45 results about "Spatial distribution pattern" patented technology

The invention relates to the field of forest ecological monitoring and operation management, and provides a method for quantifying the complexity of a three-dimensional structure of a forest canopy based on LiDAR and fractal dimension. Aiming at the key bottleneck that traditional canopy structure quantitative indexes are difficult to represent cross-scale three-dimensional space heterogeneity, high-precision three-dimensional point cloud data are acquired by using foundation and tower footing LiDAR, and precise individual tree segmentation is realized in combination with a machine learning clustering algorithm and tree structure features. According to the method, the three-dimensional void rate of the forest stand canopy is calculated, and the fractal dimension of a single tree is calculated by adopting a box counting method. The weight of a single tree is determined by comprehensively considering a single tree structure and a spatial distribution pattern of the single tree structure, the fractal dimension of a stand scale is calculated, a canopy structure complexity index is constructed in combination with a stand three-dimensional void rate, and scale-free quantification of the forest canopy three-dimensional structure complexity is achieved. According to the method, typical temperate zone secondary forest monitoring data is used for simulation and verification, and technical support is provided for forest accurate operation (cutting and complementary planting), carbon sink evaluation, biodiversity protection and the like.

The invention provides an optical fiber detection method and system for an optical time domain reflectometer, and relates to the technical field of power grids. According to the invention, the multi-wavelength backscattering signal of the optical fiber link to be detected is acquired, preprocessed and converted into the sample to be detected containing the multi-wavelength one-dimensional waveform data and the multi-wavelength two-dimensional diagram data at the same time, so that the sequence characteristic of the light intensity of each wavelength changing along with time is reserved; and spatial features of correlation and structural information between wavelengths can be reflected through data conversion construction, and multi-dimensional and structured representation of optical fiber events is realized. And combined analysis and recognition are carried out by combining a fault recognition model, and a time sequence rule in a one-dimensional waveform and a spatial distribution mode in a two-dimensional diagram are cooperatively mined, so that deep fusion and intelligent analysis of optical fiber fault spatial-temporal characteristics are realized, and the recognition capability of a complex fault mode is improved. The technical problem of low accuracy of optical fiber fault detection with a single wavelength at present is solved, and the accuracy and reliability of optical cable line fault detection are improved.

The invention discloses a mining area vegetation degradation identification method based on a remote sensing spectrum. The method comprises the following steps: collecting multispectral image data of a mining area; based on the multispectral image data of the mining area, main spectral feature vectors are extracted by adopting a principal component analysis method, and a vegetation spectral feature set is determined; based on the vegetation spectral feature set, performing health state division on vegetation pixels by adopting a support vector machine classifier to obtain a vegetation degradation classification map; extracting a spatial distribution pattern from the vegetation degradation classification map, calculating an area proportion and a position coordinate of each type of pixels, and determining a spatial distribution feature of a degradation degree; acquiring a multi-temporal remote sensing image sequence according to the spatial distribution characteristics of the degradation degree, analyzing and tracking the change of a classification graph through time sequence difference, and judging a dynamic change trend; driving mechanism indexes are integrated from the dynamic change trend, and a comprehensive monitoring and recognition report is obtained and used for revealing the trend of vegetation degradation in the mining area.

The invention relates to a global land comprehensive improvement monitoring method based on artificial intelligence and remote sensing images, and belongs to the technical field of environment management. According to the method, multi-band high-resolution remote sensing images of a target area in two time phases before and after improvement are obtained for radiation and atmospheric correction to ensure data quality, and land coverage classification maps of the two time phases are generated respectively by using a pre-trained deep convolutional neural network land coverage classification model. Through pixel-level change detection and confidence analysis, a reliable land coverage change area is identified, vectorization and spatial filtering are carried out, multi-dimensional change features are extracted, ecological influence and a spatial distribution mode of the multi-dimensional change features are comprehensively evaluated, and a comprehensive monitoring result including a spatial distribution diagram, a statistical report and trend prediction is generated. And the information is integrated to an interactive geographic information system platform for visual display and early warning. According to the invention, automatic, precise and systematic monitoring and evaluation of the global land comprehensive remediation process are realized.

The invention discloses a regional prospecting method and system based on multi-element space coupling of a metallogenic system, and belongs to the technical field of mineral resource prospecting prediction and comprehensive modeling of the metallogenic system. The method comprises the following steps: acquiring multi-source spatial data covering a mine concentration area; according to a process mechanism of a metallogenic system, converting and constructing the multi-source spatial data into a background domain, a transmission domain, an ore containing domain and a target domain to indicate spatial distribution indexes; constructing a comprehensive metallogenic potential index for representing a spatial distribution pattern of the regional metallogenic potential; an uncertainty prediction index is constructed based on the consistency deviation degree between the domain index responses and the local difference, and synchronous credibility quantification is carried out on the comprehensive mineralization potential evaluation result; and in combination with a joint discriminant relationship between the mineralization potential index and the uncertainty index, performing classified screening and target region delineation on the regional mineralization abnormal unit, so as to realize continuous quantitative prediction of the regional scale mineralization potential and optimization of the prospecting target region, and provide a mineral geological exploration service developed by using a high technology.

The invention discloses a rock mass mechanical parameter determination method and system for a rock slope blasting excavation damage area, particularly relates to the technical field of rock mass engineering stability evaluation, and is used for solving the problem of misalignment of parameter characterization caused by neglecting a damage area rock bridge and fracture zone composite structure in the prior art. The method comprises the following steps: arranging a point load test in a damage area and identifying non-unimodal distribution data; combining fracture catalog data, fusing penetrability fracture density segmentation capability and a dominant inclination angle direction failure effect to generate a collaborative isolation parameter, and dividing a residual rock bridge and fracture fracture zone spatial distribution mode according to the collaborative isolation parameter; accurately classifying the intensity data into two types of subsets based on space mapping; and finally, macro-mechanical parameters are generated through weighted fusion of the area proportion and the double-source strength statistical value, the rock mass strength prediction precision of the blasting damage area is remarkably improved, and a reliable basis is provided for slope stability analysis and support design.

The invention discloses a crop disease severity dynamic estimation method and system based on multi-scale fusion, provides a spatial stratified sampling strategy, realizes refined partitioning based on growth potential difference and disease risk clustering, ensures that sampling points cover field key areas, and meanwhile, improves the accuracy of crop disease severity estimation. The dynamic time compensation sampling strategy is combined with the field disease development situation and environmental factor self-adaptive adjustment sampling to provide high-quality and dynamic data support for estimation; multi-source features such as scab quantity and edge roughness are introduced into the leaf scale to describe scab morphology and development difference in a fine granularity manner, and the plant scale is fused with weights such as leaf space position to highlight contribution of functional leaves and key leaves to the health of the whole plant; the spatial distribution pattern of the field diseases is effectively reflected by combining the field scale with the growth potential partition weight, the risk partition weight, the health degree and the like of the plants, and finally, the constructed dynamic time integral model is fused with the propagation rate and the fertility sensitivity to obtain a dynamic comprehensive estimation result of the accumulated influence of the diseases in the monitoring period.

This invention discloses a GIS-based risk management system and method for pollutant migration in mining area watersheds, relating to the technical field of simulating pollutant migration patterns. It includes a 3D GIS functional module, a hydrodynamic-water quality model coupling module, and a mining watershed pollution risk management module. Based on watershed hydrology theory and pollutant migration and diffusion mechanisms, it fully considers the migration of pollutants in the storage area after rainfall via surface microrunoff. By studying the formation patterns of microrunoff, it simulates the watershed migration process of pollutants in the mining area under different rainfall intensities, thereby more accurately calculating pollutant migration and diffusion fluxes and managing the risk of pollutant migration in the watershed. Based on historical and real-time data and models, it uses a 3D model to display the spatial distribution patterns and development trends of pollutants, achieving accurate pollutant identification and source tracing. It can simulate pollution control measures in mining area watersheds, predict development trends, and provide early warnings for pollution events.

This invention belongs to the field of ferroalloy smelting technology and industrial process control technology. Specifically, it discloses an intelligent smelting method for high-carbon ferrochrome alloys based on a submerged arc furnace. The method includes: generating a comprehensive material surface state map by collecting material surface state and temperature data; analyzing the overall spatial distribution pattern of grid cells based on the state map to diagnose the overall furnace condition; triggering corresponding global control commands if abnormalities are found; and comparing the material surface sinking height, raw material sinking speed, current temperature, and temperature change rate of each grid cell with preset evaluation criteria to diagnose local furnace conditions if the overall furnace condition is healthy. For healthy cells, precise quantitative material replenishment is calculated when the sinking height meets the standard, and the replenishment azimuth angle is determined through polar coordinate transformation. For abnormal cells, differentiated local control commands are triggered based on their abnormal characteristics. This invention achieves closed-loop intelligent control of the submerged arc furnace smelting process from global perception to precise local control, effectively improving smelting efficiency.

This invention discloses a method for compiling mineralization center distribution maps based on the spatial distribution patterns of anomalous elements. The method involves collecting stream sediment data from the work area, performing principal component analysis, and extracting principal components with eigenvalues ​​greater than 1. It also distinguishes between medium-high temperature element combinations and medium-low temperature element combinations; calculates the mineralization center factor M; and calculates the outlier value M of the mineralization center factor. c Import the mineralization center factor M into the ZScape software and draw a contour map of the mineralization center factor in the working area; use the outlier value M of the mineralization center factor. c Create a mineralization center distribution map for the boundary; M≥M c The region is represented as the mineralization center; M≤M c The region is represented as the mineralization edge; based on the relationship between elemental zoning and temperature in stream sediments, this invention constructs mineralization center factors for magmatic-hydrothermal related deposits, realizes the quantitative compilation of mineralization center distribution maps, and innovates a new model for map compilation.

This invention discloses a method and system for anomaly detection of elevated facilities based on environmental feature fusion. The method includes: acquiring video streams, GPS location data, and timestamp information to obtain inspection data; identifying and determining specific elevated facilities and their components, and defining the target inspection area; performing dual-channel detection on specific elevated facilities based on a dynamically learned individual appearance benchmark model and an individual spatial relationship model to identify potential problems; comparing the material types and standard appearance characteristics of specific elevated facilities in each road segment with the dynamically learned individual appearance benchmark model to obtain appearance inspection results; tracking and recording the continuous spatial distribution patterns of specific elevated facilities in each road segment based on the dynamically learned individual spatial relationship model; integrating the continuous spatial distribution patterns of specific elevated facilities in each road segment with the appearance inspection results to identify potential problems; and generating an anomaly report. This invention improves the accuracy and adaptability of elevated facility detection.

The present application relates to the technical field of image processing, and more particularly to an endoscope fault image feature extraction method based on artificial intelligence, which comprises: bad point detection to determine abnormal pixels and coordinates; density ratio and cluster analysis to determine spatial distribution patterns; identification of fault types to determine the timing evolution trend; extraction of fingerprint feature vectors; prediction of development trend to determine the maintenance time window; and positioning of the physical location of the fault. The present application locates abnormal pixels through bad point detection, determines whether the bad point distribution belongs to edge priority or linear arrangement through density ratio and cluster analysis, and further determines the fault type of sealing failure or cable breakage. Meanwhile, the feature vector is extracted through the fusion of CNN and Transformer, the development trend is predicted in combination with the timing evolution to determine the maintenance window, and finally the physical location is located through coordinate mapping when the maintenance conditions are met, thereby improving the efficiency and accuracy of endoscope fault diagnosis.

The invention belongs to the technical field of computers, and particularly relates to a real-time fault positioning method for a drainage system, which comprises the following steps of: constructing a directed network model and a node-pipe section incidence matrix which reflect the complex topology of a pipe network according to a pipe network geographic information system and sensor data; calculating the flow unbalance degree of each node based on the real-time flow data and the incidence matrix, and dynamically checking and updating the network direction by using the real-time pressure or flow data; reversely tracing an upstream suspicious pipe section area in the network by taking the unbalance degree over-limit node as a starting point, and performing downstream influence propagation analysis; and finally, combining the suspicious region, the influence range and the whole network unbalance degree space distribution mode, and cooperatively judging the fault type and the accurate position. According to the method, the problems of false alarm and inaccurate positioning caused by model simplification are solved, and accurate and rapid traceability and diagnosis of complex drainage pipe network faults are realized.

This invention belongs to the field of remote sensing technology and discloses a method for optimizing habitat quality parameters in the InVEST model based on Remote Sensing Ecological Indices (RSEI). This invention overcomes the technical bottleneck of traditional InVEST models, which rely on manual experience to set parameters and are difficult to update dynamically over time. By constructing a multi-source remote sensing data-driven RSEI, and utilizing its statistical characteristics and spatial distribution patterns across different land use types, it achieves objective quantification and dynamic updating of four key parameters: habitat suitability, threat weight, threat attenuation distance, and habitat sensitivity. Without requiring data from ecological monitoring stations, it directly incorporates continuous changes in ecological environment quality into the InVEST parameter system, resulting in higher objectivity, temporal consistency, and spatial autocorrelation in habitat quality calculations. This provides a novel technical approach for the automated and time-series assessment of regional-scale habitat quality.

This invention discloses a visual servo grasping and assembly method for tunnel boring machine (TBM) segments adapted to the motion of an observation platform. The method includes the following steps: mounting a visual sensor on the observation platform to collect the three-dimensional coordinates of reference markers on the assembly machine, the assembly machine, the tunnel segments, and feature points at the target assembly position; unifying the local coordinate system of the observation platform with the base coordinate system of the assembly machine at the current moment; constructing an error vector field; based on the spatial distribution pattern of the error vector field, according to geometric decoupling mapping rules, allocating and mapping the overall error to the target motion increments of each independent joint of the assembly machine; iterative servo execution: executing the target motion increment commands in priority order, repeating the above steps until the magnitudes of all first spatial error vectors / second spatial error vectors are less than a set threshold. This invention relates to the field of automated equipment technology for tunnel construction and can solve the problem of poor engineering adaptability caused by traditional control methods relying on mechanism models and absolute pose calculations.

The invention discloses a Tor website fingerprint identification method based on a multi-feature cross representation and attention fusion mechanism. The method comprises the following steps: acquiring original traffic trajectory data generated by a user in a webpage access process in a Tor network; respectively generating a direction aggregation matrix, an interval aggregation matrix and image structure features based on the original traffic trajectory data; and inputting the direction aggregation matrix, the interval aggregation matrix and the image structure features into a pre-trained target fingerprint identification network, and outputting a fingerprint identification result for the original traffic trajectory data. According to the invention, aiming at the complex relationship among directivity, time and structural features in Tor traffic, three cross features with structural association are adopted for representation, the statistical change trend and the spatial distribution mode of the traffic can be covered at the same time, the adaptive capacity of a target fingerprint identification network to traffic disturbance and confusion strategies is enhanced, and the target fingerprint identification method is suitable for the target fingerprint identification network. Therefore, efficient and accurate identification of the Tor traffic can be realized, and the method has strong network environment adaptability.

The invention relates to a lake evapotranspiration inversion method based on a remote sensing image. The method comprises the following steps of S100, data set construction, S200, water body extraction, S300, earth surface parameter inversion, S400, evapotranspiration inversion, S500, lake evapotranspiration image extraction and S600, lake evapotranspiration space-time trend analysis. According to the method, the evapotranspiration space distribution mode and the evapotranspiration interannual change trend of the long-time-sequence lake can be systematically analyzed, the lake long-time-sequence evapotranspiration change rule can be systematically revealed, and a new technical path and theoretical support are provided for sustainable utilization of water resources under climate changes.

The invention belongs to the technical field of traffic geographic information analysis, and discloses an urban crowd activity and space structure dynamic deduction method and system based on single traffic flow, a terminal and a storage medium. Identifying a spatial distribution mode through a flow similarity measurement method in combination with a hierarchical clustering algorithm; carrying out quantity aggregation on the identified spatial distribution mode according to grid units, and constructing a spatial grade distribution model; combining the urban built environment features with spatial grade distribution in the spatial grade distribution model, and constructing a comprehensive data set suitable for machine learning regression modeling; and based on the comprehensive data set, in combination with a machine learning model interpretation method of a game theory, quantitatively analyzing a nonlinear influence mechanism of urban built environment characteristics on different spatial distribution modes, and deducing internal relevance between urban crowd activities and spatial structures. The dynamic evolution process of the urban space structure is comprehensively realized.

This invention discloses a method for predicting the spatial distribution of algal blooms in lakes based on near-surface wind vectors and a Bi-GRU-Attention model. The invention employs Empirical Orthogonal Function (EOF) analysis to extract the dominant mode of algal bloom spatial distribution and time coefficients that represent the temporal variation characteristics of algal bloom spatial patterns. Then, a bidirectional gated cyclic unit model based on an attention mechanism is constructed to extract key dynamic features from the wind vector time series and identify wind periods that significantly contribute to the formation of algal bloom spatial distribution patterns. Finally, the time coefficients of the dominant mode of algal bloom spatial distribution are predicted using the wind vector time series state, and the spatial distribution of algal blooms is reconstructed by combining the corresponding spatial modes. This invention provides a new approach for the study of wind-driven algal bloom spatial distribution.

The invention relates to the technical field of medical image processing and artificial intelligence, and discloses a brain iron deposition spatial distribution pattern intelligent identification and typing method and system, and the method comprises the steps: carrying out the spatial standardization and brain region segmentation of a whole-brain QSM image; generating a spatial distribution feature vector through a multi-scale spatial attention feature extraction network; an iron deposition mode is divided into a cortex type, a deep nuclei type, a ventricular surrounding type and a diffuse type by adopting a fuzzy clustering algorithm of spatial neighborhood constraint; establishing a mapping relation between the iron deposition mode and the vascular cognitive impairment etiology subtype; according to the method, automatic recognition and typing of the brain iron deposition space distribution mode are achieved, the mode recognition accuracy rate reaches 85% or above, and an auxiliary diagnosis basis is provided for vascular cognitive impairment pathogenesis typing.

The invention relates to the field of directed evolution screening, in particular to a visual continuous space directed evolution method, a host grows and moves in a solid culture space, the host carries an external target gene to be evolved, and the host comprises a gene element for assisting the evolvement of the target gene. The target gene is associated with the growth movement of the host; in the host growth movement process, different spatial distribution forms are formed in the solid culture space for screening to obtain an evolutionary product. The method is directly carried out in a solid culture space, different spatial distribution pattern images visible to naked eyes are locally formed, then evolution products are selected, liquid fed-batch culture equipment is not needed, meanwhile, the evolution effect is observed by naked eyes through infection spots formed in evolution, real-time monitoring equipment is not needed, operation is easy, and the flux is high.

The present application relates to the technical field of flood risk assessment and defense, more particularly, to a river network area spatial extreme water level evaluation method, through collecting disaster data and spatial data of river network area water level stations, establishing disaster extreme value data sequence, converting the disaster extreme value data sequence into unit Frechet distribution, combining different disaster data and spatial data to design complex environment factor encounter scene, considering the spatial dependence of extreme water level events between stations, establishing a multivariate spatial extreme value model, which can provide spatial expression of extreme water disasters under different complex environment factor encounter scenes, estimate the spatial distribution pattern of river network area extreme water level events under different recurrence levels, analyze the change of flood event occurrence intensity of different stations in the river network area in space and events, and effectively monitor and evaluate the composite flood.

The invention provides a medical endoscope image intelligent enhancement and focus recognition system, and relates to the technical field of data processing. The region segmentation module is used for detecting and distinguishing a suspected microvascular network region and a background region; the structure maintaining module is used for executing smoothing and color correction processing on the background area and executing texture maintaining processing on the suspected microvascular network area; the difference highlighting module is used for calculating the pixel difference between the suspected microvascular network region and the adjacent background region and executing local contrast enhancement in the region; the dynamic correction module is used for analyzing the brightness distribution relation between the suspected microvascular network area and the adjacent background area and executing self-adaptive brightness compression or lifting; the lesion recognition module is used for extracting complete blood vessel texture and a spatial distribution mode thereof, generating a lesion recognition result and outputting a suspicious lesion area; according to the invention, the accuracy of endoscope image focus identification is improved.

The invention relates to the field of directed evolution screening, in particular to a visual continuous space directed evolution method, a host grows and moves in a solid culture space, the host carries an external target gene to be evolved, and the host comprises a gene element for assisting the evolvement of the target gene. The target gene is associated with the growth movement of the host; in the host growth movement process, different spatial distribution forms are formed in the solid culture space for screening to obtain an evolutionary product. The method is directly carried out in a solid culture space, different spatial distribution pattern images visible to naked eyes are locally formed, then evolution products are selected, liquid fed-batch culture equipment is not needed, meanwhile, the evolution effect is observed by naked eyes through infection spots formed in evolution, real-time monitoring equipment is not needed, operation is easy, and the flux is high.

The invention relates to the technical field of image segmentation, in particular to a tongue coating segmentation image and digestive disease classification method. According to the method, the position of a particle center point is determined by introducing multi-scale LoG filtering, and tongue particles and tongue coating particles are distinguished according to the law of spatial distribution and density change characteristics; according to the method, local adaptive threshold adjustment is carried out on other pixel points through the change of uneven particle density of different parts, more accurate tongue coating areas and tongue quality areas are obtained through division, and subsequent classification is carried out based on the tongue coating areas and the tongue quality areas. According to the method, the tongue coating area is divided more accurately through multi-scale LoG filtering and spatial statistical characteristics, a reliable data basis is provided for subsequent auxiliary classification of digestive diseases, and the classification accuracy is improved.

The application discloses a cell space polarity quantification analysis method based on optimal transmission theory, and the steps include: 1) performing slice quality filtering, feature screening, total amount standardization and logarithmic conversion on an original space omics count matrix to obtain a standardized expression matrix; 2) performing principal component analysis on RNA and protein data respectively to construct a low-dimensional embedding for similarity calculation; 3) calculating the difference between the actual distribution of each molecule and the uniform distribution based on the first Wasserstein distance to realize continuous gradient quantification along the plant-animal axis; 4) constructing a k-nearest neighbor graph based on the distance between molecules to identify molecular communities with common spatial positioning patterns; and 5) calculating the matching significance of the observed distribution and the ideal pattern, and then controlling the false discovery rate through the Benjamini-Hochberg procedure to screen out molecules with significant gradient distribution characteristics. The application significantly improves the spatial distribution pattern recognition and quantification ability.

The invention particularly relates to a degraded grassland unmanned aerial vehicle stellera chamaejasme plant positioning and population feature analysis method and system, and solves the problem that a target algorithm is weak in plantlet contour and feature detection capability. Acquiring degraded grassland panchromatic image data through image acquisition equipment carried by the unmanned aerial vehicle; detecting and identifying a stellera chamaejasme plant in the image by using an SAM v2 model, and outputting a stellera chamaejasme plant data frame and confidence in the image; extracting center point coordinate information of each segmented image based on a Python library, and positioning a stellera chamaejasme plant in the image according to an azimuth angle and a distance; and based on K (r) and L (r) functions, distinguishing the dependence degree of the spatial distribution pattern of the stellera chamaejasme population and the distribution modes of the stellera chamaejasme population under different scales. Monitoring and real-time evaluation of the stellera chamaejasme plants in the landscape scale are achieved, and a new technical system and a new research normal form are provided for degraded grassland evaluation.

The invention provides a long-time subway passenger flow prediction method based on deep tensor decomposition and reconstruction, and relates to the technical field of rail transit operation planning. Subway passenger flow data are expressed as three-dimensional tensors of station, time slice and date dimensions, and space and time characteristics of the subway passenger flow data are decoupled and expressed; and initializing corresponding space, time and date potential factors for each dimension, and carrying out nonlinear transformation and feature enhancement on the potential factors through a deep convolutional neural network, so that a complex nonlinear space distribution mode and a time evolution mode of subway passenger flow data can be extracted, and then a reconstruction result is obtained through tensor reconstruction. The constructed tensor decomposition and reconstruction model based on the deep neural network can be used for predicting passenger flow tensor, and finally, a vector autoregression model is introduced to apply time dynamic constraint to date potential factors, so that the model has long-term prediction capability, and accurate and stable prediction of future multi-day (long-term) subway passenger flow is realized.

The invention provides a carbon dioxide hydrate growth characteristic and stability test system and method, and relates to the technical field of hydrate method seabed carbon sequestration, and the method comprises the following steps: alternately or sequentially injecting deionized water and carbon dioxide gas into a micropore model, and synchronously adjusting the confining pressure of a reaction kettle cavity, the internal pressure of the micropore model reaches the preset experimental pressure, and the confining pressure of the cavity is always kept higher than the internal pressure of the model; under the conditions of low temperature and high pressure, observing and recording the dynamic image data of the nucleation site, the growth speed and the spatial distribution mode of the carbon dioxide hydrate in real time; performing Raman spectrum scanning on the generated carbon dioxide hydrate, and collecting Raman spectrum data of the carbon dioxide hydrate; and carrying out data processing and mechanism analysis based on the dynamic image data and the Raman spectrum data. According to the technical scheme, high-precision real-time carbon dioxide hydrate growth characteristic and stability testing under the deep-sea sediment temperature and pressure condition is achieved.

The invention belongs to the field of in-situ leachable sandstone type uranium ore, and particularly relates to a method for identifying primary color and secondary color of sandstone in basin red mixed color uranium-containing construction, which comprises the following steps of: 1, analyzing macroscopic and microscopic geologic characteristics of sandstone with different colors; 2, analyzing spatial distribution rules of sandstones with different colors; step 3, analyzing geochemical characteristics of sandstones with different colors; step 4, performing quantitative reconstruction analysis on paleoclimate-paleo environment; 5, analyzing a sedimentary facies color control rule; and 6, multi-dimensional comprehensive collaborative analysis is carried out, and the primary color and the secondary color of the sandstone are identified. According to the method, the primary color and the secondary color of the sandstone in the red mottled building can be effectively and accurately identified, the geochemical zoning in the red mottled building and the front line drawing position of the oxidation zone are correctly divided, the uranium prospecting effect and probability are improved, and the sandstone type uranium mine possibly existing in the ancient depression in the center of the basin or the fracture development part is prevented from being omitted.