2426 results about "Wind field" patented technology

The invention provides a fan system multi-physics field coupling simulation and modeling method and device, and the method comprises the steps: building a coupling simulation frame of an electromagnetic field, a temperature field, a flow field and a structural mechanical field through multi-physics field collaborative modeling, employing a finite volume method FVM and multi-body dynamics MBS for collaborative solving, and precisely simulating the dynamic response of a fan under a complex working condition. And real-time simulation and verification: combining an S CADA system and a machine learning algorithm to realize online calibration and dynamic verification of a multi-physics field model, and supporting fan structure stability prediction under extreme conditions of typhoon, turbulence and the like. A multi-physics field coupling simulation software environment is developed, the functions of wake effect analysis, fan array layout optimization and the like are provided, the construction cost of an offshore wind field is reduced, and the power generation efficiency is improved.

The invention provides an intelligent detection method and system for the health state of a wind generating set in an intelligent wind field, and relates to the technical field of intelligent wind field multi-source monitoring. The method comprises the following steps: firstly, collecting multi-source operation data such as a transmission chain, structural parts and environment working conditions, and performing time reference unification; performing noise reduction, calibration, compensation and time window segmentation on the original data to form a preprocessed data set; extracting and aligning features in multiple domains to construct fusion feature representation; establishing a health baseline model based on historical normal samples and working condition variables to generate a self-adaptive alarm threshold value; inputting a health discrimination model to obtain an anomaly index, and generating an early warning event according to a trigger condition; and comprehensively fusing the features, the health base line and the judgment result to calculate a health index and output early warning information, thereby realizing accurate detection and risk early warning of the whole life cycle and the whole working condition.

The invention discloses a bionic group intelligent low-altitude logistics unmanned aerial vehicle cluster anti-wind interference cooperation method, and the method comprises the steps: collecting the historical flight data and three-dimensional wind field data of an unmanned aerial vehicle cluster, and generating a bionic formation feature set with a wind field label; inputting the bionic formation feature set into a swarm intelligence model fused with fluid mechanics, and generating a dynamic formation topology instruction; according to the dynamic formation topology instruction, adjusting the relative position and attitude angle of each unmanned aerial vehicle through a distributed cooperative control algorithm, and generating an anti-wind disturbance cooperative flight state; and continuously monitoring the deviation between the three-dimensional wind field change and the cooperative flight state, dynamically correcting the weight of the formation density-anti-wind disturbance intensity mapping relation through a reinforcement learning algorithm, updating a dynamic formation topology instruction, and realizing adaptive control of bionic group anti-wind disturbance cooperation. According to the embodiment of the invention, high-disturbance-rejection cooperative flight of the unmanned aerial vehicle cluster in the dynamic wind field can be realized, the formation energy consumption is reduced, and the obstacle avoidance capability under the sudden wind condition is improved.

The invention discloses a multi-unmanned aerial vehicle combined flight control system coping with wind power change, and relates to unmanned aerial vehicle flight control, and the system comprises a flight data collection module which is used for collecting unmanned aerial vehicle flight data in real time, and the unmanned aerial vehicle flight data comprises wind power data, attitude angle data and height data of each unmanned aerial vehicle; the data receiving and processing module is electrically connected with the flight data acquisition module, and the data receiving and processing module is used for receiving unmanned aerial vehicle flight data acquired in real time and performing noise removal on the acquired data. According to the multi-unmanned aerial vehicle combined flight control system provided by the invention, the wind field prediction model is constructed by combining the LSTM neural network with the time attention mechanism, wind power periodic change characteristics are accurately captured, real-time correction is performed in cooperation with the Kalman filter, the prediction precision and timeliness are both optimized, and the system is suitable for large-scale popularization and application. A multi-unmanned aerial vehicle cooperative kinetic model is constructed based on a prediction result, so that the formation trajectory tracking error is reduced.

The invention relates to the technical field of wind turbine generator control, and discloses a wind turbine generator control optimization method based on dynamic changes of wind speed and wind direction. According to the method, real-time wind speed time sequence data and three-dimensional wind direction vector field data of a target wind field are received, a wind field dynamic analysis model is constructed by using a space-time convolutional neural network, and a wind field energy density distribution matrix and a turbulence intensity probability graph are generated. And constructing a multi-target adaptive optimization model on the basis, generating a unit control parameter instruction set, optimizing a cooperative adjustment coefficient according to a preset unit load-power generation efficiency balance equation, and outputting an optimal control action sequence to a wind turbine generator master control system through Bayesian optimization framework iterative updating. The method can accurately sense the wind field change, effectively balance the unit load and power generation efficiency, realize multi-unit cooperative control, improve the wind energy capture efficiency, and improve the operation stability and economic benefits of the wind turbine generator.

The invention relates to the technical field of unmanned aerial vehicle dynamic dust suppression intelligent decision making based on multi-sensor data fusion, in particular to a loess photovoltaic construction dust suppression unmanned aerial vehicle spraying dynamic regulation and control device and method, and the method comprises the steps: building a dynamically updated four-dimensional concentration field through a sensor cooperation unit in combination with a turbulence diffusion model of a dust field reconstruction engine; and the strategy knowledge base is optimized to shorten the flight path planning decision-making period from the minute level of the existing offline planning to the second level response. And the anti-interference execution unit realizes accurate spray trajectory tracking under a complex wind field condition. And a real-time evaluation closed loop of the dust suppression effect is constructed by a dual-spectrum imaging and deep learning analysis technology of the efficiency feedback unit, so that a dust field model can dynamically correct boundary condition parameters. The response lag time of an existing dust suppression system is shortened, meanwhile, the spray coverage rate is increased, energy consumption is reduced on the premise that the dust suppression effect is guaranteed, and an intelligent solution is provided for photovoltaic construction flying dust treatment.

The invention relates to a wind field sensing and wind resistance control device and method for a cross-domain unmanned aerial vehicle, belongs to the technical field of air-ground unmanned aerial vehicles in the field of air-ground coordination, and solves the problem that in the prior art, an unmanned aerial vehicle is poor in wind resistance flight capacity under the conditions that the wind environment is complex and the wind speed changes drastically. Original data are collected through a multi-mode sensor group and processed to obtain a standardized data set; s2, constructing a PINN model, and taking the standardized data set as input processing to obtain a wind field prediction result; s3, the PINN model is trained, and a trained PINN model is obtained; s4, inputting a standardized data set acquired and processed in real time into the trained PINN model to obtain a wind field prediction result output in real time; s5, establishing an active-disturbance-rejection controller, and outputting a final control quantity based on a wind field prediction result output in real time; and S6, processing the final control quantity to generate a motor distribution instruction, and providing the motor distribution instruction to a motor of the unmanned aerial vehicle.

The invention discloses a wind energy resource prediction method, system and device and a storage medium, and relates to the field of renewable energy sources, and the method comprises the steps: obtaining the three-dimensional wind field observation data of a target region; performing space-time alignment processing operation on the three-dimensional wind field observation data to generate a space-time sequence; performing multi-source data fusion on the space-time sequence, and constructing a multi-dimensional wind field matrix; establishing a wind energy potential prediction model based on the multi-dimensional wind field matrix; and predicting to-be-predicted wind energy data based on the wind energy potential prediction model to obtain a wind energy resource prediction result. And efficient, accurate and reliable resource evaluation is provided for wind energy development of various terrain areas by constructing a machine learning model.

The invention provides a marine reanalysis wind field data correction method based on buoy observation data, and belongs to the technical field of meteorologies.The marine reanalysis wind field data correction method comprises the steps that firstly, abnormal value elimination and quality control are conducted on the buoy observation data, and space-time matching is conducted through a bilinear interpolation method; constructing a dynamic time window, and adaptively adjusting the time precision according to the meteorological event type; calculating wind speed and wind direction errors, and extracting spatio-temporal characteristic parameters; the method comprises the following core steps of: optimizing Gaussian process regression by using an ocean physical constraint neural network model, wherein the model comprises a surface layer physical process coding layer, a boundary layer dynamic embedding layer and a multi-head sparse attention layer; constructing a space-time covariance model to generate an error field; correcting the reanalyzed wind field data and outputting uncertainty evaluation; the technical problem that a significant space-time error exists between reanalysis wind field data and an actual observation value in a complex marine environment is solved.

The invention relates to the field of unmanned aerial vehicle environment perception, in particular to a multi-mode environment perception method and system for a low-altitude medical unmanned aerial vehicle. The method comprises the following steps: collecting a multi-modal data stream, carrying out adaptive data optimization processing, and constructing a multi-modal fusion data set; performing environment multi-level obstacle identification and evaluation on the multi-modal fusion data set to generate a threat mapping environment map; multi-dimensional environment parameters are collected based on the unmanned aerial vehicle, wind field time-varying prediction and safe flight area calculation are performed based on the threat mapping environment map, and a flight area map is constructed; performing multi-position collision risk assessment based on the multi-modal fusion data set to generate collision risk coefficients of different positions; and carrying out safe flight constraint analysis on the flight area map according to the collision risk coefficient, and extracting an optimal flight path. According to the invention, in combination with real-time environment data, comprehensive flight path planning is provided, and the flight safety and task completion efficiency of the unmanned aerial vehicle are improved.

The invention discloses an atmospheric environmental pollution event emergency monitoring and early warning method and system, and belongs to the technical field of environmental management. The method comprises the following steps: acquiring multi-scale meteorological field data and chlorine concentration real-time data of at least five monitoring stations, extracting a wind field direction and a concentration change rate, and determining a position and a wind direction of a potential pollution hot spot area. The information is sent to the unmanned aerial vehicle, and the unmanned aerial vehicle automatically plans a path and collects supplementary concentration data from three angles. And fusing the monitoring data and the supplementary sampling data, and combining with a meteorological field input pollutant three-dimensional concentration distribution model to generate three-dimensional distribution data. And inputting the CALPUFF reverse model, and carrying out inversion to obtain the specific position and the emission intensity of the pollution source. According to the scheme, the accuracy and timeliness of source tracing of the pollution source are improved, high spatial resolution and flexible response capability are achieved, and efficient and intelligent technical support is provided for intelligent early warning, rapid source tracing and scientific response of sudden toxic gas leakage events.

The invention discloses an unmanned aerial vehicle attitude feed-forward compensation control method for building wind field disturbance, and belongs to the technical field of unmanned aerial vehicle flight control, and the method comprises the steps: building a mapping relation between a building space position and a wind field disturbance characteristic; processing the real-time wind field data in combination with the mapping relation to generate a wind field prediction result; calculating a feed-forward compensation amount, and synchronously determining a dynamic weight factor reflecting the disturbance intensity of the wind field; obtaining an attitude error between a current attitude and an expected attitude of the unmanned aerial vehicle, and calculating a feedback control quantity; and performing weighted fusion on the feedforward compensation quantity and the feedback control quantity by using the dynamic weight factor, and generating a final control instruction to control the attitude of the unmanned aerial vehicle. According to the method, the wind field mapping relation is established and the wind field is predicted to calculate the feed-forward compensation quantity, and then the dynamic weight factor is used to carry out weighted fusion on the feed-forward compensation quantity and the feedback control quantity, so that the disturbance of the wind field can be actively inhibited, and the attitude control stability and precision of the unmanned aerial vehicle in a complex environment are improved.

The invention relates to an autonomous energy-saving soaring flight path planning method for a small-sized low-cost aircraft, belongs to the technical field of aircraft trajectory planning, solves the problem of low-cost wind field energy acquisition of the small-sized low-cost aircraft in the prior art, and comprises the following steps: S1, configuring a sensor for the aircraft, and measuring through the sensor to obtain observation parameters; s2, establishing a state vector of the aircraft; s3, establishing an aerodynamic force model, introducing a dynamic equation and a state transition equation, and performing accurate modeling on aerodynamic force; s4, performing multi-source data fusion by adopting extended Kalman filtering, establishing an extended Kalman filter of a nonlinear system, and executing real-time wind vector high-precision sensing; and S5, performing global wind field modeling, estimating a wind field environment, and performing energy-obtaining flight path planning to obtain an optimal energy-obtaining soaring flight path planning scheme.

The invention discloses a three-dimensional wind field spatial-temporal feature reconstruction and efficient prediction method, and relates to the field of spatial-temporal feature reconstruction and efficient prediction. Constructing a three-dimensional terrain computational domain based on the digital elevation model of the target mountain region, performing multi-scene numerical simulation by adopting a fluid mechanics method or a mesoscale meteorological model, generating a wind field training data set, and constructing and training a wind field spatial feature mapping model; training a wind speed and wind direction short-time prediction model based on the actually measured data set; inputting the monitoring data obtained in real time into the wind speed and wind direction short-time prediction model to obtain a future wind speed and wind direction prediction value of each monitoring station; and inputting the wind speed and direction predicted values into the wind field spatial feature mapping model to obtain the mountain overall wind field distribution of the target mountain region at the future moment. By constructing an'actual measurement-simulation-modeling-prediction-reconstruction 'integrated technical framework, high-temporal-spatial-resolution short-time prediction from observation of local wind speed and wind direction to the overall three-dimensional wind field of the mountainous region is realized.

The invention relates to the technical field of intelligent weather forecast, and discloses a WRF wind speed simulation correction method, system and device and a storage medium, and the method comprises the steps: obtaining multi-source basic meteorological data, and carrying out the preprocessing; constructing a fan field feature matrix according to the fan field distribution data; inputting the preprocessed data and the wind field feature matrix into a multi-source space-time convolution fusion module, and extracting and fusing time sequence features and space correlation features; inputting the fused features into a normalized convolutional neural network introducing physical constraints, and extracting spatial-temporal features of the wind speed field; and performing channel splicing on the spatial-temporal characteristics of the wind speed field and to-be-corrected wind speed data, inputting the spliced data into a multi-scale convolutional wind speed correction network, and obtaining a correction result of the wind speed simulation data through encoding and decoding operations. Through a multi-source data dynamic fusion mechanism, a spatial-temporal feature collaborative optimization algorithm and a multi-scale correction network architecture, system errors of traditional numerical mode simulation are remarkably reduced, and the accuracy of wind speed forecasting is greatly improved.

The invention provides an ocean wind field prediction method based on a neural network, and belongs to the technical field of ocean wind field prediction.The method comprises the steps that sparse ocean observation data are collected, a spatial covariance matrix is established, the spatial covariance matrix is converted into a graph structure, and then multi-hop neighborhood feature aggregation is conducted through a graph convolutional network; a tensor decomposition algorithm is combined for modeling high-order feature interaction to generate a gridding wind field, a bidirectional long-short-term memory network encoder is used for extracting space-time invariant features, a multi-layer perceptron predictor is used for directly mapping a future multi-step wind field, and a course learning strategy and a Shenchang differential equation boundary layer are matched for correction. The technical problem that sparse ocean observation data are difficult to accurately reconstruct into a high-resolution gridding wind field is solved.

The invention relates to the technical field of additive manufacturing, in particular to an air field system with uniform airflow distribution for an additive manufacturing large-breadth forming platform, which comprises an air inlet three-way pipe unit and a negative pressure air draft unit, the air inlet three-way pipe unit comprises a main air supply pipe, an upper air outlet pipe and a lower air outlet pipe; the upper air outlet pipe and the lower air outlet pipe are each internally provided with a flow dividing grating assembly, air outlets of the upper air outlet pipe and the lower air outlet pipe are divided into a plurality of sub air outlets with the same opening size through the flow dividing grating assemblies, and flow guide pipes are arranged at the air outlets of the upper air outlet pipe and the lower air outlet pipe. An airflow dispersion unit is arranged on the side, close to the sub air outlets, in the flow guide pipe, a linear air outlet hole is formed in the other side of the flow guide pipe, and a conical flow channel with a conical section is arranged between the airflow dispersion unit and the linear air outlet hole. By means of the multi-stage flow dividing and guiding design, the effect that airflow in all areas of the large-breadth forming platform is evenly distributed under the condition of low air volume is achieved.

The invention discloses an unmanned aerial vehicle wind resistance control method and system based on wind field sensing, and relates to the technical field of aircraft wind resistance control and wind field sensing. The method comprises the following steps: acquiring local wind field information around an aircraft in real time through wind speed and direction sensors arranged in a distributed manner, and performing fusion processing to form three-dimensional wind field distribution; generating a feedforward control quantity based on the wind field disturbance identification and short-term prediction model; feedforward control and a feedback control law of a flight control system are fused to form a composite control strategy, and flight attitude stabilization and control response optimization of the unmanned aerial vehicle under complex wind disturbance are realized. Correspondingly, the system comprises a flow field sensing and data processing module, a disturbance identification and feedforward control module and a flight control execution module, and has the functions of wind field real-time sensing, disturbance prediction, control preposition deployment and the like. According to the invention, the wind resistance and autonomous flight safety of the unmanned aerial vehicle in a low-altitude complex wind field environment can be remarkably improved.

The invention relates to the field of floating type fan load simulation, in particular to a floating type fan simulation analysis method considering atmospheric stability in a stormy wave environment, which comprises the following steps of: simulating WRF wind field data; carrying out WRF-SWAN wave field simulation; calculating key parameters of wind field simulation; carrying out WRF-TurbSim wind field simulation; and carrying out OpenFAST simulation and analysis. On the basis of traditional wind field driving, a Richardson number calculation and atmospheric stability classification method is introduced, a spectrum inversion and adaptive fitting mechanism is combined, key parameters of an engineering wind speed spectrum model are extracted, and a parameter mapping relation between WRF output and TurbSim input is established. A disturbance construction module driven by Richardson numbers is developed in TurbSim, adaptive generation of wind speed disturbance under the multi-stability condition is achieved, and the physical consistency and engineering adaptability of a simulated wind speed field on the aspects of a section structure and spectrum characteristics are improved. And finally, finishing floating type fan load analysis under wind wave coupling input by combining with an OpenFAST platform, and supporting system response evaluation and structural design optimization under complex sea conditions.

The invention relates to the technical field of wind power generation control, in particular to a wind turbine generator wake flow optimization cooperative control system and method. According to the technical scheme, the wind turbine generator wake flow optimization cooperative control system comprises a feedforward LiDAR array which is deployed at the upstream 1-2 km of the prevailing wind direction of a wind power plant and used for collecting upstream three-dimensional wind field data in real time; the unit embedded sensor group comprises an ultrasonic anemograph and an inertial measurement unit IMU which are arranged in a cabin of each wind turbine unit, and the sampling frequency is not lower than 20Hz; the data fusion module is used for performing space-time alignment and noise filtering on the data of the LiDAR array and the unit embedded sensor through a convolutional neural network (CNN) and a Kalman filtering algorithm to generate a dynamic wind field digital twinborn model; and the LSTM wind field predictor predicts the wind speed and wind direction change trend in the future 30 seconds based on the dynamic wind field digital twinborn model. Through multi-source sensing fusion and dynamic game optimization, the operation efficiency and safety of the wind power plant under the dynamic wind condition are remarkably improved.

The invention discloses a low-altitude three-dimensional wind field inversion method and system based on a single wind measurement laser radar, relates to the technical field of radar detection, and solves the technical problems that a traditional wind measurement laser radar cannot directly obtain a velocity vector, data missing of a key area is caused by a blocking effect, and detection nodes are not uniform. The method specifically comprises the following steps: S1, executing a multi-elevation-angle body scanning detection mode and a wind profile detection mode by using a wind measurement laser radar, and obtaining radial wind speed data of a target area; s2, quality control is carried out on the radial wind speed, isolated abnormal points are eliminated, and isolated data missing points are filled; s3, carrying out blocking correction on a single scanning surface by using a smooth spline interpolation method; s4, after the radial wind speed is corrected based on the wind profile, an initial background three-dimensional wind field is obtained; and high-precision wind field inversion of a wind field in a three-dimensional space can be realized by using the radial wind speed of multi-layer body scanning of a single wind measurement laser radar.

The invention provides a construction method of an urban low-altitude wind field digital twinning system, which comprises the following steps: constructing an urban basic road network skeleton and performing region division, generating a building block model with real textures by using oblique photography data, and forming an urban three-dimensional space geometric model library; capturing atmospheric information in real time through a radar to generate high-resolution three-dimensional wind field scanning data covering a target area; processing detection data of the laser wind finding radar, performing simulation calculation on a wind field in combination with an urban three-dimensional space geometric model, and generating sub-meter gridding dynamic wind field data of an urban low-altitude area; and performing three-dimensional reduction and vivid representation on the obtained dynamic wind field data by using a visual rendering technology to form an interactive urban low-altitude wind field digital twin system. By integrating multi-scale modeling, laser wind finding radar, wind field simulation and visual rendering technologies, real-time monitoring, dynamic simulation and visual display of an urban low-altitude three-dimensional wind field are achieved, and low-altitude flight safety and operation efficiency are improved.

The invention relates to the technical field of wind power generation. The invention provides a wind field unit comparison type fault diagnosis method, system and device and a storage medium. The method comprises the following steps: synchronously acquiring operation and corresponding environment data of units of the same type in the same wind field, and establishing a multi-dimensional data set; building a normal working condition multi-dimensional parameter reference model by using clustering analysis and updating in real time; performing multi-scale comparison on a target and a reference unit, and extracting feature fusion to generate a comprehensive health index; establishing a double-layer diagnosis model, constructing a virtual unit based on digital twinning in the first layer, comparing residual errors and positioning a potential fault source, detecting outliers by using an improved isolated forest algorithm in the second layer, determining a fault propagation path in combination with an association rule, and establishing a fault mode knowledge base; and triggering an early warning mechanism according to the comprehensive health index and the fault mode classification. The problems that a traditional operation and maintenance mode is difficult in fault prediction, high in cost and lack of a reliable evaluation system, state monitoring is limited to a single parameter, multi-unit comparative analysis means are insufficient, and fault diagnosis is lagged are solved.

The invention discloses a two-dimensional wave spectrum intelligent forecasting method based on a physical information neural network, and belongs to the technical field of ocean information prediction.The two-dimensional wave spectrum intelligent forecasting method comprises the steps that basic data are collected, and the basic data comprise high-temporal-spatial-resolution sea surface wind field data and sea wave height data within the research range; historical sea wave data are obtained, ocean and meteorological data closely related to sea wave changes are integrated and processed, and a sea wave forecasting database is constructed; physical constraints are determined according to the sea wave forecasting database; an ANN neural network model is adopted as a basic model, physical constraints are added, the neural network meets the control equation of the mode, and a PINN physical information neural network is constructed; and carrying out interpretability analysis on the model forecasting process and result. According to the method, the sea wave dynamic process and the deep learning method are fused, interpretability is added for deep learning, and the black box problem of the deep learning method is solved.

The invention relates to the technical field of wind field simulation, and provides a wind field numerical simulation method based on multiple meteorological data sources. The objective of the invention is to solve the problems of large simulation error, rough terrain boundary processing and poor turbulence model parameter adaptability caused by non-uniform coverage of a single data source, insufficient precision and unscientific multi-source fusion. The method is characterized by comprising the following steps: step 1, constructing a CFD three-dimensional grid based on a geometric model of a target area; 2, collecting original data of a ground station, satellite remote sensing, numerical forecasting and the like; 3, performing standardized preprocessing (abnormal value elimination, missing interpolation, radiation / geometric correction and resampling), determining a multi-source fusion weight by combining historical data analysis, and generating comprehensive meteorological data by adopting a weighted average method; and 4, inputting the comprehensive data into a CFD-RANS model, dynamically adjusting turbulence parameters, accurately setting terrain boundary conditions, and obtaining a wind field space-time distribution result through numerical solution. According to the invention, through combination of multi-source data fusion and CFD simulation, the wind field simulation precision and stability are improved.

The invention discloses a bridge construction intelligent scheduling risk assessment method and system based on artificial intelligence, relates to the technical field of bridge construction safety and intelligent scheduling, and discloses the bridge construction intelligent scheduling risk assessment method and system based on artificial intelligence. Through three-dimensional wind field modeling, dynamic safety operation domain calculation, multi-dimensional risk fusion and intelligent optimization scheduling, the problem that a traditional method cannot dynamically evaluate wind load risks and equipment conflicts is solved, the risks can be accurately evaluated in real time, the conflicts can be dynamically predicted, the effectiveness of a bridge construction scheduling scheme is improved, and the construction efficiency is improved. And the construction safety and efficiency are improved.

The invention discloses a matrix yaw system of a wind power plant. The matrix yaw system comprises a data acquisition module, a wind plant modeling module, a parameter extraction module and a yaw control module. The data acquisition module acquires a wind field original data set containing three-dimensional space coordinates and timestamps by using a multi-source sensor; the wind field modeling module calculates correlation between points by combining a covariance function through a Gaussian process regression algorithm, and constructs a three-dimensional dynamic wind field model; the parameter extraction module is used for extracting wind regime parameter vectors in the model by adopting a nearest neighbor interpolation algorithm on the basis of actual space coordinates of a fan; and the yaw control module utilizes a depth deterministic strategy gradient reinforcement learning model to generate a yaw angle adjustment instruction in combination with the wind regime parameter vector, the current yaw state of the fan and a preset maximum cumulative reward function. Through multi-module cooperation and intelligent algorithm optimization, accurate matching of wind field dynamic modeling and yaw control is achieved, and the energy efficiency and operation stability of the wind generating set are improved.

The invention discloses a high-rise building operation robot, and belongs to the technical field of engineering construction, the high-rise building operation robot comprises a base, the base is provided with a mechanical arm, a driving mechanism for driving the mechanical arm to move horizontally and a lifting mechanism for vertically moving along a wall surface, the end part of the mechanical arm is provided with a sand blasting gun, two sides of the sand blasting gun are provided with distance measuring sensors, and the base is provided with a wind direction and wind speed sensor; the modeling module is used for fusing sensor data to generate an environment model containing three-dimensional geometric and semantic information and a dynamic wind field distribution model; the cooperation module generates a cooperation control instruction sequence for coordinating each component based on prediction optimization control; the adjusting module corrects parameters of the sand blasting gun in real time through autonomous learning; the execution module generates a driving instruction in combination with feedforward and compliance control; and the decision module monitors and outputs an operation adjustment instruction or a re-planning instruction. The device can sense the condition of the outer wall in real time and dynamically adjust the operation strategy, the adaptability to the complex wall surface is improved, meanwhile, wind disturbance is effectively resisted, and the sand blasting quality is guaranteed.

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 wind profile radar radial speed quality control and horizontal wind field inversion method. According to the method, through the steps of multi-mode detection splicing, signal-to-noise ratio threshold value quality control, beam consistency inspection, horizontal wind component inversion, space and time continuity inspection and the like, the quality control process of the wind field data is optimized, the precision of the horizontal wind field data is remarkably improved, and particularly, the root-mean-square error and deviation in high-level data are remarkably reduced. The core innovation comprises a mode splicing strategy based on sounding data comparison, dynamic signal-to-noise ratio threshold calculation, threshold setting of beam consistency check and a wind component compensation algorithm when a vertical beam is missing. Through experimental verification, compared with a traditional wind profile radar data processing method, the wind profile radar data processing method has remarkable advantages in data accuracy and stability.