1356 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 technical field of wind turbine generator control, and discloses a wind turbine generator control optimization method based on dynamic changes of wind speed and wind direction. According to the method, real-time wind speed time sequence data and three-dimensional wind direction vector field data of a target wind field are received, a wind field dynamic analysis model is constructed by using a space-time convolutional neural network, and a wind field energy density distribution matrix and a turbulence intensity probability graph are generated. And constructing a multi-target adaptive optimization model on the basis, generating a unit control parameter instruction set, optimizing a cooperative adjustment coefficient according to a preset unit load-power generation efficiency balance equation, and outputting an optimal control action sequence to a wind turbine generator master control system through Bayesian optimization framework iterative updating. The method can accurately sense the wind field change, effectively balance the unit load and power generation efficiency, realize multi-unit cooperative control, improve the wind energy capture efficiency, and improve the operation stability and economic benefits of the wind turbine generator.

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

The invention relates to the field of 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 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 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 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 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 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 the technical field of environmental pollutant detection, in particular to a straw open-air incineration pollutant emission quantitative method based on multi-source data fusion, which comprises the following steps: based on air pollutant monitoring data, calling a sensor network to obtain PM2.5, CO and NOX concentrations, analyzing fire point heat radiation intensity, collecting meteorological data, and calculating the concentration of PM2.5, CO and NOX; and obtaining a pollution source data set by combining the time sequence change of the fire points and the meteorological parameters. By integrating an environment monitoring sensor, satellite remote sensing and ground meteorological data, the comprehensiveness and accuracy of pollution source monitoring are improved, the combination of fire point intensity and pollutant concentration time sequence data is optimized, the evaluation of fire point and pollution hot spot areas and the comprehensive analysis of wind directions and meteorological conditions are optimized, the accuracy of a pollutant diffusion model is improved, and the pollution source diffusion efficiency is improved. The pollutant release amount is dynamically adjusted, and the diffusion model is corrected by using a geographic information system, so that the scientificity of pollution prediction and emission intensity evaluation is enhanced, and more accurate pollution prevention and control decision support is provided.

The invention discloses an atmospheric boundary layer turbulence statistic inversion method and system based on a 6-beam ground-based light quantum wind measurement laser radar DBS scanning mode, a medium and a program product, and relates to the technical field of atmospheric remote sensing and boundary layer detection. A multi-direction radial wind speed time sequence is collected, and the radial speed variance of each beam is calculated on the basis of data quality control; and further establishing a relationship between the velocity variance and the covariance of the velocity in the three directions, constructing an observation equation set and inverting the turbulence statistics, and combining coordinate rotation to obtain the turbulence statistics in the downwind direction, the transverse wind direction and the vertical direction. And finally, calculating turbulent flow kinetic energy, and outputting a turbulent flow statistic vertical profile of each height layer. According to the method, continuous monitoring and high-precision description of the atmospheric boundary layer turbulence statistics can be realized, and the requirements of a service system on stability, precision and deployability of a boundary layer turbulence inversion product are met.

The invention provides an inspection robot path optimization method and system based on dynamic obstacle avoidance, and the method comprises the steps: enabling an inspection robot to carry out the multi-layer convolution processing of gas and wind direction data through obtaining the turbulence data of a gas leakage source, the wind direction data of a meteorological station, and environment reflection feature data in real time, and generating a high-precision gas diffusion prediction map, meanwhile, a transparent obstacle optical contour and an unstructured obstacle geometric parameter are extracted from reflection data, and a three-dimensional space mapping relation with a gas diffusion diagram is established. On the basis of the mapping relation and the preset task priority, path control parameters are dynamically generated, global path node distribution is optimized, and the robot path is made to adapt to the composite risk environment of gas concentration sudden change and obstacle movement in real time. According to the invention, the dynamic obstacle avoidance precision and the safety response speed of the inspection robot path in a complex dangerous environment are improved.

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 an offshore wind power prediction method, and aims to improve the accuracy and reliability of prediction. The method comprises the following steps: (1) a data preprocessing stage: detecting an abnormal value in data by using a DBSCAN clustering algorithm, reconstructing the abnormal value by using a KNN interpolation method, detecting time sequence abnormity through an LSTM automatic encoder, and performing regression reconstruction by using LSTM to ensure the retention of time sequence features; (2) a feature engineering stage: screening out key features through correlation analysis, generating a label column through K-means clustering and wind direction classification, and extracting features such as wind direction change rate, time periodicity and wind speed interaction; and (3) a model construction stage: constructing a composite model in combination with multi-scale convolution (Inception), a bidirectional long short-term memory neural network (BiLSTM) and a multi-head self-attention mechanism, extracting local features through a convolution layer, capturing a time dependency relationship through a bidirectional LSTM layer, enhancing the attention of key features by using the multi-head self-attention mechanism, and finally realizing high-precision wind power prediction.

The invention provides a multi-source data fusion-based high-wind disaster risk assessment method along a high-speed rail, and the method comprises the steps: obtaining the meteorological temperature distribution, the wind speed peak record, the rainfall cumulative value and the wind direction angle change of a meteorological station, carrying out the format conversion and time-space alignment of multi-source data through a preset standardization protocol, and obtaining a multi-source data format; generating a first data set with a unified spatial-temporal scale; acquiring a corresponding wind speed peak record, wind direction angle change and disaster loss degree through a similar case set in the third data set, calculating a first risk probability under the current condition by adopting a weighted average method based on historical cases, and generating a first risk value; updating a line state factor in the fourth data set through a fused probability value in a risk assessment result, adjusting a weight parameter of a wind direction matching coefficient by adopting an online learning method, and generating an optimized fifth data set; according to the continuous dynamic risk assessment sequence, the operation strategy of the high-speed rail line is adjusted in real time, and line safety is ensured.

The invention relates to the field of meteorological observation management, and discloses a low-altitude meteorological observation equipment operation and maintenance management method and system based on the Internet of Things, and the system comprises a meteorological sensor array module which is used for collecting low-altitude atmospheric environment parameters as observation data, including temperature, humidity, air pressure, wind speed, wind direction and particle concentration; the Internet of Things communication module is used for transmitting observation data to the edge computing node module in real time; the edge computing node module is used for performing local preprocessing on original observation data, including data filtering, abnormal value elimination and compression; the cloud operation and maintenance management platform is used for storing and analyzing the preprocessed observation data, judging an equipment state according to an analysis result, and generating an operation and maintenance instruction according to the equipment state; and the control module is used for receiving the alarm information and remotely controlling start and stop of equipment. According to the technical scheme, the safety of the low-altitude meteorological observation equipment can be 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 patent relates to a power transmission line galloping characteristic prediction and early warning system based on complex meteorological conditions. The method comprises the following steps: firstly, acquiring various meteorological parameters including wind speed, wind direction, temperature, humidity, icing thickness and the like in real time through a micro-meteorological sensor arranged along a power transmission line; secondly, analyzing and processing the meteorological data by using a deep learning algorithm and a generalized regression neural network (GRNN), and extracting correlation characteristics of meteorological factors and power transmission line galloping amplitude and frequency; then, a support vector machine (SVM) model is adopted to classify and identify the galloping risk, and a galloping risk level is generated; finally, a set of power transmission line galloping monitoring and early warning system is designed and developed, real-time acquisition of meteorological data, galloping trend prediction, risk grade evaluation and output of early warning information are realized, and safe and stable operation of the power transmission line under complex meteorological conditions is effectively guaranteed.

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.