4881 results about "Wind speed" patented technology

The invention provides an unmanned aerial vehicle low-altitude intelligent traffic dynamic airspace management and control method and system, and relates to the technical field of intelligent control, and the method comprises the steps: taking an electronic fence geographic coordinate set as a monitoring reference boundary, fusing ADS-B data, meteorological information and an unmanned aerial vehicle equipment state, generating a real-time risk thermodynamic diagram, and outputting a grading alarm instruction; receiving a real-time risk thermodynamic diagram and a grading alarm instruction, and combining wind speed prediction and dynamic airspace occupation data; when the grading alarm instruction is triggered, executing the following operations: constructing a route feasible solution space by taking a no-fly zone and a high-risk zone in the risk thermodynamic diagram as constraint conditions; and iterating an evolutionary path population through selection, intersection and mutation operations of a genetic algorithm by taking the lowest energy consumption as an optimization target, so as to output a global final obstacle avoidance bypassing path, and issuing a route updating instruction to an unmanned aerial vehicle flight control system. According to the invention, the utilization of airspace resources is maximized on the premise of ensuring safety.

An online testing and diagnosis method for vibration characteristics of blades of wind turbine is disclosed. Steps of testing and diagnosing blade vibration comprises: S1: installing vibration sensors at key positions of a blade, designing an adaptive data acquisition strategy, and automatically adjusting a sampling rate according to a vibration amplitude and environmental changes monitored in a real time; S2: extracting key features reflecting health status of the blade from massive data, and evaluating an impact of wind speed, temperature, and environmental factors on vibration characteristics; S3: designing a customized deep learning model for damages of the blade of a wind turbine, extracting a time sequence data and a vibration signal, identifying a damage among different types of damages and evaluating a damage degree; and S4: automatically adjusting a warning threshold based on a real-time data stream and a historical trend, and drafting a preventive maintenance plan.

The invention relates to the technical field of intelligent agriculture and ecological internet of things, in particular to an ecological irrigation decision dynamic optimization method and related equipment. Comprising the following steps: acquiring multi-source environment data, acquiring a soil profile humidity gradient in real time through a soil humidity sensor array, acquiring future rainfall probability distribution, a temperature change rate and a wind speed predicted value in combination with a weather forecast interface, and synchronously accessing a geographic information system to acquire terrain elevation and crop distribution data. The three technical bottlenecks of model dimension collapse, parameter estimation instability and optimization response lag in a traditional irrigation decision-making system are systematically solved by constructing a space-time coupling analysis framework and a closed-loop optimization mechanism of multi-source heterogeneous data.

The invention relates to a temperature control intelligent optimization method and system for adhesive tape production. The method comprises the following steps that S1, production data in the drying process are collected through a multi-mode sensor; s2, constructing a state prediction model based on GNN and LSTM, inputting the collected production data into the prediction model, and dynamically predicting the state of each temperature control area; s3, modeling each temperature control area into an intelligent agent based on the predicted state between the temperature control areas, and realizing dynamic distribution of heat resources of each heating interval by training a cooperative control strategy between the intelligent agents; s4, a fuzzy control optimization scheme incorporating environment compensation: for short-term changes of environment temperature and humidity, dynamically adjusting temperature zone gradient distribution between drying sections by using fuzzy control, and optimizing energy efficiency; and S5, the temperature control module adjusts the temperature and the wind speed of the temperature control area in real time according to the optimization schemes obtained in the steps S3 and S4. The intelligent level of the temperature control system in the adhesive tape production drying process is comprehensively improved.

The invention discloses a power distribution network power supply reliability evaluation method and system based on data analysis, and relates to the technical field of power distribution network detection, a meteorological station, a GIS and an equipment monitoring terminal collect temperature, rainfall, wind speed, load and fault record data, and after time sequence alignment and interpolation correction, a unified environment data set is generated to provide an accurate basis for model construction; secondly, using historical and online data, calling a nonlinear mapping function and an equipment function to train a meteorological-fault rate model, outputting the equipment fault probability in real time, and then calculating a system risk index and a line risk value in combination with confidence-weighted weather forecast and equipment load coupling operation to accurately identify a high-risk area; priority scheduling, load transfer and graded load shedding are carried out on the high-risk area, fault information is transmitted back, closed-loop control is achieved, and the operation efficiency is improved.

The invention discloses a power distribution network operation safety risk assessment method considering typhoon and rainstorm composite disasters, and the method comprises the following steps: generating different typhoon paths as main scenes based on the typhoon and rainstorm disaster forecast data, and endowing each main scene with a weight according to the occurrence probability; the influence of microtopography and rainfall intensity on the wind speed V (r) in the main scene is considered, the corrected wind speed is calculated, the total failure probability of the distribution line between the node i and the node j is calculated according to the fault probability of the distribution line between the node i and the node j and the failure probability caused by foreign matter hanging, and the power distribution equipment failure probability is calculated according to the water accumulation amount of the power distribution network area; simulating and generating N fault scenes s through a Monte Carlo method to form a fault scene set S, and endowing each fault scene with a weight; and taking the minimum expected total load loss amount in all fault scenes as an objective function, constructing a risk assessment model, and solving the risk assessment model in combination with constraint conditions.

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention discloses a rapid three-dimensional reconstruction method for an unmanned aerial vehicle mine inspection scene, and particularly relates to the technical field of image processing and three-dimensional modeling, and the method comprises four steps: environment perception collection, inclination angle adaptive path planning, image quality optimization and three-dimensional model construction. Sensing information such as illumination, wind speed and gradient through a sensor to dynamically adjust a shooting strategy, and generating a normal supplementary shooting path in a gradient sudden change area; image quality is improved through image enhancement and feature extraction, and a confidence scoring model is constructed to screen high-quality images to participate in modeling; carrying out confidence evaluation on a modeling result, triggering a supplementary shooting mechanism, and improving the precision and integrity of the model; according to the method, the feature extraction stability of the image under the condition of illumination dramatic change is improved, the image coverage integrity of a high and steep slope area is enhanced, and stable control of image acquisition and fusion in a wind disturbance environment is realized, so that the precision, continuity and robustness of three-dimensional modeling are effectively improved.

The invention belongs to the technical field of marine environment prediction, and discloses a ship navigation sea wave dynamic space-time prediction method and system based on deep learning. The method comprises the following steps: carrying out space-time alignment, missing value repair and standardization processing on acquired ship AIS data and an ERA5 reanalysis data set, and generating node feature vectors containing latitudes and longitudes, timestamps, wind speeds and significant wave heights; through node feature coding, centrality coding, space coding and time coding, ship trajectory node importance and time-space interaction relation are quantified. Constructing a SeaGraph model, and outputting an effective wave height prediction value of a target waypoint; and performing verification. According to the method, the space-time constraint of a traditional static modeling framework is broken through, the advantages of a self-attention mechanism and a graph network are integrated, the predictive modeling capability of dynamic evolution of a wave field in front of a ship navigation track is enhanced, and high-precision sea wave forecasting support is provided for intelligent ship navigation under complex sea conditions.

The invention discloses a digital intelligent management method and system for hydraulic engineering construction, particularly relates to the technical field of hydraulic engineering construction management, and is used for solving the problems of regulation lag and insufficient environmental disturbance adaptability of an existing method under the action of multi-physics field coupling. According to the method, a concrete three-dimensional temperature field model is established, initial regulation and control parameters are generated, and a heat conductivity coefficient under the seepage influence is corrected based on space-time relevance of pore water pressure and temperature data; the constraint boundary is dynamically adjusted in combination with the construction progress and the material strength curve, a cooling water pipe coordinated regulation and control matrix is constructed, and target flow instructions of space differentiation are generated through the included angle between the distance and the water flow direction; setting a lag time threshold and optimizing an execution time sequence according to the thermal inertia parameter, splitting the threshold and allocating a weight coefficient when the environmental wind speed suddenly changes, and finally updating the model parameter and the regulation and control instruction; accurate temperature control response and abnormal heat conduction suppression under multi-field coupling are achieved, and the structural safety and construction efficiency under complex working conditions are improved.

The present invention relates to the technical field of power system risk assessment, and provides a power system risk assessment method in extreme weather based on deep feature mining. The risk assessment method comprises: performing regional division on the whole power system on the basis of the geographic location; on the basis of the historical wind speed data, constructing a correlation model of the strong wind extreme weather in a plurality of regions of the power system; constructing a strong wind scenario sample set of each region on the basis of the correlation model; acquiring the power transmission line fault probability of the corresponding region under each strong wind scenario in the strong wind scenario sample set; randomly allocating a power system operation condition to each strong wind scenario of each region, and acquiring an operation risk value of the power system under the corresponding operation condition; constructing and training a risk assessment model; and using the risk assessment model to perform power system operation risk assessment. The present invention can implement real-time and accurate assessment of the power system operation risk, and can provide an auxiliary decision-making suggestion for the power system.

The invention discloses an intelligent control method and system for tunnel loudspeakers, and relates to the technical field of tunnel audio control, environmental parameters in a tunnel are collected by adopting a mode of deploying sampling equipment in a distributed manner, and data preprocessing is performed in a targeted manner for different environmental parameters; a sound propagation model is established, and attenuation and delay of sound in different environments are simulated. According to the intelligent control method and system for the tunnel loudspeakers, various temperature and humidity sensors are arranged in the tunnel, and the absolute humidity is calculated in combination with the air pressure data, so that the sound velocity is accurately corrected, and the phase difference of the multiple loudspeakers is reduced; an adaptive Kalman filtering algorithm is adopted to process wind speed data, and reliable input is provided for a sound propagation model; a deep reinforcement learning algorithm is used to carry out collaborative optimization on parameters such as amplitudes and directional angles of multiple loudspeakers, a Bayesian network is used to detect loudspeaker faults, and Delaunay triangulation and a distributed consistency algorithm are combined to realize rapid fault reconstruction.

The present invention provides a wind power off-grid hydrogen production power supply topology and control method without a step-down transformer, including: obtaining wind speed data, using a wavelet threshold algorithm for denoising, predicting power based on an LSTM-GRU hybrid neural network, and extracting power fluctuation characteristics through multi-scale entropy analysis; obtaining load data, using Kalman filtering and recursive least squares to establish an impedance model, and extracting dynamic characteristics in combination with Hilbert transform; using a differential evolution algorithm for multi-objective optimization to generate an optimal switching sequence and power allocation strategy; achieving system control through space vector modulation, balanced control, soft switch control and harmonic compensation; achieving state estimation and disturbance suppression based on a sliding mode observer and a nonlinear observer; and achieving fault diagnosis and protection using a deep belief network. The present invention improves system efficiency and reliability and improves dynamic performance.

The invention provides a power equipment meteorological monitoring and early warning system based on artificial intelligence. The power equipment meteorological monitoring and early warning system based on artificial intelligence comprises a data acquisition module, a data preprocessing module, a spatio-temporal feature fusion module and a meteorological disaster prediction model, the meteorological disaster prediction model adopts a deep reinforcement learning framework, inputs a multi-dimensional spatio-temporal feature matrix, and carries out meteorological disaster prediction on the multi-dimensional spatio-temporal feature matrix. And outputting meteorological disaster risk levels and key parameter predicted values in a future preset time period, including a wind speed, precipitation, temperature anomaly and tropical cyclone path probability, a dynamic early warning threshold generation module, an early warning decision module and a model optimization module. The power equipment meteorological monitoring and early warning system based on artificial intelligence provided by the invention has the advantages that the data interpolation precision of a complex terrain region can be improved, high-precision prediction of a typhoon path, short-time strong wind and an icing risk can be realized, and the early warning accuracy and defense response efficiency of a power system to meteorological disasters can be comprehensively improved.

The invention provides an unmanned aerial vehicle inspection route determination method and device, equipment and a medium. The method for determining the inspection route of the unmanned aerial vehicle comprises the steps that a, basic geographic information, environment state data and task requirements of a target area are obtained, the geographic information of the target area is obtained through remote sensing data, the environment state data is collected in real time through a sensor, and the task requirements comprise a starting point and an ending point of an inspection route and a specific inspection task; according to the unmanned aerial vehicle inspection route determination method and device, the equipment and the medium, by introducing the environmental factor correction model and the adaptive optimization algorithm, the problem that dynamic environmental changes are not fully considered in traditional route planning is solved. The environment factor correction model can pre-process the path in real time according to external factors such as wind speed, air temperature, atmospheric pressure and the like, so that a more accurate environment assessment map is generated, and more comprehensive reference data is provided for path planning.

The invention belongs to the technical field of intelligent agriculture, and discloses a canopy-atmospheric environment information-based soil water content prediction method and system, and the method comprises the steps: setting a canopy and atmospheric environment monitoring unit in a field, and synchronously collecting the canopy temperature, wind speed, relative humidity, and six-dimensional environment parameters of atmospheric temperature, wind speed and relative humidity; the crown temperature difference is calculated after preprocessing; the method comprises the following steps: constructing a training set by utilizing measured data of soil relative water content, constructing a soil water content prediction model by adopting a random forest algorithm, screening and confirming an optimal prediction model through grid search and cross validation, and screening key features by combining an SHAP value; the water demand is automatically calculated according to a pre-established crop whole-growth-period drought stress threshold table and a dynamic irrigation decision formula, and meanwhile, the adaptation of a general model to a regional specific model is realized by constructing an environmental parameter-actually measured water content database and transfer learning, so that the soil water content state is accurately predicted, and the soil quality is improved. And a scientific basis is provided for precise irrigation.

The invention relates to the technical field of wind power generation. The invention provides a fan main shaft backward movement monitoring and evaluating method and system. The method comprises the following steps: acquiring an axial displacement signal, a vibration signal, a bearing temperature signal and a lubrication state parameter of a fan main shaft in real time; based on a multi-parameter fusion algorithm, performing coupling analysis on the axial displacement, the vibration amplitude, the bearing temperature and the lubrication state to generate a comprehensive risk index; performing dynamic diagnosis on the comprehensive risk index in combination with a main shaft backward movement mechanism model, and identifying at least one fault root cause of axial force imbalance, thermal stress abnormality or lubrication failure; an alarm threshold value is dynamically adjusted according to wind speed change, temperature fluctuation and load working conditions, and graded early warning is triggered based on the fault root cause; and based on historical operation data and a real-time monitoring result, evaluating a main shaft backward movement risk level, and generating a decision report containing a maintenance priority and a technical improvement suggestion. The problem that existing fan main shaft backward movement monitoring depends on manual inspection and offline detection technologies, and limitation exists is solved.

The invention relates to the technical field of mine mining, and discloses an underground multi-parameter environment monitoring method, which comprises the following steps of: acquiring equipment pose, vibration spectrum and environment parameters in real time through a multi-source sensor, performing space-time alignment, filtering noise reduction and feature extraction on original data, outputting a standardized state vector, and performing data processing on the standardized state vector; fusing the pose data, the vibration signals and the environmental parameters under a unified space-time reference to construct a multi-dimensional feature matrix; and carrying out dimension reduction and redundant information elimination by adopting principal component analysis, and outputting a fused equipment state vector. According to the method, through real-time fusion of the equipment pose and the airflow dynamic state, the ventilation equipment is cooperatively regulated and controlled, the interference of mechanical vibration on the gas monitoring precision is eliminated and the reliability of gas concentration detection is guaranteed based on coupling modeling of vibration spectrum and pose drift and intelligent compensation of sensor reading errors, and through space correlation analysis and dynamic air volume optimization, the gas concentration detection accuracy is improved. Wind speed sudden drop caused by tramcar passing is actively eliminated, and roadway global continuous monitoring is achieved.

The invention discloses a coal mine ventilation and heat reduction integrated control method based on intelligent scheduling, and relates to the technical field of coal mine ventilation and heat reduction control, and the method comprises the following steps: arranging a plurality of monitoring devices on a ventilation path and a standby ventilation path of a mine, and collecting temperature, humidity, gas concentration and wind speed and volume data; the central control system gathers monitoring data in real time, analyzes the ventilation condition in the air duct and identifies whether abnormal changes exist or not. Continuous ventilation is ensured through multi-path redundancy and dynamic switching, risks of gas explosion and the like are reduced, early warning is performed by means of the airflow simulation model, and safety of miners is guaranteed. And in combination with a reinforcement learning algorithm and a simulation model, quick response and self-adaptive adjustment are realized, air distribution is optimized, shutdown is avoided, and economic benefits are improved. Meanwhile, by intelligently planning a ventilation path and dynamically adjusting the fan load, energy consumption and maintenance cost are reduced, the service life of equipment is prolonged, and sustainable operation of a mine is promoted.

The invention relates to the technical field of power supply, in particular to a new energy station power prediction method and system based on a weather forecast large model, and the method comprises the following steps: detecting a weather forecast difference value and output fluctuation, marking an abnormal time period, calculating a voltage and frequency deviation to generate a correction threshold value, and matching phase deviation to generate a smooth reference. And analyzing inertia and response rate, applying dynamic attenuation, and collecting tie line power to adjust, predict and generate an output result. According to the method, abnormal time periods are screened through reverse correlation of meteorological difference and output fluctuation, error conduction is blocked, irradiance prediction is corrected through a voltage frequency integral quantity closed loop, power grid response is improved, actually measured wind speed rolling mean value is matched with AGC phase offset, output smoothness is optimized, inertia time delay and frequency modulation rate dynamic matching is performed, and attenuation constraints are adjusted. The frequency risk is reduced, the tie line fluctuation and deviation superimposed effect is quantified, the attenuation range is dynamically adjusted, the power transmission robustness is enhanced, and wind and light curtailment is reduced.

The invention discloses a multi-source data fusion vehicle-mounted gas leakage traceability detection system and method, and relates to the technical field of gas leakage traceability detection. According to the vehicle-mounted gas leakage traceability detection method based on multi-source data fusion, the diffusion coefficient is dynamically corrected by obtaining the gas concentration and environmental data in the vehicle running process in real time, and the leakage source position and the leakage rate are reversely calculated in combination with the self-adaptive particle swarm optimization algorithm; the method can dynamically correct the diffusion coefficient according to the gas concentration and environmental data obtained in real time when the vehicle runs, and accurately and reversely calculate the leakage source position and the leakage rate through the adaptive particle swarm optimization algorithm, and the traditional detection method generally depends on static parameters and preset models, lacks the ability to cope with the fast changing environment, and is difficult to detect. According to the method, the model is adjusted in real time through multi-dimensional environment data obtained in real time, such as information of wind speed, temperature and humidity, air pressure and the like, and it is ensured that the leakage source can still be rapidly and accurately positioned in a dynamic environment.

The invention discloses a wind power plant wind speed prediction method, medium and equipment based on a space-time diagram neural network in the technical field of wind power generation, and the method comprises the steps: carrying out the frequency enhancement of an original wind speed sequence, and obtaining a frequency-enhanced wind speed sequence; inputting the frequency-enhanced wind speed sequence into a time convolution module to obtain time sequence characteristics; constructing a wind speed sequence containing unsteady state information according to the original wind speed sequence, and inputting the wind speed sequence containing the unsteady state information into a graph learning module to construct a space graph structure; performing dynamic graph convolution on the time sequence feature and the space graph structure to obtain a first spatial-temporal feature; constructing a time-space unified graph containing time sequence features and space features, and simultaneously learning the time sequence features and the space features in the time-space unified graph through a Fourier graph neural network to obtain second time-space features; and fusing the first spatio-temporal feature and the second spatio-temporal feature, and obtaining a wind speed prediction value in combination with the prediction time step. According to the method, the generalization ability of the model is improved, and hidden ground frequency information in the data can be better learned.

The invention discloses a water quality comprehensive regulation and control intelligent model based on water quality hydrodynamic force, and relates to the technical field of water quality comprehensive regulation and control, which comprises the following steps: constructing a comprehensive meteorological monitoring network, capturing key meteorological parameters of air temperature, rainfall, humidity and wind speed in real time, and formulating water area monitoring paths of an unmanned aerial vehicle and an unmanned ship according to detected environment data; the method comprises the following steps: setting an unmanned aerial vehicle monitoring team to monitor the water surface condition in real time, setting an unmanned ship formation to run according to a formulated route, carrying out key monitoring and sampling on a specific polluted area, constructing a water quality standard-reaching early warning system, and carrying out prediction and early warning simulation calculation and correction, and analyzing and evaluating the water quality condition. According to the water quality comprehensive regulation and control intelligent model based on the water quality hydrodynamic force, water area pollution can be quickly found, pollutants can be traced and researched in time, pollution expansion is prevented, meanwhile, pollution early warning can be conducted according to data, potential environmental risks can be found, and powerful technical support is provided for environmental protection work.

The invention relates to an electric power meteorological disaster risk early warning method and system based on artificial intelligence. The method comprises the following steps: cleaning and preprocessing basic meteorological data including temperature, humidity, wind speed and precipitation to obtain a cleaned basic meteorological data set; on the basis of satellite cloud picture or radar image data, meteorological changes are modeled through a neural network model, assimilation features are obtained, and the basic meteorological data and the assimilation features are spliced to form a joint feature vector; and constructing a comprehensive disaster risk assessment model based on the meteorological factors, the power grid factors and the geographic information, carrying out training and multi-objective optimization on the comprehensive disaster risk assessment model, and carrying out real-time electric power meteorological disaster risk early warning by using the optimized comprehensive disaster risk assessment model. According to the invention, meteorological data, power grid equipment states and geographic information can be monitored in real time, meteorological disasters possibly suffered by power equipment can be accurately warned, and the safety and stability of a power system are improved.

The invention discloses a mine disaster prediction method based on multi-source data, and relates to the technical field of mine safety, and the method comprises the following steps: collecting original data from different types of sensors in a mine, the data types comprising gas concentration, temperature, humidity, wind speed, ground pressure, water level and vibration information; each type of data is provided with a corresponding timestamp and a spatial position identifier. According to the method, time resampling and space mapping standardization of multi-source data are realized, so that the time-space consistency of data fusion is remarkably improved, and the accuracy of disaster prediction model input is ensured. Meanwhile, a dynamic feature matrix is constructed and a high-precision position weight mechanism is introduced, so that the sensitivity of the model to key areas and key parameters is enhanced, the real-time performance and accuracy of mine disaster prediction are effectively improved, and the risk of missing report and false report of an early warning system is remarkably reduced.

The invention relates to the technical field of ship navigation early warning, and discloses a ship direction monitoring and early warning integrated platform which comprises a ship speed acquisition unit, a ship course angle data acquisition unit, a ship navigation data analysis unit and an early warning unit. According to the ship direction monitoring and early warning integrated platform, early warning is carried out on the ship navigation direction and the navigation state by integrating the wind speed, the water flow speed and the noise data in the navigation process in the current navigation environment, the risk and the course deviation angle in the navigation process can be obtained at the same time, and under the severe navigation condition, the ship direction can be monitored and early warned. The method can still accurately obtain the current ship navigation risk and course deviation, and avoids the situation that the position information and the navigation state of the ship are acquired in real time only by means of sensors such as radar and a global positioning system (GPS), and the early warning effect is affected due to delayed data transmission possibly caused by network delay or signal interruption under the severe navigation condition. And therefore, accurate early warning can be made in a better way in a variable navigation environment.

The invention belongs to the technical field of intelligent management, and particularly discloses and provides an unmanned aerial vehicle group collaborative fire extinguishing intelligent management system, which comprises a flame position analysis module, a fire scene image is acquired by screening target unmanned aerial vehicles, and a flame area is identified to determine a fire source position. The fire behavior evaluation module divides a fire scene area according to the position of a fire source, monitors the wind speed, the combustible density and the flame temperature, analyzes a fire behavior spreading degree index, and screens out a fire behavior spreading area. The launching path analysis module screens fire extinguishing unmanned aerial vehicles based on unmanned aerial vehicle functions, and analyzes flight paths in combination with fire spreading areas, maps and unmanned aerial vehicle position information of a fire scene. The fire extinguishing agent throwing module collects data such as vertical temperature distribution, analyzes the target height and throwing distance of the fire extinguishing unmanned aerial vehicle, judges whether the target height and throwing distance are qualified or not and conducts adjustment. And the coverage area qualification judgment module is used for collecting a put fire extinguishing agent image, analyzing a coverage area qualification degree index, judging whether the fire extinguishing agent is qualified or not and performing early warning.

The invention discloses a salt mist airflow field prediction method and system in an ocean atmospheric environment, and relates to the technical field of ocean wind power plant salt mist corrosion trend prediction.The system divides a wind power plant into a plurality of salt mist monitoring areas, collects environmental data, constructs a salt mist airflow field model by adopting three-dimensional modeling and CFD simulation, and obtains the salt mist corrosion trend of the wind power plant. A digital twinborn model is generated through multi-physics field coupling, and dynamic deduction is carried out; constructing an AI corrosion prediction model by using historical data and simulated deduction data, extracting a corrosion risk feature vector FCV, and performing dynamic training to predict a corrosion trend; salt mist concentration and wind speed data are monitored in real time, a corrosion coefficient is calculated and compared with a threshold value for analysis, and a corresponding protection strategy is formulated; in combination with real-time data and digital twinborn deduction, evaluating a wind-driven salt mist coupling deposition risk, triggering early warning and optimizing a strategy; and a test report is automatically generated, future test parameters are optimized through an AI prediction model, and the corrosion resistance and the operation efficiency of the wind turbine are improved.

The invention relates to the technical field of building construction monitoring, and discloses a method for evaluating mechanical properties of a steel structure building construction whole process based on digital twinning. The method comprises the steps of establishing a digital twin model fusing multi-source information, and performing real-time linkage with a sensor network arranged on site. Collected data such as deformation, temperature and wind speed are processed through dynamic fusion and an anomaly recognition algorithm, model parameters are continuously corrected, and real-time dynamic high-fidelity mapping of the mechanical state in the construction process is achieved. And based on the updated model, the intelligent analysis module performs cooperative calculation, autonomously identifies construction abnormity, quantitatively predicts potential risks, generates a process optimization decision instruction and feeds back the process optimization decision instruction to a site. According to the method, a closed-loop regulation and control mechanism from data perception, model analysis to decision execution is constructed, and accurate online evaluation of construction mechanical properties and active prediction control of safety risks are realized.

The invention relates to the technical field of wind power prediction, and discloses a wind power plant cluster power prediction method and system based on multi-scale space-time correlation, and the method comprises the steps: obtaining the operation data, environment data and geographic position data of each fan, carrying out the preprocessing, and generating a basic feature matrix; constructing a geographic position matrix, a historical wind speed similarity incidence matrix and a wake effect dynamic relation matrix; performing multi-scale space-time correlation modeling on the basic feature matrix through a spatial feature fusion unit, and fusing the basic feature matrix with the wake effect dynamic relationship matrix to generate a multi-scale dynamic relationship matrix; performing information aggregation on in-degree neighbor nodes and out-degree neighbor nodes of the fans to generate node embedding representation of each fan; and inputting the node embedded representation of each fan into a multi-scale time convolutional neural network prediction model for processing to obtain a wind power prediction value. According to the method, the problem that spatial-temporal correlation modeling among fans is not comprehensive in a traditional method is solved, and the power prediction precision is improved.