17 results about "Climate index" patented technology

The invention discloses a medium-and-long-term runoff forecasting method coupled with a weather forecasting product and a related device, and relates to the technical field of runoff forecasting, the method comprises the following steps: firstly, acquiring runoff data, weather data and climate index data of a plurality of time periods before a to-be-predicted time period and rainfall correction forecasting data of the to-be-predicted time period; taking as an input data set; after preprocessing and redundant variable screening are carried out, an obtained input data set is input into an optimal runoff forecasting model which is determined through training and comparison, and runoff forecasting data of a to-be-predicted time period are obtained; compared with an existing physical hydrological model, runoff prediction is carried out by coupling rainfall correction forecast data, complex hydrological physical calculation is avoided, and the adaptability and generalization ability of the model are improved; compared with an existing data driving method, the method has the advantages that medium-and-long-term runoff forecasting is carried out by coupling rainfall correction forecasting data, and the defect that the influence of future weather on medium-and-long-term runoff is not fully considered in an existing method is overcome.

The invention belongs to the technical field of electric power meteorology, and provides a medium and long term wind and light resource prediction method and system considering climate remote correlation factors, and the method comprises the steps: obtaining global climate index historical data, and carrying out the standardization processing of the data, and obtaining a standardized climate index; for the historical resource sequence of the target station, calculating the mutual information value of the standardized climate index, and constructing a forecasting factor set by taking the lag time corresponding to the maximum value of the mutual information as the optimal early warning window period; extracting wind and light resource measured data of the same period in historical years to construct a reference probability density function; and taking the reference probability density function as prior distribution, combining a preset dynamic mode prediction result as a likelihood function, and outputting a prediction result containing a deterministic value and a confidence interval. By means of the characteristic that ocean signals change slowly, the method breaks through the 15-day prediction limit of an atmospheric mode, effective trend prediction from the quarterly level to the annual level is successfully achieved, and the requirement for medium and long term resource evaluation is met.

The invention discloses a land water reserve dynamic change diagnosis method based on sudden change time point analysis. The method comprises the following steps: S1, obtaining TWS abnormal data of a preset time length in a research area; s2, carrying out preprocessing on the TWS abnormal data of each grid unit; s3, according to the preprocessed TWS abnormal data of each grid unit, calculating a main mutation occurrence time point of each grid unit; s4, for each grid unit, calculating change trends of TWS abnormal data in time periods before and after a main sudden change occurrence time point; s5, classifying each grid unit into a preset dynamic transformation category; s6, acquiring regional hydrological climate index data corresponding to the time and space of the TWS abnormal data in the step S1; s7, analyzing the change trend of each hydrological climate index data in time periods before and after the main sudden change occurrence time point; and S8, carrying out correlation analysis on the dynamic transformation category of each grid unit in the step S5 and the change trend of each hydrological climate index data in the step S7, and outputting correlation information between the dynamic transformation category and the change trend.

The invention discloses a space-time pattern prediction method based on forest carbon sink climate change. The method comprises the following steps: collecting vegetation data, soil data, meteorological data, topographic data and special interference factor data of a target forest area; preprocessing the collected multi-source data, and performing space-time matching on the preprocessed data; quantifying a climate index of the target forest region; taking a Biome-BGC ecological process model as a core, and integrating a graph neural network to construct a carbon sink-climate index-terrain coupling model; setting various climatic change scenes, and predicting a carbon sink spatial and temporal distribution pattern under different scenes based on a carbon sink-climatic index-terrain coupling model. The space-time pattern prediction method based on the forest carbon sink climate change is adopted, and the prediction precision is improved.

The invention discloses a southwest wine grape climate zoning method based on multi-source data, and relates to the technical field of agrometeorological information, and the method comprises the following steps: collecting and preprocessing multi-source basic data; key climate and growth indexes are calculated; spatial interpolation analysis; generating a zoning map; analyzing and verifying the trend; according to the method, by integrating multi-source meteorological data and high-precision topographic data, a more comprehensive southwest wine grape climate zoning index system is established; according to the method, multiple factors such as heat, moisture and terrain are comprehensively considered, the limitation of single climate index analysis is made up, the zoning result can more accurately reflect the difference of microclimate under the complex terrain, a scientific basis with more regional pertinence is provided for planting layout, and the zoning practicability and guidance value are improved.

The invention belongs to the technical field of informatization agriculture, and particularly relates to a climate-soil synergistic effect-based apple quality multi-index prediction model construction method, which comprises the following steps of: data collection and preprocessing: collecting climate indexes, soil chemical indexes and quality index data corresponding to apple samples, carrying out standardization processing on all continuous variables, and carrying out calculation on the standard variables; dividing a training set and a test set according to a preset proportion; feature engineering: high-order features are constructed based on agronomy knowledge, an engineering feature pool is formed, and the high-order features comprise an interaction feature, a seasonal aggregation feature, a soil-climate balance feature and a ratio feature; according to the method, a systematic basic statistical analysis-high-order feature engineering-two-stage modeling research framework is constructed, the limitation of traditional single-factor linear analysis is broken through, the nonlinear synergistic effect of climate-soil factors is effectively captured, and high-quality feature input and a scientific modeling thought are provided for apple quality prediction.

The invention discloses a rare earth ore restoration area ecological product management method based on remote sensing image analysis, and relates to the technical field of ecological product management, and the method comprises the steps: obtaining a remote sensing image of a rare earth ore restoration area, and calculating an index value change value of each position; obtaining a meteorological index, and obtaining a mark type of the meteorological index; according to the mark type and the index value change value, obtaining the correlation degree between the position point and the climate index; obtaining all current sampling points to be detected, obtaining terrain similarity, soil similarity, species similarity and water source similarity between the sampling points and the position points, and obtaining a target index of each sampling point; and establishing a number sequence based on the target index, and judging to carry out early warning prompt on the sampling points. According to the method, the remote sensing image of the rare earth ore restoration area is analyzed, whether the currently selected sampling point is reasonable or not is judged, prompting is conducted in time, the problem of sampling data distortion is solved, and the reliability and accuracy of an ecological product value accounting result are improved.

The application discloses a method for coupling meteorological forecast product medium and long term runoff forecast and related device, relates to runoff forecast technical field, and the method comprises the following steps: firstly, runoff data, meteorological data and climate index data of several time periods before the to-be-predicted time period and precipitation correction forecast data of the to-be-predicted time period are acquired as input data sets; then, after pretreatment and redundant variable screening, the obtained input data sets are input into the optimal runoff forecast model determined through training and comparison, and runoff forecast data of the to-be-predicted time period is obtained; compared with the existing physical hydrological model, the application couples precipitation correction forecast data to carry out runoff prediction, avoids complex hydrological physical calculation, and improves the adaptability and generalization ability of the model; compared with the existing data-driven method, the application couples precipitation correction forecast data to carry out medium and long term runoff forecast, and overcomes the fact that the influence of future meteorology on medium and long term runoff is not fully considered in the existing method.

The invention relates to the technical field of disaster prevention and reduction of power systems, provides an icing trend prediction method based on climate indexes, and aims to solve the blank of icing prediction on long-term climate scales such as monthly and quarterly in the prior art. Comprising the following steps: constructing an icing strip number-AO index historical database by collecting AO index data of the southern power grid icing total strip number and corresponding time month by month in winter from 2013 to 2019; statistically analyzing the change rule of the total number of month-by-month icing of the south network in recent seven years and the AO index, and calculating to obtain a significant negative correlation between the two; the relevance between the ice coating strip number and the AO index is verified to have statistical significance through significance t test; and establishing an icing strip prediction equation taking the AO index as an independent variable by utilizing linear regression fitting, and inputting AO index prediction data to realize long-term trend prediction of the icing strip. Compared with a traditional short-term prediction method, the method is combined with large-scale climate factors, the reliability and timeliness of prediction are remarkably improved, and a scientific basis is provided for disaster prevention and reduction of a power system.

The invention provides a forest carbon sequestration amount evaluation method based on climate index influence. The method comprises the following steps: determining a complete growth cycle of a forest; calculating the forest carbon sequestration amount after biological correction; calculating to obtain a winter temperature anomaly index WTAIi, a summer temperature anomaly index STAIi, a growth season monthly rainfall anomaly index GSPAI-Si, a growth season double-month rainfall anomaly index GSPAI-Bi and an annual sunshine anomaly index YSAIi of the research area in the year Ti; and constructing a forest carbon sequestration quantity-climate index model. From the perspective of coping with climatic changes and guaranteeing the forest carbon sequestration amount, a'forest carbon sequestration amount-climatic index 'model capable of quantifying the influence of climatic abnormity on the forest carbon sequestration amount is constructed by designing the climatic index capable of representing the abnormal degree of climatic conditions, and objective and efficient evaluation of the influence of the forest carbon sequestration amount is developed.

The invention discloses a medium and long term prediction method and system for regional thunder and lightning climate, and the method enables the interannual change of thunder and lightning activity to be objectively predicted in the future 2-12 months through building the quantitative mapping relation between the thunder and lightning occurrence frequency distance level and the multi-source climate factor and the large-scale climate index. Therefore, thunder and lightning approaching early warning which is originally limited to a minute-to-hour level is expanded to seasonal and annual climate scales, and a thunder and lightning-oriented climate service prediction link is formed by uniformly utilizing historical thunder and lightning observation, air temperature, rainfall, an El Nino index and other data. The large-scale air-sea abnormal signals can be converted into quantitative judgment of too much and too little regional thunder and lightning, and the defect that in the prior art, only empirical correlation analysis stays is overcome. And meanwhile, by performing standardized grading on the lightning frequency interval, the grading results of'more, more, normal, less and less' and the like are directly output, so that the prediction product can be directly called and applied by the lightning protection and disaster reduction department, the planning and design department and the like.

The application discloses a kind of based on the construction method and system of avian influenza occurrence risk prediction model of El Nino index, belong to risk prediction technical field, including respectively constructing H5 subtype avian influenza event data set and multiple El Nino index data set and climate-avian influenza data set;Regression model is constructed to avian influenza event number on climate index based on climate-avian influenza data set;Multiple deep learning models are constructed based on multiple El Nino index data set, and optimal prediction model is selected;Future avian influenza event number is predicted based on regression model and optimal prediction model.The application uses MEI as the core representative variable of El Nino climate factor, combines the historical epidemiological characteristics and time factor of avian influenza virus, realizes the dynamic prediction of avian influenza occurrence risk under future climate condition by quantitative analysis of the influence of climate change on highly pathogenic avian influenza transmission, and can accurately predict the high-risk space-time distribution of H5 subtype strain.

The invention relates to the technical field of regulation systems, and discloses an intelligent building waterscape regulation and control system and method based on climate awareness, and the system comprises a feature index fusion module, a multi-dimensional strategy matching module, a collaborative optimization module, a state regulation and control module, and a final regulation and control module. Performing real-time feature index fusion on the multi-source heterogeneous environmental parameters of the building waterscape to obtain a real-time climate index; based on the operation strategy of the building waterscape, performing multi-dimensional strategy matching on the real-time climate index to obtain a preliminary regulation and control strategy of the real-time climate index; based on the preliminary regulation and control strategy, performing overall collaborative optimization on the building waterscape to obtain a preliminary overall regulation and control instruction of the building waterscape; performing state real-time regulation and control on the initial overall regulation and control instruction to obtain an initial optimized operation state of the building waterscape; performing strategy optimization according to the preliminary optimization operation state to obtain a final regulation and control strategy of the building waterscape; according to the invention, the accuracy of intelligent building waterscape regulation and control can be improved.

This invention discloses a method for quantifying the coupling degree of drought-heat wave and decomposing its driving forces based on a time-varying moment model (PMOE). It constructs a time-varying moment-constrained maximum entropy distribution (POME) model for heat wave and drought sequences, expressing the key Lagrange multipliers of the PMOE model as a linear function of climate indices and urbanization factors to characterize the time-varying characteristics of univariate marginal distributions. Based on pseudo-observations, a time-varying Copula model considering physical driving factors is constructed. Through the optimal combination of various parameter structures, the dynamic evolution of the interdependent structure between drought and heat waves is characterized. By calculating and comparing risk impact indicators under multiple models, the influence of climate change and human activities on the coupling degree of drought-heat waves is quantified and separated. This invention achieves simultaneous characterization of the dynamic evolution and non-stationary characteristics of complex extreme events, and effectively identifies and decomposes the contributions of key physical driving factors, providing a scientific basis for dynamic risk assessment and adaptive countermeasure formulation for regional complex events.

The invention relates to a convolutional neural network-based drought and flood sudden change event grading early warning method. The method comprises the steps of performing objective clustering-based drought and flood sudden change event grading definition and label construction; constructing a multi-source climate-driven feature set based on nonlinear dynamic optimization; a GDFAA-CNN prediction model with time sequence perception and cost sensitive characteristics is constructed; the invention relates to medium-and-long-term multi-scale rolling forecasting application of drought and flood sudden turning events. According to the method, the specific one-dimensional convolution and pooling layer structure of the convolutional neural network is utilized, time sequence accumulation and mutation characteristics can be automatically extracted from high-dimensional and complex input climate data, a data-driven model training mode is adopted, only current climate index data needs to be input in practical application, and the time sequence accumulation and mutation characteristics can be automatically extracted. According to the method, prediction results of 1-3 months in the future are output within second-level time, prediction time consumption is greatly shortened, uncertainty accumulation caused by physical model parameter calibration is avoided, and precious advance is gained for formulating emergency plans for flood and drought disaster prevention departments.

The application provides a runoff prediction method and device, a storage medium and a computer device, and comprises the following steps: obtaining historical hydro-meteorological data, historical climate index data, historical runoff return data of a plurality of preset historical periods and historical runoff data of a target basin; the historical runoff return data is runoff data of the preset historical period simulated by a plurality of different runoff simulation models according to the historical hydro-meteorological data and the historical climate index data; the historical runoff data is measured runoff data of the historical period corresponding to the historical runoff return data; training a plurality of initial runoff prediction models according to the historical hydro-meteorological data, the historical climate index data, the historical runoff return data and the historical runoff data to obtain a plurality of runoff prediction models; inputting basin hydro-meteorological data, basin climate index data and a plurality of runoff return data of the target basin into the plurality of runoff prediction models to obtain runoff data prediction data.

The application discloses a method, system, device and medium for medium and long term runoff prediction based on multi-source climate factors and deep learning. The method comprises: obtaining historical runoff time series data and multi-source climate factor data, wherein the multi-source climate factors are divided into three-dimensional space climate field factors and two-dimensional climate index factors; based on an initial space window scale, the three-dimensional space climate field factors are cropped and traversed at different space positions, a runoff prediction model is trained in a first stage, and candidate space driving region centers are determined by comparing prediction performance; taking the candidate space driving region centers as space window centers, a plurality of different space window scales are traversed, a runoff prediction model is trained in a second stage, the optimal space window scale corresponding to each candidate space driving region center and the final parameters of the model are determined by prediction performance, a plurality of candidate runoff prediction models are constructed, and the optimal model for final runoff prediction is determined by comprehensive comparison. The application can improve the prediction accuracy, stability and reliability of runoff prediction.