Wind erosion particle efficient collection system and method based on wind field reconstruction and wind speed profile guidance

Abstract

The invention discloses a wind erosion particle efficient acquisition system based on wind field reconstruction and wind speed profile guidance, which comprises a multi-dimensional wind field measurement module for performing wind field data acquisition based on a space-time synchronization algorithm, and a wind speed profile guidance module for constructing a dynamic wind speed gradient model based on an improved WAsP algorithm and predicting a wind speed change trend in combination with a Kalman filter. A collection frequency adjustment instruction is output, the efficient separation module efficiently collects wind erosion particles based on an optimization collection mechanism, and the data fusion module carries out space-time alignment on wind field data and particle trajectory data based on an LSTM neural network and outputs a wind erosion process reconstruction result; the system has multi-scale adaptability, supports a wind tunnel test and field measurement, realizes whole-process collection of particles with different heights and different motion states, and reduces observation errors; and meanwhile, multi-scale accurate analysis of the wind erosion process is also realized, and wind erosion mechanism analysis is realized through a high-precision reconstructed wind erosion process model.

Description

Technical Field

[0001] The present invention relates to the technical field of wind-sand physics, and in particular to a system and method for efficiently collecting wind-eroded particles based on wind field reconstruction and wind speed profile guidance. Background Art

[0002] In the existing technology, the wind erosion particle collection system uses a single sensor wind field measurement, which leads to an increase in the three-dimensional wind field reconstruction error. In addition, the strategy adopted by the existing system relies on a fixed threshold and cannot dynamically respond to changes in the wind speed profile. The discretization processing of data will cause distortion in the wind erosion mechanism analysis; and there is a particle size selectivity deviation in the sand collection structure, which greatly reduces the efficiency of wind erosion particle collection. Therefore, the present invention proposes an efficient wind erosion particle collection system and method based on wind field reconstruction and wind speed profile guidance to solve the problems existing in the existing technology. Summary of the Invention

[0003] In response to the above problems, the purpose of the present invention is to propose a system and method for efficiently collecting wind erosion particles based on wind field reconstruction and wind speed profile guidance. The system and method for efficiently collecting wind erosion particles based on wind field reconstruction and wind speed profile guidance have multi-scale adaptability, support wind tunnel tests and field measurements, realize the full-process collection of particles at different heights and in different motion states, and reduce observation errors; through multi-dimensional wind field reconstruction and wind speed profile dynamic guidance strategies, multi-scale and accurate analysis of the wind erosion process is achieved, and through the data fusion module, a high-precision reconstructed wind erosion process model can be used to realize wind erosion mechanism analysis, providing key technical support for the prevention and control of wind and sand disasters.

[0004] To achieve the purpose of the present invention, the present invention is implemented through the following technical solutions: a wind erosion particle efficient collection system based on wind field reconstruction and wind speed profile guidance, including a multi-dimensional wind field measurement module, a wind speed profile guidance module, an efficient separation module and a data fusion module. The multi-dimensional wind field measurement module collects wind field data based on a time-space synchronization algorithm, and can achieve a spatial resolution of 0.5m×0.5m×0.5m. The wind speed profile guidance module constructs a dynamic wind speed gradient model based on an improved WAsP algorithm and combines a Kalman filter to predict wind speed change trends, and outputs collection frequency adjustment instructions. The efficient separation module efficiently collects wind erosion particles based on an optimized collection mechanism. The data fusion module performs time-space alignment on wind field data and particle trajectory data based on an LSTM neural network and outputs wind erosion process reconstruction results.

[0005] Further improvements include: the multi-dimensional wind field measurement module is composed of a fusion architecture of a three-axis hot-wire anemometer and a particle image velocimeter. The measurement range of the three-axis hot-wire anemometer is 0-200m / s. The particle image velocimeter is composed of a double-exposure high-speed camera and a laser sheet light source combined measurement structure. The measurement frame rate of the particle image velocimeter is ≥500Hz.

[0006] Further improvements are: the wind speed profile guidance module includes a dynamic calibration module, an intelligent detection module and an analysis and adjustment module. The dynamic calibration module dynamically calibrates the measurement reference height based on Beidou satellite positioning data, which can make the starting wind speed judgment error ≤±0.3m / s, greatly improving the accuracy. The intelligent detection module is based on the wind speed gradient model and Kalman filter and combines the predicted wind speed change trend. The analysis and adjustment module generates an acquisition frequency adjustment signal based on the predicted data and sends it to the optimized collection mechanism to control the efficient separation adjustment of the multi-stage separation unit.

[0007] Further improvements are: the optimized collection mechanism includes a first-stage cyclone separator, a second-stage cyclone separator, a third-stage cyclone separator, a guide pipe and an adjustable blade machine, the first-stage cyclone separator, the second-stage cyclone separator and the third-stage cyclone separator are connected through a guide pipe, the guide pipes of the second-stage cyclone separator and the third-stage cyclone separator are connected to the side of the outlet pipe arranged above the first-stage cyclone separator and the second-stage cyclone separator, the guide pipe arranged on the first-stage cyclone separator serves as an air inlet pipe, and the guide pipes are all provided with an adjustable tablet press, and the guide pipes are connected tangentially when connected to the first-stage cyclone separator, the second-stage cyclone separator and the third-stage cyclone separator, so as to reduce turbulent loss and improve separation efficiency.

[0008] Further improvements include: the cone angle of the first-stage cyclone separator is 15-30°, the cone angle of the second-stage cyclone separator is 30-45°, and the cone angle of the third-stage cyclone separator is 45-60°. Through CFD optimization design, the separation efficiency of particles with a particle size of ≥0.1μm is ≥98%. The adjustable blade machine is equipped with adjustable rotating blades through a servo motor and an encoder is used to provide real-time feedback on the rotating blade angle. The blade's moment of inertia is ≤0.05kg·m 2 , the servo motor can realize the 90° rotation sorting action of the blade.

[0009] Further improvements are: the data fusion module includes a spatiotemporal alignment module and a wind erosion modeling module. The spatiotemporal alignment module aligns and synchronizes the multi-source data acquired through detection based on a multi-source data synchronization controller with a built-in LSTM neural network, and realizes nanosecond-level synchronization of the wind field measurement module, the high-efficiency separation module and the data acquisition card through a hardware trigger signal, with a synchronization error of ≤1μs. The wind erosion modeling module reconstructs a wind erosion process model based on the spatiotemporal aligned data.

[0010] A method for efficiently collecting wind-eroded particles based on wind field reconstruction and wind speed profile guidance includes the following steps:

[0011] Step 1: Obtain wind field time series data through a multi-dimensional wind field measurement module, and use wavelet transform to eliminate turbulence noise to obtain wind field detection data;

[0012] Step 2: Use the improved particle filter algorithm to dynamically update the wind speed profile model, output the wind speed gradient prediction value, and obtain the wind speed gradient prediction data;

[0013] Step 3: triggering the hierarchical collection instructions of the optimized collection mechanism according to the wind speed gradient prediction data, and controlling the optimized collection mechanism to collect wind erosion particles;

[0014] Step 4: Align and synchronize the collected data through the data fusion module, and reconstruct the particle size distribution based on the collected wind erosion particle samples to obtain a wind erosion model and generate a wind erosion intensity thermodynamic map.

[0015] Further improvements are: in step 2, the particle filter algorithm is improved to introduce an adaptive resampling threshold, and the wind speed mutation rate is greater than 30m / s 2 It automatically switches to the extended Kalman filter mode to ensure the stability of model prediction.

[0016] Further improvements are as follows: the collection strategy of the graded collection instruction in step three is that when the wind speed v∈[5, 10]m / s, the first-level separation mode is started to separate and collect wind-eroded particles with a particle size ≥100μm; when the wind speed v∈[10, 20]m / s, the second-level separation mode is started to synchronously separate and collect wind-eroded particles with a particle size of 50-100μm and less than 50μm; when the wind speed v≥20m / s, the third-level emergency mode is started, and the start and stop frequency of the rotating blades in the optimized collection system is increased to 20Hz.

[0017] A further improvement is that the generation of the wind erosion intensity heat map in step 4 includes the sediment transport rate and the erosion sensitivity index. The sediment transport rate is calculated by the formula Q = α·v 3 · C calculation, where α is the geomorphic coefficient, v is the wind speed, and C is the vegetation cover factor. The erosion sensitivity index is calculated by first performing nonlinear fitting of the particle size distribution D50 and D90 with the wind speed profile, and then the erosion sensitivity index is calculated by ESI = 0.65 × (D50 / D0) 2 +0.35×log(v / v0), where D0 is the average particle size, v is the near-surface wind speed, and v0 is the critical wind speed threshold for wind erosion in arid areas.

[0018] The beneficial effects of the present invention are: the system of the present invention has multi-scale adaptability, supports wind tunnel tests and field measurements, realizes the full-process collection of particles at different heights and in different motion states, and reduces observation errors; at the same time, it innovatively integrates three-dimensional wind field measurement, adaptive collection control and multi-stage separation technology, and solves the technical problems of low collection efficiency and high data discreteness existing in traditional methods.

[0019] Through multi-dimensional wind field reconstruction and dynamic guidance strategy of wind speed profile, multi-scale accurate analysis of wind erosion process is achieved. Through data fusion module, high-precision reconstructed wind erosion process model is realized to realize wind erosion mechanism analysis, providing key technical support for the prevention and control of wind and sand disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Flow chart of the acquisition method of the present invention.

[0021] Figure 2 This is a structural diagram of the optimized collection mechanism in the system of the present invention. DETAILED DESCRIPTION

[0022] In order to deepen the understanding of the present invention, the present invention will be further described in detail below with reference to the examples. The examples are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0023] according to Figure 1 and Figure 2 As shown, this embodiment provides an efficient collection system for wind erosion particles based on wind field reconstruction and wind speed profile guidance, including a multi-dimensional wind field measurement module, a wind speed profile guidance module, an efficient separation module and a data fusion module. The multi-dimensional wind field measurement module collects wind field data based on a time-space synchronization algorithm, and can achieve a spatial resolution of 0.5m×0.5m×0.5m. The wind speed profile guidance module constructs a dynamic wind speed gradient model based on an improved WAsP algorithm and combines a Kalman filter to predict the wind speed change trend, and outputs a collection frequency adjustment instruction. The efficient separation module efficiently collects wind erosion particles based on an optimized collection mechanism. The data fusion module performs time-space alignment on wind field data and particle trajectory data based on an LSTM neural network and outputs the wind erosion process reconstruction result.

[0024] The multi-dimensional wind field measurement module consists of a fusion architecture of a three-axis hot-wire anemometer and a particle image velocimeter. The measurement range of the three-axis hot-wire anemometer is 0-200m / s. The particle image velocimeter consists of a double-exposure high-speed camera and a laser sheet light source combined measurement structure. The measurement frame rate of the particle image velocimeter is ≥500Hz.

[0025] The wind speed profile guidance module includes a dynamic calibration module, an intelligent detection module and an analysis and adjustment module. The dynamic calibration module dynamically calibrates the measurement reference height based on Beidou satellite positioning data, which can make the starting wind speed judgment error ≤±0.3m / s, greatly improving the accuracy. The intelligent detection module is based on the wind speed gradient model and Kalman filter and combines the predicted wind speed change trend. The analysis and adjustment module generates an acquisition frequency adjustment signal based on the predicted data and sends it to the optimized collection mechanism to control the efficient separation adjustment of the multi-stage separation unit.

[0026] The optimized collection mechanism includes a primary cyclone separator, a secondary cyclone separator, a tertiary cyclone separator, a guide pipe and an adjustable blade machine. The primary cyclone separator, the secondary cyclone separator and the tertiary cyclone separator are connected through a guide pipe. The guide pipes of the secondary cyclone separator and the tertiary cyclone separator are connected to the side of the outlet pipe arranged above the primary cyclone separator and the secondary cyclone separator. The guide pipe arranged on the primary cyclone separator serves as an air inlet pipe. The guide pipe is provided with an adjustable tablet press. The guide pipe is connected to the primary cyclone separator, the secondary cyclone separator and the tertiary cyclone separator in a tangential direction to reduce turbulent loss and improve separation efficiency.

[0027] The cone angle of the first-stage cyclone separator is 15-30°, the cone angle of the second-stage cyclone separator is 30-45°, and the cone angle of the third-stage cyclone separator is 45-60°. Through CFD optimization design, the separation efficiency of particles with a particle size of ≥0.1μm is ≥98%. The adjustable blade machine is equipped with adjustable rotating blades through a servo motor and combined with an encoder to provide real-time feedback on the rotating blade angle. The blade's moment of inertia is ≤0.05kg·m 2 , the servo motor can realize the 90° rotation sorting action of the blade.

[0028] The data fusion module includes a spatiotemporal alignment module and a wind erosion modeling module. The spatiotemporal alignment module aligns and synchronizes the multi-source data acquired through detection based on a multi-source data synchronization controller with a built-in LSTM neural network. The wind field measurement module, the high-efficiency separation module and the data acquisition card are synchronized at the nanosecond level through hardware trigger signals, with a synchronization error of ≤1μs. The wind erosion modeling module reconstructs the wind erosion process model based on the spatiotemporal aligned data.

[0029] A method for efficiently collecting wind-eroded particles based on wind field reconstruction and wind speed profile guidance includes the following steps:

[0030] Step 1: Obtain wind field time series data through the multi-dimensional wind field measurement module, and use wavelet transform to eliminate turbulence noise to obtain wind field detection data.

[0031] Step 2: Use the improved particle filter algorithm to dynamically update the wind speed profile model, output the wind speed gradient prediction value, and obtain the wind speed gradient prediction data;

[0032] The improved particle filter algorithm introduces an adaptive resampling threshold, and the wind speed mutation rate is greater than 30m / s 2 It automatically switches to the extended Kalman filter mode to ensure the stability of model prediction.

[0033] Step 3: triggering the hierarchical collection instructions of the optimized collection mechanism according to the wind speed gradient prediction data, and controlling the optimized collection mechanism to collect wind erosion particles;

[0034] The collection strategy of the graded collection instruction is as follows: when the wind speed v∈[5, 10]m / s, the first-level separation mode is activated to separate and collect wind-eroded particles with a particle size ≥100μm; when the wind speed v∈[10, 20]m / s, the second-level separation mode is activated to synchronously separate and collect wind-eroded particles with a particle size of 50-100μm and less than 50μm; when the wind speed v≥20m / s, the third-level emergency mode is activated, and the start and stop frequency of the rotating blades in the optimized collection system is increased to 20Hz.

[0035] Step 4: The collected data are aligned and synchronized through the data fusion module. At the same time, the particle size distribution of the collected wind erosion particle samples is reconstructed to obtain a wind erosion model and generate a wind erosion intensity heat map;

[0036] The generation of the wind erosion intensity heat map includes the calculation of the sediment transport rate Q and the erosion sensitivity index ESI:

[0037] The sediment transport rate is given by the formula Q = α·v 3 · C calculation, where α is the landform coefficient, v is the wind speed, and C is the vegetation cover factor;

[0038] The calculation of erosion sensitivity index is to first perform nonlinear fitting on the particle size distribution D50, D90 and wind speed profile, and then the erosion sensitivity index is calculated by ESI = 0.65 × (D50 / D0) 2 +0.35×log(v / v0), where D0 is the average particle size, v is the near-surface wind speed, and v0 is the critical wind speed threshold for wind erosion in arid areas.

[0039] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An efficient wind erosion particle collection system based on wind field reconstruction and wind speed profile guidance, characterized by: The system includes a multi-dimensional wind field measurement module, a wind speed profile guidance module, an efficient separation module and a data fusion module. The multi-dimensional wind field measurement module collects wind field data based on a spatiotemporal synchronization algorithm. The wind speed profile guidance module constructs a dynamic wind speed gradient model based on an improved WAsP algorithm and combines a Kalman filter to predict wind speed change trends and outputs collection frequency adjustment instructions. The efficient separation module efficiently collects wind erosion particles based on an optimized collection mechanism. The data fusion module performs spatiotemporal alignment of wind field data and particle trajectory data based on an LSTM neural network and outputs wind erosion process reconstruction results.

2. The efficient wind erosion particle collection system based on wind field reconstruction and wind speed profile guidance according to claim 1 is characterized by: The multi-dimensional wind field measurement module consists of a fusion architecture of a three-axis hot-wire anemometer and a particle image velocimeter. The measurement range of the three-axis hot-wire anemometer is 0-200m / s. The particle image velocimeter consists of a double-exposure high-speed camera and a laser sheet light source combined measurement structure. The measurement frame rate of the particle image velocimeter is ≥500Hz.

3. The efficient wind erosion particle collection system based on wind field reconstruction and wind speed profile guidance according to claim 1 is characterized by: The wind speed profile guidance module includes a dynamic calibration module, an intelligent detection module and an analysis and adjustment module. The dynamic calibration module dynamically calibrates the measurement reference height based on Beidou satellite positioning data. The intelligent detection module is based on a wind speed gradient model and a Kalman filter and combines the predicted wind speed change trend. The analysis and adjustment module generates an acquisition frequency adjustment signal based on the predicted data and sends it to the optimization collection mechanism.

4. The efficient wind erosion particle collection system based on wind field reconstruction and wind speed profile guidance according to claim 1 is characterized by: The optimized collection mechanism includes a first-stage cyclone separator, a second-stage cyclone separator, a third-stage cyclone separator, a guide pipe and an adjustable blade machine. The first-stage cyclone separator, the second-stage cyclone separator and the third-stage cyclone separator are connected through a guide pipe. The guide pipes of the second-stage cyclone separator and the third-stage cyclone separator are connected to the side of the outlet pipe arranged above the first-stage cyclone separator and the second-stage cyclone separator. The guide pipe arranged on the first-stage cyclone separator serves as an air inlet pipe, and the guide pipes are all provided with an adjustable tablet press.

5. The efficient wind erosion particle collection system based on wind field reconstruction and wind speed profile guidance according to claim 4 is characterized by: The cone angle of the first-stage cyclone separator is 15-30°, the cone angle of the second-stage cyclone separator is 30-45°, and the cone angle of the third-stage cyclone separator is 45-60°. The adjustable blade machine is equipped with adjustable rotating blades through a servo motor and is combined with an encoder to provide real-time feedback on the rotating blade angle.

6. The efficient wind erosion particle collection system based on wind field reconstruction and wind speed profile guidance according to claim 1 is characterized by: The data fusion module includes a spatiotemporal alignment module and a wind erosion modeling module. The spatiotemporal alignment module aligns and synchronizes the multi-source data acquired through detection based on a multi-source data synchronization controller with a built-in LSTM neural network. The wind erosion modeling module reconstructs a wind erosion process model based on the spatiotemporal aligned data.

7. A method for efficiently collecting wind-eroded particles based on wind field reconstruction and wind speed profile guidance, characterized in that: The following steps are involved: Step 1: Obtain wind field time series data through a multi-dimensional wind field measurement module, and use wavelet transform to eliminate turbulence noise to obtain wind field detection data; Step 2: Use the improved particle filter algorithm to dynamically update the wind speed profile model, output the wind speed gradient prediction value, and obtain the wind speed gradient prediction data; Step 3: triggering the hierarchical collection instructions of the optimized collection mechanism according to the wind speed gradient prediction data, and controlling the optimized collection mechanism to collect wind erosion particles; Step 4: Align and synchronize the collected data through the data fusion module, and reconstruct the particle size distribution based on the collected wind erosion particle samples to obtain a wind erosion model and generate a wind erosion intensity thermodynamic map.

8. The method for efficiently collecting wind-eroded particles based on wind field reconstruction and wind speed profile guidance according to claim 7, characterized in that: In step 2, the particle filter algorithm is improved to introduce an adaptive resampling threshold, and the wind speed mutation rate is greater than 30m / s 2 Automatically switches to the extended Kalman filter mode.

9. The method for efficiently collecting wind-eroded particles based on wind field reconstruction and wind speed profile guidance according to claim 7, characterized in that: The collection strategy of the graded collection instruction in step three is as follows: when the wind speed v∈[5, 10]m / s, the first-level separation mode is started to separate and collect wind-eroded particles with a particle size ≥100μm; when the wind speed v∈[10, 20]m / s, the second-level separation mode is started to synchronously separate and collect wind-eroded particles with a particle size of 50-100μm and less than 50μm; when the wind speed v≥20m / s, the third-level emergency mode is started, and the start and stop frequency of the rotating blades in the optimized collection system is increased to 20Hz.

10. The system and method for efficiently collecting wind-eroded particles based on wind field reconstruction and wind speed profile guidance according to claim 7, characterized in that: The generation of the wind erosion intensity heat map in step 4 includes the sediment transport rate and the erosion sensitivity index. The sediment transport rate is given by the formula Q = α·v 3 · C calculation, where α is the geomorphic coefficient, v is the wind speed, and C is the vegetation cover factor. The erosion sensitivity index is calculated by first performing nonlinear fitting of the particle size distribution D50 and D90 with the wind speed profile, and then the erosion sensitivity index is calculated by ESI = 0.65 × (D50 / D0) 2 +0.35×log(v / v0), where D0 is the average particle size, v is the near-surface wind speed, and v0 is the critical wind speed threshold for wind erosion in arid areas.

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