1531 results about "Extreme weather" patented technology

The invention discloses a day-ahead and intra-day economic dispatching method for a wind and light storage system, which relates to the technical field of wind and light storage and comprises the following steps of: establishing a dynamic aging model based on historical operation data of a battery, fitting actual attenuation characteristics of the battery through machine learning, acquiring the health state of the battery in different operation states and the change rule of the battery along with time, and calculating the economic dispatching result. Therefore, the basis of the dynamic adjustment strategy is formed. Energy storage charging and discharging are optimized through health factors, the system monitors parameters such as SOH and SOC in real time, tasks are dynamically adjusted, battery aging is reduced, stability is improved, the SOC strategy is optimized in combination with deep reinforcement learning, the service life is prolonged, and the maintenance cost is reduced. Rolling optimization scheduling and experience playback are adopted, a system can dynamically adjust a strategy, the wind and light absorption rate is improved, power grid balance is optimized, thermal power dependence is reduced, scheduling cost is reduced, extreme weather adaptability is enhanced, and green and low-carbon development of electric power is promoted.

The invention discloses a road traffic flow prediction method based on a space-time mixed attention network, and the method breaks through the limitation of a conventional time sequence model and a single deep learning architecture based on the systematic analysis of urban road traffic flow space-time heterogeneity, periodic non-stationarity and road network topological relevance, constructs the space-time mixed attention network, and achieves the prediction of road traffic flow. Spatial heterogeneous correlation of road network nodes is captured through a graph convolution network, dynamic time sequence evolution characteristics of traffic flow are modeled by adopting a hybrid architecture, a residual attention mechanism is introduced to realize layer-by-layer refining of multi-scale spatio-temporal characteristics, and the overall architecture of the method has remarkable advantages in the aspects of spatial topology modeling and time dynamic capture compared with a traditional model. Feature decoupling learning is carried out on multi-source heterogeneous data such as weather and events, adaptive integration of environment sensitive features is realized through a parameterized gating fusion strategy, and the prediction error fluctuation amplitude in an extreme weather scene is reduced by 34.8%.

The invention relates to the technical field of electric power energy storage system planning analysis, and discloses an electric power system planning operation optimization method, system and device and a storage medium. The method comprises the following steps: collecting electrical and equipment states and environmental parameters for feature extraction, and evaluating the health state of equipment; grouping the distributed energy sources based on health state evaluation and weather early warning information to form a virtual power plant resource pool; performing task decomposition according to the characteristics of the resource pool, and formulating a collaborative scheduling strategy; arranging a maintenance plan according to the equipment state and the scheduling strategy; and optimizing system operation parameters based on the scheduling strategy and the maintenance plan to obtain a balance control scheme. According to the invention, under the condition of considering the health state of the equipment and the influence of extreme weather, the dynamic balance of the minimization of the operation cost of the power distribution network and the maximization of renewable energy consumption is realized through the intelligent arrangement technology of the virtual power plant group.

The invention discloses an urban life body full-cycle monitoring system based on a digital twinborn technology, relates to the technical field of urban governance, integrates multi-source data such as GIS data, remote sensing information, surface permeability data and an underground drainage pipe network structure, quantifies a flood high-risk area, and provides an urban life body full-cycle monitoring system based on the digital twinborn technology. The AI flood prediction module deduces the ponding range and the drainage capacity under different rainfall situations by adopting extreme weather analogue simulation based on a historical rainfall trend, a current meteorological condition and a drainage network state, and dynamically adjusts a flood diffusion trend prediction model, so that the flood risk identification is more accurate; the intelligent emergency scheduling module automatically matches flood control resources, combines flood high-risk areas, ponding point locations and traffic flow analysis, optimizes a drainage pump station start-stop strategy, remotely regulates and controls an underground drainage gate, dynamically adjusts the overflow adjustment capability of a sewage treatment plant, and optimizes an emergency risk avoiding route in combination with an intelligent traffic management system. And the accuracy and the execution efficiency of the flood control scheduling scheme are improved.

The invention relates to a groundwater safety assessment method under an extreme climate event, which comprises the following steps: collecting multi-source heterogeneous data such as meteorological data, geological data, hydrological data and remote sensing data, and constructing a unified groundwater safety knowledge graph through standardized cleaning, semantic alignment and deletion completion; monitoring an extreme climate event in real time, and updating a node relation weight and sparsifying a transmission path based on knowledge graph dynamic evolution and a time sequence attention mechanism; performing risk propagation path reasoning on the dynamic knowledge graph in combination with an improved graph neural network, identifying key pollution nodes, and outputting a structured risk level and a coping suggestion; the system continuously optimizes atlas and model parameters based on evolution feedback, and high adaptability and reasoning precision of emergency response are achieved. According to the method, the intelligence, the real-time performance and the accuracy of underground water risk assessment are improved. The problems that the underground water pollution propagation path is difficult to dynamically identify and the decision adaptability is insufficient under extreme climate events are solved.

The invention discloses a power load prediction method and device based on multi-factor influence, and particularly relates to the technical field of power load prediction. Historical weather data and power load data are collected, preprocessing and feature extraction are carried out, temperature change deviation features and power load lag features are extracted and input into a prediction model for training, the accuracy of a prediction result is evaluated, and when the prediction accuracy is low, the prediction result is subjected to prediction. The abnormal degree is further analyzed and predicted, and the power dispatching strategy is adjusted in time, so that the violent fluctuation of the load demand is dealt with, the stable operation of the power grid is ensured, the accuracy of power load prediction is effectively improved, and the risk of overload or collapse of the power grid is reduced especially in the case of extreme weather.

The invention provides a multi-mode tailing pond dam break digital twinning emergency deduction and decision optimization method, which comprises the following steps of: firstly, constructing a high-precision three-dimensional scene model, performing simulation calculation on the high-precision three-dimensional tailing pond model based on a finite element model to obtain a dam body stress-strain field, and performing simulation calculation on the dam body stress-strain field based on ANSYS + reinforcement learning. And predicting a dam break probability distribution diagram by using the dam body stress-strain field and the current environment state, generating a rescue scheduling scheme based on the dam break probability distribution diagram and the resource distribution data, building a dynamic virtual deduction scene for exercise, and verifying and optimizing the rescue scheduling scheme according to exercise record data. And a final rescue scheduling scheme is obtained. According to the method, the ANSYS + reinforcement learning hybrid engine is utilized to reinforce the deduction precision, dam break path prediction, optimal resource scheduling and plan self-optimization closed loop are realized, the problems of data fusion distortion, deduction stiffness, decision lag and the like in the traditional technology are solved, and the method is particularly suitable for variable working conditions such as tailing pond seepage field sudden change and extreme weather.

The invention discloses a multimodal transport cargo receiving, unloading and transferring multi-machine cooperation method and system. The multimodal transport cargo receiving, unloading and transferring multi-machine cooperation method comprises the steps of obtaining freight basic elements, constraint conditions and real-time environment information, and assigning a matched number of unmanned transfer vehicles and unmanned aerial vehicle sets; generating an initial optimal path; and the local path of the initial optimal path is adjusted in real time, the unmanned transfer vehicle and the unmanned aerial vehicle set advance to the unloading destination according to the global optimal path, and multi-vehicle cooperative cargo unloading is completed according to the conflict-free motion trail. A multi-machine cooperation technology is adopted, advanced technologies such as unmanned driving, the Internet of Things and an artificial intelligence algorithm are fused, the unmanned transfer trolley, the unmanned aerial vehicle and the intelligent gantry crane can work efficiently and cooperatively, various operation scenes such as indoor and outdoor switching, place layout difference and extreme weather can be dealt with, cargoes can be rapidly and accurately received, unloaded and transferred, and the working efficiency is improved. And the efficiency of multimodal transport is greatly improved.

The invention discloses a cross-platform power transaction data interaction optimization method, and particularly relates to the technical field of power transaction data digital twinning. Simulation transaction data of market participants is generated based on an antagonistic neural network model; identifying a causal relationship among market variables from the simulation transaction data of the digital twin through a causal discovery algorithm, and constructing a causal graph; modeling spatial dependence and causal conduction paths among market participants by using a graph neural network model, and performing anti-factual reasoning in a single external impact scene in a digital twinborn body by intervening key variables in a causal graph; simulating dynamic game behaviors of market participants in an extreme weather parameter distribution scene; the power market failure assessment is carried out by combining the causal conduction path to obtain the power market failure assessment index, and early warning is carried out based on the power market failure assessment index, so that the adaptability and stability of the power market in extreme weather scenes are improved.

The invention provides a new energy output prediction method and system based on Monte Carlo Dropout, and the method comprises the steps: carrying out the probabilistic prediction of new energy output through a Monte Carlo Dropout technology, generating a dynamic prediction result containing a confidence interval, and quantifying the uncertainty of meteorological sudden change and equipment state; secondly, constructing a multi-stage random dynamic programming model, discretizing a prediction interval into multi-scene input, designing a non-linear objective function based on the discharge depth, and synchronously optimizing the electricity purchase cost and the energy storage aging cost; and finally, realizing rolling optimization of the system in combination with a model prediction control framework, and dynamically adjusting an energy storage aging cost weight by updating prediction data and a scheduling instruction on line and embedding an energy storage health state real-time feedback mechanism. The photovoltaic and wind power consumption rate can be remarkably improved, the full life cycle cost of an energy storage system is reduced, and meanwhile, the robustness of a scheduling strategy in extreme weather is ensured.

The invention discloses an AI-NWP three-dimensional closed-loop bidirectional dynamic feedback coupling method for extreme rainfall event area simulation and a program product, and belongs to the technical field of meteorological numerical simulation and artificial intelligence fusion. According to the method, a mid-term forecast field is generated based on an AI global weather prediction model, an analysis field is constructed by assimilating multi-source observation data, and a high-resolution NWP region mode is driven to perform rolling simulation. Furthermore, a space-time residual field is constructed by using the difference between an NWP region simulation result and live data, a residual learning model is trained, an AI model prediction structure is fed back and corrected, and dynamic weight adjustment updating of the AI prediction model is realized. A closed-loop two-way feedback system among AI output, NWP simulation and residual evaluation is integrally formed, the space structure reduction capability and the area positioning precision of a medium-term heavy rainfall event are effectively improved, the continuous predictability and the simulation credibility of an extreme weather process are remarkably enhanced, and the method has good stability, universality and engineering expansion value.

The invention discloses a land-sea-air-space holographic sensing and collaborative decision-making system based on multi-mode edge intelligence. The system comprises a sensing layer which is used for carrying out three-dimensional monitoring on a ship driving process, the sea, the sky, a shore base and a space formed by the ship driving process, the sea, the sky, the shore base and the sea, the sky and the shore base; the network layer is used for constructing a global communication basis, preprocessing monitoring data, judging whether the ship has an abnormal condition or not, and realizing data transmission of the ocean, the sky, a shore base and a space formed by the ocean, the sky and the shore base; and the application layer is used for judging whether an abnormal condition exists in the ship driving process or not based on the data of the ocean, the sky, the shore base and the space formed by the ocean, the sky and the shore base transmitted by the network layer, performing collision early warning decision based on the abnormal condition, and performing dynamic channel capacity prediction. And navigation guidance and route tracking in extreme weather are carried out based on environmental risk early warning, underwater obstacle collision is avoided, and early warning information and decision information are fed back to a sensing layer.

The invention provides a solar power generation power real-time prediction method, system and device based on multi-mode deep learning and a storage medium. Wherein the prediction precision is improved by fusing multi-dimensional features of earth surface radiation, equipment temperature and environmental data, mutation fluctuation decomposition is performed on long and short wave components of earth surface solar radiation, and a frequency domain sub-modal sequence is extracted; constructing a correlation model of a photovoltaic module temperature field and hot spot infrared data, and generating a dynamic feature map reflecting hot spot interference space distribution; establishing a multi-mode time sequence prediction model, and synchronously processing frequency domain features of radiation sub-modes, spatial correlation of hot spot feature maps and time sequence dependence of temperature and humidity data; and integrating the prediction results of the sub-modals through a frequency domain superposition reconstruction technology. According to the technical scheme provided by the invention, through multi-modal spatial-temporal feature joint modeling and dynamic interference decoupling, the power prediction precision in extreme weather and hot spot abnormal scenes is remarkably improved, and meanwhile, the dynamic adaptive capacity of a prediction system to complex environment disturbance is enhanced.

The invention discloses a time-space characteristic analysis method for meteorological new energy load big data, and belongs to the technical field of new energy power management, and the method comprises the steps of data fusion alignment, physical causal modeling, extreme weather attribution and time-space characteristic analysis. According to the method, time-space characteristic analysis is performed by combining physical causal modeling and extreme weather attribution, the prediction precision of new energy load fluctuation under daily weather is improved, and load abnormity caused by extreme weather can be effectively identified and coped with; a double-flow space-time causal graph network model is adopted to perform physical causal modeling, and through physical constraint expression of wind power and photovoltaic characteristics, causal modeling of load change association between power stations and a dynamic feature fusion prediction mechanism of the wind power and photovoltaic characteristics, the load prediction accuracy, stability and scheduling practicability are improved; extreme weather attribution is carried out by adopting an attribution method combined with an improved disturbance response ratio, the actual influence of the extreme weather on the new energy load is accurately identified, and the interference degree of different types of weather events on the load is quantified.

The invention discloses an operation state fault monitoring method, system and device for a heavy-load overhead line, and relates to the technical field of electrical fault detection of the heavy-load overhead line. The method comprises the following steps: injecting a high-frequency test signal into a heavy-load overhead line to carry out predefined fault detection so as to obtain a total harmonic distortion rate; performing first harmonic optimization adjustment according to comparative analysis of the total harmonic distortion rate and a threshold value; performing harmonic second optimization adjustment according to the monitoring analysis result of the external load fluctuation of the heavy-load overhead line; and performing harmonic third optimization adjustment according to the monitoring analysis result of the external environment fluctuation of the heavy-load overhead line. According to the invention, through multi-stage distribution adjustment, the effect of improving the dynamic regulation and control accuracy of the high-frequency test signal injection fault monitoring method of the heavy-load overhead line in extreme weather is achieved; the problem of insufficient dynamic regulation and control accuracy of a high-frequency test signal injection fault monitoring method of a heavy-load overhead line in extreme weather exists.

The invention relates to the field of weather forecasting, and discloses a marine meteorological coupling refined forecasting method and system, which are used for improving the precision and reliability of marine meteorological coupling forecasting. Comprising the following steps: based on a non-exchangeable geometric vortex equation and an external differential form, constructing a conservation manifold dynamic field in combination with submarine topography data, and realizing strict conservation of mass flux under a complex terrain; an additional pressure item caused by vacuum fluctuation is quantified, and an interface energy field of quantum correction is generated; the recognition precision of the mesoscale vortex and the frontal surface structure is improved; a spectral element method and lattice Boltzmann method mixed solver is combined, and high-precision coupling of ocean vertical layering and the atmospheric process is achieved. According to the method, the defects of conservation, interface energy transmission, initial field non-physics and grid adaptive capacity of a traditional method are overcome, refined forecasting of 72-hour typhoon paths, ocean frontal surfaces and energy flux is supported, and key technical support is provided for extreme weather early warning and climate change research.

The invention discloses an underground pipe network intelligent sensing and safety evaluation system based on an urban lifeline, relates to the field of intelligent monitoring and management of urban infrastructures, and provides an intelligent sensing and safety evaluation system for an underground pipe network based on the urban lifeline by integrating advanced technologies such as an advanced sensor network, a deep learning algorithm, an Internet of Things technology, a digital twin model and meteorological data integration. Comprehensive real-time monitoring and accurate damage identification are carried out on the urban underground pipe network; a machine learning algorithm is combined with a physical model, a statistical method and application of a digital twinborn technology, so that the system can create a digital copy of a pipe network, synchronization of virtuality and reality is realized, and an integrated meteorological risk assessment tool and an early warning system can be combined with pipe network monitoring data and meteorological short-term and imminent forecast data; the potential influence of extreme weather on the pipe network is evaluated, and early warning is given out in time; the health monitoring, risk assessment and emergency response capabilities of the underground pipe network can be remarkably improved, and intelligent management of urban infrastructures is realized.

The invention belongs to the technical field of meteorological safety, and discloses a circum-Bohai sea low-altitude meteorological safety engine artificial intelligence algorithm based on historical reanalysis and multi-mode data. The low-altitude meteorological safety engine artificial intelligence algorithm comprises the following modules: a meteorological variable reconstruction and extraction module, an extreme event identification module, a model training module, a mode fusion module and a safety engine construction module. The low-altitude meteorological safety engine artificial intelligence algorithm of the Bohai Sea has the following advantages: (1) data fusion and timeliness improvement are realized; (2) the low-altitude small-scale extreme weather identification precision is improved; (3) performing multi-dimensional meteorological risk assessment system and standardized grading; and (4) performing dynamic adaptive optimization and real-time adjustment.

The invention belongs to the technical field of ecological risk prediction, and relates to an extreme weather event and ecological risk prediction method using a generative adversarial network, through a meteorological generation branch, spatial-temporal characteristics of a meteorological field are synchronously extracted by using a 3D residual network and Transform and a future meteorological state is predicted, and an ecological generation branch is based on a meteorological prediction result and topographic data. An ecological risk map is dynamically generated through hole convolution U-Net, and mismatch of resolution and dimension caused by cross-model interpolation is avoided; a discriminator introduces a physical conservation verification module and an ecological association module, physical rationality loss and ecological logic consistency loss of a generator are jointly optimized in adversarial training, in addition, a dynamic feedback mechanism corrects and generates errors through real-time observation data, and spectrum normalization is utilized to constrain model parameters, so that the reliability of the model is improved. The accuracy of extreme event and ecological risk coupling prediction is further improved; therefore, seamless space-time coupling of the weather and the ecological field is realized, and the generated result has both physical conservation and ecological relevance.

The invention provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

The invention discloses a low-vacuum circulating water heating dynamic load matching method based on AI regulation and control, and the method comprises the following steps: step S100, multi-source data real-time collection: building thermal environment parameters are synchronously collected through a temperature sensor array, a vacuum pressure transmitter, a flowmeter and an environment monitoring unit which are deployed in a distributed manner; according to the method, the control period is compressed to the minute level, compared with traditional PID control, the response speed is increased by several times, the indoor temperature can be controlled more accurately, and meanwhile the failure rate of equipment is reduced. According to the sudden weather coping mechanism, heat supply strengthening can be started 30 minutes ahead of time under extreme weather such as snowstorm weather, and the stability of the system is ensured. The method is characterized in that the prediction advantage of deep learning and the optimization capability of model prediction control are deeply combined, and the control problem of multivariable strong coupling of the low vacuum system is solved through a verification system of multi-source data fusion and virtual-real combination.

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 discloses a flood disaster monitoring and early warning system and a flood disaster monitoring and early warning method. A cloud, rain, water and I integrated sensing network is constructed through a full-chain monitoring capability; a hybrid prediction model coupled with HEC-HMS and SWMM physical models and an LSTM-Transformer deep learning architecture is established, parameter deviation is dynamically corrected through NSGA-II and a symbolic regression multi-objective optimization algorithm, the flood prediction period is prolonged to 10 days (the precision of the southern watershed is larger than or equal to 90%, and the precision of the northern watershed is larger than or equal to 70%), the flood peak time error is compressed to be within 30 minutes, and compared with a scheme based on a static flood risk model, the method has the advantage that the flood prediction efficiency is greatly improved. The false alarm rate is reduced from 20% to 5% through the dynamic threshold calibration technology; hierarchical response and survivability communication are adopted, Beidou satellite and NB-IoT dual-channel redundant transmission is deployed, and a Mesh ad hoc network and frequency modulation subcarrier technology are combined, so that the direct rate of early warning information in extreme weather is ensured to be greater than or equal to 99%; and three-dimensional GIS platform dynamic rendering is supported, and collaborative visualization of a submerging thermodynamic diagram, a material scheduling path and ecological flow monitoring is realized.

The invention discloses a civil aviation passenger demand prediction method and system based on artificial intelligence. The method comprises the steps of data acquisition, abnormal demand elimination, civil aviation demand side sample supplementation, civil aviation passenger demand prediction model establishment and civil aviation passenger demand prediction. The invention belongs to the field of civil aviation demand prediction, and particularly relates to a civil aviation passenger demand prediction method and system based on artificial intelligence. According to the scheme, a time sequence weighted distance is introduced, and it is guaranteed that samples with off-season and low demand characteristics are not mistakenly removed; on the basis of route similarity weighting, trusted neighbors are generated, and rare low-demand samples are supplemented in a targeted manner; feature importance scores are introduced, feature sensitivity scores of civil aviation samples are calculated, and influences of feature-time points on demand prediction are evaluated from different angles; the adaptability to festival and holiday peaks and extreme weather scenes is guided through a punishment mechanism; based on the introduction of a holiday association weight enhancement scheduling strategy, the penalty weight growth rate is dynamically improved; and the reliability of civil aviation passenger demand prediction is improved.

The invention discloses a low-altitude airway flow field sensitive area dynamic identification optimization method based on set simulation, and the method comprises the steps: building a low-altitude flow field preprocessing data base with consistent time and space based on Beidou subdivision grids and multi-source heterogeneous data fusion; constructing a low-altitude airspace digital twinning environment based on the data; based on the low-altitude airspace digital twin environment and the cellular automaton-fluid coupling model, generating a diversified flow field evolution scene covering extreme weather and equipment faults; based on a set simulation result, extracting a high-conflict probability region through a spatio-temporal clustering algorithm and quantifying region risk features; generating an air route planning scheme meeting security constraints through a multi-objective evolutionary algorithm based on the quantitative regional risk features; on the basis of a low-altitude airspace digital twin environment and an air route planning scheme, verifying the feasibility of the air route planning scheme through historical data playback and virtual-real fusion test; and according to a verification feedback result, carrying out dynamic feedback optimization on the low-altitude air route flow field sensitive area identification and air route planning scheme.

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 discloses a wind power prediction system for optimizing a neural network based on a genetic algorithm, relates to the technical field of new energy power system prediction, and improves the precision and adaptability of wind power prediction by fusing the genetic algorithm and a deep neural network. The system adopts multi-objective genetic optimization, randomly initializes a neural network parameter combination, evaluates the fitness by taking a prediction error and model complexity as double objectives, and screens out an optimal network architecture through evolution operation; in the aspect of neural network training, the system adopts an LSTM and TCN hybrid network as a basic model, dynamic weighting input features of a meteorological attention mechanism are combined, a learning rate and regularization parameters are optimized by using a genetic algorithm, model convergence is accelerated, and overfitting is prevented; in addition, for the space-time imbalance of the wind power data, a generative adversarial network is introduced to generate synthetic data in an extreme weather scene, and the generalization ability of the model is enhanced.

The invention discloses a millimeter wave radar meteorological target detection method based on deep learning, and relates to the technical field of meteorological radar processing. The method comprises the following steps: acquiring a millimeter wave radar original signal by using a transmission control protocol for preprocessing; the distance and the angle between an object and an antenna are measured through a millimeter wave radar, so that three-dimensional space coordinates of the measured object are obtained; converting the three-dimensional space coordinates through time alignment and a space coordinate system, and projecting the three-dimensional space coordinates into a visual coordinate system; pre-training a feature extractor, analyzing a data label by using ECMWF, and then performing end-to-end fine tuning; and automatically optimizing the detection threshold according to the signal-to-noise ratio, and outputting target meteorological classification, meteorological intensity and meteorological motion prediction. According to the method, the detection threshold is automatically optimized according to the signal-to-noise ratio, target classification, intensity estimation and motion vector prediction are output, weather weak signal leak detection is avoided, and the target tracking capability and the extreme weather generalization capability are improved.

The invention provides a fused salt heat storage coupled compressed air energy storage cooperative power generation system and a control method thereof.The fused salt heat storage coupled compressed air energy storage cooperative power generation system comprises a data acquisition module, a multi-target optimization decision algorithm based on dynamic programming, an energy distribution module, a dynamic adjustment module and a safety protection module; a multi-objective optimization decision algorithm based on dynamic programming automatically switches system working modes, an energy distribution module optimizes fused salt and air flow of a high-temperature fused salt air heat exchanger, and a dynamic adjusting module adjusts fused salt pump flow, compressor rotating speed and valve opening in real time. And the safety protection module is used for monitoring and preventing over-temperature, fused salt solidification and pipeline corrosion. The problems that in the prior art, a fused salt heat storage type photo-thermal power generation system is insufficient in heat storage capacity under the extreme weather condition, a compressed air energy storage system depends on fossil fuel, the afterburning efficiency is low, and the energy loss is caused due to the low system coupling degree can be effectively solved.

The invention relates to a temperature forecast correction method based on a PSD-Net and a PSD-Net, and belongs to the technical field of weather forecast, and the method comprises the steps: obtaining a dynamic meteorological variable based on the PSD-Net, and loading topographic data and a report starting time observation truth value at the same time; preprocessing the dynamic meteorological variable, the topographic data and the report starting time observation truth value; inputting the preprocessed data into a temperature forecast correction model to obtain temperature correction field data; wherein the temperature forecast correction model is obtained by training a PSD-Net model based on a training data set, and the training data set comprises historical dynamic meteorological variables, topographic data and a report starting time observation true value. Compared with the traditional numerical mode and the original output of the archaeological model, the temperature forecast MAE corrected by the method is reduced by more than 37.6%, the accuracy rate within 2 DEG C is improved by 14%-19%, and the precision advantage is more prominent especially in complex terrain areas and extreme weather events.