1859 results about "Wind run" patented technology

The invention relates to the technical field of fan blade fatigue damage prediction. The invention provides a fan blade fatigue damage prediction method and system. The method comprises the following steps: constructing a coupling finite element model based on blade anisotropy parameters; blade surface three-dimensional strain field data, blade vibration acceleration signals, environment temperature and humidity and wind speed and direction data are obtained in real time, and a multi-dimensional monitoring data set is constructed; based on the multi-dimensional monitoring data set, nonlinear coupling features of all the load components are extracted, a multi-dimensional feature tensor is obtained, and a reference stress field matched with the current working condition is generated; inputting the multi-dimensional feature tensor and the reference stress field into a bidirectional long-short-term memory network, and establishing a data-physics combined driven damage evolution model; and positioning a damage area based on a damage probability distribution diagram output by the damage evolution model. The problems that in the prior art, prediction errors are obvious, sensitivity to early damage is insufficient, the false alarm rate is high, and accurate positioning of the damage position and quantitative prediction of the residual life are difficult to achieve are solved.

The invention provides a smoke and fire detection method and system based on deep reinforcement learning and electronic equipment, and relates to the technical field of fire fighting. The method comprises the following steps: acquiring thermal imaging data of a forest area through a thermal imager, obtaining temperature distribution conditions of different positions, and determining position coordinates, the number and specific temperature values of temperature abnormal points for an area with abnormal temperature rise; if the temperature value of the temperature abnormal point exceeds a preset temperature threshold value, judging that the temperature abnormal point is a suspected fire point, and collecting a visible light video image of the area through a visible light camera according to the position of the suspected fire point; smog detection processing is carried out on the collected visible light video image, whether smog exists in the image is judged by analyzing the color, texture, motion and other characteristics of the image, and the concentration, morphological characteristics and diffusion speed of the smog are obtained; the meteorological parameters such as the wind speed and the wind direction of a fire occurrence area are obtained through the meteorological sensor, the meteorological parameters are input into the fire spreading model, and the spreading direction and the spreading range of the fire under the current wind field condition are simulated.

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 discloses a monitoring point distribution method and device based on industrial park gas monitoring and a medium, and relates to the technical field of carbon monitoring, triangular mesh division is carried out based on DT triangular mesh division, and according to the difference of monitored areas, the DT triangular mesh division method is used for setting point distribution of the monitored areas, so that the monitoring point distribution accuracy is improved. At least one monitoring point is selected in each grid for carbon dioxide concentration monitoring, point distribution setting can be carried out in different area ranges of prevailing wind directions in different states, the optimal monitoring result can be obtained through the least carbon dioxide gas monitoring points, the accuracy and credibility of the final data result are improved, and the method is suitable for popularization and application. The problems that the current industrial park monitoring coverage range is not wide, the device cost is high, and improvement of the carbon monitoring device distribution position affects the accuracy of the carbon ground monitoring technology are solved.

The invention relates to the technical field of environment perception, and discloses an optimization method and device for a multi-climate self-adaptive environment perception cooling fan system, and the method comprises the steps: collecting multi-point temperature, humidity and air pressure data, and carrying out the filtering processing, and obtaining an environment state vector; executing mathematical modeling of the multi-degree-of-freedom cooling system according to the environment state vector to obtain a linearized state space model; performing temperature gradient estimation and humidity compensation based on the linearized state space model to obtain an adaptive control law; inputting the adaptive control law into a model-free adaptive prediction controller to obtain a rolling optimization control sequence; weight self-adaptive adjustment and heat dissipation efficiency hierarchical prediction control are carried out on the rolling optimization control sequence based on climate conditions, fan rotating speed control and wind direction adjustment execution signals are generated, the problem that a traditional heat dissipation system is not sensitive to space temperature distribution and humidity changes is solved, and the heat dissipation control precision under different humidity conditions is improved.

The invention provides a wind turbine generator array wake flow management method and system for Doppler laser wind measurement, and relates to the technical field of laser, and the method comprises the steps: collecting the real-time wind speed data, wind direction data and turbulence intensity data of each generator in the front and back direction through deploying a Doppler laser radar in a wind turbine generator array; establishing a wake flow influence model containing a wake flow influence coefficient, an attenuation coefficient and a diffusion coefficient based on the data, and calculating to obtain a wake flow influence range, loss power and a recovery distance; according to the method, the wind turbine generator array is cooperatively controlled, and the power generation efficiency of the wind turbine generator array is optimized by adjusting the yaw angle or the pitch angle of the upstream unit and the rotating speed of each unit. The wind speed data acquisition precision of the wind field is improved, the wake flow influence evaluation accuracy is enhanced, and the overall power generation efficiency of the wind field is remarkably improved.

The invention relates to the technical field of wind power generation, and discloses a wind power plant energy management method based on laser radar wind measurement, comprising the following steps: S1, deploying laser radar equipment, and collecting real-time data of wind speed, wind direction and turbulence intensity in a wind power plant; s2, constructing a three-dimensional dynamic model of a wind field, and modeling wind speed, wind direction and turbulence distribution in combination with laser radar data and historical meteorological data; s3, performing short-term prediction on the future wind speed by using the deep learning model, and outputting minute-level to hour-level wind energy prediction data; s4, optimizing a dynamic scheduling strategy of the wind turbine generator based on the prediction data; and S5, docking an energy storage system, optimizing charging and discharging behaviors of energy storage equipment, and realizing smooth power output. According to the invention, by fusing the laser radar wind measurement data, the deep learning prediction model and the intelligent optimization algorithm, high-precision wind condition monitoring, dynamic scheduling optimization and power output stability improvement of the wind power plant are realized.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

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 discloses an unmanned aerial vehicle multi-mode mine inspection method and system, a medium and a program product, and the method comprises the steps: dividing an inspection target into a short-range object and a long-range object based on a preset inspection target classification list; constructing a three-dimensional grid inspection path according to the position information of the short-range object and the remote object, and setting a plurality of standby inspection entrance points in a detection area of the short-range object; acquiring real-time environment parameters, wherein the real-time environment parameters comprise illumination intensity, atmospheric visibility, electromagnetic field intensity and wind speed and wind direction; calculating the reachability score of each standby inspection entrance point based on the influence degree score of the real-time environment parameters on the unmanned aerial vehicle, and determining the standby inspection entrance point with the highest reachability score as the current inspection entrance point; and generating an inspection path according to the current inspection entrance point, and controlling the unmanned aerial vehicle to inspect according to the inspection path. According to the method, the integrity of the inspection data is improved, the accuracy of the safety evaluation data obtained based on the inspection data is improved, and the safety of a mine is further improved.

The invention relates to the field of bridge structure dynamic response analysis, and discloses a windmill bridge coupling response analysis method. According to the method, wind speed, wind direction and vehicle speed data are collected, and a data set is constructed by combining finite element and CFD coupling numerical simulation; a parallel encoder is adopted to fuse Transform feature extraction and LSTM time sequence processing to generate a hybrid prediction response; constructing a physical constraint and composite loss function based on a train-bridge motion equation, and optimizing neural network parameters through a subtraction average strategy; and finally, predicting dynamic response through forward propagation and verifying physical consistency to form a model optimization closed loop. According to the method, a deep learning method and physical equation constraints are fused, the analysis precision and calculation efficiency of windmill bridge coupling response are remarkably improved, and a more reliable dynamic evaluation means is provided for bridge wind resistance design.

The invention discloses a photovoltaic system abnormal data detection method and device, and relates to an abnormal detection technology based on a multi-head self-attention mechanism fusion variational auto-encoder and a bidirectional long short-term memory network (BiLSTM). Abnormal detection of electrical parameters (including DC voltage and current and AC voltage and current), environment parameters (relative humidity, wind direction and wind speed) and system parameters (including inverter error codes, inverter environment temperature difference, module environment temperature difference and the like) in a photovoltaic system is realized. The invention discloses a photovoltaic system abnormal data detection method. The method comprises the following steps: (1) photovoltaic system data acquisition and data processing; (2) encoding time sequence data of the photovoltaic system; (3) data sampling and data feature enhancement; (4) data decoding operation; and (5) detecting an abnormal value of the photovoltaic system.

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 is suitable for the technical field of flying dust monitoring, and provides a road construction site all-weather flying dust online monitoring method and system, and the method comprises the following steps: collecting flying dust data and wind power information through a sensor; the particulate matter concentration is judged, and the particulate matter concentration exceeding the standard and the corresponding coordinate position, wind speed data and wind direction data are screened out; determining a flying dust source area according to the coordinate position, the wind speed data and the wind direction data; a road transportation monitoring video is called, and the area where the transportation vehicle is located is determined according to the time information and the road transportation monitoring video; a road construction map is called, and the area where the transport vehicle is located is marked in the road construction map; and comparing and analyzing the flying dust source area with a road construction map, determining a potential flying dust source position, and sending the potential flying dust source position to an inspection terminal. According to the invention, the flying dust pollution source can be accurately tracked, and the inspection personnel can quickly position the potential flying dust pollution source according to the information.

The invention relates to the technical field of aviation, in particular to a low-altitude flight micrometeorological intelligent decision-making system based on multi-source sensing and AI fusion, and aims at solving the problems of difficulty in local disturbance identification, insufficient meteorological data granularity, weak flight task adaptation and the like in urban and complex terrain low-altitude flight scenes. According to the system, multi-dimensional meteorological data such as wind speed, wind direction, temperature and humidity are acquired through cooperation of fixed nodes and an air-based unmanned aerial vehicle, a risk area is dynamically identified in combination with a disturbance index model, and meteorological perception with minute-level and hectometer-level resolution is realized. The system supports intelligent perception scheduling, edge AI processing and multi-link communication transmission based on task characteristics, has task adaptability evaluation, flight path optimization, risk early warning and closed-loop learning capabilities, is suitable for scenes such as low-altitude airspace management, flight examination and approval, urban air traffic and the like, and remarkably improves the safety and intelligent level of low-altitude operation.

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 discloses a wind-blown snow motion trail simulation tracking method based on a wind-blown snow numerical model, and relates to the technical field of meteorological analysis, and the method comprises the following steps: determining the initial condition of a wind-blown snow motion trail simulation region, and setting the basic parameters of the wind-blown snow numerical model; adding a snow particle crushing process into the wind-blown snow numerical model to simulate new snow particles generated by crushing the snow particles due to impact; and jump snow particles and suspended snow particles are distinguished by adopting a threshold particle size method, and the Routh number is calculated to determine the threshold particle size. According to the invention, a fine simulation system is established by considering various physical processes of sublimation, jump and suspension of the snow particles and various environmental factors of wind speed, wind direction, temperature and humidity based on the wind-blown snow numerical model, so that the movement track and concentration distribution of the snow particles can be predicted, the sublimation rates of the snow particles under different conditions can be analyzed, and the precision of the snow particle simulation is improved. Therefore, the influence of snow blown by wind on traffic, environment and human activities can be evaluated more accurately.

The invention belongs to the technical field of pollutant diffusion simulation, and particularly relates to a pollutant diffusion simulation system and method based on a time effect. The system comprises a meteorological data acquisition module, a pollutant diffusion module, a time effect conversion module and a simulation analysis module, the meteorological data acquisition module is used for acquiring meteorological data and preprocessing the acquired data; the pollutant diffusion module is used for acquiring a three-dimensional Gaussian diffusion model changing along with time based on the Gaussian diffusion model of the continuous point source; the time effect conversion module is used for establishing a coordinate system by taking the current downwind direction as an x axis when the wind direction and the wind speed in the meteorological data are changed, acquiring a conversion relation between a reference coordinate system and a coordinate system at the current calculation moment, and performing conversion of a three-dimensional Gaussian diffusion model by utilizing the conversion relation; and the simulation analysis module is used for calculating the concentration of the pollutants according to the converted three-dimensional Gaussian diffusion model so as to realize a pollutant diffusion simulation system.

The invention relates to a data and model fused tower crane displacement response prediction method under the typhoon action, which comprises the following steps: constructing a monitoring system, acquiring actual measurement data of a construction site and a tower crane, and constructing a tower crane finite element model by utilizing a design parameter set of the tower crane; the extreme wind speed under the most unfavorable wind direction of a construction site is obtained based on typhoon full-path simulation and wind field simulation, and the tower crane finite element model is calibrated by analyzing a parameter object needing to be calibrated in the tower crane finite element model through sensitivity, so that the calibrated tower crane finite element model can accurately reflect the response condition of a tower crane structure; a calibrated tower crane finite element model is used for simulating a data set formed by displacement data under the typhoon working condition to serve as a training set, the defect of an extreme weather sample in an actually-measured training sample is overcome, then response prediction of tower crane displacement under the typhoon working condition is achieved by training a CNN-BiLSTM-Adaboost displacement response prediction model, and the method is simple and convenient to implement. The precision and robustness of tower crane displacement prediction under the extreme typhoon working condition are remarkably improved, and sufficient emergency processing time is reserved for a construction site.

The invention relates to the field of wind speed and wind direction prediction, and discloses a wind speed and wind direction prediction method based on an improved TCN-LSTM, and the method comprises the following steps: collecting real-time monitoring multi-dimensional meteorological data of an offshore wind station, and carrying out the preprocessing; performing feature fusion and feature enhancement processing on the preprocessed data, wherein the feature enhancement processing comprises spatial feature extraction, tensor reconstruction and time sequence modeling; inputting the data after feature fusion and feature enhancement processing into a wind speed and wind direction prediction model based on an improved TCN-LSTM hybrid neural network, and predicting the wind speed and wind direction at a future moment; the wind speed and direction prediction model comprises a time-space separation TCN network, a multi-head self-attention mechanism, an adaptive Dropblock mechanism, an LSTM network and a Bayesian optimization module; according to the method disclosed by the invention, the accuracy, robustness and calculation efficiency of wind power prediction in a complex marine environment can be improved.

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 discloses a wind power generation system operation optimization method based on intelligent control, and the method comprises the following steps: S1, collecting the real-time data during the operation of a wind power generation system, including the wind speed, the wind direction, the rotating speed of a generator, the output power, and the environment temperature; s2, constructing a performance model of the wind power generation system according to the real-time data; s3, based on the performance model, adjusting the operation parameters of the generator in real time to optimize the power generation efficiency of the system; and S4, when detecting that the system is abnormal, automatically triggering a fault diagnosis module, generating a fault processing suggestion and executing an optimization strategy. Through real-time data acquisition, performance model construction, operation parameter dynamic adjustment and intelligent fault diagnosis, the problems that a traditional wind power generation system is low in efficiency and not timely in fault response are solved, and the system has the advantages of dynamically optimizing power generation efficiency, timely processing system abnormity and improving operation stability.

The invention discloses a wind power plant yaw control optimization method based on a sequential optimization algorithm, and the method comprises the steps: obtaining the coordinates of all fans at a current inflow wind direction angle, queuing all fans after the coordinates are converted in sequence, and calculating the yaw angle range of each cycle according to the actual demands; (n-1) times of outer layer circulation are set for the first (n-1) fans in the preliminary optimization process, in each outer layer circulation, yaw angle traversal value taking circulation is conducted on a certain fan according to the obtained yaw range and the uniform step length, and then the optimal yaw combination and the corresponding power of the wind power plant are obtained; and then optimization is carried out to obtain the optimal yaw angle of each fan in the whole field. According to the method, the wake flow secondary deflection effect in the wind power plant and the influence effect of the inflow turbulence intensity on the wake flow are considered, the improved Gaussian wake flow model is adopted to comprehensively consider calculation of wake flow loss in the three-dimensional direction, the calculation result is more accurate, and the final control optimization effect can also conform to the actual engineering situation.

The invention discloses a full-state wind field simulation system and method for wind resistance detection of an unmanned aerial vehicle. The system comprises a system base, a distribution box, a system control cabinet, a modular wind wall, a protective net and a wind speed and direction sensor. The wind field simulation system is of a cuboid structure, independent fan arrays are embedded in modular wind walls, the three modular wind walls independently generate three single-direction wind fields forming 90-degree included angles, and wind fields of any size and any direction can be generated through space wind field vector superposition. The distribution box provides power support for the ventilation wall to work, and the system control cabinet can independently control the fan arrays. The protective net is fixed on the system base bracket, so that the unmanned aerial vehicle is prevented from causing system damage in the testing process; the wind speed and direction sensor senses wind speed and direction information of a wind field in a test area and quantitatively represents the simulated wind field. According to the invention, a wind field with any size and direction can be generated for an unmanned aerial vehicle wind resistance test, and necessary test platform support is provided for unmanned aerial vehicle wind resistance detection.

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 relates to the technical field of wind power plant wake flow testing, and discloses a 3D scanning type laser radar planning method suitable for wind power plant wake flow testing, and the method comprises the steps: determining a wind power plant wake flow wind measurement region according to a wind measurement demand; determining a mainstream wind direction and a wind speed interval of the wind power plant wake flow wind measurement area based on the wind power plant wind resource data, and obtaining unit coordinates and unit parameters of wind turbine units in the wind power plant wake flow wind measurement area; according to the mainstream wind direction, the wind speed interval, the unit coordinates and the unit parameters, performing simulation calculation on the wake flow field of the wind turbine generator in the wake flow wind measurement area of the wind power plant to generate a wake flow area cloud picture; according to the method, the arrangement position of the 3D scanning type laser radar is determined based on the wake flow area cloud picture and the actual landform of the site, the situation that the arrangement position of the 3D scanning type laser radar is repeatedly adjusted for multiple times due to arrangement errors possibly occurring during arrangement according to experience can be avoided, and the wake flow testing efficiency is improved.

The invention discloses a microplastic transport simulation and risk identification method based on multi-factor coupling, which comprises the following steps: by coupling a hydrodynamic model and a microplastic transport model, comprehensively considering multiple environmental factors such as tide, water level, runoff, wind speed, wind direction and the like, constructing a wave flow coupling microplastic migration and diffusion model suitable for complex hydrodynamic conditions of a Pearl River estuary; the model is calibrated and verified through measured data, and the precision of the model is evaluated by using indexes such as a Nash efficiency coefficient and a root-mean-square error; in combination with the simulation result and the spatial distribution characteristics of the typical sensitive area, representative sections and stations are selected, and annual-scale micro-plastic concentration change analysis is carried out; and further introducing an ecological risk index to carry out regional ecological risk grade identification, and determining a micro-plastic high-risk area and influence main control factors. The method has high regional adaptability and expansibility, and scientific support and technical reference can be provided for prevention and control of microplastic pollution of estuary and coastal water.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

The invention discloses a multi-modal offshore wind power ultra-short-term prediction method in the field of offshore wind power plant cluster power prediction, and aims to solve the technical problems of spatial-temporal feature splitting and insufficient dynamic dependency relationship modeling. The method comprises the steps of performing anomaly detection and restoration on fan data, and generating a corrected wind power cluster data set; extracting a mean value, a standard deviation and a latest value of core operation data of each fan through a dynamic time window, and constructing a multi-dimensional node feature; a static geographic similarity matrix is generated based on geographic coordinates, a basic wake effect matrix is generated in combination with real-time wind direction data, correction is carried out through the maximum mutual information quantization time-delay effect, and then a dynamic adjacency matrix is obtained through self-adaptive fusion; and integrating the multi-dimensional node features and the dynamic adjacency matrix into a space-time diagram sequence data architecture, inputting the space-time diagram sequence data architecture into a multi-scale wake flow perception diagram space-time prediction model, and outputting a multi-fan power prediction value. According to the invention, high-precision multi-fan power prediction can be realized.

The invention relates to a gas leakage early warning method and system for a chemical industry park. The early warning method comprises the following steps: acquiring real-time temperature and pressure data of a reaction kettle area and a storage tank area; obtaining a historical leakage record of a pipeline joint, and determining a diffusion path prediction boundary; extracting a wind direction and a wind speed in a diffusion path prediction boundary, and determining space-time distribution characteristics; analyzing space-time distribution characteristics, determining an overlapping region and setting concentration monitoring nodes; identifying the concentration contribution ratio of the reaction kettle area diffusion characteristic and the storage tank area diffusion characteristic; integrating the operation data of the pipeline joint diffusion characteristics and the loading and unloading area diffusion characteristics, and determining a diffusion path deviation correction coefficient; combining the deviation correction coefficient with the humidity data of the peripheral diffusion characteristics of the cooling tower; and updating diffusion parameters of the separation tower area diffusion characteristics and the public engineering area diffusion characteristics according to a source intensity estimation result. According to the invention, accurate prediction and early warning of gas diffusion in a complex chemical device area are realized, and the safety management level is improved.