Cotton drip irrigation-unmanned aerial vehicle collaborative operation field moisture rapid supplementary irrigation system

Abstract

The invention discloses a cotton drip irrigation-unmanned aerial vehicle collaborative operation field moisture rapid supplementary irrigation system, which belongs to the technical field of intelligent agricultural irrigation, and comprises a collaborative architecture of a physical execution layer, an intelligent sensing layer and a decision control layer, the physical execution layer comprises a drip irrigation pipe network subsystem and an unmanned aerial vehicle supplementary irrigation subsystem; the intelligent sensing layer comprises a soil moisture detection unit and a crop physiology monitoring unit; the decision control layer comprises an air-land irrigation cooperation module, a pulse supplementary irrigation strategy module and a three-dimensional path planning module. The method can effectively improve the operation efficiency, increase the yield per mu and reduce the cost.

Description

Technical Field

[0001] The present invention relates to the field of smart agricultural irrigation technology, and more specifically to a cotton drip irrigation-drone collaborative field water rapid irrigation system. Background Art

[0002] At present, the functions of air-ground irrigation equipment are separated, and the delay in emergency water replenishment has led to an increase in the cotton bud and boll shedding rate. At the same time, the pulse irrigation parameters are poorly adapted to the soil type, the water infiltration rate fluctuates widely, and the drone group path planning does not take into account the dynamic characteristics of battery attenuation, resulting in a low task completion rate.

[0003] Therefore, how to provide a field water rapid irrigation system that combines cotton drip irrigation with drones is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a field water rapid irrigation system for cotton drip irrigation and UAV collaborative operation, which solves the technical problems of response delay and coordination failure of air-ground irrigation system, mismatch between pulse irrigation parameters and soil permeability characteristics, and low balance in task allocation of UAV swarm.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A cotton drip irrigation and drone collaborative field water replenishment system includes a collaborative architecture of physical execution layer, intelligent perception layer, and decision-making control layer:

[0007] The physical execution layer includes:

[0008] Drip irrigation pipe network subsystem, including pressure regulating valve array and zone flow meter for water supply;

[0009] UAV refueling subsystem, used for emergency refueling;

[0010] The intelligent perception layer includes:

[0011] Soil moisture detection unit, equipped with terahertz wave radar, is used to generate three-dimensional moisture content cloud maps;

[0012] The crop physiological monitoring unit uses multispectral imaging to capture canopy transpiration characteristics and identify the water stress status of cotton plants;

[0013] The decision control layer includes:

[0014] Air-ground irrigation coordination module dynamically allocates drip irrigation and drone operation areas based on real-time moisture data;

[0015] Pulse irrigation strategy module generates atomization parameter control instructions based on soil permeability characteristics;

[0016] The three-dimensional path planning module optimizes the flight path of the drone swarm and outputs the coordinated flight trajectory of the drone swarm.

[0017] Furthermore, the drone refueling subsystem is equipped with a refueling device, which includes an atomizing nozzle and a liquid storage bag for emergency refueling.

[0018] Furthermore, the soil moisture detection unit includes:

[0019] Perform water content inversion and generate a three-dimensional water content cloud map:

[0020]

[0021] Where,∈ eff Equivalent dielectric constant; ∈ d is the soil skeleton dielectric constant; ∈ w is the free water dielectric constant; φ is the porosity correction factor.

[0022] Furthermore, the air-land irrigation coordination module includes:

[0023] Establishing a water deficit index:

[0024]

[0025] Where θ opt is the optimum volumetric moisture content of cotton; θ act is the real-time detection of volumetric moisture content; D is the water deficit index;

[0026] Determine the water deficit index and trigger the drone emergency irrigation when D ≥ 0.7.

[0027] Furthermore, the pulse replenishment strategy module includes:

[0028] Penetration acceleration model:

[0029]

[0030] Where, v p is the pulse infiltration rate; K s is the saturated hydraulic conductivity; β is the pulse gain coefficient; t on is the injection opening time; t off Injection closing time.

[0031] Furthermore, the pulse replenishment strategy module further includes:

[0032] Atomization control parameter matrix:

[0033]

[0034] Where d is the droplet size distribution; Q is the irrigation volume per unit area; and H is the flight altitude.

[0035] Furthermore, the three-dimensional path planning module includes:

[0036] Establish the objective function:

[0037]

[0038] Where, E i is the energy consumption of the UAV; T i is the operation time; w1 and w2 are dynamic weights.

[0039] Furthermore, the three-dimensional path planning module optimizes the flight path of the drone swarm and outputs the coordinated flight trajectory of the drone swarm, including using the parameter coupling mechanism of the Lévy flight strategy and the convergence factor to optimize the flight path of the drone swarm and output the coordinated flight trajectory of the drone swarm, including:

[0040] Initial iteration: Use large Lévy flight steps and high convergence factors.

[0041] Mid-iteration: Dynamically attenuate the Lévy step size and linearly reduce the convergence factor;

[0042] Late iteration: small-scale Gaussian perturbation, low convergence factor;

[0043] Among them, the Lévy flight step length generation formula is:

[0044]

[0045] The dynamic convergence factor update equation is:

[0046] φ=φ init ·e -λ·iter +φ min ;

[0047] The parameter coupling mechanism expression is:

[0048]

[0049] Where A is the basic stride, σ u is the step size scaling factor, β is the distribution parameter, φ init is the initial convergence factor, λ is the decay rate, φ min is the minimum convergence factor, D max is the maximum diagonal distance of the task area, and iter is the number of iterations.

[0050] Through the above technical solutions, it can be seen that compared with the existing technology, the present invention discloses a field rapid water replenishment system for cotton drip irrigation and drone collaborative operation. Based on the air-ground collaborative mechanism of the dynamic mask matrix, the end-to-end delay from the identification of local drought areas to the start of replenishment irrigation is reduced; through the dynamic zoning strategy of the drone and the drip irrigation network collaborative mask, the irrigation blind area is eliminated and the field moisture uniformity coefficient is ensured; through the infiltration acceleration model and droplet particle size control, ineffective evaporation and deep leakage are reduced, and water use efficiency is improved; through terahertz radar, three-dimensional moisture content modeling of the cultivated layer is achieved, and the irrigation amount is dynamically matched to the needs of the cotton growth period to avoid soil salinization caused by excessive irrigation; the improved migratory bird algorithm introduces the Lévy flight strategy and dynamic convergence factor to improve the task balance of the drone group and reduce the difference in battery energy consumption. Therefore, the present invention can effectively improve operation efficiency, increase per mu yield, and reduce costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0052] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] Example 1:

[0055] See also Figure 1 The embodiment of the present invention discloses a cotton drip irrigation-UAV collaborative field water rapid irrigation system, including a collaborative architecture of a physical execution layer, an intelligent perception layer, and a decision-making control layer:

[0056] The physical execution layer includes:

[0057] Drip irrigation pipe network subsystem, including pressure regulating valve array and zone flow meter for water supply;

[0058] UAV refueling subsystem, used for emergency refueling;

[0059] The intelligent perception layer includes:

[0060] Soil moisture detection unit, equipped with terahertz wave radar, is used to generate three-dimensional moisture content cloud maps;

[0061] The crop physiological monitoring unit uses multispectral imaging to capture canopy transpiration characteristics and identify the water stress status of cotton plants;

[0062] The decision control layer includes:

[0063] Air-ground irrigation coordination module dynamically allocates drip irrigation and drone operation areas based on real-time moisture data;

[0064] Pulse irrigation strategy module generates atomization parameter control instructions based on soil permeability characteristics;

[0065] The three-dimensional path planning module optimizes the flight path of the drone swarm and outputs the coordinated flight trajectory of the drone swarm.

[0066] Specifically, the physical execution layer includes:

[0067] Drip irrigation pipe network subsystem: responsible for regular water supply, achieving precise pressure control (0.2-0.5MPa) in different zones through pressure regulating valves, and flow sensors monitoring irrigation volume with an error of ≤1.5%.

[0068] UAV refueling subsystem: equipped with a high-precision atomization device for emergency refueling, with a single-machine coverage radius of 15m and a response delay of ≤3min.

[0069] The intelligent perception layer includes:

[0070] Soil moisture detection unit: uses terahertz radar (0.3THz) to penetrate the cultivated layer (0-50cm) and generate a three-dimensional moisture content cloud map with a spatial resolution of 3cm.

[0071] Crop Physiology Monitoring Unit: Analyzes the canopy NDVI index through multispectral imaging (400-1000nm) to identify the water stress status of cotton plants.

[0072] The decision-making control layer includes:

[0073] Air-ground collaboration module: Dynamically divides the drip irrigation and drone operation areas to avoid irrigation overlap or omissions.

[0074] Pulse irrigation module: matches pulse parameters according to soil type to improve water infiltration efficiency.

[0075] Path planning module: Optimizes the flight path of drone swarms, reduces energy consumption and improves operation coverage.

[0076] In a specific embodiment, the drone refueling subsystem is equipped with a refueling device, which includes an atomizing nozzle and a liquid storage bag for emergency refueling.

[0077] Furthermore, the soil moisture detection unit includes:

[0078] Perform water content inversion and generate a three-dimensional water content cloud map:

[0079]

[0080] Where,∈ eff Equivalent dielectric constant; ∈ d is the soil skeleton dielectric constant; ∈ w is the free water dielectric constant; φ is the porosity correction factor.

[0081] In a specific embodiment, the air-land irrigation coordination module includes:

[0082] Establishing a water deficit index:

[0083]

[0084] Where θ opt is the optimum volumetric moisture content of cotton; θ act is the real-time detection of volumetric moisture content; D is the water deficit index;

[0085] Determine the water deficit index and trigger the drone emergency irrigation when D ≥ 0.7.

[0086] In a specific embodiment, the pulse replenishment strategy module includes:

[0087] Penetration acceleration model:

[0088]

[0089] Where, v p is the pulse infiltration rate; K s is the saturated hydraulic conductivity; β is the pulse gain coefficient; t on is the injection opening time; t off Injection closing time.

[0090] In a specific embodiment, the pulse replenishment strategy module further includes:

[0091] Atomization control parameter matrix:

[0092]

[0093] Where d is the droplet size distribution; Q is the irrigation volume per unit area; and H is the flight altitude.

[0094] Particle size control: 50±5μm droplets can reduce drift loss (<8%) while ensuring leaf adsorption rate >90%; height compensation: irrigation intensity Q increases linearly with flight height H to compensate for droplet diffusion loss.

[0095] In a specific embodiment, the three-dimensional path planning module includes:

[0096] Establish the objective function:

[0097]

[0098] Where, E i is the energy consumption of the UAV; T i is the operation time; w1 and w2 are dynamic weights.

[0099] Specifically, the constraints are: energy saving is prioritized when the battery is low, and efficiency is prioritized when the battery is sufficient.

[0100] In a specific embodiment, a three-dimensional path planning module optimizes the flight path of a swarm of drones and outputs the coordinated flight trajectory of the swarm of drones, including using a parameter coupling mechanism of a Lévy flight strategy and a convergence factor to optimize the flight path of the swarm of drones and output the coordinated flight trajectory of the swarm of drones, including:

[0101] Initial iteration: Use large Lévy flight steps and high convergence factors.

[0102] Mid-iteration: Dynamically attenuate the Lévy step size and linearly reduce the convergence factor;

[0103] Late iteration: small-scale Gaussian perturbation, low convergence factor;

[0104] Among them, the Lévy flight step length generation formula is:

[0105]

[0106] The dynamic convergence factor update equation is:

[0107] φ=φ init ·e -λ·iter +φ min ;

[0108] The parameter coupling mechanism expression is:

[0109]

[0110] Where A is the basic stride, σ u is the step size scaling factor, β is the distribution parameter, φ init is the initial convergence factor, λ is the decay rate, φ min is the minimum convergence factor, D maxis the maximum diagonal distance of the task area, and iter is the number of iterations.

[0111] Specifically, the synergistic mechanism of Lévy flight strategy and convergence factor:

[0112] Initial iteration (iter<50):

[0113] Use large-scale Lévy flight steps (α~10-50m) to enhance global search capabilities

[0114] High convergence factor (φ≈2.0) expands population diversity

[0115] Mid-iteration (50≤iter<150):

[0116] Dynamic attenuation Lévy step size (α~5-20m)

[0117] Linearly reduce the convergence factor (φ≈1.5→0.8)

[0118] Late iteration (iter ≥ 150):

[0119] Small-scale Gaussian perturbation (α ~ 1-5m) improves local optimization accuracy

[0120] A low convergence factor (φ≈0.5) accelerates convergence.

[0121] When the number of iterations iter increases, σ u Automatic decay, to achieve a smooth transition from "coarse search to fine tuning", the convergence factor φ is exponentially decayed with σ u A linkage is formed to ensure synchronous optimization of step size adjustment and convergence speed.

[0122] Example 2:

[0123] The emergency irrigation test of cotton fields was conducted using a method disclosed by the present invention and a traditional drip irrigation method, including:

[0124] Test conditions:

[0125] Location: Xinjiang testing area

[0126] Soil type: Sandy loam (62% sand content)

[0127] Test area: 20 mu (3 drones, 8 drip irrigation zones)

[0128] See Table 1 for parameter setting table.

[0129] Table 1 Parameter setting table

[0130] parameter Numerical Flight altitude 2.5m±0.3m Pulse period 2s (on: 0.5s, off: 1.5s) Operation speed 4.2m / s

[0131] Data collection:

[0132] Terahertz radar scanning: 0.3THz center frequency, 50cm×50cm grid;

[0133] Canopy temperature monitoring: FLIR T1040 infrared thermal imager, sensitivity 0.03°C;

[0134] Test results:

[0135] Moisture uniformity coefficient CU = 92.4% (ISO 15886 standard);

[0136] Single-machine operating efficiency: 5.8 acres / hour (compared to 3.2 acres / hour for traditional drones).

[0137] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0138] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A cotton drip irrigation-UAV collaborative field water rapid irrigation system, characterized by: The collaborative architecture includes the physical execution layer, intelligent perception layer, and decision-making control layer: The physical execution layer includes: Drip irrigation pipe network subsystem, including pressure regulating valve array and zone flow meter for water supply; UAV refueling subsystem, used for emergency refueling; The intelligent perception layer includes: Soil moisture detection unit, equipped with terahertz wave radar, is used to generate three-dimensional moisture content cloud maps; The crop physiological monitoring unit uses multispectral imaging to capture canopy transpiration characteristics and identify the water stress status of cotton plants; The decision control layer includes: Air-ground irrigation coordination module dynamically allocates drip irrigation and drone operation areas based on real-time moisture data; Pulse irrigation strategy module generates atomization parameter control instructions based on soil permeability characteristics; The three-dimensional path planning module optimizes the flight path of the drone swarm and outputs the coordinated flight trajectory of the drone swarm.

2. The cotton drip irrigation-UAV collaborative field water rapid irrigation system according to claim 1 is characterized in that: The UAV refueling subsystem is equipped with a refueling device, which includes an atomizing nozzle and a liquid storage bag for emergency refueling.

3. The cotton drip irrigation-UAV collaborative field water rapid irrigation system according to claim 1 is characterized in that: The soil moisture detection unit comprises: Perform water content inversion and generate a three-dimensional water content cloud map: Where,∈ eff Equivalent dielectric constant; ∈ d is the soil skeleton dielectric constant; ∈ w is the free water dielectric constant; φ is the porosity correction factor.

4. The cotton drip irrigation-UAV collaborative field water rapid irrigation system according to claim 1, characterized in that: The air-ground irrigation coordination module includes: Establishing a water deficit index: Where θ opt is the optimum volumetric moisture content of cotton; θ act is the real-time detection of volumetric moisture content; D is the water deficit index; Determine the water deficit index and trigger the drone emergency irrigation when D ≥ 0.

7.

5. The cotton drip irrigation-UAV collaborative field water rapid irrigation system according to claim 1, characterized in that: The pulse replenishment strategy module includes: Penetration acceleration model: Where, v p is the pulse infiltration rate; K s is the saturated hydraulic conductivity; β is the pulse gain coefficient; t on is the injection opening time; t off Injection closing time.

6. The cotton drip irrigation-UAV collaborative field water rapid irrigation system according to claim 5, characterized in that: The pulse replenishment strategy module further includes: Atomization control parameter matrix: Where d is the droplet size distribution; Q is the irrigation volume per unit area; and H is the flight altitude.

7. The cotton drip irrigation-UAV collaborative field water rapid irrigation system according to claim 1, characterized in that: The three-dimensional path planning module includes: Establish the objective function: Where, E i is the energy consumption of the UAV; T i is the operation time; w1 and w2 are dynamic weights.

8. The cotton drip irrigation-UAV collaborative field water rapid irrigation system according to claim 1, characterized in that: The three-dimensional path planning module optimizes the flight path of the drone swarm and outputs the coordinated flight trajectory of the drone swarm, including using the parameter coupling mechanism of the Lévy flight strategy and the convergence factor to optimize the flight path of the drone swarm and output the coordinated flight trajectory of the drone swarm, including: Initial iteration: Use large Lévy flight steps and high convergence factors. Mid-iteration: Dynamically attenuate the Lévy step size and linearly reduce the convergence factor; Late iteration: small-scale Gaussian perturbation, low convergence factor; Among them, the Lévy flight step length generation formula is: The dynamic convergence factor update equation is: f=f init ·e -λ·iter +φ min ; The parameter coupling mechanism expression is: Where A is the basic stride, σ u is the step size scaling factor, β is the distribution parameter, φ init is the initial convergence factor, λ is the decay rate, φ min is the minimum convergence factor, D max is the maximum diagonal distance of the task area, and iter is the number of iterations.

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