115 results about "Climatology" patented technology

The invention relates to the field of ocean data buoy data quality control, and discloses a buoy meteorological data restoration method based on a space-time double-attention neural network, and the method comprises the following steps: data collection and preprocessing; establishing a space-time double-attention neural network model, wherein the model comprises a time coding link, a space coding link and a feature fusion layer; model training: inputting the time features containing the target elements to be predicted into a time coding link, inputting the meteorological elements into a space coding link, and outputting the target elements at the moment to be predicted by the model; after model training, evaluating the model by using the test set; and predicting a vacancy value or an abnormal value in the multi-dimensional meteorological element data acquired in real time by using the model which is qualified in evaluation, so as to realize data restoration. According to the method disclosed by the invention, time and space constraints are effectively combined, and abnormal values and vacant values in the meteorological data are predicted and replaced, so that the meteorological data are repaired.

The invention discloses a microwave monitoring rainfall field reconstruction method based on a generative model. The method comprises the following steps: S1, obtaining rainfall data from different data sources; s2, dividing a training data set and a simulation data set, and performing data preprocessing; s3, a generative adversarial neural network model Mic-GAN is constructed; s4, iteratively training the anti-neural network model Mic-GAN by adopting the training set in the step S2 until a preset number of iterations or convergence conditions are met; and S5, reconstructing the microwave rainfall field of the target area by adopting the simulation data set in the step S2 and using the model in the step S4. According to the microwave monitoring rainfall field reconstruction method based on the generative model, optimization is continuously carried out along with the increase of data, the advantages of microwave big data are effectively utilized, and the method has good expandability.

The invention relates to a shallow water dynamic process simulation method based on a local time step length, and the method comprises the steps: dividing a target region into a plurality of grids based on a shallow water dynamic process model, the target region comprising an ocean region and a land region adjacent to the ocean region; at least dividing the plurality of grids into a first grid set and a second grid set; determining a first time step length corresponding to the first grid set according to the first grid set; determining a second time step length corresponding to the second grid set according to the second grid set; determining a first simulation result of the shallow water dynamic process according to the first time step length and the second time step length; wherein the first grid set comprises a plurality of first grids, and the second grid set comprises a plurality of second grids; the first time step length is larger than the second time step length. The efficiency of the shallow water dynamic process simulation method is improved by respectively determining appropriate local time steps according to the precision requirements of different grid sets.

The invention provides a whole sea area sound scattering layer resource abundance prediction method and electronic equipment. The method comprises the following steps: constructing a standard sound scattering layer NASC data set; constructing a sound scattering layer resource abundance prediction model based on machine learning; predicting the resource abundance of the sound scattering layer based on the trained resource abundance prediction model of the sound scattering layer; and optimizing the predicted NASC value by adopting a Kriging interpolation method to obtain a resource distribution diagram of the sound scattering layer of the whole sea area. Aiming at the problem that the survey area cannot cover the whole sea area in the fishery resource acoustic survey process, the resource abundance of the acoustic scattering layer of the sea area without hydroacoustic survey can be predicted.

The invention discloses an urban air pollution early warning method based on meteorological and environmental monitoring data, which relates to the technical field of environmental monitoring, and comprises the following steps: setting a monitoring period and a monitoring area, and establishing an air pollution early warning model according to historical data characteristics; establishing an urban air pollution early warning strategy of the current monitoring period according to the data characteristics predicted by the air pollution early warning model; when the real-time data characteristics of the monitoring area do not conform to the urban air pollution early warning strategy of the current monitoring period, a sentinel node is arranged in the monitoring area, the sentinel node sends detection data to the adjacent monitoring area at regular time, and a real-time data sharing channel is established between the monitoring area and the adjacent monitoring area; the quantitative data of migration and diffusion of pollutants in the atmospheric environment are obtained through the real-time data sharing channel, the air pollution early warning model of the current monitoring period is dynamically adjusted, the urban air pollution early warning strategy of the remaining time period of the current monitoring period is updated, and the early warning accuracy of dynamic environmental pollution is improved.

The invention relates to the technical field of data processing, and discloses a numerical simulation method for a synergistic effect of an urban heat island and high-temperature heat waves. The method comprises the following steps: collecting urban building form data to construct a data set, and combining remote sensing data to carry out local climate zoning; extracting a high-temperature heat wave process and performing grading by utilizing machine learning; analyzing the urban heat island intensity time sequence change based on the remote sensing data; constructing an urban canopy mode coupling hydrology and an artificial heat source; and inputting different levels of heat wave data to simulate a synergistic effect. The technical problem that the urban heat island effect and high-temperature heat wave synergistic effect mechanism cannot be accurately simulated in the prior art is solved, and the numerical simulation method for quantitatively evaluating the urban heat island intensity amplification coefficient under the condition of different grades of high-temperature heat waves is established.

The invention relates to a multi-modal meteorological data downscaling generation method and device and computer equipment. The method comprises the following steps: acquiring topographic data and meteorological data in a to-be-measured area; inputting the topographic data and the meteorological data into a pre-trained deep learning model, carrying out feature coding on the topographic data and the meteorological data through the deep learning model to obtain a topographic feature vector and a meteorological feature vector, carrying out feature interaction on the topographic feature vector and the meteorological feature vector to obtain interaction features, and carrying out feature extraction on the interaction features; and carrying out downscaling decoding on the interaction features to obtain downscaling meteorological data. By adopting the method, the downscaling generation efficiency and the physical rationality of the meteorological data can be considered.

The invention provides a data model dual-drive-based water pollution prediction method and device, and relates to the technical field of water pollution monitoring, and the method comprises the steps: obtaining multi-source information reflecting an incidence relation between a pollution source position and water quality abnormity; training a preset random forest regression model by using the multi-source information and a particle swarm optimization algorithm to obtain a target random forest regression model, inputting real-time data of which the type is the same as that of the multi-source information into the target random forest regression model, and determining pollution source position information; constructing a one-dimensional pollutant diffusion model based on the spatial-temporal characteristic information, and expanding the one-dimensional pollutant diffusion model to a two-dimensional cellular model in combination with a cellular automaton simulation method; and inputting pollution source position information and environmental influence information into the two-dimensional cellular model to generate a water pollution diffusion prediction result. Through comprehensive application of the algorithm and the model, the accuracy and the real-time performance of water pollution prediction are improved.

The invention relates to a landslide meteorological early warning method coupling machine learning and a rainfall early warning index, which comprises the following steps: taking a frequency ratio of screened landslide evaluation factors in different grading intervals as input, taking a landslide occurrence state as output, adopting a machine learning model to carry out landslide susceptibility modeling, and establishing a landslide susceptibility partition map; modeling a quantitative relation between the rainfall scene and the rainfall early warning index; calculating early effective rainfall and induced event rainfall in different return periods by adopting Gumbel distribution based on historical rainfall statistical data of the to-be-researched area, determining a minimum rainfall early warning index and a maximum rainfall early warning index, and obtaining a rainfall early warning index distribution diagram of the to-be-researched area; and carrying out coupling superposition on the landslide susceptibility partition map and the rainfall early warning index distribution map to obtain a landslide risk meteorological early warning map of the to-be-researched area. According to the method, a scientific basis is provided for regional scale landslide early warning, and the problem of splitting of a traditional model in data utilization and mechanism interpretation is solved.

The invention discloses a method for analyzing influence of climate change and human activity on space-time evolution of water resources. The method comprises the following steps: acquiring meteorological information, hydrological information, land utilization information, soil information, social economic information and water resource information of a to-be-detected area; performing trend analysis on the water resource information to obtain a space-time evolution rule of the water resource information; performing attribution analysis on the time-space evolution rule to obtain influence factors of the time-space evolution of the water resource; obtaining the correlation degree of the influence factors and the space-time evolution rule, and completing the analysis of the climate change and human activity on the space-time evolution of the water resource. According to the method, the influence factors of water resource evolution of the to-be-detected area are researched through the multivariate statistical stepwise regression analysis model, the multicollinearity between the related influence factors of climate change and human activity is reduced through the characteristics of stepwise regression analysis, the calculation accuracy is improved, and the sustainable development of water resources is promoted.

The invention relates to the technical field of special engineering environment control, and discloses a large-aperture telescope dome cover internal thermal control system, which comprises a dome air conditioner thermal control system and a dome emergency exhaust system, and is characterized in that the dome air conditioner thermal control system has variable-temperature cooling and preset low-environment-temperature cooling functions; the thermal environment adjusting module is used for adjusting the thermal environment in the dome cover based on preset thermal control requirements; the dome emergency exhaust system is used for conducting rapid cooling adjustment when the adjusting effect of the dome air conditioner thermal control system cannot meet the preset thermal control requirement. According to the dome air conditioner thermal control system, the cooling mode can be flexibly adjusted according to different environment temperatures and observation requirements, the dome emergency exhaust system serves as a standby scheme, and when the air conditioner thermal control system cannot meet the thermal control requirement, rapid starting and rapid cooling adjustment can be conducted. The emergency mechanism ensures that the telescope can still quickly reach the preset working temperature under extreme weather or emergency conditions, and is beneficial to improving the observation benefit of the telescope.

The invention relates to the field of ocean temperature frontal surface detection, and discloses an ocean frontal surface intelligent detection method fusing dense jump connection and seasonal enhancement, which comprises the following steps: obtaining daily satellite remote sensing SST data of a research sea area, and carrying out preprocessing to obtain an SST gradient map; marking the frontal surface label to obtain frontal surface label data; constructing a seasonal enhanced multi-scale recognition network model fused with dense jump connection; the model comprises five encoders, four decoders, a head network and six intermediate states; the SST gradient map and frontal label data are divided into a training set and a test set, and the model is trained and subjected to precision evaluation; and preprocessing SST data of a sea area to be detected to obtain an SST gradient map, and inputting the SST gradient map into the qualified model for ocean frontal surface intelligent detection. The method disclosed by the invention can improve the overall expressive force of the model and the accuracy of frontal surface detection.

The invention belongs to the technical field of point cloud data noise reduction, and particularly discloses a point cloud data noise reduction method and system, a terminal and a medium. Comprising the steps of obtaining original point cloud data of a target scene; for any point in the point cloud, calculating the local point cloud density based on the spatial distance from the point to a plurality of nearest neighbor points in the neighborhood of the point, and determining the neighborhood search radius of the point according to the local point cloud density; constructing a neighborhood point set according to the neighborhood search radius, and extracting local structure consistency features in the neighborhood point set; based on the local structure consistency feature, generating a judgment condition for judging a noise point, and judging whether the target point is the noise point or not according to the judgment condition; and filtering the points which are judged to be noise points from the point cloud data to obtain the point cloud data after noise reduction. Through adaptive neighborhood construction, local structure consistency feature modeling and dynamic generation of noise discrimination conditions, robust recognition of point cloud noise points under complex weather conditions such as rain and snow is realized, and improvement of the overall quality of point cloud data is facilitated.

The invention provides an atmospheric brown carbon aerosol light absorption characteristic determination method and electronic equipment, and the method comprises the steps: obtaining aerosol observation data and organic aerosol mass spectrum data; determining a brown carbon absorption coefficient corresponding to each wavelength according to the aerosol observation data; determining a plurality of organic aerosol sources and aerosol concentrations corresponding to the organic aerosol sources according to the organic aerosol mass spectrum data; according to the brown carbon absorption coefficient corresponding to each wavelength and the aerosol concentration corresponding to each organic aerosol source, training to obtain a plurality of brown carbon source decision models, and according to each brown carbon source decision model, determining at least one brown carbon source corresponding to each wavelength; and determining the absorption spectrum of each brown carbon source according to each brown carbon source corresponding to each wavelength. According to the method, accurate inversion of the absorption spectrum of each brown carbon source can be realized, cross-scale correlation from chemical components to climate effects is realized, and the accuracy and scientificity of brown carbon climate influence assessment are improved.

The invention provides a permafrost region all-season heat pipe heat exchange system which comprises a heat pipe used for being arranged in a permafrost region and internally provided with a refrigeration working medium vaporized when the set temperature is reached, the heat pipe is provided with a condensation part, a refrigeration part and an evaporation part which are sequentially communicated from top to bottom, and the condensation part and the refrigeration part are located on the earth surface of the permafrost region correspondingly; the evaporation part is located in a frozen soil layer of the frozen soil area; the refrigerating system comprises a refrigerating device arranged on the outer side of the refrigerating part, and cold bath liquid capable of providing cold energy is arranged in the refrigerating device; and the gas circulating system exchanges heat with the cold bath liquid through internal circulating gas. Cooling of the heat pipe can be achieved through the refrigerating system, heat exchange of the refrigerating system can be achieved through the gas circulating system, and therefore it is guaranteed that the refrigerating system always has the proper temperature, continuous cooling of the heat pipe at any time period can be achieved, the defect that a traditional heat pipe cannot work in the high-temperature season is overcome, and full-year protection of frozen soil is achieved.

A method of controlling a transport climate control system is disclosed. The method includes a controller instructing the transport climate control system to operate in a continuous mode in order to provide climate control within a climate controlled space. The method also includes the controller monitoring a temperature within the climate controlled space. Upon the temperature within the climate controlled space reaching a cycle-off temperature, the controller instructing the transport climate control system to operate in a start-stop mode. The method further includes the controller determining a reference duty cycle. Also, the method includes the controller scaling the reference duty cycle to obtain a scaled duty cycle based on the reference duty cycle and a predetermined efficient climate capacity of the transport climate control system. Moreover, the method includes the controller operating the transport climate control system in the start-stop mode according to the scaled duty cycle.

The embodiment of the invention provides a compressed air energy storage system and method based on goaf heat storage regulation and control. The system is applied to the technical field of compressed air energy storage and comprises a heat storage goaf module, an energy storage sub-module and an energy release sub-module. The method comprises the following steps: in a power consumption trough period, an energy storage sub-module operates to compress ambient air to a high-temperature and high-pressure state; in the power utilization peak period, the energy release sub-module operates, heat energy is transmitted to high-pressure air which is pumped out of the air storage shaft and flows through the energy storage heater, and high-temperature and high-pressure air is formed to drive the turbo expander to generate electricity. After the turbo expander completes power generation, exhausted low-temperature and low-pressure air is introduced into a multi-stage cold energy exchange pipe network buried in the goaf, and an energy closed loop is achieved. In this way, the technical problems that in the prior art, due to inherent intermittency and uncertainty of renewable energy sources, grid-connected consumption is difficult, wind and light abandoning is serious every year, and the peak-valley difference of a power grid tends to be gradually increased can be solved.

The invention provides an ocean environment noise forecasting method based on a LightGBM algorithm. The ocean environment noise forecasting method mainly solves the problem that in the prior art, a model and a method for forecasting noise through machine learning are low in forecasting accuracy. According to the method, on the basis of actually-measured acoustic experiment data and meteorological data in a public database, noise spectrum levels, calculated by the actually-measured acoustic experiment data, of different frequency points serve as label variables, the meteorological data serve as characteristic variables, hyper-parameter optimization is conducted on a LightGBM model through a grid search and cross validation method, and the optimal LightGBM model and hyper-parameters are obtained; the method has the advantages of high training speed, high accuracy and the like during data processing, and prediction precision can be effectively improved while prediction time is shortened.

An intensification mechanism analysis and anthropogenic climate change signal identification method for terrestrial water cycle (TWC) in dry and wet regions, includes: identifying dry regions and wet regions worldwide with observed data; quantifying a precipitation increasing rate; calculating a regional warming rate and a precipitation response warming rate, and investigating a difference between dry regions and wet regions; identifying a fingerprint pattern of a precipitation increase in dry regions and wet regions in response to global warming, and calculating a signal-to-noise ratio (SNR) to quantify a possibility of an anthropogenic climate change signal in dry regions and wet regions; and detecting different external forcing signals in a precipitation change of dry regions and wet regions with an optimal fingerprinting method, and quantifying a contribution of each of the different external forcing signals to the precipitation change.

The invention discloses a method for confirming the width of a heat island corridor of an urban heat island network, and belongs to the technical field of urban ecological intelligent analysis. The method comprises the following steps: inverting the surface temperature of a research area; in combination with morphological spatial pattern analysis, landscape connectivity indexes and circuit theories, screening a heat island source ground and generating a resistance surface, and constructing a multi-grade urban heat island corridor network; constructing a multi-level buffer zone for different levels of galleries, and respectively extracting surface temperature mean values; extracting a buffer zone temperature extreme value of each grade of gallery, calculating a temperature difference parameter, and quantifying the heat exchange intensity; calculating heat transfer efficiency per unit area by combining the area of the buffer zone; analyzing the nonlinear attenuation characteristics of the heat transfer efficiency along with the offset distance of the buffer zone, extracting the inflection point of the heat transfer efficiency, and taking the offset distance of the buffer zone corresponding to the inflection point as the space action width of the heat island gallery, thereby realizing the scientific definition of the space action boundary of the heat island network. The method can also provide a scientific basis for relieving the urban heat island effect, optimizing the green land layout and the like.

The invention discloses an accumulated snow depth inversion method and device based on multi-source data fusion, and the method comprises the steps: obtaining first snow depth data through a snow depth product, obtaining second snow depth data actually measured by a site, and obtaining independent variable data; the independent variable data comprises brightness temperature data, ground surface coverage data, forest coverage rate data, altitude data, latitude and longitude data, ground surface classification data and geographic coordinates; fusing the first snow depth data and the second snow depth data to obtain fused snow depth data; combining different independent variable combinations, and fusing the snow depth data as a data set to train a random forest model to obtain a plurality of snow depth prediction initial models; and evaluating the prediction precision of the plurality of snow depth prediction initial models, and taking the model with the highest prediction precision as a target snow depth prediction model. According to the method, the snow depth product and the site actually measured snow depth are fused, and the fused high-resolution snow depth data are obtained to serve as the target variable of the random forest algorithm, so that the model precision is improved.

The present application belongs to the technical field of urban meteorological data processing and spatial information calculation, and particularly relates to a method and device for constructing urban meteorological gridded data based on spatial interpolation. The method comprises meteorological data clustering, partition optimization by using a target function during clustering, partition interpolation, spatial division and processing of regions according to the clustering results of meteorological data, statistics of the number of base stations for each region, determination of the type of a region based on the spatial distribution density of the base stations, determination of a global variation function and a regional variation function for each region, calculation of the estimated value of an interpolation point by using a corresponding Kriging interpolation method based on the variation function for different region types, interpolation of points near the boundary lines of different regions by using the meteorological data of the base stations of the regions involved, and weighting to obtain the final result. Finally, the construction of urban meteorological gridded data based on spatial interpolation is realized.

The invention provides an atmospheric pollution driving factor decoupling attribution method and device. The method aims to solve the problems that in the prior art, atmospheric pollution prediction neglects a space process, consequently, precision is insufficient, and static background and dynamic meteorological driving factor contribution cannot be quantitatively decoupled. Comprising the following steps: training a prediction model with spatial information perception capability based on multi-modal input features, and learning a spatial dependency relationship of pollutant concentration and outputting a prediction value by extracting spatial feature data containing a target position and neighborhood information of the target position as input by the model; and calculating the contribution value of each input feature by applying an attribution algorithm, and grouping and aggregating the contribution values based on preset feature group division to obtain the aggregated contribution amount of the feature group, thereby realizing the quantitative decoupling of the driving factor. According to the method, contribution of driving force of different time scales is quantitatively distinguished, so that global high-resolution accurate prediction is realized, and mechanism analysis and decision support is provided for scientific pollution control.

1. Name of the product in this design: Weather Station. 2. Purpose of this design: For meteorological observation and the collection and transmission of meteorological data. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

Method for monitoring low-temperature pipeline based on smart gas, including: obtaining gas data and pipeline data of each segment of a gas pipeline and weather data of a position of each segment of the gas pipeline; determining at least one target pipeline; determining icing data of the at least one target pipeline by processing the gas data, the pipeline data, and the weather data of the at least one target pipeline based on an icing prediction model, wherein the icing prediction model is a machine learning model; and generating a thawing instruction based on the icing data, and controlling a natural gas heating device to perform a thawing operation on the at least one target pipeline based on the thawing instruction.

The utility model is applicable to the technical field of early-warning regulation and control devices, and provides an early-warning regulation and control device for preventing grapes from being frozen in early spring, which comprises a heat preservation bin and a fan, one end of the fan communicates with the heat preservation bin, and the other end of the fan communicates with a heat conduction main pipe. The monitoring mechanism is arranged beside the heat conduction main pipe; according to the utility model, the rapid transmission and wide distribution of heat are realized, and the environmental conditions in the greenhouse are monitored in real time, so that the utilization efficiency of the heat is obviously improved, and the heat utilization rate of the greenhouse is improved; the system ensures that grapes grow in a relatively warm and stable environment, effectively prevents freeze injury in early spring, can monitor temperature and humidity data in a greenhouse in real time, provides comprehensive environment information for planters, and gives an alarm in time to remind the planters to take necessary measures when abnormal conditions are monitored, so that the planting efficiency is improved. Therefore, healthy growth and stable yield of grapes are comprehensively guaranteed.

The invention relates to the technical field of hydrological safety perception, in particular to a safety situation perception model training method of a hydrological information platform. The ice flood abnormity perception model comprises a data input layer, a weather data identification layer, a river channel data identification layer, a water source data identification layer, an ice flood abnormity perception layer and a result output layer; the weather data identification layer predicts the weather data set to obtain weather prediction data; the river channel data recognition layer recognizes the river channel data set to obtain river channel feature data; the water source data identification layer identifies the weather prediction data and the water source data set to obtain water source prediction data; and the ice flood abnormity sensing layer identifies the weather prediction data, the river channel characteristic data and the water source prediction data to obtain an ice flood abnormity coefficient. According to the method, the ice flood abnormity perception model is obtained through construction and training, and river ice flood abnormity is accurately identified.

The invention discloses a sky radiation coupling water chilling unit high-proportion renewable cold supply system and a control method, and belongs to the technical field of building air conditioner cold source systems, and the sky radiation coupling water chilling unit high-proportion renewable cold supply system comprises a sky radiation refrigerator, a water-cooled water chilling unit, a cold storage water tank, a water pump, an electric valve, a controller and a radiation ceiling tail end. According to the system, five working modes are dynamically switched, and efficient coupling utilization of a sky radiation cold source in multiple links of cold storage, cold supply and condenser heat dissipation is achieved; at night, a radiation refrigerator is used for preparing cold energy and storing the cold energy in a cold storage water tank; when a cold load exists in the daytime room and the temperature of the water tank is lower than the designed water supply temperature of the radiation ceiling, the water tank directly supplies cold and continues to accumulate cold; when the refrigerating efficiency of the radiation refrigerator is reduced, only the water tank supplies cold; when the temperature of the water tank rises to be unable to supply cold, the water chilling unit is started to supply cold, and the radiation refrigerator is utilized to dissipate heat for the condenser; and when the refrigerating efficiency of the radiation refrigerator is reduced, the water tank is switched to dissipate heat of the condenser.

The application discloses a kind of short-acting rain prediction methods of fusing satellite cloud picture and radar echo, it is related to short-acting rain weather prediction field, the specific steps of this method are as follows: synchronous acquisition preprocessed satellite cloud picture and radar echo data, respectively with symmetric time series deep learning architecture extraction feature, generate dimension alignment's rainfall probability discrete distribution;Unified distribution is obtained by double mode confrontation fusion;Probability confidence calibration is carried out, and the distribution of confidence interval is generated;According to magnitude interval mapping, the output different magnitude rainfall probability and confidence interval are calculated, and the grid point prediction is completed;The application adopts symmetric time series deep learning architecture to process multi-modal data, realizes macroscopic and microscopic collaborative representation, improves the completeness and consistency of rainfall probability generation;Combined with historical sample calibration deviation, the original logic and features are retained, and the standardized grid point prediction results are output;Optimize each link in whole process, so that the prediction result is more in line with the actual, and the business adaptability and application value are improved.

An optimization method for filtering LiDAR point clouds carried by an unmanned aerial vehicle. The method includes establishing an index of the point cloud using a KD tree; calculating and determining the maximum search radius for identifying isolated points r ; corresponding to the neighborhood threshold k 1 and the maximum search radius r for resampling; corresponding to the neighborhood threshold k 2 and the maximum search radius r for resampling. By establishing an index of the point cloud using a KD tree, the identification and removal of isolated points are achieved based on the search for neighboring points; the local tangent plane fitting at the position of each sampling point is realized using the moving least squares method, and the resampling of the point cloud is realized based on the method of projective transformation, thereby realizing the filtering and optimization processing of the original sampling data; greatly weakening the influence of sampling errors on the point cloud data, improving the consistency between the obtained data and the collected point cloud data, enhancing the universality of the airborne LiDAR point cloud data, and broadening the application field of lightweight and economically applicable unmanned aerial vehicle borne LiDAR devices.