43 results about "Climatology" patented technology

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 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 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 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.

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.

This application pertains to a meteorological data processing method. Addressing the technical problems of existing generative adversarial networks (GANs) in downscaling applications, such as unstable training processes and overly smooth high-resolution data leading to vanishing or exploding gradients and an inability to accurately represent local meteorological conditions, this application provides a meteorological data downscaling method, system, electronic device, and readable storage medium. First, statistical distribution bias correction is performed on the original low-resolution meteorological data to be downscaled, resolving the issue of significant discrepancies between the original data and measured data, thus improving overall downscaling accuracy. This application utilizes a Wasserstein GAN to solve key problems such as unstable training, pattern collapse, and vanishing or exploding gradients. Furthermore, during training, the Wasserstein GAN in this application aims to minimize the overall loss, incorporating perceptual loss to address the problem of overly smooth output results that fail to reflect local meteorological conditions.

The invention relates to the technical field of three-dimensional modeling, in particular to a numerical analysis-based complex-form karst cave distribution simulation method, and solves the technical problem of inaccurate model construction caused by elimination of irregular boundary forms in a karst cave during dimensionality reduction of karst cave point cloud data in the prior art. The method comprises the following steps: acquiring point cloud data of a karst cave; dividing the point cloud data into a plurality of local space regions, and determining a complexity coefficient based on shape features and edge features of the point cloud data in each local space region; the complexity coefficient is used for representing the form complexity of the local space region; performing adaptive dimension reduction processing on each local space region according to the complexity coefficient to obtain point cloud data of each local space region after dimension reduction; and constructing a three-dimensional distribution model of the karst cave based on the point cloud data after dimension reduction of the plurality of local space regions.

This invention relates to a method for urban multi-granularity catchment area delineation based on high-precision 3D data, comprising the following steps: acquiring high-precision 3D urban data using laser point cloud technology; classifying the point cloud data using a progressive triangulation filtering method to extract vector boundary lines of building, road, and overpass elements; performing hydrological analysis based on a high-precision DEM to extract catchment areas and merge them with boundary lines to construct a macroscopic boundary layer; dynamically dividing irregular grids using a quadtree grid subdivision and merging mechanism to construct a detailed grid layer; dividing the detailed grid layer into runoff generation, runoff confluence, and runoff-confluence raster layers; integrating the macroscopic boundary layer and the detailed grid layer, assigning hydrological parameters to form a multi-granularity 3D urban catchment area delineation; and achieving efficient storage and rapid retrieval through object-oriented spatial data organization and GeoJSON storage. This invention improves the precision of catchment area delineation and the accuracy of dynamic simulation in complex urban environments.

The application discloses a rocky desertification rapid identification method and system, and the method comprises the following steps: inputting a ground surface image into a lightweight semantic segmentation model, and outputting bare rock area pixels, vegetation area pixels and other area pixels from the lightweight semantic segmentation model; determining a base rock exposure rate and a vegetation coverage degree according to the bare rock area pixels, the vegetation area pixels and the other area pixels; determining an interference intensity grade according to selection information; inputting a soil layer thickness measurement value, slope data, the base rock exposure rate, the vegetation coverage degree and the interference intensity grade into a multi-factor comprehensive evaluation model to calculate a rocky desertification comprehensive index; and calculating a rocky desertification grade according to a preset grade determination threshold and the rocky desertification comprehensive index, and displaying a calculation result. The application solves the problem that a single index cannot comprehensively reflect the rocky desertification degree, and unifies different factors into a comprehensive index, so that the evaluation result is scientific, the work efficiency of ground investigation is improved, and the work intensity of workers is reduced.

The present application belongs to the technical field of geochemistry, and particularly relates to the estimation of the carbon flux of the earth system, and especially relates to a method and system for estimating the carbon flux of the deep-time earth system. The method for estimating the carbon flux of the deep-time earth system provided by the present application solves the four defects in the traditional technology caused by the fixed carbon pool assumption, the neglect of the atmospheric / biosphere carbon content, the isotopic error, and the failure to consider the fractionation effect through dynamic carbon pool segmentation, the inclusion of multi-sphere isotopic composition, and the correction of fractionation effect. The high-precision and verifiable model framework not only improves the scientificity of the deep-time carbon cycle research, but also provides a key tool for analyzing the climate evolution mechanism in the geological history period and predicting the future climate change trend, and has important theoretical innovation and practical application value.

The application discloses a lunar soil water ice drilling process sample temperature rise simulation prediction method and system, belongs to the moon water ice detection and temperature field simulation technical field, wherein the method comprises the following steps: matching lunar soil characteristic parameters according to the polar lunar soil properties, and establishing a lunar soil water ice particle system according to the lunar soil characteristic parameters; acquiring the inner diameter, the outer diameter and the wall thickness of a drill rod, and the heat output and the heating power input of an instantaneous heat source, and inputting the EDEM simulation software to establish a drill theoretical temperature rise model; performing a single-tooth cutting simulation test based on the water ice particle system and the drill theoretical temperature rise model, and determining a heat source point and a heat generation energy distribution ratio; and integrating the lunar soil water ice particle system, the drill theoretical temperature rise model and the heat generation energy distribution ratio to establish a moon-land interaction discrete element temperature rise model. The method increases the simulation of heat transfer forms such as convection and thermal radiation, the established simulation model is closer to the actual situation, and then the simulation model can predict the temperature rise of the drill and the lunar soil water ice in the drilling process.

The application provides a method for determining the light absorption characteristics of atmospheric brown carbon aerosols and an electronic device, wherein the method comprises: obtaining aerosol observation data and organic aerosol mass spectrum data; determining the brown carbon absorption coefficient corresponding to each wavelength according to the aerosol observation data; determining a plurality of organic aerosol sources and the aerosol concentration corresponding to each organic aerosol source according to the organic aerosol mass spectrum data; training a plurality of brown carbon source decision models according to the brown carbon absorption coefficient corresponding to each wavelength and the aerosol concentration corresponding to each organic aerosol source, and determining at least one brown carbon source corresponding to each wavelength according to each brown carbon source decision model; and determining the absorption spectrum of each brown carbon source according to each brown carbon source corresponding to each wavelength. The application can accurately retrieve the absorption spectrum of each brown carbon source, realize the cross-scale correlation from chemical composition to climate effect, and improve the accuracy and scientificity of the evaluation of the climate impact of brown carbon.

The application discloses a radar echo space-time extrapolation method based on asymmetric space modeling, belongs to the cross field of meteorological radar data processing and artificial intelligence technology, and first constructs an asymmetric input space sample containing a target region and a surrounding extended region by preprocessing original meteorological radar observation data, builds an asymmetric space modeling prediction network based on a 3D U-Net architecture, adopts a regional mask mean square error loss function to complete model training, and finally realizes accurate space-time extrapolation of the target region radar echo. The application solves the technical problems of information fragmentation and significant long-time prediction error accumulation of the traditional symmetric modeling method, improves the precision and stability of radar echo extrapolation under complex weather systems, and is suitable for 0-1 hour nowcasting and meteorological disaster accurate early warning scenes.

A method, computer equipment, medium, and product for calculating forest stand density are disclosed, relating to the field of forest resource monitoring. The method includes: acquiring three-dimensional point cloud data of the forest area to be measured; separating the three-dimensional point cloud data into a ground point set and a vegetation point set; performing elevation normalization processing on the vegetation point set based on the ground point set to obtain a vegetation point cloud; constructing a canopy height model based on the vegetation point cloud, and extracting the local highest points from the canopy height model and adding them to a candidate vertex set for individual trees; constructing a planar Delaunay triangulation network based on the candidate vertex set for individual trees, and determining the vertex pairs connected by each edge in the planar Delaunay triangulation network as adjacent vertex pairs; calculating the branch topological adhesion of adjacent vertex pairs; if the branch topological adhesion is greater than a preset adhesion threshold, identifying false vertices in the adjacent vertex pairs and removing them from the candidate vertex set for individual trees to obtain a true vertex set for individual trees; counting the total number of vertices in the true vertex set for individual trees, and dividing the total number of vertices by the total area of ​​the forest area to be measured to obtain the forest stand density calculation result.

The invention discloses a hydrological data analysis and forecasting method based on machine learning, which comprises the following steps: acquiring a digital elevation model of a target area, and determining an earth surface confluence direction based on the digital elevation model; obtaining shrinkage crack trend information of the target area and determining a crack diversion direction; acquiring a rainfall time sequence and river channel monitoring time sequences of the first river channel and the second river channel; inputting the rainfall time sequence, the riverway monitoring time sequence of the first riverway, the riverway monitoring time sequence of the second riverway, the earth surface confluence direction and the crack diversion direction into a cross-catchment area water delivery judgment model, and outputting a cross-basin water delivery judgment result; and when the inter-basin water delivery judgment result represents that inter-basin water delivery exists, the effective rainfall of the rainfall event is redistributed from the basin corresponding to the first river channel to the basin corresponding to the second river channel, and a forecast water level or forecast flow is generated respectively. According to the method, the problems of false report and missing report caused by cross-basin hidden water delivery under the shrinkage crack condition are solved.

This invention discloses a method for sub-basin delineation in small and medium-sized watersheds based on a distributed model, comprising the following steps: S1, collecting digital elevation model (DEM) data, performing depression filling processing, and extracting preliminary river network information; S2, extracting the actual river system and calculating the elevation difference of the main stream grid; S3, adjusting the DEM data based on the elevation difference to optimize the river network extraction results; S4, calculating watershed morphological parameters and classifying the watershed using a manifold learning method; S5, constructing an optimal threshold calculation model for sub-basins and dynamically adjusting the sub-basin delineation threshold; S6, performing flow direction calculation, confluence analysis, and sub-basin delineation, and optimizing the sub-basin boundaries; S7, validating the method using a distributed hydrological model, analyzing the impact of the delineation results, and optimizing the sub-basin delineation parameters. This invention integrates multi-scale features, dynamic threshold optimization, and hydrological network topology to improve the accuracy of sub-basin delineation, boundary continuity, and hydrological simulation stability, achieving intelligent watershed delineation.

The invention relates to the technical field of climate resource development and health-care industry, and discloses a method for planning three-dimensional climate health-care resources in a low-latitude plateau mountainous area, which is characterized in that the climate shows a vertical differentiation characteristic of'ten days different from each other 'due to large altitude fall (generally 2000-3000 meters) in the low-latitude plateau mountainous area, so that the three-dimensional climate health-care resources in the low-latitude plateau mountainous area can be developed. Therefore, the mapping relation of altitude gradient-microclimate-health value needs to be established in general survey, and during specific implementation, automatic meteorological stations (such as 600 meters, 900 meters, 1200 meters... 3200 meters) are arranged at intervals according to the altitude of 300 meters. According to the three-dimensional climate health resource planning method for the low-latitude plateau mountainous area, when the high-altitude research and study requirements are insufficient, by combining the phototherapy course developed by the ultraviolet light resources, the market blank is filled, the deep value of the three-dimensional climate can be played, the resource waste is avoided, the continuous attraction is kept, and the mode that development is not damaged and adjustment follows up is adopted. The three-dimensional climate health resources are ensured to benefit people at present and benefit descendants for a long time.

The invention discloses a road area vegetation carbon sink assessment method and system based on the Internet of Things, and a storage medium, relates to the technical field of carbon sink monitoring, and solves the technical problem that the carbon sink assessment precision is low due to the fact that the prior art often depends on a single data source and lacks consideration of the influence of dynamic factors such as traffic interference and soil parameters. Multi-source fusion data is generated based on remote sensing data, sensor data and traffic data; inputting the multi-source fusion data corresponding to the region ID into a vegetation classification model to obtain vegetation result data corresponding to the region ID; generating a regional carbon sink total amount based on the vegetation result data; an alarm signal and an optimization scheme are generated based on the total amount of regional carbon sinks, multiple data sources are considered in real time, fusion weight coefficients are dynamically distributed for all the data sources, multi-source fusion data are more accurate, accurate vegetation result data are obtained through a pre-trained vegetation classification model, accurate data support is provided for carbon sink evaluation, and the carbon sink evaluation efficiency is improved. And the accuracy of the carbon sink evaluation method is improved.

The invention discloses a regional sea wave forecasting method based on single-point buoy observation and a graph neural network, and belongs to the field of marine environment forecasting. A solution based on a graph neural network (GNN) and fused with a MeshGraphNet model of single-point buoy observation is innovatively provided, so that the efficiency, precision and timeliness of sea wave significant wave height forecasting are remarkably improved. The method comprises the following steps: carrying out time sequence feature extraction and short-time forecasting on an observation sequence of a single buoy station through an LSTM network, and then taking a forecasting result output by the LSTM and ERA5 space wind field features as node features of a space graph neural network; an encoder of the graph neural network encodes node wind field features and edge geometric features into a multi-dimensional potential space, edge updating, node aggregation and global state updating are carried out through a message transmission layer to fuse local neighborhood information and a global sea area state, and finally a decoder outputs the significant wave height at a forecast moment.

The invention discloses a method for quantitatively evaluating the influence of marine environment forecast product errors on sound field forecast, and relates to the technical field of ocean engineering. The method is characterized by comprising the following steps: S1, taking seawater sound velocity profile data measured in a target sea area field as real sound velocity; taking seawater sound velocity profile data output by the marine environment forecasting product in the corresponding space-time range as forecasting sound velocity; s2, data space-time alignment and ocean forecast sound velocity error calculation; s3, generating a sound velocity error spatial distribution map; s4, constructing a sound velocity disturbance probability model and a sound velocity disturbance sample set based on the sound velocity forecast error; calculating sound field propagation loss by adopting a parabolic equation model to obtain reference sound propagation loss and a sound field propagation loss disturbance sample; s5, extracting a convergence region distance; and S6, generating a sound field convergence region distance error spatial distribution diagram according to the first convergence region distance error and the second convergence region distance error in each grid space unit.

The application relates to the field of new energy power generation technology, and discloses a wind-solar-storage power station active power optimization control method and device, which comprises the following steps: determining wind-solar-storage active power control optimization variables based on active output scheduling instruction values and actual output active power values of the wind-solar-storage power station; calculating wind-solar-storage active power control costs based on the actual output active power values of the wind-solar-storage power station, wind-solar ultra-short-term power prediction values and the wind-solar-storage active power control optimization variables; constructing a wind-solar-storage power station active power optimization control model based on the wind-solar-storage active power control optimization variables and the wind-solar-storage active power control costs; and optimizing and solving the wind-solar-storage power station active power optimization control model to obtain optimal control results of the wind-solar-storage power station active power. The wind-solar-storage active power control optimization variables fully consider the active control costs of wind power, photovoltaic power and energy storage, and the wind-solar-storage active power output is optimized and controlled.

The dustproof heat dissipation type LED mining lamp and the using method are applied to the technical field of mining lamps, an illumination mechanism is arranged, a light-emitting assembly can provide illumination for a target area after being powered on and started, a dust discharging assembly can blow dust at the light-emitting assembly to a dust guiding assembly, and the dust guiding assembly can guide the dust to the target area; positive ions and negative ions can be adopted for purifying air, meanwhile, static electricity in dust can be eliminated, danger caused by the static electricity is avoided, the dust guiding assembly can guide the dust and the air, the dust is guided into the intercepting assembly, and the dust collecting assembly can collect the dust; according to the dust removal assembly, the dust is prevented from returning to the air to cause secondary pollution to the air, the workload of the dust removal assembly is reduced, the intercepting assembly can intercept the dust and convey the dust into the conveying assembly, and the conveying assembly can convey the dust intercepted by the intercepting assembly into the dust collection assembly along with flowing of the air.

The invention provides a storm surge dam overtopping risk dynamic assessment method fused with multi-source remote sensing data, and relates to the technical field of risk prediction. The method comprises the following steps: collecting multi-source observation data of each stage of storm surge, uniformly preprocessing, and constructing a standardized multi-dimensional time sequence state vector; a disturbance seed function is introduced to construct a feature enhancement module, local response changes are extracted in a disturbance window, a disturbance response sequence is generated, frequency domain offset reconstruction is carried out, and a spectrum stability index is established to enhance feature robustness; in combination with a disturbance response path and a non-normalized enhancement factor, context feature mapping decoding is guided, and risk area response focusing is realized; a disturbance feedback dynamic strategy optimization mechanism is designed, disturbance amplitude modulation, channel adjustment and threshold control strategies are fused, and the risk score prediction precision is improved; and a joint loss function is constructed, the spectrum disturbance consistency and the risk scoring error are jointly optimized, and the identification and dynamic prediction performance of the potential dam overtopping risk in the complex environment is significantly enhanced.

The invention relates to the field of information retrieval, in particular to an astronomical data index construction method and device based on an R tree and a program product. The method for constructing the astronomical data index based on the R tree comprises the steps that an astronomical star catalogue is obtained, the astronomical star catalogue comprises a plurality of data lines, and fields of the astronomical star catalogue comprise spatial positions; dividing the celestial coordinate system into a plurality of pixel blocks with different spatial codes based on a predetermined sky area division protocol, mapping the data rows to the corresponding pixel blocks based on the spatial positions of the data rows to obtain the spatial codes corresponding to the data rows, and dividing the data rows with the same spatial codes in the astronomical star catalogue into one data block to obtain a plurality of data blocks; constructing an R tree index for each data block in the plurality of data blocks based on the spatial position of the data row to obtain a block index of the data block; and performing one-to-one correspondence on each spatial code and the block index to obtain an astronomical data index. According to the method, the astronomical data retrieval efficiency is improved.

This invention discloses a method for determining the target phase change temperature and target phase change enthalpy of phase change materials based on the snow melting and de-icing needs of different regions, belonging to the technical field of road snow melting and de-icing and phase change energy storage parameter design. The method includes: obtaining the latitude, altitude, winter minimum temperature, duration near freezing point, snowfall intensity, and total winter solar radiation of the target region; determining the characteristic winter temperature based on latitude and altitude; automatically identifying the snow melting and de-icing demand type by combining the above parameters; inputting the regional parameters and demand type into a phase change material thermal parameter prediction model to obtain the predicted values ​​of the target phase change temperature and target phase change enthalpy; after engineering constraint correction, matching with a candidate phase change material library, and outputting recommended phase change temperature range, phase change enthalpy range, and candidate material category. This invention can provide a basis for determining phase change snow melting and de-icing parameters for transportation infrastructure such as roads, bridge decks, airport runways, and railway turnouts.