12 results about "Predictive capability" patented technology

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.

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 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 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 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 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.