26 results about "Predictive capability" patented technology

The invention relates to the technical field of meteorological simulation, in particular to a dynamic three-dimensional grid meteorological simulation model based on historical observation data. Based on abundant historical observation data, the meteorological element change rule can be accurately reflected, the accuracy of meteorological simulation is greatly improved, and a reliable basis is provided for meteorological research. A dynamic three-dimensional grid result shows complex evolution of weather in a three-dimensional space in all directions, limitation of a traditional model is overcome, weather change details in the vertical direction and the horizontal direction are clearly shown, and deep understanding of a weather mechanism is facilitated. Physical driving and data driving models are fused, the advantages of the physical driving and data driving models are brought into full play, the simulation and prediction capability of various meteorological phenomena is enhanced, and particularly for complex and extreme meteorological phenomena, meteorological disaster early warning is better served. Through visualization and interaction functions, a vivid three-dimensional meteorological scene can be generated, a user can conveniently observe and analyze visually, and development and decision making of the meteorological field and related industries such as aerospace and agriculture are powerfully promoted.

The invention discloses a rainfall prediction method and system for the Beijing, Tianjin and Hebei region, and relates to the technical field of rainfall prediction, and the method comprises the steps: obtaining multi-source data, carrying out the preprocessing of the data, extracting the core characteristics of the data, carrying out the dynamic rainfall modeling according to the core characteristics of the data, and simulating a rainfall evolution path; and constructing a rainfall prediction model, performing rainfall intensity prediction based on the rainfall evolution path, performing extreme weather early warning based on a prediction result, and generating a rainfall distribution diagram of a high-risk area. Through fusion of multi-source data, the generative adversarial network and spectral analysis, the spatial resolution of the data and the rainfall evolution prediction capability are improved, the real-time performance and the accuracy of rainfall prediction are improved in combination with dynamic rainfall modeling, a fractional order differential equation and a multiple regression model, and the prediction accuracy is improved. The rainfall prediction challenge under complex weather conditions can be effectively dealt with, and accurate prediction of rainfall intensity and extreme weather events is realized.

The invention discloses an ENSO prediction method, system and device based on a space-time fusion neural network, and belongs to the field of deep learning model prediction. The invention designs a time-space fusion Transform network, the network designs a parallel encoder-decoder structure, and the time-space information can be effectively extracted from the ocean surface temperature data and the # imgabs0 # index at the same time, so that the prediction precision of the # imgabs1 # index is improved. Meanwhile, the network extracts global features by using an attention mechanism in an encoder of the network, and establishes a remote dependency relationship on a target. Compared with a comparison method, the method has better prediction capability in multiple prediction advance periods, the problem that an existing ENSO prediction method underestimates the ENSO event amplitude along with the increase of the prediction advance periods is solved, and the ENSO prediction precision is improved.

The present invention proposes a flood monitoring and early warning method and system based on the fusion of multi-source remote sensing satellite data. The present invention collects various types of remote sensing satellite data of flood areas; pre-processes the collected remote sensing satellite data; integrates the pre-processed multiple remote sensing satellite data with features of different scales to obtain feature fusion; generalizes the feature fusion based on the revisited channel attention module from the same-layer scene to the cross-layer scene; utilizes the fused remote sensing images to identify floods and extract the boundaries and scope of the flood area; conducts flood warnings based on the identified flood areas and historical flood data, combined with meteorological and hydrological information, to obtain warning results; and conducts a comprehensive analysis of the causes, processes, and impacts of flood events. By integrating multi-source satellite data, the present invention aims to improve the accuracy of large-scale flood detection and enhance the ability to predict flood trends, thereby ensuring the accuracy and reliability of spatiotemporal flood warnings.

This invention relates to the field of meteorological services and provides a method and system for correcting subseasonal model precipitation based on climate zoning and sea surface temperature (SST) background constraints. The method includes: climate zoning, adding SST background constraints, reanalysis data modeling, model historical return data modeling, real-time prediction model matching, and prediction result correction. The system includes: a data preprocessing unit, a climate zoning unit, an SST background constraint unit, a reanalysis data modeling unit, a model historical return data modeling unit, and a real-time model matching and prediction result correction unit. This invention considers the importance of regional differences in precipitation and SST background constraints, solving the problems of significant regional differences in daily summer precipitation in my country, the tendency for overfitting in single-point modeling, and the lack of physical constraints that make correction methods unsuitable. It effectively utilizes physically meaningful predictability sources to modify the cumulative probability density correction method based on percentile mapping, thereby improving my country's summer subseasonal precipitation prediction capabilities.

The invention belongs to the technical field of water environment monitoring and early warning, and discloses a lake and reservoir chlorophyll a concentration prediction method based on an SO-KNN model. According to the invention, multi-time scale meteorological cumulative effect features are introduced to enrich information representation, and an SO-KNN intelligent prediction model is constructed. According to the method, under the conditions of data scarcity and non-equilibrium, the chlorophyll a concentration, especially the high-precision and strong-generalization prediction capability of the water bloom risk critical point, is remarkably improved. The model is simple in structure and efficient in calculation, the common defects of overfitting, insufficient generalization ability and the like of a complex machine learning model in the scene are effectively overcome, and a reliable and practical innovative technical solution is provided for early water bloom warning of northern reservoirs and water areas with similar data conditions.

The present application relates to the technical field of geological exploration, in particular to a coal mine GIS transparent geological management system fusing multi-source geological exploration data, comprising a multi-source geological and gas monitoring data acquisition module: obtaining structural geological data and dynamic gas data, forming a structure-gas joint observation data set; a structure-gas coupling modeling module: constructing a three-dimensional geometric model, dynamically adjusting the fracture development zone and the stress concentration area, and generating a structure-gas double-field coupling three-dimensional geological model; a model dynamic updating and local feedback correction module: when the gas sensor detects abnormal changes or new drilling data is accessed, triggering an automatic updating mechanism; a GIS transparent visualization and risk labeling module: using a multi-layer fusion and asynchronous drawing mechanism for real-time rendering, and labeling and displaying high-risk areas. The present application can truly reflect the dynamic correlation between geological structure and gas evolution, and has stronger prediction ability and safety evaluation adaptability.

This application relates to the field of marine ecological technologies, particularly to a construction method of marine ecological multi-agents and its interaction system. Among them, the construction method includes: a step of constructing a marine ecological large model, constructing a fine-tuning data set, and training a marine ecological large model based on the fine-tuning data set; a step of constructing a parallel application module, constructing a domain knowledge base, marine ecological multi-agents, and building a multi-modal large model, deploying the domain knowledge base, marine ecological multi-agents, multi-modal large model of the parallel application module and the marine ecological large model on a GPU server, where the marine ecological large model is used to parse user requests and distribute them to the parallel application module, triggering the parallel application module to generate response results and feedback them to the marine ecological large model for semantic integration and then output. Through this application, the intelligent level in the field of marine ecology is improved, the prediction ability of the model is enhanced, and the prediction results are made more accurate and reliable.

The invention discloses a method for predicting meteorological disasters based on weather classification, and relates to the technical field of weather prediction. Acquiring spatial meteorological data of the target area, and performing three-dimensional processing on the spatial meteorological data to obtain training data; obtaining a terrain distribution diagram of the target area, and inputting the training data and the terrain distribution diagram into a weather prediction model for training to obtain a weather prediction result; and determining a weather type of the target area according to the weather prediction result, and performing meteorological disaster prediction according to the weather type. According to the method, the spatial meteorological data of the target area are comprehensively obtained, three-dimensional processing is carried out, and the training data are input into an advanced weather prediction model in combination with detailed terrain distribution map information, so that the prediction capability of the model can be remarkably improved. According to the process, the weather type of the target area can be more accurately determined, powerful support is provided for meteorological disaster prediction, and the prediction accuracy is effectively enhanced.

The present invention provides a method and device for predicting chlorophyll concentration in the ocean surface, and relates to the technical field of ocean data analysis and processing; the method is to first import water body chlorophyll data and satellite remote sensing Rrs reflectivity, then perform time matching, standardization processing and logarithmic conversion, then screen satellite data set features to construct a target data set, and then integrate a chlorophyll concentration prediction model based on multiple gradient models, and finally input the satellite target data set and the in-situ data set as independent variables and dependent variables respectively into the model and MDN network prediction, and merge the results using a regression method. The present invention effectively extracts key information from complex data, improves model capture and prediction capabilities, makes up for feature deficiencies, reduces the impact of data distribution, improves the limitations of fusion algorithms, and uses data reasonably, the model is highly targeted, and the prediction results are scientific and practical.

The application discloses a time-frequency cooperative significant wave height prediction method and system based on cross attention and adaptive weighting, and the method specifically comprises the following steps: acquiring hourly historical observation data of an observation station; constructing an ATF-CAN model based on cross attention and adaptive weighting, wherein the model comprises a learnable frequency domain decomposition module, a multi-path encoder group, a two-stage cross attention module, an adaptive weighting fusion module and a position embedding decoder; training the ATF-CAN model, verifying the trained ATF-CAN model by using a verification set; and predicting the significant wave height of a test set by using the trained ATF-CAN model. The application improves the generalization prediction ability of the model under multiple sea conditions.

The invention relates to the technical field of climate event analysis, in particular to a regional composite thermal wave population exposure risk analysis method based on multi-source data. The method comprises the following steps: acquiring day-by-day temperature observation data, month-by-month reanalysis data of various meteorological elements and population distribution data in a target area; judging a composite heat wave event by adopting a percentile threshold method, and counting the composite heat wave frequency; on the basis of an inverse algorithm, calculating a whole-layer atmospheric heat source according to monthly meteorological reanalysis data; analyzing the correlation between the composite heat wave frequency in the target area and an atmospheric heat source, and establishing a linear regression equation; and calculating the population exposure degree of the composite heat wave in the target area caused by the abnormal change of the atmospheric heat source, and quantitatively evaluating the population exposure risk level. The method disclosed by the invention reveals the formation mechanism of the composite heat wave event from a new perspective, assists in improving the prediction capability, provides a solid scientific basis for accurately identifying the distribution of related high-risk crowds, and has important scientific significance and application prospects.

The application discloses a meteorological satellite cloud map extrapolation method based on a geographical attention mechanism, characterized by a deep learning framework constructed by GeoAttX, the introduction of a geographical attention mechanism to enhance the modeling ability of spatial features, and the synergistic effect of geographical semantic modeling, prediction path optimization and distribution correction mechanism, using the observation data of meteorological satellites to realize high-precision and low-error accumulation cloud prediction. The method uses a reverse greedy algorithm to realize flexible forecast time by reducing the autoregressive step, and proposes a second-order standardization method to normalize the pixel distribution, to maintain the consistency of brightness statistics and reduce error propagation. Compared with the prior art, the application has the advantages of using real-time stationary meteorological satellite observation data, having error suppression capability, geographical structure modeling capability, long-term stable prediction capability, high precision and stability, and can effectively improve the cloud motion extrapolation capability, and has important application value for weather forecasting and disaster warning.

The present invention discloses a spatial prediction method of sea ice density based on an MLR model, including calculating the lead-lag correlation coefficient between the Weddell Sea sea ice density and the global sea surface temperature, the mean sea level pressure and the surface air temperature in different lead times, so as to determine the atmospheric and oceanic prediction factors affecting the SIC of the Weddell Sea; constructing an MLR spatial prediction model based on the prediction factors, wherein the SIC data of the past 12 months are respectively included in the prediction factors; according to the predicted object, the MLR model performs multiple linear regression on the SIC of each grid point in the Antarctic Weddell Sea region based on the above six prediction factors, so as to realize the monthly prediction of the spatial distribution of sea ice. The present invention is to establish a spatial monthly prediction model of the Weddell Sea SIC based on the MLR statistical method, which can incorporate the climate signals of the distant atmosphere and ocean into the sea ice prediction, and can consider the influence of the persistence of the atmosphere, ocean and sea ice itself on the prediction respectively, and the MLR model shows good prediction ability.

The invention discloses a multi-time-efficiency short-temporary rainfall prediction method based on time-space joint feature extraction, and the method comprises the steps: carrying out the down-sampling processing of preprocessed multi-dimensional meteorological data through employing an encoder based on a U-Net architecture, and carrying out the semantic embedding of input information through employing a time information embedding module, and the sub-module has the time sensing capability. And the features after down-sampling processing are input into a Transform-Mama hybrid module for efficiently extracting space and time features, and window division is used in front of each sub-module for parallel processing. And performing up-sampling reduction on the feature data by using a decoder, complementing information lost due to down-sampling by using jump connection, and finally inputting the information into a multi-layer perceptron for prediction to obtain a precipitation result of the target area. The method can effectively carry out multi-time local high-precision rainfall prediction in a short critical time, realizes the complexity of a lightweight model, and has excellent prediction capability.

The invention discloses a ConvLSTM sea level height change prediction method based on reduced gravity hypothesis, and the method is suitable for the prediction of global sea level height anomaly (SLA). The method comprises the following steps: S1, acquiring and preprocessing global satellite altimeter data; s2, based on a ConvLSTM model, extracting spatio-temporal characteristics of sea level height anomaly; s3, introducing wind stress data, and combining physical constraints of a reduced gravity model to enhance the prediction capability of the model for wind-driven Rogobby waves; and S4, outputting a high-precision sea level height prediction result through error evaluation and optimization. According to the method, global satellite altimeter data in 1993-2014 are used for training, experiments in a black tide extension sea area show that the prediction correlation coefficient reaches 0.98, the effective prediction time efficiency can reach 365 days, the performance is more excellent in a low-frequency change area, and the accuracy, stability and timeliness of sea level prediction are remarkably improved.

The present invention discloses a flood forecasting method and system based on a support vector machine model, which belongs to the field of runoff forecasting; the method and system include S1, collecting hydrological and meteorological data of a study area; S2, determining the parameter CN value of a runoff generation module of an SCS-CN model based on the collected hydrological and meteorological data; S3, improving the runoff generation module of the SCS-CN model by using rainfall characteristic factors and the parameter CN value of the runoff generation module; S4, establishing an SCS-CN three-water source confluence model by coupling the runoff generation module improved in S3 with a three-water source confluence module; S5, constructing a dynamic confluence hysteresis parameter through a support vector machine model to improve the SCS-CN three-water source confluence model; S6, forecasting the flood process line of a watershed outlet section by using measured rainfall data; considering the influence of rainfall characteristic factors, improving the prediction ability of the model in areas with relatively dry climates; considering factors such as the spatial distribution of rainfall events and previous rainfall, so as to reduce the risk of prediction deviation of the hydrological model due to uneven spatial distribution of rainfall.

The present invention proposes a high-resolution ocean thermocline depth rapid reconstruction method based on random forest, which is used for rapid reconstruction and quasi-real-time acquisition of the ocean thermocline depth using sea surface data; by establishing a random forest model, using sea surface data, such as sea surface temperature, salinity, sea surface height anomaly, etc., combined with historical data of thermocline depth calculated based on measured temperature data, the sea surface elements and the thermocline depth are fitted with a regression relationship, thereby achieving the goals of multi-source ocean data fusion and high-resolution thermocline depth rapid reconstruction; in order to train the model, a large amount of measured data is used for sample fitting and optimization to improve prediction accuracy; the present invention has the advantages of accuracy, high efficiency and wide applicability, can improve the spatiotemporal resolution of ocean thermocline depth calculation, enhance the understanding and prediction ability of ocean temperature field, and is of great significance to the fields of ocean engineering and physical oceanography.

The invention belongs to the technical field of marine environment monitoring and prediction, and discloses a non-linear sea wave reconstruction and prediction method and system based on coherent radar, and the method comprises the steps: obtaining sea wave data through the coherent radar; processing the sea wave data to obtain the radial speed of the sea surface; constructing a propagation operator matrix based on a linear wave theory; introducing a second-order nonlinear correction term to correct the linear wave theoretical model; solving a nonlinear equation containing the second-order nonlinear correction term to obtain a model coefficient; and reconstructing and predicting the wave height time calendar based on the model coefficient. The sea surface elevation is predicted by using the coherent radar, so that more reliable sea wave data can be obtained; after the radial velocity of the sea waves is obtained through a spectral distance method, band-pass filtering is carried out on the radial velocity, and therefore the more accurate radial velocity of the sea waves is obtained. By introducing a second-order nonlinear term, the method has higher reconstruction and prediction capabilities on nonlinear sea waves, and finally, integration of sea wave reconstruction and prediction is realized.

The invention relates to the field of meteorological services, and provides a sub-seasonal mode rainfall correction method and system based on climate partitioning and sea temperature background constraint, and the method comprises the steps: climate partitioning, sea temperature background constraint addition, reanalysis data modeling, mode historical return data modeling, real-time prediction model matching, and prediction result correction. The system comprises a data preprocessing unit, a climate partitioning unit, a sea temperature background constraint unit, a reanalysis data modeling unit, a mode historical return data modeling unit and a real-time model matching and prediction result correction unit. According to the method, the importance of rainfall regional differences and sea temperature background constraints is considered, and the problem that the correction method is not applicable due to the fact that the daily rainfall regional differences in summer in China are obvious, overfitting is likely to occur to single-point modeling and physical constraints are lacked is solved; a predictable source with physical significance is effectively utilized to transform the cumulative probability density correction method of percentile mapping, and the rainfall prediction capacity of China in the summer sub-seasons is improved.

The invention belongs to the technical field of marine environment meteorological analysis, and particularly relates to a temperature and humidity data model construction method based on a K-means clustering algorithm and a Bayesian network, the K-means clustering process is optimized by introducing three evaluation indexes of SilhouetteCoffeient, Clinski-HarabaszIndex and Davies-BouldinIndex, the rationality and accuracy of the clustering result are ensured, and the accuracy of the temperature and humidity data model is improved. Therefore, different temperature and humidity change modes can be distinguished more finely. Besides, a local causal network structure is constructed by using a K2 Bayesian network structure learning algorithm in combination with a Bayesian-Dirichlet equivalent scoring function, so that the learning efficiency of the model is improved, and the prediction capability of the temperature and humidity change trend under a specific meteorological condition is enhanced. Through statistics of sample frequency and Laplacian smooth estimation of conditional probability distribution, high-precision prediction of future temperature and humidity changes is realized. According to the method, the limitation in the traditional technology is overcome, and powerful technical support is provided for meteorological monitoring and early warning in coastal areas.

The invention discloses a meteorological element forecasting method and device based on a genetic algorithm, equipment and a medium. The method comprises the following steps: acquiring actual meteorological data and initial meteorological forecasting data of different climate modes; obtaining target weather forecast data of each observation station according to the initial weather forecast data and the position information of each observation station; searching an optimal solution set in the plurality of initial weight candidate solution sets by using a genetic algorithm to obtain a target weight of each climate mode; and performing weighted average according to the predicted meteorological data in each climate mode and the target weight to obtain target predicted meteorological data. According to the method, the optimal weight combination is searched in a plurality of initial weight candidate solution sets by using the genetic algorithm, so that the optimal weight combination more reasonably reflects the prediction capability of different climate modes, weighted averaging is performed through the optimal weight combination and the weather forecast data, more accurate target prediction weather data is obtained, and the prediction efficiency is improved. Therefore, the accuracy of weather forecast data is improved.

The present invention discloses a rainfall prediction method and system for the Beijing-Tianjin-Hebei region, which relates to the technical field of rainfall prediction. It includes obtaining multi-source data, preprocessing the data, extracting the core characteristics of the data, dynamically modeling rainfall according to the core characteristics of the data, and simulating the rainfall evolution path; constructing a rainfall prediction model, predicting the rainfall intensity based on the rainfall evolution path, giving early warnings for extreme weather based on the prediction results, and generating a rainfall distribution map of high-risk areas. Through the fusion of multi-source data, the present invention uses a generative adversarial network and spectral analysis to improve the spatial resolution of the data and the prediction ability of precipitation evolution. Combining dynamic rainfall modeling, fractional differential equations, and multiple regression models, it improves the real-time performance and accuracy of rainfall prediction, can effectively cope with the challenges of rainfall prediction under complex weather conditions, and realizes accurate prediction of rainfall intensity and extreme weather events.

The application discloses a kind of offshore low-permeability reservoir geology dessert prediction methods, comprising the following steps: low-permeability reservoir geology dessert prediction method includes the acquisition of low-permeability reservoir original well logging quality parameter and the determination of prestack inversion elastic parameter objective function, the construction of the isochronal inversion stratum framework of sedimentary mode guidance seismic recognizable scale, the construction of low-frequency model under paleogeomorphology constraint, prestack simultaneous inversion based on partial stack seismic data, reservoir quality parameter inversion based on bayesian discrimination, plane attribute extraction and characterization of three-dimensional RQI data body.The application is characterized by the distribution prediction of each period geology dessert of target interval by extracting the plane attribute of three-dimensional RQI data body, realizes the accurate characterization of offshore middle-deep low-permeability reservoir geology dessert, has better prediction ability, and then provides favorable support for development well site optimization deployment.

The application discloses a kind of sea-land interactive zone pollution interface pollutant migration path identification and influence factor analysis method, it is related to environmental science and pollutant migration simulation field, the analysis method includes the following steps: S1, obtain the adsorption kinetics parameter and thermodynamic parameter of target pollutant in porous medium, and determine the influence on adsorption behavior;S2, construct the dynamic migration process simulation of pollutant, monitor concentration field and the conductivity field of salt and fresh water interface;S3, compare concentration field and conductivity field, identify spatial migration path and interface position relationship;S4, correlate adsorption parameters and migration path, establish quantitative relationship model, output analysis result.By dynamic simulation and synchronous monitoring, the specific migration path and distribution mode of pollutant near the interface are directly revealed;At the same time, by correlating the static adsorption mechanism and the macro migration phenomenon, the scientific cognition and accurate prediction ability of the pollutant migration law in the coastal zone are significantly improved.

The invention discloses a WO-AHP-EWM-based novel transformer area line loss prediction capability comprehensive evaluation method. The method comprises the steps of S1, establishing a transformer area line loss prediction key technology comprehensive evaluation index system; s2, preprocessing the obtained line loss prediction related data of the target transformer area; s3, determining the index weight of each factor by using an analytic hierarchy process; s4, calculating a weight; s5, the weight is optimized through a sea image algorithm, and the consistency of the weight and the judgment matrix is checked at the same time; and S6, carrying out comprehensive evaluation on the line loss prediction capability of the transformer area by utilizing the improved index system. The method has the advantages of simple principle, high accuracy, wide application range and the like.