CN113673181A - Intelligent airport area wind shear identification method based on multi-source wind field data - Google Patents
Intelligent airport area wind shear identification method based on multi-source wind field data Download PDFInfo
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Abstract
An airport area wind shear intelligent identification method based on multi-source wind field data comprises the following steps of 1) generating a three-dimensional refined wind field of an airport area based on the multi-source wind field data; 1-1: constructing a refined wind field numerical simulation model of an airport area; 1-2: constructing a simulation wind field database based on the CFD model; 1-3: calculating the matching degree of the current measured multi-source wind field data and each group of CFD data, and finding the CFD simulation data with the highest matching degree with the measured multi-source wind field data; 2) performing wind field data fusion based on a CFD technology and a WRF mode to obtain a three-dimensional wind field; 3) performing numerical correction on the three-dimensional wind field obtained by fusion based on the current measured data to obtain all-weather non-difference airport regional digital wind field data; 4) the wind shear is intelligently identified based on the convolutional neural network to obtain the position and the shear strength of the wind shear, and wind shear marking is carried out in a three-dimensional wind field data graph by combining refined three-dimensional wind field data.
Description
Technical Field
The invention relates to the technical field of wind shear identification, in particular to an airport regional wind shear intelligent identification method based on multi-source wind field data.
Background
For aviation flight, flight safety is the first element. In actual flight activities, there are many factors affecting flight safety, with low-altitude wind shear being one of the most important factors. Because the aircraft is in low altitude in the take-off and landing stages and the flight speed of the aircraft is slow, if the aircraft meets low altitude wind shear at the moment, the pilot can hardly react in time, and serious flight accidents are easy to occur. Thus, low altitude wind shear is also known as a "stealth aircraft killer". The method is an important ring for flight safety guarantee work by timely identifying wind shear and sending out early warning information. Wind shear identification and early warning, two core technologies are designed, wherein firstly, wind field monitoring, namely wind field information acquisition, is carried out; the second is a wind shear identification algorithm, i.e. wind shear information mining based on the acquired data. With the progress of technology, various wind field detection means have been developed, and each wind field detection means develops a corresponding wind shear identification algorithm.
As early as the 70's of the 20 th century, the united states began monitoring wind shear caused by downbursts and the like by constructing multiple wind stations at airports, known as the low altitude wind shear warning system (LLWAS). The initial LLWAS-I consists of 6 wind stations, 1 deployed in the middle of the runway and 5 deployed around the runway, detecting wind shear by detecting the difference in wind speed from the central wind station to the other wind stations. However, because of the large spacing between the 6 wind stations, it is difficult to detect minor downbursts when small scale downbursts occur between the 2 wind stations, and therefore LLWAS-I cannot detect the downburst and has a high probability of false alarms. The improved LLWAS-II upgrades the software algorithm to improve the accuracy of detecting microbursts and reduces the number of false alarms. At present, LLWAS-II is completely eliminated and replaced by LLWAS-III, up to 32 wind measuring sensors can be installed, and a new triangular area detection algorithm is adopted, so that the early warning accuracy rate is improved, and meanwhile, the false warning rate is reduced. LLWAS-III divides 3 or 2 wind measuring sensors into several groups, and calculates the wind field and its variation according to the detected wind speed and direction and the relative position of each sensor. If the wind field is divergent, calculating the change of the wind speed according to a micro downburst model established by the symmetrical assumption, and judging the outflow center of the downburst. However, the LLWAS system has a limited monitoring height, only detects horizontal wind shear near the surface, and the monitoring effect is affected by the number and location of surface wind stations deployed.
With the application of doppler weather radar in meteorological services, wind shear algorithms have been developed rapidly. In 1985, foreign scholars proposed a difference filtered synthetic shear algorithm that synthesizes two-dimensional wind shear by calculating the radial and tangential wind shear along the weather radar, respectively, and can be used to identify wind shear caused by cold front crossing, shear lines, radial lines, cyclone, etc. Meanwhile, partial scholars improve the existing cyclone identification algorithm and provide a gust front identification algorithm according to a radial velocity radial line. In 1989, another scholars added double verification of continuity check of a vertical wind field in the algorithm, and wind shear identification caused by gust fronts is realized. In 2013, a scholars uses a networking fusion technology to judge the difference of adjacent lattice points to identify the position of wind shear. In 2000, domestic scholars proposed a method for calculating synthetic wind shear by using a least square method, so that the successful detection of airport airspace humidity wind shear is realized. In 2013, domestic scholars use a bidirectional gradient and radial line identification algorithm to accurately position the front of the gust wind front and successfully identify wind shear caused by the gust wind front. Therefore, through development and improvement for many years, the technology for identifying the wet wind shear caused by wind gusts, downburst currents and the like based on the doppler weather radar is mature, but the detection of the dry wind shear caused by aircraft wake vortexes, radiation inverse temperature, terrain obstacles and the like still has more problems.
With the continuous maturation of the laser wind radar technology, the laser wind radar wind shear monitoring algorithm is rapidly developed. At present, the mainstream algorithms for monitoring wind shear by the laser wind measuring radar include an IRIS algorithm of Vaisala Finland and a Rainbow algorithm of Germany, and the two algorithms are widely applied to a plurality of international large airports internationally and have good effects. The research on laser wind measuring radar wind shear monitoring algorithm was originally developed by national hong Kong astronomical observatory research teams, a lower slideway scanning mode and a single slope detection algorithm adapted to the same are newly provided, and the method is applied to hong Kong airport service guarantee. Meanwhile, hong kong astronomical research teams proposed wind shear identification algorithms based on F factor and eddy current dissipation ratio in 2012 and 2014, respectively. A large amount of wind shear recognition algorithm researches are carried out at China civil aviation university, firstly, a wind shear early warning algorithm of a hong Kong astronomical observatory research team is improved, and a lower slide double-slope detection algorithm and a small-scale wind shear detection algorithm for correcting an F factor are provided; meanwhile, a wind shear early warning algorithm based on self-adaptive multi-scale gradient search is provided for wind field data in a PPI (pulse-beat pulse-order) scanning mode of the laser wind radar, and the wind shear early warning rate is improved. Compared with the traditional wind measuring technology, the laser wind measuring radar has obvious advantages, but the detection performance of the laser wind measuring radar is influenced by weather conditions because laser is attenuated by air molecules, aerosol particles and cloud and rain particles when the laser is transmitted in the atmosphere, and the laser wind measuring radar is not suitable for detecting a wet wind field.
In recent years, with the development of artificial intelligence technology, a plurality of wind shear intelligent recognition algorithms are proposed successively. The Chinese university of civil aviation team does a lot of work in this regard: 2012, a wind shear type identification algorithm based on an image processing method is provided, and compared with the traditional wind shear identification algorithm, the algorithm omits meteorological factors, completely converts the meteorological factors into an image processing problem, and realizes the type identification of low altitude wind shear; in 2013, a support vector machine parameter optimization method based on a predation search strategy genetic algorithm is applied to wind shear identification; in the same year, a low-altitude wind shear type identification method based on wavelet transform extraction texture features and a BP neural network is proposed; in 2014, aiming at the problem that a standard support vector machine cannot provide posterior probability in laser radar wind shear image recognition, a probability support vector machine recognition method is provided; in the same year, aiming at the shape characteristic relation among the low downburst, the low-altitude torrent, the downwind and the crosswind low-altitude wind shear sample images, the characteristic extraction technology of the wavelet invariant moment is provided to be applied to wind shear identification; in 2015, a texture feature fusion method combining rotation invariant non-subsampled Contourlet transformation and Weber descriptor is provided; aiming at laser radar scanning images of 4 different low-altitude wind shears, namely, micro downburst, low-altitude torrent, direct wind and crosswind in the same year, an identification method based on combination of shape features and texture features is provided; in 2019, a multi-layer feature extraction and adaptive fusion algorithm based on a deep convolutional neural network is provided. Although various wind shear recognition algorithms are proposed at present, each algorithm needs specific applicable conditions, the recognition effect difference under different conditions is large, and the actual effect is yet to be tested.
In summary, in the prior art, theoretically, an ideal effect cannot be obtained through a shear identification algorithm of a certain single device, and wind shear identification based on multi-source wind field data cannot be solved, including detection of dry or wet wind shear caused by different reasons.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides an airport regional wind shear comprehensive intelligent identification method based on multi-source wind field data, which can fuse various wind measurement results to obtain a three-dimensional refined wind field of a guarantee region, further excavate wind shear information from a three-dimensional wind field and finally obtain a three-dimensional wind field data map marked with a wind shear identification result.
The technical scheme is as follows: in order to achieve the above object, the present invention provides an intelligent identification method for wind shear of an airport area based on multi-source wind field data, comprising the following steps,
step 1: generating a three-dimensional refined wind field of the airport area based on the multi-source wind field data;
step 2: performing wind field data fusion based on a CFD technology and a WRF mode to obtain a three-dimensional wind field; WRF refers to weather forecast mode, mesoscale weather forecast mode;
and step 3: performing numerical correction on the three-dimensional wind field obtained by fusion based on the current measured data to obtain all-weather non-difference airport regional digital wind field data;
and 4, step 4: the wind shear is intelligently identified based on the convolutional neural network to obtain the position and the shear strength of the wind shear, and wind shear marking is carried out in a three-dimensional wind field data graph by combining refined three-dimensional wind field data.
Further, in the present invention: the generating of the three-dimensional refined wind field further comprises the following steps,
step 1-1: constructing a refined wind field numerical simulation model of an airport area;
step 1-2: constructing a simulation wind field database based on the CFD model;
step 1-3: calculating the matching degree of the current measured multi-source wind field data and each group of CFD data, and finding the CFD simulation data with the highest matching degree with the measured multi-source wind field data;
step 1-4: performing numerical correction on the CFD simulation data based on the current actually measured multi-source wind field data;
step 1-5: and obtaining the current three-dimensional refined wind field after data reconstruction.
Further, in the present invention: the wind farm data fusion further comprises the steps of,
step 2-1: outputting and processing WRF data;
step 2-2: generating a CFD refinement network;
step 2-3: performing data exchange on the WRF and the CFD;
step 2-4: and finally outputting the WRF data and processing the WRF data.
Further, in the present invention: the numerical correction further comprises the steps of,
step 3-1: for the area with credible actually measured wind field data, the actually measured data is taken as the main;
step 3-2: supplementing the area without actually measured wind field data based on the simulation data of the CFD under the current meteorological condition;
step 3-3: when more than two wind measuring results exist at the same position, performing weighted calculation, wherein the weight of each wind measuring device is determined by the weather condition, the wind measuring principle of each wind measuring device, the observation position and the like;
step 3-4: the area with the actually measured wind field data has influences of different weights on the wind field data in a certain peripheral range, and the specific influence range and influence weight need to be determined by combining a sensitivity experiment.
Further, in the present invention: the intelligent identification of wind shear based on a convolutional neural network further comprises the steps of,
step 4-1: constructing a wind shear recognition convolutional neural network structure and establishing a wind field plane image set with wind shear characteristics;
step 4-2: extracting image characteristics, and training wind shear to identify convolutional neural network node parameters;
step 4-3: constructing a plane scanning scheme of regional refined three-dimensional wind field data;
step 4-4: outputting the position and the shear strength of wind shear;
and 4-5: and marking the shear position and the shear strength in the three-dimensional wind field data graph.
The invention also provides an airport regional wind shear intelligent identification method based on multi-source wind field data, which comprises the following steps,
step 1: each wind measuring device measures the airport area;
step 2: fusing the measurement results based on the priority;
and step 3: and obtaining wind shear early warning information based on the fusion result.
Further, in the present invention: the wind measuring equipment comprises a wind direction anemometer, a Doppler weather radar, a wind profiler and a laser wind measuring radar.
Further, in the present invention: the wind direction anemoscope is erected at the top of a wind rod, and the wind rod is arranged around an airport area; the Doppler weather radar is used for detecting a wet wind field; the wind profiler is used for detecting a dry wind field; the laser wind-finding radar is used for detecting the slideway of the airport area. And constructing a CFD wind field simulation model. The type and the number of wind measuring instruments needing to be controlled in multiple points. The wind measuring instrument needs to be accurate on site, the wind measuring radar is mainly combined with a ground wind lever due to the advantages of the laser wind measuring radar, and when other wind measuring instruments (a wind profiler, a Doppler weather radar and the like) exist in a station, a measuring result can be accessed into the system according to a required data format. The number of instruments needs to be reduced as much as possible on the premise of accuracy and effectiveness of regional wind field monitoring. The method comprises the steps of obtaining wind field information of a set detection node, taking the wind field information of one part of nodes as an initial value, taking the wind field information of the other part of nodes as a target value, combining an established CFD wind field simulation model by bringing the initial value into the target value, calculating to obtain the wind field information of the target value, comparing, finding the most sensitive position, and arranging a corresponding instrument. And obtaining multi-source wind field data.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that: the wind shear identification method provided by the invention is based on multi-source wind field data and a wind shear identification algorithm of a convolutional neural network, can detect wind shear information under different meteorological conditions, and realizes accurate identification of wind shear under various meteorological conditions.
Drawings
FIG. 1 is a schematic overall flow chart of an intelligent identification method for wind shear in an airport area based on multi-source wind field data, which is provided by the invention;
FIG. 2 is a schematic flow chart of generating a three-dimensional refined wind field according to the present invention;
FIG. 3 is a schematic flow chart of wind field data fusion according to the present invention;
fig. 4 is a schematic flow chart of the intelligent wind shear identification based on the convolutional neural network in the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Due to different wind measuring principles of different equipment, the detection accuracy of each wind measuring equipment under different meteorological conditions is different, so that the success rate of wind shear early warning is different. Generally, the detection accuracy of the wind anemoscope is highest, but the wind anemoscope is usually erected on the top of a 10m high wind rod and can only detect near-stratum single-point horizontal wind, the detection area and height are limited, and the number of sites to be built needs to be increased for detecting a wind field in a larger area. Due to its limited detection height, it can only be used to monitor low altitude wind shear caused by downburst or the like. The tracer of the Doppler weather radar is cloud rain particles, so that the Doppler weather radar can only detect a region with cloud rain, has a certain effect on wet low-altitude wind shear accompanied by downburst, has larger working blind area and poorer distance resolution, is difficult to aim at a lower slideway and a runway region of an airplane for observation, and has poorer detection effect on small-microscale wind shear such as micro downburst; and is limited by the tracer itself and does not have the detection capability of dry wind shear. The tracer of the wind profiler is atmospheric turbulence and is suitable for dry wind field detection, but a single wind profiler can only detect vertical wind field information of a headspace region, and if the detection range is to be expanded, multiple networking detections are required, so that the economy is poor, and the actual implementation is difficult. In addition, the detection result is easily interfered by ground clutter, the credibility of a near stratum wind field is low, a detection blind area exists, and the low-altitude wind shear is not easy to identify. The tracer of the laser wind measuring radar is aerosol particles, is suitable for dry wind field detection, has the unique advantages of strong anti-interference capability, small blind area, high data spatial resolution, capability of quickly acquiring refined low-altitude three-dimensional wind field information and the like, and is widely applied to aviation safety guarantee work at present. The laser is attenuated by air molecules, aerosol particles and cloud and rain particles when transmitted in the atmosphere, and the detection performance of the laser wind-measuring radar is influenced by weather conditions, so that the laser wind-measuring radar is not suitable for detecting a wet wind field, but is an optimal detection means for a dry wind field, and is particularly suitable for detecting wind fields and wind shear in key areas such as a glidepath, an airplane runway and the like.
As shown in fig. 1, an overall flow diagram of an intelligent identification method for wind shear in an airport area based on multi-source wind field data provided by the present invention is shown, and the method includes the following steps:
step 1: generating a three-dimensional refined wind field of the airport area based on the multi-source wind field data;
step 2: performing wind field data fusion based on a CFD technology and a WRF mode to obtain a three-dimensional wind field; the Computational Fluid Dynamics (CFD) model is a discrete solution for solving a Fluid mechanics control equation by a numerical method, is used for simulating a near-ground wind field, can fully reproduce the flow of air under a complex terrain, and is a development trend of a wind field analysis technology; according to the geographic model of the service guarantee area, CFD numerical simulation is carried out, and CFD wind field simulation results with different directions, different wind speeds and different gust characteristics are obtained; the selection of the size of the grid of the service guarantee area needs to be determined by combining with the terrain and landform conditions so as to achieve the minimum calculated amount and the optimal simulation result;
and step 3: performing numerical correction on the three-dimensional wind field obtained by fusion based on the current measured data to obtain all-weather non-difference airport regional digital wind field data;
and 4, step 4: the wind shear is intelligently identified based on the convolutional neural network to obtain the position and the shear strength of the wind shear, and wind shear marking is carried out in a three-dimensional wind field data graph by combining refined three-dimensional wind field data.
Wherein the generation of the three-dimensional refined wind field further comprises the following steps,
step 1-1: constructing a refined wind field numerical simulation model of an airport area;
the airport area refined wind field numerical simulation model further comprises the steps of collecting the topographic and geomorphic characteristics of the airport area, obtaining historical wind field data of the airport area, carrying out characteristic analysis on the historical wind field of the airport area, generating a model boundary condition, and combining the topographic and geomorphic characteristics of the airport area to obtain the airport area refined wind field numerical simulation model.
Step 1-2: constructing a simulation wind field database based on the CFD model;
step 1-3: calculating the matching degree of the current measured multi-source wind field data and each group of CFD data, and finding the CFD simulation data with the highest matching degree with the measured multi-source wind field data;
step 1-4: performing numerical correction on the CFD simulation data based on the current actually measured multi-source wind field data;
step 1-5: and obtaining the current three-dimensional refined wind field after data reconstruction.
The wind field data fusion algorithm is used for processing direct wind measurement results which are obtained by various means and have inconsistent space-time and different measurement precision, and all-weather and non-differential airport area digital wind field data are obtained after fusion and are used for further wind shear identification. In the embodiment, the measured wind field data is combined with the CFD mode and the mesoscale WRF mode wind field simulation result to perform wind field data fusion, and meanwhile, the numerical correction is performed on the three-dimensional wind field obtained through fusion based on the current measured data.
Specifically, the wind field data fusion further comprises the following steps,
step 2-1: outputting and processing WRF data;
wherein the method further comprises the following steps of,
step 2-1-1: the WRF simulates a coarse resolution wind field of a specific airport;
step 2-1-2: processing wind field data of the side boundary through interpolation;
step 2-1-3: a polynomial is fitted to the function of the variation of the side boundary wind field data with height.
Step 2-2: generating a CFD refinement network;
wherein the method further comprises the following steps of,
step 2-2-1: generating a high resolution grid based on the STL format elevation data;
step 2-2-2: SnapppyHexMesh generates the computational network and imports the CFD.
Step 2-3: performing data exchange on the WRF and the CFD;
wherein the method further comprises the following steps of,
step 2-3-1: writing the simulated side boundary function into an initial file of the CFD;
step 2-3-2: and realizing the assignment of the side boundary grid through a groovy function library.
Step 2-4: and finally outputting the WRF data and processing the WRF data.
Wherein the method further comprises the following steps of,
step 2-4-1: monitoring WRF output and inflow direction;
step 2-4-2: WRF exchanges data in each step of CFD;
step 2-4-3: and (5) calculating the CFD fine grid numerical value and outputting the result.
Specifically, the numerical correction is performed by data assimilation, and the numerical correction further includes the steps of,
step 3-1: for the area with credible actually measured wind field data, the actually measured data is taken as the main;
step 3-2: supplementing the area without actually measured wind field data based on the simulation data of the CFD under the current meteorological condition;
step 3-3: when more than two wind measuring results exist at the same position, performing weighted calculation, wherein the weight of each wind measuring device is determined by the weather condition, the wind measuring principle of each wind measuring device, the observation position and the like;
step 3-4: the area with the actually measured wind field data has influences of different weights on the wind field data in a certain peripheral range, and the specific influence range and influence weight need to be determined by combining a sensitivity experiment.
Wherein the intelligent identification of wind shear based on a convolutional neural network further comprises the steps of,
step 4-1: constructing a wind shear recognition convolutional neural network structure and establishing a wind field plane image set with wind shear characteristics;
step 4-2: extracting image characteristics, and training wind shear to identify convolutional neural network node parameters;
step 4-3: constructing a plane scanning scheme of regional refined three-dimensional wind field data;
step 4-4: outputting the position and the shear strength of wind shear;
and 4-5: and marking the shear position and the shear strength in the three-dimensional wind field data graph.
The invention also provides an airport regional wind shear intelligent identification method based on multi-source wind field data, which comprises the following steps,
step 1: each wind measuring device measures the airport area; the wind measuring equipment comprises a wind direction anemometer, a Doppler weather radar, a wind profiler and a laser wind measuring radar.
Specifically, the wind direction anemoscope is erected on the top of a wind rod, and the wind rod is arranged around an airport area, in this embodiment, the height of the wind rod is not lower than 10 m; the Doppler weather radar is used for detecting a wet wind field; the wind profiler is used for detecting a dry wind field; the laser wind-measuring radar is used for detecting the slideway of the airport area; fusing the measurement results based on the priority; and fusing and early warning of the multi-equipment wind shear recognition result. The fusion is embodied in the release of the wind shear identification result at different spatial positions and under different weather conditions.
Specifically, the detection accuracy of the anemoscope is the highest, so that the measurement result is set to be issued preferentially, but the anemoscope can only be used for detecting near-formation single-point horizontal wind, the detection area and height are limited, so that the anemoscope can only be used for monitoring low-altitude wind shear caused by downburst and the like, and in order to detect a wind field in a large area, the number of constructed stations needs to be increased.
The tracer of the Doppler weather radar is cloud rain particles, so that only a region with cloud rain can be detected, namely the Doppler weather radar is used for detecting a wet wind field, and the prediction result of wet low-altitude wind shear accompanying downburst is accurate. However, the doppler weather radar has a large working blind area and poor distance resolution, is difficult to aim at a glidepath and a runway area for observation, has poor detection effect on small micro-scale wind shear such as micro downburst, is limited by a tracer, and does not have the detection capability of dry wind shear. Therefore, the wind shear warning information of the airport area outside the slide way is the first best release of the prediction result of the doppler weather radar under wet conditions, namely, in cloudy, rainy, snowy and foggy weather.
The tracer of the wind profiler is atmospheric turbulence, and is suitable for dry wind field detection, but the perpendicular wind field information of its headspace area can only be detected to single wind profiler, and the detection range is little, if needs to enlarge the detection range, then need carry out many networking detections, and economic nature is poor, is difficult to implement in practice. In addition, the detection result of the wind profiler is easily interfered by ground clutter, the reliability of a near-stratum wind field is low, a detection blind area exists, and low-altitude wind shear is not easy to identify, so that the detection result of the wind profiler is used as supplementary information, and the priority is lowest.
The tracer of the laser wind measuring radar is aerosol particles, is suitable for dry wind field detection, has the unique advantages of strong anti-interference capability, small blind area, high data spatial resolution, capability of quickly acquiring refined low-altitude three-dimensional wind field information and the like, and is widely applied to aviation safety guarantee work at present. The laser is attenuated by air molecules, aerosol particles and cloud and rain particles when transmitted in the atmosphere, and the detection performance of the laser wind-measuring radar is influenced by weather conditions, so that the laser wind-measuring radar is not suitable for detecting a wet wind field, but is an optimal detection means for a dry wind field, and is particularly suitable for detecting wind fields and wind shear in key areas such as a glidepath, an airplane runway and the like. Therefore, the wind shear early warning information of the lower sliding track area is issued by taking the laser wind-measuring radar as the first priority.
The fusion early warning here refers to the comprehensive early warning of multiple equipment identification results, that is, the early warning is not based on a single equipment identification result. The fusion is embodied in a method for issuing the wind shear identification result at different spatial positions under different weather conditions.
The specific scheme refers to fusion and early warning of multi-equipment wind shear recognition results, and the method comprises the following steps of 1) constructing a geographic information model of a service guarantee area containing information such as elevation and roughness by using accurate topographic information obtained by a GIS and combining topographic features (distinguishing winter, summer, spring and autumn) of the service guarantee area; 2) constructing a Computational Fluid Dynamics (CFD) wind field simulation model: the Computational Fluid Dynamics (CFD) model is a discrete solution for solving a Fluid mechanics control equation by a numerical method, is used for simulating a near-ground wind field, can fully reproduce the flow of air under a complex terrain, and is a development trend of a wind field analysis technology; according to the geographic model of the service guarantee area, CFD numerical simulation is carried out, and CFD wind field simulation results with different directions, different wind speeds and different gust characteristics are obtained; the selection of the size of the grid of the service guarantee area needs to be determined by combining with the terrain and landform conditions so as to achieve the minimum calculated amount and the optimal simulation result;
3) analyzing sensitivity of observation points, and determining the type and the number of wind measuring instruments needing to be distributed and controlled at multiple points in a service guarantee area according to the landform characteristics; the method comprises the steps of adopting a laser wind measuring radar to combine with a ground wind pole, obtaining wind field information of a set detection node, taking the wind field information of one part of nodes as an initial value, taking the wind field information of the other part of nodes as a target value, bringing the initial value into the wind field information, combining an established CFD wind field simulation model, calculating to obtain the wind field information of the target value, comparing, finding the most sensitive position, and arranging a corresponding instrument.
4) Establishing a regional wind field monitoring system; and 3) constructing a multi-point distribution control wind field observation system according to the sensitivity test result in the step 3), and acquiring wind fields in any horizontal direction and any vertical direction in the area range according to the actually measured wind field observation result by utilizing a CFD wind field simulation model established under the geographic and geomorphic conditions and combining a table look-up mode with an optimization interpolation method.
According to specific service requirements, flight take-off and landing conditions of different aircrafts for crosswind, low-altitude horizontal wind shear and vertical wind shear, medium-altitude horizontal wind shear and vertical wind shear are provided, and safety early warning and safety time window information is provided.
The wind field CFD simulation general flow under various terrains is divided into three parts, namely pre-processing, simulation calculation and post-processing. The preprocessing part comprises the work of acquiring and processing topographic data, modeling ground and simulation areas, generating grids and the like, and is a prerequisite for wind field CFD simulation; the simulation calculation part comprises the steps of boundary condition setting (inlet wind profile, outlet self-outflow, wall surface, symmetrical boundary and the like), turbulence model selection, discrete format selection, solver selection, simulation initialization and the like, and is a main solving process of wind field CFD simulation: the post-processing part comprises the extraction and analysis of the simulation result, and aims to carry out qualitative and quantitative inspection on the rationality of the wind field CFD simulation result more intuitively and extract related data on the basis to carry out wind field analysis.
When other wind measuring instruments (wind profilers, Doppler weather radars and the like) are arranged at the station, the measurement result can also be accessed into the system according to a required data format. The detection result of the wind profiler as auxiliary information is fused with the results of other devices.
And step 3: and obtaining wind shear early warning information based on the fusion result.
It should be noted that the above-mentioned examples only represent some embodiments of the present invention, and the description thereof should not be construed as limiting the scope of the present invention. It should be noted that, for those skilled in the art, various modifications can be made without departing from the spirit of the present invention, and these modifications should fall within the scope of the present invention.
Claims (8)
1. An airport regional wind shear intelligent identification method based on multi-source wind field data is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
step 1: generating a three-dimensional refined wind field of the airport area based on the multi-source wind field data;
step 2: performing wind field data fusion based on a CFD technology and a WRF mode to obtain a three-dimensional wind field;
and step 3: performing numerical correction on the three-dimensional wind field obtained by fusion based on the current measured data to obtain all-weather non-difference airport regional digital wind field data;
and 4, step 4: the wind shear is intelligently identified based on the convolutional neural network to obtain the position and the shear strength of the wind shear, and wind shear marking is carried out in a three-dimensional wind field data graph by combining refined three-dimensional wind field data.
2. The intelligent identification method for wind shear of the airport area based on the multi-source wind field data according to claim 1, characterized in that: the generating of the three-dimensional refined wind field further comprises the following steps,
step 1-1: constructing a refined wind field numerical simulation model of an airport area;
step 1-2: constructing a simulation wind field database based on the CFD model;
step 1-3: calculating the matching degree of the current measured multi-source wind field data and each group of CFD data, and finding the CFD simulation data with the highest matching degree with the measured multi-source wind field data;
step 1-4: performing numerical correction on the CFD simulation data based on the current actually measured multi-source wind field data;
step 1-5: and obtaining the current three-dimensional refined wind field after data reconstruction.
3. The intelligent identification method for wind shear of the airport area based on the multi-source wind field data according to claim 2, characterized in that: the wind farm data fusion further comprises the steps of,
step 2-1: outputting and processing WRF data;
step 2-2: generating a CFD refinement network;
step 2-3: performing data exchange on the WRF and the CFD;
step 2-4: and finally outputting the WRF data and processing the WRF data.
4. The intelligent identification method of wind shear of the airport area based on the multi-source wind field data of claim 3, characterized in that: the numerical correction further comprises the steps of,
step 3-1: for the area with credible actually measured wind field data, the actually measured data is taken as the main;
step 3-2: supplementing the area without actually measured wind field data based on the simulation data of the CFD under the current meteorological condition;
step 3-3: when more than two wind measuring results exist at the same position, performing weighted calculation, wherein the weight of each wind measuring device is determined by the weather condition, the wind measuring principle of each wind measuring device, the observation position and the like;
step 3-4: the area with the actually measured wind field data has influences of different weights on the wind field data in a certain peripheral range, and the specific influence range and influence weight need to be determined by combining a sensitivity experiment.
5. The intelligent identification method of wind shear of an airport area based on multi-source wind farm data as claimed in claim 4, wherein: the intelligent identification of wind shear based on a convolutional neural network further comprises the steps of,
step 4-1: constructing a wind shear recognition convolutional neural network structure and establishing a wind field plane image set with wind shear characteristics;
step 4-2: extracting image characteristics, and training wind shear to identify convolutional neural network node parameters;
step 4-3: constructing a plane scanning scheme of regional refined three-dimensional wind field data;
step 4-4: outputting the position and the shear strength of wind shear;
and 4-5: and marking the shear position and the shear strength in the three-dimensional wind field data graph.
6. An airport regional wind shear intelligent identification method based on multi-source wind field data is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
step 1: each wind measuring device measures the airport area;
step 2: fusing the measurement results based on the priority;
and step 3: and obtaining wind shear early warning information based on the fusion result.
7. The intelligent identification method of wind shear of an airport area based on multi-source wind farm data as claimed in claim 6, wherein: the wind measuring equipment comprises a wind direction anemometer, a Doppler weather radar, a wind profiler and a laser wind measuring radar.
8. The intelligent identification method of wind shear in airport area based on multisource wind farm data as claimed in claim 7, wherein: the wind direction anemoscope is erected at the top of a wind rod, and the wind rod is arranged around an airport area; the Doppler weather radar is used for detecting a wet wind field; the wind profiler is used for detecting a dry wind field; the laser wind-finding radar is used for detecting the slideway of the airport area.
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