108 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 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 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 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 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 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 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 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.

The invention provides a method and system for predicting drought and flood sudden turning disasters under climate change, and the method comprises the steps: calculating a drought and flood sudden turning index of a target drainage basin in a first time period, and carrying out the fitting of a hook-shaped Hook response function of a relation between a drought and flood sudden turning index prediction value and a near-earth temperature; based on the air temperature data of the target watershed output by the plurality of global climate modes, determining a preliminary prediction value of a drought and flood sudden turning index in a second time period; constructing a three-variable emergence constraint model according to the temperature change trend of each global climate mode in the third time period and the corresponding drought and flood sudden turning index preliminary prediction value; substituting the temperature change trend of the fourth time period into the emergence constraint model to obtain a drought and flood sudden turning index prediction value of the target drainage basin in the second time period; and based on the corrected prediction result of the drought and flood sudden turning index in the future period, identifying the drought and flood sudden turning risk of the target drainage basin in the future period, and evaluating possible social and economic risks.

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.

This invention belongs to the field of building performance simulation and provides an hourly downscaling method for monthly weather forecast data based on distribution-adjusted time mapping. The method includes the following steps: identifying representative cities, collecting data from a typical meteorological year, determining a global climate model and a monthly average resolution dataset, acquiring a primary variable dataset, determining candidate statistical distributions, performing maximum likelihood estimation, screening optimal distributions, constructing an empirical cumulative distribution function, modifying distributions, calculating quantiles, mapping quantiles, adjusting distributions, and finally model prediction. By using specific distribution adjustment and quantile mapping methods, the method achieves hourly downscaling of monthly forecast data for different weather variables in an atmospheric circulation model (GCM). This method not only maintains the physical properties of the data and the statistical characteristics of historical data, but also reflects long-term trends and extreme weather conditions predicted by GCMs, providing reliable future weather data for building performance simulation.

The invention discloses a measuring device and method for researching soil greenhouse gas emission under interaction of different plant species. The measuring device comprises an interspecific interaction chamber and plant planting chambers arranged at the two ends. The two plant planting chambers and the interspecific interaction chamber are connected in any two of the following modes (1)-(3): (1) a blank separation frame; 2) a 20 [mu] m nylon net separation frame; and 3) a plastic film separation frame: a static box is arranged on the interseed interaction chamber, so that forced airflow circulation and gas sampling are realized. According to the method, the influence of the root interaction and litter mixing effect in the intercropping / mixed forest system on greenhouse gas emission can be quantified, and theoretical support is provided for low-carbon agricultural measures (such as optimized planting combination); policy support: accurately evaluating the greenhouse gas source / sink function of the agriculture and forestry ecosystem, assisting carbon accounting and emission reduction technology research and development, and responding to global climate change to cope with demands.

The present invention discloses a method for tracking four-dimensional soil moisture drought events and identifying warming signals, the method comprising the following steps: data acquisition; four-dimensional soil moisture drought event identification; four-dimensional soil moisture drought event feature quantification; human-induced warming signal detection of the temporal evolution of four-dimensional soil moisture drought features in historical periods; four-dimensional soil moisture drought future evolution prediction; and four-dimensional soil moisture drought event driving factors. The present invention has the beneficial effects of exploring the future spatiotemporal evolution characteristics of four-dimensional soil moisture drought, analyzing the driving factors and physical mechanisms of four-dimensional soil moisture drought, and providing a strong scientific basis for responding to global climate change, ensuring water resource security, and formulating disaster prevention and mitigation strategies.

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 present invention discloses a deep learning-based sea ice classification and density inversion method, which relates to the field of remote sensing image processing technology and aims to solve the problems of the existing technology. The method comprises obtaining raw dual-polarization synthetic aperture radar data and preprocessing it; performing superpixel segmentation on the preprocessed data to obtain multiple superpixels; calculating the posterior probability of the superpixel using a conditional random field model to determine the uncertain superpixel unit; using the uncertain superpixel unit as input, outputting the sea ice and seawater boundary lines within the uncertain superpixel unit based on the Ice-WaterNet network model, and combining them to obtain a sea ice classification result map; and performing sea ice density inversion on the sea ice classification result map to obtain a sea ice density inversion result. The present invention improves the inversion accuracy of sea ice classification and density, and can play a more important role in global climate change monitoring, marine environmental protection, polar resource development and utilization, and other aspects.

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.

This invention discloses a generative deep learning-based downscaling correction method for climate model predictions. This method uses an unsupervised generative adversarial network to identify biases in global climate model hydrological and meteorological variable predictions. Using adversarial training of a generator and a discriminator, the adversarial loss is continuously reduced, enabling the trained generative deep learning network to handle complex nonlinear relationships, achieving a comprehensive optimization of downscaling and bias correction. This method can be used to correct the average bias of ensemble predictions of multi-model variables (precipitation, temperature, etc.) and improve their resolution. Inventive examples demonstrate that this method can effectively downscale multi-model ensemble predictions and achieves superior downscaling performance compared to commonly used quantile mapping and convolutional neural networks.

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.