25 results about "Downburst" patented technology

The invention relates to a bridge non-stationary wind speed intelligent prediction and abnormal vibration early warning method and system, and a storage medium, and the method comprises the steps: constructing a first training set based on historical wind speed-vibration response data with a timestamp; and meanwhile, constructing a second training set based on the historical wind speed feature vector marked with the abnormal state. And respectively training the CNN-LSTM network and the KNN classification model by using the first training set and the second training set. And inputting real-time wind speed data into the CNN-LSTM network to obtain a non-stationary wind speed prediction feature vector in a future time period. And the prediction feature vector is imported into a t-SNE-KNN dimension reduction classification algorithm, unstable abnormal wind and normal wind are distinguished, and an early warning boundary and safe and dangerous areas are established. Compared with the prior art, the method has the advantages that an accurate intelligent prediction and abnormal vibration early warning decision method can be constructed for extreme wind events such as downburst, the problems in the prior art are solved, and reliable guarantee is provided for safe operation of a large-span bridge under the extreme wind events.

The invention discloses a low-altitude aircraft route real-time meteorological risk early warning method and system, and belongs to the technical field of flight risk identification. The method is used for solving the technical problem that the missing report rate of sudden weather phenomena such as wind shear and micro downburst in an existing scheme is high. The three-dimensional wind shear and turbulence intensity is identified through bimodal cooperation, and the limitation of traditional single-parameter identification is broken through; through independent modeling of normal rigidity and tangential damping, the risk characteristics of vertical airflow and horizontal wind shear are decoupled, so that the risk identification precision can be effectively improved; a height self-adaptive threshold value can effectively reduce the false alarm rate of a low-altitude complex terrain scene; according to the nonlinear attenuation control, through a tanh function, a dynamic response that the larger the risk overshoot is, the stronger the control force is is, and the risk energy propagation depth can be effectively reduced; by using the amplification effect of fractional differential on weak disturbance, the detection rate of risk abrupt change can be effectively improved, and missing report of sudden risks is avoided.

The invention relates to the technical field of meteorological disaster monitoring and early warning and numerical forecasting fusion, in particular to an extreme gale weather forecasting method based on a weather forecasting large model, which comprises the following steps: acquiring a numerical weather forecasting three-dimensional physical field, a weather radar, a weather satellite and ground meteorological observation and assimilating the numerical weather forecasting three-dimensional physical field into a consensus field; time advances, sinking potential energy and cold pool diagnosis are obtained through a micro-downburst calculation program, coarse-resolution gust is formed through boundary layer similarity mapping and a hysteresis kernel, and a fine-scale base map is generated under cold pool frontal surface limitation through optimal transmission of advection constraint; the conditional diffusion probability generation model outputs pixel gust distribution and a threshold exceeding probability and extracts a wind damage polygon; radar, satellites, lightning and ground gust are fused, monotonous normalized flow calibration is used, parameters are updated through intersection-to-parallel ratio and shape-preserving coverage inspection and online amplitude limitation, and the credibility of spatial positioning, occurrence time and probability description is improved.

An ultra-stable structural laminate with fire resistance and a lateral nail pull strength from 44 to 300 pounds of force and an insulation R value from 1 to 40, the ultra-stable structural laminate of a cementitious material with a nano-molecular veneer and a foam component catalytically reacted into an expanded closed cell foam having a thickness from ⅛th inch to 8 inches, a density from 1.5 pounds / cubic foot to 3 pounds / cubic foot that self-adheres to the cementitious material forming an ultra-stable structural laminate with fire resistance and a lateral nail pull strength from 44 pounds to 300 pounds of force, an insulation R value from 1 to 40, a resistance to seismic impact for earthquakes over 3.1 on the Richter Scale, a break point from 7 lbs / inch to 100 lbs / inch; and a resistance to wind shear equivalent to a 15 mph downburst.

The present invention discloses a collaborative observation method, system, equipment and storage medium for the entire life history of a convective cell. The method includes: tracking the motion trajectory of the same convective cell; screening a suitable convective cell as a target for collaborative observation; and selecting a weather radar to conduct continuous collaborative observation of the target convective cell. This application ensures that the observation target at the previous and next moments is the same convective cell through the identification and tracking technology of convective cells. At the same time, a method for selecting a collaborative observation radar based on the change in the azimuth angle of the observed target is proposed to ensure that the observation results at the previous and next moments are comparable. The method provided by this application is of great significance for monitoring the intensity, vertical structure changes and physical parameter calculation of hail, tornadoes, downbursts and thunderstorms, and is of great value for improving the application of collaborative observation results in severe convection monitoring, early warning and mechanism research.

The invention provides a downburst maximum horizontal wind speed diagnosis method and system. The method comprises the steps that the vertical speed and the jet radius of downburst vertical jet airflow in a high-voltage transmission corridor area are obtained; constructing a downburst impingement jet model based on the Bernoulli principle, and obtaining a static pressure difference parameter; establishing a momentum equation based on a momentum conservation law and the static pressure difference parameter; a mass conservation equation is established based on the jet radius and the mass conservation law, and the horizontal flow thickness is obtained; obtaining a diagnosis model formula of the maximum horizontal wind speed according to the horizontal flow thickness and the momentum equation; and determining an efficiency coefficient according to the turbulence energy loss, correcting the diagnosis model formula based on the efficiency coefficient, and obtaining a corrected diagnosis formula of the maximum horizontal wind speed, thereby realizing accurate estimation of the downburst maximum horizontal wind speed, and providing key dynamic support for disaster prevention and reduction of power grid facilities.

The application discloses a non-stationary turbulent wind field large eddy simulation entrance synthesis method and device, comprising the following steps: a continuous derivable sine wave is used to simulate a non-stationary time-varying average wind speed; a PRFG 3 The random flow method generates three-dimensional anisotropic turbulence with specific vertical wind shear parameters, turbulence intensity and turbulence integral scale; the sine wave and the random flow are superimposed to generate a non-stationary turbulent wind field with accurately adjustable non-stationary parameters. By adopting the technical scheme of the application, the sudden rise and fall of the time-varying average wind speed and the vertical wind shear characteristics can be accurately simulated, and the time-varying turbulence intensity and integral scale of the fluctuation component can also be accurately simulated, so that the application can be used for large eddy simulation research on strong typhoons, thunderstorm winds and downbursts and the like.

The invention discloses an angle-adjustable multi-fan array type downburst three-dimensional wind field physical simulation device. The angle-adjustable multi-fan array type downburst three-dimensional wind field physical simulation device comprises a modularized detachable frame and a multi-fan array installed in the frame. The fan array is composed of a plurality of fan rows, and each row comprises a plurality of independent fan units. Each fan unit is provided with an independent pitching and yawing adjusting mechanism which is driven by a servo motor, and accurate control over the outflow angle is achieved. A vertical guide rail is arranged on the frame, each row of fans is matched with the guide rail through a sliding block mechanism, and accurate positioning of the height of the whole row is achieved through an independent height adjusting mechanism. By independently controlling the rotating speed, the pitch angle, the yaw angle and the height of each row of each fan unit and utilizing the space vector synthesis technology, the three-dimensional flow field characteristic diffused after the downburst impacts the ground is effectively reproduced, and the problems that a traditional simulation technology depends on a wind tunnel, and a three-dimensional wind field structure is difficult to truly restore are solved.

The present invention proposes a method for identifying downbursts based on near-ground divergence characteristics, comprising: step 1, obtaining horizontal wind field grid data (gridData); step 2, constructing a region block (block) with ideal divergence characteristics; step 3, comparing a region within the horizontal wind field grid data (gridData) with the region block (block) with ideal divergence characteristics; step 4, obtaining the difference between the region and the region block; and step 5, identifying downbursts based on the difference obtained in step 4. The method can use near-ground horizontal wind field data obtained by laser wind radar scanning to identify divergence characteristics and thus downbursts, and can also obtain data on the downburst's center point, range, and intensity.

The invention discloses a large-vortex simulation inlet synthesis method and device for a non-stationary turbulent wind field. The method comprises the following steps: simulating a non-stationary time-varying average wind speed by adopting a continuous and derivable sine wave; a PRFG3 random flow method is adopted to generate three-dimensional anisotropic turbulence with a specific vertical wind shear parameter, turbulence level and turbulence integral scale; and superposing the sine wave and the random flow to generate a non-stationary turbulent wind field with non-stationary parameters capable of being accurately adjusted. By the adoption of the technical scheme, the sudden rise and sudden drop of the time-varying average wind speed and the vertical wind shear characteristic can be accurately simulated, the time-varying turbulivity and the integral scale of the pulsation component can also be accurately simulated, and the method can be used for large eddy simulation research of specific strong wind such as strong typhoon, thunderstorm wind and downburst.

The utility model belongs to the technical field of special wind field simulation test devices, and particularly relates to a wind field generation device for simulating tornado and downburst. The bottom air duct is improved to be of the regular hexagon structure, the first draught fans on the side walls of all the faces of the bottom air duct share the same set of air guide adjusting mechanism, and the construction difficulty and the implementation cost of the whole device are greatly reduced. In addition, the bottom air duct is improved to be of a regular hexagon structure and is matched with a modularized splicing type structure, and later reconstruction and expansion can be further facilitated. Besides, a sliding rail and a test working platform capable of reciprocating along the sliding rail are additionally arranged in the bottom air duct, and during implementation, a target building is fixed on the test working platform, so that the condition of a mobile wind field can be simulated.

The invention belongs to the technical field of data processing, and particularly relates to a thunderstorm and gale classification forecasting method and system.The method comprises the steps that radar observation data, numerical mode output and ground observation data are obtained, and a unified grid multi-source data set is obtained through preprocessing and space-time registration; extracting radar morphological characteristics through a convolutional neural network, and identifying a thunderstorm and gale overall risk area in combination with environmental factors output in a numerical mode; then extracting local radar features in the region, estimating the cold pool strength and obtaining a time sequence mutation signal; and finally, inputting the three types of information into a classification model, outputting two types of risk judgment results of common thunderstorm gale and downburst, and presenting the results in probability and uncertainty intervals. According to the method, the downburst can be accurately identified, and the refinement level of medium and short time thunderstorm gale forecast is improved.

The invention discloses a method for determining the most unfavorable working condition of downburst wind load of a power transmission line. The method comprises the following steps: 1) obtaining the line wind load of the power transmission line under downburst; 2) performing quasi-static linear analysis on the power transmission line-insulator system under the action of downburst wind to obtain wind vibration response of the multi-span power transmission line-insulator system; 3) with seeking maximum transverse and longitudinal wind loads of wires of a tangent tower and a strain tower as an optimization target, constructing a worst working condition parameter combination optimization model when the counterforce transmitted to the tower by the multi-span power transmission line-insulator system is maximum; 4) solving the model to obtain the most unfavorable working conditions of the tangent tower and the strain tower; and 5) selecting power transmission lines of different conductor types as research objects, and optimizing the maximum values of the transverse and longitudinal wind loads of the conductors of the tangent tower and the strain tower to obtain the most unfavorable wind effect parameter configuration under different working conditions. The method provided by the invention remarkably reduces the time cost and the calculation overhead required by numerical simulation.

The invention provides a downburst maximum vertical wind speed diagnosis method and system, and the method comprises the steps: obtaining meteorological element data of a target high-voltage power transmission corridor with sparse meteorological data, extracting an environment temperature profile based on the meteorological element data, and determining the starting height and ending height of a sinking air block in downburst according to the meteorological element data; according to the initial height, the final height and the dry adiabatic rate change, a sinking air block temperature value changing along with the height is obtained; based on the initial height, the end height, the temperature numerical value set and the environment temperature profile, effective potential energy of sinking convection is calculated according to numerical integration; identifying the maximum value of sinking convection effective potential energy in a preset time sequence; based on the law of conservation of energy, the maximum value of effective potential energy of sinking convection is converted into the maximum vertical wind speed of downburst; according to the method, the diagnosis of the maximum vertical wind speed of the downburst is obtained by establishing a direct calculation method from the DCAPE to the maximum vertical wind speed of the downburst.

The invention discloses a downburst identification method and device for a power grid fault area, equipment and a medium, and belongs to the technical field of electric power weather forecast. Aiming at the problem of difficulty in identifying downburst of a remote power transmission line due to dependence on radar monitoring in the prior art, the method comprises the following steps of: obtaining reanalysis meteorological data of a three-dimensional space of a power grid fault area, and combining high-low altitude circulation configuration coupling analysis to determine large-scale circulation characteristics; constructing a vertical structure model under an air temperature-air pressure logarithmic coordinate system, and analyzing the atmospheric junction stability and convection potential through the relationship among an environment temperature profile, a dew point temperature profile and an adiabatic line; and carrying out multi-dimensional identification by combining a high-altitude humidity criterion, sinking convection effective potential energy and ground strong wind radiation characteristics. According to the method, numerical reanalysis data is adopted to replace traditional observation equipment, accurate recognition of downburst is achieved under the condition that no field meteorological monitoring exists, and reliable technical support is provided for power grid fault attribution and extreme weather early warning.

The application discloses a method, device and equipment for identifying downburst in a power grid failure area and a medium, and belongs to the technical field of power weather forecasting. In view of the problem that the existing technology relies on radar monitoring, leading to difficulty in identifying downburst in remote power transmission lines, the method obtains reanalysis meteorological data of a three-dimensional space of the power grid failure area, determines large-scale circulation characteristics in combination with high and low air circulation configuration coupling analysis, constructs a vertical structure model in a logarithmic coordinate system of air temperature and air pressure, analyzes atmospheric stratification stability and convective potential through the relationship among the environmental temperature profile, the dew point temperature profile and the adiabatic line, and comprehensively identifies in multiple dimensions the high-altitude humidity criterion, the sinking convective available potential energy and the ground strong wind divergence characteristics. The method uses numerical reanalysis data to replace traditional observation equipment, realizes accurate identification of downburst under the condition that there is no on-site meteorological monitoring, and provides reliable technical support for power grid failure attribution and extreme weather warning.

The invention discloses an air line wind field risk automatic detection method and system based on meteorological grid data. The method comprises the steps of obtaining starting and ending point coordinates of a target area; geometrically segmenting the route based on a preset interval to generate a route point sequence; selecting a representative point set for wind field analysis from the grid wind field data based on the route point sequence; based on the representative point set, constructing a geometric analysis structure for wind field analysis; and based on a geometric analysis structure and the grid wind field data, calculating and integrating a wind field divergence index, a wind gain index and a wind loss index, carrying out alarm arbitration, and generating alarm information for micro downburst and / or wind shear. According to the method, meteorological grid data are processed by combining geometric segmentation of the air line and wind field divergence analysis, and micro-downburst and wind shear influencing the air line are automatically identified and positioned. And the continuity, the positioning precision and the anti-interference capability of low-altitude flight route wind field risk monitoring are remarkably improved.

The invention discloses a downburst symmetric and asymmetric structure analysis method facing meteorological space field data and based on Fourier wave number decomposition, a medium and a program product, and belongs to the technical field of meteorological data processing and diagnosis. According to the method, for the attributes of small temporal-spatial scale and rapid evolution of downburst, a downburst disaster point is determined by using a ground observation maximum wind speed threshold criterion, an event constraint area is determined, and a target variable field Var is collected; and calculating the horizontal center of the weather system based on Var amplitude weighting. A radius and azimuth angle sampling frame is constructed by taking a horizontal center as an original point, interpolation and polar coordinate resampling are realized by adopting natural adjacent point interpolation, and a regularized polar coordinate variable field Var (r, theta) is obtained. Fourier series expansion is executed along an azimuth angle, a zero wave number symmetric component and a multi-order non-zero wave number asymmetric mode are separated, an amplitude-phase parameter set is output, structural feature extraction and quantitative characterization are achieved, and support is provided for disaster falling area analysis and business evaluation.

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.

The present application relates to the technical field of meteorological disaster monitoring and early warning and numerical prediction, and more particularly to an extreme gale weather prediction method based on a meteorological prediction large model, which comprises the following steps: obtaining a numerical weather prediction three-dimensional physical field and weather radar, meteorological satellite and ground meteorological observation and assimilating them into a consistent field; time advancing and obtaining sinking potential energy and cold pool diagnosis through a micro-downburst calculation program, forming a coarse resolution gust through boundary layer similar mapping and hysteresis kernel, generating a fine scale base map under the restriction of a cold pool front by using an optimal transport of advection constraint, outputting an image element gust distribution and an over-threshold probability by a conditional diffusion probability generation model and extracting a wind damage polygon; fusing radar, satellite, lightning and ground gust, using monotonic normalization flow calibration and intersection and union ratio and conformal coverage verification, updating parameters with limited online amplitude, and improving the reliability of spatial positioning, occurrence time and probability description.

The invention discloses a power transmission tower downburst protection system and method based on multi-mode cooperative control and intelligent reinforcement. The system comprises an intelligent real-time monitoring and early warning subsystem, a multi-mode cooperative vibration reduction control subsystem and a structural intelligent dynamic reinforcement subsystem. The intelligent real-time monitoring and early warning subsystem obtains downburst wind field characteristics and dynamic response of the power transmission tower, outputs an early warning level according to an over-limit threshold value, and obtains an instruction of a corresponding processing scheme; after receiving an instruction of the intelligent real-time monitoring and early warning subsystem, the multi-mode cooperative vibration reduction control subsystem controls a pneumatic vortex suppression module and a structure tuning module to complete vibration reduction control; and after receiving an instruction of the intelligent real-time monitoring and early warning subsystem, the structural intelligent dynamic reinforcing subsystem controls the wind-resistant inhaul cable reinforcing module and the carbon fiber clamp reinforcing module to perform reinforcing treatment. According to the method, dynamic risk assessment early warning is realized, the timeliness of downburst early warning and corresponding decision making is greatly improved, and the survivability of the power transmission tower under extreme wind disasters is improved.

The invention relates to the technical field of wind engineering and artificial intelligence, and discloses a downburst average wind speed prediction method and system based on a regression random forest, and the method comprises the steps: building a CFD numerical simulation model of mobile downburst based on an impact jet theory; secondly, performing uniform sampling in a two-dimensional parameter space formed by an environment wind speed and a storm center moving speed by using a Haarton sequence to generate a parameter combination sample; thirdly, constructing a training data set containing multiple groups of input features and target output through batch CFD calculation; then, constructing a regression random forest model, and optimizing the number and the maximum depth of the decision trees by using parameter sensitivity analysis; and finally, using the trained model to rapidly predict any to-be-predicted working condition. According to the method, the wind speed double-peak characteristics of the mobile downburst can be accurately captured, the calculation cost is greatly reduced while the high prediction precision is ensured, and real-time rapid prediction of the downburst wind field is realized.

The invention provides a navigation risk weather identification method based on laser wind-finding radars, and the method comprises the steps: carrying out the cooperative PPI scanning through arranging the laser wind-finding radars at two ends of an airport runway, and carrying out the horizontal wind field data meshing through employing the PPI scanning data obtained by the two laser wind-finding radars in real time as basic data; and identifying a wind shear region based on the wind shear characteristics in the horizontal wind field. In a wind shear area, a judgment method based on navigation risk weather characteristics is combined, monitoring and early warning of medium and small-scale severe convection weather (micro downburst, gust front, turbulence, strong wind areas and the like) are achieved, reliable data support is provided for airport wind shear early warning, and the coping capacity of an aircraft to sudden wind shear in the take-off and landing stage is improved.