed in view of the existence of the above-mentioned technical defects.

[0003] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgement or any form of suggestion that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a method, device, equipment and medium for judging and optimizing the uniformity of UAV sowing, so as to solve the problems of large error, long cycle and high cost in actual flight testing in related technologies.

[0005] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention provides a method for evaluating and optimizing the uniformity of UAV seeding, comprising:

[0007] Establishing a drone seeding model;

[0008] Preset multiple groups of influencing parameter combinations and conduct spreading tests in sequence;

[0009] Through multiple spreading tests, the particle distribution data corresponding to each combination of influencing parameters is obtained;

[0010] According to the particle distribution data corresponding to each combination of influencing parameters, the coefficient of variation is calculated to judge the uniformity;

[0011] The influencing parameter combination with the smallest coefficient of variation is selected as the optimal influencing parameter combination.

[0012] Furthermore, the establishment of the drone seeding model includes:

[0013] Model the drone and particle receiving device using 3D modeling software;

[0014] Export the model file to the discrete element numerical simulation software;

[0015] The particles were modeled using discrete element numerical simulation.

[0016] Furthermore, the three-dimensional simulation model of the drone at least includes: a material box, a roller and a spinning plate, wherein the roller is arranged in the material box, and the spinning plate is arranged below the material box;

[0017] The particle receiving device is located directly below the drone, and is provided with a plurality of drop areas of different diameters arranged around the center of the circle, and a plurality of receiving slots are evenly distributed in the drop area of ​​each diameter;

[0018] The particles are loaded and accumulated in the material box, fall onto the throwing plate through the rotation of the roller, and are thrown by the throwing plate into the receiving groove provided in the particle receiving device.

[0019] Furthermore, the preset multiple groups of influencing parameter combinations and sequentially performing the spreading test include:

[0020] The influencing parameters include roller speed and spinning disc speed;

[0021] Preset multiple groups of roller speeds with different parameters to match the spinner speeds, and conduct sowing tests in sequence;

[0022] The roller speed ranges from 50 to 300 r / min, with each 50 r / min being a value; the spinner speed ranges from 500 to 1000 r / min, with each 500 r / min being a value.

[0023] Furthermore, the step of obtaining the particle distribution data corresponding to each group of influencing parameter combinations through multiple spreading tests includes:

[0024] The roller speed of each group with different parameters matches the spinning disc speed, and the particle spreading test is carried out in turn;

[0025] The number of particles in each diameter of the receiving slot of the particle receiving device under each set of parameters is counted.

[0026] Furthermore, according to the particle distribution data corresponding to each group of influencing parameter combinations, the coefficient of variation is calculated to judge the uniformity, including:

[0027] Calculate the standard deviation and mean of the number of particles in the receiving slot of each diameter according to the number of particles in the receiving slot of each diameter obtained by statistics;

[0028] The coefficient of variation at each diameter is obtained by dividing the standard deviation of the number of particles in the receiving slot of each diameter by the mean number of particles in the receiving slot of each diameter, thereby judging the uniformity.

[0029] Furthermore, the selected influencing parameter combination with the smallest coefficient of variation as the optimal influencing parameter combination includes:

[0030] The influencing parameter combination with the smallest coefficient of variation at each diameter is selected as the optimal roller speed and the optimal spinning disk speed matching each diameter.

[0031] In a second aspect, the present invention provides a UAV sowing uniformity evaluation and optimization device, comprising:

[0032] Model building module, used to build drone seeding models;

[0033] The spreading test module is used to preset multiple groups of influencing parameter combinations and perform spreading tests in sequence;

[0034] A data acquisition module is used to obtain the particle distribution data corresponding to each group of influencing parameter combinations through multiple spreading tests;

[0035] The data processing module is used to calculate the coefficient of variation to judge the uniformity based on the particle distribution data corresponding to each group of influencing parameter combinations;

[0036] The parameter selection module is used to select the influencing parameter combination with the smallest coefficient of variation as the optimal influencing parameter combination.

[0037] In a third aspect, the present invention provides an electronic device, the electronic device comprising:

[0038] at least one processor; and

[0039] a memory communicatively connected to the at least one processor; wherein,

[0040] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the drone sowing uniformity simulation test method described in any embodiment of the present invention.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the drone spreading uniformity simulation test method described in any embodiment of the present invention when executed.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention uses a discrete element simulation method to obtain the particle distribution data corresponding to multiple groups of influencing parameter combinations. The coefficient of variation is calculated through the particle distribution data corresponding to each group of influencing parameter combinations to judge whether the uniformity meets the requirements, thereby determining the optimal influencing parameter combination. This provides an accurate and efficient judgment and optimization method for the uniformity of UAV sowing, reduces the test cost, and shortens the test cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 is a flow chart of a method for judging and optimizing the uniformity of UAV sowing provided by Embodiment 1 of the present invention;

[0046] Figure 2 A three-dimensional simulation model diagram of a drone provided in the first embodiment of the present invention;

[0047] Figure 3 This is a diagram of a drone seeding model provided in the first embodiment of the present invention;

[0048] Figure 4 This is a simulation diagram of particle spreading provided by the first embodiment of the present invention;

[0049] Figure 5 This is a simulation diagram of particle spreading provided by the first embodiment of the present invention;

[0050] Figure 6 A particle distribution data diagram provided in Example 1 of the present invention;

[0051] Figure 7 is a line graph of particle distribution data provided in Example 1 of the present invention;

[0052] Figure 8 is a structural diagram of a UAV seeding uniformity evaluation and optimization device provided in Embodiment 2 of the present invention;

[0053] Fig. 9 It is a structural diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0055] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0056] Embodiment 1:

[0057] Figure 1 This is a flow chart of the method for evaluating and optimizing the spreading uniformity of a UAV provided in the first embodiment of the present invention. This embodiment can be applied to the spreading uniformity simulation test of a UAV. The method can be executed by a UAV spreading uniformity evaluation and optimization device. The UAV spreading uniformity evaluation and optimization device can be implemented in the form of hardware and / or software. The UAV spreading uniformity evaluation and optimization device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0058] Step 1: Establishing a UAV seeding model, wherein establishing a UAV seeding model includes:

[0059] Model the drone and particle receiving device using 3D modeling software;

[0060] Export the model file to the discrete element numerical simulation software;

[0061] The particles were modeled using discrete element numerical simulation.

[0062] Specifically, the three-dimensional modeling software may be any one of Solidworks, Pro / E, and CATIA, and the discrete element numerical simulation software may be EDEM.

[0063] See also Figure 2-5The three-dimensional simulation model of the UAV at least includes: a material box, a roller and a spinning plate, wherein the roller is arranged in the material box, and the spinning plate is arranged below the material box;

[0064] The particle receiving device is located directly below the drone, and is provided with a plurality of drop areas of different diameters arranged around the center of the circle, and a plurality of receiving slots are evenly distributed in the drop area of ​​each diameter;

[0065] The particles are loaded and accumulated in the material box, fall onto the throwing plate through the rotation of the roller, and are thrown by the throwing plate into the receiving groove provided in the particle receiving device.

[0066] It should be noted that the present invention does not specifically limit the key parameter settings of the particle receiving device.

[0067] In this embodiment, the key parameter settings of the particle receiving device can be set as follows:

[0068] The particle receiving device is located 3.5 meters below the UAV, and the depth of its drop area is 1.5 meters. The diameter of the drop area is 3-15 meters respectively, and each diameter of the drop area is divided into 60 receiving slots.

[0069] When performing the simulation test, the particles are first accumulated in the material box under the action of gravity, and then the roller and the spinner start to rotate at a set speed, and then the particles fall into the receiving slot of the particle receiving device, and the test ends.

[0070] Step 2: Preset multiple groups of influencing parameter combinations and perform spreading tests in sequence. The presetting multiple groups of influencing parameter combinations and performing spreading tests in sequence include:

[0071] The influencing parameters include roller speed and spinning disc speed;

[0072] Multiple sets of roller speeds with different parameters are preset to match the spinner speeds, and spreading tests are carried out in sequence.

[0073] During the test, a comprehensive experimental design method is required, and roller speeds and spinner speeds with different parameters are selected for matching, and simulation tests are carried out multiple times.

[0074] In order to make the simulation test have practical significance, most of the particle models need to fall into the drop area. Therefore, the parameters that affect the falling trajectory of the particle model during the simulation process need to be limited to keep them within a reasonable range. The parameters that affect the falling of the particle model include the roller speed and the spinning disk speed. Among them, the roller speed is limited to a range of 50-300r / min, and every 50r / min is taken as a value to match the spinning disk speed; the spinning disk speed is limited to a range of 500-1000r / min, and every 500r / min is taken as a value to match the roller speed. The sowing test is carried out on the drone sowing model established in step 1.

[0075] Table 1 shows all influencing parameter combinations:

[0076]

[0077] 200 500 200 1000 250 500 250 1000 300 500 300 1000

[0078] Table 1

[0079] Step 3: obtaining the particle distribution data corresponding to each group of influencing parameter combinations through multiple sowing tests, wherein obtaining the particle distribution data corresponding to each group of influencing parameter combinations through multiple sowing tests includes:

[0080] The roller speed of each group with different parameters matches the spinning disc speed, and the particle spreading test is carried out in turn;

[0081] The number of particles in each diameter of the receiving slot of the particle receiving device under each set of parameters is counted.

[0082] During the simulation test, the particle spreading test was carried out in sequence according to the combination parameters listed in Table 1, and the number of particles in the receiving slot of each diameter of the particle receiving device under each group of influencing parameters was recorded.

[0083] See also Figure 6 and Figure 7 , Figure 6 For this embodiment, a set of parameters, in which the roller speed is 100 r / min and the spinner speed is 500 r / min, is selected for the sowing test to obtain the particle distribution data diagram. Figure 7 For the generated line graph (part), when statistics are performed, the horizontal axis is each group of receiving slots, the vertical axis is the number of particles received in each group of receiving slots, and the broken lines are different diameters.

[0084] In order to reduce calculations, this embodiment divides the 60 receiving slots of each diameter into 20 groups, with 3 receiving slots as a group, and counts the number of particles received in each group of receiving slots, and then obtains the number of particles in each group of receiving slots of each diameter of the particle receiving device under each set of parameters. The grouping of the receiving slots is not limited here.

[0085] Table 2 shows the number of particles received in 20 groups of receiving slots (selected partial diameter data) when the roller speed is 100 r / min and the spinner speed is 500 r / min:

[0086]

[0087]

[0088] Table 2

[0089] Step 4: Calculate the coefficient of variation to judge the uniformity according to the particle distribution data corresponding to each group of influencing parameter combinations. Calculate the coefficient of variation to judge the uniformity according to the particle distribution data corresponding to each group of influencing parameter combinations, including:

[0090] Calculate the standard deviation and mean of the number of particles in the receiving slot of each diameter according to the number of particles in the receiving slot of each diameter obtained by statistics;

[0091] The coefficient of variation at each diameter is obtained by dividing the standard deviation of the number of particles in the receiving slot of each diameter by the mean number of particles in the receiving slot of each diameter, thereby judging the uniformity.

[0092] Specifically, the coefficient of variation is obtained by standard deviation / mean, that is, the coefficient of variation at each diameter can be obtained by the standard deviation of the number of particles in the receiving groove of each diameter / the mean of the number of particles in the receiving groove of each diameter, so as to judge whether the uniformity meets the requirements. If the coefficient of variation is greater than 0.3, that is, the uniformity does not meet the standard, the combination of this group of influencing parameters under the sowing diameter should be eliminated.

[0093] Table 3 shows the coefficient of variation for each diameter when the roller speed is 100 r / min and the spinner speed is 500 r / min (selected partial diameter data):

[0094] Spreading diameter(m) 4 6 8 10 12 Coefficient of variation 0.595904389 0.210963357 0.260860404 0.162741135 0.302844947

[0095] Table 3

[0096] Step 5: Selecting the influencing parameter combination with the smallest coefficient of variation as the optimal influencing parameter combination, wherein selecting the influencing parameter combination with the smallest coefficient of variation as the optimal influencing parameter combination includes:

[0097] The influencing parameter combination with the smallest coefficient of variation at each diameter is selected as the optimal roller speed and the optimal spinning disk speed matching each diameter.

[0098] This embodiment uses a comprehensive experimental method to perform discrete element simulation and calculates the coefficient of variation of each group of influencing parameter combinations under each diameter. By comparing the coefficient of variation under each group of influencing parameter combinations, the influencing parameter combination corresponding to the minimum value of the coefficient of variation is selected as the optimal condition under the diameter. A user manual is formulated to guide users in the best usage plans in different operating scenarios.

[0099] Embodiment 2:

[0100] Figure 8 This is a structural schematic diagram of a UAV sowing uniformity evaluation and optimization device provided in Example 3 of the present invention.

[0101] like Figure 8 As shown, the device comprises:

[0102] Model building module, used to build drone seeding models;

[0103] The spreading test module is used to preset multiple groups of influencing parameter combinations and perform spreading tests in sequence;

[0104] A data acquisition module is used to obtain the particle distribution data corresponding to each group of influencing parameter combinations through multiple spreading tests;

[0105] The data processing module is used to calculate the coefficient of variation to judge the uniformity based on the particle distribution data corresponding to each group of influencing parameter combinations;

[0106] The parameter selection module is used to select the influencing parameter combination with the smallest coefficient of variation as the optimal influencing parameter combination.

[0107] The drone spreading uniformity evaluation and optimization device provided in the embodiment of the present invention can execute the drone spreading uniformity evaluation and optimization method provided in any embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method.

[0108] Embodiment three:

[0109] Fig. 9 A schematic diagram of an electronic device that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. 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 invention described and / or required herein.

[0110] like Fig. 9 As shown, the electronic device includes at least one processor, and a memory connected to the at least one processor, such as a read-only memory (ROM), a random access memory (RAM), etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) or the computer program loaded from the storage unit to the random access memory (RAM). In the RAM, various programs and data required for the operation of the electronic device can also be stored. The processor, ROM and RAM are connected to each other via a bus. The input / output (I / O) interface is also connected to the bus.

[0111] Multiple components in an electronic device are connected to the I / O interface, including: input units, such as keyboards, mice, etc.; output units, such as various types of displays, speakers, etc.; storage units, such as disks, optical disks, etc.; and communication units, such as network cards, modems, wireless communication transceivers, etc. The communication unit allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunication networks.

[0112] The processor may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of processors include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor executes the various methods and processes described above, such as the drone seeding uniformity evaluation and optimization method.

[0113] In some embodiments, the drone spreading uniformity evaluation and optimization method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via a ROM and / or a communication unit. When the computer program is loaded into RAM 43 and executed by the processor, one or more steps of the drone spreading uniformity evaluation and optimization method described above can be performed.

[0114] Alternatively, in other embodiments, the processor may be configured to execute the drone spreading uniformity evaluation and optimization method in any other appropriate manner (e.g., by means of firmware).

[0115] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0116] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0117] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM 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 of the foregoing.

[0118] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the 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).

[0119] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0120] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0121] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0122] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for judging and optimizing the uniformity of UAV seeding, characterized in that: include: Establishing a drone seeding model; Preset multiple groups of influencing parameter combinations and conduct spreading tests in sequence; Through multiple spreading tests, the particle distribution data corresponding to each combination of influencing parameters is obtained; According to the particle distribution data corresponding to each group of influencing parameter combinations, the coefficient of variation is calculated to judge the uniformity; The influencing parameter combination with the smallest coefficient of variation is selected as the optimal influencing parameter combination.

2. The method according to claim 1, characterized in that The establishment of the UAV seeding model includes: Model the drone and particle receiving device using 3D modeling software; Export the model file to the discrete element numerical simulation software; The particles were modeled using discrete element numerical simulation.

3. The method according to claim 2, characterized in that The three-dimensional simulation model of the drone at least includes: a material box, a roller and a spinning plate, wherein the roller is arranged in the material box, and the spinning plate is arranged below the material box; The particle receiving device is located directly below the drone, and is provided with a plurality of drop areas of different diameters arranged around the center of the circle, and a plurality of receiving slots are evenly distributed in the drop area of ​​each diameter; The particles are loaded and accumulated in the material box, fall onto the throwing plate through the rotation of the roller, and are thrown by the throwing plate into the receiving groove provided in the particle receiving device.

4. The method according to claim 3, characterized in that The preset multiple groups of influencing parameter combinations and the sequentially performing spreading tests include: The influencing parameters include roller speed and spinning disc speed; Preset multiple groups of roller speeds with different parameters to match the spinner speeds, and conduct sowing tests in sequence; The roller speed ranges from 50 to 300 r / min, with each 50 r / min being a value; the spinner speed ranges from 500 to 1000 r / min, with each 500 r / min being a value.

5. The method according to claim 4, characterized in that The particle distribution data corresponding to each group of influencing parameter combinations obtained through multiple spreading tests includes: The roller speed of each group with different parameters matches the spinning disc speed, and the particle spreading test is carried out in turn; The number of particles in each diameter of the receiving slot of the particle receiving device under each set of parameters is counted.

6. The method according to claim 5, characterized in that According to the particle distribution data corresponding to each combination of influencing parameters, the coefficient of variation is calculated to judge the uniformity, including: Calculate the standard deviation and mean of the number of particles in the receiving slot of each diameter according to the number of particles in the receiving slot of each diameter obtained by statistics; The coefficient of variation at each diameter is obtained by dividing the standard deviation of the number of particles in the receiving slot of each diameter by the mean number of particles in the receiving slot of each diameter, thereby judging the uniformity.

7. The method according to claim 6, characterized in that The influencing parameter combination with the smallest coefficient of variation selected as the optimal influencing parameter combination includes: The influencing parameter combination with the smallest coefficient of variation at each diameter is selected as the optimal roller speed and the optimal spinning disk speed matching each diameter.

8. A drone spreading uniformity evaluation and optimization device, characterized in that: include: Model building module, used to build drone seeding models; The spreading test module is used to preset multiple groups of influencing parameter combinations and perform spreading tests in sequence; A data acquisition module is used to obtain the particle distribution data corresponding to each group of influencing parameter combinations through multiple spreading tests; The data processing module is used to calculate the coefficient of variation to judge the uniformity based on the particle distribution data corresponding to each group of influencing parameter combinations; The parameter selection module is used to select the influencing parameter combination with the smallest coefficient of variation as the optimal influencing parameter combination.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the drone sowing uniformity simulation test method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the drone sowing uniformity simulation test method according to any one of claims 1 to 7 when executed.

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