458 results about "Weather prediction" patented technology

The invention relates to the technical field of photovoltaic prediction, in particular to a distributed photovoltaic power prediction method and system based on high-dimensional gridding numerical weather forecast, and the method comprises the steps: carrying out the standardization of the numerical weather forecast data and photovoltaic power historical data of a target region, and achieving the time-space alignment based on a preset grid, generating a gridding data set; utilizing convolution processing to extract local space features, and converting and fusing the local space features into a feature sequence containing space and historical time sequence information at the same time; modeling is carried out through an encoder-decoder architecture, an encoder excavates historical power dependence, and a decoder dynamically couples future meteorological characteristics with historical power through an attention mechanism and outputs a grid-level predicted value; aggregating to obtain a system total power prediction result; by establishing a unified space-time grid, refined alignment of data is realized, cross-space-time dynamic fusion is performed in combination with convolution and an attention mechanism, and prediction precision and stability can be kept in complex weather.

The invention provides a coordination control method and device of a photovoltaic energy storage inverter for automobile charging based on artificial intelligence, and belongs to the technical field of automobile photovoltaic energy storage coordination control, and the method comprises the steps: monitoring the output power of a photovoltaic array, the state of charge (SOC) of an energy storage system, the state of a power grid and the power demand of a load in real time; a reinforcement learning algorithm is introduced, weather forecast and historical load data are combined, photovoltaic output and load demands in a future time period are predicted, and a power distribution instruction is generated; deciding a current operation mode and generating a mode switching instruction; seamless switching between grid connection and grid disconnection is controlled, and after switching is completed, the charging and discharging proportion of the lithium battery and the super capacitor is coordinated; integrating the energy storage real-time state and the power distribution instruction, and optimizing and adjusting the control parameters of the inverter in real time; continuously monitoring a running state and a switching process, and starting a standby mode when a fault is detected; and the data is uploaded to a monitoring platform. The system operation is efficiently optimized, the service life is prolonged, and stability is guaranteed.

The invention relates to the technical field of artificial intelligence and electric power system prediction, and discloses a wind power short-term output prediction method based on multi-modal data, and the method comprises the steps: obtaining the multi-modal data, such as historical output, numerical weather forecast, actually measured weather of an anemometer tower, landform and fan operation state; performing sliding window segmentation on the output sequence and identifying a mutation interval; calculating a local optimal alignment path of each mode in the mutation interval based on a dynamic time warping algorithm; non-uniform resampling is carried out in this way, and a time-synchronized multi-modal alignment feature sequence is generated; and inputting a hybrid neural network formed by a gating circulation unit and an attention mechanism, and outputting a high-precision output prediction value in the next 15 minutes. The system comprises corresponding function modules. According to the method, through dynamic time alignment and cross-modal feature fusion, the wind power short-term prediction precision is remarkably improved, the root-mean-square error in a sudden change scene is reduced by 23.7%, and reliable support is provided for power grid dispatching.

The invention relates to a meteorological deduction method and device fusing physical constraints and a neural network, and the method comprises the steps: obtaining multi-source meteorological data, and constructing a spatial-temporal feature input tensor; the spatio-temporal feature input tensor is subjected to standardization processing and then input into a deep learning network model, and a future weather prediction result is obtained; the model extracts time sequence evolution features and space attention features through a neural network module and a space attention module respectively, and integrates the time sequence evolution features and the space attention features in a splicing form; for a forecast task of a future gamma day, a deep learning network model and a physical mode are adopted for prediction respectively, and a splicing time point is determined according to an error minimum principle, so that splicing of prediction results is carried out; when the physical mode is used for prediction, the improved regional numerical weather prediction model is used as a basis, atmospheric basic equation sets are integrated, and weather prediction at future moments is carried out. Compared with the prior art, the method has the advantages that the atmospheric physical law and data driving advantages are fused, and the extreme weather prediction precision and stability are improved.

The invention provides a wind field inversion method and device based on multi-source prior data, equipment and a medium, and the method comprises the steps: building a target function of a three-dimensional fusion wind field based on the multi-source prior data and a fluid mechanics model simulation wind field, carrying out the iterative optimization of the target function, and determining the three-dimensional fusion wind field; constructing an initial deep learning neural network model, taking the three-dimensional fusion wind field as a truth value label, inputting the urban underlying surface features, the terrain elevation and the numerical weather forecast wind field into the deep learning neural network model, and training to obtain a target deep learning neural network model; and inputting the new numerical weather forecast wind field, the urban underlying surface features and the terrain elevation into the target deep learning neural network model, and outputting a refined wind field. According to the technical scheme provided by the embodiment of the invention, through the trained deep learning neural network model, high-precision rapid inversion of the low-altitude wind field is realized without depending on laser radar and ground observation data and depending on prior information such as numerical prediction and terrain.

The invention relates to the technical field of power systems and automation thereof, in particular to a generative adversarial network-based source-load joint scene generation method, which comprises the following steps of: constructing a multi-source time sequence database and extracting weather, time, space and historical state driving factors; establishing a joint probability distribution model based on a vine connection function; taking a numerical weather forecast path and a date type as conditional input, constructing a generative adversarial network embedded with a physical constraint microloss function of the power system, and forming a physical information generator; performing dependent structure fidelity verification on the generated scene by using the joint probability distribution model; generator parameters are fixed, potential space vectors are optimized through a gradient ascending method to maximize power grid risk indexes, and a high-risk source-load joint scene set is generated. According to the technical scheme, accurate generation of the source-load joint scene which is physically feasible and reasonable in statistics and focuses on the high-risk working condition is realized, and the safe operation toughness and the risk early warning capability of the novel power system are remarkably improved.

The invention provides a micrometeorological prediction method and system, and belongs to the technical field of meteorological prediction. The method comprises the following steps: acquiring data of an unmanned aerial vehicle sensor, a ground meteorological station, satellite remote sensing and numerical weather forecast, and respectively constructing feature vectors of data sources; performing weighted average fusion on the multi-source feature vectors based on an attention mechanism at a target space-time position to obtain a fusion feature matrix; and training a target neural network by using the fusion feature matrix of the plurality of space-time positions, and taking the trained target neural network as a micro-meteorological prediction model to realize high-precision prediction of future micro-meteorological elements. According to the method, multi-source heterogeneous data and a deep learning technology are fused, the temporal-spatial resolution and accuracy of micrometeorological prediction are effectively improved, and the method is particularly suitable for unmanned aerial vehicle flight safety early warning and route dynamic optimization.

The invention discloses an active fluctuation collaborative stabilizing method and system for a high-proportion distributed new energy power grid, and relates to the technical field of power grid dispatching. According to the method, a physically consistent weather prediction model is established through multi-source meteorological data fusion, and a regional fluctuation propagation rule is accurately captured; identifying a high-risk fluctuation cluster based on dynamic time warping and spectral clustering, simulating a fluctuation propagation path by using a digital twin platform, and quantifying resource requirements; energy storage resource configuration is optimized by adopting mixed integer programming and a column generation algorithm, and multi-dimensional stability verification is carried out through a digital twin environment; a self-adaptive optimization mechanism based on reinforcement learning is established, continuous evolution of the system is realized, the technical bottlenecks of a traditional method in the aspects of fluctuation perception, resource allocation, system self-adaption and the like are solved, a collaborative stabilization mechanism with accurate prediction, intelligent recognition and decision optimization is formed, and a complete solution is provided for safe and stable operation of a high-proportion new energy power grid.

The invention discloses a transformer area distribution transformer reverse heavy overload prediction method and system, and the method comprises the steps: obtaining data which comprises the historical electrical quantity characteristics of a photovoltaic user, the numerical weather forecast characteristics of the geographic position of a transformer area, and a prior graph structure formed according to the topological graph of the transformer area; preprocessing the data to construct a data set; the method comprises the steps of establishing a space-time diagram prediction model, inputting historical electrical quantity characteristics and numerical weather forecast characteristics of N photovoltaic users and a prior diagram structure into the model to obtain a future K-step area distribution transformer load prediction result, performing heavy overload judgment based on a prediction value, and finally obtaining a future K-step area distribution transformer reverse heavy overload prediction result. According to the method, the topological structure of the power distribution network, the electrical characteristic data and the numerical weather forecast characteristics are comprehensively utilized, high-precision prediction of the multi-time-step power of the photovoltaic grid-connected system is achieved through the space-time diagram neural network model, and the method is suitable for application scenes such as operation optimization and early warning management of the power distribution network with distributed photovoltaic access.

The invention belongs to the technical field of power generation power prediction, and particularly relates to a photovoltaic power prediction method based on multi-source data fusion and deep learning, and the method comprises the steps: firstly obtaining the historical power, satellite irradiance and numerical weather forecast data of a photovoltaic power station; performing preprocessing such as space-time alignment, missing value processing and abnormal value elimination, and constructing periodic time features; training a deep learning model taking a long short-term memory network as a core by using the processed data so as to capture a complex nonlinear time sequence relationship between the weather and the power; inputting the satellite and forecast data in a to-be-predicted time period into the model, and outputting a future multi-step power predicted value; and finally, carrying out online deviation correction and physical amplitude limiting post-processing to obtain a final prediction result. By effectively fusing multi-source data and deep learning, the precision and practicability of short-term photovoltaic power prediction are remarkably improved.

The invention provides a wind power prediction method and system based on a multivariate combination prediction model and a medium, and relates to the technical field of machine learning, and the method comprises the steps: obtaining a historical wind power data time sequence and a historical numerical weather forecast data time sequence of a wind power plant; analyzing a nonlinear dependency relationship by using correlation, and optimally training a multi-modal parallel time sequence prediction model by using a variational mode decomposition algorithm in combination with a sliding window length; and based on the final sliding window length, constructing an input sample, inputting the input sample into a multi-mode parallel time sequence prediction model, outputting future prediction values of a plurality of intrinsic mode function components, and carrying out summation to obtain a wind power prediction result. According to the method and the device, the technical problem of insufficient wind power prediction precision caused by strong volatility and nonlinearity of a wind power sequence and limited learning ability of a single prediction model in the prior art can be solved, and the wind power prediction precision is improved by combining variational mode decomposition with a machine learning model for prediction.

The invention discloses an end-to-end scheduling method and system for a multi-source weather-driven urban integrated energy system. The method comprises the following steps: constructing an integrated energy system model; constructing a source load prediction model based on multi-source numerical weather forecast data in combination with historical photovoltaic output data and power load and thermal load data; the method comprises the following steps: establishing a comprehensive energy system optimization scheduling model with minimization of system operation cost as an optimization target, designing a differentiable optimization layer, and reversely transmitting the gradient of the optimization target in the scheduling model to a source load prediction model parameter to the source load prediction model through a back propagation algorithm by the differentiable optimization layer, the parameters of the driving source load prediction model are updated, and end-to-end linkage optimization from prediction to scheduling is achieved; and periodically obtaining updated multi-source numerical weather forecast data and source load data, readjusting prediction model parameters, and executing optimization solution of the integrated energy system optimization scheduling model. According to the invention, cooperative training and iterative optimization of the prediction model and the scheduling decision are realized.

The present invention discloses a system and method for optimizing energy distribution in renewable energy plants. The system (100) comprises a solar power plant or a wind power plant to generate electrical energy. A renewable energy controller (108) monitors real-time energy output and manages energy flow to a grid (112). The renewable energy controller (108) optimizes charging cycles based on forecasts. The renewable energy controller (108) generates dispatch schedules through an intelligent bidding unit (114), considering market prices and grid conditions, while ensuring grid code compliance. A battery digital twin unit (122) is operatively connected to the renewable energy controller (108), to simulate future states of the plant based on historical data, real-time operational data, and weather forecasts, providing inputs for the intelligent bidding unit (114) to optimize energy dispatch and storage.

The invention relates to the technical field of building energy consumption management, in particular to a building heating ventilation air conditioner intelligent control method for energy efficiency optimization, and the method comprises the steps: dividing a building into a plurality of independent temperature control regions, and deploying multifunctional sensor nodes; acquiring real-time environment data, personnel data and weather forecast data; the ultra-short-term load prediction model is maintained to predict the cold / heat load demand of the area in the future 15 minutes, and a cold / heat load demand prediction value is obtained; reporting the cold / heat load demand prediction value to a central processing unit in a standardized data format; the central processing unit optimizes the energy distribution of the whole building through a multi-objective optimization algorithm based on the prediction requirements of all the regions, and obtains a resource scheduling list in the next 15-minute period; and according to the resource scheduling list, a regulation and control instruction is issued to equipment in each independent temperature control area. Therefore, the problems of response lag, extensive control and the like in the prior art are solved.

The invention discloses a rainfall downscaling method and system based on a deep learning network model fusing rainfall priori knowledge, and the method comprises the steps: firstly collecting the topographic data and low-resolution day-by-day rainfall data of a target region, and taking the data as input data; a short-term high-resolution precipitation field generated in a mesoscale weather forecast WRF mode is used as training truth value data; according to the method, the function of accurately downscaling the rainfall data in combination with the convolutional neural network and the long and short term memory network is realized, the spatial-temporal correlation of rainfall is fully considered in the downscaling process, and meanwhile, a likelihood function combined with coupled censored data, Box-Cox conversion and time variation variance Gaussian distribution is adopted as a rainfall loss function; the method not only can represent zero expansibility, skewness and heterovariance characteristics of rainfall, but also can improve the rainfall downscaling precision and quantify the uncertainty of rainfall downscaling, and is suitable for wide popularization and use.

The invention provides a hectometer-level short temporary rainfall forecasting method based on generative adversarial network downscaling and physical constraint, which belongs to the technical field of weather forecast, and is characterized in that S-band radar jigsaw and high-resolution X-band radar observation data are fused through an adaptive rule to generate a fused radar echo combined reflectivity, and the combined reflectivity is used for forecasting the rainfall of the hectometer-level short temporary rainfall. A super-resolution downscaling model is constructed by using a generative adversarial network, a long-sequence high-resolution radar echo training data set is reconstructed, a deep learning forecasting model is constructed, a high-resolution radar echo sequence is used as input, space-time modeling training is performed by using a composite loss function, and a future radar echo forecasting field is output. The Z-R relation is converted into quantitative rainfall forecast, and finally the quantitative rainfall forecast with the spatial resolution reaching the hectometer level is generated. The interpretability of the model is enhanced through physical constraint, the bottleneck of insufficient hectometer-level resolution training data is effectively solved, and the spatial refinement degree of short temporary rainfall forecasting and the forecasting capacity for a severe convection system are remarkably improved.

The invention discloses an interval generation and probability correction method and system for new energy power prediction, and belongs to the technical field of power system operation and control, and the method comprises the steps: generating a multi-dimensional environment feature code through numerical weather forecast data, and calling a basic point prediction model to obtain a point prediction result and basic probability distribution; inputting the multi-dimensional environment feature code and the point prediction result into a dual-channel dynamic interval generator to form a preliminary prediction interval, calculating a short-term error sequence based on the actual power of new energy power generation and historical prediction data, analyzing the trend characteristics of the short-term error sequence, and correcting the basic probability distribution according to the trend characteristics; and extracting a probability verification signal from the corrected probability distribution, feeding back the probability verification signal to an interval generator, carrying out optimization adjustment on the preliminary prediction interval, and outputting an optimized prediction interval. According to the technical scheme, the preliminary interval is generated by adopting a dual-channel mechanism, and feedback correction and closed-loop optimization are performed in combination with the short-term error trend, so that the adaptive capacity, accuracy and reliability of the prediction interval can be improved.

The invention relates to a wind power prediction method based on cross-modal space-time attention fusion. Accurate wind power prediction in different weather scenes is realized. The method comprises the following steps: preprocessing wind power numerical weather forecast data to obtain preprocessed data; performing feature extraction on the preprocessed data by adopting a Transform model to obtain a time feature; performing feature extraction on the preprocessed data by adopting a GAT model to obtain spatial features; performing dual-scale feature fusion on the time feature and the spatial feature by adopting a preset neural network to obtain a fused feature; and carrying out standardization processing on the fusion features to obtain standard wind power data.

The invention discloses a laser wind-finding radar and a data processing and assimilation method thereof. The method comprises the following steps: acquiring observation data of the laser wind-finding radar, atmospheric stability parameters and topographic relief feature data; a three-dimensional space weight distribution function is constructed based on the parameters, and initial influence weights of the observation points on surrounding grids are represented; dynamically adjusting the horizontal influence range and the vertical attenuation characteristic of the function according to the geomorphic stratification degree and the atmospheric vertical stratification change information, and generating dynamic weight distribution matched with the local atmospheric physical and topographic characteristics; the distribution serves as a space adjustment factor of an observation error covariance matrix and is introduced into a cost function, and the wind field analysis field and the dynamic weight distribution are synchronously optimized by minimizing the cost function; and outputting the optimized wind field analysis field for numerical weather forecast. According to the method, accurate and efficient fusion of observation information in three-dimensional variational assimilation is realized, and the authenticity and forecast reliability of wind field analysis under complex conditions are remarkably improved.

The invention discloses a convective gale recognition and analysis method based on machine learning, and belongs to the technical field of weather forecast and artificial intelligence technology crossing, and the method comprises the following steps: multi-source data acquisition and feature fusion: obtaining multi-source meteorological data of a target region in a preset time window, the multi-source meteorological data at least comprises numerical mode forecast data, satellite remote sensing data and ground observation data. According to the invention, by introducing the space-time deep learning model and combining the three-dimensional convolution and recurrent neural network, the evolution rule of the weather system in the space-time dimension is effectively captured, and the perspectiveness and continuity of convective gale recognition are improved. According to the invention, by integrating an interpretability technology and a credibility evaluation mechanism, the transparency and reliability of the model in business application are improved. According to the method, through a self-supervised pre-training and lightweight fusion strategy, the adaptability and the calculation efficiency of the model are improved while the performance is ensured.

The invention discloses an emergency method for extreme weather prediction of a power system. The method comprises the following steps: comprehensively analyzing data from a meteorological satellite, a ground meteorological station and a radar detection system by using a multi-source data fusion algorithm; developing a precise weather prediction model by integrating numerical weather prediction, machine learning optimization and data visualization technologies; carrying out vulnerability analysis on the key components of the power system to establish a risk assessment model, and assessing the operation risk of the power system under the extreme weather event; based on a risk assessment result, a real-time monitoring system and an automatic early warning mechanism are established, and an early warning signal is sent to an electric power system operator in time; strategy measures are formulated and implemented according to different weather situations, so that reliable power supply of the key area is ensured; and according to geographical and climate conditions of different regions, configuration and function optimization are carried out on the power management software. Through accurate meteorological data processing and model prediction, the weather prediction accuracy is improved, and early warning information is sent out in time.

The invention discloses an unmanned aerial vehicle autonomous route planning method and system capable of adapting to environmental changes. The method comprises the steps of obtaining a dynamic three-dimensional situation map of a flight area of an unmanned aerial vehicle based on multi-source data of the flight area of the unmanned aerial vehicle; acquiring a current no-fly zone and weather forecast information of the flight area of the unmanned aerial vehicle, and analyzing and acquiring a flight risk distribution condition of the flight area of the unmanned aerial vehicle based on the dynamic three-dimensional situation map; considering the flight risk distribution condition of the unmanned aerial vehicle flight area, the unmanned aerial vehicle flight task and the unmanned aerial vehicle self-state information to generate an unmanned aerial vehicle reference route; and automatically adjusting the flight speed, the flight height and the flight course of the unmanned aerial vehicle according to a preset flight parameter adjustment model based on the acquired multi-source data and the state information of the unmanned aerial vehicle in the process of flying along the reference route. According to the invention, the reliability and safety of flight task execution of the unmanned aerial vehicle in a complex dynamic environment can be improved.

The invention relates to the technical field of flood control dispatching, and discloses a reservoir flood control water level dynamic control and flood recycling method and system, and the method comprises the steps: obtaining a surface rainfall forecast value of a reservoir basin based on real-time rainwater condition data and numerical weather forecast; a hydrodynamics and hydrology coupled drainage basin runoff model is driven, and an in-reservoir flood hydrograph in a predicted period is generated; judging the flood scale and grade based on the hydrograph, and when the flood is medium and small flood, calculating the dynamic flood control storage capacity of a downstream flood control object by adopting an equivalent flood control effect algorithm taking into account the pre-discharge scheduling capability; according to the dynamic flood control storage capacity, a dynamic flood control water level control value is obtained through backstepping of a water level-storage capacity relation curve of the reservoir; and finally, generating and executing a reservoir dispatching instruction based on the control value, and storing the water level of the reservoir to not exceed the dynamic value. The problems that a fixed flood control water level method is low in water resource utilization rate and inflexible in dispatching are solved, and flood resource efficient utilization on the premise of flood control safety is achieved.

The invention relates to an unmanned aerial vehicle track decision-making method based on turbulence prediction, and belongs to the technical field of unmanned aerial vehicle navigation and weather prediction. Aiming at the problems of difficulty in monitoring and predicting low-altitude turbulence and high air route planning risk, the defects of data heterogeneity, insufficient numerical weather forecast resolution and the like exist in the prior art; according to the method, a three-dimensional turbulence intensity field is generated through multi-source meteorological observation data fusion to serve as an observation benchmark, a diagnosis model is constructed in combination with numerical weather forecast data, linear calibration is carried out, or short-term prediction is generated by adopting an observation extrapolation model when forecast data is lacked; further, the turbulence field is mapped into a weighted graph, the height, the climbing rate and the airspace constraint are combined, the optimal track is solved by using a path search algorithm, and the accumulated turbulence cost is minimized; the method is clear in structure, full-process optimization from data fusion to decision making is achieved through multi-model complementation, and the method is suitable for the fields of low-altitude logistics, urban air travel and the like.

The invention provides a chemical adding control method and a chemical adding control system. The dosing control method comprises the following steps: collecting target measuring point data, and generating a working condition family label based on an hour period, a water quality grade and a load grade; respectively training each candidate model according to a candidate model library and historical data so as to obtain a prediction file of each candidate model, obtaining a champion model of each hour period according to the prediction file and the working condition family label, and generating a time period model mapping table; and determining the dosage based on the time period model mapping table, and issuing the dosage to medicament adding equipment. According to the method, the hour segments serve as working condition family labels, the errors of the multiple candidate models are evaluated hour by hour in the back-testing window, the champion model is selected, the optimal model can be adopted in a self-adaptive mode according to the differences of SCD1 / SCD2 distribution, water diversion / generated water instantaneous flow change and the like of different hour segments, the problem that errors of a single model are large in part of the hour segments is avoided, and the accuracy of the model is improved. Therefore, the all-weather prediction stability is improved.

The invention relates to the technical field of atmospheric science and numerical calculation, discloses an atmospheric numerical simulation method based on multiphase state water substance conservation constraint and application, and aims at evolution calculation of all phase state water substances under the conditions of discrete grids and discrete time steps for atmospheric numerical mode cloud microphysical parameterization. The method comprises the following steps: acquiring the density and velocity field of each component in a grid unit, and constructing a mass ratio variable normalized by wet air density; defining a growth rate per unit volume and per unit time, and representing a phase change source sink by using a component continuity equation; and establishing a coupling discrete updating rule according to the mass ratio accurate evolution equation, calculating a total water substance growth rate residual error and a ratio residual error corresponding to the equivalent conservation expression, and obtaining a corrected growth rate meeting conservation constraint through consistency correction so as to write back and update the mass ratio field. The method can inhibit the income and expenditure drift of water substances, reduces the phase distribution error under the condition of mixed phase cloud, and is suitable for numerical weather forecast and regional numerical simulation.

The invention relates to the field of weather prediction, and particularly discloses an intelligent hail monitoring and early warning method and system based on morphological learning, equipment and a medium, and the method mainly comprises the steps: obtaining the multi-source data of hail cloud, and carrying out the recognition and extraction of the key morphological features of the hail cloud based on the multi-source data; a space-time evolution model is constructed, the space-time evolution model comprises a ConvLSTM space-time prediction model and optical flow method motion estimation, and predicted hail cloud key morphological characteristics are output based on the space-time evolution model; and the multi-feature fusion module is configured to perform multi-feature fusion on the key morphological features of the hail cloud, the multi-feature fusion comprises weighted fusion and an attention mechanism, a loss function is set for optimization, and a hail prediction result is obtained. According to the method, the hail cloud form change under different hail disasters is learned through the form learning network according to the form change in the time sequence change process of the hail cloud, the hail is dynamically predicted in combination with the form change, and accurate and dynamic prediction of the hail is achieved.

The invention relates to the technical field of power system automation and digital twinning, in particular to a regional power grid static safety risk early warning system and method. Comprising a multi-source heterogeneous data intelligent fusion module, a high-fidelity static security analysis module, a multi-dimensional dynamic risk assessment module, a hierarchical linkage early warning decision module and a full-link result tracing and optimizing module. Uniform access of four kinds of heterogeneous data including SCADA, WAMS, equipment online monitoring and meteorological prediction is supported, a data island of a traditional system is broken through, timestamp alignment is achieved by adopting a dynamic time warping algorithm, spatial deviation is corrected based on GIS Kriging interpolation, the problem of analysis distortion caused by time / space deviation of the traditional system is solved, and the analysis accuracy of the system is improved. A sparse matrix compression technology and an MPI parallel computing framework are adopted, so that the efficiency is improved, the real-time analysis requirement is met, and an N-1 / N-2 fault scene is dynamically generated based on an equipment health index, including N-1 verification of health equipment and N-2 verification of sub-health equipment.

The invention discloses a multivariable time sequence prediction system and method, and belongs to the technical field of artificial intelligence and time sequence analysis. The system sequentially comprises a data preprocessing module, a local-global feature extraction module, a convolution enhancement Transform encoder module, a self-correction residual compensation module and a prediction output module. According to the corresponding method, an enhanced time sequence input matrix is constructed through data preprocessing, and short-term mutation and long-term trend features are fused through a local-global feature extraction module (multi-scale cavity convolution, bidirectional BiLSTM and gated attention). The method comprises the following steps: firstly, modeling global dependency and multivariable interaction by a convolution enhanced Transform encoder (causal convolution position coding and compressed multi-head self-attention), and finally, inhibiting multi-step prediction error accumulation by a self-correction residual compensation module, and outputting a prediction result which has the characteristics of low error, strong stability and fast response. The method can be applied to the fields of power dispatching, traffic control, weather prediction and the like.

The invention provides a route planning system capable of improving the operation efficiency of a power generation floating body. A route planning system plans the route of a power generation floating body that generates power while sailing on the sea. This course planning system is provided with a planning means for planning, as a course, a course in which a power generation floating body circulates between a meeting point between a transport ship for recovering power generation energy from the power generation floating body and the power generation floating body and a turning point different from the meeting point. The planning means changes, on the basis of at least one of the weather prediction accuracy and the sea image prediction accuracy, the number of turns in which the power generation floating body travels around the course until the power generation floating body meets the transport ship.