69 results about "Global climate" patented technology

The invention relates to the field of gene engineering for enhancing the cold resistance of plants, and relates to a recombinant expression vector, a recombinant strain and a primer pair of a pennisetum purpureum PpMYB2 gene, a method for improving the cold resistance of the plants and application of related materials in improving the cold resistance of the plants. One purpose of the invention is to provide application of the Pennisetum purpureum PpMYB2 gene and related materials thereof in improving the cold resistance of the plant, the amino acid sequence of the encoding protein of the Pennisetum purpureum PpMYB2 gene is as shown in SEQ ID NO.1, and the plant is arabidopsis thaliana or Pennisetum purpureum. Under the background that extreme low temperature events caused by global climate change are increasingly frequent, the cold resistance enhancing technology provided by the invention has important significance on guaranteeing the stability of agricultural production and reducing loss caused by low temperature. Through overexpression of the gene PpMYB2, the adaptability of perennial pennisetum purpureum to low-temperature stress can be remarkably improved, so that the planting range of perennial pennisetum purpureum is expanded to regions with lower temperature, and the survival and growth performance in different ecological environments is enhanced.

The invention discloses a summer rainfall forecast correction downscaling method and system based on deep learning, and belongs to the technical field of meteorological prediction and climate simulation. In order to solve the technical problems of systematic deviation and spatial detail missing in forecasting, a deep learning downscaling model is constructed for summer multi-mode climate forecasting data published six months ahead of time in combination with high-resolution observation data and topographic data, deviation correction and spatial super-resolution reconstruction are performed on a summer rainfall forecasting result, and a deep learning downscaling model is constructed. And outputting the kilometer-level summer precipitation field of the target area. The model improves the spatial feature capture capability of multi-scale climate elements through an attention mechanism, and realizes the adaptive fusion of meteorological elements and topographic factors through a topographic perception module, thereby enhancing the medium and long term prediction precision of summer rainfall in a complex topographic region. According to the method, global climate information and local topographic features can be effectively integrated, and high-precision technical support is provided for medium-and-long-term prediction of regional summer rainfall, disaster prevention and reduction in flood seasons and water resource scheduling.

The invention discloses a vegetation vulnerability constraint estimation method and system under dry-heat composite stress. The vegetation vulnerability constraint estimation method comprises the following steps: 1, acquiring observed vegetation total primary productivity data, meteorological data and similar meteorological data of each lattice point in a global climate mode; 2, determining a standardized rainfall evapotranspiration index time scale of each lattice point and determining dominant stress types of different lattice points in growing seasons; 3, respectively constructing two-dimensional Copula models of corresponding dominant stress and total primary productivity, and determining a stress threshold value corresponding to induced vegetation loss; 4, calculating future estimation of dominant stress based on a climate mode, and constructing an emergence constraint model; 5, correcting a future estimated mean value and uncertainty of the dry heat stress index based on the emergence constraint relation; and 6, inputting the corrected mean value and uncertainty range of the future dry heat stress indexes into a Copula model, and calculating a corresponding loss probability. According to the method, the response difference of different vegetation ecosystems to dry heat stress is considered while the prediction accuracy of the drought heat waves and the compound events thereof is improved.

The invention relates to a multi-source ocean data fusion method based on multi-scale optimal interpolation. The method comprises the following steps: acquiring multi-source ocean data; generating a regional grid, and marking the sea-land distribution of the grid; interpolating global climate state data to the regional grid to generate a regional climate state background field; calculating a position index of the low-resolution observation data in the regional grid, and removing invalid data in the region; fusing the regional climate state background field and the low-resolution observation data by adopting a multi-scale optimal interpolation algorithm to generate a regional low-resolution analysis field; calculating a position index of the high-resolution observation data in the regional grid, and removing invalid data in the region; and fusing the regional low-resolution analysis field and the high-resolution observation data by adopting a multi-scale optimal interpolation algorithm to generate a regional high-resolution analysis field. Compared with the prior art, the method has the advantages of being capable of efficiently achieving data fusion, small in occupied memory and the like.

The invention discloses a remote sensing multi-parameter integrated inversion normal form method, system and equipment based on AI-Agent. According to the method, a deep learning neural network is dynamically driven through AI-Agent, a refining mechanism (RM)-Transformer-MoE size nested model, a physical method, a statistical method and expert knowledge are coupled, a DL-C-PSK normal form is constructed, a high-precision multi-source database is established based on the normal form, an appropriate radiation transfer equation is constructed through geophysical logical reasoning, and a high-precision multi-source database is established. And inversion of parameters such as surface temperature, surface emissivity, atmospheric water vapor content and near-surface air temperature is realized. According to a causal relationship between an input wave band and an output parameter, a direct synchronous inversion or iterative inversion mode is adopted to ensure multi-parameter high-precision synchronous inversion. Wherein the core of the deep learning neural network comprises RM logic derivation, SHAP model interpretation, Transform model architecture and a Transform-MoE size nested model, so that the interpretability, the adaptability and the precision of the model are improved. Through an AI-Agent driven RM-Transform-MoE nested model, deep coupling of physics-statistics-knowledge is realized, compared with a traditional SW method, the inversion precision is greatly improved, and verification shows that the technology is suitable for the fields of global climate observation, environment monitoring and the like.

The invention relates to a machine learning and multi-factor coupled concrete carbonization depth prediction model establishment method. The method specifically comprises the steps of obtaining future global climate data; building a dynamic temperature and humidity coupling carbonization model; building a diffusion-reaction equation of CO2, Ca (OH) 2 and relative humidity, and correcting a CO2 diffusion coefficient; setting initial and boundary conditions of the equation; future climate data is dynamically mapped to concrete surface concentration calculation under boundary conditions, and a surface mass transfer correction coefficient equation driven by wind speed is introduced; performing space-time discretization on the equation, introducing Taylor expansion to construct a third-order precision difference format, and solving a nonlinear coupling equation set; future global climate data are input into the dynamic temperature and humidity coupling carbonization model, the surface boundary condition of the coupling carbonization model in each time and space step length is updated, the internal diffusion coefficient is corrected in real time, and the change of the carbonization depth along with time is calculated. The method provides a scientific basis for the durability design of the infrastructure, and has remarkable economic benefits and social values.

The application belongs to the field of sub-seasonal climate prediction, and provides a sub-seasonal climate prediction method and system based on the combination of dynamic model downscaling and machine learning downscaling, which comprises the following steps: S1, generating initial field and model boundary field information required for dynamic downscaling based on global climate model output data; S2, driving regional climate model to perform dynamic downscaling by using the initial field and model boundary field information; S3, generating input field required for machine learning downscaling based on the output circulation field information of the regional climate model; S4, performing machine learning downscaling correction optimization on the output circulation field of dynamic downscaling based on a convolution model; S5, performing machine learning super-resolution based on the output data of machine learning downscaling correction; and S6, generating sub-seasonal prediction information of double downscaling of dynamic model and machine learning. The application utilizes the complementary advantages of dynamic downscaling and deep learning downscaling, improves the sub-seasonal prediction skill, and copes with new challenges brought by climate change.

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 climate public opinion data analysis and system based on multi-agent collaboration. The technical problems that in information acquisition and analysis of public opinions related to global climate changes, the data processing capacity is insufficient, cross-language understanding is limited, the real-time performance is poor, and the prediction capacity is weak are solved. The core technology module comprises a cue word embedded neural network crawler information collection module which is used for realizing intelligent collection and analysis of public opinion data in a network space; the multi-agent collaborative instant text analysis and content generation module is used for rapidly tracking global climate policy evolution and public opinion information; and the energy climate public opinion AI report generation module based on construction information splicing is used for dynamically fusing the generated content to provide depth and breadth coexistence information support for climate negotiation.

The invention discloses a method and a system for evaluating a hysteretic effect of an influence factor in a species growth-suitable area, and the method comprises the steps: obtaining the distribution data of a to-be-researched species, and simulating the habitat condition of the to-be-researched species according to the distribution data, thereby obtaining a habitat distribution diagram; acquiring environmental factor data influencing the growth of the species to be studied, projecting environmental factors into the habitat distribution map, and processing to obtain environmental factor data of the suitable growth area; calculating the explanatory force of the environment factor data of the growth-suitable area to the spatial differentiation of the growth-suitable area of the species to be researched, and screening out key factors according to the explanatory force; and performing causal relationship analysis on each key factor by adopting a dynamic causal inference method GCCM to obtain a causal relationship and strength, and evaluating the hysteresis effect of the key factors according to the causal relationship and strength. According to the method, the problem that the time delay effect of an ecological system on environment change is difficult to depict in the prior art can be solved, and the influence of global climate change and human activities on biocenosis can be evaluated more accurately.

A technological improvement to a system for vehicle navigation is provided. The improvement diminishes a contribution to global climate change, e.g., global warming, by generating a flight path (generated flight path) or modifying the flight path (modified flight path) of a vehicle to avoid an atmospheric region in which the vehicle would create a contrail if the vehicle travelled through the atmospheric region.

This invention relates to a method for estimating design floods using coupled GAMLSS and BMA models under changing environments, and pertains to the field of hydrological response and its impact assessment technology. The invention first quantitatively identifies changes in meteorological and hydrological elements under future changing environments, and then performs inconsistency frequency analysis based on their distribution characteristics and variation patterns. Then, based on the inconsistency frequency analysis results obtained from different global climate models, it integrates design flood data using the BMA model. This effectively avoids the problem of homogenization in the ensemble simulation results caused by the uncertainty of input data when directly applying the BMA method to runoff sequences, which leads to significant deviations in the design flood analysis results. This invention solves the uncertainty and inconsistency problems faced by current conventional methods.

The application discloses a land water storage prediction method and system based on artificial intelligence and electronic equipment. The prediction method comprises the following steps: collecting atmospheric reanalysis data sets and gravity satellite land water storage data, and obtaining various global climate model output data; a long short-term memory model for simulating long series of land water storage is constructed; the long short-term memory model is driven by the corrected grid global climate model output data to simulate the first prediction result of the land water storage in the future prediction period; a hook-shaped response function of the land water storage and the dew point temperature is fitted, and the second prediction result of the land water storage in the future prediction period is obtained; and an emergent constraint model for correcting the prediction result is constructed. The application can fully reflect the spatio-temporal evolution law of the land water storage under the influence of climate change, significantly prolong the prediction period of the land water storage, improve the prediction effect, and solve the defect that the prediction of the land water storage has great uncertainty in the prior art.

The invention describes a method by which it is possible to predict past and future climate changes by measuring changes in local and global magnetic fields on the Earth's surface in time and space and comparing them with older measurements and effects.

The invention belongs to the related technical field of climate and environmental science, and particularly relates to a method for determining the influence of a climate geographic grid on global atmospheric component variables, which comprises the following steps of: constructing global climate mean time sequence data; performing signal decomposition on the global climate mean time sequence data and the global variable time sequence data to obtain a first target sequence and a second target sequence; determining the optimal time delay between the two sequences, aligning the two sequences and carrying out linear fitting, and taking a fitting quadratic term as a fitting sequence of a second target sequence; respectively calculating a first variance of an original second target sequence and a second variance of the fitting sequence; sequentially neglecting each climate grid, updating the fitting sequence and the second variance after each grid is neglected, and taking the permillage of the difference value of the two second variances before and after updating and the first variance as the coupling expressive force of the neglected grid to the second target sequence.

The invention relates to the technical field of hydro meteorology, in particular to a method and a system for predicting and tracing extreme hydrological events in a reservoir area. The method comprises the following steps of S1, collecting meteorological and hydrological data, a global climate mode data set, geographic space data and stable isotope data of a reservoir area, and unifying a multi-source data scale through a time sequence fusion algorithm; according to the method, isotope data is adopted as an independent evidence constraint model, and compared with a traditional single model method, the method can more comprehensively capture a complex mechanism of occurrence of an extreme event, so that a simulation result is closer to an actual situation, and countermeasures can be taken in the face of an extreme climate event; the climate prediction and the isotope traceability technology are coupled for the first time, how causes of the extreme events change under different future climate scenes can be pre-judged in advance, and the research method and the model framework are not only suitable for specific areas, but also can be widely applied to research of the extreme events of large reservoirs and drainage basins.

The application relates to the field of atmospheric science and discloses an El Nino event identification and La Nina transition early warning method, equipment and medium. The method uses the sea surface temperature and the near-surface 2-meter air temperature data in the fifth-generation atmospheric reanalysis product ERA5 of the European Centre for Medium-Range Weather Forecasts, calculates the lag cross-correlation coefficient between the sea temperature grid points and the air temperature grid points, and constructs a directed network between the global air temperature and the equatorial mid-east Pacific and equatorial mid-Pacific sea temperature. The average link strength is used to quantify the importance of the air temperature node, the spatial and temporal changes of the link number and the link strength are combined, the key area with a leading response to the El Nino is identified, and the transition of the El Nino event to the La Nina event is early warned. The application helps us to deeply understand the influence of the El Nino event on the global climate system, improves the prediction accuracy of the La Nina event, and thus provides a scientific basis for the formulation of disaster prevention and response strategies.

The invention describes an outflow and extraction method using suspended or fixed Coanda bodies. This outflow-assisted extraction of the flow can also be achieved conventionally with existing extraction systems, in the case of kitchens, for example, with conventional extractor hoods with stationary filter elements or via special rotating filter elements. The blowers used can be conventional radial, axial, meridional, or capillary blowers, such as blower modules or filter blowers. The invention is explained using a kitchen extractor hood as an example, but is also applicable to all pollutants distributed in a fluid in an industrial environment. An outflow device according to this method can also be used as an artistic or painterly flow sculpture to symbolize a chaotic system, such as...our global climate, can be used, especially when different flow regimes of the sphere flow can be traversed.

This invention provides a method for climate-adaptive reconfiguration of power distribution networks, comprising: obtaining typical climate datasets, extreme climate datasets, and expected climate datasets based on global and regional climate models; constructing power distribution network optimization models based on the typical, extreme, and expected climate datasets respectively; performing a risk quantification assessment of the power distribution network based on the optimization models to form candidate reconfiguration schemes; and selecting the candidate reconfiguration scheme with the smallest normalized integration assessment value as the climate-adaptive reconfiguration scheme through normalized integration evaluation of the expected net present value and expected integration level of the power distribution network under each candidate reconfiguration scheme. This invention enables power distribution network reconfiguration to adapt to various climate scenarios.

The application discloses a climate mode multivariate statistical downscaling method, a storage medium and equipment, relates to the technical field of climate mode statistical downscaling, and comprises the following steps: acquiring mode circulation information, measured precipitation and temperature information output by a global climate mode, and dividing the information into a training set and a test set; constructing a climate mode multivariate statistical downscaling initial model, including a circulation information compression module, a precipitation processing module, a temperature processing module, a temperature and precipitation output module; training the circulation information compression module, a new circulation information compression module retaining an encoder after training, and replacing the circulation information compression module of the initial model; using the training set and the test set to train and test the climate mode multivariate statistical downscaling model, inputting mode circulation information to be processed into the model after training and testing, and obtaining down-scaled precipitation and temperature information. The application avoids multiple loading of input data and waste of running time and resources.

The application discloses a set weather forecast method based on variational Bayes, comprising the following steps: 1, preprocessing global climate fifth generation atmospheric reanalysis dataset ERA5 and dividing windows; 2, establishing a three-dimensional neural network based on Swin-Transformer; 3, quantifying the probability distribution of model parameters by using a variational Bayes method; 4, model sampling is performed on the probability distribution, set forecast reasoning is realized, and the uncertainty of set forecast results is given. The application can improve the prediction skill of the current numerical method-based weather forecast model, and significantly improve the reasoning speed and efficiency, and reduce energy consumption.

The present application relates to icing precursor signal forecasting technical field, especially in icing signal analysis method and system based on circulation index, integrated modern meteorological analysis tool and data processing technology, aims at improving the prediction accuracy and response efficiency of icing event, through the comprehensive use of the fifth generation global climate atmospheric reanalysis data provided by the European center for medium-range weather forecasts, combined with the actual icing record of the specific area transmission line, the analysis and calculation of key atmospheric circulation index, including Siberia high pressure intensity index, east Asia trough intensity index, etc., through these data support, can accurately track and predict the possibility and severity of icing occurrence, application of advance-lag correlation analysis method, further reveals the dynamic relationship between icing and atmospheric circulation index, provides scientific basis for formulating effective icing response measures.

The present application belongs to the technical field of meteorological data processing and remote sensing mapping, and particularly relates to a rice maturity suitability zoning method based on time series meteorological data analysis. The present application obtains an annual representative meteorological mode through annual inter-period normalization and time series reconstruction, obtains annual precipitation accumulation and temperature supply through precipitation and temperature index calculation, and combines rice growth mechanism and rice maturity law to perform threshold segmentation on large-area precipitation and temperature indexes, so as to realize rice maturity suitability zoning. The method provided by the present application effectively reconstructs the annual meteorological mode under the background of global climate change and regional climate anomaly, analyzes and extracts meteorological indexes most significantly affecting rice maturity suitability, performs threshold segmentation on key meteorological indexes in combination with rice growth mechanism and rice maturity law, can provide clear and accurate guidance and suggestions for rice maturity planning and agricultural policy making, and solves the problem of blindness in current rice maturity planning.

The present invention provides a method for simulating changes in particle number concentration and particle size spectrum distribution during new particle generation, including calculating relevant characteristic parameters during a new particle generation event; calculating the impact of atmospheric transport on the new particle number concentration during a new particle generation event; determining upper and lower boundaries of the simulation based on actual measurements of the particle number concentration and particle size spectrum distribution; and simulating changes in the particle number concentration and particle size spectrum distribution during a new particle generation event using aerosol dynamics. The present invention improves existing dynamic equations for calculating new particle generation events, considers the impact of atmospheric transport on changes in particle number concentration, and improves the accuracy of simulating changes in particulate matter during new particle generation events. The present invention can quantitatively evaluate the impact of various parameters on the particle number concentration during a new particle generation event, which helps to evaluate the impact of new particle generation events on atmospheric particulate matter. It can also evaluate the causes of atmospheric pollution and provide an analytical method for global climate change analysis.

The invention discloses a fast regional self-adaptive ACM method based on meta-learning, and belongs to the technical field of low-orbit satellite communication. Aiming at the technical problems of dependence on a large amount of local data, long debugging period, unstable performance and the like during cross-region deployment of the existing adaptive coding modulation technology, the method comprises the following steps of: constructing a meta-training task set covering various global climate characteristics, and training to obtain a meta-initial model with strong generalization ability; when a ground station is deployed in a new region, an optimized localized ACM strategy can be quickly adapted through several steps of gradient updating by using a very small amount of initial communication data collected by the station. Simulation results show that the method only needs 30 samples and 2-minute fine tuning to achieve the approximate optimal performance, the spectrum efficiency is improved by 35.8% compared with a traditional fixed threshold value method, the sample demand is reduced by 99% and the deployment time is shortened by more than 98% compared with a supervised learning method, the regional self-adaption problem in global rapid deployment of the satellite communication system is effectively solved, and the method has good application prospects. And the operation cost is obviously reduced.

This invention discloses a method, system, and apparatus for water body extraction based on conditional random fields and sub-pixel localization, relating to the field of remote sensing image processing technology. The method includes: preprocessing the data to be extracted to obtain low-resolution raw data, then inputting it into the generator network of a super-resolution generative adversarial network model to obtain corresponding high-resolution image data after super-resolution reconstruction; performing image binarization and connected component processing on the high-resolution image data to remove noise and obtain potential water body regions; expanding the boundaries of the potential water body regions according to a preset threshold to generate a lake buffer super-resolution image; and segmenting the lake buffer super-resolution image into several superpixel objects to obtain the final water body extraction result. The water body extraction method for the Larsmann Hills region in Antarctica obtained by this invention can provide a deeper understanding of the stability of the Antarctic ice sheet, hydrological cycle, ecosystem health, and the impact of global climate change.

The application discloses a kind of glacial identification model modeling method based on deep learning network and semi-supervised learning, belong to surveying and mapping technical field.The present application includes steps S1: establishing glacial identification remote sensing image dataset, segmentation glacial identification remote sensing image dataset, obtain labeled data and unlabeled data;S2: using deep residual network constructs deep learning convolutional neural network model, pre-trains to deep learning convolutional neural network model;S3: using semi-supervised learning model is consistent with regularizing data, obtain semi-supervised learning dataset, through semi-supervised learning dataset deep learning convolutional neural network model combination, constructs glacial identification model.The present application realizes from high-resolution remote sensing image automatic identification and detection glacial area change, the work of the manpower research identification, searching is given to the computer trained by deep learning model to complete batch system, provides basis for analyzing glacial ablation speed and monitoring global climate change.

The invention is related to a method and system that both meets the hot water requirements and provides heating in places that need heating such as buildings and greenhouses with zero emission by easily replacing the traditional solar heating systems used for hot water use with active circulation and separate tank (4) systems and including an induction heating system in the pipeline during the transition process to renewable energy due to global climate changes.

The invention discloses a de-trending-based hydro meteorological time sequence reconstruction system and application, and the method comprises the steps: carrying out the de-trending processing of a hydro meteorological sequence, and eliminating the influence of the global climate balance temperature rise on cycle recognition; performing noise reduction processing on the hydro meteorological sequence to eliminate the influence of white noise; selecting an optimal discrete wavelet generating function and a multi-resolution number by using a signal-to-noise ratio so as to achieve an optimal denoising effect; and carrying out period identification on the de-trended and de-noised hydro meteorological sequence by adopting a continuous wavelet function, identifying relative intensities of disturbance under different scales and main periods in the sequence, and revealing hidden period changes in the time sequence. According to the method, the influence of tendency and noise on the hydro meteorological sequence data information quality and the interference of period identification are overcome, period mining can be carried out on the hydro meteorological time sequence under the condition of considering climate change and white noise, and the accuracy of period analysis is improved on the premise of ensuring the optimal denoising effect.

The invention discloses a large-scale linear cultural heritage multi-scene long-time-sequence flood inundation evolution simulation method and system, and belongs to the field of climate change influence evaluation and cultural heritage disaster simulation, and the method comprises the steps: obtaining multi-scene climate data, and carrying out the deviation correction; calculating an extreme rainfall index based on the data after deviation correction; matching the hourly rainfall pattern distribution of rainfall with an extreme rainfall day to obtain an hourly sequence and a spatial weight of extreme rainfall; based on the digital elevation model data and the land utilization data, obtaining parameter data of each drainage basin; and inputting the hour-by-hour sequence and the spatial weight of each drainage basin and the parameter data into a flood inundation hydrodynamic model, and carrying out flood inundation evolution simulation on the large-scale linear cultural heritage. According to the method, the problems of coarse temporal-spatial resolution, large systematic deviation and the like of global climate model data in regional application are effectively solved, and meanwhile, the bottleneck that a traditional high-resolution hydrodynamic model is too high in calculation load in long-time-sequence and multi-scene simulation is overcome.