3729 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 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 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 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 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 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 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 solar irradiance prediction, and discloses a solar irradiance prediction method and system based on data fusion, and the method comprises the steps: obtaining a cloud layer gray image, a wind speed vector, a terrain elevation, a slope inclination angle, a solar azimuth angle and an elevation angle, calculating the movement speed and direction of a cloud layer based on the cloud layer image and the wind speed vector, and obtaining a prediction result. Calculating a shielding path in combination with a terrain elevation and a sun position; generating a terrain shielding influence coefficient matrix; fusing a gradient and shielding data to generate a dynamic effect graph; performing cloud-ground shielding analysis based on the dynamic effect graph and a cloud trajectory to obtain cloud-ground coupling influence distribution; using a random forest algorithm to fuse the coupling distribution and the shielding coefficient to calculate an initial irradiance probability, and forming a preliminary prediction result; and inputting the time sequence prediction model, the cloud trajectory and the shielding coefficient into a trained time sequence prediction network, and outputting an optimization result. According to the method, prediction requirements under complex terrains and rapid weather changes can be met.

The invention provides a mobile henhouse ventilation regulation and control method and system based on an environment model, and relates to the technical field of intelligent breeding, and the method comprises the steps: obtaining original temperature data, original humidity data, a three-dimensional space wind speed component, an opening geometric distribution data set, and original latitude and longitude coordinate data and time data; calculating a dynamic environment weight coefficient matrix according to the original latitude and longitude coordinate sequence and the time data; generating a self-adaptive heat exchange flow field according to the dynamic environment weight coefficient matrix, the original temperature data and the original humidity data; according to the self-adaptive heat exchange flow field and the trepanning geometric distribution data set, a ventilation compensation amount demand curve is obtained through calculation; generating a ventilation intensity adjusting instruction set according to the ventilation compensation amount demand curve; and according to the ventilation intensity adjusting instruction set, outputting an optimal ventilation control parameter. According to the method, ventilation response self-adaptive accurate matching, airflow dead zone self-suppression and energy consumption-effect dynamic balance under the moving sudden change working condition are achieved.

The invention discloses a base station air conditioner energy consumption management energy storage method and system, and the method comprises the steps: collecting multi-source data in real time according to the load data of a temperature sensor, a humidity sensor, an air conditioner operation state monitoring device and a communication device in a base station room, and generating a multi-source fusion data set; for the multi-source fusion data set, dynamic refrigeration requirements in a future time window are predicted, and a dynamic optimization model of air conditioner energy consumption is constructed through an energy consumption efficiency curve; according to the prediction result of the dynamic refrigeration demand and the energy consumption optimization model, the operation mode, the wind speed and the temperature set value of the air conditioner are dynamically adjusted, the energy storage scheduling strategy is optimized, and a combined optimization scheme of air conditioner operation and energy storage scheduling is generated; and monitoring the operation state of the air conditioner in real time, dynamically adjusting the joint optimization scheme, and generating a final base station air conditioner energy consumption management scheme. By utilizing the embodiment of the invention, the energy consumption management of the air conditioner has real-time performance and accuracy, and more efficient energy consumption control and energy storage scheduling optimization can be realized.

The invention belongs to the technical field of wind power prediction, and discloses an ultra-short-term wind power prediction model construction method based on signal decomposition and parameter optimization, and the method specifically comprises the following steps: S1, original wind power data processing: employing a self-adaptive noise complete set empirical mode decomposition algorithm (CEEMDAN) to decompose the original wind power data; according to the method, a hybrid model fusing a bidirectional gating cycle unit (BiGRU), a bidirectional time convolution network (BiTCN) and a multi-head attention mechanism (MHA) is constructed, an improved parameter optimization algorithm is designed, the capacity of the model for capturing wind power short-term fluctuation characteristics is enhanced, the parameter optimization efficiency is improved, the local optimum problem is effectively avoided, and the method is suitable for the wind power short-term fluctuation characteristic capturing capability. According to the method, the limitation in traditional feature extraction is effectively improved, high-precision and high-efficiency ultra-short-term wind power prediction is realized, a reliable basis is provided for optimizing a power generation scheduling strategy for a power system, and the method can be popularized and applied to multivariate time sequence prediction scenes such as wind speed prediction and photovoltaic power generation prediction.

The invention relates to a pipe network station inspection task execution method and system based on multi-modal fusion, and belongs to the technical field of industrial facility detection. According to the method, multiple types of sensors are carried through an unmanned aerial vehicle and a ground robot, illumination, temperature and humidity, wind speed and rain and fog data are collected in real time, and an optimal sensor combination is dynamically activated; a feature level fusion strategy is adopted, the weight of each modal is adjusted in combination with environmental parameters, and the defect detection accuracy is improved; the system performs digital twinborn simulation verification on an abnormal result, so that the false alarm rate is reduced; an inspection path is adaptively adjusted according to a detection result, and a high-risk area is emphatically scanned; the edge computing nodes realize real-time data processing, and upload key information after compression; the maintainer rechecks the result through the AR glasses and marks a misinformation case; according to the method, the problems of poor environmental adaptability and insufficient utilization of multi-modal data of traditional inspection are solved, and the detection efficiency and reliability are improved.

The invention relates to the technical field of air-cooled air conditioner control, in particular to an air-cooled air conditioner control method and system based on deep learning, and the method comprises the following steps: obtaining sensor readings of indoor temperature, outdoor temperature and humidity and Wi-Fi channel state information, judging the position number and the activity state of personnel, calculating the position distribution of the personnel in combination with a time window, and calculating the position distribution of the personnel. And integrating to obtain a multi-dimensional environment and load state vector. According to the method, indoor and outdoor temperature, humidity, Wi-Fi channel state information and personnel position distribution and activity states are integrated through a multi-dimensional environment and load state vector, the personnel density change trend is dynamically calculated through a time window, the comprehensiveness and real-time performance of environment perception are enhanced, and high-dimensional data support is provided for control decision making. And a basic comfort degree deviation index is constructed based on the temperature deviation value and the humidity deviation value, and a multi-target performance index set is generated in combination with the wind speed value, the compressor start-stop frequency and the energy consumption power.

The invention discloses a method for performing wind power generation prediction through a fine-tuning pre-training large model, and the method comprises the steps: obtaining a historical multi-dimensional time series data set, and carrying out the spatial-temporal feature fusion through a sliding window, and generating a wind speed trend sequence; based on the wind speed gradients of the adjacent time windows, generating step length adjustment parameters by using a normalization function; performing dynamic parameter adjustment on the pre-trained time sequence prediction model based on the step length adjustment parameters, and performing domain adaptation on top network parameters of the time sequence prediction model through an adaptive optimization algorithm in combination with a hierarchical transfer learning strategy to obtain an optimized time sequence prediction model; and processing the real-time multi-dimensional time sequence data set by using the optimized model to generate a wind power generation prediction result. By dynamically adjusting model parameters and a hierarchical transfer learning strategy, the precision of wind power generation prediction and the model adaptation capability are effectively improved, and the method is suitable for time sequence prediction scenes in the field of wind power generation.

The invention discloses a cold station multi-equipment combination energy efficiency optimization method and system, relates to the field of cold station energy efficiency optimization, and aims to solve the problems of low energy efficiency and response lag caused by manual adjustment. The method comprises the following steps: S1, acquiring key operation data (power, state, temperature, flow and frequency) of a water chilling unit, a cooling tower and a water pump and external environment data (temperature and humidity, wind speed and load prediction) in real time; s2, constructing an equipment energy efficiency model based on historical data, and quantifying the change of the equipment efficiency along with the load rate, the temperature difference and the environment temperature; s3, according to the current load and environment prediction, simulating and traversing the energy consumption of the equipment start-stop combination and the water pump frequency combination by applying a Monte Carlo algorithm, and screening a combination strategy with the lowest total energy consumption of the system; s4, converting the optimization strategy into a control instruction, and issuing the control instruction to equipment for execution; and S5, comparing the actual energy consumption with the predicted value, and if the deviation exceeds 5%, correcting model parameters by adopting a least square regression algorithm to realize closed-loop optimization. And the energy efficiency and the response speed of the cold station are obviously improved.

The invention relates to the technical field of construction control, and particularly discloses a method and a system for dynamically regulating and controlling the installation precision of an inclined-span steel box arch bridge based on machine learning. Wherein the real-time data comprises real-time stress of the arch rib, real-time deformation of the arch rib, real-time environment wind speed and real-time hoisting parameters; constructing a prediction model capable of analyzing the coupling effect of the wind load and the hoisting unbalance load based on a large amount of historical construction data; inputting real-time data into the prediction model to obtain a real-time coupling effect analysis result; outputting a hoisting sequence optimization instruction based on the decision engine, the coupling effect analysis result and a preset mechanical constraint condition of the obliquely-spanning steel box arch bridge; based on the deviation between the real-time deformation amount of the arch rib and a preset installation precision threshold value, a cable force grading adjustment scheme is generated; construction efficiency of the inclined-span steel box arch bridge is effectively improved, construction safety and installation precision are guaranteed, and stability and reliability of the bridge structure are guaranteed.

The invention relates to the technical field of methanol safety monitoring, in particular to a methanol leakage prediction system based on artificial intelligence. According to the method, structural extraction of potential leakage paths is realized through dynamic sorting and surge correlation combination of methanol concentration change trends in continuous time periods, and weighted calculation is carried out on conduction characteristics between monitoring nodes in double dimensions of time series and spatial topology by virtue of a graph attention mechanism, so that the detection accuracy of the leakage paths is improved. The method comprises the following steps: establishing a path, forming quantitative expression of node influence factors in the path, capturing synchronous abnormal behaviors under an environment disturbance background through a multi-parameter coupling sudden change identification mode of air pressure and wind speed, further constructing a high-timeliness sudden change event set, and performing index fusion on the influence degree and sudden change frequency of monitoring nodes on the basis to obtain a high-timeliness sudden change event set. And space risk score distribution is formed, and a leakage possibility region prediction map is generated in combination with two-dimensional coordinates, so that boundary fine depiction of a high-risk region is realized.

The invention provides a hydropower station natural disaster intelligent early warning and emergency decision-making method and system based on multi-source data fusion and dynamic threshold optimization, and the method comprises the steps: collecting the water regimen data of a hydropower station and a drainage basin in real time through the deployment of a water level gauge, a rain gauge, a wind speed sensor and a thunder and lightning monitoring device, and combining weather forecast data and historical hydrological data, thereby achieving the intelligent early warning and emergency decision-making of the hydropower station. And dynamically calculating a disaster threshold value, and generating a graded early warning signal. The system comprises a data acquisition module, a dynamic threshold matching module, an early warning generation module, a case retrieval module, an information issuing module and a response tracking module. The early warning threshold is dynamically adjusted through multi-source data fusion and a machine learning technology, and the early warning accuracy is improved; a historical case library is constructed by using a knowledge graph technology, and intelligent recommendation of a disaster disposal scheme is realized; and early warning information and disposal suggestions are directionally pushed through multiple channels, so that the emergency response efficiency is improved. The conversion from passive response to active early warning is realized, and the influence of natural disasters on the operation of the hydropower station is obviously reduced.

The invention discloses a regional power grid wind power generation power prediction optimization method and system, and particularly relates to the technical field of power grid wind power generation. Multi-source environment data is collected, wind speed change characteristics are extracted, a time series data set is constructed, a hybrid prediction model is constructed in combination with a long-short term memory network and a gradient boosting decision tree, a time series trend and a nonlinear wind speed-power mapping relation are captured respectively, and prediction weights of the time series trend and the nonlinear wind speed-power mapping relation are adaptively adjusted through an attention mechanism. According to the method, the model hyper-parameters are adaptively adjusted by further combining Bayesian optimization, short-term prediction errors are corrected by using Kalman filtering, the real-time adaptability and stability of prediction are enhanced, the problems of prediction misalignment and the like caused by high nonlinearity and data scarcity of a wind speed mode in a complex terrain environment are effectively solved, and the prediction accuracy is improved. The wind power prediction precision is improved, a more stable and reliable scheduling reference is provided for a regional power grid, and the standby capacity demand and the operation cost are reduced.

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 provides a desert photovoltaic sand damage dynamic monitoring method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the flying dust data of a photovoltaic power station region, generating an initial data set, and enabling the flying dust data to be collected through a multi-mode sensor carried by the unmanned aerial vehicle; performing multi-modal data fusion processing on the initial data set to obtain a dust feature matrix, the dust feature matrix being used for representing dust reflectivity, terrain elevation and wind speed correlation features; calculating migration probability distribution of the flying dust particles based on the sand and dust characteristic matrix, wherein the migration probability distribution is used for representing spatio-temporal evolution laws of the particles with different particle sizes; a three-dimensional dynamic sand and dust concentration field model is generated according to the migration probability distribution, a sand damage dynamic monitoring result is output based on the three-dimensional dynamic sand and dust concentration field model, and the sand damage dynamic monitoring result comprises deposition density distribution and an erosion risk area. By adopting the method, the photovoltaic sand damage of the desert can be accurately and dynamically monitored with high timeliness.

The invention relates to the technical field of wind power generation, and discloses a wind power plant wind speed correction method and system based on dynamic space-time modeling, and the method comprises the steps: obtaining a whole power curve, obtaining the whole wind speed of a historical period, and constructing a multi-modal training data set; inputting a convolutional neural network to extract local features, inputting a long-short-term memory network, calculating the correlation of each time step feature, obtaining an attention weight, and finally obtaining global feature representation; setting two multi-layer perceptron branches to carry out wind speed prediction correction to obtain a common weather branch prediction value and an extreme weather branch prediction value; constructing a correction curve of each sector and obtaining a correction curve prediction value; and according to the common weather branch prediction value, the extreme weather branch prediction value and the correction curve prediction value, carrying out weighted fusion to obtain a final wind speed correction value. According to the method, the correction precision and robustness are improved, and the interpretability and applicability of the model are enhanced.

The invention relates to the technical field of gas monitoring, and discloses an air quality monitoring method based on an olfactory chip, which comprises the following steps: step 1, preparing a multi-modal sensor system which comprises an olfactory chip module, a surface enhanced Raman scattering module and a microfluidic time division multiplexing module, the sensor surface of the olfaction chip module is modified with a metal organic framework material, and the substrate of the surface enhanced Raman scattering module is made of a core-shell structure nano material; and 2, synchronously collecting resistance response data of the olfaction chip module, spectral data of the surface enhanced Raman scattering module and temperature, humidity and wind speed data of the environment sensor. According to the invention, a multi-modal sensor cooperative detection technical scheme is adopted, the high-sensitivity detection effect on low-concentration harmful gas in air is achieved, and compared with a technical scheme of a single sensor and a fixed fusion algorithm in the prior art, the defect that accurate recognition of trace gas cannot be realized under ppb-level concentration is overcome.

The invention relates to an offshore wind speed prediction method based on a TVFEMD-FE-TCN-Transform model, and the method comprises the steps: obtaining historical offshore wind speed data, carrying out the preprocessing of the data, decomposing the original wind speed data through TVFEMD to obtain a plurality of IMF components, improving the data stability, and carrying out the prediction of the offshore wind speed. According to the method, four types of signals including a high-frequency signal, an intermediate-frequency signal, a low-frequency signal and a trend signal are generated through reconstruction according to IMF component complexity through fuzzy entropy FE, calculation complexity is reduced, a time domain convolutional network TCN is adopted to extract reconstructed signal features, fusion is performed, the reconstructed signal features are input into Transform for wind speed prediction, meanwhile, Transform model parameters are optimized through MWOA, and the wind speed prediction accuracy is improved. Predicting the offshore wind speed by using the optimal parameter combination Transform model to obtain a final offshore wind speed prediction value; according to the method provided by the invention, the problems of insufficient signal decomposition, weak feature extraction capability and low prediction precision of a single prediction model are effectively solved, and powerful support is provided for operation and maintenance management and power grid dispatching of the offshore wind power plant under the condition that the offshore wind speed has intermittent and fluctuation characteristics.