296 results about "Soil parameters" patented technology

The invention relates to the technical field of farmland fertilization management, and discloses an intelligent fertilization management method and system based on machine learning. The method comprises the steps that a soil parameter set and an environment parameter set of a target farmland are collected, soil parameters comprise soil humidity, nitrogen phosphorus and potassium content and pH value, and environment parameters comprise illumination intensity, temperature and rainfall; constructing a soil nutrient dynamic change model according to historical data, and predicting a soil nutrient consumption trend in a future preset period; generating an initial fertilization scheme based on the nutrient consumption trend and the crop growth stage characteristics; monitoring the growth state of crops in real time by using a multi-mode sensor, and obtaining a leaf surface color index, a stem height and a fruit development progress to form a growth state data set; and inputting the growth state data set and the initial fertilization scheme into a fertilization decision model, and comparing growth state deviation to adjust the nutrient distribution ratio to generate an optimized fertilization scheme.

The invention relates to the technical field of earthwork compaction, and discloses a pipe ditch backfilling and tamping system based on excavator modification. The system comprises an earthwork condition dynamic sensing module which captures a bucket tooth track, a hydraulic oil cylinder pressure pulse and a soil mass vibration spectrum in real time and combines a historical database to construct a model to output a theoretical compaction degree curve; a multi-source compaction difference analysis module performs three-dimensional deviation detection on the theoretical curve and the actual settlement volume data to generate a construction grid-level compaction difference tensor; the defect area association mapping module inputs the tensor into a geological topology analysis network, generates a loose defect propagation path probability cloud chart by combining soil parameters and identifies weak sections; and the tamping strategy self-adaptive configuration module automatically sets operation parameters according to the cloud picture, activates high-precision monitoring on a high-probability defect section, and applies a dynamic excitation force test on adjacent grids, so that intelligent management and control of the backfilling and compacting process are realized.

The invention provides a finite element-based method for estimating pulling-out ultimate bearing capacity of a grouting screw anchor in gravelly soil, and relates to the technical field of civil structure engineering.A three-dimensional finite element model containing the grouting screw anchor and surrounding gravelly soil is established, model parameters cover the structure and material characteristics of the grouting screw anchor, and the grouting screw anchor and the surrounding gravelly soil are subjected to finite element modeling; a coordinate axis is established with the center of the anchor bottom as an original point for grid division, a stress concentration area is analyzed, principal stress is calculated to serve as critical bearing stress, vertical pull-out loads are applied step by step through a grouting pull-out test, a pull-out stress influence area is determined, soil stress of the area is analyzed, and soil binding power and friction force are calculated. And a grouting coefficient and a rock crack coefficient are generated, soil parameters are corrected, and the ultimate pulling bearing capacity of the grouting screw anchor is comprehensively obtained. According to the method, the pulling ultimate bearing capacity of the grouting screw anchor is accurately evaluated by establishing the three-dimensional finite element model and the graded loading experiment.

The invention discloses a farmland precise fertilization control method based on multi-source data fusion and deep learning, and the method comprises the steps: obtaining the multi-dimensional heterogeneous data of a target farmland in real time, including the crop spectrum time series data of a satellite image, the ion concentration data collected by a soil sensor network, and the microenvironment time series monitored by a meteorological station; and constructing a Delaune triangulation network spatial index, and re-sampling satellite image data to be matched with a soil sensor network space. Calculating a time-varying mutual information entropy, extracting related microenvironment parameters as coupling factors, combining the coupling factors with soil basic parameters to form a three-dimensional feature matrix, and converting the three-dimensional feature matrix into a growth period feature vector fused with time-space correlation; the collaborative decision model is input, the main branch predicts the basic fertilization amount, the auxiliary branch detects the ion concentration space mutation area and corrects the fertilization amount, a fertilization control instruction is output, and the fertilization device is controlled to conduct precise fertilization. The problem that fertilization control cannot be accurately and efficiently performed on farmland in the prior art is effectively solved.

The invention discloses an offshore wind turbine foundation scouring full-period intelligent monitoring method and system based on multi-source data fusion and a dynamic coupling model, and relates to the technical field of intelligent monitoring, and the method comprises the steps: deploying a multi-source monitoring module, and constructing a finite element model; carrying out load calculation and parameter inversion; and training full-cycle dynamic updating of the washout failure function model. According to the method, a self-adaptive Kriging-Bayesian method is adopted, a Bayesian inversion framework and a self-adaptive agent model are fused to solve optimal soil body parameters, full-period model dynamic updating based on dynamic monitoring data is achieved, a multi-fidelity deep kernel learning model is adopted, three types of data are fused into a training set, full-period intelligent monitoring of offshore wind turbine foundation scouring is achieved, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. The dynamic identification of soil parameters is realized by combining a self-adaptive inversion framework with a displacement error closed-loop optimization mechanism, and the technical problem that a traditional static model cannot adapt to the spatial-temporal variability of seabed geology is solved.

The invention belongs to the technical field of intelligent soil monitoring, and particularly relates to a multi-sensor networking soil parameter high-precision detection device and method.The device adopts sensing, transmission and data processing, a four-layer architecture is applied, a sensing layer collects soil texture, temperature and salinity parameters, an environment interference factor matrix is constructed, and a data processing layer is constructed; outputting non-interference data through a layered interference calibration model; the transmission layer completes data transmission through wired and wireless dual modes; the data processing layer extracts spatial-temporal characteristics after preprocessing, and obtains a soil parameter fusion value through dynamic weight fusion; and the application layer matches the fusion value with a preset template to realize scene generation and visual rendering. According to the invention, the problems that the precision of the existing multi-sensor networking system is easily influenced by the outside and the intelligent level is insufficient can be solved.

The invention relates to an iron tower foundation safety analysis method considering the dry-wet cycle effect. The method comprises the following steps that 1, a soil body strength degradation constitutive model is established; 2, determining iron tower foundation structure parameters and soil body parameters; 3, establishing an iron tower foundation-soil body three-dimensional geometric model by utilizing three-dimensional modeling software and rock-soil finite element simulation software; 4, applying a load transmitted to the iron tower foundation by the power transmission iron tower in the iron tower foundation-soil body three-dimensional geometric model, defining initial parameters, and establishing a rainwater seepage and dry-wet cycle coupling effect analysis model; 5, analyzing and predicting the cycle index when the safety coefficient of the iron tower foundation is reduced to a threshold value from the two aspects of tower foundation ultimate bearing performance and iron tower foundation-soil body interaction on the basis of a rainwater seepage and dry-wet cycle coupling effect analysis model; according to the method, an efficient and accurate numerical analysis means can be provided for safety assessment of the power transmission tower foundation in the collapsible loess area.

The invention discloses an irrigation index prediction method based on meteorological data and field moisture capacity, and relates to the technical field of intelligent agriculture, and the method comprises the steps: collecting real-time meteorological data, real-time soil volumetric water content data, soil matrix potential data and future precipitation forecast data, and carrying out quality control and time alignment processing to obtain a multi-source data set; the method comprises the following steps: calculating evapotranspiration of a reference crop according to real-time meteorological data, obtaining actual water demand of the crop in combination with a crop coefficient, calculating a moisture stress coefficient according to real-time soil volumetric water content data and a field moisture capacity static parameter value, and obtaining a basic irrigation demand index in combination with future precipitation forecast data. According to the method, the evapotranspiration amount is separated by adopting the Shuttleworth-Wallace model, the irrigation index integrating the crop evapotranspiration amount, the water deficit amount and the effective precipitation amount is generated on the basis of multi-period rolling water balance calculation and risk decision rules, and online calibration of soil parameters and optimization of irrigation decision are achieved.

The invention relates to a rock-soil-building block collaborative slope reliability analysis method considering spatial variability. The method comprises the following steps: establishing a slope finite element model; aiming at the finite element model, constructing an autocorrelation random field which takes a soil mass parameter as a variable and has a discrete characteristic by using a Cholesky Decomposition midpoint method; coupling the self-correlation random field based on a Copula model to form a cross-correlation standard uniform random field, and performing equal probability conversion on the cross-correlation standard uniform random field to form a cross-correlation random field in logarithmic normal distribution; and inputting the generated random field into a finite element model, obtaining a safety coefficient by using a strength reduction method, and determining the slope reliability based on a Monte Carlo method. According to the method, the finite element analysis result is closer to the actual situation, and the accuracy of the reliability analysis result is remarkably improved.

The invention provides a landslide probability prediction method and system, computer equipment and a storage medium, and belongs to the field of geological disaster early warning, and the method comprises the steps: obtaining the soil cohesion, internal friction angle and permeability coefficient of rock soil in a landslide region to be predicted; random field generation is carried out on the soil body cohesion, the internal friction angle and the permeability coefficient of the rock soil in the landslide area to be predicted, and soil body cohesion random field data, internal friction angle random field data and permeability coefficient random field data are obtained; carrying out discrete processing on the soil mass cohesion random field data, the internal friction angle random field data and the permeability coefficient random field data to obtain random variables of rock soil in the landslide area to be predicted; inputting the random variables of the rock and soil of the landslide area to be predicted into a pre-trained machine learning model, and calculating and outputting a plurality of slope safety coefficients; and determining the failure probability of the rock soil of the to-be-predicted landslide area according to the plurality of slope safety factor sets. According to the method, soil parameter spatial variability is considered, and the landslide probability prediction accuracy is improved.

The invention relates to the technical field of agricultural engineering, in particular to a water and fertilizer regulation and control method and system for saline-alkali soil grain-fed crops, and the method comprises the following steps: S1, synchronously obtaining soil salinity concentration, soil moisture content and available nitrogen concentration; s2, calculating a salinity stress index, and dividing stress levels; s3, if the soil moisture content is lower than the preset crop suitable lower limit, the irrigation compensation amount is increased; if the soil moisture content is normal, the pulse irrigation mode is adjusted according to the stress level; s4, the nitrogen fertilizer notch amount is calculated according to comparison between the available nitrogen concentration in the S1 and the nitrogen demand threshold value in the crop growth stage; and S5, dynamically adjusting the fertilizer injection concentration and the irrigation duration in the water and fertilizer integrated application. By monitoring soil parameters in real time and dynamically adjusting water and fertilizer supply, accurate regulation and control of water and fertilizer of saline-alkali soil crops are achieved, the utilization efficiency of the fertilizer is improved, the growth environment of the crops is optimized, and resource waste and environmental pollution are reduced.

The invention belongs to the technical field of calculation of horizontal bearing capacity of foundations, and particularly discloses and provides an accurate calculation method for horizontal bearing capacity of an offshore wind power foundation, which comprises the following steps: integrating wave spectrum, wind regime, tide and soil mass parameters to form a multi-dimensional data set; a three-dimensional pile-soil model is constructed, and wave force and wind load are coupled; calculating a stiffness coefficient and identifying a pile body type in combination with a length-diameter ratio; quantifying an environment deviation compensation coefficient according to the pile body type; after the initial horizontal bearing capacity is calculated, the bearing capacity failure critical state is judged based on real-time displacement monitoring data, and if the critical state is reached, the initial bearing capacity is corrected through linear weighting of the displacement deviation compensation coefficient and the environment deviation compensation coefficient. According to the method, the time-varying environment load is dynamically coupled, the pile-soil interaction is finely simulated, a rigidity self-adaptive grading mechanism and a double compensation feedback system are established, and the calculation precision and the real-time early warning capability of the horizontal bearing capacity in the complex marine environment are remarkably improved.

The invention provides a method for analyzing deformation characteristics of an exit slope of a flood discharge building, which comprises the following steps of: firstly, collecting geotechnical parameters, hydraulic load parameters, GNSS displacement, pore water pressure and clinometer data of the slope; then, constitutive parameters of the slope stability model are dynamically corrected, GNSS displacement is decomposed into creep and elastic displacement components, and the time delay error of pore water pressure data is compensated; thirdly, seepage-stress coupling stability calculation is conducted in combination with the compensated pore water pressure and inclinometer data, and the slope stability safety coefficient is obtained. And finally, outputting the creep displacement, the elastic displacement and the stability safety coefficient to an early warning terminal. According to the invention, the accuracy and reliability of slope stability evaluation can be improved.

The invention relates to the technical field of environmental science, and discloses a water and soil conservation carbon sink measuring and calculating method and system based on artificial intelligence, and the method comprises the steps: obtaining soil parameters, climate parameters, terrain parameters and environmental parameters, carrying out the adaptive uncertainty quantification processing, and constructing a standardized data set; on the basis of the standardized data set, combined with a biomass-carbon sink dynamic constraint model, forest and grass carbon sink data are generated; engineering carbon sink data are generated through the microtopography-carbon sink dynamic correction model; further generating cultivation carbon sink data through a carbon turnover-cultivation interaction stability feedback model; performing nonlinear collaborative causal reasoning on the forest and grass carbon sink data, the engineering carbon sink data and the cultivation carbon sink data, and fusing to generate total carbon sink data; and finally, performing error correction prediction on the total carbon sink data, and outputting a carbon sink prediction result. According to the method, accurate quantification of water and soil conservation carbon sequestration with multi-measure cooperation and multi-path interweaving is realized, and the measurement and calculation precision and reliability are effectively improved.

The invention provides a soil moisture content calculation method based on an optical fiber temperature measurement technology, which comprises the following steps: acquiring a dry soil heat conductivity coefficient, dry soil density and porosity of a target area and a heat conductivity coefficient and density of water in the target area as soil basic parameters; based on an active heating method, obtaining temperature measurement values of the target area under different radial distances after the target area is heated to a steady state; the soil body basic parameters and the temperature measurement value are substituted into a soil body moisture content formula, and the soil body moisture content of the target area is obtained; wherein the soil mass moisture content formula is pre-constructed based on a soil mass steady-state heat conduction equation and the relationship between the heat conduction coefficient and the soil mass basic parameters, and represents the relationship among the heat conduction coefficient of the soil mass, the temperature and the soil mass moisture content. According to the method, the soil moisture content formula representing the relation among the soil basic parameters, the temperature measurement value and the soil moisture content is constructed, and tedious laboratory operation needed by an existing active heating method is simplified.

The invention belongs to the technical field of transformer substation grounding grid monitoring, and discloses a transformer substation main grounding grid real-time safety monitoring system based on multi-sensor fusion, which adopts a MySQL and InfluxDB mixed architecture through a dynamic association database module, and stores static data such as transformer substation position, grounding grid material and the like, and dynamic data such as soil parameters, corrosion rate and the like respectively; meanwhile, a soil parameter and sampling frequency dynamic mapping table is built in, and when it is recognized that the soil salinity is larger than 15 mS / cm or the pH is smaller than 5.5, a sampling frequency adjusting instruction can be sent to the sensor array module, and the automatic soil sample collecting device is started; the module also establishes a material corrosion digital twinborn model and a climate partition library, provides scene data for the scheme dynamic adaptation and operation and maintenance module, enables the scheme dynamic adaptation and operation and maintenance module to output an adaptive scene corrosion prevention scheme, calculates a grounding electrode replacement period according to a corrosion rate, constructs a dynamic association between a soil working condition and corrosion protection, and effectively improves the durability of a grounding grid.

The invention discloses a grounding grid corrosion prediction method fused with a soil parameter knowledge graph, and relates to the related field of power system grounding grid corrosion monitoring, and the method comprises the steps: laying a multi-parameter soil sensor based on a grounding grid region for real-time collection, and generating a soil parameter data set; performing knowledge association based on the soil parameter data set in combination with soil corrosion mechanism data, and constructing a soil corrosivity knowledge graph; acquiring an operation electrical data set of the grounding grid in real time, and performing fusion evaluation on the operation electrical data set and the soil corrosivity knowledge graph to generate a grounding grid corrosion grade evaluation result; and performing corrosion prediction on the grounding grid according to a grounding grid corrosion level evaluation result, obtaining a corrosion prediction trend graph, backtracking to the soil corrosivity knowledge graph for verification, performing operation analysis on the grounding grid according to a verification result, and generating a grounding grid residual life prediction report. The technical problem that existing grounding grid corrosion prediction is insufficient in accuracy is solved, and the technical effect of improving corrosion state prediction accuracy is achieved.

The invention discloses a grounding resistance thermal-electric coupling prediction method and system suitable for various soil scenes. The method comprises the following steps: acquiring an electrode parameter, a soil parameter, a grounding fault parameter and initial temperatures of an electrode and soil; calculating an electrode current and a soil leakage current of the time step based on the electrode and soil resistivity of the time step; based on the electrode current and the soil leakage current of the time step, calculating the electrode temperature, the soil radial temperature distribution and the grounding potential rise of the time step by adopting a finite difference method; based on the electrode temperature and soil radial temperature distribution of the time step, updating the electrode resistivity and the soil resistivity of the time step + 1; if the end of the fault time is not reached, returning to continuously calculate the temperature of the electrode and the soil at the time step + 1; otherwise, outputting the electrode temperature and soil radial temperature distribution of each time step. According to the invention, thermal-electric dynamic coupling is considered, and accurate evaluation of the performance of the grounding system is realized.

The invention relates to the technical field of irrigation saline-alkali soil improvement, in particular to an irrigation saline-alkali soil improvement method based on straw adsorption and water conservancy regulation, which comprises the following steps: acquiring multi-depth soil parameters and surface meteorological data of an irrigation area, and establishing a soil water-salt dynamic migration coupling model; calculating a vertical moisture flux and a plowing layer net salt flux by using the model; calculating an accumulated salt return risk index to obtain a salt return risk grade; building a straw decomposition index by using multi-depth soil parameters; on the basis of the straw decomposition index, updating the soil water and salt dynamic migration coupling model; in response to the fact that the salt return risk level is a high risk, constructing an optimization objective function; resolving and optimizing the objective function to obtain an optimal irrigation strategy; outputting an optimal irrigation strategy to an irrigation control system, and feeding back the optimal irrigation strategy to the model as an irrigation rate; according to the invention, threats can be identified before a high-risk salt return event occurs, the timeliness and initiative of regulation and control are improved, and the balance between treatment benefits and economic cost is realized.

The invention discloses an agricultural big data multivariate fusion supervision system, and relates to the technical field of agricultural supervision, and the system comprises a soil analysis module which is used for obtaining soil parameter data of an agricultural region, carrying out the analysis of the soil parameter data through a K-Means clustering model, and outputting the quality score and improvement suggestion information of agricultural products in the agricultural region; the meteorological analysis module is used for collecting historical meteorological data of the agricultural region, constructing a time sequence prediction model, predicting a meteorological trend of a future time window and generating meteorological forecast information; through multi-source data fusion of the meteorological analysis module, the agricultural product quality supervision module and the market prediction module, the agricultural product quality, the future meteorological trend and the agricultural product market supply and demand state of a land parcel in an agricultural area are comprehensively judged, and a crop variety recommendation scheme is output based on a decision tree and a scoring function through the planting recommendation module. The decision making is more scientific and accurate, so that the crop yield and the economic benefit are improved.

The invention belongs to the field of rock-soil simulation, and provides an ice-water phase change finite element simulation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining soil parameters, temperature change data and hydraulic characteristic data of a target tailings pond; constructing a tailing pond frozen soil model by adopting a finite element method according to the soil parameters; determining a temperature field of the target tailing pond according to the temperature change data and a tailing pond frozen soil model; determining a seepage field of the target tailing pond according to the hydraulic characteristic data and the tailing pond frozen soil model; according to the seepage field, calculating the tailing pond frozen soil model to obtain initial seepage characteristics of the tailing pond frozen soil model when the tailing pond frozen soil model is not melted; and performing heat flow coupling calculation on the tailing pond frozen soil model according to the initial seepage characteristics and the temperature field to obtain a frozen soil melting simulation result of the target tailing pond. The method has the beneficial effect that the accuracy of ice-water phase change tailing pond frozen soil melting and seepage characteristic finite element simulation is improved.

The invention provides a cotton irrigation method and device and terminal equipment, and is suitable for the technical field of data processing, and the method comprises the steps: obtaining initial cotton moisture stress parameter information according to historical meteorological parameter information, historical soil parameter information, historical cotton physiological parameter information and an initial cotton moisture stress parameter prediction model; screening the initial cotton moisture stress parameter prediction model to obtain a target cotton moisture stress parameter prediction model; obtaining target cotton moisture stress parameter information according to the current meteorological parameter information, the current soil parameter information, the current cotton physiological parameter information and a target cotton moisture stress parameter prediction model; and according to the target cotton water stress parameter information and the cotton water stress parameter threshold information, generating a cotton irrigation instruction so as to irrigate cotton through the cotton irrigation instruction. The accuracy and timeliness of cotton moisture judgment are improved, so that the effectiveness of cotton irrigation is improved.

The invention discloses an intelligent feedback control construction quality monitoring method and system, and the method comprises the steps: collecting soil parameters, and obtaining a soil parameter set; presetting a slurry soil property contrast database, and obtaining slurry proportioning data according to the soil property parameter set; the soil property parameter set and the grout proportion data are analyzed, and a rotating speed parameter set and a grouting pressure parameter set are generated; obtaining slurry monitoring data, comparing the slurry monitoring data with the slurry proportioning data, and obtaining a passing instruction; presetting pile-forming verification indexes, collecting pile-forming acceptance data, comparing the pile-forming acceptance data with the pile-forming verification indexes to obtain pile-forming correction parameters, and correcting the slurry soil texture contrast database according to the pile-forming correction parameters; the system is used for setting the slurry soil contrast database according to different soil layer conditions to obtain slurry ratio data, so that the pile forming quality is monitored through intelligent feedback.

The invention relates to the technical field of information processing, in particular to a soil monitoring method, device and equipment based on the Internet of Things and a storage medium, node deployment density suggestions are generated according to a soil difference atlas, a plurality of intelligent sensing nodes are deployed in a target soil monitoring area based on the node deployment density suggestions, the rationality of node deployment can be improved, and the reliability of node deployment is improved. According to the method, abnormal data detection is carried out on multi-parameter data based on multi-parameter space-time correlation, dynamic adaptive filtering processing is carried out on the multi-parameter data according to a detection result, the reliability of data processing is improved, and a LoRa ad hoc network or an NB-IoT network is selected according to an output result of a decision matrix to transmit effective monitoring data to a cloud platform. The data transmission flexibility is improved, the soil parameter early warning threshold value is dynamically updated according to the growth stage information and the short-term weather forecast information, the early warning accuracy is improved, farming operation suggestions are generated according to the effective monitoring data and the soil parameter early warning threshold value, and operable intelligent decision support is provided.

The invention relates to the technical field of civil engineering, in particular to a method for calculating the seismic response of a pile group under the action of seismic P waves, a pile-soil coupling model is established based on a continuous elastic dynamics theory, and pile-pile dynamic interaction factors are calculated by decoupling displacement of a free field and a scattered field of a soil body and combining coordinate transformation, so that the seismic response of the pile group is calculated. The displacement response of a pile group system is solved according to the superposition principle, the influence of pile spacing and soil mass parameters on the anti-seismic performance of the pile group is quantitatively evaluated by adopting a method of combining frequency domain amplitude-frequency characteristics and time domain PGA attenuation analysis, and theoretical support is provided for pile foundation optimization design.

The invention discloses an agricultural planting soil parameter analysis system, which relates to the technical field of soil intelligent analysis, and comprises a data acquisition module, a data determination module, a quality evaluation module and a dynamic optimization module, the data acquisition module is used for acquiring soil parameters and farming operation record data of each planting area; the data determination module is used for analyzing the soil component difference value of each planting area to generate a first influence difference; the quality evaluation module is used for analyzing the spatial correlation of the first influence difference by using the interaction correlation to obtain a soil spatial distribution prediction map; and the dynamic optimization module is used for dynamically regulating and controlling the soil content according to the soil spatial distribution prediction map. The farmland ecological environment is protected, agricultural sustainable development is promoted, and the intelligent level of soil parameter adjustment is improved.

The invention discloses a negative pressure screw anchor horizontal ultimate bearing capacity prediction method, and relates to the technical field of geotechnical engineering, and the method comprises the following steps: generating a construction resistance energy feature vector by using penetration time sequence data, inverting a soil body improvement factor through an energy-soil property mapping model, and correcting undisturbed soil parameters; and calculating and superposing the resistance of each subitem based on the correction parameter and the geometric parameter to obtain a predicted value. The method is used for solving the problem that an existing prediction method neglects the influence of construction disturbance on soil parameter remodeling, and consequently the bearing capacity prediction precision is insufficient.

The invention provides a microwave remediation efficiency prediction method for polycyclic aromatic hydrocarbon contaminated soil, and belongs to the field of physical remediation of soil. The method comprises the steps that soil parameters, remediation parameters based on microwave soil remediation and the polycyclic aromatic hydrocarbon removal rate are obtained; based on a data fitting method, determining a corresponding relation between the soil parameter and / or the remediation parameter and the polycyclic aromatic hydrocarbon removal rate; according to the corresponding relation between the soil parameters and / or the remediation parameters and the polycyclic aromatic hydrocarbon removal rate, predicting the polycyclic aromatic hydrocarbon removal rate under the known soil parameters and / or the remediation parameters; according to the method, accurate prediction of the polycyclic aromatic hydrocarbon removal rate of the soil can be realized, and a theoretical basis is provided for optimizing microwave remediation process parameters.

The invention provides a method for constructing a soil fertility index comprehensive evaluation model based on multiple soil indexes, and the method comprises the steps: (1) collecting soil sample image data of different regions, and carrying out the preprocessing of the data, so as to obtain a panoramic looking-around soil profile hyperspectral image; (2) extracting soil sample data and standard reflectivity plate data; (3) carrying out radiation correction and preprocessing on the soil sample data and the standard reflectivity plate data; (4) carrying out characteristic wavelength extraction on the spectral reflectivity data of the soil samples at different depths; (5) respectively measuring parameter data of the soil samples at different depths; (6) constructing a multi-parameter inversion model under different depths; (7) establishing a soil fertility comprehensive evaluation model; and (8) obtaining color distribution diagrams of different soil parameters and soil fertility indexes. According to the method, the spectrum data of the soil profiles at different depths can be quickly acquired in a low-loss manner, so that the soil index data is inverted, and the soil fertility evaluation efficiency is greatly improved.

The invention discloses a freezing design method for an inclined shaft freezing crossing strong permeable pebble bed, and particularly relates to the technical field of freezing method construction. Acquiring rock and soil parameters and regional historical environment temperature of a pebble bed of a crossing section of the target inclined shaft; establishing a three-dimensional inclined shaft geometric model, and determining the arrangement position, inclination angle and spacing of the freezing pipe along the axis of the inclined shaft; constructing a frozen region space model coupled with the pebble bed; the method comprises the following steps: constructing a freezing-heat transfer-seepage multi-field coupling model according to historical environment temperature and freezing pipe arrangement information, performing numerical simulation on a frozen body forming process, and obtaining an evolution function of a freezing boundary expansion range and a freezing thickness at different times; according to the method, multi-physical field coupling modeling and freezing body evolution accurate control of the freezing process can be achieved, and the method has the advantages of being high in predictability, uniform in freezing effect and the like and is particularly suitable for inclined shaft freezing design and construction under complex geological conditions.