139 results about "Numerical weather prediction" patented technology

The invention discloses a photovoltaic power generation power prediction method and system based on a large language model. The method comprises the following steps: converting historical power data and numerical weather forecast data into time sequence embedded representation; through cross-modal semantic alignment, semantic embedding representation is generated; constructing a natural language prompt containing task context information, encoding the natural language prompt into prompt embedding, combining prompt embedding with semantic embedding representation to form a fusion input sequence, inputting the fusion input sequence into a pre-trained large language model, and outputting implicit features; synchronously generating an initial power prediction result and a weather prediction result obtained by correcting the numerical weather prediction data through a parallel collaborative prediction mechanism; and taking the meteorological prediction result as a correction signal, performing joint optimization on the preliminary power prediction result, and outputting a power generation power prediction value. According to the method, the problem of deep fusion of heterogeneous data is effectively solved, and the prediction accuracy is improved.

The invention relates to a photovoltaic power grid energy storage optimization regulation and control method based on multi-scale prediction. The method comprises the following steps: A1, obtaining historical power generation data, real-time meteorological data and numerical weather forecast of a photovoltaic power station; a2, generating a multi-time-scale photovoltaic output prediction sequence; a3, establishing an energy storage dynamic model of charge and discharge efficiency, capacity attenuation and operation constraint; a4, generating an energy storage charging and discharging demand curve under different time scales; a5, constructing a multi-time scale coupled optimization model by taking power grid operation cost minimization and renewable energy consumption maximization as targets; a6, updating an energy storage scheduling instruction in a rolling manner based on latest prediction data by adopting a model prediction control framework; a7, monitoring the deviation between the actual photovoltaic output and the power grid load, and dynamically adjusting the energy storage charging and discharging power; and A8, correcting a prediction error through Kalman filtering and a closed-loop feedback mechanism. According to the invention, high-efficiency operation can be realized, and power grid cost minimization and renewable energy consumption maximization can be realized.

The invention discloses a high-precision distributed photovoltaic output prediction method and system based on micrometeorology and feature hierarchical clustering, and relates to the technical field of distributed photovoltaic output prediction.The method comprises the steps that numerical weather forecast data are collected, an initial micrometeorological field is generated through space-time alignment and self-adaptive KNN interpolation, and the initial micrometeorological field is subjected to feature clustering; a WRF-LES system and a bidirectional LSTM are combined to establish cross-scale mapping, a dynamic residual correction field is fused to generate hectometer-level high-resolution micrometeorological data, and the problem of insufficient resolution of traditional numerical forecasting is solved. MIC and PA-DTW are used for jointly analyzing the characteristics of the power station, and dynamic clustering is achieved through a sliding time window and incremental spectral clustering. According to the method, a physical information graph network and causal expansion convolution are coupled to extract features, federal learning cross-power-station cooperative training is combined, the distributed photovoltaic output prediction precision and robustness are improved, and privacy security is considered.

The invention discloses a drainage basin intelligent flood control scheduling method and system based on digital twinborn, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting static data and dynamic data of a drainage basin, building a hydrological and hydrodynamic coupling model based on the static data and the dynamic data, and forming a drainage basin digital twinborn body; inputting the received numerical weather forecast into the digital twin of the watershed for simulation, generating a plurality of flood routing scenes in a future time period, and calculating a dynamic flood risk probability graph; the method comprises the following steps: constructing a simulation training environment by using historical flood data and a high-precision drainage basin digital twinborn body, carrying out offline training on a scheduling strategy network in the simulation training environment based on a reinforcement learning algorithm, and outputting a scheduling instruction according to a real-time drainage basin state to complete training of the scheduling strategy network. According to the method, the core problem that the traditional method is insufficient in decision timeliness and weak in adaptive capacity in an uncertain environment is effectively solved.

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 RIME-RF spatial downscaling-based photovoltaic output prediction method comprises the steps of collecting photovoltaic power data and local meteorological observation LMD data of a photovoltaic power station in a target area, extracting common data of numerical weather forecast NWP data and the local meteorological observation LMD data, and constructing an input feature set; the method comprises the following steps: optimizing hyper-parameters of a random forest (RF) algorithm based on a frost ice optimization (RIME) algorithm, constructing an RIME-RF model, and performing spatial downscaling on numerical weather forecast NWP data; a VMD-CNN-GRU-SE attention mechanism photovoltaic power prediction model optimized based on BKA is adopted, original numerical weather forecast NWP data is combined with photovoltaic power data to train the prediction model, and numerical weather forecast NWP data after spatial downscaling is combined with the photovoltaic power data to train the prediction model. According to the prediction method, changes of fine meteorological factors influencing the photovoltaic power can be more accurately captured, downscaling errors are remarkably reduced, and short-term power prediction precision is 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 discloses a numerical weather forecast deviation correction method and system based on physical conservation optimization. The method comprises the following steps: acquiring a specified prognostic variable in a numerical weather forecast system; inputting the prognostic variable into a prediction deviation correction model pre-trained by a loss function based on physical conservation optimization so as to obtain a deviation correction prediction result of numerical weather prediction; in the loss function of the physical conservation optimization, the physical conservation optimization refers to adding part or all of Coriolis force and barometric gradient force loss, static equation constraint derivation loss and vertical integral loss to the loss function of the value weather forecast deviation correction model. The invention aims to solve the problem of systematic deviation correction in mid-term numerical forecasting, better capture the complex dependency relationship in meteorological data, quantify the uncertainty of numerical weather forecasting and improve the forecasting precision.

The invention discloses a wind power prediction error decoupling evaluation method considering unit faults and real-time capacity changes. The method comprises the following steps: combining an actual wind power plant prediction process, and dividing wind power prediction into three links of numerical weather prediction, wind-electricity model conversion and power correction; for a power correction link, a unit fault prediction model is constructed, capacity reduction caused by faults is pre-judged in advance, a health index and a capacity attenuation coefficient are combined, a fault unit is removed in real time, the available capacity of the unit is dynamically corrected, and the equivalent actual capacity is calculated according to the health index in a weighted mode; finally, the equivalent actual capacity of the remaining unit is introduced into an error decoupling model, and quantitative evaluation is conducted on prediction errors caused by all links. And determining the proportion of the prediction error caused by each link, and determining the wind power error source after considering the unit fault and the capacity attenuation. The method can realize accurate decoupling of the wind power prediction error, and is suitable for wind power plant power prediction scenes with frequent meteorological sudden change and equipment aging.

The invention relates to the technical field of numerical weather forecast, in particular to a meteorological large model prediction method based on a data correction model, which comprises the following steps of: firstly acquiring multi-source atmospheric observation, screening observation by topology-optimal transmission quantum annealing, and constructing a weighted error covariance; applying mass, energy and earth rotation gradient, and generating a conservation assimilation field through diffusion implicit sampling; calculating a mutual information mask and coupling a cloud top optical flow fine tuning phase; cloud motion consistent field pulse codes are sent to the symplectic decomposition pulse neural network for neural form hardware reasoning, a pulse threshold is adjusted in a closed loop to control energy drift, and an uncertainty field is output through parallel disturbance reasoning. The method has the advantages of high resolution, low power consumption and probability prediction capability, and the extreme weather path and intensity prediction precision is obviously 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 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 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 discloses a wind power prediction method based on a long short-term memory network, and the method comprises the following steps: 1, obtaining a multi-dimensional data set of a target wind power plant in real time through a data collection system, and the multi-dimensional data set at least comprises historical power data, numerical weather forecast data, fan operation state parameters and geographic information data; according to the invention, through a multi-dimensional feature fusion mechanism and a dynamic weight optimization strategy, the prediction precision is significantly improved; the method comprises the following steps: firstly, fusing weather forecast, a fan operation state and topographic feature data, constructing feature data with physical significance, and enhancing the characterization capability of a model to a complex power grid engineering environment; secondly, a self-adaptive feature selection module is introduced to dynamically optimize the input weight, and redundant data interference is reduced; and finally, an attention mechanism is adopted to enhance feature extraction of key time nodes, so that the fluctuation ratio of a prediction curve is reduced.

The invention discloses a day-ahead solar irradiance data correction method, and the method comprises the steps: screening historical numerical weather forecast data of the same weather type based on the weather type of day-ahead weather forecast; calculating a Spearman correlation coefficient and a variance of daily solar irradiance, dividing historical days into different clustering groups through a Kmeans clustering algorithm, and obtaining a clustering center; determining a cluster group to which the day-ahead numerical forecast belongs, and selecting historical data with matched weather types and groups to construct a training set; training a COSTLSTM model capable of decomposing an input sequence trend and a seasonal component to obtain a correction model; and inputting the numerical weather data of the day-ahead weather forecast into the trained solar irradiance correction model, and outputting to obtain the corrected day-ahead solar irradiance. Compared with the prior art, the method can effectively cope with complex mixed modes and seasonal periodic differences in the solar irradiance, and realizes accurate data correction of the day-ahead solar irradiance in complex weather.

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.

A weather prediction method includes generating radiance differences as a difference between measured radiances from satellites and forecast satellite radiances generated by a radiative transfer model and forecasted state profiles output by a numerical weather prediction (NWP) model. When the radiance differences exceed a noise threshold, the method includes generating updated state profiles by generating radiance-sensitivities using a Jacobian model and the forecasted state profiles; constructing a Kalman-gain matrix from background error covariance (BEC) matrices and the radiance-sensitivities; generating filtered state-profile changes from the Kalman-gain matrix and the radiance differences; updating the state profiles by adding the filtered state-profile changes to the forecasted state profiles to yield updated state profiles.

The present application relates to the field of photovoltaic power prediction, and particularly relates to a photovoltaic power prediction method, which obtains spectral response function data of different photovoltaic components in a photovoltaic power station, and measures spectral irradiance distribution of surfaces of each photovoltaic component in real time; according to the spectral response function data and the spectral irradiance distribution, effective spectral irradiance of each component type is calculated according to component type grouping; temperature coefficients of batches to which each photovoltaic component belongs are counted according to historical data, and a component-level temperature-power correction factor is established; string-level temperature correction power is obtained according to string topology relationship; the string-level temperature correction power, effective spectral irradiance and global irradiance data of numerical weather prediction are used to output a corrected power station-level photovoltaic power prediction value; the problem of low photovoltaic power prediction accuracy caused by inaccurate modeling due to photovoltaic component parameter difference and temperature influence in photovoltaic power station power prediction is solved.

This invention discloses a short-term intelligent numerical weather prediction method and system based on environmental perception, belonging to the field of meteorological management technology. The system includes a data acquisition and fusion module, an intelligent zoning and feature mining module, a short-term forecasting and risk assessment module, and a visualization and focusing module. The data acquisition and fusion module collects and fuses environmental data from meteorological radar and weather stations, as well as GIS maps. The intelligent zoning and feature mining module divides the data into grids based on the GIS map, analyzes the rainfall synergy between grids based on historical environmental data to construct analysis groups, and selects and constructs an impact set for each analysis group. The short-term forecasting and risk assessment module fits near-term precipitation intensity relationships and short-term precipitation intensity relationships based on the impact sets of each analysis group. The visualization and focusing module calculates and outputs the near-term and short-term predicted precipitation intensities for each analysis group based on the relationships, identifies key areas of concern using topographic and hydrological models, and enhances visualization.

The application provides an artificial intelligence driven typhoon path dynamic tracking method and system, the system comprises a running environment, a data control program and a typhoon positioning program, the data control program is responsible for managing data flow input and output, including initial coordinate processing, meteorological data preprocessing and data post-processing process, the path tracking coordinates are updated through cyclic iteration, and finally complete typhoon tracking data is output, and the typhoon positioning program adopts a three-layer progressive processing architecture, including dynamic window tracking, multi-scale feature fusion network reasoning and path coordinate generation.The application has the beneficial effects that based on the deep learning multi-scale feature fusion network, the typhoon path can be tracked accurately and quickly by using numerical weather prediction data, the running environment is compatible with the running of GPU and CPU environments, and the running efficiency is much higher than that of traditional positioning algorithms.

The invention relates to the technical field of water conservancy projects and emergency management, and discloses a cascade reservoir group flood emergency scheduling method and system fused with dynamic risk assessment, and the method comprises the steps: a dynamic risk assessment stage, employing an ensemble forecast product of numerical weather forecast to drive a hydrological model to generate a flood set, calculating the instantaneous dam overtopping risk probability of each reservoir, and calculating the flood overtopping risk probability of each reservoir; a risk chain type propagation network model is constructed to evaluate the overall accident probability of the system; and the intelligent decision support stage is triggered when the overall failure probability of the system exceeds a risk threshold value, similar historical cases are retrieved by adopting a graph neural network based on the water conservancy field knowledge graph, and an optimal disposal scheme is generated and recommended through a multi-attribute utility evaluation model. According to the method, dynamic quantification of risks, accurate identification of systematic risks and intelligence of emergency decision making are achieved, the problems that traditional risk assessment is static and isolated, and emergency decision making depends on experience are solved, and the flood control emergency response capacity of the cascade reservoir group is improved.

The present disclosure discloses a four-dimensional assimilation method for wind farm data based on physical constraints and generative AI, belonging to the technical field of numerical weather prediction and wind energy prediction. The method includes: collecting various types of wind-detecting observation data from a wind farm and performing quality control; performing multi-scale preprocessing and uncertainty encoding on raw data of background field; encoding the wind-detecting observation data into conditional features; using the raw data of background field and the conditional features as joint conditional inputs to drive a generative model, and performing a reverse denoising sampling to generate a preliminary 3D analysis field; applying soft constraints and a projection correction based on physical constraints such as MO similarity theory and wake model; and performing multi-sample generation and statistical integration to output a mean and a uncertainty of analysis field and products for key height layers of wind power.

The invention relates to the technical field of new energy prediction, and particularly provides a photovoltaic abnormal output prediction method and device based on an atmospheric circulation field, and the method comprises the steps: calculating the difference between potential height grid prediction data of a preset height layer of a preset date in numerical weather prediction and climate state data of the preset height layer corresponding to the preset date, obtaining a forecast distance flat field of a preset height layer on a preset date; and predicting the abnormality of the photovoltaic output of the preset date based on the similarity between the forecast distance flat field of the preset height layer of the preset date and each element in a pre-constructed circulation feature set. According to the technical scheme provided by the invention, the application level of atmospheric circulation characteristics is enhanced in photovoltaic output prediction, so that the prediction capability of photovoltaic output, especially abnormal output, is improved.

The invention relates to the technical field of numerical weather forecasting, in particular to a hierarchical adaptive Gaussian conversion method and device for cloud and rain control variables, which can obtain observation data and input the observation data into a numerical forecasting model to obtain three-dimensional cloud and rain control variables; constructing a variational assimilation model comprising a forward transformation operator, an inverse transformation operator, a tangent linear operator and an adjoint operator; the forward conversion operator can calculate and adjust the conversion intensity layer by layer, so that finer and more accurate Gaussian processing is realized; the tangent linear operator and the adjoint operator are used for assimilating minimization iterative calculation in the model, and then the model is restored to the original physical magnitude through the inverse transformation operator to output a three-dimensional cloud and rain control variable analysis field according with reality. According to the technical scheme, the vertical distribution difference of cloud and rain variables can be accurately adapted, and the compatibility with an existing assimilation system is ensured by providing a complete operator chain, so that the quality of an analysis field and the forecasting capability are improved.

The invention discloses a wind power short-term prediction method and system based on multi-source numerical weather forecast, and the method comprises the steps: obtaining an original wind speed according to the multi-source numerical weather forecast; based on the threshold value of the power derivative, performing dynamic wind speed stage division on the original wind speed, synchronously dividing corresponding original power, and obtaining multi-stage wind speed data and multi-stage power data; constructing a first dual-attention prediction architecture and a second dual-attention prediction architecture with the same network architecture; the multi-stage wind speed data is used as training data of the first double attention prediction architecture; the multi-stage power data is used as training data of the second dual attention prediction architecture; and applying the trained first double attention prediction architecture and second double attention prediction architecture to real-time wind power short-term prediction. According to the method, the problem of insufficient adaptability of the wind speed stage is solved through division of the wind speed states according to different prediction expressions of different numerical weather forecasts in the wind starting stage, the wind reducing stage and the stable stage.

The application discloses a power meteorological data fusion method and system based on multi-source meteorological prediction and a storage medium, and combines power meteorological monitoring and multi-source numerical weather prediction data. Through error analysis on the multi-source numerical weather prediction data, the numerical weather prediction is changed from single deterministic prediction to multi-source fusion prediction, which is beneficial to the real application of meteorological information to actual business work of the power grid. The application can provide precision evaluation of different prediction data sources, provide multi-source prediction fusion data, improve prediction precision, and provide technical support for power meteorological fine prediction.

The application discloses a numerical weather prediction optimization method and system based on meteorological resource coupling, belongs to the technical field of numerical weather prediction, focuses on the multi-source data in the target area, and constructs a double-dimension correlation between the power grid and the meteorology, integrates the meteorological resources, and is targeted to gather the power grid output, so that the subsequent numerical weather prediction is more in line with the power prediction requirements of the power grid, and through the correlation matrix and the comprehensive influence coefficient, the qualitative coupling relationship is converted into quantitative indexes, linear and nonlinear are considered, and therefore the prediction accuracy under complex weather and complex terrain is greatly improved, high-precision meteorological driving data is provided for photovoltaic short-term power prediction, and the problem that the existing numerical weather prediction is mostly based on single dimension for prediction, so that it is difficult to guarantee the numerical accuracy when facing environmental changes is solved.

The invention relates to the technical field of cross-platform software transplantation, in particular to a migration adaptation method for regional numerical weather forecast software. According to the method, through the software adaptation step, the problem of compatibility of an instruction set and a system environment when existing regional numerical weather forecast software migrates from an X86 architecture to an ARM architecture is solved, and native operation of the regional numerical weather forecast software on a domestic platform is achieved; through a containerization packaging step, software and a complete dependency environment thereof are packaged into a standard mirror image, so that one-time construction and deployment at any place are realized, and the problem of deployment complexity in different operating system environments is thoroughly solved; through the verification step, containerized software is operated, strict cross-platform comparison testing is carried out, prediction precision is quantitatively evaluated, service functions are ensured to be complete, results are reliable, and key technical support is provided for autonomous controllable and safe sustainable development of a meteorological core service system.