26 results about "Wind shear" patented technology

The application discloses an airborne radar downburst optimized detection method and belongs to the technical field of radar signal processing. An airborne Doppler radar is used to emit a radar waveform to a possible wind shear dangerous area in front of a route, and wind speed estimation in a transmission direction is obtained by analyzing radar echo IQ data. The application sets a pitch angle of radar scanning according to a take-off or landing stage of an airplane. After receiving the IQ signal, frequency domain correction is performed, data is preprocessed according to spectrum width information, and a wild value area is eliminated. After the preprocessing is completed, a frequency domain clutter suppression algorithm is used to suppress a ground clutter area near zero frequency, and then wind shear wind speed estimation is performed. The application proposes a novel vertical wind speed estimation model, establishes a connection between a downburst core area range and a vertical wind speed gradient, forms a detection and sensing capability for a three-dimensional area of the downburst, and proposes an airplane scanning strategy for optimizing the model.

This invention belongs to the interdisciplinary field of numerical meteorology and computational fluid dynamics, and discloses a mesoscale-microscale atmospheric coupling simulation method and related apparatus. The mesoscale-microscale atmospheric coupling simulation method includes: acquiring initial field, lateral boundary conditions, and terrain data; based on the initial field, lateral boundary conditions, and terrain data, performing time integration of the microscale meteorological model within a preset time period to obtain the microscale simulation results for all main time steps; and performing spatiotemporal matching and fusion of the microscale simulation results of all main time steps with the background field data of the mesoscale meteorological model to obtain the mesoscale-microscale atmospheric coupling simulation results. The technical solution disclosed in this invention solves the problem of computational collapse in existing atmospheric coupling simulations due to steep terrain or strong wind shear, and can improve the robustness and success rate of simulation operation while ensuring the physical realism of the simulation.

This invention provides a method, apparatus, and equipment for rainfall forecasting in plains areas, relating to the field of weather forecasting technology. The method includes: detecting the circulation pattern of the area to be forecasted to determine if it belongs to a typical rainfall circulation type; if the circulation pattern belongs to a typical rainfall circulation type, obtaining the rainfall triggering conditions corresponding to the circulation type, as well as the low-level jet stream characteristics and vertical wind shear characteristics of the area to be forecasted; if the low-level jet stream characteristics and vertical wind shear characteristics meet the rainfall triggering conditions, determining that rainfall will begin in the area to be forecasted after a set duration, and determining the rainfall intensity level and duration based on the low-level jet stream characteristics and vertical wind shear characteristics. This invention can improve the accuracy of rainfall forecasting in plains areas.

This invention provides an artificial intelligence-based method for identifying gust front wind shear, comprising: performing noise filtering, missing value imputation, data smoothing, wind shear value calculation, and sample selection and extraction on collected radial velocity data to obtain gust front wind shear samples; based on the gust front wind shear samples, performing coordinate system transformation, sample set partitioning, and data annotation to obtain an expanded dataset; designing and training a Mask R-CNN model architecture to obtain a gust front wind shear identification model; and inputting the expanded dataset into the gust front wind shear identification model for gust front wind shear detection. This invention reduces the dependence on reflectivity factor data, focusing on building the identification model based on gust front radial velocity data, which can not only accurately identify gust front wind shear but also achieve pixel-level segmentation and localization of wind shear regions, improving identification efficiency.

The present application relates to radar wind field data related technical field, specifically including an airport wake vortex radar wind field data enhancement method based on spectral width anomaly detection, the method comprises: based on the initial error matrix, the spatial error component of each detection partition, configure dynamic data enhancement matrix, under the constraint of wake vortex dissipation time iteration optimization enhancement parameter, carry out the enhancement decision of wind field data deviation. The technical problems of unable to accurately decompose the spatial error component of horizontal wind direction angle and vertical wind shear, easy to confuse the action law of different interference sources, and difficult to effectively improve the signal-to-noise ratio of wind field data are solved, the technical effects of introducing the spatial correlation mapping of the detection partition wind field direction vector and the wake vortex influence area, optimizing the enhancement coefficient, accurately splitting the horizontal and vertical direction spatial error component, extracting the environmental disturbance enhancement factor, efficiently decoupling the atmospheric turbulence, electromagnetic interference and equipment noise, improving the space-time consistency of wind field data, and reducing the wake vortex early warning false alarm rate are realized.

The present disclosure is directed to a method for determining loads on wind turbines. The method includes creating a grid structure with grid points from randomly varied wind conditions as parameters, the wind conditions including one or more of a turbulence, a turbulence intensity, and a wind shear, performing load simulation of a wind turbine for each of the grid points, providing a prediction model for predicting loads based on wind conditions, training the prediction model based on the load simulation for each of the grid points, and determining the loads for any combination of wind conditions using the prediction model.

This invention discloses a method for calculating vertical wind shear based on impeller azimuth and pitch motor current, comprising the following steps: obtaining the real-time impeller azimuth and pitch motor current of the wind turbine; calculating the sum of the pitch motor currents of each blade at the corresponding impeller azimuth position within a preset operating cycle, and superimposing the real-time pitch motor current of each blade at the corresponding impeller azimuth position; calculating the average pitch motor current of each blade within the impeller azimuth interval within each preset operating cycle; calculating the impeller azimuth range corresponding to the maximum pitch motor current of each blade; establishing a vertical wind shear vs. impeller azimuth relationship table; and determining the vertical wind shear value of each blade within the operating cycle by querying the vertical wind shear vs. impeller azimuth relationship table according to the impeller azimuth range. This invention can obtain accurate vertical wind shear values, reduce errors, and improve the accuracy of simulation tests.

A system includes: a balloon module; a sail module; a ballast module; and a bridle assembly. The balloon module includes an inflatable element containing a volume of lifting gas. The sail module defining a first edge and a second edge and including: a control surface extending between the first edge and the second edge; payload sensors; and a motorized spool arranged proximal the second edge of the sail module. The ballast module: is arranged below the sail module; and includes a container containing a ballast material. The bridle assembly includes: a set of fixed sail cables coupling the balloon module to the first edge of the sail module; and a sail control cable wound about the motorized spool and coupling the balloon module to the second edge of the sail module.

The application discloses a regional atmospheric pollution source distribution and influence evaluation system and relates to the technical field of atmospheric pollution monitoring and treatment.The system can accurately identify the spatial distribution characteristics of pollution sources by identifying the spatial distribution of agricultural waste such as straw, dry grass and livestock manure through remote sensing technology, and can comprehensively evaluate the combustion emission characteristics and diffusion influence of pollution sources based on static analysis and dynamic monitoring means.In addition, the temperature collecting unit and the inversion layer identifying unit identify the inversion phenomenon in the region, calculate the inversion layer effect factor, the vertical wind shear factor and the precipitation washing effect factor in combination with the wind speed data and the precipitation data, and calculate the dynamic diffusion coefficient classification evaluation in combination with the dimensionless processing method, and generate a targeted regulation and control strategy to further improve the accuracy of pollution diffusion prediction.

Embodiments of the present application provide a kind of multi-unmanned aerial vehicle cooperative wind field data acquisition method and system, the method comprises: obtaining target wind field space parameter, unmanned aerial vehicle is assigned based on space parameter, and the track of unmanned aerial vehicle is planned;The timestamp of main measuring point unmanned aerial vehicle and dynamic measuring point unmanned aerial vehicle data acquisition is compensated alignment, generates pseudo-synchronous frame data;The initial value of the wind vector of each grid element center point in target wind field is calculated, and representative sampling point is selected, and representative sampling point, vertical wind profile and wind shear exponent are transmitted to ground station, construct global three-dimensional environmental wind vector field, and calculate wind shear exponent distribution based on global three-dimensional environmental wind vector field;Unmanned aerial vehicle state is continuously monitored, and task exception is detected in combination with unmanned aerial vehicle state, when task exception, the task cost function of replacement unmanned aerial vehicle is calculated, and replacement unmanned aerial vehicle is assigned based on task cost function.

The application provides a wind shear early warning method and device based on mechanism information fusion data, which comprises the following steps: obtaining flight parameter data in a target time; inputting the flight parameter data in the target time into a time sequence prediction model to respectively perform time sequence feature extraction and cross-variable feature extraction on the input flight parameter data, and performing fusion prediction based on the extracted time sequence features and cross-variable features to obtain a flight parameter prediction result; determining a wind shear risk according to the flight parameter prediction result, obtaining a wind shear anomaly probability sequence, and combining a wind shear control limit to obtain a wind shear early warning result. The application comprehensively understands the flight state from a multi-dimensional perspective, significantly improves the robustness and accuracy of the prediction result, and avoids the deviation caused by a single feature perspective; at the same time, the application realizes wind shear early warning based on conformal prediction, significantly improves the sensitivity and accuracy of wind shear early warning, and avoids the influence of flight unsafe events on the safety quality of the aircraft.

This invention relates to the field of low-level wind shear warning technology, and particularly to a method, device, equipment, and medium for low-level wind shear warning based on alarm threshold verification and dynamic adjustment. The warning method includes the following steps: obtaining the glide slope radial wind speed; calculating the first glide slope head wind profile and wind shear intensity factor; calculating the wind field type and first wind shear warning information; obtaining QAR data and calculating the second glide slope head wind profile, wind field type, and second wind shear warning information; and obtaining a judgment result based on the first and second wind shear warning information. This invention objectively verifies the first wind shear warning information by spatiotemporally matching the first and second wind shear warning information. Compared with existing static threshold wind shear warning methods, this method can verify the alarm threshold through objective verification, thereby improving the accuracy and reliability of wind shear warnings.

This application discloses a method, system, electronic device, and storage medium for determining wind turbine loads, belonging to the technical fields of wind power new energy technology and atmospheric environmental element detection technology. The method for determining wind turbine loads includes: determining the measured radial wind speed based on measured data collected by lidar; constructing a wind field model based on a wind profile model and a sensing zone model, and determining an expression for calculating the simulated radial wind speed based on the wind field model; fitting the measured radial wind speed and the simulated radial wind speed to obtain a wind shear index; constructing a three-dimensional simulated wind field based on an atmospheric turbulence model using the wind shear index; and operating a wind turbine model based on the three-dimensional simulated wind field to obtain wind turbine load data. This application can generate a three-dimensional simulated wind field that conforms to actual wind conditions, improving the accuracy of wind turbine load assessment.

The application provides a kind of robot inspection path planning method and system, it is related to mobile robot automatic control technical field, the application obtains three-dimensional environment topology map, environmental meteorology, heat source distribution and robot dynamics parameter by multidimensional situation data analysis;Subsequently, hidden constraint boundary modeling is carried out, and the dynamic mirror reflection blind area, local high-frequency heat flow disturbance area, narrow pipe effect wind shear area and dynamic bearing capacity attenuation area and other non-geometric characteristic risk areas are quantitatively extracted;In the global planning stage, the system generates a candidate path in the space after eliminating the above risk areas, and combines the path length, safety margin and curvature change calculation space multidimensional penetration cost coefficient to determine the target path optimally;In the operation process, the system monitors the sensor perception confidence and pose offset residual in real time, and when positioning divergence is caused by strong reflection interference or unmapped obstacles, a local dynamic re-planning mechanism is triggered.

This invention discloses a low-altitude wind shear recognition system and method based on convolutional neural networks, belonging to the field of meteorological monitoring technology. The system includes: a low-altitude wind shear dataset construction module, which generates a wind shear image dataset containing crosswind shear, low-altitude jet stream, headwind and tailwind shear, and micro-downbursts; a data preprocessing module, which performs preprocessing on the images; a convolutional neural network module, which incorporates a lightweight convolutional neural network with a progressive channel design, trained using a two-stage transfer learning strategy, and utilizes an improved loss function obtained by weighted summation of cross-entropy loss, flow direction consistency constraint loss, and global vortex intensity constraint loss to obtain a wind shear recognition model; and a wind shear intelligent classification module, used to identify the type of wind shear in low-altitude wind field images in real time. This invention has the advantages of high recognition accuracy, low computational load, good real-time performance, and low hardware cost, and can effectively ensure the flight safety of UAVs in complex low-altitude wind field environments.

This application relates to the field of meteorological monitoring equipment technology, and in particular to a low-altitude wind shear detection device based on wind profiler radar, which includes a base, a rotating detection mechanism, a height adjustment mechanism, and a data recording mechanism. The base has adjustable horizontal support legs to ensure stable installation. The wind cup assembly in the rotating detection mechanism senses wind force and converts it into rotation. The height adjustment mechanism uses components such as an inflatable airbag and a return spring to flexibly adjust the height of the detection mechanism. A transmission conversion component converts the rotation of the wind cup assembly into the up-and-down movement of a recording pen, which, in conjunction with recording paper wound around the outside of the active and driven rollers, completes the recording of wind shear data. This device has a reasonable structural design, effectively solving the problems of complex structure and poor environmental adaptability of existing equipment. It can achieve accurate detection and convenient data recording of low-altitude wind shear, providing reliable data support for fields such as aviation.

The application relates to the technical field of meteorological services, and discloses a system for predicting migration of atmospheric pollutants under influence of meteorological parameter gain in complex terrain, which comprises the following modules: a terrain topography analysis module that extracts spatial features of a target region and constructs a terrain topography feature matrix; a meteorological background preprocessing module that extracts a potential temperature gradient and a vertical wind shear parameter; an atmospheric layer evaluation module that determines a local Richardson number according to the parameters; a terrain gain correction module that corrects matrix weights according to the local Richardson number and outputs a terrain dynamic correction coefficient; a microscale field reconstruction module that couples the coefficient with background field data to generate a microscale meteorological field; and a diffusion trend prediction module that outputs a migration trajectory of atmospheric pollutants. The application can correct terrain dynamic gain weights in real time through identification of a feedback mechanism of stability of a layer and a forcing action of terrain, eliminate physical distortion in the process of reconstruction of a microscale flow field, and improve physical fitting degree of prediction of pollutant diffusion in a complex terrain region.

This invention discloses a method and system for wind shear line identification and movement path prediction based on two-dimensional wind vectors, belonging to the field of lidar meteorological detection technology. The method includes using a variational algorithm to invert radial wind speed data into two-dimensional horizontal wind vector field data; setting an orthogonal vector detection window to calculate the two-dimensional composite vector difference to filter vector shear feature segments; extracting the core points of vector abrupt changes to reconstruct the wind shear line to construct a comparison area; and determining the dynamic guidance zone driving the movement of each local shear line segment based on the two-dimensional horizontal wind vector field data within the comparison area. The spatial displacement vector is determined based on the average two-dimensional wind vector of the corresponding area of ​​the dynamic guidance zone, and the corresponding local shear line segments are translated, extrapolated, and spliced ​​based on the spatial displacement vector to output the future movement path trajectory of the wind shear line. This invention solves the problems of easy missed detection in one-dimensional radial detection and failure of wind shear line identification and movement path prediction in radar blind zones in traditional methods.

The application discloses a kind of extreme vertical wind shear identification and load reduction control method and system of wind turbine unit, comprising: obtaining the blade root bending moment of two directions of blade root, i.e. blade root flapping bending moment and blade root edgewise bending moment, according to current measured variable pitch angle, obtain blade root out-of-plane bending moment by rotation transformation;Current blade azimuth angle is obtained, and the blade root out-of-plane bending moment is carried out coordinate transformation, and the wind wheel pitch bending moment is calculated;Based on wind wheel pitch bending moment and current average wind speed, the vertical wind shear coefficient is calculated, when the vertical wind shear coefficient exceeds preset threshold, the current wind condition is identified as extreme vertical wind shear;When the current wind condition is identified as extreme vertical wind shear, additional independent variable pitch instruction is calculated and output, which is superimposed on the variable pitch instruction output by the variable pitch controller, to generate additional bending moment on the wind wheel, which cancels out the wind wheel pitch bending moment.The application is based on the measured blade root load, identifies whether the current is extreme vertical wind shear, and reduces the load by additional independent variable pitch instruction.

This application discloses a method for constructing real-time wind fields around urban buildings based on transient CFD simulation. The method acquires building outlines, heights, and DEM data of the target area, performs quality control, and constructs an urban building scene model. Based on this model, transient CFD simulations are performed for multiple inlet wind directions and preset height layers to obtain time-series wind field results for multiple time steps, generating a three-dimensional gridded wind field database containing instantaneous wind fields, average wind fields, and maximum gust fields. Then, combined with real-time low-altitude wind direction and speed observation data collected by multi-source meteorological observation equipment, the corresponding CFD simulation results are matched and fused with the gridded background wind field to obtain the real-time wind field around urban buildings. Furthermore, turbulence intensity and wind shear intensity are calculated. This method balances real-time performance with spatial precision, enabling rapid construction of real-time wind fields in complex urban low-altitude environments.

This invention relates to the field of wind farm safety management and discloses a wind farm safety management system and method based on meteorological data analysis. The system includes collecting wind speed, wind direction, wind shear, turbulence, topography, and sensitive locations; mapping sensitive locations to a weighted matrix; continuously processing and combining these data to form a wind farm safety status dataset; generating micro-region wind force response maps using numerical simulations and historical measurement data; dividing the wind farm into micro-regions that can be dynamically updated with real-time meteorological changes; calculating the wind flow parameters for each micro-region; mapping the micro-region wind flow to each wind turbine location; establishing a wind turbine structural capacity model and determining the controllable power range; establishing a wind flow flexible deformation tensor; deriving the wind flow redistribution and corresponding energy field under candidate power conditions; constructing an energy minimization optimization model with constraints on sensitive locations, equipment capacity, and wind shear; solving for the optimal wind turbine power; and generating a unified wind flow optimization scheme. This invention has the advantage of improving wind flow guidance effectiveness.

The application discloses a kind of self-adapting weather background's civil aviation low-altitude wind shear detection method and system, belong to civil aviation technical field.The detection method includes: correlation analysis is carried out to historical meteorological data, historical wind shear factor, and characteristic set is obtained by screening;Characteristic set is used as model input, and pilot report real label is used as model output label, and dynamic threshold model is trained;Real-time meteorological data and current period wind shear factor are input into the dynamic threshold model trained, and the adaptive wind shear discrimination threshold suitable for current weather background is automatically calculated and output;Threshold decision is carried out, and the output result is output;The new data of current period is added to training set, and the dynamic threshold model is updated online.The application also discloses the self-adapting weather background's civil aviation low-altitude wind shear detection system.The application can dynamically adjust the detection threshold according to airport real-time meteorological environment and weather type, improve the accuracy and robustness of wind shear early warning.

The application discloses an airborne weather radar wind shear detection speed unblurring method, complete radar waveforms are generated based on A-wave and B-wave grouping waveforms; after receiving radar echo for digital sampling, rearrangement processing is carried out, and data of fast-slow time dimensions of A-group and B-group waveforms are obtained; then, along the fast time dimension, matching filter processing is carried out, and the fast time dimension is converted into a distance dimension; after Doppler components caused by the speed of an airplane itself are compensated, along the slow time dimension, FFT transformation processing is carried out, and distance-speed dimension spectrum data are obtained; along the distance dimension, first moment calculation processing is carried out on Doppler velocity spectrum for each distance gate to obtain blurred wind speed measurement values, and corresponding A-wave and B-wave data are extracted; unblurring operation processing is carried out based on the A-wave and B-wave data to obtain wind speed measurement values. The application has extremely small calculation amount, and can complete speed unblurring in a signal processing link, and does not affect the data refresh rate of wind shear detection.

This invention discloses a method for correcting incoming wind speed in offshore wind farms based on SAR satellite observations. First, SAR satellite images are preprocessed and masked to remove interference from bright targets such as wind turbines. Then, a high-resolution near-sea surface wind speed is retrieved from the images using an improved ConvNeXtv2 deep learning network. This is then matched with numerical weather prediction (NMR) wind fields, and the amplitude and spatial structure of the NMR wind fields are corrected using the LightGBM model. Finally, the wind shear index is corrected by combining atmospheric elements at the wind height level with the wind speed extrapolation formula, thereby correcting the extrapolation ratio and obtaining high-precision wind speed extrapolation results at the wind turbine hub height. This invention integrates the high spatial resolution of SAR with the continuity of numerical weather prediction, effectively eliminating wind turbine target interference and numerical weather prediction system bias, significantly improving the accuracy of incoming wind speed at the wind turbine hub height, and providing reliable wind field data for offshore wind farm power prediction and safe operation.