350 results about "Soil type" patented technology

The embodiment of the invention provides a soil nutrient spectrum detection and regulation method and system, and belongs to the technical field of soil nutrient spectrum detection and regulation. The method comprises the following steps: collecting soil multispectral original data and environmental data, generating a calibration matrix and a nonlinear correction parameter, executing optical reflectivity conversion, and synchronously fusing a time domain reflectometry dielectric constant and a spectral moisture index to obtain fused moisture content; selecting a nutrient diagnosis characteristic wave band pair to obtain a moisture inhibition type spectral index; and predicting the soil nutrient content by using a pre-trained transfer learning model to obtain a soil nutrient content prediction value, and performing fertilization regulation and control based on the fertilization decision risk coefficient. According to the invention, extreme soil moisture measurement deviation is solved through a soil type adaptive moisture fusion technology; through multi-source risk quantitative decision and PID self-adaptive regulation and control, the fertilization amount can be dynamically optimized, execution safety is guaranteed, and a detection-decision-execution precision agriculture closed loop is formed.

The invention relates to the technical field of wetland carbon evaluation, and discloses a wetland carbon flux evaluation and management system based on three-dimensional digital twinning. The system comprises a three-dimensional modeling module, a flux monitoring module, a model optimization module and a twin updating module. The three-dimensional modeling module integrates wetland geographic space coordinates, an ecological parameter set and elevation, vegetation coverage density and soil type layering information, constructs a three-dimensional terrain grid model, and generates a three-dimensional digital twinborn body with a geographic mark; the flux monitoring module is used for collecting data such as atmospheric temperature gradient based on real-time environmental parameters, extracting carbon flux change characteristics through time sequence analysis and outputting a dynamic distribution state value; the model optimization module uses an adaptive evolutionary algorithm to adjust the parameter matching degree, iteratively updates constraint conditions, and generates an optimization parameter set; and the twin updating module recognizes abnormal grid units according to the abnormal grid units, corrects attributes by combining measured data, calibrates a topological relation, and outputs an updated evaluation model.

The invention relates to the technical field of intelligent agriculture and precise irrigation, in particular to an intelligent control system for agricultural irrigation. Comprising a water-saving valve module, a sensing layer module, a control layer module, an execution layer module and a data and application layer module, and the water-saving valve module is used for achieving microsecond level flow regulation and control through piezoelectric ceramic driving and energy recovery and integrating a self-maintenance function. According to the invention, the LSTM network and the Duelling DQN algorithm are utilized to accurately discriminate the soil type and formulate an extreme weather coping scheme, dynamic optimization of an irrigation strategy is realized based on low-power-consumption edge calculation and an intelligent control algorithm, the adaptability and decision accuracy of the system are greatly improved, multiple advanced technologies are integrated, and the system is suitable for large-scale popularization and application. Comprising a magneto-rheological buffer, a TSN valve controller, an SMA emergency pressure release valve and a variable frequency water pump driver, pressure balance of a pipe network, high-precision synchronization of valves and safe treatment of sudden failures are ensured, and the stability and flexibility of an irrigation system are comprehensively improved.

The invention provides a farmland yield prediction method and system based on soil parameter inversion, and the method specifically comprises the steps: collecting the soil profile information and earth surface three-dimensional information of a target farmland through a ground penetrating radar and a laser radar, and carrying out the precise inversion of the water content and porosity of soil based on a water-porosity inversion model, therefore, a high-precision three-dimensional soil distribution model is constructed, crop growth monitoring data is combined, a farmland yield prediction model is established, the relationship among soil structure characteristics, crop growth indexes and meteorological factors is quantified, and high-precision farmland yield prediction is realized. According to the method, through accurate modeling of the double-pore structure and optimization of the multiple linear regression prediction model, yield prediction is more accurate and reliable, and the method is particularly suitable for farmland management under different soil types and meteorological conditions.

The invention discloses a drainage basin water quality pollution source flux identification method based on an SWAT model, computer equipment and a storage medium, and the method comprises the steps: obtaining topographic data, land utilization data, soil type data and meteorological data, and enabling the topographic data, the land utilization data, the soil type data and the meteorological data to form a drainage basin spatial database; based on the preprocessed digital elevation model, determining a water flow direction and setting a reasonable water collection area threshold value, and dividing sub-basins to construct a basin calculation unit network topology structure; obtaining pollution source data, and constructing an SWAT-CUP model according to the pollution source data; performing system calibration and verification on the watershed hydrological process by adopting an SUFI-2 algorithm in the SWAT-CUP model; carrying out system calibration and verification on the total nitrogen load of the drainage basin by adopting an SWAT-CUP model; and based on the calibrated SWAT hydrological model, the input condition of the pollution source is changed. The method can cover the whole watershed and quantify the contribution rate of different pollution sources; cost is high, operation is simple, and long-term change can be dynamically simulated.

The invention discloses a soil improvement and soil quality optimization comprehensive management method based on big data analysis, and the method comprises the following steps: S1, collecting and preprocessing multi-source soil data, and constructing a soil data input matrix; s2, initializing parameters of the fuzzy C-means clustering model, and constructing a model basic structure; s3, introducing a hosting crab optimization algorithm, and optimizing clustering model parameters; s4, executing clustering analysis, and generating a fuzzy soil type distribution map; s5, constructing an improved strategy scheme space, and fusing crop types, historical records and resource information; s6, making a multi-target scoring standard, and selecting an optimal improvement strategy; s7, deploying an optimal improvement strategy to a farmland operation system, and collecting soil feedback data in real time; and S8, updating model input and parameters based on feedback data, and re-executing optimization to realize a closed loop. According to the invention, big data and an intelligent optimization technology are fused, and accurate matching and efficient configuration of soil improvement strategies are realized.

The invention discloses a soil type prediction model training method, drawing method and system based on multi-modal feature fusion and spatial neighborhood constraint, and the training method comprises the steps: constructing a multi-source heterogeneous digital soil drawing data sample set which comprises a continuous environment variable, a discrete environment variable and a multi-source remote sensing image; extracting the characteristic quantity of the data sample set and carrying out multi-modal adaptive characteristic fusion, and adaptively determining a fusion weight by using a cross attention mechanism to obtain a joint characteristic quantity; constructing a composite loss function including a spatial neighborhood smooth regular term and classification cross entropy loss; and performing joint optimization training on a preset deep learning model according to the joint characteristic quantity and the composite loss function until a preset requirement is met, and determining a soil type prediction model. According to the embodiment of the invention, the accuracy and spatial continuity of the mapping result expression output by the model and the generalization performance of the model can be effectively improved, and the method can be widely applied to the technical field of digital soil mapping.

The invention provides an operation monitoring method for a steam pipe network. The operation monitoring method comprises the following steps: acquiring pipeline burial depth information, a leakage signal, temperature, pressure and soil thermal conductivity change information of an underground steam pipe network; constructing a pipe network space model, marking pipe fitting positions, obtaining soil types, and calculating attenuation coefficients of leakage signals in different soil media; simulating a leakage signal propagation path based on a pipe network space model, obtaining a complex area of a pipe network structure according to the pipe burial depth information, and if the leakage signal is superposed or interfered in the complex area, separating out an independent signal component to obtain an initial propagation direction of the leakage signal; according to the position of the leakage point, in combination with the pipeline burial depth and the soil type attribute, a regression analysis method is adopted to calculate the quantitative relation between the intensity of the leakage signal and the leakage degree, and an evaluation result of the leakage degree is obtained.

The invention relates to the technical field of ecological restoration scene modeling, in particular to an ecological restoration scene modeling method and system platform applied to territorial space planning, and the method comprises the following steps: based on a successful ecological restoration technical scheme case and a to-be-restored land parcel unit. According to the method, normalization processing is carried out on multi-dimensional attributes between successful ecological restoration technical scheme cases and to-be-restored land parcel units, a source-target land parcel feature fingerprint set is constructed, and specific attributes such as annual average temperature, precipitation, soil types, pH values, altitudes, native vegetation types and labor cost are combined; modeling is realized on data source selection granularity and expression dimension. Furthermore, an importance coefficient is set according to the influence degree of each dimension in a historical repair success case, feature difference weight calculation is guided, and a technical migration adaptation degree score is obtained by combining normalized difference calculation, so that a quantifiable source-place adaptation capability evaluation index is established, and the limitation that judgment depends on experience in the prior art is broken through.

The invention relates to the related technical field of climate monitoring, in particular to a freezing circle climate change monitoring method and system suitable for an Asian alpine region, and the method comprises the steps: configuring a monitoring network, receiving a frozen soil multi-source monitoring data set, constructing a frozen soil and climate coupling response model, embedding a region climate assessment engine, and generating a frozen soil degradation risk grade distribution map. And judging whether risk reminding is triggered or not according to the dynamic frozen soil degradation threshold value, and if the risk reminding is triggered, marking an abnormal region in the frozen soil degradation risk level distribution diagram. The technical problems that Asian alpine region freezing circle climate change monitoring abnormity reminding mostly depends on static data, the dynamic evolution process of frozen soil cannot be accurately captured, and the frozen soil degradation risk identification precision is limited are solved, a frozen soil and climate coupling response model constructed based on multi-source data is achieved, and the frozen soil degradation risk identification accuracy is improved in combination with a dynamic degradation threshold value set according to the frozen soil type. And risk reminding is triggered in time and an abnormal region is marked, so that the frozen soil degradation risk identification precision is improved.

The invention belongs to the technical field of pile foundation construction, and particularly relates to an inversion method of a pile foundation construction and geological exploration technology, which comprises the following steps of: 1, collecting geological exploration data; the method comprises the following steps: acquiring property data of an underground soil layer through drilling and in-situ testing operation, including the thickness, compactness and soil type of the soil layer; and calculating the numerical values of the thickness, the compactness and the soil type of the soil layer through a static sounding test method. According to the inversion method of the pile foundation construction and geological exploration technology, accurate geological exploration data can be provided, specific characteristics of an underground soil layer are calculated through the inversion method, and accurate parameters are provided for pile foundation design. Meanwhile, the interaction between the pile foundation and the soil layer is simulated through a finite element method, deformation and stress distribution in the construction process are predicted, and a scientific basis is provided for engineering design. In addition, settlement and pile sinking resistance of the pile foundation need to be monitored in real time, so that a construction scheme is adjusted in time, and construction safety and quality are ensured.

The invention specifically relates to a forest stock dynamic exploration system and method based on unmanned aerial vehicle multispectral fusion, and relates to the technical field of forest resource monitoring. A data preprocessing module; a feature extraction module; a model construction and estimation module; and a result output and visualization module. According to the method, a multi-dimensional data fusion and dynamic calibration mechanism changes the limitation of a traditional fixed soil regulation coefficient; when the soil regulation coefficient is obtained, four core factors of soil type, terrain illumination, soil humidity and texture partition distance are comprehensively considered; interference of different soil backgrounds, terrain illumination and humidity changes on vegetation index calculation is effectively eliminated, the enhanced vegetation index can reflect the coverage and health condition of forest vegetation more truly, a high-precision data basis is provided for subsequent forest stock estimation based on the vegetation index, and compared with a traditional method, the method has the advantage that the method is more accurate. And the data accuracy and reliability are improved.

The invention relates to the technical field of soil moisture content monitoring, and discloses a method and a system for accurately monitoring soil moisture content through remote sensing inversion of an unmanned aerial vehicle. Acquiring original spectral data of different soil types and environmental conditions through a spectral sensor to obtain an initial multispectral image data set; dividing soil types by using a support vector machine classification method according to pixel reflection characteristics to form a soil type grouping set; calculating a multispectral band reflectivity variance for each group, and screening candidate bands to construct a set; processing the candidate wave band set through a random forest regression model, adjusting the weight in combination with an environment variable, and performing iterative screening to form an optimized wave band combination sequence; extracting spectral features of the optimized wave band, and carrying out weighted fusion on the soil variation region to output a processed image; and calculating the soil moisture content by using a linear inversion model based on the pixel reflectivity of the processed image, and outputting a distribution diagram when the deviation reaches the standard. The method is suitable for different soil and environments, the wave band is accurately selected, the precision is improved, and high-quality data is provided for agricultural and environmental governance.

The invention discloses an accounting method for carbon sequestration and emission reduction of soil. The accounting method comprises the following steps: S1, acquiring the average soil temperature # imgabs0 # average soil humidity # imgabs1 # average soil acidity and alkalinity # imgabs2 # average soil microbe number # imgabs3 # average soil microbe activity # imgabs4 # average environment temperature # imgabs5 # average environment humidity # imgabs6 # S2 of selected type soil from a moment t to a moment t + delta t; inputting the average soil temperature # imgabs7 #, the average soil humidity # imgabs8 #, the average soil acidity and alkalinity # imgabs9 #, the average soil microbe number # imgabs10 #, the average soil microbe activity # imgabs11 #, the average environment temperature # imgabs12 #, the average environment humidity # imgabs13 # and the soil type of the selected type of soil into a trained soil carbon sequestration emission reduction amount accounting model; and obtaining the carbon sequestration amount and greenhouse gas emission reduction amount of the selected type of soil. The method can quickly and accurately reflect the actual carbon sequestration and emission reduction conditions of the soil.

The invention relates to the field of remote sensing image processing, in particular to a farmland soil type classification-oriented remote sensing image data acquisition method, which comprises the following steps of: regularly acquiring remote sensing image data of a target area according to a preset period; obtaining a distance measure between any two pixel points in each remote sensing image, and segmenting each remote sensing image to obtain a ground feature region; analyzing the trend and the amplitude of each pixel point on the spectral curve, and determining the red edge expression degree of each pixel point; determining the vegetation growth performance degree of each pixel point; determining the farmland soil segmentation quality of each remote sensing image; and screening a to-be-classified image from all remote sensing images of the target area, screening a farmland soil area to construct a bare soil mask image, and performing farmland soil classification on a ground feature area in the to-be-classified image in combination with a neural network. According to the method, the farmland soil area in the remote sensing image is accurately divided, and the accuracy of farmland soil type classification is improved.

The invention relates to the technical field of water resource management, in particular to a flow hydrograph simulation method based on coupling of a Xinanjiang model and a depression, and the method comprises the steps: obtaining the geographic space data and meteorological data of a target drainage basin; the geospatial data at least comprises a digital elevation model, land utilization data and soil type data; and carrying out sub-drainage-basin division on the target drainage basin based on the digital elevation model, identifying depressions in the digital elevation model by adopting an improved priority flood algorithm, and constructing a spatial topological relation of the depressions. According to the method, the hydrological characteristics of the depression are fused into the Xinanjiang model, the regulation and storage function of the depression group on the drainage basin runoff is quantified, the hydrological simulation precision of the depression dominant drainage basin is remarkably improved, and by improving the depression recognition algorithm, pre-construction of the slope confluence time matrix based on the cells and rapid judgment of the depression merging mechanism, the stability of the depression confluence time matrix is improved. The calculation speed of the distributed hydrological model is greatly improved, and the efficiency requirement of the real-time hydrological model can be met.

The invention relates to a soil erosion amount dynamic monitoring method and system based on multi-source remote sensing data fusion. The method comprises the following steps: acquiring multi-source remote sensing data and auxiliary data, and performing geometric fine correction and atmospheric correction on an image based on high-resolution digital elevation data to obtain a data set with consistent space; performing inversion based on the data set to obtain a corrected terrain factor and a water and soil conservation measure factor; generating a time sequence rainfall erosivity factor and a time sequence vegetation coverage factor in combination with the multi-temporal satellite rainfall data; and calculating a preliminary soil erosion modulus in combination with the soil type map, and correcting by using multi-stage high-resolution digital elevation data to obtain a dynamic monitoring result of the soil erosion amount. By adopting the method, the problem of multi-source data fusion matching can be solved, and the factor inversion precision is improved, so that the accuracy of a dynamic monitoring result of the soil erosion amount is improved.

The invention provides a cohesive soil early consolidation pressure detection method and device, and belongs to the technical field of geotechnical engineering detection.The cohesive soil early consolidation pressure detection method comprises the steps that a structured database is established based on engineering area historical drilling data and geotechnical test data; extracting data subsets corresponding to the soil texture types from the database, and respectively constructing mucky soil and conventional cohesive soil early consolidation pressure prediction models; acquiring a standard volume soil sample and density and water content data thereof; drilling position coordinates, orifice elevation, sampling depth and soil sample specific gravity parameters are input; selecting a prediction model according to the soil texture type, inputting related data, and outputting an early consolidation pressure value; and comparing the early consolidation pressure value with the current effective self-weight stress, and judging the consolidation state of the soil body. According to the method, the detection period of the early consolidation pressure of the cohesive soil body is shortened, subjective factors in the manual curve drawing process are eliminated, and the data accuracy and the working efficiency are improved.

Disclosed in the present invention is an intelligent energy efficiency management system for an LNG dual-fuel powered trailing suction hopper dredger, comprising: an online intelligent LNG energy efficiency monitoring module, configured to collect energy efficiency key factor parameters of a main energy consumption device of the LNG dual-fuel powered trailing suction hopper dredger to form a unified LNG powered trailing suction hopper dredger energy efficiency data dictionary; an intelligent energy efficiency analysis and evaluation module, configured to construct, on the basis of the LNG powered trailing suction hopper dredger energy efficiency data dictionary, an operating condition identification model, an energy efficiency indicator calculation model, a carbon emission calculation model, an energy efficiency level model and a periodic energy efficiency analysis model to form an energy efficiency analysis and evaluation system; and a dredging operation energy efficiency auxiliary decision-making module, configured to construct an energy consumption prediction model for ten-thousand cubic meters of soil on the basis of soil type, design dredging depth, construction parameters and historical fuel consumption data, and recommending a dredging construction operation parameter combination by means of an optimization algorithm. Also disclosed is an intelligent energy efficiency management method for an LNG dual-fuel powered trailing suction hopper dredger. The present invention increases the energy efficiency.

The invention discloses a soil color exploration and identification system and method, belongs to the technical field of soil color identification, can effectively reduce the influence of ambient light on soil color discrimination through a standardized color correction board and software image processing, and performs hierarchical division on a soil color identification image along a vertical plane, thereby improving the accuracy of soil color identification. Pixel point color information of a plurality of independent soil layers is obtained, so that the color distribution uniformity of a single layer is judged, then the color distribution condition of the whole image is comprehensively analyzed to divide soil types, and different color recognition modes are selected according to the judged soil types to be compared with a colorimetric card digital image. Compared with a mode of directly extracting color information of an overall image and comparing a colorimetric card, hierarchical division is adopted, single-to-overall soil type judgment is carried out, and a proper recognition mode is selected according to a judgment result, so that the color recognition accuracy is effectively improved.

The invention relates to the technical field of soil component detection, in particular to a soil component detection method and system based on spectral analysis, and the system comprises an intelligent mobile platform and a multifunctional integrated spectrum acquisition module. In the advancing process, soil is continuously scanned through the spectrum collection module, spectrum information is obtained in real time, data are processed through the spectrum feature extraction unit, the soil component type and distribution proportion are obtained, and a soil component distribution diagram and quantitative data are generated. According to the method, the detection efficiency can be remarkably improved, the problem of data missing of a traditional fixed-point sampling mode is avoided, meanwhile, the adaptability to complex terrains and diversified soil types is enhanced, and high precision and wide applicability of a detection result are ensured.

The invention discloses a DEM adaptive multi-algorithm fusion lifting method for a high-precision distributed hydrological model, and relates to the field of hydrological model basic data construction. The method comprises the following steps: acquiring original DEM data, land utilization data and soil type data; performing standardization processing on the original DEM data to obtain a unified grid DEM; calculating a basic terrain field based on the unified grid DEM; generating at least one enhanced topographic parameter (such as an enhanced gradient grid or topographic index) through a predetermined optimization pipeline based on the base topographic field; finally, hydrological response units are divided in combination with the at least one enhanced topographic parameter, the land utilization data, and the soil type data. Through the optimization assembly line, the problem that traditional filtering is insufficient in geomorphic feature reservation is solved, and the physical rationality of hydrological response unit division is improved.

The invention provides a high-production planting method and system for realizing agricultural multi-dimensional elements based on geographic information, and relates to the technical field of geographic information, and the method comprises the steps: S1, obtaining multi-source geographic information data of a target planting region, comprising soil type spatial distribution data, climate condition statistical data, landform three-dimensional feature data and pest and disease damage occurrence records; s2, carrying out spatial superposition and correlation analysis on the multi-source geographic information data, and generating a multi-dimensional geographic element comprehensive layer integrating the soil fertility grade, the moisture infiltration capacity and the accumulated temperature illumination adaptation degree; and S3, presetting two target detection points in the multi-dimensional geographic element comprehensive layer, wherein the detection points are respectively located at a geographic feature turning point and a region center point of the planting region boundary. By integrating multi-source geographic information, accurate division of planting areas, intelligent matching of varieties and parameters and dynamic regulation and control of growth cycles are realized.

The invention provides a mountain freeze-thaw desertification early warning method and related equipment. The method comprises the following steps: acquiring thermal neutron intensity and epithermal neutron intensity of a target area; based on a target intensity ratio and a pre-obtained empirical fitting parameter set, target parameters are determined, and the target intensity ratio is the ratio of the thermal neutron intensity to the epithermal neutron intensity of the target area; determining a target moisture content according to the target parameter and the thermal neutron intensity or epithermal neutron intensity of the target area; and when the target moisture content is lower than a preset critical soil moisture threshold value, carrying out mountain freeze-thaw desertification early warning. Soil moisture is inverted through a cosmic ray neutron detection technology, environmental interference factors such as a non-soil water hydrogen source are corrected, local soil moisture of different vegetation types and soil types is accurately captured, freeze-thaw desertification is judged in combination with a critical soil moisture threshold, and early warning is given.

The invention belongs to the field of remote sensing, and provides a straw coverage rate estimation method based on soil type zoning, which comprises the following steps: determining the soil type in a research area, and performing soil zoning; aiming at each subarea of different soil types in the research area, acquiring an unmanned aerial vehicle image, and calculating a straw coverage rate; acquiring an optical satellite remote sensing image at the same time as the ground survey of the research area, and calculating features; for each soil type partition, performing correlation analysis on the features, selecting the features related to the straw coverage rate as input, and combining the features of different types to form a feature combination; and for each soil type partition, taking the sample and feature combination as input, establishing a straw coverage rate estimation model by using a random forest regression algorithm, and estimating the straw coverage rate of a non-sample point area. According to the method, the collection efficiency and reliability of ground straw coverage rate samples are remarkably improved, and meanwhile, the precision of straw coverage rate estimation under the background of various soil types is greatly improved.

The invention discloses a rhizomatic traditional Chinese medicinal material harvesting system, and relates to the technical field of traditional Chinese medicinal material harvesting.The rhizomatic traditional Chinese medicinal material harvesting system comprises a multi-modal soil detection module which can obtain the dielectric constant, the porosity, the organic matter content, the hardness and the humidity of soil and conduct weighted summation according to a dynamic weight distribution strategy to obtain digging force; the traditional Chinese medicinal material rhizome identification module can identify rhizomes and excavated objects to obtain an optimal excavation path; the self-adaptive digging module can dig the rhizomes of the traditional Chinese medicinal materials according to the digging force and the optimal digging path; the soil removing module is used for removing soil from the rhizomes of the traditional Chinese medicinal materials; the storage module is used for storing Chinese herbal medicine rhizomes. The harvester can solve the problems of low digging efficiency and high damage rate of rhizome traditional Chinese medicinal materials caused by incapability of automatically adjusting digging force and digging depth and incapability of adapting to traditional Chinese medicinal materials of different soil types in an existing rhizome traditional Chinese medicinal material harvesting mode, and improves the harvesting efficiency of the rhizome traditional Chinese medicinal materials and the quality of the traditional Chinese medicinal materials.

The invention belongs to the field of seismic liquefaction discrimination of geotechnical engineering, and relates to a three-dimensional site liquefaction discrimination method based on SPT, which comprises the following steps: obtaining site seismic parameters; geological soil layer conditions are obtained; establishing a three-dimensional site grid model; carrying out a field standard penetration test on a soil layer of a typical soil texture type; generating a three-dimensional field actual measurement average standard penetration field; generating a three-dimensional field critical standard penetration field; generating a three-dimensional site anti-liquefaction safety coefficient field; and determining the liquefaction possibility of the three-dimensional site. According to the invention, through two times of correction of the overlying effective stress, the detection cost is reduced while the data reliability is ensured; through a stress coupling correction technology, dynamic coupling analysis of a stress state and a standard penetration value is realized, and the discrimination precision is improved; by establishing a three-dimensional site grid model, spatial distribution characteristics and physical properties of a soil layer and a soil body are represented; and through a three-dimensional visual discrimination system, the liquefaction evaluation of the site can be integrally and visually carried out.

The invention discloses a pre-drilling engineering carbon loss calculation method, which belongs to the technical field of carbon emission estimation, and is characterized by comprising the following steps: S1, calculating and determining the carbon loss in a pre-drilling construction project; s2, the carbon sequestration amount of different types of vegetation is obtained through detection; s3, obtaining carbon sequestration quantity influence parameters of different types of vegetation; s4, detecting and obtaining the carbon sequestration amount of soil of different soil types and different depths; and S5, according to the survey data of the standard sample plot, biomass of different types of vegetation is calculated, and carbon reserves, including forest stand carbon reserves and farmland carbon reserves, of the different types of vegetation are calculated in combination with the carbon content of the different types of vegetation. According to the method, the calculation precision of the pre-drilling engineering carbon loss amount is greatly improved by establishing the logic relation based on time, geographical environment and space conditions and the pre-drilling engineering carbon loss amount of petroleum and natural gas, so that a carbon emission evaluation system of petroleum and natural gas extraction activities is more perfect and accurate.

The invention relates to the technical field of soil spectral analysis, in particular to a hyperspectral soil classification method, which comprises the following steps: acquiring hyperspectral images of various types of soil to be detected, and extracting a spectral feature group and a texture feature group from the hyperspectral images; obtaining topographic data, and extracting a topographic feature group from the topographic data; calculating separability indexes of the three feature groups among different soil classes; weighting the three feature groups based on a separability index to obtain an input feature vector; simplifying the input feature vector by adopting a recursive feature deletion method to obtain an optimal feature vector; and inputting the optimal feature vector into a middle layered random forest model for soil type detection to obtain a predicted soil type. According to the method, hyperspectral spectral features, spectral indexes, texture features and topographic features are integrated, a high-dimensional multi-source feature optimization mechanism based on factor weighting and recursive feature screening is introduced, and classification precision and model robustness are improved while feature dimensions are remarkably compressed.

The invention relates to the technical field of geological disaster early warning, in particular to a rainfall type landslide critical early warning method and system, and the method comprises the steps: obtaining multi-source geographic data of a target region, and generating grid data through a spatial interpolation method; extracting terrain parameters and terrain humidity indexes based on a DEM model, and determining landslide susceptibility evaluation factors in combination with stratum lithology, soil types and human activity data; distributing weights by adopting an analytic hierarchy process, dividing a landslide susceptible area and positioning a prediction area; based on historical disaster data and rock-soil body permeability coefficients, landslide critical curves of different stratum lithology are dynamically fitted, and a landslide occurrence probability is output through a random forest algorithm; and multiplying the susceptibility by a probability result to generate a gridding risk value, and dividing early warning grades. The method has the effect of improving the precision and timeliness of landslide early warning analysis.