270 results about "Wind shear" patented technology

The invention relates to the technical field of hydrological monitoring, in particular to a multi-satellite collaborative hydrological monitoring system. The method comprises the following steps that a water level height measurement module obtains radar pulse recovery time delay data through a height measurement satellite and calculates the water level height, abnormal points are removed to generate river water level data, a water remote sensing module collects river water images through a remote sensing satellite to extract water boundary features, water area change data are deduced, and the water level height measurement module calculates the water level height. The meteorological wind shear analysis module obtains river channel meteorological data through a meteorological satellite, microwave scattering measurement and wind speed inversion are carried out, wind-induced shear stress parameters are generated, and the flow estimation module carries out section dynamic analysis and estimates the water flow in combination with river channel water level and water area data. And the flow correction module corrects the river water flow based on the wind-induced shear stress parameter and carries out real-time monitoring, and data are synchronously uploaded to the control terminal, so that comprehensive dynamic monitoring of the hydrological state of the river is realized. According to the invention, a more efficient multi-satellite collaborative hydrological monitoring system is realized.

The invention discloses a multi-channel meteorological risk early warning information self-adaptive targeted publishing system and method based on low-altitude flight, and solves the problems that existing low-altitude meteorological early warning is low in temporal-spatial resolution, single in risk identification and insufficient in information pertinence. The system integrates air-based, space-based, foundation, social and topographic data, and generates a low-altitude meteorological dynamic database through fusion; micro-scale risks such as wind shear are extracted and graded through an AI model, a digital twin simulation response is constructed in combination with the state of the aircraft, and a risk grid model and coordinates are output; calculating a comprehensive risk index, generating evasion guidance, converting into multi-role early warning information, generating a three-dimensional thermodynamic diagram, and finally adaptively selecting a publishing channel according to user roles, terminals and network states. According to the invention, the accuracy and timeliness of low-altitude flight meteorological risk early warning are improved, and the safety of low-altitude flight is guaranteed.

The invention discloses a TCBL wind field high-resolution LES simulation method facing gray area parameterization, a medium and a program product. According to the method, a TCBL idealized initial gradient wind field is constructed based on a modified Rankine vortex model, controllability and stability of initial conditions are ensured by specifying key kinetic parameters, and thermodynamic profiles under different wind speed conditions are obtained based on synthetic analysis of tropical cyclone sounding data. And correcting the bottom of the boundary layer by adopting a quadratic and logarithmic function fitting method. Through high-resolution grid configuration and dynamic boundary conditions, secondary grid turbulence is processed in combination with an SGS-TKE closed model, and adaptability of the model to strong rotation and high wind shear environments is optimized by adopting a dynamic correction mechanism. Besides, the stability of thermodynamic variables is maintained through large-scale tendency correction, and high-resolution three-dimensional wind field and turbulence statistical reference data are output and used for verifying and improving a turbulence parameterization scheme of the TCBL under gray region resolution.

The invention discloses a numerical simulation prediction method and system for typhoon binocular wall structure reconstruction judgment and maintenance duration after terrain interference and a medium, and relates to the technical field of typhoon fine structure evolution description and numerical simulation. According to the method, the binocular wall typhoon is identified through multi-source data fusion, a high-resolution numerical simulation system is constructed, and a typhoon structure is divided into four quadrants for differential diagnosis according to the wind shear direction based on vertical wind shear phase limit analysis. And the distribution characteristics of the rapid vortex silking area are captured by calculating vortex silking time parameters. And establishing a ditch sinking airflow detection mechanism, and quantitatively evaluating the contribution of radial advection, tangential advection, vertical advection and friction force items to tangential wind evolution by using tangential wind income and expenditure diagnostic analysis. And calculating an energy growth rate, and predicting a double-eye wall maintaining time length. According to the method, the outer eye wall re-formation judgment is output, the outer eye wall radius zone, the maintenance duration interval and the uncertainty are predicted, and the method is suitable for real-time business and research.

The invention discloses a PPI wind shear extraction method and device based on a laser wind finding radar and a storage medium, and the method comprises the following steps: S1, obtaining the radial data of N light beams in a period based on PPI scanning, and carrying out the screening and marking of the radial data; s2, inverting a horizontal wind field based on a light beam sliding window to obtain a horizontal wind speed and a horizontal wind direction of the light beam; s3, grid division is carried out on the horizontal wind field, and interpolation is carried out based on the detection distance; compared with the prior art, the method takes radial data obtained by PPI scanning as a basis, adopts a sliding window method, and takes the horizontal wind speed and the horizontal wind direction calculated by a previous sliding window as initial values of model parameters to be substituted into the gradient descent solving model of the current light beam, so that the horizontal wind speed and the horizontal wind direction of the current light beam are inverted; the wind field inversion error when the wind direction and the light beam direction tend to be perpendicular is reduced, and the inversion precision of the horizontal wind field is improved.

The invention relates to the technical field of wind power generation. The invention provides a yaw static and dynamic error adaptive method based on big data backtesting, which comprises the following steps of: constructing a multi-dimensional feature vector through multi-source data fusion data, and establishing a dynamic feature data set; based on the dynamic characteristic data set, a space-time diagram convolutional network is adopted to establish a wind power plant dynamic error prediction model, and space-time evolution rules of wind shear, turbulent flow and wake flow effects are captured; constructing a variational self-coding reference model based on historical full wind speed section data, calculating a residual error between a current working condition and the variational self-coding reference model in real time, and taking the residual error as a static error prediction value; performing adaptive weight fusion on the static error prediction value and the dynamic error prediction value to obtain a fusion error; and the fusion error is converted into the yaw angle correction amount, and the wind facing action is executed. The problems that an existing yaw error recognition technology is high in data dependence, lack of dynamic analysis, poor in model adaptability and difficult in complex wind field processing are solved.

The invention discloses a civil aviation wind shear and visibility early warning method, which comprises the following steps of: acquiring an echo signal by using a high-precision and high-temporal-spatial-resolution coherent laser radar, and simultaneously acquiring civil aviation meteorological conventional observation data; calculating wind field distribution through a wind field inversion algorithm, and calculating visibility distribution through a visibility inversion algorithm; positioning a wind shear region by using a wind shear recognition algorithm, analyzing a visibility space-time evolution trend by combining a space-time deduction algorithm, and generating a visibility profile; inputting the wind shear identification data, the visibility profile data and the meteorological routine observation data into a machine learning model for modeling training; generating a wind shear and visibility early warning scheme over the airport based on the trained prediction model; and generating a wind shear alarm signal and visibility early warning information based on the early warning scheme, and outputting the signals to a civil aviation meteorological early warning system. According to the invention, high-precision wind shear and visibility combined early warning can be realized, and the early warning accuracy under complex weather conditions is improved.

The invention relates to the field of meteorological detection and analysis, and discloses a low-altitude complex wind field detection and inversion method based on a vehicle-mounted laser radar, and the method comprises the steps: determining a scanning point location based on the terrain, building distribution and simulation data; optimizing the driving path of the vehicle-mounted radar to cover the high-risk area; acquiring three-dimensional wind field data by adopting an RHI and PPI multi-mode scanning strategy; the influence of vehicle-mounted movement on speed measurement is corrected; calculating turbulence intensity and vortex nucleus position by using spectral width characteristics; and identifying the wind shear position and strength through a slope algorithm. According to the invention, low-cost and high-precision wind field detection is realized through maneuvering deployment and multi-mode cooperation, and reliable technical support is provided for safe flight of the unmanned aerial vehicle, low-altitude traffic management and meteorological early warning.

A system includes: a lower sail module; an upper sail module; and a bridle assembly. The lower sail module defines a first edge and a second edge and includes: a first control surface extending between the first edge and the second edge; a set of payload instruments; and a motorized spool arranged proximal the second edge of the lower sail module. The upper sail module: is arranged above the lower sail module; defines a third edge and a fourth edge and includes; and includes a second control surface extending between the third edge and the fourth edge. The bridle assembly includes: a set of fixed sail cables coupling the upper sail module to the first control surface proximal the first edge; and a sail control cable wound about the motorized spool and coupling the upper sail module to the first control surface proximal the second edge.

The invention provides an aircraft landing stage short-time abnormal risk early warning and visual simulation adaptive method based on deep learning, and the method comprises the following steps: synchronizing a multi-source sensor signal, separating the multi-source sensor signal into an impact channel, a wind shear channel and a residual channel, and mapping the multi-source sensor signal into aircraft-runway-wind field graph node features; features are extracted through a space-time diagram convolutional network, and a future trajectory is predicted in combination with a dual-channel attention decoder under physical constraints such as an energy attenuation template; high-risk actions are rapidly inhibited on the basis of real-time risks, the risk probability is calculated through weighted submerged space sampling, graded alarms are output, meanwhile, the alarms drive visual simulation to be adaptively updated, and model online safety iteration is achieved through hard sample mining after landing. According to the method, physical driving modeling, space-time fusion perception and probabilistic risk assessment are adopted, the dependence of a traditional method on an empirical threshold value is overcome, and the early warning precision, the confidence coefficient and the landing safety are remarkably improved.

The invention discloses a method for improving a TCBL grey area scale perception similarity function based on LES simulation optimization, a medium and a program product, and aims to improve the prediction precision of a numerical weather prediction model on a tropical cyclone path and strength. According to the method, a high-resolution LES simulation framework is constructed, a high-resolution reference data set of TCBLs with different intensities is generated, scale turbulence kinetic energy and secondary grid turbulence kinetic energy are calculated and analyzed, and an improved grey area scale perception similarity function is constructed based on multi-scale filtering and function fitting. The similarity function can more accurately represent the next grid turbulence characteristic of the gray region resolution, and the effectiveness of the similarity function in TC path and intensity prediction is verified by integrating the similarity function with a numerical weather prediction model. According to the method, the simulation precision of the model under the conditions of complex wind shearing and strong rotation is remarkably improved, the universality of the model is enhanced, important support is provided for weather forecast and tropical cyclone research, and the method has wide scientific value and application prospects.

The airport available runway monitoring and early warning system and method based on multi-source data fusion are high in real-time performance, good in forecasting effect and large in monitoring range. The system comprises a laser wind finding radar (1), a CFD numerical simulation module (2), a mesoscale weather forecast module (3), a machine learning model (4) and a system server (5). The method comprises the following steps that: a system server performs data assimilation by combining real-time wind shear detection data measured by a laser wind measurement radar and forecast data of a current time point output by a mesoscale weather forecast module, processes the assimilated data by applying a CFD numerical simulation module, and calculates the real-time wind shear detection data by combining terrain and building parameters near an airport; according to the method, real-time sub-meter gridding three-dimensional wind field data is generated, and a sub-meter three-dimensional wind field structure is reconstructed, so that future wind shear of an airport is forecast and early warned, and then available runway prediction is carried out. The method is applied to the technical field of wind field prediction and judgment.

The invention discloses a calculation method for a complex vertical wind field cloud catalysis operation influence area, and the method comprises the steps: obtaining flight information, selecting the data of a sounding station closest to the flight position of an airplane, and carrying out the matching of the data of a wind field at the same height of the sounding station according to the sowing height of the airplane; and calculating an influence area position parameter of the height of the sowing layer based on the airplane sowing operation time, the sowing concentration and the obtained sowing height horizontal movement data, calculating a distance movement parameter caused by shear, and adding the influence area position parameter and the distance movement parameter caused by shear to obtain a final sowing influence area position. According to the method, the influence of the wind field on the sowing layer can be simulated more accurately, the calculation accuracy is improved, and the influence of the complex wind field on the sowing operation is considered by defining the wind shear and calculating the distance movement caused by the shear; and the diffusion path and range of the spread substance can be predicted more accurately.

The invention provides a pilot manipulation quality real-time analysis method and system in a wind shear environment, and relates to the technical field of aviation flight, and the method comprises the steps: firstly capturing flight manipulation and environment interaction data including flight state feedback data and pilot manipulation execution data; secondly, establishing a response association link set of the manipulation action and the environment change, taking the wind shear environment change as a trigger node, taking pilot manipulation execution as a response node, and taking flight state feedback as an association node; tracing the matching relation between the time sequence track of the control action and the environment change, and outputting a time sequence matching relation set; analyzing the adaptability dynamic change of the manipulation action based on the time sequence fit relationship set evolution, and outputting an adaptability dynamic change result; and finally, generating a manipulation adjustment guide based on the dynamic change result of the adaptability, assisting a pilot in optimizing manipulation actions in real time, and improving the flight safety and manipulation quality.

The invention relates to the technical field of low-altitude flight path planning, in particular to a low-altitude flight path automatic generation method and system, and the method comprises the following steps: S1, synchronously obtaining original wind field distribution data and original terrain elevation data of a flight airspace; s2, performing wind shear strength analysis on the original wind field distribution data; meanwhile, carrying out obstacle contour recognition on the original terrain elevation data to generate an obstacle basic coordinate set; s3, calculating a real-time risk coefficient of each obstacle; s4, executing iterative calculation to generate an initial obstacle avoidance path point sequence; and S5, under the condition that the minimum safety height constraint set based on task requirements is maintained, compressing the path points in the vertical direction to generate a final path instruction set. According to the method, wind shear analysis, obstacle risk assessment and energy consumption constraint path optimization are fused, so that automatic generation of the low-altitude flight path suitable for a dynamic complex environment is realized, and the safety and the performability of the path are improved.

The invention discloses a wind profile fitting method suitable for time sequence prediction of a complex mountain wind field, and the method comprises the steps: obtaining topographic data and meteorological data, determining a wind shear coefficient of a target region, and constructing a wind profile fitting model; target area real-time topographic data and target area real-time meteorological data are obtained, the wind profile fitting model receives the target area real-time topographic data and the target area real-time meteorological data, and a wind profile set is generated; and for the target area, dynamically adjusting the wind profile parameters in real time, and generating a wind field time sequence prediction result. According to the method, complex mountain landforms, meteorological factors and space-time change characteristics of a wind field are considered, accurate fitting of wind profiles of different terrain sub-regions is realized on the basis of a terrain and meteorological data set, and wind profile fitting better fits the actual situation; on one hand, terrain and meteorological data are updated in real time, on the other hand, a dynamic self-adaptive updating mechanism is established for wind field time sequence prediction, and calculation input reasonability is guaranteed.

The invention relates to the technical field of laser radar meteorological detection, in particular to a wind field feature detection method and device based on a laser radar, and the method comprises the following steps: S1, controlling the laser radar to execute dynamic hierarchical scanning, and generating three-dimensional laser scanning data; s2, carrying out aerosol backscattering signal preprocessing on the three-dimensional laser scanning data; s3, extracting an aerosol motion vector of each range gate, and decomposing the aerosol motion vector into a horizontal wind field component and a vertical wind field component; s4, performing spatial gradient calculation to generate a spatial gradient distribution map; s5, respectively calculating a horizontal energy spectrum and a vertical energy spectrum; and S6, outputting wind field characteristic parameters. According to the invention, through fusion of the dynamic features of the laser radar point cloud, the wind shear space gradient and the energy spectrum analysis method, precise extraction of the turbulence intensity, the main energy scale and the three-dimensional vortex features is realized, and the accuracy and the spatial resolution of wind field structure identification are improved.

The invention provides a gust front wind shear identification method based on artificial intelligence, and the method comprises the steps: carrying out the noise filtering, missing value supplementary measurement, data smoothing, wind shear value calculation and sample screening extraction of collected radial speed data, and obtaining a gust front wind shear sample; performing coordinate system conversion, sample set division and data annotation on the basis of gust and front wind shear samples to obtain an expanded data set; designing and training a Mask R-CNN model architecture to obtain a gust and front wind shear identification model; and inputting the expanded data set into the gust and front wind shear identification model to carry out gust and front wind shear detection. According to the method, dependence on reflectivity factor data can be reduced, an identification model is constructed based on gust front radial speed data, gust front wind shear can be accurately identified, pixel-level segmentation and positioning of a wind shear area can be realized, and identification efficiency is improved.

The invention discloses a weather early warning method and device based on radar data and meteorological data and storage equipment, and the method comprises the following steps: S1, obtaining high-temporal-spatial-resolution wind field data, an inversion wind shear field and a visibility field through a high-precision high-temporal-spatial-resolution coherent laser radar; s2, detecting atmospheric parameters of an overhead area of the airport in real time to form airport auxiliary observation data; s3, based on the wind shear field, the visibility field and the airport auxiliary observation data, artificial intelligence machine learning is used for data processing, and a forecasting scheme is formed. Compared with the prior art, the method is based on the high-precision and high-temporal-spatial-resolution coherent laser radar, atmospheric parameters of the area above the airport are detected in real time, high-temporal-spatial-resolution wind field data are obtained, and the prediction accuracy is improved. And inverting a wind shear field and a visibility field, and processing by using methods such as artificial intelligence machine learning and the like to form a forecasting scheme.

The invention discloses a wind power plant unit load reduction optimization method and system based on cabin type laser radar real-time wind measurement, and relates to the technical field of wind power generation. Constructing a load estimation model; performing real-time detection on a blade root bending moment predicted value sequence and a tower bottom vibration acceleration predicted value sequence, constructing a multi-objective optimization function including blade root bending moment, tower bottom vibration acceleration and power generation power, and setting a dynamic weight coefficient of the multi-objective optimization function according to three-dimensional wind field prediction sequence data; obtaining an optimal control variable through a multi-objective genetic algorithm based on the multi-objective optimization function; and according to the optimal control variable and vertical wind shearing in the three-dimensional area, differential adjustment is conducted on the pitch angle of each blade, and a personalized variable pitch angle sequence of each blade is obtained. According to the method, by establishing the optimization objective function fusing the load and the power, multi-variable collaborative optimization is achieved, and the safety and the economical efficiency of wind power plant unit operation are remarkably improved.

The invention relates to a wind shear wind speed extrapolation calculation method based on a neutral equivalent wind speed. The method comprises the following steps: (1) determining input wind speed measurement data; (2) classifying the atmospheric stability, and converting the wind speed at each moment into an equivalent wind speed in a neutral state; (3) performing extrapolation wind speed calculation according to the neutral equivalent wind speed; and (4) outputting an extrapolated wind speed result, and calculating wind shear. According to the method, the wind speed extrapolation error is effectively reduced, the wind speed extrapolation calculation is more accurate, and particularly, the method has outstanding performance in complex terrains or marine environments.

The invention relates to the technical field of risk analysis, in particular to an offshore wind power project risk assessment method and system, and the method comprises the following steps: collecting a wind speed value according to the hub height of multiple units of an offshore wind power plant, obtaining the unit power, yaw angle change and vibration amplitude, and calculating the wind speed change rate; according to the method, through combination of blade vibration, yaw driving feedback and tower stress monitoring data and multi-source information cross analysis, the calculation precision of the unit load fluctuation rate is improved, the wind shear influence is quantified through the wind shear strength coefficient, and the risk prediction capability under the condition of dramatic change of the wind speed is improved; yaw error cumulative effect dynamic evaluation optimizes unit yaw precision management and control, reduces influence of long-term operation deviation on equipment stability, computes unit operation state unstable factors through multi-dimensional data fusion, comprehensively quantifies wind speed change, load fluctuation and yaw error influence, perfects a risk evaluation system, optimizes a wind turbine generator risk evaluation model, and improves wind turbine generator risk evaluation accuracy. And the safety management level of offshore wind power projects is improved.

The invention discloses a laser radar-assisted wind power generation wind shear modeling method, and relates to the technical field of wind shear modeling, and the method comprises the steps: collecting the wind speed measurement data of a laser radar at a plurality of heights in a target fan region, and constructing a measurement volume integral operator matrix; constructing a unit induction field correction model based on large eddy simulation in combination with a gradient boosting tree; coupling the measurement volume integral operator with the unit induction field correction model, and obtaining a high-precision wind speed vertical section and a corresponding wind shear parameter through inversion; on the basis of the high-precision wind speed vertical section and the corresponding wind shear parameters, physical constraints are fused, a wind shear model is generated, and uncertainty evaluation is carried out; according to the wind shear modeling method, the problems that the measurement error of the laser radar is difficult to correct, the wind shear modeling lacks the physical constraint and the result credibility cannot be quantified are solved by constructing the volume measurement integral operator and the unit induction field correction model for coupling inversion and fusing the physical constraint and the Bayesian uncertainty evaluation method.

The invention relates to the technical field of floating type wind turbine generator operation, and discloses an evaluation method and device for coupling response characteristics of offshore wind regimes and floating type draught fans, and the method comprises the steps: obtaining sea area meteorological observation data corresponding to a target floating type draught fan, and calculating offshore wind regime data based on the sea area meteorological observation data corresponding to the target floating type draught fan; wherein the offshore wind regime data comprises a sea level roughness coefficient and a vertical wind shear coefficient, obtaining a target floating fan equipment parameter, constructing a floating fan simulation model based on the target floating fan equipment parameter, and finally performing coupling response characteristic evaluation by using the floating fan simulation model based on the offshore wind regime data. And obtaining an evaluation value of the coupling response characteristics of the offshore wind regime and the floating fan. According to the method, the response characteristics of the floating fan under the influence of the offshore wind condition are effectively evaluated, and a data basis is provided for ensuring the safe operation of the floating fan.

The invention relates to the field of wind power generation, and discloses a wind turbine generator wind speed and wind direction acquisition method which comprises the following steps: 1) acquiring wind speed and wind direction data of a wind turbine generator operation area in real time through a sensor; 2) eliminating high-frequency disturbance based on a turbulence model; 3) correcting wind speed and wind direction data based on a wind shear model, and compensating a wind speed gradient; 4) constructing a wind field optimization model, and dynamically optimizing wind speed and wind direction data by using a global optimization method; 5) fusing the macroscopic trend and the local details based on multi-scale decomposition to generate final wind speed and wind direction data; and 6) outputting high-precision corrected wind speed and wind direction data. By introducing turbulence correction and wind shear models, main measurement errors caused by turbulence disturbance and height difference are eliminated for complex wind field characteristics in a wind turbine generator operation area. The layering correction method effectively reduces the randomness and deviation of the wind speed and direction data, and enables the output data to have high reliability and accuracy in various wind field environments.

The invention relates to the technical field of path planning, in particular to a low-altitude airspace aircraft flight path planning system which comprises a task analysis module, an airspace modeling module, a path generation module, a feasibility verification module and a path output module. According to the method, sight distance flight rule grade judgment is introduced in airspace boundary extraction to eliminate airspace sections which do not meet the minimum sight distance reference requirement, a connection diagram is constructed, combined calculation of the wind shear angle and the building projection density is executed, and through quantitative analysis of the ratio of the path section height to the wind pressure change gradient, the wind shear angle and the building projection density are calculated. The method comprises the following steps: performing intensity risk labeling on a path in an unstable airflow area to realize path continuity and stability discrimination, constructing a continuous instruction point group based on a stable fragment sequence in a path combination stage, and setting speed and steering adjustment thresholds, so as to enhance the adaptability and execution robustness of a path execution sequence to flight control; and the air route feasibility and the risk avoidance capability in a complex low-altitude environment are improved.

The invention discloses a wind shear and shear line identification method and system based on a laser radar in the technical field of radar detection, and the method comprises the steps: identifying all wind shear slopes from single radial wind speed data, and screening a stronger wind shear slope according to a wind shear intensity factor; and calculating a total wind shear value of each wind shear slope, comparing the total wind shear value with a wind shear grade criterion, and identifying a wind shear slope with a strong wind shear grade. Extracting all radial wind speed data from radar echo signals of a target area to obtain a significant wind shear slope in the whole target area, and performing clustering analysis on all wind shear slope shear points according to position coordinates of the wind shear slope shear points to form a plurality of clusters; and screening clusters conforming to shear line conditions, and performing curve fitting to obtain all shear lines. According to the invention, the position and intensity of wind shear in the target area can be effectively identified, real-time identification and early warning of the shear line are realized, and the timeliness and accuracy of weather detection and early warning are improved.

The invention provides a non-linear flight control method and system for an anti-wind disturbance unmanned aerial vehicle. The method comprises the following steps: acquiring wind field disturbance characteristics in real time by using a preset meteorological radar; inputting the wind field disturbance characteristics into a preset aerodynamic disturbance prediction model to generate an aerodynamic torque fluctuation prediction value; preset dynamic filtering processing is applied to the amplitude characteristic of the aerodynamic torque fluctuation predicted value so as to generate a control instruction correction signal; and the control instruction correction signal is coupled with a preset nonlinear control law of an unmanned aerial vehicle flight control system, so that unmanned aerial vehicle attitude offset caused by wind disturbance is inhibited in real time, and unmanned aerial vehicle nonlinear flight control is carried out. According to the method, the composite defect of hysteresis and filtering mismatch of traditional linear control in a strong wind sudden change scene is overcome, stable attitude control of the unmanned aerial vehicle in low-altitude wind shear and gust sudden attack is realized, the anti-interference response speed of the unmanned aerial vehicle is remarkably improved, and the out-of-control risk of the unmanned aerial vehicle is greatly reduced.

The invention provides an impeller unbalance monitoring method based on fusion of wind speed and blade root load, which comprises the following steps of: firstly, constructing a dynamic synthesis model of local effective wind speed of a blade, and establishing a dynamic effective wind speed model of each point of the blade by combining wind shear, a pitch angle, a rotation effect and an incoming flow wind direction so as to provide high-precision input for theoretical aerodynamic load calculation; then, a multi-sensor dynamic data fusion method is fully adopted, real-time wind speed field data and direct measurement data of a blade root load sensor are dynamically fused, a real-time correction algorithm based on a physical model is constructed, interference of wind speed fluctuation on load judgment is eliminated, and unbalance detection precision is improved. And finally, based on a dynamic threshold early warning mechanism adopted by theoretical-actual load deviation, a sliding window standard deviation analysis and machine learning algorithm is introduced to set a dynamic abnormal threshold of the load deviation. Therefore, the unbalance state of the impeller is recognized in real time, and real-time monitoring, analysis and early warning of pneumatic unbalance of the fan are achieved.

The invention discloses a wind shear risk assessment prediction method based on an improved PKO optimization combination model, and the method comprises the steps: S1, constructing a wind shear risk assessment index system, obtaining historical wind shear event data, dividing the historical wind shear event data into a plurality of time span groups, and calculating the span group wind shear average risk of each time span group, collecting according to a time sequence to form a wind shear average risk data set; and S2, constructing a combination model, inputting the wind shear average risk data set into the combination model to carry out model training, carrying out model parameter optimization processing by adopting an improved emerald optimization algorithm, and outputting a span group wind shear average risk after the current time by the combination model according to a time sequence. According to the method, the trend characteristic, the seasonal characteristic, the time sequence characteristic and the nonlinear relation of the historical time sequence wind shear average risk data set can be better captured, the future span group wind shear average risk prediction after the current time can be realized, and technical support is provided for aviation risk trend analysis.