Gradient wind field coupling type wind erosion particle intelligent collection system and working method

Abstract

The invention discloses a gradient wind field coupling type wind erosion particle intelligent collection system and a working method, and relates to the technical field of wind erosion monitoring, the system comprises a gradient wind field generation module, an intelligent environment sensing module, a multifunctional collection module, an intelligent control terminal, a visual platform and an intelligent power supply module; an adjustable gradient wind field is generated through a multi-stage axial flow fan array, multi-dimensional cooperative control of wind speed, wind direction, temperature and humidity is achieved in combination with a wind guide barrel angle dynamic adjusting mechanism and an environment sensing network mechanism, spatial and temporal change characteristics of a natural wind field are simulated, particle motion trails are accurately guided, and the dynamic control of the wind speed, the wind direction, the temperature and the humidity is achieved. And meanwhile, the system adopts a wind field coupling model and a space-time dynamic evaluation unit to output a wind erosion particle collection strategy, the particle collection efficiency and environmental adaptability are remarkably improved, and the problems that a traditional wind erosion particle collection system is insufficient in dynamic response and low in efficiency are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind erosion monitoring, and in particular to a gradient wind field coupled wind erosion particle intelligent collection system and a working method. Background Art

[0002] Wind erosion is the process by which wind erodes, transports, and deposits loose surface materials (such as soil and sand). It is an important natural force in the evolution of surface morphology in arid and semi-arid regions, and one of the main causes of land degradation and desertification. The occurrence of wind erosion depends on the interaction between wind intensity, surface material properties, and surface cover. Wind erosion is the result of the combined effects of nature and human activities, and its prevention and control requires a combination of ecological restoration, engineering technology, and sustainable land use.

[0003] Wind erosion particles refer to surface particles that are eroded and transported by wind under the action of wind. When the wind speed reaches a certain threshold, the sand particles on the surface begin to jump and undergo forms of movement such as saltation, suspension and displacement. By collecting wind erosion particles, we can conduct in-depth research on the wind erosion process, reveal its internal mechanism, and provide a scientific basis for the protection and management of soil slopes. This is of great significance for preventing and controlling wind erosion disasters and protecting the ecological environment.

[0004] Most existing wind erosion particle collection systems have a single function and are unable to adjust the wind field gradient according to real-time environmental parameters, resulting in low particle collection efficiency and poor dynamic response capability of the wind field. In addition, the coupling relationship between wind speed gradient and particle movement is ignored during the collection process, and the influence of climate factors such as temperature and humidity on wind erosion is not integrated, resulting in limited adaptability. In addition, it is impossible to analyze the spatiotemporal distribution characteristics of the wind field and the migration laws of particles in real time, making it difficult to optimize the collection strategy, resulting in a lack of intelligent monitoring and evaluation of the entire collection process. Therefore, the present invention proposes a gradient wind field coupled wind erosion particle intelligent collection system and working method to solve the problems existing in the prior art. Summary of the Invention

[0005] In response to the above problems, the purpose of the present invention is to propose a gradient wind field coupled wind erosion particle intelligent collection system and working method to solve the problems that the existing wind erosion particle collection system cannot adjust the wind field gradient according to real-time environmental parameters, resulting in low particle collection efficiency, and ignores the coupling relationship between wind speed gradient and particle movement during the collection process, fails to integrate the influence of climate factors such as temperature and humidity on wind erosion, and cannot analyze the spatiotemporal distribution characteristics of the wind field and the migration laws of particles in real time, making it difficult to optimize the collection strategy.

[0006] To achieve the objectives of the present invention, the present invention is implemented through the following technical solutions: a gradient wind field coupled wind erosion particle intelligent collection system, comprising a gradient wind field generation module, an intelligent environment perception module, a multifunctional collection module, an intelligent control terminal, a visualization platform, and an intelligent power supply module, wherein the gradient wind field generation module comprises an axial flow fan unit for generating a stable airflow and an outlet angle adjustment mechanism for driving the axial flow fan unit to generate a stepped wind speed gradient;

[0007] The intelligent environment perception module includes a sensor network for collecting wind field parameters and environmental data in real time and a multi-sensor fusion unit for fusing the data of various parameters;

[0008] The multifunctional collection module includes a detachable air guide for dynamically adjusting the particle trajectory and a particle collector for collecting wind erosion particles;

[0009] The intelligent control terminal includes a wind field coupling model for establishing a mapping relationship between wind speed gradient, particle size, and collection efficiency, a spatiotemporal dynamic evaluation unit for outputting a wind erosion particle collection strategy, and an edge computing unit for generating control instructions for the axial flow fan unit and the detachable air guide tube;

[0010] The visualization platform is used for human-computer interaction and real-time display of wind field distribution, wind erosion particle collection efficiency and environmental parameters;

[0011] The intelligent power supply module provides intelligent power supply for the system.

[0012] A further improvement is that the axial flow fan unit adopts axial flow fans distributed in an array and the speed is adjusted by a frequency conversion controller, and the air outlet angle adjustment mechanism supports ±30° dynamic inclination adjustment.

[0013] Further improvements are: the sensor network includes anemometers, temperature and humidity sensors, and a particle concentration monitoring unit arranged at the inlet, outlet, and particle collector area of the detachable air guide duct; the multi-sensor fusion unit fuses the data collected by the sensor network through multi-sensor fusion technology to generate a spatiotemporal distribution map of the wind field and a particle movement path.

[0014] Further improvements are: the detachable air guide is equipped with a cylinder angle adjustment mechanism to dynamically adjust the airflow inclination angle according to the wind speed gradient; the particle collector has a built-in centrifugal separation bin and a grading filter module to store particles according to particle size, and an integrated weighing unit and an optical metering unit to count the mass and particle size distribution in real time.

[0015] Further improvements are as follows: the wind field coupling model establishes a mapping relationship between wind speed gradient, particle size and collection efficiency based on the Navier-Stokes equation and particle size distribution; the spatiotemporal dynamic evaluation module uses a convolutional neural network to model the wind field and particle trajectory and outputs a wind erosion particle collection strategy; the edge computing unit generates control instructions in real time based on the ARM+FPGA architecture to adjust the parameters of the axial flow fan unit and the wind guide angle of the detachable air guide tube.

[0016] Further improvements are as follows: the spatiotemporal dynamic evaluation module uses a convolutional neural network to perform spatiotemporal modeling of the spatiotemporal distribution of the wind field and the particle motion trajectory, outputs the optimal collection strategy, and dynamically optimizes the angle of the detachable air guide and the speed of the axial flow fan unit to achieve efficient capture of particles of different particle sizes.

[0017] A further improvement is that the intelligent power supply module includes a solar power supply unit powered by solar photovoltaic components and a local battery unit powered by rechargeable batteries, providing redundant power support for the system and achieving low-power operation through energy management algorithms.

[0018] A working method of a gradient wind field coupled wind erosion particle intelligent collection system comprises the following steps:

[0019] Step 1: The sensor network of the intelligent environmental perception module collects three-dimensional wind field data, environmental parameters, and particle concentration distribution in real time. The multi-sensor fusion unit uses Kalman filtering and data registration algorithms to generate a high-precision spatiotemporal distribution map of the wind field and reconstruct the particle motion trajectory.

[0020] Step 2: The wind field coupling model of the intelligent control terminal establishes a nonlinear mapping relationship between wind speed gradient and target particle collection efficiency. The spatiotemporal dynamic evaluation unit predicts the spatiotemporal sequence of wind field evolution and generates a dynamic collection strategy.

[0021] Step 3: The edge computing unit analyzes the particle size distribution at the inlet of the detachable air duct, calculates the optimal wind speed gradient curve, generates the variable frequency control parameters of the axial flow fan unit, and determines the adjustment amount of the air duct inclination angle;

[0022] Step 4: The detachable air guide tube drives the cylinder to dynamically adjust in three-dimensional space through the air outlet angle adjustment mechanism, and combines the centrifugal separation chamber and the filter classification module to achieve particle classification and enrichment according to the Stokes number;

[0023] Step 5: The built-in weighing unit and optical metering unit of the particle collector of the multifunctional collection module provide real-time feedback on the collection efficiency. The intelligent control terminal periodically updates the control strategy and maintains the target collection efficiency through PID closed-loop control.

[0024] The beneficial effects of the present invention are: the present invention generates an adjustable gradient wind field through a multi-stage axial flow fan array, combines the dynamic adjustment mechanism of the air guide tube angle and the environmental perception network mechanism, realizes multi-dimensional coordinated control of wind speed, wind direction, temperature and humidity, simulates the spatiotemporal variation characteristics of the natural wind field, accurately guides the particle movement trajectory, and makes particles of different particle sizes settle in layers to the multifunctional collection module, thereby improving the collection efficiency. At the same time, the system adopts a wind field coupling model and a spatiotemporal dynamic evaluation unit to output a wind erosion particle collection strategy, which significantly improves the particle collection efficiency and environmental adaptability, and solves the problems of insufficient dynamic response and low efficiency of traditional wind erosion particle collection systems. It is suitable for wind erosion mechanism research, sandstorm prevention and control, and ecological restoration projects, and can provide technical support for wind erosion mechanism research and prevention projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a structural schematic diagram of the gradient wind field coupled wind erosion particle intelligent collection system of the present invention. DETAILED DESCRIPTION

[0026] 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0027] Soil erosion is a movement phenomenon under the action of wind. The wind carries various soil particles, causing them to move in different forms and distances, which is called the transportation effect of wind. When the wind speed near the ground is greater than 5m per second, soil with a particle size of 0.1 to 0.25mm can be transported to form wind erosion flow. Usually, the particle size of soil particles blown by the wind is proportional to the wind speed. The sand content in soil wind erosion flow is related to the height. It has been observed that most of the soil particles in soil wind erosion flow are below 10cm near the surface, and increase with the increase of wind speed.

[0028] Example 1

[0029] See also Figure 1 This embodiment provides a gradient wind field coupled wind erosion particle intelligent collection system, which is composed of a gradient wind field generation module, an intelligent environment perception module, a multifunctional collection module, an intelligent control terminal, a visualization platform, and an intelligent power supply module, wherein:

[0030] The gradient wind field generation module consists of an axial flow fan unit and an air outlet angle adjustment mechanism. The axial flow fan unit generates a stable airflow, and the air outlet angle adjustment mechanism is used to drive the axial flow fan unit to generate a stepped wind speed gradient to simulate the temporal and spatial variation characteristics of the natural wind field, including the saltation and suspension motion of particles.

[0031] The intelligent environment perception module consists of a sensor network and a multi-sensor fusion unit. The sensor network is used to collect wind farm parameter environment data in real time, and the multi-sensor fusion unit is used to fuse the parameter data collected by the sensor network.

[0032] The multifunctional collection module consists of a detachable air guide tube and a particle collector. The detachable air guide tube is used to dynamically adjust the particle trajectory, and the particle collector is used to collect wind erosion particles.

[0033] The intelligent control terminal consists of a wind field coupling model, a spatiotemporal dynamic evaluation unit, and an edge computing unit. The wind field coupling model is used to establish a mapping relationship between wind speed gradient, particle size, and collection efficiency. The spatiotemporal dynamic evaluation unit is used to output a wind erosion particle collection strategy. The edge computing unit is used to generate control instructions for the axial flow fan unit and the detachable air guide duct.

[0034] The visualization platform uses a 3D visualization interface developed with the Unity3D engine, supporting VR interactive operations (90Hz refresh rate, 6DoF control). It is used for human-computer interaction and displays wind field distribution, wind erosion particle collection efficiency, and environmental parameters in real time, making it easy for operators to monitor system status and provide decision support.

[0035] The intelligent power supply module provides intelligent power supply for each component module of the system. It consists of a solar power supply unit and a local battery unit. The solar power supply unit is powered by a double-sided double-glass photovoltaic module (540Wp, conversion efficiency 21.6%), and the local battery unit is powered by a lithium iron phosphate battery pack (48V / 200Ah, cycle life >3000 times). It provides redundant power support for the system and achieves low-power operation through energy management algorithms. When there is sufficient sunlight, the solar power supply unit is used to convert solar energy into electrical energy, providing clean energy for the system. At the same time, it charges the local battery unit and releases the stored energy when there is insufficient sunlight to ensure continuous power supply to the system.

[0036] The axial flow fan unit of this embodiment adopts a multi-stage programmable axial flow fan array (industrial-grade fan with a diameter of 300mm). The array layout adopts a hexagonal honeycomb structure with a spacing coefficient of 1.2D (D is the impeller diameter) to eliminate boundary layer interference. The single-machine power is adjustable from 0.75 to 3kW and the speed is adjusted by a frequency converter. The frequency converter supports stepless speed regulation from 0 to 100Hz and a response time of <50ms. It cooperates with the PID closed-loop control algorithm to achieve a wind speed accuracy of ±0.5m / s. The air outlet angle adjustment mechanism adopts a servo motor to drive a worm gear mechanism and is equipped with a 16-bit high-precision encoder. The inclination adjustment resolution reaches 0.1° and the dynamic response bandwidth is 5Hz. It supports ±30° dynamic inclination adjustment. The dynamic adjustment algorithm adopts adaptive sliding mode control and supports a 10Hz angle update frequency.

[0037] The sensor network of this embodiment consists of anemometers, temperature and humidity sensors, and a particle concentration monitoring unit arranged at the inlet, outlet, and particle collector area of the detachable air duct. The specific layout topology is as follows: a 3×3 array ultrasonic anemometer (range 0-30 m / s, accuracy ±0.1 m / s) is arranged at the inlet of the detachable air duct, a hot wire anemometer (frequency response 1 kHz) is configured at the outlet of the detachable air duct, and a laser particle size analyzer (detection range 0.1-500 μm) and a β-ray particle mass monitor are deployed in the particle collection area.

[0038] The multi-sensor fusion unit of this embodiment fuses the data collected by the sensor network through multi-sensor fusion technology, adopts extended Kalman filter (EKF) to fuse spatiotemporal asynchronous data, and combines with computational fluid dynamics (CFD) to generate a spatiotemporal distribution map of the wind field and the particle movement path.

[0039] The detachable air guide tube of this embodiment adopts a carbon fiber composite material cylinder (wall thickness 2mm, stiffness coefficient ≥50GPa) and is equipped with a cylinder angle adjustment mechanism to dynamically adjust the airflow inclination angle according to the wind speed gradient. The cylinder angle adjustment mechanism adopts an electric push rod (thrust 500N, stroke 300mm) to drive the four-bar mechanism, supports continuous adjustment of 0~45°, dynamic adjustment frequency 10Hz, and repeat positioning accuracy of ±0.5°.

[0040] The particle collector of this embodiment has a built-in centrifugal separation chamber and a grading filter module. The centrifugal separation chamber adopts a double volute structure (separation efficiency >95%@10μm), is equipped with a brushless motor to drive the impeller (speed adjustable from 0 to 6000rpm), and has a dynamic balance level of G2.5. The grading filter module adopts a three-stage gradient filter (20 mesh, 100 mesh, 500 mesh), and is equipped with an electromagnetic vibration cleaning system (cleaning cycle adjustable from 1 to 60min) to realize the classification and storage of particles by particle size. It also integrates a high-precision weighing unit (0.01g resolution, Max50kg) and a laser particle size analyzer (Mie scattering principle, 0.1 to 1000μm range) to perform real-time statistics on mass and particle size distribution.

[0041] The wind field coupling model of this embodiment is based on the Navier-Stokes equations and the wind erosion climate factor optimization algorithm, and combines the wind speed gradient and particle size distribution to establish a mapping relationship between wind speed gradient-particle size-collection efficiency, so as to provide a basis for the subsequent adjustment of the axial flow fan unit parameters and the wind guide angle of the detachable air guide tube.

[0042] The spatiotemporal dynamic evaluation module of this embodiment uses a convolutional neural network to perform spatiotemporal modeling of the spatiotemporal distribution of the wind field and the particle motion trajectory, outputs the optimal collection strategy, and dynamically optimizes the angle of the detachable air guide and the rotation speed of the axial flow fan unit to achieve efficient capture of particles of different particle sizes.

[0043] The edge computing unit of this embodiment deploys an embedded controller (ARM+FPGA architecture), which analyzes data in real time based on the ARM+FPGA architecture and generates control instructions to adjust the parameters of the axial fan unit and the air guide angle of the detachable air guide tube.

[0044] Example 2

[0045] This embodiment provides a working method of a gradient wind field coupled wind erosion particle intelligent collection system, comprising the following steps:

[0046] Step 1: The sensor network of the intelligent environmental perception module collects three-dimensional wind field data (wind speed and direction), environmental parameters (temperature and humidity), and particle concentration distribution at the inlet and outlet of the detachable air duct and the collection area in real time. The multi-sensor fusion unit uses Kalman filtering and data registration algorithms to generate a high-precision spatiotemporal distribution map of the wind field and reconstruct the particle motion trajectory.

[0047] Step 2: The wind field coupling model of the intelligent control terminal combines the Navier-Stokes equations with real-time collected particle size distribution data to establish a nonlinear mapping relationship between wind speed gradient (0.5-15 m / s) and target particle collection efficiency (0.1-100 μm). The spatiotemporal dynamic evaluation unit uses a CNN-LSTM hybrid network to predict the spatiotemporal sequence of wind field evolution and generate a dynamic collection strategy for the next 5-30 seconds.

[0048] Step 3: The edge computing unit of the intelligent control terminal is based on the FPGA parallel computing architecture and completes the following within 50ms:

[0049] Analyze the particle size distribution at the inlet of the detachable air guide;

[0050] Calculate the optimal wind speed gradient curve (±0.2m / s accuracy);

[0051] Generate variable frequency control parameters (0.1Hz step) for axial flow fan units;

[0052] Determine the inclination adjustment of the detachable air duct (±0.5° accuracy);

[0053] Step 4: The detachable air guide tube of the multifunctional collection module is driven by the cylinder angle adjustment mechanism to dynamically adjust the cylinder in three dimensions (maximum inclination angle ±30°). Combined with the centrifugal separation chamber and the classification filter module, the particles are classified and enriched according to the Stokes number.

[0054] Step 5: The built-in weighing unit and optical metering unit of the particle collector of the multifunctional collection module provide real-time feedback on the collection efficiency. The control terminal updates the control strategy every 10 seconds and maintains the target collection efficiency (±2% fluctuation) through PID closed-loop control.

[0055] The above workflow realizes an intelligent closed loop of environmental perception → modeling and prediction → dynamic regulation → collection optimization → feedback correction. Compared with traditional wind erosion particle collection devices, it has higher collection efficiency and lower energy consumption.

[0056] 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 in the scope of protection of the present invention.

Claims

1. A gradient wind field coupled wind erosion particle intelligent collection system, comprising a gradient wind field generation module, an intelligent environment perception module, a multifunctional collection module, an intelligent control terminal, a visualization platform, and an intelligent power supply module, characterized by: The gradient wind field generation module includes an axial flow fan unit for generating a stable airflow and an air outlet angle adjustment mechanism for driving the axial flow fan unit to generate a stepped wind speed gradient; The intelligent environment perception module includes a sensor network for collecting wind field parameters and environmental data in real time and a multi-sensor fusion unit for fusing the data of various parameters; The multifunctional collection module includes a detachable air guide for dynamically adjusting the particle trajectory and a particle collector for collecting wind erosion particles; The intelligent control terminal includes a wind field coupling model for establishing a mapping relationship between wind speed gradient, particle size, and collection efficiency, a spatiotemporal dynamic evaluation unit for outputting a wind erosion particle collection strategy, and an edge computing unit for generating control instructions for the axial flow fan unit and the detachable air guide tube; The visualization platform is used for human-computer interaction and real-time display of wind field distribution, wind erosion particle collection efficiency and environmental parameters; The intelligent power supply module provides intelligent power supply for the system.

2. The gradient wind field coupled wind erosion particle intelligent collection system according to claim 1, characterized in that: The axial flow fan unit adopts axial flow fans distributed in an array and the speed is adjusted by a frequency conversion controller. The air outlet angle adjustment mechanism supports ±30° dynamic inclination adjustment.

3. The gradient wind field coupled wind erosion particle intelligent collection system according to claim 1, characterized in that: The sensor network includes anemometers, temperature and humidity sensors, and a particle concentration monitoring unit arranged at the inlet, outlet, and particle collector areas of the detachable air guide. The multi-sensor fusion unit fuses the data collected by the sensor network through multi-sensor fusion technology to generate a spatiotemporal distribution map of the wind field and the particle movement path.

4. The gradient wind field coupled wind erosion particle intelligent collection system according to claim 1, characterized in that: The detachable air guide is equipped with a cylinder angle adjustment mechanism to dynamically adjust the airflow inclination angle according to the wind speed gradient. The particle collector has a built-in centrifugal separation bin and a grading filter module to store particles according to particle size, and integrates a weighing unit and an optical metering unit to count the mass and particle size distribution in real time.

5. The gradient wind field coupled wind erosion particle intelligent collection system according to claim 1, characterized in that: The wind field coupling model establishes a mapping relationship between wind speed gradient, particle size and collection efficiency based on the Navier-Stokes equation and particle size distribution. The spatiotemporal dynamic evaluation module uses a convolutional neural network to model the wind field and particle trajectory and outputs a wind erosion particle collection strategy. The edge computing unit generates control instructions in real time based on the ARM+FPGA architecture to adjust the parameters of the axial fan unit and the air guide angle of the detachable air guide tube.

6. The gradient wind field coupled wind erosion particle intelligent collection system according to claim 5, characterized in that: The spatiotemporal dynamic assessment module uses a convolutional neural network to perform spatiotemporal modeling of the spatiotemporal distribution of the wind field and the particle motion trajectory, outputs the optimal collection strategy, and dynamically optimizes the angle of the detachable air guide and the speed of the axial flow fan unit to achieve efficient capture of particles of different particle sizes.

7. The gradient wind field coupled wind erosion particle intelligent collection system according to claim 1, characterized in that: The intelligent power supply module includes a solar power supply unit powered by solar photovoltaic components and a local battery unit powered by rechargeable batteries, which provide redundant power support for the system and achieve low-power operation through energy management algorithms.

8. A working method for a gradient wind field coupled wind erosion particle intelligent collection system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: The sensor network of the intelligent environmental perception module collects three-dimensional wind field data, environmental parameters, and particle concentration distribution in real time. The multi-sensor fusion unit uses Kalman filtering and data registration algorithms to generate a high-precision spatiotemporal distribution map of the wind field and reconstruct the particle motion trajectory. Step 2: The wind field coupling model of the intelligent control terminal establishes a nonlinear mapping relationship between wind speed gradient and target particle collection efficiency. The spatiotemporal dynamic evaluation unit predicts the spatiotemporal sequence of wind field evolution and generates a dynamic collection strategy. Step 3: The edge computing unit analyzes the particle size distribution at the inlet of the detachable air duct, calculates the optimal wind speed gradient curve, generates the variable frequency control parameters of the axial flow fan unit, and determines the adjustment amount of the air duct inclination angle; Step 4: The detachable air guide tube drives the cylinder to dynamically adjust in three-dimensional space through the air outlet angle adjustment mechanism, and combines the centrifugal separation chamber and the filter classification module to achieve particle classification and enrichment according to the Stokes number; Step 5: The built-in weighing unit and optical metering unit of the particle collector of the multifunctional collection module provide real-time feedback on the collection efficiency. The intelligent control terminal periodically updates the control strategy and maintains the target collection efficiency through PID closed-loop control.

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