2295 results about "Wind speed" patented technology

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 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 discloses a radar lifting control method and system based on meteorological monitoring, and relates to the technical field of radar lifting control, and the method comprises the steps: completing the switching of a power supply and communication after a radar is powered on, initializing a controller, collecting the data of a meteorological station, and generating a future fusion wind speed in real time through a Kalman filtering physical model and a residual neural network; future fused wind speed is converted into wind pressure for evaluation, the risk degree is judged according to the evaluation result, early warning is given out, and the controller is preheated to enter a lifting preparation state. The input stability is improved through meteorological data sliding window smoothing and feature extraction, wind speed dynamic prediction and uncertainty quantification are achieved through XGBoost prediction and residual variance estimation, the time sequence consistency and robustness are enhanced through remote API interpolation correction and adaptive extended Kalman filtering, residual correction is conducted through a neural network, the prediction precision is improved, and the prediction accuracy is improved. And a reliable decision basis is provided for radar lifting control.

The invention discloses a truss structure wind-induced dynamic response prediction method and system based on physical enhancement. The method comprises the following steps: carrying out feature extraction and alignment fusion on input data containing condition parameters and wind speed time sequence data by utilizing a long short-term memory network and a physical enhancement attention mechanism; extracting multi-scale features from the fusion features through expansion convolution, and performing weighted aggregation on the multi-scale features; the physical priori knowledge of structural vibration is fused into position coding and a self-attention mechanism so as to carry out response prediction; and integrating physical model information of the truss structure and a dynamic control equation into a loss function, and calculating physical information residual loss so as to improve the physical interpretability of a prediction result. According to the method, data heterogeneity can be eliminated, complementary information can be fused, the multi-scale characteristic of wind-induced response is coped with, the accuracy and efficiency of wind-induced dynamic response prediction of the truss structure are effectively improved, and the physical interpretability and generalization ability are enhanced.

The invention relates to the technical field of weather forecast, discloses a method for classifying severe convection weather forecast, and aims to solve the problem that complexity of severe convection weather requires multi-dimensional data support, so that a three-dimensional data acquisition network covering'ground-air-sky 'needs to be constructed. Ground observation data need to include minute-level rainfall, hourly air temperature and humidity (emphatically paying attention to humidity difference between 850hPa and 500hPa and reflecting unstable stratification) and 10-minute average wind speed of a meteorological station, and high-altitude detection data need to extract temperature vertical profiles (calculating convective condensation height LCL) and wind speed vertical shear (shear values of 0-3km and 0-6km) at 08 o'clock and 20 o'clock every day. According to the method for classifying severe convection weather forecast, new signals (such as sudden cloud top brightness temperature drop) observed in real time are rapidly absorbed, and meanwhile, the method is adaptive to severe convection characteristic differences of different areas (such as mountainous areas and plains) and different seasons, so that the forecast precision is improved, and the requirements of refined disaster prevention for high-accuracy and high-timeliness forecast are met.

The invention discloses a wind power generation energy storage load intelligent prediction and power distribution management method, and relates to the technical field of new energy power generation, and the method comprises the steps: generating a clock synchronization signal when a phase gradient quantity exceeds a stable threshold value, correcting the node voltage phase deviation of a power distribution network according to the clock synchronization signal, and outputting a whole network synchronization voltage waveform; inputting an energy storage charging and discharging control instruction into the power flow optimization model, and generating a voltage suppression control vector through a dynamic power deviation compensation algorithm; and extracting stability parameters in the wind power operation data, carrying out weight distribution and state matching on the energy storage charging and discharging control instruction and the voltage suppression control vector, and outputting a cooperative control instruction. According to the method, heterogeneous data such as wind speed, power and voltage are fused into multi-dimensional sequence parameters through a space-time dynamic coupling method, the phase gradient quantity is generated through phase field gradient extraction, a quantitative correlation model of wind speed fluctuation and power grid response is established, and the load prediction accuracy is improved.

The invention discloses an existing wind power blade electrothermal film deicing control method based on big data, and relates to the technical field of wind turbine generator operation control and ice prevention and removal. Frost ice / glaze ice probability output by an ice type discrimination model does not only give an alarm any more, but is directly converted into an increase / decrease coefficient constrained by boundary and monotonicity; energy can be fully put in a glaze ice scene, and overheating and over-consumption can be avoided in a frost ice scene; according to the method, the change trend of wind speed, temperature and relative humidity is introduced as modulation factors, the working condition of forming / turning glaze ice is responded in advance, and insufficient deicing caused by static threshold hysteresis is reduced; according to power deviation correction, a unit wind speed-power curve serves as a reference, a dead zone, segmented gain and accumulative saturation structure is adopted, on the premise of not depending on an external icing sensor, extrinsic performance of pneumatic degradation is closed into controlled quantity, sensitivity and stability are considered, and start-stop oscillation and duration drift are reduced.

The invention provides a weather forecasting method and system for thunderstorm and gale. The method comprises the following steps: acquiring multi-source weather observation data in a past preset time period; inputting the multi-source meteorological observation data into a pre-trained progressive wind speed forecasting model to extract and fuse multi-scale spatial-temporal characteristics in the multi-source meteorological observation data, and sequentially generating average wind speed forecasting results covering a plurality of future time periods; inputting the average wind speed forecast result into a pre-trained gust mapping model, and performing nonlinear mapping to obtain gust wind speed forecast results corresponding to a plurality of future time periods; and based on the gust speed forecast result, whether thunderstorm and gale risks exist in the future time period is judged. In the mode, the multi-source meteorological observation data is acquired and prediction is performed in combination with the progressive wind speed prediction model and the gust mapping model, so that the accuracy of wind speed and gust prediction can be improved, the thunderstorm and gale risk can be identified in advance, and the disaster early warning and disaster prevention and reduction capabilities are further improved.

The invention discloses a short-term wind speed prediction method for multiple offshore wind power plants, relates to the technical field of power system intellectualization, and constructs a dynamic graph structure fusing the correlation between geographic distance and wind speed according to the correlation between the geographic position of a target area and the wind speed, namely a multi-wind-plant connected graph, and represents the spatial topological relation of a wind power plant group. A wind power plant group is mapped into a node network by constructing a dynamic graph structure fusing geographic distance and wind speed correlation, spatial dependence intensity between nodes is quantized by using a weighted adjacent matrix, spectral domain convolution operation is carried out by a graph convolution network based on a normalized Laplacian matrix, and the spatial dependence intensity between nodes is quantized by using a normalized Laplacian matrix. Efficient neighborhood feature aggregation is achieved through Chebyshev polynomial approximation, complex spatial association caused by geographic position difference and meteorological condition interaction can be accurately captured, the defect of non-Euclidean spatial relationship modeling in a traditional method is overcome, and the representation capacity of the spatial dependency relationship in the multi-wind-power-plant environment is remarkably improved.

The invention discloses a data center machine room energy-saving optimization method and system based on thermal environment prediction. The method comprises two stages of offline modeling and online prediction optimization. In the off-line stage, a CFD simulation model is constructed based on a machine room physical structure, equipment layout and thermal load parameters, and high-precision temperature field data is generated; using the thermal environment prediction model to input equipment parameters and load change to output future space temperature distribution; meanwhile, an XGBoost hybrid energy consumption prediction model is constructed based on the wind speed ratio or the fan frequency, the number of running fans and related characteristics. In the online stage, the lowest energy consumption and the minimum temperature deviation serve as targets, and a Pareto optimal solution set is generated through an MOEA / D algorithm; the optimal wind speed ratio / frequency and equipment number combination is selected as required to control operation of the air conditioner; and dynamically updating model parameters by sliding a time window to realize long-term robust control of the system. According to the method, intelligent energy-saving control of the machine room is realized by fusing CFD simulation, time sequence prediction, energy consumption modeling and a multi-objective optimization algorithm.

The invention discloses a wind power plant energy management system and a dispatching optimization system, and belongs to the field of wind power generation. The system comprises a data acquisition module, a wind speed prediction module, a wake effect analysis module, a power prediction and distribution module, an optimization scheduling module, a dynamic adjustment module, an energy efficiency evaluation module and a communication control module which work cooperatively. Through multi-source data fusion and dynamic collaborative optimization, the comprehensive performance of the wind power plant is remarkably improved, on the operating efficiency level, the system combines the space-time convolutional neural network and the multi-target optimization algorithm, high-precision prediction of minute-level wind speed and optimal distribution of whole-field power are achieved, energy loss caused by the wake effect is effectively reduced, and the wind power generation efficiency is improved. The output strategy is dynamically adjusted according to the health state of the fan, and the fatigue loss of the equipment is delayed while the generating capacity is maximized; on the power grid adaptability level, a self-learning mechanism based on the frequency disturbance qualified rate is introduced, and frequency modulation response parameters are optimized in real time.

The invention provides an unmanned aerial vehicle autonomous obstacle avoidance and dynamic path planning method and system based on multi-modal perception and hybrid intelligent decision. Environmental parameters such as wind speed, illuminance, temperature and humidity and image quality indexes are collected in real time through an airborne weather station, an IMU and a visual sensor, a flight parameter-image quality coupling model is established, and multi-target optimization is achieved through support vector regression (SVR) and particle swarm optimization (PSO). And designing a dynamic strategy optimization module based on Q-learning, and designing a reward function in combination with image quality, obstacle avoidance safety and energy consumption. An obstacle three-dimensional model is constructed in real time through ORB-SLAM3, a dynamic danger coefficient is calculated, and an obstacle avoidance track is generated by adopting an improved APF-RRT * algorithm. And the online cooperative control module optimizes the control quantity by using a BFGS algorithm so as to ensure the flight stability and the task efficiency. The method realizes high-precision obstacle avoidance and path planning of the unmanned aerial vehicle in a complex environment, has the characteristics of high robustness and wide adaptability, and is suitable for practical application scenes such as routing inspection, surveying and mapping and the like.

The invention relates to the technical field of multi-peak spectrum demodulation, and particularly provides a fiber bragg grating multi-peak spectrum demodulation method and system. The method comprises the following steps: extracting local spectral features based on an experimental reference spectrum to form initial atoms, and performing translation offset and normalization processing to obtain an over-complete spectral atom dictionary; performing global offset preliminary estimation based on the dictionary, and obtaining preliminary estimation values of peak sites of the measurement spectrum and the reference spectrum through cross-correlation calculation; executing constraint orthogonal matching pursuit sparse recovery based on the estimated value, and recovering atomic displacement from the measurement spectrum by using block sparsity, translation consistency and non-negative constraint; and performing wind speed inversion and calibration based on the atomic displacement, and converting the wind speed into a wind speed estimated value through a nonlinear calibration model to obtain a final result. According to the method, a dictionary based on experimental data is constructed, dependence on large-scale labeled data is reduced, a physical mechanism and sparsity prior are fused, and the problems that a traditional method is insufficient in precision and weak in generalization ability in a complex environment are solved.

The invention relates to a method for detecting surface cracks of a steel rail, which comprises the following steps of: controlling the environmental temperature difference change of the steel rail to enable thermal response to generate a hysteresis phenomenon, inducing slight asynchronous thermal fluctuation with discontinuous and asymmetric thermal diffusion in a local area of a material surface layer, and exposing a potential crack boundary; non-contact low-intensity laser thermal disturbance multi-angle scanning is used for executing a disturbance penetration test, non-structural thermal anomalies are removed, and a real crack area is determined; carrying out attenuation curve recording on the thermal response after multiple rounds of disturbance excitation, and reversely deducing the integrity degree of the microstructure below the crack according to the disturbance settlement time so as to judge the structural fatigue grade; environmental steady state comparison is carried out on the same detection area, through comparison of temperature and humidity, wind speed and radiation environment parameter changes before and after disturbance, if the crack area still continuously shows thermal response abnormity under a constant environment condition, it is confirmed that the thermal characteristic is caused by a structural crack, and otherwise, misjudgment caused by external accidental interference is eliminated.

The invention relates to the technical field of weather forecast, and discloses a wind speed forecast correction method, device and equipment and a readable storage medium, and the wind speed forecast correction method comprises the steps: carrying out the space-time alignment processing of multi-source wind speed data, and obtaining the wind speed alignment data corresponding to an observation station; constructing a corresponding initial spatial-temporal feature based on the wind speed alignment data, and performing standardization processing on the initial spatial-temporal feature to obtain a standardized spatial-temporal feature; inputting the standardized spatial-temporal characteristics into a prediction model to obtain predicted wind speed information; and generating a wind speed forecast correction result according to a dynamic fusion strategy based on the predicted wind speed information and the multi-source wind speed data. The accuracy and the stability of wind speed forecasting are remarkably improved, and the problem that the correction capability is insufficient under complex terrains and extreme weather is effectively solved.

The invention relates to the technical field of power transmission line safety monitoring, and particularly discloses a self-adaptive calibration method for an ultrasonic wind speed sensor, which comprises the following steps of: arranging an orthogonal ultrasonic probe array at the top of a tower, and compensating probe offset caused by swinging of a tower body in real time through GNSS / IMU (Global Navigation Satellite System / Inertial Measurement Unit) fusion positioning; integrating a temperature, humidity and pressure sensor and vibration spectrum analysis to construct an environmental parameter compensation model; double-frequency ultrasonic waves are adopted to detect the ice accumulation thickness of the protective cover and correct the sound path distance; deploying a lightweight LSTM model at an edge computing unit to realize minute-level dynamic parameter adjustment; electromagnetic interference is suppressed by combining linear frequency modulation spread spectrum and beam forming technologies; and establishing a three-height-layer wind speed gradient model to generate a galloping early warning. The problem of measurement distortion caused by tower deformation, icing and parameter solidification of a traditional sensor is solved, all-working-condition operation from-40 DEG C to 60 DEG C is supported, and the monitoring reliability in a strong electromagnetic environment is remarkably improved.

The invention relates to the technical field of construction methods, in particular to a large-angle inclined steel column self-balancing construction method based on BIM. Comprising the following steps that a database containing steel column parameters, environment parameters and construction parameters is built through a BIM model, and the temperature change value, the instantaneous wind speed, the steel column real-time stress and the hoisting section weight in the construction process are collected in real time; based on the database and the real-time collected data, a torque deviation calculation model under the temperature and wind power coupling effect is established, and the dynamic adjustment amount of the lifting point is deduced according to a deviation value output by the model; calculating a real-time counterweight compensation amount in combination with the lifting point adjustment amount and a stress accumulation effect of previous construction; and according to the balance weight compensation amount, the hoisting posture of the steel column is dynamically regulated and controlled, self-balance control in the construction process is achieved, and the problem that a traditional static method cannot adapt to dynamic stress changes can be solved.

According to the typhoon disaster dynamic risk estimation method and system based on intelligent grid forecasting, meteorological data of an intelligent grid forecasting system with high refinement degree and good accuracy are adopted, intelligent grid forecasting data are corrected by fusing observation data, and data deviation is effectively reduced. The typhoon wind and rain comprehensive index can objectively reflect the typhoon composite disaster-inducing effect, and the typhoon wind and rain comprehensive index is based on the nature of a disaster-inducing mechanism, and the limitation of single-element evaluation is solved by dynamically fusing wind speed and rainfall data. The disaster-pregnant environment influence coefficient considering the dynamic influence of the terrain factors is constructed, the limitation of traditional fixed geographic parameters is broken through, and the risk assessment error caused by neglecting the dynamic nature of the disaster-pregnant environment in a traditional model is solved. A typhoon disaster risk assessment model based on index weight is designed, the risk assessment model focuses on disaster-causing risk, exposure degree and vulnerability key factors, the model structure is simplified, and the calculation efficiency is improved.

Disclosed are an ultra-short-term wind power forecasting method and system, relating to the technical field of artificial intelligence. The method comprises: obtaining an original dataset of a wind farm, processing the original dataset, and performing training on the basis of processed original data; decomposing wind speed data in the trained original data, calculating each decomposition component, and constructing a feature matrix on the basis of the calculation results; and introducing a residual attention mechanism to reconstruct the feature matrix, using the reconstructed result to establish a network model, performing secondary training, and forecasting ultra-short-term wind power. The present invention improves the accuracy and reliability of ultra-short-term wind power forecasting and achieves significant advances in algorithm optimization, thereby providing effective support for the stable power supply of renewable energy sources such as wind farms and for power grid operation.

The invention discloses a method and a device for monitoring the running state of a railway overhead line system. The method comprises the following steps: receiving environmental data acquired by various sensors; the environment data comprises at least one of humidity data, wind speed data, temperature data, ultraviolet intensity data and electromagnetic induction intensity signals; predicting the environmental data through a pre-established risk prediction model to obtain a predicted contact network risk level; wherein the risk prediction model is established based on historical environment data; based on historical fault data, the environment data and an actual detection result, the contact network state is evaluated, and a contact network state evaluation grade is obtained; and generating early warning information according to the contact network risk level and the contact network state evaluation level. According to the invention, operation state monitoring, risk level discrimination and intelligent early warning linkage based on multi-source data can be realized, so that the safety and operation and maintenance efficiency of the contact network system are improved.

The invention provides a multi-scale tensor diagram space-time network wind power prediction method fusing space-time correlation and environmental factors, which comprises the following steps of: firstly, realizing high-quality completion of incomplete wind power data on the basis of tensor ring decomposition and in combination with diagram structure constraint, time smooth constraint and non-negative constraint; the importance of each constraint item is dynamically balanced through an adaptive weight mechanism; then, extracting local space-time characteristics under different time granularities by adopting multi-scale three-dimensional convolution generated by a dynamic convolution kernel and combining frequency domain transformation; and finally, constructing a double-layer dynamic graph structure consisting of a long-term dependency graph and a short-term dependency graph, dynamically adjusting an edge weight in combination with a physical model, and realizing multi-time-span time sequence modeling in multi-scale expansion time convolution. According to the method, missing data can be effectively reconstructed, the complex space-time dependency relationship between fans can be accurately described, minute-level wind speed mutation and hour-level environment change characteristics are considered, and higher precision and stability are shown in a long-time prediction task.

The invention belongs to the technical field of photovoltaic power generation, and discloses a photovoltaic tracking method and a photovoltaic tracking controller, and the method comprises the steps: collecting three-dimensional illumination distribution data through a multispectral imaging sensor and a grid irradiation sensor, and constructing a real-time sun position model in combination with an MEMS inertial measurement unit; predicting a local shadow moving path by adopting a generative adversarial network, and processing irradiance time sequence data based on a time sequence convolution module to generate a pre-compensation tracking instruction; based on the deployed deep reinforcement learning model, taking a weighting function of real-time generated power and a mechanical loss coefficient as a reward mechanism, and dynamically optimizing the rotating speed and the steering control quantity of the double-shaft motor; when the wind speed is larger than a preset threshold value, fluid mechanics simulation is started to calculate the non-horizontal wind-resistant posture, and the support is locked. According to the invention, through multi-modal sensor data fusion and deep reinforcement learning, high-precision and adaptive all-weather sun tracking is realized, and a digital twinborn model is introduced to carry out predictive maintenance.

The invention discloses a blade-free wind power generation device based on vortex-induced vibration and dynamic regulation and control and a regulation and control method. The blade-free wind power generation device comprises an energy capturing mechanism, a support, a dynamic regulation and control system and a power generation mechanism. The energy capturing mechanism, the power generation mechanism and the dynamic regulation and control system are all installed on the support. The energy capturing mechanism is arranged outside the support and is blown by wind to vibrate so that wind energy can be converted into mechanical energy. The power generation mechanism is arranged in the middle of the support and converts mechanical energy into electric energy. And the dynamic regulation and control system is used for regulating and controlling the vibration amplitude of the energy capturing mechanism at different wind speeds, so that the generated power of the energy capturing mechanism is kept stable. Compared with the prior art, the blade-free wind power generation method has the advantages that more stable electric energy output is realized by dynamically regulating and controlling the tightness of the mooring rope, the efficient, stable and environment-friendly blade-free wind power generation device is provided, and the maintenance cost is lower.

Disclosed in the present invention are a lifting synchronous control system and control method for a high-rise steel structure corridor. The control system comprises a data acquisition module comprising a sensor network, an execution module, and a data-processing and decision-making module comprising a central control unit. The execution module comprises a lifting drive provided with a receiver. The sensor network comprises a coordinate sensor, an inclination angle sensor, a temperature and humidity sensor, and a wind speed sensor. The sensor network continuously acquires coordinate information, inclination angle information, and environment information and sends same to the central control unit. By means of a machine learning or deep learning algorithm, the central control unit performs self-optimization and regulates the lifting drive in real time on the basis of the continuously collected environment data and structural data. In the present invention, by combining dynamic regulation of an intelligent algorithm with the application of an Internet of Things technology, all components in the control system are modularly designed, thereby obviously mitigating the defects of conventional synchronous control systems, and significantly improving the stability of the control system and the capability of resisting the influence of external factors.

The invention relates to the technical field of dew point measurement, in particular to an intelligent temperature control system and method based on a high-precision chilled mirror dew point meter, and the system comprises a chilled mirror temperature difference positioning module, a main signal selection module, a power response matching module, an interference airflow recognition module, and a current output instruction generation module. A cold mirror end temperature difference is obtained, a section and a lifting direction are judged, synchronous measuring points are screened, a minimum deviation signal is extracted, a power section is matched, a power-on instruction is constructed, a rheumatism disturbance level is identified, parameters are spliced to generate a conduction structure, and a cold mirror temperature control instruction is output. According to the method, section identifiers are constructed through temperature differences and temperature change directions, signals are screened based on time alignment, interference is avoided, power control positions section deviation directions through difference values, disturbance identification is combined with wind speed and humidity differences, disturbance levels are refined, power-on control generates time sequence instructions through multi-parameter splicing, instruction stability and control accuracy are improved, and the method is suitable for large-scale popularization and application. And dynamic closing logic is formed.

The invention relates to the technical field of material transportation, in particular to a conveying pressure control method and device of a powder and particle pneumatic conveying system. The method comprises the steps that the material humidity, the conveying pressure and the air speed of a frequency conversion fan are obtained; determining a humidity adjusting coefficient; in the pressurization stage, a suppression coefficient is determined according to the current wind speed and the target wind speed; according to the humidity adjustment coefficient and the inhibition coefficient, inhibiting the proportionality item to obtain a target proportionality coefficient; enhancing the differential term in combination with the humidity adjustment coefficient and the pressure deviation to obtain a target differential coefficient; in the pressure reduction stage, the standard humidity is compared with the air speed and the conveying pressure under the material humidity at the current sampling moment and the humidity adjusting coefficient, the integral item is adjusted, and a target integral coefficient is obtained; and adjusting the conveying pressure based on the adjusted PID coefficient. The problem of asymmetry of bidirectional adjustment of pressurization and depressurization can be solved, the adjustment logic is optimized, and the conveying efficiency and stability of pneumatic conveying of powder and particle materials are improved.

The invention discloses a forest fire risk prediction method based on multi-source data, and belongs to the technical field of forest fire prevention. Aiming at the problem of low prediction precision caused by one-sided information of a single data source and insufficient multi-source data fusion in the prior art, the method is realized by the following steps: acquiring micrometeorological data including temperature and humidity, wind speed and air pressure, and image data including an infrared image and a visible light image; the data of the mountain fire-prone area comprises historical fire frequency, vegetation type and topographic information; uTC + 8 time synchronization and WGS84 coordinate system space calibration are carried out on the data, and missing values and abnormal values are processed; carrying out feature layer fusion by adopting an attention mechanism, and extracting core features such as a temperature and humidity coupling index and vegetation dryness; spatial correlation features are captured through CNN, a time sequence trend is captured through LSTM, a mountain fire occurrence probability is output by using a Sigmoid function after decision-making layer fusion, and a result is calibrated in combination with sub-region features. Through multi-source data deep fusion and spatial-temporal feature collaborative analysis, the accuracy and timeliness of forest fire risk prediction are improved, a new data source can be expanded and accessed, and the method is suitable for a complex forest fire prevention scene.

The invention relates to a wind power cluster short-term power prediction method and device of a space-time diagram neural network fused with physical information and computer equipment, and the method comprises the steps: obtaining related information data of each wind power plant in a wind power cluster, and carrying out the preprocessing; forming a physical prior data set through an engineering analysis model fusing the wake flow analysis model and the blocking effect model; taking each wind power plant as a node of the graph, constructing graph structure data for predicting the power of the wind power plant, and forming a dynamic adjacent matrix; constructing a space-time diagram neural network WB-STGNN model architecture comprising a diagram convolutional neural network module, a gating time convolutional network and a multi-layer perceptron; the method comprises the following steps: pre-training by using a physical prior data set, and then performing formal training based on historical power data and a dynamic adjacency matrix to obtain a space-time diagram neural network WB-STGNN model; inputting the wind speed of the prediction day, and predicting the active power of the whole wind power cluster in 24 hours of the prediction day. By adopting the method, the precision and efficiency of wind power cluster power prediction can be effectively improved.

The invention discloses a multi-stage wind power abnormal data combination cleaning method, and belongs to the field of new energy power generation data processing. Aiming at the problems of various types of abnormal values of wind power original data, serious interference and low cleaning precision, and single anomaly detection means, easy missing detection and misjudgment and the like in the prior art, the invention provides a staged combined cleaning strategy, which comprises the following steps of: firstly, dividing equal interval sections of wind speed and power, and eliminating isolated point type anomalies in distribution by using double quartile analysis; a CFSFDP density peak value clustering algorithm is introduced, low-density anomaly clusters are mined according to a density-distance joint criterion, and the recognition capability of structural aggregation anomaly is improved through two rounds of clustering; performing segmentation modeling on a wind speed-power relation by using upper and lower envelope line fitting based on a function, and removing envelope outer drift type noise; and finally, complementing edge missing data by using an interpolation algorithm. According to the method, the systematicness, precision and adaptability of the wind power data cleaning process are remarkably enhanced, high-quality data are provided for subsequent power prediction and energy optimization scheduling, and the application prospect is wide.

The invention provides a method for predicting strong wind along a high-speed rail based on multi-scale modeling and time-frequency feature fusion. The method comprises the following steps: step 1, collecting and preprocessing historical data of wind speed monitoring stations along the high-speed rail; step 2, carrying out decomposition algorithm processing and down-sampling processing on the preprocessed data; step 3, constructing a multi-scale time-frequency fusion prediction network; the multi-scale time-frequency fusion prediction network comprises a TCN network layer, an LSTM network layer and a cross attention mechanism; step 4, training the multi-scale time-frequency fusion prediction network; and 5, performing future wind speed prediction by using the trained multi-scale time-frequency fusion prediction network. The method is suitable for a multi-time scale prediction task in a complex wind speed time sequence scene, and can be used for strong wind early warning in the running process of a high-speed train.