777 results about "Wind run" patented technology

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 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 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 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 the technical field of mine environmental protection and intelligent control, and discloses an intelligent dust suppression system and method for an open-pit coal mine, which are used for solving the problems of fixed spraying mode, poor real-time performance and waste of water resources in the dust suppression process of the open-pit coal mine. According to the intelligent dust suppression system and method for the open pit coal mine, a dust concentration sensor, a wind speed and direction sensor and a meteorological sensor are arranged in a mining area, real-time monitoring of a dust diffusion source and environmental parameters is achieved in combination with a video monitoring unit, and multi-source data are fused in a control system for diffusion trend prediction; according to the method, a dust high-risk area can be dynamically identified, then a targeted spraying strategy is generated according to a prediction result, and accurate control over the spraying position, the spraying time and the spraying flow is achieved, so that the problems that in the prior art, a spraying operation mode is fixed and is difficult to adapt to environmental changes are solved, the water consumption per unit time is reduced, and the working efficiency is improved. The air quality of an operation area is guaranteed, and the operation environment safety and the environmental protection standard-reaching rate are improved.

The invention provides a multi-source data fused SAR sea surface significant wave height deep learning inversion method, and belongs to the technical field of remote sensing ocean, and the method specifically comprises the steps: preprocessing buoy data, and obtaining sea wave significant wave height data observed by a buoy; acquiring dual-polarization single-view complex SAR data collected in an interference wide-width mode; preprocessing the SAR data, and taking significant wave height data obtained by buoy observation as a label of the SAR data; acquiring auxiliary data corresponding to the SAR data, wherein the auxiliary data comprises wind speed, wind direction data, rainfall data and OCN data; and constructing a multi-source data-fused SAR significant wave high-depth learning inversion model, training the multi-source data-fused SAR significant wave high-depth learning inversion model, and testing and verifying the trained model by using the test set and the verification set to obtain a final significant wave height inversion result. According to the method, the SAR data inversion capability of the model is improved.

The invention provides a dynamic space-time air quality prediction method based on a physical constraint graph attention network, and the method comprises the steps: carrying out the collection and preprocessing of multi-source data, and obtaining the pollutant concentration and meteorological element historical sequence of a monitoring station; constructing a dynamic space-time diagram network fusing distance attenuation and a wind direction driving transmission path based on the longitude and latitude of the station and the real-time meteorological field; multi-cycle time sequence features are extracted in a self-adaptive mode through an enhanced time sequence network ETNet, and attention weight distribution is guided through physical priori; time sequence features are embedded into the dynamic graph, a physically constrained graph diffusion attention network PC-GDAN is input, and space-time diffusion modeling is achieved through a differential operator embedded into an atmospheric diffusion equation; time sequence and space features are fused, mass conservation and diffusion smoothness constraints are introduced in decoding prediction, and a concentration prediction result conforming to a physical rule is generated. The method effectively fuses physical mechanisms and data, has strong space-time modeling capability and physical interpretability, and significantly improves the precision and robustness of air quality prediction.

The invention discloses an air pollution intelligent early warning method and system based on machine learning, and the method comprises the steps: obtaining the multi-source data of environment, wind direction, geography, traffic, emission, population and the like, carrying out the multi-scale decomposition of the environment data, extracting the short-term fluctuation and long-term trend, and achieving the time series prediction in combination with a historical mode; constructing a regional association graph by using a graph convolutional network, identifying cross-regional diffusion features, and fusing wind direction and space information to determine a high-risk region and a potential diffusion path; carrying out weighted calculation in combination with traffic and emission data to obtain a comprehensive risk score, and completing region division and early warning level determination; and finally, generating a regional visual early warning report. According to the invention, pollution trend accurate prediction and diffusion path identification can be realized.

The invention relates to the technical field of vehicle aided driving, and provides a crosswind early warning method for vehicle aided driving, and the method comprises the steps: carrying out the multi-dimensional data collection and feature extraction, carrying out the correlation analysis of crosswind interference, outputting a quantitative parameter representing the influence of crosswind interference, inputting the quantitative parameter into a risk analysis model, and carrying out the fusion calculation, thereby achieving the early warning of crosswind interference. The early warning level is determined according to a calculation result, a dynamic prediction model is constructed by integrating multi-dimensional data such as driver data, real-time wind direction data, driving data, altitude information and climate information, a risk early warning coefficient is calculated in real time, prediction precision and real-time performance are guaranteed, warning response time is prolonged through a pre-prediction means, and the early warning efficiency is improved. And the high-speed driving safety is effectively improved.

The invention relates to the technical field of power transmission line operation and maintenance, in particular to a power transmission conductor galloping prediction method based on an ice wind load coupling effect, and the method comprises the steps: collecting micrometeorological data, icing parameters, conductor tension, span and other multi-source monitoring data in real time; constructing a three-node cable unit model containing a torsional degree of freedom based on the icing parameters, and simulating an asymmetric icing form; constructing a spatial large rotating beam unit model of the transmission tower based on Beidou positioning and point cloud scanning data; coupling the models, inputting wind speed, wind direction and temperature parameters, and calculating a galloping amplitude, a galloping frequency and an alternating stress amplitude through a self-adaptive step size algorithm; dynamically generating an early warning threshold according to the voltage grade and the span; when the galloping amplitude exceeds a threshold value, triggering graded early warning and outputting an affected tower disposal suggestion; through precise modeling and multi-source data fusion, the galloping prediction precision and response speed are improved, dynamic and precise early warning is realized, and safe and stable operation of the power transmission line can be effectively guaranteed.

The invention discloses a radiation correction method and system for an airborne hyperspectral imager. The radiation correction method and system are used for improving the radiation correction precision in complex weather. The method comprises the following steps: calculating a theoretical sun position based on unmanned aerial vehicle initial coordinates, ephemeris data and atmospheric parameters; performing course and differential compensation on a solar azimuth angle by combining IMU course and wind speed and direction data, performing refraction correction on an elevation angle, and outputting a high-precision sun position parameter; hyperspectral images and irradiance data are synchronously acquired, quaternion interpolation is combined with IMU data to realize space-time alignment, and attitude disturbance is eliminated; a hemispherical space irradiance model is constructed based on alignment data, cloud layer modes (clear sky, thin cloud and thick cloud) are divided according to atmospheric optical thickness variance, and Lambert body proportion correction, a discrete longitudinal standard method or a deep convolutional spectral network are respectively adopted for radiation correction. The method remarkably improves the correction precision and generalization ability, supports large-area image splicing, and is suitable for quantitative application of geological exploration, environment monitoring and the like.

The invention provides a day-ahead prediction method and device for wind power cluster power, equipment and a storage medium. Relates to the technical field of wind power generation prediction. The method comprises the steps that clustering is completed based on historical multivariate information of all stations in a wind power cluster, cluster virtual nodes are set, and the visual angle of global information is expanded for cluster modeling; and information propagation characteristics between nodes are explored based on a cluster prevailing wind direction. Constructing an adjacent matrix of the dynamic graph, and quantifying the dynamic evolution process of the cluster topology; and finally, mining cluster dynamic information by using a frequency domain channel attention mechanism and STGCN, and completing short-term power prediction of the wind power cluster. The method is a prediction method which considers multivariate information space-time diversity and is suitable for the day-ahead wind power cluster power, and is simple in calculation, high in prediction performance, clear in physical significance, effective in prediction result and high in practicability.

The invention relates to the technical field of project management intelligent decision making, in particular to a project management intelligent decision making system integrating multi-target resource scheduling optimization. The system comprises an equipment state sensing module, an environment monitoring module, a data acquisition module, a conflict evaluation module and a dynamic decision module. The equipment state sensing module is used for collecting operation parameters of key construction equipment in real time, wherein the operation parameters comprise equipment real-time position coordinates, actual speed, fault early warning coefficients, actual working time and actual completion time. The data acquisition module is used for acquiring supply chain data and construction plan data. The equipment state sensing module, the data acquisition module and the environment monitoring module are arranged for cooperative use, so that the system can automatically sense environment changes such as wind speed and wind direction, the equipment offset risk is calculated through the conflict evaluation module in real time, and meanwhile, the resource scheduling instruction is generated, so that the error correction response time is greatly shortened, and the error correction efficiency is improved. And meanwhile, the delay time of the construction period is reduced.

The invention discloses a wind power plant wind power prediction method, system, equipment and medium in combination with fan spatial-temporal characteristics and adjacent matrix optimization, and relates to the field of wind power plant wind power prediction.The wind power plant wind power prediction method comprises the steps that wind power plant historical wind power data and real-time wind direction and wind speed data are collected, and the collected wind power plant historical wind power data are preprocessed; dynamically generating an adjacent matrix between the fans based on the spatial position information of the fans and the real-time wind direction and wind speed data; constructing a prediction network for extracting spatial and temporal characteristics of the wind power data, wherein the prediction network is used for establishing a time dependency relationship and a spatial coupling relationship of fan power changes; and predicting the wind power in a future time period by using the prediction network, and evaluating a prediction result to verify the performance of the model. The adjacent matrix is adjusted through the real-time wind speed and wind direction data, the spatial correlation and wake effect between the fans are accurately described, the temporal and spatial features are extracted in combination with the time sequence convolution layer and the graph convolution layer, and the prediction precision is improved.

The invention relates to the field of power transmission line operation and maintenance, in particular to a power transmission line tree obstacle intelligent supervision system and method based on the Internet of Things, and the system comprises an intelligent sensing module, a data cloud transmission module, a fusion prediction module, an analysis decision module and a linkage execution module. According to the coupling prediction, historical data are processed through an algorithm model, future tree growth and conductor wind deviation are predicted, and the scientificity and accuracy of prediction are ensured through cooperation of a state space model and a Kalman filtering algorithm, so that the prediction accuracy is improved through a sensor deployed at the front end and an intelligent algorithm at the cloud end. The device can continuously and automatically monitor for 7 * 24 hours, not only measures the current static distance, but also predicts the future growth trend of the tree based on historical data, and calculates the dynamic windage yaw of the lead by combining the real-time wind speed and wind direction, thereby being convenient to give out early warning before the tree actually approaches the safe distance, realizing the process from post-event disposal to pre-event prediction, and improving the working efficiency. And the preventive safety level of the power grid is improved.

The invention discloses a ventilation system online monitoring method based on the Internet of Things, and relates to the technical field of ventilation system management, and the method comprises the steps: 1, setting a plurality of air environment monitoring units at different heights in a to-be-monitored space region in a vertical direction, and enabling each air environment monitoring unit to synchronously collect environment parameter data at a fixed sampling period; organizing the environmental parameter data according to the spatial position and the acquisition time, and constructing a three-dimensional pollution concentration and flow field snapshot; and 2, analyzing the change trend of the pollutant concentration through a sliding time window. Air quality data are collected and analyzed in real time through the multi-dimensional air environment monitoring units, the multiple air environment monitoring units are arranged in the space, the factors such as the wind speed and the wind direction are combined, and the system can comprehensively sense the vertical distribution and horizontal propagation characteristics of pollutants, so that the air quality data can be analyzed in real time. And accurate data is provided for pollution source positioning and pollution path modeling.

The invention relates to the technical field of environment control of earth excavation, in particular to an earth excavation construction flying dust suppression method and system.The earth excavation construction flying dust suppression method comprises the steps that construction intensity prediction is conducted, specifically, the construction activity intensity and duration in a specific time period in the future are predicted by accessing a construction management system; vehicle information acquisition and fusion: identifying and classifying incoming and outgoing vehicles to obtain accurate vehicle passing frequency and type distribution; raising dust risk assessment and grade determination: establishing a raising dust risk assessment model based on the predicted construction intensity, real-time vehicle passing data and meteorological parameters, and performing grade determination on raising dust risks of different areas; and the regional dynamic spraying control module is used for dividing the construction site into a plurality of independent control regions according to the dust raising risk level and the wind direction information, dynamically adjusting the start-stop time sequence, the water quantity and the spraying angle of a spraying device in each region, and realizing regional and differentiated dust raising suppression. On the premise of saving water and electricity, the problem of flying dust pollution in earth excavation operation is solved.

The invention relates to the technical field of new energy resource evaluation, in particular to a wind and light resource dynamic evaluation method and system based on multi-source remote sensing data, and the method comprises the following steps: inputting a region remote sensing image, calculating a boundary confidence coefficient, generating an interference classification identifier, reconstructing a resource prediction output value, predicting a trend analysis result, and evaluating a development suitability level. And generating a regional resource evaluation result. According to the invention, effective quantification of boundary stability and data confidence is realized through cooperative judgment of a remote sensing image boundary structure and reflected light intensity distribution, fine identification of wind flow offset and area shielding is carried out based on a terrain gradient and a wind direction included angle, and wind speed radiation data is accurately interpolated according to a space grid connection combination and a neighborhood change trend. And meanwhile, climate factors and resource change trends are accurately coupled, and actual path connectivity is determined in combination with terrain and land attributes, so that the spatial suitability and development accuracy of wind and light resources are effectively improved.

The invention relates to the technical field of garden landscape design, in particular to a garden landscape design method based on a virtual reality technology, which comprises the following steps: step 1, multi-source data acquisition and three-dimensional modeling: adopting a laser radar to scan a design site to obtain high-precision terrain point cloud data and generate a three-dimensional terrain model, the method comprises the following steps: performing three-dimensional scanning on garden plants to construct a plant resource library containing plant morphological parameters, growth characteristics and a growth cycle algorithm, acquiring ecological data of field illumination intensity, soil pH value, annual precipitation and wind direction and wind speed through a sensor array, and establishing a field ecological parameter database; and 2, constructing an immersive virtual scene: importing the terrain model into a VR engine, and automatically generating a basic landform based on an ecological parameter database, and through growth simulation and space-time rendering, realizing full-cycle presentation from short-term landscape to long-term ecological change, improving the customer scheme recognition degree to more than 90%, and reducing the modification rate by 60%.

The invention discloses a wind measurement radar attitude angle depolarization estimation and correction method, which comprises the following steps: acquiring original wind field data acquired by a wind measurement radar, including a horizontal wind speed, a horizontal wind direction and a vertical wind speed; performing quality control on the original wind field data, removing invalid samples and screening effective wind speed data; constructing an error model based on the ratio of the vertical wind speed to the horizontal wind speed, and establishing a mapping relation between the ratio and the horizontal wind direction; estimating the attitude deflection angle of the radar by adopting a robust fitting method, including data density weighting and robust fitting processing, so as to improve the estimation stability and the anti-interference capability; based on the estimated attitude deflection angle, performing coordinate transformation on a radar beam direction, correcting a wind field inversion result, and outputting corrected three-dimensional wind speed data; the wind speed measurement precision is improved.

The invention relates to the technical field of data regulation and control, in particular to an intelligent control method of an air conditioning system for temperature control of a server room, which comprises the following steps: acquiring temperature data, wind direction data and load power data of each monitoring position of the server room in a time monitoring window; temperature data and total load data of the channel position corresponding to each air conditioner are obtained; according to the fluctuation of the temperature data and the difference of the wind direction data of each monitoring position, a heat regulation and control demand index is obtained by combining the change cooperation condition between the temperature data and the load power data; obtaining a heat radiation characteristic index according to the cooperation condition of the temperature data change of the adjacent channel positions and the cooperation condition of the total load data change of the adjacent channel positions; and according to the heat regulation and control demand index and the heat radiation characteristic index corresponding to each air conditioner, the air conditioner temperature is regulated and controlled. According to the invention, the temperature regulation and control result is more reasonable, dynamic heat balance among different channels can be realized, and the temperature stability of the server room is maintained.

The invention discloses an outdoor space public facility layout optimization method based on big data analysis, and belongs to the field of outdoor space public facility layout optimization, and the method comprises the steps: predicting the number of participants in a data set based on demands, and combining the illumination intensity, wind direction, temperature and rainfall in a comprehensive data set, calculating a meteorological suitability score of each region, and generating a preliminary facility layout data set including candidate position points and meteorological suitability scores thereof; on the basis of the adjusted candidate position data set, in combination with the average decibel value of each region, spatial analysis is executed, position points with the average decibel value lower than 60 decibels are screened, and an optimized position data set is generated and comprises the position points, a meteorological suitability score and a noise level; and based on the optimized position data set, in combination with the facility coverage range and the construction and maintenance cost, calculating the facility number and type of each region, and generating a facility layout planning data set including a facility configuration list of each region.

The invention relates to the technical field of wind power monitoring, and discloses a construction engineering tower crane wind power monitoring control method, which comprises the following steps: collecting wind power environment data around a tower crane structure through a multi-source sensor network, including wind speed, wind direction, wind pressure and tower crane boom vibration parameters, and generating a tower crane wind power original data set; performing wind load fusion evaluation processing based on the tower crane wind power original data set, calculating actual wind load distribution of each part of the tower crane by combining a tower crane structure model and a computational fluid dynamics principle, and generating tower crane wind load evaluation data; when wind power monitoring and controlling of the tower crane in constructional engineering are carried out, wind power environment data and structure response data around a tower crane structure are collected through a multi-source sensor network, a tower crane wind power original data set is generated, wind load fusion evaluation processing is carried out, and the wind load actually borne by the tower crane can be reflected; and meanwhile, the calculated tower crane wind load evaluation data and the tower crane safety threshold are compared and analyzed in real time, and a reliable basis is provided for control decision.

The invention relates to the technical field of knowledge reasoning, in particular to a forest fire danger cognition decision-making method based on an ecological knowledge graph, which comprises the following steps of: acquiring monitoring node data, cleaning and constructing a situation grid, and generating a fire source propagation edge set by combining temperature difference wind direction consistency and gradient accessibility; and verifying wind speed dryness and load grade logic to endow the initial label or mark with abnormity, counting frequency correction abnormal nodes by utilizing neighborhood gradient load continuity, identifying multipath intersection risk points by fusing path information, and generating a forest fire danger cognitive decision identification set. According to the method, continuous observation mapping is constructed through monitoring data, a propagation path is generated in combination with temperature difference wind direction consistency and gradient connectivity, a multi-factor conflict model is established, abnormal nodes are corrected by using neighborhood frequency, continuous risk distribution at path intersections is identified, a reasoning result is constructed by fusing labels, and the risk evolution and situation expression depth is enhanced. And the fire danger reasoning accuracy and the space matching capability under complex conditions are improved.

The invention provides a power grid photovoltaic output short-term prediction method and system fused with a meteorological time sequence, and relates to the technical field of photovoltaic power, and the method comprises the steps: reading multi-source meteorological observation data of a target region; performing cloud field motion estimation, and establishing a cloud motion vector field; activating a temperature corrector, reading environment temperature and photovoltaic module temperature, constructing a temperature-module efficiency dynamic model by utilizing a temperature reading result and wind speed and wind direction data of a weather station, and establishing a power upper limit correction factor; after an illumination disturbance signal is established by using the cloud motion vector field, a short-time scale dynamic output prediction curve is generated; and performing power grid operation constraint mapping adjustment, and outputting a calibration prediction result. According to the method and the device, the technical problem of low photovoltaic output short-term prediction precision caused by instability and unpredictability due to the fact that photovoltaic power generation is easily influenced by weather changes in the prior art is solved, and the photovoltaic output prediction precision is improved and the power grid dispatching and operation are optimized by fusing the meteorological time sequence.

The invention relates to the technical field of pollution traceability, in particular to a precise traceability system and method for an atmospheric pollution source. The method comprises the following steps: collecting the concentration value, the wind speed and the wind direction of pollutants; carrying out statistics on common pollutants of different monitoring points, and determining a change characteristic value and concentration variability based on a concentration change condition so as to obtain a pollution approximation degree between the monitoring points; determining a pollutant propagation factor based on the concentration change direction and the wind direction of the monitoring point and the wind speed; therefore, optimization factors are determined to optimize the pollution approximation degree; clustering the optimized pollution approximation degree, and determining a pollution characteristic value of the characteristic point in a cluster based on the pollution approximation degree between the monitoring points and the distance at the wind direction convergence position; and completing source tracing of the pollution source based on the pollution characteristic values of the characteristic points. According to the invention, the traceability precision and efficiency in a multi-pollution source complex scene are improved.

The invention discloses a performance-based fire-resistant design method for a cable system of a large-span suspension bridge. The method comprises the steps of bridge fire-resistant category division, fire intensity division, fire-resistant target determination, adverse fire scene determination, fire response calculation, fire-resistant checking calculation, fire-resistant protection and the like. The bridge fire-resistant category, the fire intensity, the fire-resistant performance and the fire-resistant target are all divided into three levels. The adverse fire scene is determined based on the fire type, fire combustion mode, fire position, wind speed and wind direction influence and combined with the structure stress state; fire response is calculated through thermal-mechanical coupling by adopting a finite element method; the fire-resistant checking calculation can adopt a design method based on overall structure fire-resistant checking calculation or component fire-resistant checking calculation; and when the fire-resistant checking calculation of each component of the cable system does not meet the fire-resistant target, fire-resistant protection design is carried out, and a fire-resistant protection scheme is designed by combining tests and numerical simulation. The fire-resistant design method based on the performance of the suspension bridge cable system is constructed, and reference can be provided for fire-resistant design of the suspension bridge.

The invention provides a factory unattended dust suppression method and system, a medium and a product. In the method, a dust suppression system firstly establishes a quantitative corresponding relation between the running state of dust production equipment and the theoretical dust production level, then sets dust monitoring points and the dust production equipment as nodes, and constructs node feature vectors containing environmental parameters and dust related data, so that each node not only can reflect the own environmental attribute, but also can reflect the theoretical dust production level. And direct association with dust can be reflected. Then, the dust suppression system calculates an edge weight through the wind speed, the wind direction and the node distance, the diffusion possibility and strength of the dust among the nodes are reflected, and then the constructed dust diffusion directed graph can visually present the possible propagation path of the dust; and finally, the dust suppression system analyzes the associated information of each node by using the topological structure and node characteristics of the directed graph through the graph neural network, and identifies a dust generation source. According to the method, the dust pollution source is positioned, the problem of blind dust suppression only according to the concentration of the monitoring point is solved, and the dust suppression efficiency is improved.

The invention relates to the technical field of new energy, and discloses an optimization method for an intelligent yaw system of a wind generating set, and the method comprises the steps: building the intelligent yaw system, enabling the system to be internally provided with seven modules, and enabling a multi-sensor data collection module to collect wind direction, wind speed and yaw angle data in real time, the data fusion module performs Kalman filtering fusion processing to obtain a high-confidence state estimation value, the intelligent decision output module generates an optimal yaw control instruction by adopting a multi-mode control strategy and a parameter self-adaptive mechanism based on fused data, an execution mechanism is driven to realize accurate wind alignment, and the system integrated hardware-in-the-loop test module is used for realizing accurate wind alignment. Omnibearing verification can be carried out before deployment, after actual operation, operation indexes are continuously monitored through the performance evaluation module, control parameters and fusion algorithm parameters are adjusted online through the optimization module on the basis of methods such as machine learning, closed-loop optimization is formed, finally, yaw precision, response speed and system stability are remarkably improved, and maintenance cost is reduced.

The invention discloses a plateau rainfall fusion method and a plateau rainfall fusion system based on multi-source meteorological collaboration, and relates to the technical field of plateau rainfall fusion methods, and the method comprises the steps: obtaining a multi-source rainfall product, a meteorological covariable and high-resolution digital elevation model data, carrying out the time-space alignment and normalization processing of the multi-source rainfall product and the meteorological covariable, and obtaining the high-resolution digital elevation model data; forming a multi-source meteorological data set; based on physical variables in the multi-source meteorological data set, constructing four physical process links of water vapor transportation, terrain uplift, convection triggering and cloud microphysics, generating attention weight vectors through sub attention branches corresponding to the links, obtaining a causal attention weight matrix after weighted fusion, and performing weighted fusion on multi-source rainfall products to obtain a multi-source rainfall data set; generating a causal fusion feature map; wavelet decomposition is carried out on the causal fusion feature map and meteorological variables, large-scale components and small-scale components are separated out, and meanwhile the terrain uplift rate and the included angle between the wind direction and the slope direction are calculated according to the digital elevation model data and the wind field components.