176 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 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 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 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.

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 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 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 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 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 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 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 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 embodiment of the invention discloses a low-altitude wind shear early warning method and device based on multi-source meteorological data. A specific embodiment of the method comprises the steps of generating a first region of interest for a target region according to a contour topographic map; determining a second region of interest according to the real-time rainfall distribution diagram and the real-time air temperature distribution diagram; performing region optimization on the first region of interest according to the real-time wind field graph to obtain a third region of interest; generating a fourth region of interest according to the second region of interest and the third region of interest; and according to the fourth region of interest and a pre-trained low-altitude wind shear prediction model, generating wind shear region early warning information. According to the embodiment, accurate prediction of the wind shear is realized, so that early warning is performed on the low-altitude aviation equipment, and the navigation safety of the low-altitude aviation equipment is ensured.

The invention provides a civil aircraft safety perception method and system based on virtual flight, and relates to the technical field of aircraft safety perception. A dynamic and hierarchical three-dimensional wind shear field model is constructed through space-time alignment and fusion of multi-source meteorological data; deep coupling of wind disturbance and aircraft dynamic response is realized based on a small disturbance theory and a flight dynamics model; continuous and dynamic quantification of risks is realized by combining and analyzing flight parameters and a safety threshold value dynamically adjusted based on wind disturbance intensity and utilizing multi-parameter risk level weighted integration; according to the method, the risk coefficient is coupled with the three-dimensional terrain and the virtual flight trajectory, hierarchical color mapping is adopted to carry out visual space visualization, and a complete closed loop from environment modeling to risk early warning is formed, so that the identification accuracy and timeliness of the flight risk in the wind shear environment are greatly improved, and the scientificity and practicability of security situation awareness are enhanced.

The invention relates to a wide-range wind shear generation system, a parameter determination method and a parameter control method, and belongs to the technical field of wind field simulation. The system comprises a wind field generation device, a wind direction adjustment device and a wind field simulation cabin which are arranged in sequence; the wind field generating device comprises a plurality of fans, the fans are connected with a control module, and the control module is connected with an upper computer; the wind field simulation cabin is internally provided with a plurality of wind speed sensors which are located in the wind field simulation cabin, have the same flow direction and are connected with the control module. The wind direction adjusting device comprises a mounting frame, a plurality of cross rods and vertical rods which are arranged in a staggered mode are rotationally connected into the mounting frame, driving parts which drive the cross rods and the vertical rods to rotate and are connected with the control module are arranged on the mounting frame, baffles are fixedly connected to the cross rods and the vertical rods, and when all the baffles are located on the same plane, the edges of every two adjacent baffles are attached to each other. The method has the advantages that the performance and safety characteristics of the aircraft in the complex and changeable low-altitude wind environment are comprehensively known, and safety guarantee is provided.

The application discloses a wind shear working condition recognition method and system for a wind generating set, the method is that the test data of the wind generating set is obtained through the independent variable pitch equipment of the wind generating set, the bending moment of the wind generating set in the static load coordinate system is calculated, the change rate time sequence is calculated according to the bending moment, the direction and the duration of the change rate in the change rate time sequence are compared with the preset threshold condition, if the direction and the duration of the change rate in the change rate time sequence meet the threshold condition, the wind generating set is recognized to be in the wind shear working condition; the test data can be obtained based on the independent variable pitch equipment, the difference between the wind shear working condition and the normal power generation working condition can be recognized, the load optimization and the clearance optimization of the wind shear working condition can be realized, the power generation capacity of the normal power generation working condition is not affected, and the loss of the power generation capacity of the wind generating set is prevented.

The invention relates to a wind power plant unit operation optimization method and system based on artificial intelligence, and the method comprises the following steps: S1, data collection and processing: collecting the output power data, vibration data, temperature data, rotating speed data, yaw angle data and blade root strain data of each unit, meanwhile, acquiring upwind speed, wind direction, wind shear and turbulence intensity by using a laser radar at the top of a cabin, and performing time synchronization and preliminary noise filtering on the data; according to the invention, unit output power data, vibration data, temperature data, rotating speed data, yaw angle data and blade root strain data are continuously acquired through a data acquisition module, upwind speed, wind direction, wind shear and turbulence intensity are acquired by a laser radar at the top of a cabin, and time synchronization and preliminary noise filtering processing are combined to obtain a wind speed and a wind direction; discrete noisy points generated by wind speed pulsation, equipment jitter and environment interference can be effectively eliminated, so that the acquired data is more stable and reliable.

The invention discloses a quadrotor unmanned aerial vehicle attitude estimation method and device based on an improved UKF and a medium, and the method comprises the steps: constructing a kinetic model of a quadrotor unmanned aerial vehicle, deducing an attitude angle kinetic equation through a Newton-Euler method, and superposing a wind shear model and a turbulent wind field model; the method comprises the following steps: collecting multi-source sensor data, and performing hierarchical filtering processing on the multi-source sensor data through a multi-source data fusion and dynamic noise suppression technology; and performing attitude estimation on the quad-rotor unmanned aerial vehicle by adopting a UKF (Unscented Kalman Filter) algorithm of an improved adaptive factor based on the deduced and superposed kinetic model and the processed multi-source sensor data. Through the above method, the method is closer to a real flight environment, the influence of environment sudden change on data quality is inhibited, and high-precision and high-stability attitude estimation is realized.

The invention relates to a wind shear factor calculation method based on one-dimensional wind speed measurement, and the method comprises the following specific wind speed estimation steps after preprocessing: 1, obtaining the distribution of the radial speed of meteorological echoes scanned by all horizontal azimuth angles of a radar along with the distance through a frequency domain analysis algorithm; 2, according to the radial wind speed distribution, the horizontal plane coordinates of the center position of wind shear are obtained; 3, calculating the distance from each effective range gate on the expected flight path of the airplane to the wind shear center; and 4, calculating the vertical wind speed on the expected flight path of the airplane by using a corrected vertical wind speed calculation method based on the axial symmetry non-rotation field approximation of the wind shear wind field and the non-fluid static pressure model of the near-surface wind field. The nonlinear wind speed model estimation of the vertical wind speed of the central area of the wind shear wind field is provided, the calculation amount of the calculation model is small, the method does not depend on preset parameters, and compared with a traditional method, errors are small.

The invention relates to an unmanned aerial vehicle control method considering weather risks, and the method comprises the steps: collecting the flight environment data of an unmanned aerial vehicle in real time, synchronously receiving a cloud evolution map and a wind shear early warning signal of a ground radar, and carrying out the processing, thereby obtaining multi-mode meteorological data; predicting storm intensity distribution in the track area in 2-5 minutes in the future, and generating a three-dimensional threat level grid map; aiming at the conventional meteorological risk, constructing a threat level-evasion strategy mapping table, judging the threat level, calling a corresponding evasion strategy, and guiding the unmanned aerial vehicle to execute a corresponding evasion action; when encountering an extreme condition, activating an anti-interference navigation mode, enhancing flight stability through rotating speed-pitch angle cooperative control, and triggering a standby landing emergency protocol; after the avoidance action is completed, the flight path is re-planned in combination with space-time constraints, and the flight speed is reasonably adjusted. The invention also relates to an unmanned aerial vehicle control system considering the weather risk. According to the invention, the flight safety and stability of the unmanned aerial vehicle under complex weather conditions can be improved.

The invention relates to the technical field of low-altitude meteorological science and aviation safety, in particular to a low-altitude turbulence prediction method based on multi-source observation data. The method is realized based on multi-source data, and specifically comprises the steps of data acquisition and preprocessing, unification of observation data of different data sources to a target height layer and realization of space-time matching, calculation of turbulent kinetic energy by physical characteristic quantity, wind shear, turbulence intensity weighted calculation, design of a height weight function of exponential attenuation, and calculation of turbulence intensity through weighted combination. Therefore, turbulence intensity distribution in a two-dimensional vertical plane is obtained. Through systematic multi-source data fusion, characteristic quantity extraction based on a physical mechanism, intelligent weight design and reliability evaluation, high-precision two-dimensional and three-dimensional low-altitude turbulence prediction is realized, and powerful technical support is provided for low-altitude flight safety. Meanwhile, the adaptability scheme of the single-source data ensures the practicability of the method under different equipment configuration conditions, and the method has a wide application prospect.

The invention discloses a pollution emission monitoring and early warning method based on physical prior and information value driving, and relates to the technical field of environment monitoring and pollution control, and the method comprises the following steps: collecting production side certificate data, executing time alignment and self-calibration drift removal, and constructing a standardized multi-source database; judging whether a heat island effect occurs or not, and if yes, collecting volume heat capacity, solar irradiance and heat island intensity data of a single building; according to the method, the heat island effect is introduced to judge and generate a new time-space diagram network, real-time heat island intensity changes caused by building materials and solar irradiation can be dynamically captured, and building wind field and plume data are accurately corrected. The problem that a conventional method cannot adapt to irregular fluctuation of vertical wind shear caused by the real-time heat island effect is solved, the pollution plume low-altitude compression phenomenon caused by the heat island effect is effectively corrected, and the monitoring direction is always aligned with the real diffusion path of pollutants.

The invention relates to the technical field of wind shear early warning, and provides a wind shear early warning method based on an airport wind lidar, which comprises the following steps of: 1, acquiring high-resolution three-dimensional wind field detection data based on high-resolution multi-mode continuous observation of the airport wind lidar; 2, performing data quality control on the three-dimensional wind field detected by the wind measurement laser radar based on a K-field frequency method; 3, performing wind shear identification inversion on the quality control data; 4, according to different influences of different wind shear sizes on flight control of the aircraft, performing early warning classification of different degrees on the wind shear of the horizontal wind; step 5, using divergence and a logistic regression model to optimize a wind shear value, and reducing a false alarm rate; and step 6, based on the airplane report of the airport, selecting a wind shear process, and checking and evaluating a wind shear early warning result. According to the invention, wind shear early warning can be well carried out.

The present application relates to a kind of unmanned aerial vehicle control method considering weather risk, including the following steps: real-time acquisition unmanned aerial vehicle flight environment data, synchronous receipt cloud layer evolution map and ground radar's wind shear early warning signal, process to obtain multimodal meteorological data;Predict the storm intensity distribution in future 2-5 minutes track area, and generate three-dimensional threat level grid map;For conventional meteorological risk, build threat level-avoidance strategy mapping table, judge threat level, and call corresponding avoidance strategy, guide unmanned aerial vehicle to execute corresponding avoidance action;When encountering extreme situation, activate anti-interference navigation mode, enhance flight stability by rotating speed-pitch angle collaborative control, trigger emergency protocol of alternate landing;After completing avoidance action, re-plan flight path in combination with space-time constraint, reasonably adjust flight speed.The present application also relates to a kind of unmanned aerial vehicle control system considering weather risk.The present application can improve the flight safety and stability of unmanned aerial vehicle under complex weather conditions.