176 results about "Global climate" patented technology

The invention discloses a sea ice classification and intensity inversion method based on deep learning, relates to the technical field of remote sensing image processing, and aims to solve the problems in the prior art, and the method comprises the steps: obtaining original dual-polarization synthetic aperture radar data, and carrying out the preprocessing of the original dual-polarization synthetic aperture radar data; performing superpixel segmentation on the preprocessed data to obtain a plurality of superpixels; calculating the posterior probability of the superpixel by using a conditional random field model, and determining an uncertain superpixel unit; taking the uncertain super-pixel unit as input, outputting sea ice and seawater boundary lines in the uncertain super-pixel unit based on an Ice-WaterNet network model, and combining to obtain a sea ice classification result graph; and performing sea ice concentration inversion on the sea ice classification result map to obtain a sea ice concentration inversion result. According to the method, the inversion precision of sea ice classification and density is improved, and the method can play a more important role in the aspects of global climate change monitoring, marine environment protection, polar region resource development and utilization and the like.

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 belongs to the field of building performance simulation, and provides a monthly meteorological prediction data hourly downscaling method based on distribution adjustment time mapping. Comprising the steps of representative city determination, typical meteorological year data collection, global climate mode and monthly average resolution data set determination, main variable data set acquisition, candidate statistical distribution determination, maximum likelihood estimation, optimal distribution screening, empirical cumulative distribution function construction, distribution correction, quantile calculation, quantile mapping, distribution adjustment and model prediction. According to the method, hourly downscaling of monthly prediction data of different weather variables in the atmospheric circulation model is realized through a specific distribution adjustment and quantile mapping method, so that the physical characteristics of the data and the statistical characteristics of historical data are kept, and the long-term trend and extreme weather conditions of GCM prediction can be reflected; and reliable future meteorological data is provided for building performance simulation.

The invention discloses an ocean and atmosphere multi-physical field prediction method and system based on a multi-scale map neural network, electronic equipment and a medium, and mainly solves the problems of one-sidedness and lack of ocean and atmosphere physical field interaction mechanisms in ocean-atmosphere coupling modeling in the prior art. According to the implementation scheme, ocean and atmosphere historical observation data are obtained and preprocessed; a multi-scale graph neural network model comprising an encoder, multiple grids, a processor and a decoder is constructed, the multiple grids are connected with a local high-resolution grid and a global low-resolution grid through shared nodes and cross-scale edges, and bidirectional information flow is achieved; iteratively optimizing parameters of the multi-scale graph neural network model by using the training set; and the performance of the model is evaluated and optimized through the test set, and finally a high-precision prediction result is generated. The method can synchronously predict the multi-physical field parameters such as the hybrid wave height, the wave period, the temperature and the wind speed, remarkably improves the calculation efficiency and the prediction precision, and can be widely applied to global climate analysis, marine disaster early warning and air-sea interaction research.

The invention relates to the technical field of environmental science and engineering, and discloses a regional carbon emission accounting system, which adopts a plurality of sensors and intelligent equipment to collect carbon emission data in real time, and transmits the data through wireless communication. And the data integration module performs cleaning and standardization processing on the carbon emission data, and evaluates the reliability of a data source based on a fuzzy rule to form an accurate comprehensive data set. And the dynamic evaluation module analyzes the comprehensive data set by adopting a machine learning algorithm to generate evaluation feedback, and feeds back the evaluation feedback to the intelligent monitoring module to optimize the monitoring strategy and the data acquisition frequency. And through a visual display module, an analysis result is presented in a chart form, user interactive data query and retrieval are supported, and scientificity and real-time performance of carbon emission management are improved. The system provides an important basis for policy making and effective emission reduction while improving the carbon emission accounting precision, and assists in coping with global climate changes.

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.

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 staged design flood estimation method under the influence of climate change and reservoir regulation and storage. The method comprises the following steps: acquiring meteorological and hydrological data, human water consumption data and global climate mode output data of a reservoir; a reduced natural reservoir runoff series is obtained based on a water balance method, a GR4J-9 hydrological model is calibrated to achieve natural reservoir runoff simulation, and a long-short-term memory model is constructed to simulate the influence of human activities such as reservoir regulation and storage on runoff; based on a global climate mode set and a quantile deviation correction method, driving a GR4J-9 hydrological model and a long-short term memory model to obtain a reservoir runoff series under climate change and performing flood season staging; establishing a staging design flood calculation model of a future scene based on a Copula function, and deducing the most probable staging design flood; and constructing an emergence constraint model based on the dew point temperature so as to obtain a corrected stage design flood result. According to the method, the comprehensive influence of climate change and reservoir regulation and storage is considered, and a reference basis is provided for deducing stage design flood.

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 discloses a climate mode pre-estimation downscaling correction method based on generative deep learning, and the method comprises the steps: recognizing the global climate mode hydro meteorological variable pre-estimation deviation through an unsupervised generative adversarial network, continuously reducing the adversarial loss through the adversarial training of a generator and a discriminator, and improving the accuracy of the adversarial training. Therefore, the trained generative deep learning network has the capability of processing a complex non-linear relationship so as to achieve the comprehensive optimum of downscaling and deviation correction. The method can be used for correcting the estimated average deviation of a multi-mode variable (rainfall, temperature and the like) set and improving the resolution ratio of the multi-mode variable set. The embodiment of the invention shows that the method can effectively estimate and downscale a multi-model set, and has a better downscaling effect than common quantile mapping and a convolutional neural network.

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 construction method of an earth system coupling device and the earth system coupling device, and the construction method comprises the steps: a first component module construction step: constructing a plurality of first component modules of a global climate system based on full coupling, the plurality of first component modules comprise an atmospheric component module, a land component module, a sea ice component module, a ground ice component module, a river component module, a first ocean component module and a sea wave component module; a second component module construction step: constructing a second component module based on the ocean circulation simulation system, wherein the second component module is a second ocean component module; a replacement step: replacing the first ocean component module with the second ocean component module; and a coupling step: reconstructing a coupling application interface of the coupler of the global climate system based on full coupling, and carrying out data exchange and interaction among different component modules through the coupling application interface.

The invention discloses a land water reserve prediction method and system based on artificial intelligence and electronic equipment. The prediction method comprises the following steps: collecting an atmosphere reanalysis data set and gravity satellite land water reserves data, and obtaining various global climate mode output data; constructing a long-short-term memory model for simulating long-series land water reserves; adopting the corrected lattice global climate mode output data to drive a long-short term memory model, and simulating a first prediction result of land water reserves in a future prediction period; fitting a hook-shaped response function of the land water reserves and the dew-point temperature, and obtaining a second prediction result of the land water reserves in the future prediction period; and constructing an emergence constraint model for correcting the prediction result. According to the method, the land water reserve space-time evolution rule under the influence of climate change can be fully reflected, the land water reserve prediction period is remarkably prolonged, the prediction effect is improved, and the defect that in the prior art, land water reserve prediction is large in uncertainty is overcome.

The invention discloses a device and a method for determining the denitrification rate of sediments in situ. According to the device, through field determination, transparent design and fluidity design, the accuracy and ecological correlation of denitrification rate determination are greatly improved, and an innovative technical support is provided for research and management of nitrogen circulation of a water body. And further determining the denitrification rates of rivers, shallow lakes and intertidal zones by using a method of combining stable isotopes with an in-situ culture room. The method can help understand back mechanisms of environmental problems such as algal blooms and oxygen deficit, improve water quality, provide important data support for ecological restoration, wetland protection and agricultural management, promote health and stability of an ecological system, provide important data for studying the effect of nitrogen circulation in global climate change, and promote implementation of sustainable management practice. Compared with a traditional laboratory measurement method, the method has remarkable advantages in the aspects of simulating natural conditions, spatial heterogeneity and ecological interaction, and the denitrification mechanism can be more comprehensively understood.

The invention relates to the technical field of data prediction processing, in particular to a flood risk prediction method and device based on artificial intelligence and emergence constraint, and the method comprises the steps: segmenting a base flow through employing a preset digital filtering algorithm, and generating a surface runoff series of a research basin; obtaining a drainage basin average daily air temperature series, and dividing the same-day air temperature-surface runoff combination in the target research time period into a plurality of box elements with the same sample capacity by using a box element scaling function; separating a thermodynamic item and a dynamic item of the drainage basin extreme rainfall scale coefficient; predicting a preliminary prediction value of the extreme surface runoff driven by the thermal action; and constructing an emergence constraint model according to the data of the global climate mode and the predicted initial predicted value of the extreme surface runoff so as to predict the flood risk driven by the future thermal action of the target. Therefore, the problem that the flood risk under the driving of the future thermal action is predicted without comprehensively considering the atmospheric thermal power and power separation mechanism and without considering the emergence constraint in the related technology is solved.

The invention relates to a water body carbon emission long-term trend prediction method based on a Bayesian additive regression tree, and belongs to the field of water body carbon emission prediction. The method comprises the steps that water-gas interface carbon flux data and related hydrological and water environment data are collected, the data are preprocessed and subjected to dimension unification, and predictive variables are determined through correlation analysis and a structural equation model. Thirdly, constructing a prediction model by using a BART algorithm model, and verifying the performance of the model through a test set; in addition, the method further comprises the steps of utilizing a BART algorithm model to invert historical carbon emission conditions, and coupling with a global climate mode to predict carbon emission trends under different climate change scenes in the future. The method provides an effective way for evaluation and long-term trend prediction of the carbon emission level of the water body, and is helpful for deeply understanding and alleviating the influence of global climate change.

The invention provides a generating capacity pre-judgment method based on a cloud computing integration technology, and relates to the field of power generation, and the method comprises the following steps: S100, collecting global climate model prediction data, power grid load data and power plant equipment health degree monitoring data in real time through a cloud computing platform; the global climate model prediction data comprises temperature, humidity, wind speed, wind direction and precipitation. The climate-power grid-equipment ternary fusion generating capacity pre-judgment system combining Al deep learning and cloud computing is an innovative solution, not only can analyze the relationship between historical meteorological data and generating capacity, but also can integrate global climate model prediction, power grid load dynamics and power plant equipment health degree monitoring data in real time; by introducing a microclimate simulation technology, high-precision prediction is performed on local climate around a power plant, and power generation efficiency prediction is dynamically adjusted in combination with an equipment aging prediction model, so that accurate pre-judgment is realized.

The invention discloses a statistical correction method for land water resource availability simulation data, and belongs to the technical field of climate data analysis. Constructing a space-time distribution simulation capability evaluation model, screening out a global climate system mode with a relatively good simulation effect, and determining historical simulation data # imgabs0 # and future simulation data # imgabs1 #; a cumulative distribution function is established, and the variable quantity and the change rate from historical simulation data # imgabs2 # of the same quantile to future simulation data # imgabs3 # are quantized; selecting a mode simulation trend storage mode, and obtaining deviation-corrected future simulation data # imgabs4 # by using a quantile mapping method; and sequencing the future simulation data # imgabs5 # subjected to deviation correction to obtain future simulation data # imgabs6 # based on a time sequence, and performing deviation correction on the multi-period simulation data to form a perfect historical simulation data set and a future simulation data set. According to the method, the WA simulation precision is fundamentally improved by taking the WA as a correction target variable, the statistical correction effect of the quantile mapping method on the WA variable is analyzed, and most climate scale research requirements are met.

The invention discloses an Antarctic iceberg surrounding volume estimation method and system based on an optical remote sensing image, and the method comprises the steps: obtaining optical remote sensing image data, and partitioning the optical remote sensing image surrounding the Antarctic iceberg to form a plurality of fan-shaped regions; extracting a plurality of icebergs and iceberg shadows based on the optical remote sensing image data of each fan-shaped region; estimating the water outlet height of each iceberg based on the plurality of icebergs and the iceberg shadows; calculating the overwater volume of each iceberg by combining the iceberg area obtained by extracting the iceberg contour based on the water outlet height of each iceberg, and calculating the total volume of each iceberg by combining the Archimedes buoyancy law; according to the total volume of each iceberg, the total volume of all the icebergs in the corresponding sector area is obtained, and then the total volume of the icebergs around the Antarctic pole is obtained through calculation. According to the method, the iceberg volume calculation accuracy is improved, and more accurate scientific data and theoretical support are provided for global climate change research, polar region ecological environment protection and international shipping safety.

The invention belongs to the technical field of ocean measurement, and discloses a method and a system for measuring the sea surface height of deep and far sea based on Beidou PPP-B2b. The method comprises the following steps: calculating an antenna instantaneous geodetic height of a four-antenna GNSS receiver based on a WGS84 coordinate system; correcting the attitude of the buoy by using a buoy attitude correction algorithm to obtain the height of the buoy in the vertical direction; calculating the buoy draft according to a built-in instantaneous average water depth algorithm; the sea surface instantaneous height is calculated through a sea surface instantaneous height algorithm; carrying out noise reduction processing on the calculated instantaneous height of the sea surface through a filtering calculation unit; and the final result is sent to a monitoring service platform through a data transmission module. The method not only supports ship route planning and navigation safety, helps to avoid risks, but also provides long-term, stable and reliable data support for global climate change monitoring, marine environment protection and deep sea scientific research.

The invention relates to the field of carbon neutralization, and discloses a four-dimensional soil moisture drought event tracking and warming signal identification method, which comprises the following steps: data acquisition; four-dimensional soil moisture drought event identification; four-dimensional soil moisture drought event characteristic quantification; artificial warming signal detection of time evolution of four-dimensional soil moisture drought characteristics in historical periods; performing four-dimensional soil moisture drought future evolution estimation; four-dimensional soil moisture drought event driving factors; the method has the advantages that future space-time evolution characteristics of four-dimensional soil moisture drought are explored, driving factors and physical mechanisms of four-dimensional soil moisture drought are analyzed, and a powerful scientific basis is provided for coping with global climate changes, guaranteeing water resource safety and formulating disaster prevention and reduction strategies.

The invention mainly relates to the technical field of hydrological disaster assessment, in order to accurately predict the flood risk of a variable environment drainage basin, the invention provides an intelligent prediction method for the flood risk under climate change, and the core idea of the method is that the hydrological process of the drainage basin in the future is simulated and studied based on a global climate mode and a hydrological-deep learning coupling model; feature parameters of a Budyko formula serve as covariables, a time-varying Copula function is adopted to consider hydrological series inconsistency under the influence of climate change and human activities, a joint probability distribution function of flood duration and flood volume is constructed, and the most probable combination of the flood duration and the flood volume is solved; according to the method, future flood risk changes are assessed according to the joint recurrence period difference of the most probable combination of flood duration and flood volume in historical and future periods, the social and economic exposure degree caused by flood risk increase in the future is predicted, the method has high physical significance and statistical basis, and the change characteristics of future flood driven by water circulation variation can be effectively represented; and the flood risk prediction accuracy is improved.

The invention discloses a drought-resistant gene OsDEL1, an amplification method and application of the drought-resistant gene OsDEL1 in plant drought-resistant improvement, and belongs to the technical field of plant genetic engineering. The invention provides a drought-resistant gene OsDEL1. The nucleotide sequence of the drought-resistant gene OsDEL1 is as shown in SEQ ID NO. 1. The invention also provides a primer pair for amplifying the drought-resistant gene OsDEL1, a method for amplifying the drought-resistant gene OsDEL1 by using the primer pair, and an application of the drought-resistant gene OsDEL1 in improving the drought resistance of plants. The drought-resistant gene OsDEL1 and the corresponding protein are applied to genetic engineering genetic breeding of crops, a new variety of drought-resistant rice is cultivated, the safety hazard of abnormal drought caused by global climate change to food crops is reduced, and the drought-resistant gene OsDEL1 and the corresponding protein have important reference significance for related research on drought resistance of other crops.

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.

According to the common vetch and oat mixed cropping planting method in the high and cold agricultural and pastoral areas, through proper combination of common vetch and oat pasture and different-row sowing, the light energy and the land utilization rate are improved, inter-seed competition is reduced, and the field cultivation technology of high quality and high yield of pasture is achieved; the yield of forage grass, economic benefits, land utilization efficiency and nitrogen fertilizer utilization efficiency are remarkably improved, the use amount of industrial nitrogen fertilizer and economic, environmental and energy burdens caused by the nitrogen fertilizer are reduced, and the method is an effective way for improving the productivity of agriculture and animal husbandry in the Qinghai-Tibet Plateau and is also a key measure for coping with global climate changes.

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 invention discloses a nature-society coupled water transaction prediction method and system, and a continuation medium. The method comprises the steps of obtaining hydro meteorological data, global climate mode output data, social population-economy data and water resource data in a research area; relative humidity and specific humidity are determined, and four hydrological model parameters are calibrated based on hydrometeorological data; training four hydrological models by adopting the corrected future-period global climate mode output data to obtain future-period daily runoff series data; obtaining probability distribution characteristics of natural runoff volume of each sub-basin in a future period in the research area; evaluating a water demand threshold value of an ecological system in each sub-basin; constructing a natural-social coupled water transaction dynamic prediction optimization model; and estimating the corrected water shortage area in the future period, and finally obtaining the evolution trend of the water shortage area after the water transaction in the future period. The method can accurately predict the response characteristics of the dynamic change of the water transaction under the future climate change and the future economic trend, and provides a scientific basis for the decision making of the water transaction.

The invention relates to the technical field of ring main units, in particular to an environment-friendly gas insulation ring main unit. In order to solve the problems that although a ring main unit adopting SF6 gas as an insulating medium becomes the mainstream of the industry due to performance advantages, a large amount of SF6 gas is discharged into the atmosphere during operations such as daily maintenance, troubleshooting or equipment replacement, and long-term serious negative effects are generated on the global climate, the invention provides the following technical scheme: the ring main unit comprises a cabinet body with an opening on one side; two groups of partition plates are fixedly connected in the cabinet body; the cabinet doors are hinged to the two sides of the cabinet body; the insulation box is installed in the cabinet body, and a load switch is installed in the insulation box; and the supplementing mechanism is arranged below the insulation box and used for discharging an insulation medium into the insulation box, and a positioning assembly is installed on the side face of the supplementing mechanism. The SF6 gas is effectively prevented from being directly discharged into the atmosphere, the harm of the SF6 gas to the environment is reduced, and the environment protection concept and the double-carbon target are met.

The invention discloses an ET0 prediction method based on signal decomposition and an improved deep learning model. The ET0 prediction method comprises the following steps: step 1, collecting data; 2, missing value processing, abnormal value detection, normalization operation and interpolation are conducted on the collected data, the data obtained after interpolation are linearly mapped to be between 0 and 1 through Min-Max standardization, and normalized data are obtained; 3, calculating ET0 according to the P-M model; 4, signal decomposition is conducted on the ET0 obtained through calculation through a CEEMDAN algorithm, the signal is decomposed into a plurality of IMFs, and the IMFs are different frequency components in the signal and used for extracting data features under different time scales; step 5, combining all IMFs obtained by decomposition of the CEEMDAN algorithm with other meteorological feature vectors to form a complete input feature, and constructing a sample for the input feature by using a sliding window technology; the problem that ET0 cannot be accurately predicted under the background of global climate change and increasingly tense water resources is solved.

The invention relates to the technical field of coal mine fire prevention and control, and discloses a CCS-based environment-friendly oxygen-isolating fire preventing and extinguishing material and a preparation method and application thereof.The CCS-based environment-friendly oxygen-isolating fire preventing and extinguishing material comprises a water-permeable wear-resistant cover, a solid waste-based self-foaming carbon capture material, a waterproof sealing compound fabric and a net-shaped support, the net-shaped support serves as a supporting frame to be evenly distributed and connected between the water-permeable wear-resistant cover and the waterproof sealing composite fabric, and the structure of the net-shaped support is filled with the solid waste-based self-foaming carbon capture material. According to the method, integrated innovation of multiple technologies of efficient utilization of solid waste, improvement of material performance, prevention and control of goaf disasters and mineralization and storage of CO2 is embodied, air leakage can be effectively reduced through application of the oxygen insulation material and geological storage of CO2, and prevention and control of goaf coal spontaneous ignition are achieved; the efficient application of the solid waste material and the mineralization and storage of CO2 can provide a new thought and direction for solving the problems of global climate change and resource shortage in the coal mine field.