A method, device and equipment for determining the weight of a fertilizer and a storage medium

Abstract

The application discloses a fertilizer weight determination method, device, equipment and storage medium. The method comprises the following steps: acquiring the roller speed at a previous moment and the weight of the loaded fertilizer at the previous moment; collecting the current weight of the loaded fertilizer at a current moment; inputting the weight at the previous moment, the roller speed and the current weight into a filter to output the actual weight of the fertilizer at the current moment. The method estimates the actual weight of the fertilizer at the current moment by combining the fertilizer weight data at the previous moment and the fertilizer weight collected at the current moment, and considers the influence of the roller speed on the fertilizer weight change by inputting the roller speed, so that the actual weight of the fertilizer at the current moment is obtained.

Description

Technical Field

[0001] This invention relates to the field of drone fertilization technology, and in particular to a method, apparatus, equipment and storage medium for determining fertilizer weight. Background Technology

[0002] In agriculture, obtaining the weight of the remaining fertilizer is crucial for precise application when using drones to spread fertilizer. Although drones are equipped with weighing sensors to measure the real-time weight of the fertilizer, the weight is constantly changing, and the drone is affected by various factors such as wind, pressure, and weightlessness during flight, making it impossible for the weighing sensors to provide an accurate weight value. Therefore, accurately determining the weight of the remaining fertilizer during drone application is a problem that current technology urgently needs to solve. Summary of the Invention

[0003] This invention provides a method, apparatus, device, and storage medium for determining fertilizer weight, in order to solve the problem in the prior art that the weight of the remaining fertilizer cannot be accurately determined when the fertilizer is spread by drone.

[0004] According to one aspect of the present invention, a method for determining fertilizer weight is provided, the method comprising:

[0005] Obtain the previous moment's wheel rotation speed and the previous moment's weight of the fertilizer loaded on the drone;

[0006] Collect the current weight of the fertilizer carried by the drone at the current moment;

[0007] The weight of the fertilizer at the previous moment, the rotational speed of the roller, and the current weight are input into the filter to output the actual weight of the fertilizer at the current moment.

[0008] According to another aspect of the present invention, a fertilizer weight determination device is provided, the device comprising:

[0009] The acquisition module is used to acquire the previous moment's roller rotation speed and the previous moment's weight of the loaded fertilizer;

[0010] The data acquisition module is used to collect the current weight of the fertilizer carried by the drone at the current moment.

[0011] The input / output module is used to input the weight of the previous moment, the rotational speed of the roller, and the current weight into the filter, and output the actual weight of the fertilizer at the current moment.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: at least one processor; and

[0013] A memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the fertilizer weight determination method according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the fertilizer weight determination method according to any embodiment of the present invention.

[0016] This invention discloses a method, apparatus, device, and storage medium for determining fertilizer weight. The method includes: acquiring the roller rotation speed and the weight of the fertilizer loaded on a drone at the previous moment; acquiring the current weight of the fertilizer loaded on the drone at the current moment; inputting the weight at the previous moment, the roller rotation speed, and the current weight into a filter, and outputting the actual weight of the fertilizer at the current moment. This method estimates the actual weight of the fertilizer at the current moment by combining the fertilizer weight data from the previous moment and the fertilizer weight acquired at the current moment. Furthermore, by inputting the roller rotation speed, it also considers the influence of the roller rotation speed on the change in fertilizer weight, thereby obtaining the actual weight of the fertilizer at the current moment.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a method for determining fertilizer weight according to Embodiment 1 of the present invention.

[0020] Figure 2 This is a flowchart illustrating a method for determining fertilizer weight according to Embodiment 2 of the present invention;

[0021] Figure 3 A flowchart illustrating a method for determining fertilizer weight provided in an embodiment of the present invention;

[0022] Figure 4 This is a filtering result of fertilizer weight provided in an embodiment of the present invention;

[0023] Figure 5 This is another filtering result for fertilizer weight provided in an embodiment of the present invention;

[0024] Figure 6 This is a schematic diagram of a fertilizer weight determination device provided in Embodiment 3 of the present invention;

[0025] Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention. It should be understood that the various steps described in the method embodiments of the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0027] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0030] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0031] Since the flight speed, flight angle, and type of fertilizer spread by drones vary, different roller speeds need to be set for different situations. Therefore, when determining the fertilizer weight, it is necessary to obtain an accurate relationship between the roller speed and the fertilizer spreading rate.

[0032] To address this issue, existing methods typically involve pre-calibrating the drone before takeoff, gradually increasing the roller speed to spray fertilizer or pesticides, and then reading data from the weighing sensor to derive the mapping relationship between the roller speed and the fertilizer application rate. However, during actual spraying, two factors can occur: firstly, the drone's flight angle may change; secondly, over time, fertilizer may become stuck in various parts of the roller, creating a "sticky" phenomenon. These two phenomena lead to varying fertilizer application rates at the same roller speed. Consequently, the weight prediction curve obtained from the pre-calibrated mapping relationship will differ significantly from the actual curve. Therefore, this application proposes a fertilizer weight determination method that can accurately obtain weight change data that reflects actual conditions.

[0033] Example 1

[0034] Figure 1 This is a flowchart illustrating a fertilizer weight determination method provided in Embodiment 1 of the present invention. This method is applicable to situations where the weight of fertilizer is determined when a drone is spreading fertilizer. The method can be executed by a fertilizer weight determination device, which can be implemented by software and / or hardware and is generally integrated into an electronic device. In this embodiment, the electronic device includes, but is not limited to, devices such as computers.

[0035] like Figure 1 As shown, the fertilizer weight determination method provided in Embodiment 1 of the present invention includes the following steps:

[0036] S110: Obtain the rotation speed of the drone's rollers and the weight of the fertilizer it was carrying at the previous moment.

[0037] The drone can be used for spreading fertilizer. The roller speed can be the rotational speed of the drone's rollers, which can be obtained by a sensor installed on the drone capable of detecting the rotational speed, or by the drone's controller; this embodiment does not limit this. The weight can be the weight of the remaining fertilizer on the drone, which can be obtained by a weighing sensor installed on the drone.

[0038] In this embodiment, the drone's roller rotation speed and fertilizer weight data from the previous moment can be obtained from the database.

[0039] S120: Collect the current weight of the fertilizer carried by the drone at the current moment.

[0040] The current weight can be the weight of the remaining fertilizer at the current moment.

[0041] In this embodiment, the current weight of the fertilizer carried by the drone can be obtained through a weighing sensor.

[0042] S130: Input the weight of the previous moment, the rotation speed of the roller, and the current weight into the filter, and output the actual weight of the fertilizer at the current moment.

[0043] The filter can be a Kalman filter, an extended Kalman filter, or other similar type; this embodiment does not limit this. The actual weight can be the actual remaining weight of the fertilizer.

[0044] In this embodiment, the weight of the fertilizer at the previous moment, the rotation speed of the roller, and the current weight of the fertilizer can be input into the filter to predict the actual weight of the fertilizer at the current moment.

[0045] This invention provides a method for determining fertilizer weight, comprising: acquiring the rotational speed of a drone's rollers and the weight of the fertilizer loaded on the drone at the previous moment; acquiring the current weight of the fertilizer loaded on the drone at the current moment; inputting the weight at the previous moment, the roller rotational speed, and the current weight into a filter, and outputting the actual weight of the fertilizer at the current moment. This method estimates the actual weight of the fertilizer at the current moment by combining the fertilizer weight data from the previous moment and the fertilizer weight acquired at the current moment. Furthermore, by inputting the roller rotational speed, it also considers the influence of the roller rotational speed on the change in fertilizer weight, thereby obtaining the actual weight of the fertilizer at the current moment.

[0046] Based on the above embodiments, modified embodiments of the above embodiments are proposed. It should be noted that, in order to keep the description brief, only the differences from the above embodiments are described in the modified embodiments.

[0047] In one embodiment, the filter is a Kalman filter, and before inputting the previous weight, the roller speed, and the current weight into the filter, the following additional steps are included:

[0048] Establish the relationship between fertilizer weight and roller speed and determine the proportionality coefficient;

[0049] The state equation of the Kalman filter is constructed based on the weight of the fertilizer at the previous moment, the rotational speed of the roller, the current weight at the current moment, and the scaling factor.

[0050] Among them, the Kalman filter is one of the most important and common estimation algorithms. The Kalman filter is an algorithm that uses the state equations of a linear system and the system's input and output observation data to make an optimal estimate of the system state. Since the observation data includes the influence of noise and interference in the system, the optimal estimation can also be regarded as a filtering process. The relationship between fertilizer weight and roller speed can be second-order, third-order, or even higher-order; depending on the relationship, the number of proportionality coefficients can also vary accordingly.

[0051] In this embodiment, the actual weight of the fertilizer can be estimated using a filter. Before estimating the actual weight, the relationship between the fertilizer weight and the roller rotation speed can be used as a proportionality coefficient. The state equation of the Kalman filter is constructed based on the fertilizer weight at the previous moment, the roller rotation speed, the current weight at the current moment, and the coefficient. The initial value of the proportionality coefficient can be set arbitrarily. In this embodiment, the proportionality coefficient can be used to simulate the situation where the fertilizer is stuck inside the roller.

[0052] In one embodiment, when the relationship is a second-order mapping relationship, the scaling factor includes a first scaling factor, a second scaling factor, and a third scaling factor.

[0053] In this embodiment, the relationship between fertilizer weight and roller rotation speed can be a second-order mapping relationship. Correspondingly, three proportionality coefficients can be set: a first proportionality coefficient, a second proportionality coefficient, and a third proportionality coefficient. This embodiment uses the three proportionality coefficients in the second-order mapping relationship as four-dimensional state variables, and sets the roller rotation speed as a control variable. For example, a quadratic polynomial can be used to construct the relationship between the roller rotation speed v and the current fertilizer weight Δm:

[0054]

[0055] m k =m k-1 -Δm k *Δt

[0056] Where, m k The current weight of the fertilizer is m. k-1 v represents the fertilizer weight at the previous moment. k-1 Let α be the roller speed at time k-1, set as a known quantity (control quantity) without added noise. 0 Δ 1 α 2 Δt is the proportionality coefficient used to simulate the situation where fertilizer is stuck inside the roller, and Δt is the sampling interval of the weighing sensor, which is dynamically changing.

[0057] In one embodiment, the state equation is:

[0058]

[0059]

[0060]

[0061]

[0062] Where k is the current time, k-1 is the previous time, m is the weight of the fertilizer, and α is the weight of the fertilizer. 0 α is the first proportionality coefficient. 1 α is the second proportionality coefficient. 2 Δt is the third proportionality coefficient, v is the roller speed, and Δt is the sampling interval of the weighing sensor.

[0063] In this embodiment, the state equation of the Kalman filter can be constructed using the fertilizer weight at the previous moment, the roller speed, the current weight at the current moment, and the coefficients. Besides constructing the state equation, the observation equation, state variables, state transition matrix, and observation matrix of the Kalman filter can also be set. The observation equation is: z k =H*x k +v k .

[0064] Based on the state equation, the state variables can be extracted as follows:

[0065] The transition matrix is:

[0066] Since only the weighing sensor collects the measurement data of fertilizer weight in practical applications, we set the observation matrix as: H=(1,0,0,0).

[0067] Example 2

[0068] Figure 2 This is a flowchart illustrating a method for determining fertilizer weight according to Embodiment 2 of the present invention. Embodiment 2 is an optimization based on the above embodiments. For details not covered in this embodiment, please refer to Embodiment 1.

[0069] like Figure 2 As shown in Embodiment 2 of the present invention, a method for determining fertilizer weight includes the following steps:

[0070] S210: Obtain the rotational speed of the drone's rollers and the weight of the fertilizer it was carrying at the previous moment.

[0071] S220: Collect the current weight of the fertilizer carried by the drone at the current moment.

[0072] S230. Based on the weight at the previous moment and the rotational speed of the roller, estimate the estimated weight of the fertilizer at the current moment using the state equation, and determine the covariance matrix corresponding to the estimated weight.

[0073] The estimated weight can be the fertilizer weight predicted by the Kalman filter, which can be determined by the fertilizer's weight at the previous moment and the roller's rotation speed. The covariance matrix can be a matrix used to measure the error.

[0074] In this embodiment, the weight and roller rotation speed of the previous moment can be input into the Kalman filter. The estimated weight of the fertilizer at the current moment is predicted through the state equation, and the covariance matrix corresponding to the estimated weight is determined. Before inputting the data into the Kalman filter, the parameters of the Kalman filter can also be initialized, and the state variable X (i.e., x) can be set. k The initial values ​​of the covariance matrix P, the prediction noise covariance matrix Q, the observation matrix H, and the observation noise covariance matrix R are:

[0075] For example, Figure 3 This is a flowchart illustrating a method for determining fertilizer weight according to an embodiment of the present invention. Figure 3 As shown, the roller rotation speed at time k-1 can be obtained, and the state equation (state transition model) from time k-1 to time k can be established. The fertilizer weight x at time k-1 is input. k-1 The predicted weight at time k is estimated using the equation of state.

[0076]

[0077] covariance matrix According to the state transition matrix A corresponding to the state equation k Predicted noise covariance matrix Q k and the covariance matrix P of the previous time step k-1 Sure:

[0078]

[0079] Among them, A k Let k be the state transition matrix of the state equation at time k. For A k The transpose of P k-1 This is the covariance matrix updated at time k-1 based on the Kalman gain.

[0080] S240. Determine the observed weight of the fertilizer at the current moment based on the current weight and the observation matrix.

[0081] The observed weight can be the weight of fertilizer obtained through observation.

[0082] In this embodiment, the current weight collected by the weighing sensor can be input into the observation matrix to calculate the observed weight.

[0083] S250. Determine the actual weight of the fertilizer at the current moment based on the estimated weight, the observed weight, and the covariance matrix.

[0084] In this embodiment, the actual weight of the fertilizer at the current moment can be determined based on the estimated weight, observed weight, and covariance matrix.

[0085] This invention provides a method for determining fertilizer weight, comprising: acquiring the rotational speed of a drone's rollers and the weight of the fertilizer it carries at the previous moment; acquiring the current weight of the fertilizer carried by the drone at the current moment; estimating the estimated weight of the fertilizer at the current moment using a state equation based on the weight at the previous moment and the roller rotational speed, and determining the covariance matrix corresponding to the estimated weight; determining the observed weight of the fertilizer at the current moment based on the current weight and the observation matrix; and determining the actual weight of the fertilizer at the current moment based on the estimated weight, the observed weight, and the covariance matrix. This embodiment estimates the actual weight of the fertilizer at the current moment by combining the fertilizer weight data from the previous moment and the fertilizer weight collected at the current moment, and by inputting the roller rotational speed, it also considers the influence of the roller rotational speed on the change in fertilizer weight, thereby obtaining the actual weight of the fertilizer at the current moment.

[0086] In one embodiment, determining the actual weight of the fertilizer at the current moment based on the estimated weight, the observed weight, and the covariance matrix—that is, determining the actual weight of the fertilizer at the current moment based on the estimated weight, the observed weight, the covariance matrix, and the observed noise covariance matrix—includes:

[0087] The Kalman gain is determined based on the covariance matrix, the observation noise covariance matrix, and the observation matrix corresponding to the observation weight.

[0088] The actual weight of the current fertilizer is determined based on the Kalman gain, the estimated weight, and the observation matrix.

[0089] The observation noise covariance matrix can be the error matrix corresponding to the observation weight.

[0090] In this embodiment, the Kalman gain can be determined using the covariance matrix, the observation noise covariance matrix, and the observation matrix. Therefore, the actual weight of the fertilizer can be determined based on the Kalman gain, the estimated weight, and the observation matrix. For example, the Kalman gain K... k The calculation formula is as follows:

[0091]

[0092] Among them, Rk To observe the noise covariance matrix, H k This is the observation matrix.

[0093] After obtaining the Kalman gain, the actual weight x of the fertilizer can be estimated by analyzing the predicted and observed weights. k :

[0094]

[0095] Among them, z k The equation is the observation equation.

[0096] By determining the state variables at time k-1, the estimated weight of the fertilizer at time k can be calculated. Then, based on the observed weight at time k, the estimated weight is further corrected using a Kalman filter model to obtain the actual weight x. k Then, iterative calculations are performed to obtain the actual weight of the fertilizer at each moment.

[0097] In one embodiment, after determining the Kalman gain, the following is included:

[0098] The covariance matrix at the current time is updated based on the Kalman gain and the observation matrix to obtain the updated covariance matrix.

[0099] In this embodiment, the Kalman gain K can be used to... k Observation matrix H k The covariance matrix at time k The covariance matrix P is updated to obtain the updated covariance matrix. K The covariance matrix P K This can be used for the next Kalman filter calculation; the updated formula is as follows:

[0100]

[0101] Based on the technical solutions of the above embodiments, this invention provides a specific implementation method.

[0102] As a specific implementation method of this embodiment, a simulation test was conducted, and the steps are as follows:

[0103] Step 1: Simulate ideal weight data;

[0104] Step 2: Add Gaussian noise to the ideal data to simulate the weight data measured by the load cell.

[0105] Step 3: The observed data is fed into a Kalman filter for filtering to obtain filtered data.

[0106] Figure 4 and Figure 5 These are filtering results for fertilizer weight provided in an embodiment of the present invention. Figure 4 The filtered result of fertilizer weight when the rotation speed is set to an arbitrary value. Figure 5 The simulation results show the filtering effect on fertilizer weight when the rotational speed is set to a constant value but can change abruptly. The simulation results demonstrate that, based on the quadratic polynomial and four-dimensional state variable equation proposed in this invention, the Kalman filter's filtering result closely approximates the ideal data, with a scaling factor α... 0 α 1 and α 2 It can also converge well from the initial value of 0 to a certain value.

[0107] This embodiment of the fertilizer weight determination method takes into account the actual conditions of the drone flying in the air and the "stickiness" phenomenon that occurs when the drone spreads fertilizer. It eliminates the pre-calibration step in the general method. At the same time, it uses a second-order polynomial instead of a simple linear relationship to fit the relationship between the roller speed and the fertilizer spreading rate, which is more in line with the actual situation of drone fertilizer spreading and can obtain a more accurate fertilizer weight.

[0108] Example 3

[0109] Figure 6 This is a schematic diagram of a fertilizer weight determination device provided in Embodiment 3 of the present invention. The device is applicable to the situation where the weight of fertilizer is determined when a drone is spreading fertilizer. The device can be implemented by software and / or hardware and is generally integrated into an electronic device.

[0110] like Figure 6 As shown, the device includes:

[0111] The acquisition module 310 is used to acquire the rotational speed of the drone's rollers and the weight of the fertilizer it was carrying at the previous moment.

[0112] The data acquisition module 320 is used to collect the current weight of the fertilizer carried by the drone at the current moment;

[0113] The input / output module 330 is used to input the weight of the previous moment, the rotation speed of the roller, and the current weight into the filter, and output the actual weight of the fertilizer at the current moment.

[0114] This embodiment provides a fertilizer weight determination device, comprising: an acquisition module for acquiring the roller rotation speed and the weight of the loaded fertilizer at the previous moment of a drone; a collection module for collecting the current weight of the fertilizer loaded by the drone at the current moment; and an input / output module for inputting the weight at the previous moment, the roller rotation speed, and the current weight into a filter and outputting the actual weight of the fertilizer at the current moment. By combining the fertilizer weight data from the previous moment and the fertilizer weight collected at the current moment, the actual weight of the fertilizer at the current moment is estimated. Furthermore, by inputting the roller rotation speed, the influence of the roller rotation speed on the fertilizer weight change can be taken into account, thereby obtaining the actual weight of the fertilizer at the current moment.

[0115] Furthermore, the filter is a Kalman filter, and before the input / output module 330, it also includes:

[0116] Establish the relationship between fertilizer weight and roller speed and determine the proportionality coefficient;

[0117] The state equation of the Kalman filter is constructed based on the weight of the fertilizer at the previous moment, the rotational speed of the roller, the current weight at the current moment, and the scaling factor.

[0118] Furthermore, when the relationship is a second-order mapping relationship, the scaling factor includes a first scaling factor, a second scaling factor, and a third scaling factor.

[0119] Furthermore, the state equation is:

[0120]

[0121]

[0122]

[0123]

[0124] Where k is the current time, k-1 is the previous time, m is the weight of the fertilizer, and α is the weight of the fertilizer. 0 α is the first proportionality coefficient. 1 α is the second proportionality coefficient. 2 Δt is the third proportionality coefficient, v is the roller speed, and Δt is the sampling interval of the weighing sensor.

[0125] Furthermore, the input / output module 330 includes:

[0126] Based on the weight at the previous moment and the rotational speed of the roller, the estimated weight of the fertilizer at the current moment is estimated using the state equation, and the covariance matrix corresponding to the estimated weight is determined.

[0127] The observed weight of the fertilizer at the current moment is determined based on the current weight and the observation matrix, and the observation noise covariance matrix corresponding to the observed weight is also determined.

[0128] The actual weight of the fertilizer at the current moment is determined based on the estimated weight, the observed weight, the covariance matrix, and the observed noise covariance matrix.

[0129] Furthermore, the step of determining the actual weight of the fertilizer at the current moment based on the estimated weight, the observed weight, and the covariance matrix, including the estimated weight, the observed weight, the covariance matrix, and the observation noise covariance matrix, comprises:

[0130] The Kalman gain is determined based on the covariance matrix, the observation noise covariance matrix, and the observation matrix corresponding to the observation weight.

[0131] The actual weight of the current fertilizer is determined based on the Kalman gain, the estimated weight, and the observation matrix.

[0132] Furthermore, after determining the Kalman gain, the following is included:

[0133] The covariance matrix at the current time is updated based on the Kalman gain and the observation matrix to obtain the updated covariance matrix.

[0134] The fertilizer weight determination device described above can execute the fertilizer weight determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0135] Example 4

[0136] Figure 7 A schematic diagram of an electronic device 10 that can be used to implement embodiments 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 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0137] like Figure 7As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0138] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0139] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the fertilizer weight determination method.

[0140] In some embodiments, the fertilizer weight determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the fertilizer weight determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the fertilizer weight determination method by any other suitable means (e.g., by means of firmware).

[0141] Various embodiments 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-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0142] Computer programs used to implement 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, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0143] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0144] To provide interaction with a user, the systems and techniques described herein can 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 pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0145] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0146] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0147] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0148] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining fertilizer weight, characterized in that, The method includes: Obtain the previous moment's wheel rotation speed and the previous moment's weight of the fertilizer loaded on the drone; Collect the current weight of the fertilizer carried by the drone at the current moment; The weight of the previous moment, the rotation speed of the roller, and the current weight are input into the filter to output the actual weight of the fertilizer at the current moment. The previous weight, the roller speed, and the current weight are input into a filter to output the actual weight of the fertilizer at the current moment, including: Based on the weight at the previous moment and the rotational speed of the roller, the estimated weight of the fertilizer at the current moment is estimated using the state equation, and the covariance matrix corresponding to the estimated weight is determined. The observed weight of the fertilizer at the current moment is determined based on the current weight and the observation matrix; The actual weight of the fertilizer at the current moment is determined based on the estimated weight, the observed weight, and the covariance matrix.

2. The method according to claim 1, characterized in that, The filter is a Kalman filter, and before inputting the previous weight, the roller speed, and the current weight into the filter, it also includes: Establish the relationship between fertilizer weight and roller speed and determine the proportionality coefficient; The state equation of the Kalman filter is constructed based on the weight of the fertilizer at the previous moment, the rotational speed of the roller, the current weight at the current moment, and the scaling factor.

3. The method according to claim 2, characterized in that, When the relationship is a second-order mapping relationship, the scaling factor includes a first scaling factor, a second scaling factor, and a third scaling factor.

4. The method according to claim 3, characterized in that, The state equation is: ； in, For the current moment, The previous moment before the current moment. This is the weight of the fertilizer. The first proportionality coefficient, This is the second proportionality coefficient. The third proportionality coefficient, The rotational speed of the roller. This represents the sampling interval of the weighing sensor.

5. The method according to claim 1, characterized in that, Determining the actual weight of the fertilizer at the current moment based on the estimated weight, the observed weight, and the covariance matrix includes: The Kalman gain is determined based on the covariance matrix, the observation noise covariance matrix, and the observation matrix corresponding to the observation weight. The actual weight of the fertilizer at the current moment is determined based on the Kalman gain, the estimated weight, and the observation matrix.

6. The method according to claim 5, characterized in that, After determining the Kalman gain, the following is included: The covariance matrix at the current time is updated based on the Kalman gain and the observation matrix to obtain the updated covariance matrix.

7. A fertilizer weight determination device, characterized in that, The device includes: The acquisition module is used to acquire the previous moment's roller rotation speed and the previous moment's weight of the loaded fertilizer; The data acquisition module is used to collect the current weight of the fertilizer carried by the drone at the current moment. The input / output module is used to input the weight of the previous moment, the rotation speed of the roller, and the current weight into the filter, and output the actual weight of the fertilizer at the current moment. Input / output modules, including: Based on the weight at the previous moment and the rotational speed of the roller, the estimated weight of the fertilizer at the current moment is estimated using the state equation, and the covariance matrix corresponding to the estimated weight is determined. The observed weight of the fertilizer at the current moment is determined based on the current weight and the observation matrix, and the observation noise covariance matrix corresponding to the observed weight is also determined. The actual weight of the fertilizer at the current moment is determined based on the estimated weight, the observed weight, the covariance matrix, and the observed noise covariance matrix.

8. An electronic device, characterized in that, The device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the fertilizer weight determination method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the fertilizer weight determination method according to any one of claims 1-6.

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