Mechanical arm spraying parameter regulation and control system for artificial rainfall simulation

Abstract

The invention discloses a mechanical arm spraying parameter regulation and control system for artificial rainfall simulation, particularly relates to the technical field of agricultural irrigation control, and is used for solving the technical problems of water pressure self-adaption lagging and spraying parameter instability during alternate irrigation mode switching caused by pipeline extension and retraction of existing mechanical arm irrigation equipment. According to the system, by detecting the length variation of a telescopic water pipe of the mechanical arm and the real-time water pressure value of a water pipe inlet, the flow entropy yield space gradient and the turbulent flow energy dissipation rate are calculated to generate the water pressure compensation amount; regulating and controlling the water pump pressure based on the water pressure compensation amount, and synchronously analyzing a target spraying parameter combination of the alternate irrigation instruction; pressure compensation and parameter adjustment are executed in a linkage mode in the moving process of the mechanical arm, and kinetic energy distribution is collected through a water drop impact sensor array; when the kinetic energy distribution uniformity does not reach the standard, the entropy production and turbulence calculation logic is reversely corrected; precise and stable control over spraying parameters under dynamic operation is achieved, and the multi-area and multi-mode switching requirement is met.

Description

Technical Field

[0001] This invention relates to the field of agricultural irrigation control technology, and more specifically, to a robotic arm spraying parameter control system for simulating artificial rainfall. Background Technology

[0002] In agricultural research and field trials, artificial rainfall simulation is a core means of studying crop response and optimizing irrigation strategies. Current mainstream technologies use fixed sprinkler systems or large mobile rainfall simulation devices to achieve regional coverage through preset sprinklers. In static scenarios, they can maintain basic spray uniformity. However, when faced with alternating irrigation needs in multiple regions and modes (such as adjacent experimental plots needing to simulate heavy rain and cloudy / rainy weather respectively), manual disassembly and reassembly or the addition of independent equipment is required, resulting in low deployment efficiency and difficulty in meeting the experimental requirements of modern precision agriculture for randomness and flexibility.

[0003] While existing robotic arm irrigation equipment attempts to improve mobility by incorporating extendable structures, the sudden changes in fluid resistance caused by variations in pipe length during dynamic operations result in significant water pressure adaptive lag. Specifically, when the robotic arm moves and extends / retracts in the field, the water pressure within the pipe cannot respond in real time to changes in length, causing instability in spray flow and rainfall intensity parameters. This defect is further amplified in alternating irrigation scenarios (such as when adjacent areas need to quickly switch between heavy rain and cloudy / rainy weather modes), leading to deviations between simulated rainfall parameters and preset values, thus compromising the controllability and data validity of the experiment. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a robotic arm spraying parameter control system for artificial rainfall simulation to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A robotic arm spraying parameter control system for simulating artificial rainfall includes:

[0007] The parameter detection module is used to detect the change in length of the extendable water pipe of the robotic arm and obtain the real-time water pressure value at the pipe inlet.

[0008] The entropy loss calculation module is used to calculate the spatial gradient of flow entropy yield and turbulent kinetic energy dissipation rate based on the length change and real-time water pressure value.

[0009] The compensation generation module is used to generate the water pressure compensation required to maintain the target spray flow rate based on the spatial gradient of flow entropy yield and turbulent kinetic energy dissipation rate.

[0010] The pressure control and analysis module is used to control the output pressure of the adjustable pressure water pump based on the water pressure compensation amount, receive alternating irrigation mode instructions, and analyze the spraying parameter combination required for the target area.

[0011] The linkage acquisition module is used to synchronously adjust the combination of water pressure compensation and spraying parameters during the movement of the robotic arm, and to collect the distribution of water droplet impact kinetic energy through a water droplet impact sensor array.

[0012] The feedback correction module is used to correct the calculation logic of the flow entropy yield spatial gradient and turbulent kinetic energy dissipation rate when the uniformity coefficient of the water droplet impact kinetic energy distribution does not reach the set standard value.

[0013] Furthermore, the measurement of the change in length of the extendable water pipe by the robotic arm and the acquisition of the real-time water pressure value at the pipe inlet include:

[0014] The change in the rotation angle of the robotic arm joint is detected in real time by photoelectric encoder. Based on the linear mapping relationship between the change in the joint rotation angle and the change in the length of the extendable water pipe, it is converted into the change in the length of the extendable water pipe.

[0015] The water pressure fluctuation signal at the water pipe inlet is collected in real time by a piezoresistive pressure sensor. The water pressure fluctuation signal is then processed by Kalman filtering to reduce noise, and the real-time water pressure value at the water pipe inlet is output.

[0016] Furthermore, based on the length change and real-time water pressure value, the spatial gradient of flow entropy yield and turbulent kinetic energy dissipation rate are calculated, including:

[0017] The average velocity change of the fluid in the pipe is calculated based on the change in the length of the expandable pipe. Combined with the real-time water pressure at the pipe inlet, the spatial gradient of the flow entropy yield is solved by the viscous dissipation term of the Navier-Stokes equations.

[0018] Based on the average flow velocity change and the real-time water pressure at the pipe inlet, the turbulent pulsation intensity is calculated using the turbulent kinetic energy transport term in the Reynolds time-averaged equation. The turbulent kinetic energy dissipation rate is then generated based on the product relationship between the turbulent pulsation intensity and the fluid kinematic viscosity.

[0019] Furthermore, based on the spatial gradient of flow entropy yield and turbulent kinetic energy dissipation rate, the water pressure compensation required to maintain the target spray flow rate is generated, including:

[0020] The mechanical energy loss of fluid in a stretchable water pipe is calculated based on the spatial gradient of flow entropy yield, and the pressure oscillation attenuation caused by turbulence is quantified by combining the turbulent kinetic energy dissipation rate.

[0021] The total energy compensation value required to offset the sudden change in pipeline resistance is determined based on the sum of mechanical energy loss and pressure oscillation attenuation.

[0022] The total energy compensation value is converted into the equivalent water pressure increment to maintain the target spray flow rate using Bernoulli's equation, and this increment is used as the water pressure compensation amount.

[0023] Furthermore, based on the water pressure compensation amount, the output pressure of the adjustable pressure water pump is controlled, and alternating irrigation mode instructions are received and the required spraying parameter combination for the target area is analyzed, including:

[0024] The water pressure compensation is superimposed on the reference output pressure set value of the adjustable pressure water pump through the proportional-integral control algorithm, generating a dynamic pressure adjustment command and sending it to the motor driver of the adjustable pressure water pump.

[0025] The regional code identifier in the alternating irrigation mode instruction is parsed, matched with the preset irrigation mode database, and the rainfall intensity parameters and raindrop size distribution parameters corresponding to the target area are extracted.

[0026] Combine rainfall intensity parameters and raindrop size distribution parameters to form the spraying parameter combination required for the target area.

[0027] Furthermore, the water pressure compensation is superimposed onto the reference output pressure setpoint of the adjustable pressure pump using a proportional-integral control algorithm, including:

[0028] Read the reference output pressure setpoint of the adjustable pressure water pump and use the water pressure compensation amount as the process variable input of the proportional-integral control algorithm;

[0029] The proportional term of the proportional-integral control algorithm responds in real time to changes in the amplitude of water pressure compensation, while the integral term accumulates the time integral error of the water pressure compensation, outputting a dynamic pressure adjustment command.

[0030] Furthermore, during the movement of the robotic arm, the linkage adjustment of the water pressure compensation amount and the spraying parameter combination is performed simultaneously, and the water droplet impact kinetic energy distribution is collected through a water droplet impact sensor array, including:

[0031] The water pressure compensation amount is synchronously transmitted to the pressure controller of the adjustable pressure water pump via industrial fieldbus, and the rainfall intensity parameter in the spraying parameter combination is converted into a pulse width modulation duty cycle signal and sent to the solenoid valve driver.

[0032] During the spraying process at the end of the robotic arm, the voltage pulse signal of the raindrop impacting the plane is collected in real time by a piezoelectric sensor array. Based on the direct proportionality between the voltage pulse signal amplitude and the raindrop mass, and the inverse proportionality between the pulse rise time and the raindrop impact velocity, the impact kinetic energy of the water droplets at each spatial sampling point is calculated.

[0033] The impact kinetic energy of water droplets at each spatial sampling point is mapped according to the spatial coordinates of the robotic arm's movement trajectory to generate the water droplet impact kinetic energy distribution.

[0034] Furthermore, the impact kinetic energy of water droplets at each spatial sampling point is mapped according to the spatial coordinates of the robotic arm's movement trajectory, including:

[0035] By using a robotic arm joint encoder to record the three-dimensional spatial coordinate sequence of the end nozzle in real time, a time-synchronized correspondence between the physical position of the water droplet impact sensor array and the spatial coordinate sequence is established.

[0036] Based on the time synchronization correspondence, the water droplet impact kinetic energy values ​​collected by each water droplet impact sensor are marked to the corresponding three-dimensional spatial coordinate points, generating a spatial discrete point set of water droplet impact kinetic energy distribution.

[0037] Furthermore, when the uniformity coefficient of the water droplet impact kinetic energy distribution does not reach the set standard value, the calculation logic for the flow entropy yield spatial gradient and turbulent kinetic energy dissipation rate is corrected in reverse, including:

[0038] The Christensen uniformity coefficient is calculated based on the spatial discrete point set of the water droplet impact kinetic energy distribution, and the difference between the Christensen uniformity coefficient and the set standard value is compared.

[0039] If the difference exceeds the allowable threshold, extract the robotic arm motion parameters and water pressure fluctuation data corresponding to the spatial region with the largest coefficient of variation in the water droplet impact kinetic energy distribution.

[0040] Based on the abrupt acceleration characteristics in the motion parameters of the robotic arm and the high-frequency oscillation components in the water pressure fluctuation data, the turbulent viscosity calibration coefficient of the viscous dissipation term in the calculation of the spatial gradient of flow entropy yield is corrected, and the turbulent model constants of the Reynolds time-averaged equation in the calculation of turbulent kinetic energy dissipation rate are adjusted.

[0041] Furthermore, the Christensen uniformity coefficient is calculated based on the spatial discrete point set of the water droplet impact kinetic energy distribution, including:

[0042] Extract the water droplet impact kinetic energy values ​​from all sampling points in the spatial discrete point set of water droplet impact kinetic energy distribution, and calculate the average value of the water droplet impact kinetic energy values;

[0043] The absolute deviations of the water droplet impact kinetic energy values ​​from the average value at all sampling points are summed, and the Christensen uniformity coefficient is generated by combining the product of the total number of sampling points and the average value.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. By establishing an adaptive coordination between the dynamic operation of the robotic arm and fluid parameters, precise and stable control of spraying parameters in multi-regional alternating irrigation scenarios is achieved. Unlike the static control limitations of existing fixed sprinkler irrigation systems, this system is based on the coupled analysis of the change in the length of the expandable water pipe and the real-time water pressure value. It quantifies the sudden change law of pipe resistance through the spatial gradient of flow entropy productivity and turbulent kinetic energy dissipation rate, and generates water pressure compensation to offset the pressure fluctuations caused by the movement of the robotic arm in real time. This solves the problem of water pressure lag caused by pipe expansion and contraction in traditional equipment, ensuring that the spraying flow rate and the target rainfall intensity parameters remain synchronous and stable during the continuous movement of the robotic arm, thereby improving the accuracy of rainfall simulation in dynamic scenarios.

[0046] 2. By using spatial mapping technology for the kinetic energy distribution of water droplet impacts, the movement trajectory of the robotic arm is correlated with the raindrop kinetic energy collection points in real time. The spraying quality is dynamically evaluated based on the Christensen uniformity coefficient. When the uniformity does not meet the standard, the entropy production and turbulence calculation logic are corrected in reverse. The turbulence model parameters are adaptively adjusted using the abrupt acceleration characteristics of the robotic arm and the high-frequency oscillation components of water pressure. This significantly improves the robustness of the system under complex motion conditions. A single device can meet the switching needs of heavy rain / rainy weather in adjacent areas, reducing the deployment cost of multiple devices and providing a highly flexible and reliable dynamic irrigation solution for precision agriculture experiments. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the structure of a robotic arm spraying parameter control system for simulating artificial rainfall according to the present invention. Detailed Implementation

[0048] 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 only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0049] Example: Figure 1 A schematic diagram of a robotic arm spraying parameter control system for simulating artificial rainfall is provided. This system includes the following modules:

[0050] The parameter detection module is used to detect the change in length of the extendable water pipe of the robotic arm and obtain the real-time water pressure value at the pipe inlet.

[0051] The entropy loss calculation module is used to calculate the spatial gradient of flow entropy yield and turbulent kinetic energy dissipation rate based on the length change and real-time water pressure value.

[0052] The compensation generation module is used to generate the water pressure compensation required to maintain the target spray flow rate based on the spatial gradient of flow entropy yield and turbulent kinetic energy dissipation rate.

[0053] The pressure control and analysis module is used to control the output pressure of the adjustable pressure water pump based on the water pressure compensation amount, receive alternating irrigation mode instructions, and analyze the spraying parameter combination required for the target area.

[0054] The linkage acquisition module is used to synchronously adjust the combination of water pressure compensation and spraying parameters during the movement of the robotic arm, and to collect the distribution of water droplet impact kinetic energy through a water droplet impact sensor array.

[0055] The feedback correction module is used to reverse the calculation logic of the flow entropy yield spatial gradient and turbulent kinetic energy dissipation rate when the uniformity coefficient of the water droplet impact kinetic energy distribution does not reach the set standard value.

[0056] The system detects the change in length of the extendable water pipe of the robotic arm and obtains the real-time water pressure value at the pipe inlet, including the following continuously executed operations:

[0057] A photoelectric encoder installed at the center of the robotic arm's rotary joint axis detects the change in joint rotation angle in real time. The photoelectric encoder collects raw rotation angle data every 10 milliseconds. The raw rotation angle data is the increment of the angular displacement of the joint axis relative to the initial zero coordinate. The raw rotation angle data is input into an angle-to-length converter. The angle-to-length converter has a built-in linear mapping relationship between the change in joint rotation angle and the change in the length of the extendable water pipe. This linear mapping relationship is determined by the robotic arm's structural parameters, specifically the product of the radius of the roller around the joint axis and the number of turns of the extendable water pipe. For example, when the roller radius is 50 mm, each degree of joint rotation corresponds to a water pipe extension of 0.873 mm. The angle-to-length converter calculates the change in joint rotation angle based on the difference between the currently collected raw rotation angle data and the raw rotation angle data of the previous cycle, and then multiplies it by the linear mapping coefficient of 0.873 mm / degree to output the value of the change in the length of the extendable water pipe.

[0058] Water pressure fluctuation signals are acquired in real time using a piezoresistive pressure sensor welded to the flange connection at the water pipe inlet. The piezoresistive pressure sensor has a range covering 0 to 1 MPa, and the sampling frequency is set to 1000 Hz. The water pressure fluctuation signal contains high-frequency noise components caused by fluid pulsation. The water pressure fluctuation signal is input to a Kalman filter noise reduction processor. The Kalman filter noise reduction processor sets the state variables to the true water pressure value and its first derivative. The process noise covariance matrix is ​​set to 0.01 times the square of the range based on the damping characteristics of the water pipe material, and the observation noise covariance is set to 0.05% of the full-scale square based on the sensor accuracy. The Kalman filter noise reduction processor uses a prediction-correction iterative process. In the prediction stage, the water pressure state at the next moment is calculated based on the fluid continuity equation. In the correction stage, the measured value and the predicted value of the water pressure fluctuation signal are weighted and fused. The weighting weight is dynamically adjusted by the noise covariance. Finally, the real-time water pressure value at the water pipe inlet is output. The output frequency of the real-time water pressure value is 1000 Hz, and the fluctuation amplitude is controlled within ±0.1% of the range.

[0059] The process of establishing the linear mapping relationship includes: during the robotic arm assembly stage, measuring the change in the exposed length of the water pipe as the joint rotation angle increases from 0 degrees to 90 degrees using a laser rangefinder, and recording 18 sets of angle-length data points at 5-degree intervals; obtaining a linear regression equation by fitting the angle-length data points using the least squares method, with the slope of the linear regression equation being the linear mapping coefficient, and the intercept being zeroed; the linear mapping coefficient is written into the non-volatile memory of the angle-length converter and self-tested and loaded each time the robotic arm is powered on. The parameter calibration of the Kalman filter noise reduction processor includes: collecting 1000 pressure samples under static water pressure conditions and calculating the sample standard deviation as the observation noise benchmark; in the sinusoidal pressure oscillation experiment, adjusting the process noise covariance until the error between the filtered output and the standard pressure gauge is less than 0.05% of full scale.

[0060] The water pressure fluctuation signal acquisition process includes: the millivolt-level voltage signal output from the Wheatstone bridge of the piezoresistive pressure sensor is converted into a digital signal by a 24-bit analog-to-digital converter; the digital signal passes through an anti-aliasing low-pass filter with a cutoff frequency set to 500 Hz to suppress noise above the Nyquist frequency; the filtered digital signal is transmitted to a Kalman filter noise reduction processor at a rate of 1000 Hz. Real-time water pressure value output verification includes: continuously recording 300 real-time water pressure value samples under a steady-state pressure of 0.5 MPa, calculating the sample mean square error to be no more than 0.001 MPa.

[0061] The precision control of the length change value of the extendable water pipe includes: an angular resolution of no less than 0.1 degrees for the photoelectric encoder, corresponding to a length resolution better than 0.0873 mm; and an update delay of no more than 20 milliseconds for the length change value. Environmental compensation for the real-time water pressure value at the water pipe inlet includes: activating the temperature compensation coefficient matrix when the ambient temperature changes by more than 10 degrees Celsius. The coefficient matrix is ​​obtained through laboratory calibration; for example, it compensates 0.005% of the full scale for every 1 degree Celsius increase in temperature.

[0062] Fault diagnosis for the angle-to-length converter includes: triggering an angle anomaly flag and freezing the length change output when the joint rotation angle change exceeds the mechanical limit of 90 degrees / second for 10 consecutive cycles. Robustness assurance for the Kalman filter noise reduction processor includes: automatically resetting the state covariance matrix to its initial value to prevent divergence when the amplitude of the water pressure fluctuation signal changes by more than 20% of the range.

[0063] The spatial gradient of flow entropy yield and turbulent kinetic energy dissipation rate are calculated based on the length change of the scalable water pipe and the real-time water pressure at the pipe inlet, including the following continuously executed operations:

[0064] The average velocity change of the fluid within the pipe is calculated based on the change in the length of the expandable water pipe. Specifically: A correspondence is established between the length change and the fluid volumetric flow rate, where the fluid volumetric flow rate equals the product of the pipe's cross-sectional area and the average fluid velocity. The pipe's cross-sectional area is a fixed geometric parameter, calculated by measuring the pipe's inner diameter and using pi (3.1416). For example, a pipe with an inner diameter of 50 mm has a cross-sectional area of ​​1963.5 square millimeters. The change in the expandable water pipe's length acquired in the current period is divided by the sampling time interval to obtain the change in the pipe's volume per unit time, which equals the change in the fluid volumetric flow rate. The average velocity change within the pipe is output by dividing the change in fluid volumetric flow rate by the pipe's cross-sectional area, with the unit for the average velocity change being meters per second (m / s).

[0065] By combining the real-time water pressure at the pipe inlet, the spatial gradient of the flow entropy productivity is solved using the viscous dissipation term of the Navier-Stokes equations. Specifically: First, an axial pressure gradient model of the fluid within the expandable pipe is constructed, with the real-time water pressure at the pipe inlet as the boundary condition. The pressure drop is calculated segment by segment according to the fluid movement direction. The change in the average fluid velocity is substituted into the transient term of the Navier-Stokes equations, and the pressure gradient is substituted into the pressure term. The viscous dissipation term in the Navier-Stokes equations is then extracted. The viscous dissipation term is derived from the fluid... The dynamic viscosity is formed by the square product of the velocity gradient. The spatial distribution of the viscous dissipation term along the pipe axis is solved by a three-dimensional mesh discretization method, with the mesh size set to 1 / 20 of the pipe diameter. For example, a 2.5 mm mesh is used for a 50 mm diameter pipe. The local entropy productivity is calculated at the mesh nodes, which is equal to the viscous dissipation term divided by the absolute temperature of the fluid. The difference in entropy productivity between adjacent mesh nodes along the pipe axis is differentially calculated to generate the spatial gradient of the flow entropy productivity, with the unit of the spatial gradient being watts per cubic meter Kelvin.

[0066] Based on the change in average flow velocity and the real-time water pressure at the pipe inlet, the intensity of turbulent fluctuations is calculated using the turbulent kinetic energy transport term in the Reynolds time-averaged equation. Specifically: the change in average fluid velocity is input as the time-averaged velocity into the Reynolds time-averaged equation; the initial turbulent fluctuation intensity is determined using a turbulence intensity estimation model, which is set according to the geometric abrupt change characteristics of the pipe inlet, for example, the initial turbulence intensity corresponding to a right-angle inlet is 5% of the time-averaged velocity; the turbulent kinetic energy transport term in the Reynolds time-averaged equation is solved, which includes convection, diffusion, generation, and dissipation terms; the generation term is calculated by the product of Reynolds stress and velocity gradient, and the Reynolds stress is closed using the Busineske eddy viscosity model with an eddy viscosity coefficient of 0.09; the turbulent kinetic energy transport equation is solved iteratively using the finite volume method, with the convergence condition being that the residual turbulent kinetic energy between two adjacent steps is less than 0.001; finally, the root mean square value of the turbulent fluctuation intensity is output, with the unit of turbulent fluctuation intensity being meters per second.

[0067] The turbulent kinetic energy dissipation rate is generated based on the product of turbulent pulsation intensity and fluid kinematic viscosity. Specifically, the fluid kinematic viscosity is determined by referring to a table using the real-time water temperature value obtained from a water temperature sensor. For example, the kinematic viscosity is 1.004 × 10⁻⁶ m² / s at a water temperature of 20 degrees Celsius. The square of the turbulent pulsation intensity is multiplied by an empirical coefficient of 0.3 and then divided by the turbulent characteristic length scale. The turbulent characteristic length scale is taken as 1 / 10 of the hydraulic diameter of the water pipe, which is equal to 4 times the cross-sectional area divided by the wetted perimeter. For a circular pipe, this is the inner diameter. The above calculation result is multiplied by the fluid kinematic viscosity to finally output the turbulent kinetic energy dissipation rate, with the unit being m² / m³ / s.

[0068] The validation of the pressure gradient model includes: measuring the pressure difference between the inlet and outlet of the water pipe under a steady flow velocity of 1.5 m / s, and comparing the error between the calculated value and the measured value, ensuring the error does not exceed 5%; if the error exceeds this range, the pipe roughness parameter is adjusted. The roughness parameter is set according to the water pipe material, for example, 0.015 mm for stainless steel pipes. The calibration of the turbulence intensity estimation model includes: obtaining the actual turbulence fluctuation distribution through particle image velocimetry and correcting the mapping relationship between the initial turbulence intensity and geometric features.

[0069] Temperature compensation for fluid dynamic viscosity includes: dynamically updating the viscosity value according to the viscosity-temperature relationship formula when the water temperature change exceeds 5 degrees Celsius. The viscosity-temperature relationship formula is as follows:

[0070] Dynamic viscosity = 2.414 × 10 -5 ×10 247.8 / ( T+140 )

[0071] The unit of dynamic viscosity is Pascal-second (T is temperature in Celsius). The convergence control of 3D mesh discretization includes: when the mesh size is reduced to half of the current value, the maximum deviation of the entropy yield spatial gradient must be less than 0.5%; otherwise, the mesh is further refined.

[0072] The boundary conditions for the Reynolds time-averaged equations include: the pipe wall is treated with a standard wall function, and the dimensionless distance y+ of the first layer of the wall mesh is controlled between 30 and 300. Stability guarantees for the finite volume method solution include: the convection term uses a second-order upwind scheme, the diffusion term uses a central difference scheme, and the time step is set to 1 / 10 of the sampling interval.

[0073] The dimensional verification of turbulent kinetic energy dissipation rate includes: dividing the square of the turbulent fluctuation intensity (m² / s²) by the characteristic length (m) to obtain a dimension of 1 / s, then multiplying by the kinematic viscosity (m² / s²) to finally obtain m² / cubic second, which conforms to the international standard dimension of turbulent kinetic energy dissipation rate. The rationality verification of the output parameters includes: the turbulent kinetic energy dissipation rate value should be within the range of 10⁻⁶ to 10⁻² m² / cubic second; if it exceeds this range, a sensor data verification command is triggered.

[0074] The water pressure compensation required to maintain the target spray flow rate is generated based on the spatial gradient of the flow entropy yield and the turbulent kinetic energy dissipation rate, including the following continuously executed operations:

[0075] The mechanical energy loss of fluid in a retractable pipe is calculated based on the spatial gradient of the flow entropy yield. Specifically: the spatial gradient of the flow entropy yield represents the rate of mechanical energy loss per unit volume of fluid due to viscous dissipation; the spatial gradient of the flow entropy yield is numerically integrated along the total length of the retractable pipe, with the integration interval from the pipe inlet to the end nozzle, and the integration step size consistent with the grid size for entropy yield calculation; during the integration process, the spatial gradient of the entropy yield of each grid cell is multiplied by the cell volume, which is equal to the product of the cross-sectional area of ​​the pipe and the grid length; the calculation results of all grid cells are summed to output the total mechanical energy loss power of the fluid in the retractable pipe; the total mechanical energy loss power is divided by the target spray flow rate to obtain the mechanical energy loss per unit mass of fluid per unit time, with the unit of mechanical energy loss being joules per kilogram.

[0076] The pressure oscillation attenuation caused by turbulence is quantified by combining the turbulent kinetic energy dissipation rate. Specifically: the turbulent kinetic energy dissipation rate reflects the rate at which turbulent pulsating kinetic energy is converted into heat energy; the turbulent kinetic energy dissipation rate is multiplied by the fluid density, which is determined by referring to a table based on the real-time water temperature value obtained by a water temperature sensor, for example, 998 kg / m³ when the water temperature is 20 degrees Celsius; the product represents the intensity of the turbulent pulsating pressure attenuation; the intensity of the turbulent pulsating pressure attenuation is spatially averaged by volume, with the volume averaging region covering the entire flow channel from the water pipe inlet to the end nozzle; the volume average is multiplied by a characteristic time scale, which is the turbulent vortex dissipation time, equal to the cube root of the reciprocal of the turbulent kinetic energy dissipation rate; finally, the pressure oscillation attenuation caused by turbulence is output, with the unit of pressure oscillation attenuation being Pascals.

[0077] Based on the sum of mechanical energy loss and pressure oscillation attenuation, the total energy compensation value required to offset the sudden change in pipeline resistance is determined. Specifically, the mechanical energy loss and pressure oscillation attenuation are unified into energy form with the same dimensions. The mechanical energy loss is kept in joules per kilogram, and the pressure oscillation attenuation is converted to joules per kilogram by dividing by the fluid density. The two values ​​are algebraically added together. Before adding, an empirical weighting coefficient of 0.5 is applied to the pressure oscillation attenuation. The weighting coefficient is obtained through fluid dynamics experiments, which measure the actual pressure attenuation under known turbulence intensity. The superposition result is output as the total energy compensation value. The physical meaning of the total energy compensation value is the additional fluid energy per unit mass required to maintain the target spray flow rate.

[0078] The total energy compensation value is converted into the equivalent water pressure increment to maintain the target spray flow rate using Bernoulli's equation. Specifically: Bernoulli's equation establishes a mechanical energy conservation relationship between the water pipe inlet and the terminal nozzle; the target spray flow rate corresponds to the design flow velocity of the terminal nozzle, which is calculated through the nozzle's geometric parameters, for example, a 3 mm diameter nozzle has a flow velocity of 15 m / s under a pressure of 0.3 MPa; the total energy compensation value corresponds to the energy difference term in Bernoulli's equation; according to the equivalence between the pressure and energy terms in Bernoulli's equation, the total energy compensation value is equal to the equivalent water pressure increment divided by the fluid density; multiplying the total energy compensation value by the fluid density directly outputs the equivalent water pressure increment value as the water pressure compensation amount, with the unit of water pressure compensation being Pascal.

[0079] Boundary treatment for calculating mechanical energy loss includes: using a curvature correction factor for the mesh elements of the pipe bend section. The curvature correction factor is determined based on the ratio of the bend radius to the pipe diameter; for example, when the curvature radius is 300 mm, the correction factor is set to 1.2. Constraints on turbulent vortex dissipation time include: when the calculated value is less than 0.001 seconds, it is forcibly set to 0.001 seconds to prevent numerical overflow.

[0080] The weighting coefficient calibration experiment included: measuring the pressure oscillation attenuation at different turbulence intensities (5%–20%) on a turbulence generator test platform, fitting the relationship curve between the attenuation and the turbulent kinetic energy dissipation rate, and taking the median value of the curve slope as the benchmark for the weighting coefficient. The range verification of the total energy compensation value included: setting a reasonable threshold of 0 to 500 joules per kilogram, triggering a flow channel blockage warning when the threshold is exceeded.

[0081] The real-time guarantee of equivalent water pressure increment includes: when the target spray flow rate changes by more than 10%, the conversion calculation from total energy compensation value to water pressure compensation value is completed within 10 milliseconds. The output filtering of water pressure compensation value includes: using a first-order low-pass filter to smooth abrupt changes, with the cutoff frequency set to 10 Hz to retain effective adjustment bandwidth.

[0082] Dimensionality verification: Mechanical energy loss: [J / kg] = [m 2 / s 2 Pressure oscillation attenuation: [Pa] = [N / m] 2 ] = [kg / (m·s) 2 )] → Divide by density [kg / m³] 3 ]→[m 2 / s 2 Total energy compensation value: [J / kg] = [m 2 / s 2 Equivalent water pressure increment: [Pa] = [kg / (m·s)] 2 )] = density [kg / m 3 ]×[m 2 / s 2 ].

[0083] The engineering adaptation of the output parameters includes: converting the water pressure compensation amount into a 4-20 mA analog signal output, corresponding to a range of 0 to 0.5 MPa, with a conversion relationship of 0.03125 MPa compensation for every 1 mA. Dynamic response testing includes: under a step disturbance, the response time for the water pressure compensation output to reach 90% of its steady-state value does not exceed 100 milliseconds.

[0084] The system controls the output pressure of the adjustable water pump based on water pressure compensation, receives alternating irrigation mode commands, and analyzes the required spraying parameter combinations for the target area, including the following continuously executed operations:

[0085] The proportional-integral (PI) control algorithm superimposes the water pressure compensation onto the reference output pressure setpoint of the adjustable pressure pump, generating a dynamic pressure adjustment command which is then sent to the pump's motor driver. The specific implementation process includes: reading the reference output pressure setpoint from the adjustable pressure pump's non-volatile memory; this setpoint is preset to a fixed value, such as 0.35 MPa, based on the irrigation system's design head; using the water pressure compensation calculated for the current cycle as the process variable input to the PI control algorithm, with the process variable's range limited to -0.1 to +0.1 MPa; and setting the proportional gain coefficient of the PI control algorithm to 2.0, ensuring the proportional term output equals the proportional gain. The gain coefficient of the proportional term is multiplied by the instantaneous value of the water pressure compensation, which is used to respond in real time to changes in the amplitude of the water pressure compensation. The time constant of the integral term is 0.5 seconds, and the output of the integral term is equal to the integral term gain coefficient multiplied by the time integral value of the water pressure compensation. The gain coefficient of the integral term is the proportional term gain coefficient divided by the time constant. The integral operation accumulates the time integral error of the water pressure compensation. The reference output pressure setpoint, the proportional term output, and the integral term output are algebraically superimposed, and the superposition result is used as a dynamic pressure adjustment command. The output frequency of the dynamic pressure adjustment command is 100 Hz, and the command value is converted into a 0-10 volt voltage signal by a digital-to-analog converter and sent to the motor driver of the adjustable pressure water pump.

[0086] The process involves parsing the regional code identifier in the alternating irrigation mode command, matching it against a pre-defined irrigation mode database, and extracting the rainfall intensity parameters and raindrop size distribution parameters corresponding to the target area. Specifically, the alternating irrigation mode command is received via a wireless communication module. The command data structure includes a 16-bit regional code identifier and an 8-bit mode type code. The regional code identifier is composed of a geographic zone number and a crop type number; for example, A01 represents wheat crop in zone 1. The irrigation mode database is stored in a flash memory chip, organized as a two-dimensional data table with the regional code as the primary key. Each record contains a rainfall intensity parameter field and a raindrop size distribution parameter field. The data is then processed using hashing... The indexing algorithm matches the region code identifier with the database primary key, and the hash algorithm uses the modulo division and remainder method. The hash table size is 256. After a successful match, the rainfall intensity parameter value corresponding to the target region is read. The rainfall intensity parameter represents the amount of water sprayed per unit time, in millimeters per hour, and the value range covers 1 to 100 millimeters per hour. For example, a rainstorm mode corresponds to 50 millimeters per hour. At the same time, the raindrop size distribution parameter is read. This parameter consists of the median size and the dispersion coefficient. The median size represents the center value of the raindrop size distribution, and the dispersion coefficient represents the degree of dispersion of the size distribution. For example, a median size of 2.0 mm and a dispersion coefficient of 0.3 indicate that the size is mainly distributed in the range of 1.4 to 2.6 mm.

[0087] The process of combining rainfall intensity parameters and raindrop size distribution parameters into the required spraying parameters for the target area includes: constructing a spraying parameter combination data structure containing two independent fields; the first field stores the numerical value and unit of measurement of the rainfall intensity parameter, with the value retained to one decimal place; the second field stores the composite structure of the raindrop size distribution parameter, which includes two sub-items: median particle size and dispersion coefficient, with the sub-item values ​​retained to two decimal places; the spraying parameter combination is output in binary data packet format, with the data packet header containing a timestamp and a region check code, the timestamp being accurate to the millisecond level, and the region check code being the cyclic redundancy check value of the region code identifier; the data packet is broadcast to the execution terminal via a fieldbus, using the Modbus RTU format as the transmission protocol, with the baud rate set to 19200 bits per second.

[0088] The anti-saturation processing of the proportional-integral (PI) control algorithm includes: activating the integral separation strategy when the integral term output exceeds the actuator limit, with the separation threshold being 90% of the maximum value of the dynamic pressure regulation command; freezing the integral calculation in the separation state, retaining only the proportional term's effect. The irrigation pattern database update mechanism includes: pushing new irrigation pattern data packets via a remote server, using a differential update method for the data packets, and reloading the hash table index when the version number changes.

[0089] The physical verification of raindrop size distribution parameters includes: measuring the droplet size distribution of different patterns using a laser diffraction particle size analyzer under laboratory conditions, with the median particle size error not exceeding ±0.1 mm and the dispersion coefficient error not exceeding ±0.05. The fault-tolerant design of the spraying parameter combination includes: verifying the cyclic redundancy check (CRC) code in the data packet header at the receiving end; requesting retransmission if verification fails; and using the default parameter combination after three consecutive failures.

[0090] The safety limits for dynamic pressure regulation commands include: the upper limit of the command output is 110% of the rated pressure of the adjustable pressure pump, and the lower limit is 0.1 MPa, the minimum operating pressure of the system; exceeding the limits triggers the overpressure protection circuit of the hardware. Matching rainfall intensity parameters with the actuator involves dividing the rainfall intensity parameter value by 3600 to convert it to millimeters per second, then multiplying it by the effective area of ​​the nozzle to calculate the target flow rate, which is used to drive the solenoid valve opening control.

[0091] Example of range conversion relationship: Dynamic pressure command 0 volts corresponds to 0 MPa, 10 volts corresponds to 0.5 MPa, conversion factor 0.05 MPa / volt; Rainfall intensity parameter 50 mm / h → 0.0139 mm / s → Nozzle area 78.5 square millimeters → Flow rate 1.09 ml / s.

[0092] Timing synchronization is guaranteed: the proportional-integral control cycle is 10 milliseconds; the broadcast interval for spray parameter combinations is 100 milliseconds; and the command execution delay is less than 5 milliseconds.

[0093] The persistent storage of output parameters includes: recording runtime data of dynamic pressure adjustment commands and spray parameter combinations every 5 minutes, stored on a waterproof industrial-grade SD card in CSV table format.

[0094] During the movement of the robotic arm, the linkage adjustment of water pressure compensation and spraying parameters is performed simultaneously, and the kinetic energy distribution of water droplet impact is collected through a water droplet impact sensor array, including the following continuously executed operations:

[0095] The water pressure compensation value is synchronously transmitted to the pressure controller of the adjustable pressure water pump via an industrial fieldbus. Simultaneously, the rainfall intensity parameter in the spraying parameter combination is converted into a pulse width modulation (PWM) duty cycle signal and sent to the solenoid valve actuator. The specific implementation process includes: establishing a control command transmission channel using the PROFINET industrial fieldbus protocol, with a bus cycle time set to 2 milliseconds; encapsulating the currently calculated water pressure compensation value into a 16-bit integer data frame, containing a sign bit and 12 significant bits, achieving a range resolution of 0.00024 MPa; and transmitting the data through periodic producer-consumer communication. The mode is sent to the pressure controller of the adjustable pressure water pump; the rainfall intensity parameter value in the spray parameter combination is extracted synchronously. The rainfall intensity parameter is in millimeters per hour. The rainfall intensity parameter is linearly mapped to the duty cycle signal through a pulse width modulation converter. The mapping relationship is that the duty cycle percentage is equal to the quotient of the rainfall intensity parameter value divided by the maximum rainfall intensity value of 100 mm per hour, multiplied by 100%. For example, 50 mm per hour corresponds to 50% duty cycle; the duty cycle signal is output to the solenoid valve driver in the form of a 5-volt square wave. The square wave frequency is fixed at 1 kHz, and the duty cycle update period is synchronized with the bus period of 2 milliseconds.

[0096] During the spraying process at the end of the robotic arm, a piezoelectric sensor array is used to collect voltage pulse signals from raindrop impacts on the surface in real time. The specific implementation process includes: the piezoelectric sensor array consists of 64 ceramic piezoelectric units arranged in an 8×8 matrix with a unit spacing of 20 mm, covering an impact surface of 1.4 square meters; each piezoelectric unit is connected to a charge amplifier with a gain of 5 picocoulombs per Newton and a bandwidth covering 0.1 Hz to 10 kHz; when a raindrop impacts the surface of the piezoelectric unit, a charge proportional to the impact force is generated, and the charge amplifier converts the charge into a voltage signal with a range of ±10 volts; the voltage pulse signal is collected by a synchronous sampling analog-to-digital converter at a sampling rate of 100,000 times per second, with a resolution of 16 bits, and each sampling point is marked with a timestamp accurate to the microsecond level.

[0097] Based on the direct proportionality between voltage pulse signal amplitude and raindrop mass, and the inverse proportionality between pulse rise time and raindrop impact velocity, the impact kinetic energy of water droplets at each spatial sampling point is calculated. The specific implementation process includes: the peak amplitude of the voltage pulse signal reflects the raindrop mass; an amplitude-mass mapping curve is established through laboratory calibration. The calibration method involves using a standard water droplet generator to produce a water droplet of known mass impacting the sensor and recording the corresponding voltage amplitude; for example, a 100-milligram water droplet produces a 1.2-volt peak. The pulse rise time is defined as the time interval between the voltage signal rising from 10% peak to 90% peak, and this time interval is inversely proportional to the raindrop impact velocity. The inverse proportionality coefficient is determined through wind tunnel calibration; for example, a rise time of 0.5 milliseconds corresponds to an impact velocity of 8 meters per second. The impact kinetic energy of the water droplet is calculated using Newton's law of motion: kinetic energy equals half the raindrop mass multiplied by the square of the impact velocity. Each piezoelectric unit independently performs the above calculations and outputs the water droplet impact kinetic energy value for the corresponding spatial sampling point, with a calculation period not exceeding 0.1 milliseconds.

[0098] The water droplet impact kinetic energy at each spatial sampling point is mapped to the spatial coordinates of the robotic arm's movement trajectory to generate a water droplet impact kinetic energy distribution. The specific implementation process includes: recording the three-dimensional spatial coordinate sequence of the end-effector nozzle in real time using a robotic arm joint encoder. The joint encoder contains absolute position sensors for six rotating axes, with a sampling frequency of 1 kHz; establishing a time-synchronized correspondence between the physical position of the water droplet impact sensor array and the spatial coordinate sequence. Time synchronization is achieved through a hardware trigger signal, issued by the robotic arm controller when the nozzle reaches a preset reference point, with a trigger accuracy error of less than 10 microseconds; aligning the timestamp of each piezoelectric unit's water droplet impact kinetic energy value with the timestamp of the robotic arm's spatial coordinate sequence based on the time-synchronized correspondence, and determining the corresponding three-dimensional spatial coordinate point using nearest neighbor interpolation; marking the water droplet impact kinetic energy values ​​of all spatial sampling points to the corresponding three-dimensional spatial coordinate points, forming a water droplet impact kinetic energy distribution dataset containing 64 discrete points. The data storage format is a sequence of quadruples containing three-dimensional coordinates and kinetic energy values.

[0099] Temperature compensation for the piezoelectric sensor array includes activating a temperature-sensitivity correction coefficient when the ambient temperature changes by more than 5 degrees Celsius. This coefficient is obtained through temperature chamber calibration; for example, the voltage output decreases by 0.3% for every 1 degree Celsius increase in temperature. Dynamic calibration for impact kinetic energy calculation includes periodically spraying standard steel balls to simulate raindrop impacts, ensuring the mass calculation error does not exceed ±2% and the velocity calculation error does not exceed ±3%.

[0100] Accuracy assurance measures for spatial mapping: The kinematic model of the robotic arm calculates the end-effector pose in real time at a frequency of 1 kHz; the deviation between the sensor array installation position and the rigid connection of the end nozzle is compensated by measuring with a laser tracker; the timestamp alignment adopts an FPGA hardware timing circuit with a clock jitter of less than 1 microsecond.

[0101] The redundancy design of the industrial bus includes: automatic switching to the CAN bus backup channel when PROFINET communication is interrupted, with a switching delay of less than 50 milliseconds. Anti-interference processing of the duty cycle signal includes: setting an RC low-pass filter at the input of the solenoid valve driver with a cutoff frequency of 500 Hz to suppress high-frequency noise.

[0102] Example of a storage structure for the kinetic energy distribution of a water droplet impact: X coordinate: floating point, range 0.0-2.0 meters, precision 0.001 meters; Y coordinate: floating point, range 0.0-2.0 meters, precision 0.001 meters; Z coordinate: floating point, range 1.0-3.0 meters, precision 0.001 meters; kinetic energy value: floating point, range 0-100 millijoules, precision 0.01 millijoules.

[0103] Real-time visualization of the output data includes: transmitting the water droplet impact kinetic energy distribution dataset to a host computer, generating a 3D heatmap using an OpenGL engine, with the color gradient continuously changing from blue (low kinetic energy) to red (high kinetic energy). Data validity verification includes: triggering a nozzle blockage alarm when a single sensor unit fails to detect an impact signal for 10 consecutive times.

[0104] When the uniformity coefficient of the water droplet impact kinetic energy distribution does not reach the set standard value, the calculation logic of the flow entropy yield spatial gradient and turbulent kinetic energy dissipation rate is corrected in reverse, including the following continuously executed operations:

[0105] The Christensen uniformity coefficient is calculated based on a spatial discrete point set of water droplet impact kinetic energy distribution. The specific implementation process includes: extracting the water droplet impact kinetic energy values ​​of all sampling points from the spatial discrete point set data structure of the water droplet impact kinetic energy distribution, with the number of sampling points matching the number of piezoelectric sensor array units; performing an arithmetic mean calculation on the water droplet impact kinetic energy values ​​of all sampling points, ignoring invalid data points (defined as kinetic energy values ​​less than 0.01 mJ or greater than 100 mJ); outputting the average value of the water droplet impact kinetic energy values, retaining three decimal places; and calculating the uniformity coefficient for each droplet impact kinetic energy value individually. The absolute deviation of the water droplet impact kinetic energy at each sampling point from the average value is calculated by taking the absolute value of the deviation. The absolute deviations of all sampling points are summed, and the summation is divided by the total number of sampling points to obtain the average absolute deviation. The average absolute deviation is then divided by the average value, and the quotient is subtracted from 1 and multiplied by 100% to finally output the Christensen uniformity coefficient. The Christensen uniformity coefficient ranges from 0 to 100%. For example, if the average kinetic energy of 64 sampling points is 5.2 mJ, the total absolute deviation is 78.4 mJ, the average absolute deviation is 1.225 mJ, and the uniformity coefficient is 76.44%.

[0106] The Christensen uniformity coefficient is compared with a set standard value. The specific implementation process includes: the set standard value is preset as a fixed threshold according to the irrigation level requirements, for example, 85% for fine irrigation mode and 75% for conventional irrigation mode; the difference comparison uses arithmetic subtraction to calculate the absolute difference between the Christensen uniformity coefficient and the set standard value; the allowable threshold is set to 5 percentage points, and this threshold is determined through irrigation experiments to effectively distinguish between uniformity fluctuations and systematic deviations; when the absolute difference exceeds the allowable threshold, a reverse correction flag is triggered, and the calculation logic correction process is initiated.

[0107] If the difference exceeds the allowable threshold, extract the robotic arm motion parameters and water pressure fluctuation data corresponding to the spatial region with the largest coefficient of variation in the water droplet impact kinetic energy distribution. The specific implementation process includes: dividing the spatial discrete point set of the water droplet impact kinetic energy distribution into 5 continuous sub-regions according to the robotic arm motion trajectory, with each sub-region containing 12 to 14 sampling points; calculating the coefficient of variation for each sub-region, which is equal to the standard deviation of the kinetic energy value in the sub-region divided by the average value; comparing the coefficient of variation values ​​of all sub-regions and selecting the sub-region with the largest coefficient of variation as the target correction region; extracting the motion data of the target correction region for the corresponding time period from the historical record of robotic arm motion parameters, including the three-dimensional spatial coordinate sequence of the end nozzle, joint angular velocity, and angular acceleration; and extracting the original fluctuation signal of the real-time water pressure value at the water pipe inlet for the same time period from the water pressure fluctuation database, with the original fluctuation signal sampling frequency of 1000 Hz.

[0108] Based on the acceleration abrupt change characteristics in the robotic arm motion parameters and the high-frequency oscillation components in the water pressure fluctuation data, the turbulent viscosity calibration coefficient of the viscous dissipation term in the calculation of the flow entropy yield spatial gradient is corrected. The specific implementation process includes: performing differential calculation on the angular acceleration sequence in the robotic arm motion parameters to identify acceleration abrupt change points. The criteria for determining the abrupt change point is that the rate of change of acceleration exceeds 50 degrees per square second within an adjacent 0.1 seconds; statistically analyzing the number and intensity of acceleration abrupt change points within the target correction area, and quantifying the intensity as the maximum rate of change value; establishing a correction function based on the negative correlation between the acceleration abrupt change intensity and the turbulent viscosity calibration coefficient, for example, reducing the turbulent viscosity calibration coefficient by 0.5% for each abrupt change exceeding 100 degrees per square second detected; and outputting the corrected turbulent viscosity calibration coefficient for subsequent calculation of the flow entropy yield spatial gradient.

[0109] The adjustment of the turbulence model constants in the Reynolds time-averaged equation for calculating turbulent kinetic energy dissipation rate involves the following steps: performing spectral analysis on the high-frequency oscillation components in the water pressure fluctuation data, and using the Fast Fourier Transform algorithm to extract the energy proportion in the 30-200 Hz frequency band; defining the intensity index of the high-frequency oscillation component as the percentage of the amplitude integral value in that frequency band to the total integral value in the entire frequency band; establishing adjustment rules based on the positive correlation between the intensity of the high-frequency oscillation component and the turbulence model constant, for example, increasing the turbulence model constant by 0.02 for every 5% increase in intensity; setting the baseline value of the turbulence model constant to 0.09, with the adjustment range limited to between 0.07 and 0.12; and outputting the updated turbulence model constants for subsequent calculations of turbulent kinetic energy dissipation rate.

[0110] The verification of the Christensen uniformity coefficient included: in a standard uniform spraying test, the kinetic energy distribution at 64 points was manually measured, and the error between the algorithm-calculated value and the measured value was no more than ±2 percentage points. The optimization of acceleration abrupt change feature extraction included: using wavelet transform to denoise the angular acceleration sequence to improve the accuracy of abrupt change point identification; the denoising threshold was set to 3 times the noise standard deviation.

[0111] The persistent storage of corrected parameters includes writing the corrected turbulent viscosity calibration coefficients and turbulence model constants into non-volatile memory. The storage format is a record table containing timestamps and parameter values, with a new record generated for each correction. Iterative control of reverse correction includes recalculating spray uniformity after correction. If the uniformity is still not met after three consecutive corrections, a system maintenance alarm is triggered.

[0112] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0113] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0114] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0115] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0117] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0118] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A robotic arm spraying parameter control system for simulating artificial rainfall, characterized in that, include: The parameter detection module is used to detect the change in length of the extendable water pipe of the robotic arm and obtain the real-time water pressure value at the pipe inlet. The entropy loss calculation module is used to calculate the spatial gradient of flow entropy yield and turbulent kinetic energy dissipation rate based on the length change and real-time water pressure value. The compensation generation module is used to generate the water pressure compensation required to maintain the target spray flow rate based on the spatial gradient of flow entropy yield and turbulent kinetic energy dissipation rate. The pressure control and analysis module is used to control the output pressure of the adjustable pressure water pump based on the water pressure compensation amount, receive alternating irrigation mode instructions, and analyze the spraying parameter combination required for the target area. The linkage acquisition module is used to synchronously adjust the combination of water pressure compensation and spraying parameters during the movement of the robotic arm, and to collect the distribution of water droplet impact kinetic energy through a water droplet impact sensor array. The feedback correction module is used to correct the calculation logic of the flow entropy yield spatial gradient and turbulent kinetic energy dissipation rate when the uniformity coefficient of the water droplet impact kinetic energy distribution does not reach the set standard value.

2. The robotic arm spraying parameter control system for simulating artificial rainfall according to claim 1, characterized in that, The system detects the change in length of the extendable water pipe of the robotic arm and obtains the real-time water pressure value at the pipe inlet, including: The change in the rotation angle of the robotic arm joint is detected in real time by photoelectric encoder. Based on the linear mapping relationship between the change in the joint rotation angle and the change in the length of the extendable water pipe, it is converted into the change in the length of the extendable water pipe. The water pressure fluctuation signal at the water pipe inlet is collected in real time by a piezoresistive pressure sensor. The water pressure fluctuation signal is then processed by Kalman filtering to reduce noise, and the real-time water pressure value at the water pipe inlet is output.

3. The robotic arm spraying parameter control system for simulating artificial rainfall according to claim 2, characterized in that, Based on the length change and real-time water pressure, the spatial gradient of flow entropy yield and the turbulent kinetic energy dissipation rate are calculated, including: The average velocity change of the fluid in the pipe is calculated based on the change in the length of the expandable pipe. Combined with the real-time water pressure at the pipe inlet, the spatial gradient of the flow entropy yield is solved by the viscous dissipation term of the Navier-Stokes equations. Based on the average flow velocity change and the real-time water pressure at the pipe inlet, the turbulent pulsation intensity is calculated using the turbulent kinetic energy transport term in the Reynolds time-averaged equation. The turbulent kinetic energy dissipation rate is then generated based on the product relationship between the turbulent pulsation intensity and the fluid kinematic viscosity.

4. The robotic arm spraying parameter control system for simulating artificial rainfall according to claim 3, characterized in that, Based on the spatial gradient of flow entropy yield and turbulent kinetic energy dissipation rate, the water pressure compensation required to maintain the target spray flow rate is generated, including: The mechanical energy loss of fluid in a stretchable water pipe is calculated based on the spatial gradient of flow entropy yield, and the pressure oscillation attenuation caused by turbulence is quantified by combining the turbulent kinetic energy dissipation rate. The total energy compensation value required to offset the sudden change in pipeline resistance is determined based on the sum of mechanical energy loss and pressure oscillation attenuation. The total energy compensation value is converted into the equivalent water pressure increment to maintain the target spray flow rate using Bernoulli's equation, and this increment is used as the water pressure compensation amount.

5. The robotic arm spraying parameter control system for simulating artificial rainfall according to claim 4, characterized in that, The system controls the output pressure of the adjustable water pump based on water pressure compensation, receives alternating irrigation mode commands, and analyzes the required spraying parameter combinations for the target area, including: The water pressure compensation is superimposed on the reference output pressure set value of the adjustable pressure water pump through the proportional-integral control algorithm, generating a dynamic pressure adjustment command and sending it to the motor driver of the adjustable pressure water pump. The regional code identifier in the alternating irrigation mode instruction is parsed, matched with the preset irrigation mode database, and the rainfall intensity parameters and raindrop size distribution parameters corresponding to the target area are extracted. Combine rainfall intensity parameters and raindrop size distribution parameters to form the spraying parameter combination required for the target area.

6. The robotic arm spraying parameter control system for simulating artificial rainfall according to claim 5, characterized in that, The water pressure compensation is superimposed onto the reference output pressure setpoint of the adjustable pressure pump using a proportional-integral control algorithm, including: Read the reference output pressure setpoint of the adjustable pressure water pump and use the water pressure compensation amount as the process variable input of the proportional-integral control algorithm; The proportional term of the proportional-integral control algorithm responds in real time to changes in the amplitude of water pressure compensation, while the integral term accumulates the time integral error of the water pressure compensation, outputting a dynamic pressure adjustment command.

7. The robotic arm spraying parameter control system for simulating artificial rainfall according to claim 5, characterized in that, During the movement of the robotic arm, the linkage adjustment of water pressure compensation and spraying parameters is performed synchronously, and the distribution of water droplet impact kinetic energy is collected through a water droplet impact sensor array, including: The water pressure compensation amount is synchronously transmitted to the pressure controller of the adjustable pressure water pump via industrial fieldbus, and the rainfall intensity parameter in the spraying parameter combination is converted into a pulse width modulation duty cycle signal and sent to the solenoid valve driver. During the spraying process at the end of the robotic arm, the voltage pulse signal of the raindrop impacting the plane is collected in real time by a piezoelectric sensor array. Based on the direct proportionality between the voltage pulse signal amplitude and the raindrop mass, and the inverse proportionality between the pulse rise time and the raindrop impact velocity, the impact kinetic energy of the water droplets at each spatial sampling point is calculated. The impact kinetic energy of water droplets at each spatial sampling point is mapped according to the spatial coordinates of the robotic arm's movement trajectory to generate the water droplet impact kinetic energy distribution.

8. The robotic arm spraying parameter control system for simulating artificial rainfall according to claim 7, characterized in that, The impact kinetic energy of water droplets at each spatial sampling point is mapped to the spatial coordinates of the robotic arm's movement trajectory, including: By using a robotic arm joint encoder to record the three-dimensional spatial coordinate sequence of the end nozzle in real time, a time-synchronized correspondence between the physical position of the water droplet impact sensor array and the spatial coordinate sequence is established. Based on the time synchronization correspondence, the water droplet impact kinetic energy values ​​collected by each water droplet impact sensor are marked to the corresponding three-dimensional spatial coordinate points, generating a spatial discrete point set of water droplet impact kinetic energy distribution.

9. The robotic arm spraying parameter control system for simulating artificial rainfall according to claim 7, characterized in that, When the uniformity coefficient of the water droplet impact kinetic energy distribution does not reach the set standard value, the calculation logic for the reverse correction of the spatial gradient of the flow entropy yield and the turbulent kinetic energy dissipation rate includes: The Christensen uniformity coefficient is calculated based on the spatial discrete point set of the water droplet impact kinetic energy distribution, and the difference between the Christensen uniformity coefficient and the set standard value is compared. If the difference exceeds the allowable threshold, extract the robotic arm motion parameters and water pressure fluctuation data corresponding to the spatial region with the largest coefficient of variation in the water droplet impact kinetic energy distribution. Based on the abrupt acceleration characteristics in the motion parameters of the robotic arm and the high-frequency oscillation components in the water pressure fluctuation data, the turbulent viscosity calibration coefficient of the viscous dissipation term in the calculation of the spatial gradient of flow entropy yield is corrected, and the turbulent model constants of the Reynolds time-averaged equation in the calculation of turbulent kinetic energy dissipation rate are adjusted.

10. The robotic arm spraying parameter control system for simulating artificial rainfall according to claim 9, characterized in that, The Christensen uniformity coefficient is calculated based on a spatially discrete set of points representing the kinetic energy distribution of water droplet impacts, including: Extract the water droplet impact kinetic energy values ​​from all sampling points in the spatial discrete point set of water droplet impact kinetic energy distribution, and calculate the average value of the water droplet impact kinetic energy values; The absolute deviations of the water droplet impact kinetic energy values ​​from the average value at all sampling points are summed, and the Christensen uniformity coefficient is generated by combining the product of the total number of sampling points and the average value.

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