1068 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 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 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 relates to the technical field of wind power generation control, in particular to a wind turbine generator wake flow optimization cooperative control system and method. According to the technical scheme, the wind turbine generator wake flow optimization cooperative control system comprises a feedforward LiDAR array which is deployed at the upstream 1-2 km of the prevailing wind direction of a wind power plant and used for collecting upstream three-dimensional wind field data in real time; the unit embedded sensor group comprises an ultrasonic anemograph and an inertial measurement unit IMU which are arranged in a cabin of each wind turbine unit, and the sampling frequency is not lower than 20Hz; the data fusion module is used for performing space-time alignment and noise filtering on the data of the LiDAR array and the unit embedded sensor through a convolutional neural network (CNN) and a Kalman filtering algorithm to generate a dynamic wind field digital twinborn model; and the LSTM wind field predictor predicts the wind speed and wind direction change trend in the future 30 seconds based on the dynamic wind field digital twinborn model. Through multi-source sensing fusion and dynamic game optimization, the operation efficiency and safety of the wind power plant under the dynamic wind condition are remarkably improved.

The invention discloses a 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.

The invention discloses a wind-solar complementary intelligent charging system and heat management optimization. The system comprises a wind power generation unit, a photovoltaic power generation unit, an energy storage unit, an intelligent control unit and a heat management unit, the wind power generation unit adopts an efficient wind generating set, is provided with an intelligent control system, and can automatically adjust a pitch angle and a yaw angle according to real-time wind speed and wind direction; the photovoltaic power generation unit selects a high-efficiency photovoltaic cell assembly and tracks the position of the sun in real time in combination with an intelligent light following system; and the energy storage unit adopts a high-performance energy storage battery pack and a battery management system. Through accurate prediction and analysis of local wind and light resources, dynamic configuration and cooperative power generation of the wind power generation unit and the photovoltaic power generation unit are realized. Compared with a traditional fixed configuration mode, local wind and light resources can be fully utilized, and the power generation efficiency is improved. Meanwhile, the multi-energy complementary cooperative power generation technology is adopted, organic combination of wind and light power generation and energy storage is achieved, and stable power supply of the system is 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 discloses a remote monitoring and management rice seedling raising irrigation plant protection system, computer equipment and a medium, and relates to the technical field of rice seedling raising irrigation, the remote monitoring and management rice seedling raising irrigation plant protection system comprises a sensing acquisition module, an edge processing module, a data fusion module, a prediction modeling module, an execution control module, a user interaction module and an energy safety component; the sensing acquisition module is used for acquiring soil humidity, temperature, pH value, air temperature and humidity, illumination intensity, wind speed and direction, rainfall and crop image information through multiple types of sensors deployed in a rice seedling raising field, and outputting original environment data; the edge processing module is connected with the sensing acquisition module and is used for preprocessing the original environment data, generating preprocessed data and uploading the preprocessed data to a cloud server through an NB-IoT protocol; and the data fusion module is deployed in the cloud server and is used for receiving the preprocessed data from the edge processing module.

The invention relates to the technical field of new energy power generation monitoring, in particular to a new energy wind power system fault detection method, system and device based on multi-parameter fusion and a medium. The method comprises the following steps: acquiring various operating parameters such as wind speed, wind direction, generator speed, temperature, voltage and current of a wind power system in real time, and performing feature extraction on the operating parameters to obtain key features related to a fault; analyzing the key features based on a deep learning algorithm, and judging the fault type of the wind power system; and outputting a fault diagnosis result in a visual mode, and sending the fault diagnosis result to a control center for remote monitoring and fault early warning. The technical problems that fault detection of a traditional wind power system is poor in real-time performance, not high in accuracy, low in intelligent degree and the like are solved, and reliable guarantee is provided for safe and stable operation of the wind power system.

The invention provides a low-cost and high-integration meteorological detection system for low-altitude meteorological service, and belongs to the technical field of meteorological detection. The low-cost and high-integration-level meteorological detection system for the low-altitude meteorological service comprises a plurality of small meteorological stations and MEMS sensors integrated with temperature, humidity, air pressure, wind speed, wind direction and the like. A plurality of small laser wind finding radars are based on an optical fiber laser and a VCSEL (Vertical Cavity Surface Emitting Laser) technology and adopt a modular design. And the data fusion module is used for fusing multi-source data of a ground station, a laser radar, a satellite and the like by adopting a four-dimensional variation assimilation algorithm to generate a 100-meter-resolution grid meteorological product. The technical bottleneck of traditional low-altitude meteorological detection is broken through, and a meteorological service solution is provided for the fields of urban fine management, navigation safety, intelligent agriculture and the like. The system has the characteristics of high cost performance, high flexibility and high resolution, and promotes low-altitude meteorological observation to be upgraded from sparse monitoring to global sensing.

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 discloses a multi-source data fusion wind power plant micro-siting dynamic evaluation method, relates to the technical field of wind power plant siting, and solves the problem that a mainstream method still depends on single anemometer tower data or static GIS analysis during wind power plant siting in the prior art. The method comprises the following steps: firstly, acquiring wind measurement data of multiple sources, unifying the wind measurement data of different sampling frequencies into a set time window, and realizing spatial scale alignment through an interpolation method; then obtaining a wind energy score according to the annual average wind speed and the main wind direction frequency, obtaining a suitability score according to the distance to the residential area or the main road, obtaining an environmental impact factor according to the noise and the ecological protection area, and constructing a target function based on the wind energy score, the suitability score and the environmental impact factor; and finally, combining a genetic algorithm and a real-time optimization model of a wake flow interference model, and dynamically adjusting the evaluation tendency according to the evaluation score of the fan layout inversion site, so that the evaluation result is more comprehensive, scientific and reasonable.

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 belongs to the technical field of traffic pollution prediction, and particularly relates to a LightGBM-based multi-source data fusion pollutant concentration prediction method, which comprises the following steps of: 1, integrating and processing multi-source data, and constructing a plurality of feature sets; and 2, carrying out feature analysis on meteorological, pollutant and traffic index variables through correlation analysis and time sequence analysis, and providing support for modeling and model interpretation. And step 3, dividing a training test set according to a time sequence, and training and optimizing the LightGBM pollution prediction model through parameter tuning by taking minimization of RMSE as a target. And 4, verifying the performance of the model from prediction precision, spatial distribution and wind direction influence. And 5, analyzing and displaying the key driving factor and quantifying the contribution of the key driving factor. According to the invention, through feature engineering processing and model optimization of heterogeneous data such as weather and traffic, high-precision prediction and influence factor analysis of lane-level pollutant concentration are realized.

The invention provides a photovoltaic module support amplitude stabilizing device, which relates to the technical field of photovoltaic equipment and comprises a support amplitude stabilizing execution module, a data acquisition module, a windproof control type determination module and a windproof control module. By constructing a three-level windproof control strategy system, accurate response to different wind levels is achieved, a first-level windproof control strategy is adapted to the vibration frequency and amplitude of a support in real time by dynamically adjusting the damping coefficient of a damping shock absorber, and a second-level windproof control strategy is adapted to the length of a windproof hydraulic cylinder based on wind speed and wind direction data. According to the three-level windproof control strategy, when strong wind exceeds a mechanical counteracting range, the dip angle of the photovoltaic panel is adjusted to a safe and efficient conversion angle, the wind resistance and the light energy utilization rate of the structure are balanced, and the wind energy utilization rate of the photovoltaic panel is increased. And the reliability and the economical efficiency of the photovoltaic system under the severe weather condition are improved.

The invention discloses a dust suppression method and system for railway track dry bulk cargo loading. The method comprises the steps that if it is detected that loading operation is started on an iron road trunk line, an area to be subjected to dust suppression is determined based on loading operation field data; acquiring environment information corresponding to the region to be subjected to dust suppression, wherein the environment information comprises wind direction information; determining a to-be-loaded article based on the loading field operation data, and determining the moisture content corresponding to the to-be-loaded article; and a control strategy corresponding to the spraying system is determined based on the wind direction information, the water content and the area corresponding to the area to be subjected to dust suppression, and operation parameters corresponding to the spraying system are adjusted based on the control strategy so as to perform dust suppression treatment on the loading operation process. According to the invention, accurate treatment of railway track loading flying dust pollution is realized, limitation of traditional fixed dust suppression is broken through, and pollution prevention and control efficiency and resource utilization efficiency are remarkably improved.

The invention relates to the technical field of power system monitoring, in particular to a power transmission line on-line monitoring system and method. The method comprises the following steps: continuously acquiring wind speed, temperature, wind direction and humidity through an analog signal transmission channel of a host to obtain meteorological sensor data; collecting wire temperature and tower inclination data through a digital quantity signal transmission channel; performing threshold comparison on the meteorological sensor data, and marking abnormal meteorological characteristic parameters when detecting that the meteorological sensor data exceeds a preset safety range; thermodynamic coupling analysis is carried out on the abnormal meteorological characteristic parameters and the conductor temperature data, and the conductor temperature rise trend is calculated; and carrying out mechanical structure association on the abnormal meteorological characteristic parameters and the tower inclination data, and evaluating the structure safety margin. Through an intelligent identification perception technology and a multi-mode communication management technology, accurate monitoring and efficient operation and maintenance of the power transmission line are realized, and finally the safety and the operation and maintenance efficiency of a power grid are improved.

The invention provides an unmanned aerial vehicle two-dimensional plane anti-wind disturbance trajectory correction method, device and equipment and a medium, and the method comprises the steps: obtaining the wind field collection information of an unmanned aerial vehicle corresponding to the current moment, the wind field collection information comprising wind speed data and wind direction data; determining main wind field mode data of the unmanned aerial vehicle corresponding to the current moment based on the wind field acquisition information of the unmanned aerial vehicle corresponding to the current moment; performing wind field change prediction on the main wind field mode data of the unmanned aerial vehicle corresponding to the current moment to obtain wind field change data in a preset time period; and determining the optimal thrust compensation amount of the unmanned aerial vehicle corresponding to the current moment based on the wind field change data in the preset time period, and generating a correction control instruction based on the optimal thrust compensation amount, so as to perform wind disturbance resistance track correction on the unmanned aerial vehicle by adopting the correction control instruction. Therefore, the accuracy of wind disturbance resistance track correction of the unmanned aerial vehicle is effectively improved, and the flight safety is ensured.

The invention relates to the technical field of soil splash erosion, in particular to a soil splash erosion simulation analysis system which comprises a rainfall simulator for simulating rainfall with different intensities and different pH values; the wind simulator adjusts wind speed and wind direction; raindrop data are measured by raindrop characteristic measuring equipment; the splash erosion simulation equipment simulates the process that raindrops drop on target soil to cause splash erosion and generate splash erosion substances; the data acquisition equipment acquires the corresponding initial pH value and initial conductivity of the target soil before rainfall; collecting the target pH value, the target conductivity and the target denudation depth of the target soil after rainfall, and the average weight diameter and the total target splattering amount of the target splattering substance corresponding to the splattering substance; the electronic equipment determines and establishes a splash erosion prediction model based on the raindrop pH value, the raindrop data, the wind speed, the wind direction and the data acquired by the data acquisition equipment, and the soil splash erosion simulation analysis system capable of quantitatively analyzing soil splash erosion in real time is developed.

The invention discloses a wind power plant micro-siting assessment method based on spatial analysis and climate adaptability, relates to the technical field of wind power plant siting, and solves the problems that in the prior art, spatial analysis depends on static topographic data, climate adaptability assessment uses dynamic meteorological data, and the static topographic data and the dynamic meteorological data lack a space-time synchronization mechanism; and a prediction result has a large deviation. The method comprises the following steps: firstly, acquiring multi-source data and fusing to realize time-space synchronization of the data; then, constructing and solving an objective function to obtain a candidate point set, and screening out an optimized point distribution scheme of which the prevailing wind direction coverage is greater than a set threshold value; and finally, inputting prediction data of various extreme climates into the cost-per-kilowatt-hour model, introducing extreme climate toughness parameters into calculation to carry out climate adaptability data correction, and calculating return on investment cycles under different carbon emission scenes. Through the steps, the limitation that only static wind resources are concerned on traditional site selection is broken through, and combined evaluation of the regional climate adaptability and the complex terrain adaptability is achieved.

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 discloses a marine oil spill path planning system and method based on multi-source data fusion, and belongs to the technical field of marine environment protection. According to the marine oil spill path planning method, real-time image data of an oil spill area is obtained through a multispectral camera carried by an unmanned aerial vehicle, oil spill thickness, flow velocity and oil contamination component data are collected through multiple sensors carried by a ship, meteorological data such as wind speed, wind direction and sea wave height are obtained, multi-source data are input into a reinforcement learning model, and a marine oil spill path is planned. And data fusion is carried out through dynamic weight distribution, a global optimal path is generated based on fused data, and ship-unmanned aerial vehicle cooperative operation is realized to execute an oil spill recovery task. The system comprises an unmanned aerial vehicle module, a ship module and a ground control module. Experiments show that compared with a ship oil cleaning method which does not adopt multi-source data fusion, the system and the method have the advantages that the path planning efficiency can be improved by 25%, the oil spill diffusion area can be reduced by more than 20%, the recovery efficiency can be improved by 30%, and the response time is less than or equal to 10 minutes.

The invention relates to the field of wind power generation, and discloses a wind power generation control system and method. The data acquisition module is used for acquiring front three-dimensional wind field information detected by the laser radar, near-field wind speed and direction information measured by the ultrasonic sensor, regional wind regime data issued by the meteorological satellite and operation parameters of the wind generating set. According to the invention, through multi-source information fusion, wind regime accurate perception is realized, power generation stability is improved from the source, through multi-source fusion of the laser radar, the ultrasonic sensor and meteorological satellite data, a three-dimensional wind regime perception system is constructed, wind speed and wind direction changes can be predicted in advance, wind regime measurement errors are reduced, and power generation efficiency is improved. The problem of power generation fluctuation caused by wind energy intermittency is solved from the source, and accurate data support is provided for dynamic adjustment of the pitch angle.

The invention relates to the technical field of coal mine underground dust prevention and control, in particular to a coal mine dust concentration over-limit spraying and dust-settling device and a control system thereof.The coal mine dust concentration over-limit spraying and dust-settling device comprises a dust sensing array composed of a plurality of dust concentration sensors arranged in a spatially separated mode, a main sensor arranged close to a dust generation source, and a dust concentration sensor arranged close to the dust generation source, the at least two auxiliary sensors are sequentially arranged at a set interval; the wind speed sensing unit is mounted in a preset range of the upwind direction of the main sensor and is used for synchronously capturing real-time wind speed; the partition spraying actuator comprises independent spraying units corresponding to the positions of the sensors, and each unit is provided with a quick-response electromagnetic valve and an atomizing nozzle; the multi-source information fusion controller is connected with the dust sensing array, the wind speed sensing unit and the partition spraying actuator through an industrial field bus; through dynamic threshold setting, dust diffusion path monitoring and prediction interception technologies, the problems of response lag and excessive spraying of a traditional fixed threshold system are solved.

The invention discloses a multi-source cross-domain behavior data fusion analysis system and method based on a knowledge graph, and belongs to the technical field of data processing, and the method specifically comprises the steps: deploying a gas sensor in a public vehicle in a target region, and periodically detecting the concentration value and position coordinates of pollution gas on a driving path; constructing a space-time knowledge graph structure which comprises a static building layer, a dynamic path layer and a fusion data layer; calculating the spatial relative relationship between the public vehicle and the building facades on the two sides, and mapping the gas concentration values to virtual monitoring points of the building facades in combination with the real-time wind direction and wind speed parameters to generate a concentration distribution diagram of the building facades; building gas permeation flux is calculated according to the window state, and the indoor pollution gas concentration change process of the target building is deduced; integrating indoor pollution gas concentration deduction results according to a time axis to form an air quality spatio-temporal evolution graph of the target area; according to the invention, accurate control and effective treatment of the air quality of the crowded city scene are realized.