1673 results about "Wind field" patented technology

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 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 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 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 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 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 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 wind field data modeling method and system for a small and medium-sized unmanned aerial vehicle wind resistance test, and the method comprises the steps: collecting the topographic data, wind speed data and fan data of a test wind field, and determining a preset region and an unmeasured region according to the preset trajectory of an unmanned aerial vehicle based on the test wind field; the method comprises the following steps: acquiring boundary conditions of an unmeasured area and disturbance grid wake flow through fluid simulation based on topographic data and a preset area, performing wind speed vector decomposition on a grid according to a preset wind field boundary condition, and acquiring a preset wind field model according to a disturbance time sequence, an unmanned aerial vehicle maneuvering weight and the disturbance grid wake flow, and obtaining a predicted wind field of the unmeasured area through Gaussian process regression according to the boundary condition of the unmeasured area based on a preset wind field model, and coupling the predicted wind field through a grid time sequence by using different unmanned aerial vehicle preset trajectories to obtain a target wind field model. According to the method, through space-time collaborative interpolation and fluid simulation of Gaussian process regression, the inlet boundary condition better conforms to the fluid mechanics law, and the physical consistency and reliability of the target wind field are improved.

The invention discloses a low-altitude wind field prediction method and system based on a space-time diagram convolutional network, and relates to the technical field of weather forecast and wind energy utilization, and the method comprises the steps: collecting wind field observation data and physical field data of all nodes of a target region, a dynamic space-time diagram is constructed based on a flow function-vorticity theory through a dynamic diagram construction module; extracting spatial information through a graph attention network to obtain a spatial feature tensor; the spatial feature tensor and the physical field data are processed by a PhysFusion-TransTCN encoder to obtain the deep spatial and temporal features of the wind field; performing hierarchical feature aggregation on the wind field deep spatial-temporal features through an output module to obtain a wind field prediction result of the target area; a wind field physical mechanism is deeply fused, multi-scale spatial-temporal feature fusion is realized, and prediction result precision and physical rationality are ensured.

The invention relates to an unmanned aerial vehicle self-adaptive wind-resistant flight control method and system based on multi-mode environment perception. The method comprises the steps that a miniature wind speed and direction sensor array obtains real-time wind speed and wind direction information of the surrounding environment of an unmanned aerial vehicle; the inertial measurement unit obtains real-time attitude angle, angular velocity and acceleration information of the unmanned aerial vehicle; the visual flow sensor obtains real-time image data of the surrounding environment of the unmanned aerial vehicle; the data fusion and wind field estimation module receives multi-source data collected by the miniature wind speed and direction sensor array, the inertial measurement unit and the visual flow sensor, carries out fusion processing on the multi-source data based on an extended Kalman filtering algorithm, and predicts to obtain predicted wind field information in a future short time window; the flight control core module adopts an adaptive sliding mode control algorithm to calculate a compensation control quantity for counteracting wind disturbance, and performs real-time control on the unmanned aerial vehicle based on the compensation control quantity; and the wind resistance and the operation reliability of the unmanned aerial vehicle in a complex dynamic environment are improved.

The invention relates to a multi-source data fusion rainfall revision model construction method based on dynamic physical constraints, and belongs to the technical field of artificial intelligence and meteorology and hydrology crossing. The method comprises the steps that multi-source heterogeneous data such as satellites, reanalysis, sites and terrains are acquired and fused, and a standardized space-time sample set is constructed; constructing a special deep learning network comprising a double-flow encoder, a cross-scale physical attention fusion module and a decoder; constructing a composite loss function fusing data fidelity, terrain uplift constraint and wind field advection constraint; designing a dynamic constraint gating mechanism, and adaptively adjusting a physical constraint weight according to a real-time atmospheric state; and a high-precision precipitation revision model is obtained by optimizing the composite loss function training network. According to the method, the key physical process is embedded into the model in a dynamic adjustable mode, the problems that a traditional method is poor in physical consistency and generalization ability are solved, and a revised precipitation field which is physically reasonable and higher in precision can be generated under complex terrains and changeable weather.

The invention relates to the technical field of storm surge disaster monitoring and intelligent forecasting, and provides a regional collaborative storm surge water increase forecasting method of a coupling graph attention and gated circulation network, which comprises the following steps of: obtaining storm surge water increase residual field data based on a mixed wind field driving two-dimensional hydrodynamic model formed by typhoon parameterization and reanalysis data fusion, registering the water-increasing residual field data and the observation time sequence in time and space, and taking the registered data as physical prior input; constructing a dynamic edge weight graph structure based on a space-time causal correlation analysis method, and adaptively updating the dependency relationship among multiple observation stations; physical priori and key features are used as node input, a graph attention mechanism of GATv2 and a time sequence feature extraction capability of GRU are fused, and time-space coupling modeling is carried out on storm surge water increase; and carrying out multi-station joint prediction through regional cooperation, carrying out model evaluation and result optimization, and outputting a storm surge water increase prediction sequence with 15-minute time steps and 15-4-hour advance.

The invention relates to the technical field of quality detection, in particular to a gas atomization powder sphericity degree multi-dimensional online analysis system and a method thereof.The gas atomization powder sphericity degree multi-dimensional online analysis system comprises a dynamic dual-mode imaging module, a transverse wind field auxiliary detection unit and a multi-dimensional feature fusion analysis module; the transverse wind field auxiliary detection unit is used for applying controllable transverse wind power through an airflow nozzle orthogonal to the powder falling direction and measuring the deflection track of gas atomization powder ball particles in combination with a laser displacement sensor. The online calibration module is used for spraying standardized spherical particles to the powder flow according to a preset period; the traditional density detection depends on destructive sampling and off-line measurement, the efficiency is low, and the whole production line particles cannot be covered; through an orthogonal wind field trajectory inversion technology, a density value and an internal defect mark are synchronously output through non-contact dynamic measurement, 100% lossless online full inspection of a production line is realized, and sampling limitation and aging bottleneck of a traditional means are broken through.

The invention provides a snow eave growth monitoring system based on terrain coupling and a dynamic evolution model, and relates to the technical field of mountain snow disaster monitoring. A sensitive area identification module is used for obtaining environment data of ridge terrain and a wind field, constructing a wind and terrain micro-scale coupling tensor field according to the environment data, and sending the wind and terrain micro-scale coupling tensor field to a dynamic evolution module; identifying a potential formation sensitive area of the snow eave according to a wind and terrain micro-scale coupling tensor field; and the dynamic evolution module is used for establishing a dynamic evolution model fusing pneumatic jump accumulation and a microcosmic sintering mechanism according to the identified snow eave potential formation sensitive area, simulating a snow eave generation and growth process according to the dynamic evolution model, and obtaining a structure expansion result generated by the snow eave according to simulation. According to the invention, a sensitive area identification module fusing a wind field and a terrain micro-scale coupling tensor field is constructed, and a snow eave structure evolution model, a multi-dimensional sensing data fusion mechanism and a zoning risk output module are combined, so that the growth process of a snow eave under specific terrain and meteorological conditions is dynamically described.

The invention discloses a wind wave-seabed-pile foundation-photovoltaic array multi-physics field coupling calculation system for an ocean photovoltaic power station. The system comprises a wave model construction module, a photovoltaic array wind load numerical simulation module and a multi-physics field coupling numerical model integration module. The wave model construction module is used for extracting wave elements and constructing a wave model; the photovoltaic array wind load numerical simulation module is used for performing photovoltaic array wind load numerical simulation based on the extracted wave elements and the constructed wave model; and the multi-physics field coupling numerical model integration module is used for carrying out multi-physics field coupling numerical model integration, solving and verification based on a numerical simulation structure. According to the method, the wind wave load, the seabed seepage field, the pile foundation stress field and the photovoltaic array wind field are cooperatively calculated by building the multi-field dynamic coupling model, and therefore the accuracy and reliability of offshore photovoltaic power station pile foundation bearing performance evaluation are improved.

The invention discloses a near-shore wave-flow field-sediment coupling simulation method considering a wave breaking nonlinear effect, and belongs to the field of near-shore hydrodynamics, and the method comprises the steps: inputting WRF high-resolution wind field, tide boundary conditions and water depth information, and initializing a wave module, a flow field module and a sediment module; calculating local wave breaking dissipated energy by adopting an improved wave breaking energy dissipation formula to obtain a wave energy density spectrum and a breaking probability; a near-shore flow field is calculated under radiation stress nonlinear correction, and hydrodynamic boundary conditions are updated; calculating sediment suspension and deposition by using a coupling diffusion coefficient, and updating sediment concentration distribution; waves, flow fields and sediment are mutually coupled through loop iteration, and coastal wave-flow field-sediment simulation is achieved under extreme conditions such as typhoons, storm surge and strong storm waves. The method can improve the prediction precision of the wave height, the flow velocity and the sediment concentration under the condition of offshore strong storm waves, and is used for typhoon storm surge prediction, harbor basin deposition evaluation and offshore ecological environment management.

The invention relates to the technical field of digital data processing, and discloses an aircraft safety data multi-source heterogeneous data acquisition method, which comprises the following steps of: synchronously controlling multiple types of sensors to concurrently execute physical obstacle three-dimensional investigation, micro-meteorological wind field construction and space electromagnetic spectrum surveying and mapping operation through a single unmanned aerial vehicle platform; and integrating the acquired three types of heterogeneous data into a three-dimensional spatio-temporal data base with a unified timestamp in real time. According to the method, endogenous space-time consistency of multi-source safety data at a collection source is ensured, information dislocation during traditional data fusion is avoided, a data base can truly reflect the instant coupling state of multiple risks, meanwhile, the exploration positioning capacity of the dynamic risks is arranged at a collection end in advance, and the data collection efficiency is improved. And an accurate and reliable decision basis is provided for flight safety assessment.

Disclosed in the present invention are a wind field correction method and system applied to a single-station remote sensing device. The method comprises the following specific steps: S1, acquiring wind field data by means of a data information acquisition apparatus, and obtaining radial wind speed information of a current acquisition point by means of a data processing module; S2, on the basis of the hypothesis of an uniform wind field, using a classical wind speed reconstruction algorithm for inversion to obtain a three-dimensional wind speed vector of the acquisition point; S3, establishing an error model; and S4, on the basis of an error factor β calculated by means of the error model, performing data correction on the three-dimensional wind speed vector of the acquisition point obtained by means of inversion in S2. Compared with the prior art, the present invention provides a high-precision correction method, which can be operated on low-end hardware, and can also be directly installed and operated on hardware of a measurement device, so that measurement results can be corrected in real time, no additional complexity can be brought to users, and costs are reduced; and the present invention is suitable for solving complex system problems when accurate boundary conditions cannot be obtained.

The invention discloses a multi-agent cooperative control system based on adaptive learning, and belongs to the technical field of multi-agent system control, and the system specifically comprises the steps: collecting and analyzing the wind field data of the position of each agent; distinguishing two typical working conditions of a global uniform wind field and a local disturbance wind field based on a consistency analysis result; for a global uniform wind field, a model prediction control method is adopted to generate a wind field compensation strategy for keeping formation stability; for a local disturbance wind field, an accurate airflow disturbance state is obtained through environment three-dimensional reconstruction and fluid simulation, then the expected track deviation of each agent is predicted, and a collaborative avoidance instruction set is generated in combination with a relative position relationship. According to the method, adaptive cooperative control of multiple agents under different wind field working conditions is realized, the hidden conflict problem caused by local wind field disturbance is effectively solved, and the flight safety and task reliability of an agent cluster in a complex environment are remarkably improved.

The invention discloses an optical cable fitting fatigue damage detection method and system, and relates to the technical field of power transmission line state monitoring, and the method comprises the following steps: S1, constructing a wind field and structure coupling observation baseline, obtaining a full-time-domain vibration response signal of an optical cable fitting in a non-uniform wind field environment, generating a phase consistency distribution map, and obtaining a phase consistency distribution map; establishing a corresponding relation between the multi-path reflection source group and the time correlation sequence as a traceable reference for phase analysis; and S2, based on the phase consistency distribution map, performing causal beam demixing processing, performing arrival time difference densification calculation and curvature spectrum separation analysis on each sound wave propagation path, and extracting crack propagation pointing data. According to the method, through wind field-structure coupling observation, path unmixing, phase regression and error checking, multi-path propagation recognition and correction are achieved, crack propagation topology is reconstructed, polarization rotation and a phase suppression mechanism are combined, and direction recognition stability and detection adaptivity are improved.

The invention discloses a dynamic tracking method for an air pollution source, which relates to the technical field of pollution source tracking and comprises the following steps of: acquiring a pollution concentration raster data set, a UAV sniffing set and a meteorological field data set, reconstructing a wind field and predicting pollution transportation, generating a candidate source hypothesis, matching fingerprints and calculating weights, adaptively scheduling sensors, and performing Bayesian inversion fusion and result output. Unified pollution concentration and meteorological data are generated through multi-source data collection and three-dimensional flow field reconstruction, second-level wind field calculation is achieved through a dimensionality reduction CFD model, candidate source hypotheses are generated in combination with Lagrange backtracking and Euler transport, and a weight matrix is formed through UAV mass spectrum fingerprint bidirectional matching. Secondary cruise of the unmanned aerial vehicle and densification sampling of ground nodes are driven through confidence ranking, and sampling density is adaptively optimized. Bayesian joint likelihood and Markov chain are adopted to fuse multi-source data, dynamic source coordinates and emission rate are output, and a closed-loop iterative air pollution source tracking system is realized.

The invention belongs to the technical field of wind power weather forecasting, and provides an all-region three-dimensional wind speed correction method and system, and the method comprises the steps: constructing a weather numerical forecasting model, and obtaining wind field forecasting data; fusing the preprocessed multi-source data by adopting an optimal interpolation method to obtain three-dimensional space-time continuous wind field analysis data; based on the wind field forecast data and the wind field analysis data, features are extracted and fused, then a historical forecast error sample set is constructed, a wind speed correction model is constructed, and the historical forecast error sample set is utilized to train the wind speed correction model; introducing an initial value, a physical parameter and boundary condition disturbance, calculating a mean value and a standard deviation of each set result, extracting a probability distribution feature of a wind speed, constructing a confidence interval, estimating a probability density function, and quantifying an occurrence probability of an extreme wind speed event; and the prediction result of the wind speed correction model and the multi-source wind field observation data are fused to generate final three-dimensional wind field data, so that the actual requirements of wind power prediction and power grid dispatching can be met.

The invention discloses a wind power plant cluster power prediction method. The method comprises the following steps: step 1, embedding priori knowledge into a learning module; step 2, constructing a memory enhanced adaptive graph structure; step 3, an endogenous feature learning module; step 4, an exogenous feature learning module; 5, performing memory enhancement gating fusion on the MAGF; and step 6, outputting and predicting. According to the method, special learning modules are respectively constructed by decoupling endogenous power signals and exogenous meteorological factors, and a dynamic graph structure adaptive to a sample is retrieved by utilizing a meta-memory library, so that context sensing modeling of a complex space-time dependency relationship is realized. Meanwhile, a memory enhanced gating fusion mechanism (MAGF) is introduced, adaptive semantic alignment and fusion of double-flow features are realized, the precision and robustness of the model in multiple prediction time domains are remarkably improved, and more reliable technical support is provided for power grid dispatching and wind field operation.

The embodiment of the invention provides a wind field correction method based on a PPWNet model, and is applied to the technical field of wind field correction. The method comprises the steps of obtaining high-resolution grid wind field data, low-resolution grid wind field data, three-dimensional geographic coordinates and neighborhood data of a target prediction point and wind field data actually observed by a sounding point; wherein the neighborhood data comprises high-resolution grid wind field data and three-dimensional geographic coordinates of K nearest neighbor points around the target prediction point, and K is a positive integer; and inputting the acquired data into a pre-trained PPWNet model, and outputting three-dimensional corrected wind field data covering a target prediction point and a wind field divergence deviation index. In this way, the problem of data fusion of different sources, resolutions and structures can be solved, it is ensured that the output wind field is not only accurate in numerical value but also reasonable in physics, prediction results violating physical intuition are reduced, and wind field data are closer to the real situation.

The invention belongs to the technical field of wind power generation equipment state monitoring and intelligent fault diagnosis, and discloses a wind turbine generator blade acoustic fault detection method based on transfer learning, and the method comprises the steps: building a standardized sample through acoustic signal simulation and multi-dimensional data enhancement, and extracting weak fault features through an STFT-Mel frequency spectrum; a dual-scale time-frequency attention module is introduced into the lightweight MobileNetV3, so that the focusing and recognition capability on early crack features is improved; by combining transfer learning with MMD domain difference and entropy minimization regularization, efficient alignment of a simulation domain and actually-measured wind field data is achieved, and the generalization performance of the model under the small sample condition is enhanced; a transverse difference spectrogram and longitudinal historical baseline self-evolution double-flow feature fusion mechanism is adopted, common-mode noise is suppressed, and single-blade positioning, progressive degradation early warning and synchronous aging recognition are achieved. The method is small in parameter quantity, low in calculation overhead and suitable for high-precision and low-cost intelligent detection of early damage of the wind turbine generator blades.

The invention discloses a regional wind power plant group multi-scale meteorological data assimilation and space-time reconstruction method, and aims to solve the problems that existing wind power plant meteorological prediction precision is insufficient, multi-source data fusion is difficult and the like. According to the method, effective assimilation of multi-source heterogeneous meteorological data is realized by constructing a deep convolution variational auto-encoder, space-time reconstruction of a high-resolution three-dimensional wind field is realized in combination with a heterogeneous graph network and a Shenchang differential equation technology, and physical constraint conditions are introduced to ensure physical consistency of output results. Experimental results show that the method can significantly improve the meteorological prediction precision of the wind power plant, and provides reliable meteorological data support for fine operation management of the wind power plant group.

The invention discloses a wind field rapid prediction method and system based on optimized Latin hypercube sampling and POD-BPNN, and the method comprises the steps: carrying out Latin hypercube sampling to generate an initial sample point set, introducing a sensitivity weight, and carrying out the iterative optimization of sample distribution through a greedy strategy; assembling the CFD numerical simulation flow field data of all sample points in the sample point set into a flow field snapshot matrix, and determining a dynamic multi-target truncation order and a corresponding POD mode and coefficient; building and training a BPNN model, packaging the trained BPNN model, the POD modal matrix, the mean field and the flow field reconstruction logic into an FMU module, obtaining the FMU module which can be called in a cross-platform manner, and achieving the real-time prediction of a wind field. The invention relates to the technical field of wind field prediction, significantly improves the precision and calculation efficiency of wind field prediction, and provides an efficient and accurate solution for the rapid prediction of a wind field.

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 discloses a mountain cloud sea landscape universalization forecasting method based on a vertical layering meteorological factor, and relates to the technical field of landscape forecasting, and the method comprises the following steps: obtaining forecast meteorological data of a target mountain, the meteorological data comprising a meteorological profile; based on the forecast meteorological data, predicting the dynamic evolution trend of the thermal inversion layer to obtain a thermal inversion layer evolution prediction result; correcting the initial meteorological profile based on a thermal inversion layer evolution prediction result; based on the corrected meteorological profile, analyzing and judging a space matching relationship among a thermal inversion layer, a wet layer and a wind field, and forming an ornamental index set of the cloud sea landscape; and comprehensively judging the ornamental value index set, and outputting a cloud sea landscape observability result of the target mountain land in the forecasting time period. According to the method, the dynamic evolution trend of the thermal inversion layer is identified, and the cloud sea landscape ornamental index is generated and predicted in combination with terrain disturbance correction, so that the problems of inaccurate generation and elimination depiction of the thermal inversion layer and difficulty in quantitative prediction of the cloud sea landscape in the prior art are solved.

The invention provides a four-rotor unmanned aerial vehicle anti-wind-disturbance robust flight control method and system, and belongs to the technical field of unmanned aerial vehicle control, and the method comprises the steps: collecting the real-time state data of an unmanned aerial vehicle, the real-time state information comprises the position, speed, height, three-axis attitude angle and three-axis angular speed; acquiring an expected inspection track instruction, generating an expected attitude angle instruction and a total lift instruction in combination with the real-time state data, performing wind disturbance estimation by adopting a neural network, and compensating the expected attitude angle instruction and the total lift instruction; calculating a control torque increment by adopting an increment backstepping method based on the expected attitude angle instruction and the real-time state data, and obtaining a control torque instruction by combining the control torque at the previous moment; and the control torque instruction and the total lift instruction are distributed to the rotating speeds of four motors of the four-rotor unmanned aerial vehicle, and the motors execute the rotating speed instructions to drive the unmanned aerial vehicle to complete a wind disturbance resisting flight task. The method can actively adapt to wind field changes, and it is guaranteed that the unmanned aerial vehicle can still fly accurately, stably and safely under disturbance.