A method, system, device, medium and product for quantitative positioning control of fertilization

Abstract

The application discloses a fertilization quantitative positioning control method, system, device, medium and product, relates to the fertilization field, and the method comprises the following steps: calculating a target real-time prediction fertilization amount of a prescription operation of a fertilization machine; performing coupling simulation on a computational fluid dynamics and a discrete element method of a pneumatic auxiliary conveying process of granular fertilizer to obtain a fertilization amount prediction model; determining a current fertilization amount according to the fertilization amount prediction model; calculating a fertilization amount difference value based on the target real-time prediction fertilization amount and the current fertilization amount; and adopting a fuzzy control algorithm to perform adaptive control on the fertilization machine based on the fertilization amount difference value. The application can improve the accuracy of fertilization of the fertilization machine.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fertilization, in particular to a fertilization quantitative positioning control method, system, device, medium and product. BACKGROUND

[0002] At present, the precise fertilization technology mainly includes two forms of controlling the variable fertilization operation of the fertilization machinery based on prescription information and real-time sensing information of sensors. The variable operation system based on the prescription map needs to rely on the satellite positioning system to accurately position the current position of the operation implement and synchronously obtain the fertilization amount data of the corresponding position in the prescription map in the operation process. The variable operation system based on the vehicle-mounted sensor needs to convert the farmland information sensed by the sensor in real time into the fertilization amount information required by the current operation implement. Since there is a certain distance between the installation position of the sensor (satellite positioning system receiving antenna, vehicle-mounted crop growth sensor) and the position of the fertilization part, the variable fertilization operation equipment has a time lag between the fertilizer discharge operation amount and the current fertilization information obtained by the sensor, and the operation width of the agricultural equipment does not match the farmland sensing resolution, resulting in poor operation quality of the intelligent fertilization machine. At the same time, since there is a long material conveying distance between the fertilizer discharge device of the fertilization machine and the fertilizer dropping port, there is a time lag from fertilizer discharge to fertilizer landing, which causes the conventional fertilization machine to easily form the phenomenon of missing planting during the start-up and acceleration stage and the phenomenon of over-planting during the parking and deceleration stage. SUMMARY

[0003] The purpose of the present application is to provide a fertilization quantitative positioning control method, system, device, medium and product, which can improve the accuracy of the fertilization of the fertilization machine.

[0004] To achieve the above-mentioned purpose, the present application provides the following solutions:

[0005] In a first aspect, the present application provides a fertilization quantitative positioning control method, comprising:

[0006] calculating a target real-time predicted fertilization amount of a prescription operation of a fertilization machine;

[0007] performing coupling simulation of computational fluid dynamics and discrete element method on the pneumatic auxiliary conveying process of the granular fertilizer to obtain a fertilization amount prediction model;

[0008] determining a current fertilization amount according to the fertilization amount prediction model;

[0009] calculating a fertilization amount difference value based on the target real-time predicted fertilization amount and the current fertilization amount;

[0010] adopting a fuzzy control algorithm to adaptively control the fertilization machine based on the fertilization amount difference value.

[0011] In a second aspect, the application provides a fertilization quantitative positioning control system, comprising:

[0012] a target real-time prediction fertilization amount calculation module configured to calculate a target real-time prediction fertilization amount of a prescription operation of the fertilizer distributor;

[0013] a fertilization amount prediction model determination module configured to perform coupling simulation of computational fluid dynamics and discrete element method on a pneumatic auxiliary conveying process of the granular fertilizer to obtain a fertilization amount prediction model;

[0014] a current fertilization amount determination module configured to determine a current fertilization amount according to the fertilization amount prediction model;

[0015] a fertilization amount difference calculation module configured to calculate a fertilization amount difference based on the target real-time prediction fertilization amount and the current fertilization amount;

[0016] a fertilizer distributor control module configured to perform adaptive control on the fertilizer distributor by using a fuzzy control algorithm based on the fertilization amount difference.

[0017] In a third aspect, the application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the fertilization quantitative positioning control method described above.

[0018] In a fourth aspect, the application provides a computer readable storage medium having a computer program stored thereon, and the computer program is executable by a processor to implement the fertilization quantitative positioning control method described above.

[0019] In a fifth aspect, the application provides a computer program product comprising a computer program, and the computer program is executable by a processor to implement the fertilization quantitative positioning control method described above.

[0020] According to the specific embodiments provided by the application, the application has the following technical effects:

[0021] The application provides a fertilization quantitative positioning control method, system, device, medium and product, by calculating the difference between the target real-time prediction fertilization amount and the current fertilization amount of the prescription operation, the adaptive control on the fertilizer distributor is performed by using the fuzzy control algorithm, the fertilization amount is adjusted, and the accuracy of the fertilizer distributor is improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Figure 1 A flowchart of a fertilization quantitative positioning control method provided by an embodiment of the present application is shown in the figure.

[0024] Figure 2 A variable fertilization operation machine position lag model is shown in the figure.

[0025] Figure 3 The influence of the inlet air speed and the fertilizer discharge wheel rotation speed on the fertilizer discharge performance is shown in the figure. (a) is the influence of the speed and the rotation speed on the discharge amount, and (b) is the variation coefficient of the discharge amount under different speeds and rotation speeds.

[0026] Figure 4 A fertilization machine travel path is shown in the figure.

[0027] Figure 5 A structural diagram of a computer device provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0029] The purpose of the present application is to provide a fertilization quantitative positioning control method, system, device, medium and product to solve the problem of inaccurate fertilization of the current fertilization machine.

[0030] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0031] In an exemplary embodiment, as shown in Figure 1 A fertilization quantitative positioning control method is provided, which is executed by a computer device, specifically, can be executed by a terminal or a server, or can be executed by a terminal and a server together. In the embodiments of the present application, the method is applied to a server as an example, which includes the following steps S1 to S5. Wherein:

[0032] S1: calculating the target real-time predicted fertilization amount of the prescription operation of the fertilization machine.

[0033] In a specific embodiment, step S1 includes steps S11 to S14:

[0034] S11: establishing a variable fertilization operation machine position lag model.

[0035] Obtain the environmental information of the current fertilization operation, which includes: field coordinates, prescription information, position information of the sensor (satellite positioning system receiving antenna, vehicle-mounted crop growth sensor) relative to the fertilizer pipe mounting bracket, machine operation width information, machine operation real-time positioning, speed information, center position information of the fertilizer pipe mounting bracket, etc.

[0036] According to the environmental information, a variable fertilization operation machine position lag model is established, as shown in the formula (1). Figure 2 Figure 2 In the formula (1), L sj is the distance from the sensor (satellite positioning system receiving antenna, vehicle-mounted crop growth sensor) to the fertilizer pipe mounting bracket, L wf is the machine operation width, the prescription map resolution is a x a, the field prescription starting point coordinates are (x0, y0), and when the satellite positioning coordinates are (X, Y), the prescription map interval (i, j) to which the center position of the fertilizer pipe mounting bracket belongs can be obtained by the following formula:

[0037]

[0038] In the formula, j is the grid number of the longitudinal distance starting position in the prescription map interval block, and i is the grid number of the transverse distance starting position in the prescription map interval block.

[0039] Meanwhile, the interval block numbers m and n of the left and right endpoints of the fertilizer pipe mounting bracket along the x-axis direction from the center can be obtained by the following formula:

[0040]

[0041] In the formula, the intermediate variables b1 = X - [(i-1)a+x0], and b2 = (ia+x0)-X.

[0042] S12: Determine the target fertilization amount based on the variable fertilization operation machine position lag model.

[0043] The target fertilization amount can be obtained by the following formula:

[0044]

[0045] In the formula, A kj is the fertilization amount of the (k, j)th prescription map interval block, kg / hm 2 .

[0046] S13: Calculate the target real-time fertilization amount based on the target fertilization amount.

[0047] The calculation formula of the target real-time fertilization amount Q p (t) is:

[0048] ​

[0049] wherein Q p (t) is the target real-time fertilization amount at time t, v is the variable fertilization working machine forward speed (m / s).

[0050] S14: Based on the target real-time fertilization amount, a speed change prediction model is used to determine the target real-time predicted fertilization amount of the prescription operation. Specifically, the speed change prediction model is used to predict the speed of the fertilizer machine at different stages; the position of the fertilizer machine is predicted based on the speed; and the target real-time predicted fertilization amount at the corresponding position is determined based on the target real-time fertilization amount.

[0051] V c is the set forward speed during normal operation, V z is the set turning speed during turning, L0 is the distance required to accelerate from zero speed to the specified speed during the starting stage, L z is the distance required to accelerate from the specified speed to the turning during the turning, h is the distance required to accelerate from the turning speed to the specified operation speed after turning, e is the distance required to accelerate from the specified speed to the stop during the stopping operation,

[0052] Figure 4 is a schematic diagram of the fertilizer machine travel path planning, the starting stage accelerates uniformly from zero speed to the specified speed, and the real-time speed and displacement relationship can be obtained according to the acceleration formula wherein v is the real-time predicted speed, L1 is the real-time forward distance of the fertilizer machine obtained according to the satellite positioning system, 0≤L1≤L0, 0≤v≤V c ; the speed during normal operation is v=v c ; the speed during turning is uniformly decelerated, the initial speed is v c , the acceleration is a2, wherein L3 is the real-time forward distance of the fertilizer machine obtained according to the satellite positioning system, 0≤L3≤L z , V z ≤V≤V c ; the speed from the turning speed to the specified operation speed after turning is uniformly accelerated, the acceleration is a3, wherein L4 is the real-time forward distance of the fertilizer machine obtained according to the satellite positioning system, 0≤L4≤L h , V z ≤v≤V c ; the speed from the specified speed during the stopping operation is uniformly accelerated, the acceleration is a4, wherein L7 is the real-time forward distance of the fertilizer machine obtained according to the satellite positioning system, 0≤L7≤L e , 0≤v≤V c .

[0053] Based on field boundary coordinate information and Figure 4 The fertilizer applicator's path planning information shown allows us to determine its current stage and speed, and uses a speed change prediction model to determine the target fertilizer application amount Q in real time. s (t).

[0054]

[0055] Figure 4 L2, L5, and L6 represent the real-time forward distance of the fertilizer applicator during its uniform-speed operation segment.

[0056] The time from when the fertilizer is discharged from the fertilizer dispenser of the fertilizer applicator to when it hits the ground.

[0057] average speed

[0058] The current location L of the fertilizer applicator can be determined based on satellite positioning information and vehicle speed sensor data. d and speed v d The current prescription diagram shows the horizontal application rate of seeds and fertilizers. Simultaneously, based on the speed prediction model, the speed of the fertilizer applicator at t can be obtained. d Average speed over time And the distance L forward b The number of prescription tiles covered in the forward direction is... If the floor sign is used, then at k i (k0≤k i ≤k f ) target real-time fertilizer application rate According to Q in step S13 p The formula (t) yields the following result: Then, the target is to predict the amount of fertilizer to be applied in real time.

[0059] S2: A coupled simulation of computational fluid dynamics and discrete element method was performed on the pneumatic-assisted conveying process of granular fertilizer to obtain a fertilizer application rate prediction model.

[0060] The fertilizer discharge rate q of the fertilizer applicator in this application r and its velocity V at the discharge outlet outThe parameters were simulated using commercial ANSYS Fluent and DEDM software, employing a coupled computational fluid dynamics and discrete element method (DEM) simulation of the pneumatic-assisted transport process of granular fertilizer. Specifically, since the volume fraction of granular fertilizer in the airflow field of the pneumatic-assisted transport device is less than 10%, the transport process is defined as dilute phase transport. Therefore, the DEM-CFD coupled simulation in this application uses the Eulerian-Lagrangian method to couple the gas-solid two-phase flow during transport. The drag model for the airflow and particles adopts the Freestream Equation mode, and the lift model selects the Saffman Lift mode (lift generated by the velocity gradient) and the Magnus Lift mode (lift generated by particle rotation). The turbulent motion of the airflow in the pneumatic conveying device was calculated using the standard k-ε model in ANSYS Fluent software. The Hertz-Mindlin no-slip contact model was used in the EDEM simulation. A soft sphere model was selected for the granular fertilizer, which was dynamically produced by a granulation factory at a rate of 1000 granules / s. In the coupled simulation, the fertilizer discharge wheel was made of aluminum alloy, the model shell was made of 45# steel, conventional rice and wheat seeds were used, granular compound fertilizer or urea was selected as the granular fertilizer, and the density of the gas phase was 1.225 kg / m³. 3 The viscosity coefficient of the gas phase is 1.7894 × 10⁻⁶. -5 kg / (m·s). During the simulation, the time steps for EDEM and Fluent were set to 1×10⁻⁶. -5 s and 5×10 -4 To ensure that all fertilizer granules are conveyed within the simulation time, the total simulation duration is set to 2.0 seconds. After the simulation, the inlet wind speed, fertilizer discharge wheel speed, and granule discharge rate q at the outlet of the pneumatically assisted conveying device are obtained through EDME's post-processing function. r Fertilizer application rate prediction model.

[0061] To construct a predictive model for fertilizer discharge from a pneumatically assisted conveying precision fertilizer discharge device, Matlab software was used to perform quadratic multiple regression fitting on the inlet wind speed, fertilizer discharge wheel speed, and discharge rate. Figure 3 As shown. The regression equation for the displacement rate of particulate matter is as follows, with a coefficient of determination of R. 2 =0.9868.

[0062] q r =-101.621+0.198x1+19.529x2+0.063x1x2+0.017x1 2 -0.828x2 2

[0063] In the formula: q rThe fertilizer particle discharge rate (g / s) is x1, the discharge wheel rotation speed (r / min) is x2, and the inlet air speed (m / s) is x2.

[0064] It is assumed that the airflow movement in the pneumatic auxiliary conveying device is turbulent movement, and the movement of the granular fertilizer in the airflow field follows Newton's second law of motion. The Euler-Lagrange method assumes that the airflow is a continuum and follows Newton's law of motion, and analyzes the airflow movement characteristics by solving the gas continuity equation and Navier-Stokes equation. Since the airflow movement follows the law of conservation of mass and the law of conservation of momentum, it corresponds to the continuity equation and the momentum equation in computational fluid dynamics, respectively.

[0065] The continuity equation of the gas phase is:

[0066]

[0067] In the formula, α g is the volume fraction of the gas phase, ρ g is the density of the gas phase, is the velocity vector of the gas phase.

[0068] The momentum equation of the gas phase is:

[0069]

[0070] In the formula, P is the pressure of the gas phase, is the gravitational acceleration vector, is the velocity vector of the solid phase, K sg is the momentum exchange coefficient between the gas-solid two-phase flow, τ g is the stress tensor of the gas phase.

[0071] S3: determining the current fertilizer application rate according to the fertilizer application rate prediction model.

[0072] Real-time monitoring of the discharge wheel rotation speed, auxiliary conveying air speed and other information, and obtaining the current fertilizer application rate Q c of the fertilizer applicator through the fertilizer application rate prediction model. r

[0073] S4: calculating the fertilizer application rate difference based on the target real-time predicted fertilizer application rate and the current fertilizer application rate.

[0074] S5: based on the fertilizer application rate difference, using a fuzzy control algorithm to adaptively control the fertilizer applicator.

[0075] Comparing Q s and Q c to obtain the difference ΔQ, and using a fuzzy control algorithm to adaptively control the fertilizer applicator according to ΔQ.

[0076] ​Based on the same inventive concept, the embodiments of the present application also provide a system for implementing the fertilization quantitative positioning control method described above. The implementation scheme of the problem solving provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more fertilization quantitative positioning control system embodiments provided below can refer to the limitations of the fertilization quantitative positioning control method described above, and will not be repeated here.

[0077] In an exemplary embodiment, a fertilization quantitative positioning control system is provided, comprising:

[0078] A target real-time predicted fertilization amount calculation module is configured to calculate a target real-time predicted fertilization amount of a prescription operation of a fertilizer applicator.

[0079] A fertilization amount prediction model determination module is configured to perform coupling simulation of computational fluid dynamics and discrete element method on a pneumatic auxiliary conveying process of granular fertilizer to obtain a fertilization amount prediction model.

[0080] A current fertilization amount determination module is configured to determine a current fertilization amount according to the fertilization amount prediction model.

[0081] A fertilization amount difference calculation module is configured to calculate a fertilization amount difference based on the target real-time predicted fertilization amount and the current fertilization amount.

[0082] A fertilizer applicator control module is configured to perform adaptive control on the fertilizer applicator based on the fertilization amount difference using a fuzzy control algorithm.

[0083] In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, and the memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program. The computer device can be a server or a terminal, and its internal structure diagram can be as shown in Figure 5 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is used to store data to be processed. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with the terminal outside through the network connection. The computer program is executed by the processor to implement a fertilization quantitative positioning control method.

[0084] Those skilled in the art can understand that Figure 5 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above method embodiments.

[0085] In an exemplary embodiment, a computer readable storage medium is provided, storing a computer program, which is executed by a processor to implement the steps in the above method embodiments.

[0086] In an exemplary embodiment, a computer program product is provided, including a computer program, which is executed by a processor to implement the steps in the above method embodiments.

[0087] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0088] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

[0089] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0090] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0091] The principles and implementation modes of the present application are described by applying specific examples in this paper, and the above-mentioned examples are only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method of controlling the amount of fertilizer to be applied at a specific location, characterized by, The method comprises the following steps: calculating a target real-time predicted fertilizer application amount of a prescription operation of a fertilizer applicator; performing coupling simulation of computational fluid dynamics and discrete element method on a pneumatic auxiliary conveying process of granular fertilizer to obtain a fertilizer application amount prediction model; determining a current fertilizer application amount according to the fertilizer application amount prediction model; calculating a fertilizer application amount difference value based on the target real-time predicted fertilizer application amount and the current fertilizer application amount; adopting a fuzzy control algorithm to perform adaptive control on the fertilizer applicator based on the fertilizer application amount difference value; wherein, the target real-time predicted fertilizer application amount of the prescription operation of the fertilizer applicator is calculated in the following steps: establishing a variable fertilizer application operation machine position lag model; determining a target fertilizer application amount based on the variable fertilizer application operation machine position lag model; calculating a target real-time fertilizer application amount based on the target fertilizer application amount; the calculation formula of the target fertilizer application amount is: wherein, is the target application rate, is the application rate for the interval block of the prescription map, is the number of interval blocks of the left and right end points of the fertilizer tube installation support from the center along the x-axis direction, is the distance from the sensor to the fertilizer tube installation support, is the width of the machine operation, is the resolution of the prescription map, , is an intermediate variable, is the horizontal coordinate of the starting point of the field block prescription, is the number of grids of the starting position of the horizontal distance in the interval block of the prescription map, is the horizontal coordinate of the satellite positioning; adopting a speed change prediction model to determine the target real-time predicted fertilizer application amount of the prescription operation based on the target real-time fertilizer application amount.

2. The method of claim 1, wherein The variable fertilizer application operation machine position lag model is established in the following steps: obtaining environmental information of a current fertilizer application operation; the environmental information comprises field coordinates, prescription information, position information of a sensor relative to a fertilizer pipe installation support, machine operation width information, machine operation real-time positioning information, speed information, and center position information of the fertilizer pipe installation support; establishing a variable fertilizer application operation machine position lag model according to the environmental information.

3. The method of claim 1, wherein The calculation formula of the target real-time fertilizer application amount is: wherein, is the target real-time fertilizer application amount at time t, is the target fertilizer application amount, is the implement working width, is the variable rate fertilizer application implement forward speed.

4. The method of claim 1, wherein adopting a speed change prediction model to determine the target real-time predicted fertilizer application amount of the prescription operation based on the target real-time fertilizer application amount, which comprises the following steps: adopting a speed change prediction model to predict the speed of the fertilizer applicator at different stages; determining the position of the fertilizer applicator based on the speed; determining the target real-time predicted fertilizer application amount at the corresponding position based on the target real-time fertilizer application amount.

5. A fertilization dosing and positioning control system characterized by, The method comprises the following steps: a target real-time predicted fertilizer application amount calculation module is configured to calculate a target real-time predicted fertilizer application amount of a prescription operation of a fertilizer applicator; a fertilizer application amount prediction model determination module is configured to perform coupling simulation of computational fluid dynamics and discrete element method on a pneumatic auxiliary conveying process of granular fertilizer to obtain a fertilizer application amount prediction model; a current fertilizer application amount determination module is configured to determine a current fertilizer application amount according to the fertilizer application amount prediction model; a fertilizer application amount difference value calculation module is configured to calculate a fertilizer application amount difference value based on the target real-time predicted fertilizer application amount and the current fertilizer application amount; a fertilizer applicator control module is configured to adopt a fuzzy control algorithm to perform adaptive control on the fertilizer applicator based on the fertilizer application amount difference value; wherein, the target real-time predicted fertilizer application amount of the prescription operation of the fertilizer applicator is calculated in the following steps: establishing a variable fertilizer application operation machine position lag model; determining a target fertilizer application amount based on the variable fertilizer application operation machine position lag model; calculating a target real-time fertilizer application amount based on the target fertilizer application amount; the calculation formula of the target fertilizer application amount is: wherein, is the target application rate, is the application rate for the th prescription map interval block, is the number of interval blocks for the left and right end points of the fertilizer tube installation support from the center in the x-axis direction, is the distance from the sensor to the fertilizer tube installation support, is the machine working width, is the prescription map resolution, , is an intermediate variable, is the field prescription starting point horizontal coordinate, is the number of grids for the horizontal distance starting point position in the prescription map interval block, is the satellite positioning horizontal coordinate; adopting a speed change prediction model to determine the target real-time predicted fertilizer application amount of the prescription operation based on the target real-time fertilizer application amount.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the fertilizer application quantitative positioning control method in any one of claims 1-4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the fertilization quantitative positioning control method in any one of claims 1-4.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the fertilization quantitative positioning control method in any one of claims 1-4.

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