48 results about "Global climate" patented technology

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 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 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.

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 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 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 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 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 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.

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 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.

The application provides an earth gravity field inversion method in an inter-satellite tracking mode based on an optical clock, comprising: using a numerical simulation method, the accuracy of the inversion of the earth gravity field in the inter-satellite tracking mode of the satellite-borne optical clock satellite is systematically evaluated. The simulator generates simulated observation data as input in the orbit of the gravity satellite (such as the GOCE and GRACE orbits), and estimates the gravity field coefficients by using a strict least square method. The accuracy of the results is evaluated by comparing the coefficient difference between the inversion model and the reference model. The application evaluates the accuracy of the inversion of the earth gravity field in the inter-satellite tracking mode based on the optical clock through the numerical simulation method, and verifies the potential of the optical clock in the high-precision gravity field measurement. In the future research, the optical clock noise model and the multi-sensor fusion method will be further optimized to improve the inversion accuracy of the high-order gravity field, and more accurate data support will be provided for the earth science and global climate change research.

The invention discloses a method for resolving mass change of an ice cover with high spatial resolution based on a three-dimensional acceleration point mass method. The method comprises the following steps: determining a ground point and a spatial point; calculating perturbation acceleration of a certain point in space caused by mass change of all ground points; calculating ice cover mass change data solved by the satellite height measurement data; and by taking the calculated ice cover mass change data as a limiting condition, constructing indirect adjustment with the limiting condition according to the calculated perturbation acceleration, and calculating a mass change vector of the ground point to be solved. According to the invention, the three-dimensional information observed by the satellite is fully fused with the satellite height measurement data, so that the mass change of the ice cover is calculated, and the spatial resolution of the ice cover is improved. According to the method, more accurate scientific data and theoretical support are provided for more precise estimation of ice cover mass change and analysis of global climate change.

This method, platform or patent is planned economy under a market economy, eliminates random or blind production by factories and random or blind consumption by consumers and supply according to demand all people in the world from birth to death with the best product, goods, and service quality and the lowest prices through “non-retail” in a nontraditional sense, “no fixed cost expenses such as bank interest required by typical businesses,”“eliminating random or blind production by manufacturers and consumption by consumers,”“thoroughly eliminating waste in the production and consumption process,”“substituting, eliminating, and thoroughly eliminating inefficient small, medium, large, and even huge enterprises in every industry for every product, commodity, or service,”“consumers being able to buy the best quality, lowest price, and best value products, commodities, or services in the world,”“saving people's time and money in consumption,”“helping the poor and reducing poverty,”“green and low-carbon,”“helping to address global climate change,” and so on. Therefore, this method, this platform, and this patent change the way people work and live, as well as the way enterprises produce products in the production process.This patent represents a revolutionary and disruptive redefinition, reshaping, and replacement of modern economic theory, including micro and macroeconomics, since the patent changes, replaces, disrupts, redefines, and reshapes modern business rules and revolutionizes and redefines the course of human civilization and human civilization itself.This method will establish a wave that includes Industry 4.0 or 5.0, and transform manufacturing into a service industry this method eliminates the traditional concept of primary, secondary, and tertiary industries and merge into one.Producers under the method or patent have no advertising expenses, no sales expenses or costs, and no market expenses or costs even no financial costs, This method's revolutionary and transformative reshaping and redefining of the traditional financial industry, especially the banking sector, and human social and economic activities. This method will help and serve almost all consumers from birth to near-death around the world save time and money, optimize the way they spend their money, helps poor people and reduces the poverty rate, eliminates waste in various industries from production to consumption, therefore, is environmentally friendly and low-carbon and effectively respond to global climate change.

The invention discloses a climate data error correction method, and relates to the technical field of meteorological data processing, and the method comprises the steps: determining a target region, obtaining CMIP6 global climate mode data and ERA5 reanalysis data of the target region, and unifying the computational grids of the two types of data; in the time dimension, correcting the data sequence by using methods of wavelet analysis, wavelet decomposition, quantile mapping and wavelet reconstruction; in the spatial dimension, correcting the spatial sequence in the longitude direction by using wavelet analysis, quantile mapping and wavelet reconstruction methods; correcting the space sequence in the latitude direction by using wavelet analysis, quantile mapping and wavelet reconstruction methods; and storing time, longitude and latitude correction fields as standard data files. According to the method, systematic deviation of CMIP6 data can be remarkably reduced, extreme value performance and spatial resolution are improved, and a reliable data basis is provided for high-precision downscaling simulation of future climate scenes.

The invention discloses a salinity-adaptive seawater alkalization synergistic method, and belongs to the field of ocean engineering and environmental protection. According to the seawater alkalization engineering alkalinity monitoring and control scheme based on water body treatment flow rate adjustment, an adaptive alkaline material is selected according to the salinity gradient of a treated water body, the target alkalinity of effluent is accurately set based on the salinity of inlet water, and the water body alkalinity is monitored in real time and compared with the target alkalinity; the alkalization effect is accurately controlled by dynamically adjusting the flow velocity of the treated water body. Aiming at water bodies with different salinity (low salinity lt, 0.5%, medium salinity of 0.5-25%, and high salinity gt, 25%), a differentiated alkalinity improvement target and an alkaline material formula are adopted. The system optimizes the residence time of the water body in the reaction kettle through an iterative decrement flow rate control strategy, and improves the alkalization efficiency. According to the method, the artificial storage increment of the atmospheric CO2 in the seawater alkalization process can be accurately calculated, an efficient and controllable technical scheme is provided for ocean negative emission engineering, and the method has important significance in relieving global climate change.

This invention provides a deep learning-based method and system for boundary layer height inversion from wind profiler radar. The method includes: first, proposing a sensitivity analysis theory for boundary layer height inversion to analyze the impact of the number of local peaks on boundary layer inversion, laying a theoretical foundation for algorithm improvement; then, selecting numerous atmospheric parameter profiles from wind profiler radar products based on boundary layer physical characteristics and their importance, filtering out the input parameter profiles for the inversion model; inputting the filtered profiles and the actual boundary layer height obtained from sounding inversion into a convolutional neural network model for training; and finally, the fully trained model can directly output high-precision boundary layer height inversion results. This invention combines the advantages of deep learning in discovering complex structures in high-dimensional data with the inclusion of multiple atmospheric parameter profiles, significantly improving the accuracy and stability of boundary layer height inversion, providing strong support for boundary layer research and global climate change studies.

This invention provides a method, apparatus, and electronic device for downscaling and correcting future meteorological data. The method includes: acquiring historical raster data and corresponding regional measured meteorological data from various stations, verifying the reliability of the historical raster data to determine a historical measured baseline dataset; acquiring historical meteorological data and future scenario meteorological data from multiple global climate models, as well as geographic information data of the study area; downscaling the historical meteorological data and future scenario meteorological data from the global climate models based on the geographic information data; and performing multi-model ensemble processing and bias correction on the historical meteorological data and future scenario meteorological data from the global climate models to obtain downscaled and corrected future scenario meteorological data. This invention improves the reliability of the correction baseline and solves the problem of insufficient spatial downscaling accuracy when processing global climate model data.

The invention discloses a solid waste biodegradation and carbon capture device which comprises a degradation tower and a capture tower, the degradation tower is provided with a solid waste inlet, a solid waste outlet and a gas outlet, the capture tower is provided with a gas inlet, a gas outlet, an absorbent inlet and an absorbent outlet, the gas inlet is communicated with the gas outlet, and the gas outlet is communicated with the absorbent inlet. The absorbent inlet is used for conveying an absorbent into the trapping tower so as to absorb carbon dioxide in degraded gas, the absorbent absorbing the carbon dioxide is discharged through the absorbent outlet, and the degraded gas absorbing the carbon dioxide is discharged through the exhaust port. According to the invention, the landfill and incineration requirements of solid wastes are reduced through biodegradation, and the environmental pollution is reduced. And through a carbon capture technology, the emission of carbon dioxide is reduced, and the global climate change can be slowed down. Combustible gas such as methane in the degraded gas can be collected and used as energy; the residue can be used as a raw material or a land improvement material. And wastes are converted into useful products, so that the overall utilization efficiency of resources is improved.

A method for tracking a four-dimensional soil moisture drought event and identifying a warming signal, includes following steps: data acquisition; identification of four-dimensional soil moisture drought events; characteristic quantification of four-dimensional soil moisture drought events; anthropogenic warming signal detection based on temporal evolution of four-dimensional soil moisture drought characteristics in a historical period; estimation of future evolution of four-dimensional soil moisture drought; and attribution of four-dimensional soil moisture drought events to driving factors. The future spatial-temporal evolution characteristics of four-dimensional soil moisture drought are explored, and the driving factors and physical mechanisms of four-dimensional soil moisture drought are analyzed. A strong scientific basis is provided for tackling global climate change, safeguarding water resource security, and formulating disaster prevention and mitigation strategies.

The application discloses a method for predicting meteorological dust accumulation cleaning requirements of a photovoltaic module based on a numerical mode, selects a SSP-RCP scenario and future simulation data of multiple global climate modes and historical precipitation data from a CMIP6 data set; for a target region, the future and historical precipitation of each grid point is calculated; then, the annual effective deposition of each grid point is calculated, the core of which is to correct the conversion of vertical deposition to the inclined surface of the photovoltaic module through the effective deposition coefficient, and to quantify the wind loss through the wind erosion correction; finally, based on the annual effective deposition, the theoretical annual cleaning frequency of the photovoltaic module of each grid point is calculated, thereby providing a quantitative basis for differentiated operation and maintenance.

The application discloses a cloud-aerosol hierarchical classification method based on semantic segmentation. The semantic segmentation technology is introduced into the cloud and aerosol hierarchical classification of atmospheric remote sensing, and multiple channel observation results and texture information are combined to continuously classify original signals pixel by pixel. Firstly, the multi-channel attenuated backscatter coefficient of a spaceborne laser radar CALIOP is collected, preprocessed through denoising and credibility screening, and then the volume depolarization ratio and the attenuation color ratio are calculated as RGB three-channel values to form an image to obtain a training sample set, which is input into a constructed semantic segmentation neural network to be trained, so that a cloud-aerosol hierarchical classification model is obtained, and the hierarchical classification of clouds and aerosols in the atmosphere is realized. Compared with the CALIOP hierarchical classification result of the existing method, the method has the advantages of high accuracy, high resolution, rich hierarchy and the like, is suitable for multiple scenes, and is helpful for observing the distribution of clouds and aerosols, predicting global climate change and the like.

The application discloses an experimental device and experimental method for simulating aircraft wake cloud generation and evolution, and relates to the field of experimental devices for simulating aircraft wake cloud generation and evolution. The experimental device for simulating aircraft wake cloud generation and evolution comprises a gas supply system, an altitude simulation system, a wake cloud generation and evolution system and a gas extraction system. The altitude simulation system comprises a test cabin provided with an aero-engine. The first gas supply system delivers low-temperature gas towards the aero-engine. The wake cloud generation and evolution system comprises an environment cabin, a nozzle and a dilution assembly. The dilution assembly receives and dilutes exhaust gas of the aero-engine and delivers the diluted exhaust gas to the nozzle. The nozzle sprays the exhaust gas into the environment cabin. The second gas supply system delivers low-temperature and high-humidity gas towards the environment cabin. The gas extraction system extracts gas in the test cabin, the aero-engine and the environment cabin. The experimental device for simulating aircraft wake cloud generation and evolution can realize the generation and evolution of simulated wake clouds, thereby facilitating the subsequent research on the influence of aviation emissions on global climate.