866 results about "Weather station" patented technology

The present invention relates to the technical field of mine safety management, and discloses a method and system for dynamic monitoring and early warning of mine environment, including the following steps: multi-source data acquisition, collecting dynamic data of mine environment through remote sensing imaging equipment, ground sensors and meteorological stations; data preprocessing, noise cleaning, time alignment and spatial reference coordinate unification of the data; spatiotemporal data fusion, mapping multi-source data to a unified feature space through feature extraction, cross-modal fusion and attention mechanism; intelligent analysis and disaster prediction, predicting surface deformation, landslide risk and tailings dam stability in mining areas based on multi-task joint learning models; risk assessment and early warning response, dividing risk levels according to comprehensive risk scores, and dynamically generating early warning information, evacuation paths and dangerous area isolation boundaries. The present invention solves the problems of difficulty in multi-source data fusion, low disaster prediction accuracy and delayed early warning response in the prior art.

The invention discloses a power construction unmanned aerial vehicle path planning method and system, and relates to the technical field of space calculation, and the method comprises the following steps: determining a target region needing power construction and a preset construction point coordinate; obtaining a topographic map of the target area based on the geographic information database, constructing a three-dimensional map model, and marking the preset construction point coordinates; generating an initial construction path of the unmanned aerial vehicle; the method comprises the following steps: acquiring meteorological data in real time through meteorological data of a meteorological station in a target area, establishing a meteorological obstacle three-dimensional model, and dividing a preset radius threshold with a meteorological obstacle as a circle center into non-flying areas according to the severity level of the meteorological obstacle; adjusting the initial construction path based on the non-flying area; the cruise height is adjusted according to the meteorological data and topographic relief, a relief tracking path is generated, and the constant relative height is kept; and combining the battery capacity, the flight speed and the task priority, dynamically distributing inspection road sections, and generating a corresponding power construction unmanned aerial vehicle flight path.

The invention relates to the technical field of air conditioner load dispatching, and discloses a regional air conditioner cooling load dispatching system and method. According to the system and the method, external meteorological data and internal environment data are acquired by applying a temperature and humidity sensor, an air quality sensor, a human flow meter and a meteorological station through a data acquisition module, and the acquired data are processed and analyzed by a data processing and analyzing module; the cooling load scheduling strategy module is used for calculating the current cooling load demand, the load prediction value, the energy cost, the environmental impact factor, the regional building thermal load, the system energy efficiency ratio, the temperature and humidity comfort index and the overall energy consumption level, and generating an optimal cooling load scheduling scheme based on a multi-objective optimization algorithm according to the calculated values and the analysis result; and the control and execution module controls the air conditioners according to the scheduling strategy, adjusts the air conditioner output cooling load of each area, controls ventilation and air return, and optimizes air circulation.

The invention provides a multivariable multi-step runoff prediction method based on dynamic variational mode decomposition, and belongs to the technical field of drainage basin runoff prediction.The method comprises the steps that various hydrometeorological data of a research drainage basin are collected; decomposing an original runoff sequence by adopting a dynamic variational mode decomposition strategy VMD, performing sliding window slicing processing on drainage basin underlying surface and meteorological data and upstream hydrologic station runoff data, and combining target hydrologic station runoff data subsequences obtained by decomposition into a first characteristic variable; meteorological factor screening is carried out on the meteorological station data, a screening result is used as a second characteristic variable, and the first characteristic variable and the second characteristic variable are combined together to form an input characteristic variable; a sparrow search algorithm SSA is adopted to optimize parameters of a long short-term memory network LSTM to construct the long short-term memory network LSTM, and future runoff is predicted. According to the method, the defect that future forecast information is introduced by binding decomposition is effectively overcome, and the applicability of the decomposition-integration forecast model in actual engineering is enhanced.

The invention relates to a runoff hydropower station generating capacity prediction method based on depth data fusion, and the method comprises the following steps: S1, carrying out data layer fusion, integrating multi-source hydro meteorological data, obtaining multi-source data from a meteorological station, a hydrological station, a satellite remote sensing platform and a numerical weather forecasting system, carrying out the cleaning, interpolation and normalization processing of the data, and carrying out the prediction of the generating capacity of a runoff hydropower station; performing data space-time alignment; s2, carrying out feature layer fusion, firstly inputting a multi-source data set of a unified space-time reference, carrying out feature extraction, carrying out feature fusion based on a self-attention mechanism, embedding a water balance equation in a deep learning model, and obtaining a comprehensive feature vector; and S3, performing prediction, inputting the comprehensive feature vector in the step S2, and performing power generation prediction by using a deep neural network.

A regional extreme rainfall forecasting and early warning method based on a conditional generative adversarial network (cGAN) comprises the following steps: preprocessing multi-source meteorological data, including standardization and space consistency alignment, so as to ensure the availability of the data; and through a coding module of the cGAN, extracting and fusing interannual daily average features and horizontal features, constructing a multi-scale feature pyramid, and enhancing the characterization capability of a rainfall spatial mode. Forecasting data are generated through a cGAN generator and a condition discriminator, rainfall collaborative factors are introduced into the discriminator to serve as physical constraint conditions, and network parameters are optimized through an adversarial loss function and physical consistency loss. The forecasting data is post-processed through a graph neural network (GNN), and spatial dependency modeling and optimization correction are carried out by taking a meteorological station observation value as a true value, so that the forecasting precision and reliability of an extreme rainfall event are remarkably improved.

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.

Provided in the present invention are a power-distribution-network self-healing method and system taking photovoltaic output into consideration. The method comprises: acquiring historical operation data of a photovoltaic power station and irradiance observation data from a meteorological station; on the basis of the historical operation data of the photovoltaic power station and the irradiance observation data from the meteorological station, predicting the generated power of the photovoltaic power station by using a convolutional long-short-term memory recurrent neural network model that takes sparrow search into consideration; on the basis of the generated power of the photovoltaic power station, a segment-switch state of a power distribution network and a network topology of the power distribution network, constructing an objective function and a constraint condition for a power-distribution-network self-healing model, and obtaining the power-distribution-network self-healing model; solving the power-distribution-network self-healing model by using a propagation search algorithm, so as to obtain an optimal recovery strategy; and executing the optimal recovery strategy by means of segmented switches and node loads. The present invention can realize self-healing of a power distribution network while ensuring the minimum power generation cost of a distributed power source, the minimum network loss and the minimum node voltage deviation.

The invention discloses an artificial precipitation data acquisition system and evaluation method based on a remote sensing image, and relates to the technical field of ecological environment restoration and meteorological science. Comprising the following steps: acquiring remote sensing image data and rainfall data of meteorological stations in a region, performing geometric correction and radiation correction on the remote sensing image data based on ENVI software to obtain corrected remote sensing data, and calculating the rainfall data of the meteorological stations on the basis of the corrected remote sensing data and regional historical rainfall data. The rainfall change data and the vegetation change data in different time periods before and after artificial rainfall enhancement are respectively obtained through a regression analysis method and a pixel dichotomy method, the rainfall change data and the vegetation change data in different time periods before and after artificial rainfall enhancement are compared, the influence of rainfall enhancement on vegetation recovery is quantitatively analyzed, and the rainfall enhancement effect is analyzed by utilizing spatial-temporal dynamic change. According to the method, the influence of rainfall enhancement on the ecological environment can be evaluated more comprehensively and accurately, and a new scientific basis is provided for ecological restoration of arid regions.

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

The invention relates to the technical field of meteorological remote sensing, in particular to a self-adaptive partition water vapor chromatography method and system based on multi-scale Bayesian prior, and the method comprises the steps: S1, obtaining and processing multi-source observation data of a to-be-analyzed region; s2, constructing a multi-scale grid of the tomographic inversion region; s3, multi-scale representation of the water vapor density is established, and a Bayesian prior constraint model is constructed; s4, constructing a three-dimensional chromatography inversion equation set; and S5, carrying out Bayesian inversion solution. According to the method, global navigation satellite system observation data, meteorological station data and sounding station data are fused, Bayesian prior constraints are constructed, and a multi-scale representation and self-adaptive partitioning method is adopted, so that the number of parameters is effectively reduced, and the stability, precision and calculation efficiency of tomography inversion are improved.

The invention discloses an urban blue-green space microclimate prediction system and method based on AI fusion. The method comprises the following steps of: 1, acquiring field microclimate measured data through linkage of a portable weather station and a GPS (Global Positioning System); 2, utilizing a machine learning downscaling algorithm and a deep learning semantic segmentation technology to extract high-resolution land surface temperature and urban land surface coverage classification data from the remote sensing image; 3, constructing a time-space aligned multi-modal GIS data set, and constructing a high-precision microclimate prediction model by using an ensemble learning algorithm; and 4, introducing an SHAP interpretability framework, carrying out deep analysis on the model, and quantifying the contribution degree of various environmental elements to a microclimate prediction result and the complex nonlinear influence of the contribution degree. According to the method, unprecedented high-precision and explainable scientific decision support can be provided for formulating urban planning, landscape design and thermal environment mitigation strategies, and development of healthy, sustainable and climate-flexible cities is powerfully promoted.

The invention discloses a big data platform system for emergency disposal of overwater oil spill and hazardous chemical substance leakage accidents, and the system comprises a data integration module which is used for collecting and integrating remote sensing data and environmental data from satellites, unmanned aerial vehicles, oceans and meteorological stations in real time; the monitoring module is used for dynamically displaying real-time diffusion conditions and affected areas of spilled oil and hazardous chemical substance leakage; the analysis and prediction module is used for predicting future diffusion paths of oil spilling and leakage based on the collected data by adopting an artificial intelligence algorithm; and the risk assessment module assesses the event risk and provides an emergency response strategy and a resource allocation suggestion. According to the invention, the multi-source data real-time acquisition and integration capabilities of satellites, unmanned aerial vehicles, ocean sensors, meteorological stations and the like are utilized, the data updating speed and the information integrity are improved, the real-time diffusion conditions of oil spill and hazardous chemical substance leakage are dynamically displayed through a map engine and a real-time data processing algorithm, and rapid decision making and resource allocation are supported.

The invention discloses a mining mechanical equipment type selection management control platform based on a network, and relates to the technical field of mining mechanical equipment, and the control platform comprises a data collection module, a data processing and analysis module, and a type selection recommendation module. Multi-source heterogeneous data from a mining area geological exploration department, an equipment sensor, a satellite remote sensing image and a meteorological station are ingeniously fused, and the data are effectively integrated together through normalization processing and weight distribution, so that a comprehensive and accurate information basis is provided for subsequent model selection recommendation; the data processing and analysis module further performs cleaning, classification, coding and feature extraction on the data, a quantitative relation model between working condition factors and equipment performance is established, and the intelligent data analysis and processing capability enables the platform to perform quantitative analysis according to actual working condition requirements of a mining area. And equipment models and parameters with high adaptability and lowest full life cycle cost are accurately recommended.

The invention discloses a new energy station intelligent micrometeorological monitoring device suitable for extreme weather and a power prediction method, and relates to the technical field of new energy station meteorological monitoring and power prediction. The device integrates a plurality of meteorological sensors, combines satellite meteorological data and ground meteorological station data, improves meteorological monitoring accuracy through data fusion, and gives 8-15-day long-time scale weather forecast. According to the method, based on meteorological data collected in real time and historical meteorological data of a new energy station, a long-short term memory network model and a segmentation multi-target risk method are adopted to carry out risk assessment on extreme weather, and an extreme weather early warning signal is given. A deep neural network is utilized to establish a power nonlinear prediction model of a new energy station, and a higher-precision power prediction result is given through a random forest dynamic correction algorithm. The meteorological and generated power prediction accuracy of the new energy station is improved, and the operation safety of the new energy station is improved.

The invention relates to the technical field of reservoir storage capacity analysis, in particular to an intelligent reservoir storage capacity analysis method and system based on multi-source data fusion, and the method comprises the following steps: collecting ground water level sensor data, meteorological station data, LiDAR point cloud data and aerial image data, synchronizing the data through a unified timestamp, integrating the data, summarizing the integrated data, and carrying out the analysis of the reservoir storage capacity. And generating a comprehensive multi-source data set. According to the invention, through real-time updating and accurate tracking of daily reservoir capacity changes, the accuracy of reservoir capacity estimation is significantly improved, daily cumulative statistics of water level, rainfall and evaporation capacity data is integrated, dynamic and detailed management is promoted, seasonal changes and long-term trends of the reservoir water level are analyzed, and the reservoir capacity estimation accuracy is improved. The method can accurately reveal the direct effect of manual intervention and the influence of the manual intervention on the natural supply mode of the reservoir, can make a scientific decision based on a comprehensive data view, integrates short-term prediction and real-time data flow monitoring to enable an early warning system to quickly respond, and optimizes the efficiency and effectiveness of disaster management.

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

The invention relates to the technical field of port operation state sensing and monitoring, and particularly discloses a port operation state sensing and monitoring system based on a data center, which monitors a wind speed value and a visibility value at each current time point in real time through a micro weather station array, and synchronously obtains tidal phase data of an observatory. Comprehensively generating multi-dimensional meteorological data in a set time period; dynamically calculating the berthing safety coefficient of the target ship through a multi-dimensional constraint condition in combination with the load tonnage of the target ship; according to the target ship berthing safety coefficient, a dynamic ship berthing strategy of the target ship is generated; through comprehensive application of multi-dimensional meteorological data acquisition, berthing safety coefficient calculation, dynamic ship berthing strategy generation and a feedback terminal, a port manager can monitor and optimize the ship berthing process in real time. Therefore, the operation safety and efficiency of the port are improved, a more scientific and flexible berthing scheme is provided for the ship, and the sustainability and economic benefits of port operation are ensured.

The invention provides a wide-area multi-level soil attribute fine mapping method based on spatial downscaling. According to the method, long-time-sequence optical data information and ground elevation information are collected through a satellite sensor, long-time-sequence temperature and rainfall data are collected through a meteorological station, and spatial position auxiliary data extracted from the long-time-sequence temperature and rainfall data are combined with existing national-level soil grid database data to construct a high-quality data set; the method is used for training a random forest model constructed based on an integrated learning technology so as to realize high-precision and high-reliability refined soil attribute mapping under a wide-area scale. The method is reasonable in conception, high-precision and high-reliability refined soil attribute mapping under a wide-area scale is realized, a specific region multi-level soil attribute database with higher resolution can be constructed, and the method is suitable for popularization and application.

The embodiment of the invention provides a multi-element deep learning correction method and device for numerical mode forecasting. The method is applied to the technical field of data processing, and comprises the following steps: marking observation points, and performing loss function construction on the positions of the observation points to obtain a model optimization target; carrying out iterative updating on model parameters, and carrying out training process optimization by adopting a dynamic learning rate adjustment strategy to obtain a forecast field correction model; and applying the prediction field correction model to target numerical mode prediction data acquired in real time, performing prediction effect evaluation, and performing standardization processing and anti-standardization processing to obtain a correction prediction result. According to the method, the information extraction capability of a deep learning visual algorithm model is utilized, fine deviation correction of data of a numerical mode forecast space grid by meteorological station observation data is completed, meanwhile, multiple types of meteorological elements are fused, the deep learning model is utilized to learn mutual influences and restrictions between related elements, and the prediction precision of the numerical mode forecast space grid is improved. And finer deviation correction is completed.

The invention relates to the technical field of microwave communication, and discloses a microwave communication data transmission method, which comprises the following steps: S1, sensing real-time rainfall intensity and atmospheric turbulence intensity parameters through combination of a multi-band radar and a meteorological sensor; s2, establishing a fractional order random channel model of a rainfall and turbulence coupling effect; s3, generating a dynamic coded modulation control strategy based on deep fractional order reinforcement learning; s4, optimizing a programmable metasurface reflection coefficient matrix according to the dynamic coded modulation control strategy and a channel state to reconstruct an electromagnetic propagation path; s5, executing power-codebook joint optimization of space-time coding and non-orthogonal multiple access; and S6, the system stability is monitored on line, and a degradation fault-tolerant mechanism is triggered. According to the invention, a Ku / W dual-band radar and meteorological sensor variational Kalman fusion technical scheme is adopted, and compared with a single-band radar or sparse meteorological station isolated observation scheme in the prior art, the problem of signal sudden drop misjudgment in a rainstorm scene is solved.

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

The invention relates to the technical field of wind resource assessment and wind power plant optimization, and discloses a multi-source information fusion measurement system combining laser radar data and wind resource assessment, and the system comprises a data collection module which is used for collecting laser radar data, synthetic aperture radar data, numerical weather forecast data and ground weather station data; the data preprocessing module is used for carrying out noise filtering, time alignment, space alignment and data calibration on the collected data; the information fusion module is used for dynamically fusing the preprocessed multi-source data and outputting high-precision wind speed and turbulence characteristic distribution; and the wind field modeling and prediction module is used for constructing a dynamic three-dimensional wind field based on the fusion data and performing short-time wind speed prediction. Through multi-source data deep fusion, dynamic wind field modeling and intelligent optimization modules, the comprehensive effects of high-precision wind field evaluation, nonlinear wind speed prediction and fan layout global optimization are achieved.

The invention discloses a hybrid photovoltaic prediction method based on SARIMA-LSTM, and the method comprises the steps: carrying out the calculation according to historical photovoltaic power generation data in an SCADA system, wherein the historical photovoltaic power generation data comprise the power generation power and the accumulated power generation amount, and the meteorological data comprise the irradiance, the temperature, the humidity, the wind speed, the cloud cover and the like, and are obtained from a meteorological station or an open-source data platform; statistics is carried out by combining an SARIMA model with a seasonal prediction function and an LSTM model for processing a time sequence, and a more accurate photovoltaic power generation prediction value is obtained after combined prediction and evaluation. Compared with the prior art, short-term prediction and long-term prediction can be considered, and seasonal change data prediction is more accurate.

The invention discloses a method and device for predicting regional temperature, a processor and a readable storage medium, and the method comprises the steps: determining a target numerical mode of a target region, the target numerical mode being used for carrying out simulated weather forecast on the target region; determining an initial field of a target numerical pattern according to a forecast result of the global numerical pattern; obtaining observation data of a plurality of ground meteorological stations corresponding to the target area; correcting the initial field of the target numerical pattern according to the observation data to obtain a corrected initial field of the target numerical pattern; and performing numerical simulation calculation according to the corrected initial field of the target numerical mode to determine the predicted temperature of the target area. The observation data of the ground meteorological station corresponding to the target area is assimilated into the target numerical mode, so that the ground temperature in the initial field in the target numerical mode is closer to the temperature field in the real atmosphere, the temperature deviation in the numerical mode is reduced, and the accuracy of the temperature prediction result is improved.

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 provides a spatio-temporal data-based early warning method and a spatio-temporal data-based early warning system for an extreme weather-induced power failure, and aims to accurately and timely predict and warn possible influences and failures of an extreme weather event on a power system so as to improve the reliability and the toughness of the power system. The method comprises the following steps: firstly, collecting spatio-temporal data and other influence factors observed by a meteorological station, including but not limited to meteorological data, power grid structure and operation data, geographical and time data and the like; secondly, a ConvLstm-based deep space-time residual network is utilized to fuse space-time data and external factors to realize time-space two-dimensional prediction of meteorological characteristics, and an SMOTE-NC-NCL mixed sampling method is adopted to relieve data imbalance; afterwards, a complex relation between an extreme weather event and a power system fault is finally predicted and identified by using a LightGBM algorithm, early warning information is provided for a power grid in time, an electric power company is assisted to make a quick response and a decision, and the stability of power grid operation is ensured. Therefore, economic loss is reduced and social public safety is improved.

The invention relates to the technical field of water resource management, in particular to a cascade reservoir ecological scheduling system based on artificial intelligence, which comprises a multi-source data integrated processing module, an ecological influence prediction module, a scheduling strategy optimization module, a self-adaptive water quality management module and an intelligent user interaction module. Wherein the multi-source data integrated processing module is used for collecting various data of a meteorological station, a hydrometric station and an ecological monitoring point; the ecological influence prediction module is used for constructing an ecological influence model; the scheduling strategy optimization module is used for formulating a reservoir scheduling strategy; the self-adaptive water quality management module is used for adjusting the reservoir outlet flow and the adjusting strategy of the reservoir; and the intelligent user interaction module provides a human-computer interface. According to the invention, by integrating multi-source real-time data, applying reinforcement learning to optimize the scheduling strategy and providing an efficient user interaction interface, the real-time responsiveness and decision-making efficiency of reservoir management are significantly improved, and optimal utilization of water resources and protection of the ecological environment are ensured at the same time.

The invention relates to the technical field of meteorological monitoring and lightning protection, and discloses a radio signal-based lightning protection detection method for a meteorological station, which comprises the following steps of: receiving electromagnetic wave signals generated by lightning discharge in real time through radio receiving equipment of a plurality of meteorological stations, and acquiring characteristic data of time, frequency and intensity of the signals; an effective light velocity model is adopted to correct the influence of the propagation velocity of the lightning signal in the atmosphere, and the effective propagation velocity of the lightning signal is calculated by considering the ionospheric and atmospheric refraction effects; the position of the lightning power supply is estimated by measuring the arrival time difference and the time difference of lightning signals received by different sites and utilizing the geometric positions of the multiple sites. The positioning precision of the lightning power supply is greatly improved by adopting the cooperative work of the radio receiving equipment of a plurality of meteorological stations and combining the technologies of accurate lightning signal propagation speed correction, time synchronization error correction, geometric precision optimization and the like.

The invention provides a dynamic aggregation peak regulation method and system based on distributed photovoltaic power prediction and computing equipment, and is applied to the technical field of dispatching and control of a power system. The dynamic aggregation peak regulation method comprises the following steps: acquiring data of a photovoltaic power station through a photovoltaic power station monitoring system, a meteorological station and a geographic information system; performing photovoltaic power prediction by using the distributed photovoltaic power prediction model; load demand prediction is carried out on the load in the future time period; performing network division and dynamic aggregation on the photovoltaic area; and carrying out peak regulation strategy optimization based on the output of the dynamic aggregation. According to the method, the peak regulation pressure of power grid operation can be reduced, and the stability and economical efficiency of the system are improved.