529 results about "Soil parameters" patented technology

The invention provides a stratum disturbance analysis method and system under a shield construction coupling effect. Cutting vibration spectrum data are collected in real time through a cutter vibration sensor, and a feature fingerprint database containing a vibration energy distribution mode and a critical grouting interval is constructed in combination with soil parameters. And aligning the vibration spectrum with the soil bin pressure in a space-time manner, and generating a disturbance field distribution diagram for displaying an energy gradient distribution curve and a stress diffusion path topology. And matching the energy distribution curve and correcting formation interface propagation parameters through the feature matching network optimized by transfer learning, and outputting a formation type identification result and disturbance dynamic parameters. And based on the mapping relation between the parameters and the critical grouting interval, the ground surface displacement data are linked to dynamically regulate and control the grouting pressure, graded early warning and parameter regulation instructions are generated, and a stratum disturbance monitoring-regulation and control closed loop is formed. According to the technical scheme, dynamic optimization of the grouting pressure is achieved, and the stratum deformation risk caused by shield construction is remarkably reduced.

The invention discloses a large-diameter shield tunneling parameter optimization method and system based on digital twinning, and relates to the technical field of shield tunnel numerical simulation, machine learning and digital twinning, and the method comprises the steps: obtaining shield tunnel construction site data, and constructing a virtual digital twinning body; building a training sample set based on virtual numerical twinborn generation data; training an agent model by using the training sample set; during construction, actual stratum response data and tunneling parameter data are obtained in real time, the real-time data are input into the soil mass parameter inversion agent model, and the optimal value of the soil mass parameter is rapidly output; the optimal value is input into the virtual digital twin, and dynamic real-time updating of numerical simulation is carried out; and on the basis of the updated soil parameter data and the tunneling parameter data in the next construction step, stratum response prediction data are obtained through the stratum response prediction agent model, so that the tunneling parameter data are adjusted and optimized. According to the invention, more accurate numerical simulation and safe and efficient tunneling of the shield tunnel can be realized.

The invention discloses a farmland soil quality dynamic diagnosis method based on multi-source remote sensing collaborative inversion, and relates to the technical field of agricultural information, and the method comprises the steps: collecting physical and chemical parameters of soil temperature, humidity, pH value, organic matter content, nitrogen phosphorus and potassium concentration and the like in real time through a multi-source sensor network; removing abnormal values by using a data preprocessing algorithm, performing standardization processing, generating a standardized soil parameter set for subsequent climate and management factor correlation analysis, automatically generating a risk early warning signal, pushing the risk early warning signal to an agricultural management decision platform, and completing full-process analysis from data acquisition to decision support; the farmland soil quality dynamic diagnosis method based on multi-source remote sensing collaborative inversion can provide a scientific basis for agricultural management decision, effectively guide farmland management practice, and improve soil quality and agricultural production efficiency.

The invention relates to the technical field of water and fertilizer integration, and provides an intelligent water and fertilizer integration optimization method and device based on the Internet of Things technology, and the method comprises the following three core stages and interaction mechanisms: 1, a data perception and edge processing stage; 2, an intelligent decision-making and dynamic optimization stage; and step 3, an accurate execution and closed-loop control stage. Soil parameters, meteorological parameters and crop physiological parameters are comprehensively collected through a multi-modal sensor network, the data transmission efficiency is improved by using an LoRa / NB-IoT hybrid transmission protocol, data cleaning and standardization processing are completed in combination with edge computing nodes, and the data fusion depth and quality are improved; a multi-objective optimization function is constructed based on a crop growth model, a water and fertilizer regulation and control strategy is dynamically generated through a machine learning algorithm, and collaborative optimization of water saving, yield increasing and environmental protection is achieved.

The invention provides an agricultural environment monitoring method and system based on the Internet of Things. According to the method, multi-source data weights are dynamically distributed through a soil sensor array connected with Internet of Things nodes, and a global soil parameter set is generated; deploying a micro-electro-mechanical micro-fluidic chip in the coverage area to carry out soil solution selective permeation, converting the target ion concentration into an electric signal, and synchronously transmitting the electric signal and soil parameters; historical time series data are extracted, coherence is established through time correlation analysis, and undeployed area data are filled in combination with a spatial interpolation algorithm to construct a dynamic prediction model; according to the nutrient space change trend output by the model, crop growth requirements are matched to generate a fertilization amount adjustment instruction, and the fertilization amount adjustment instruction is issued to field fertilization equipment through the Internet of Things to execute dynamic regulation. Space-time precise sensing of soil nutrients and self-adaptive fertilization regulation and control are realized, and the utilization efficiency of agricultural resources and crop growth sustainability are improved.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

The invention discloses a traditional Chinese medicinal material quality evaluation method and system based on big data analysis, and relates to the field of traditional Chinese medicinal material detection, and the main scheme is as follows: obtaining soil parameter data of different medicinal material planting areas, and analyzing to obtain a soil fertility index and a planting suitability index; obtaining effective component contents of the traditional Chinese medicinal materials, and calculating effective component predicted values of the traditional Chinese medicinal materials; calculating a machining process stability index by analyzing the machining process parameters; calculating a mildew evaluation index according to the environmental mildew data and the medicinal material mildew data in the storage environment; a quality evaluation comprehensive index is obtained through comprehensive analysis; comparing the quality evaluation comprehensive index with a quality evaluation comprehensive index threshold value, judging whether the comprehensive quality evaluation of the traditional Chinese medicinal materials is qualified or not, and if the comprehensive quality evaluation is not qualified, judging a link causing a quality problem; the problems that a traditional Chinese medicinal material quality evaluation method in the prior art focuses on a single component and only focuses on the quality of traditional Chinese medicinal materials in a certain specific link are solved.

The invention discloses an intelligent land engineering carbon sink regulation and control system and method based on dynamic management, and belongs to the technical field of land engineering carbon sink regulation and control. The multi-source spatio-temporal data comprises vegetation indexes, soil parameters, meteorological data and carbon dioxide flux data; the data processing and analyzing module is used for performing fusion processing on the multi-source spatio-temporal data, inverting and calculating the carbon sink or carbon source state of the target area in real time based on a built-in carbon sink accounting model, and generating a carbon sink spatio-temporal distribution diagram; the intelligent decision-making module is used for diagnosing a carbon sink capacity limiting factor based on the carbon sink space-time distribution diagram and generating land engineering regulation and control strategies for different sub-regions; and the execution and feedback module is used for issuing the regulation and control strategy to an execution terminal or a management platform. The method improves the carbon sequestration capacity and regulation and control efficiency of land engineering.

The invention discloses an artificial intelligence rice water and fertilizer real-time monitoring method and system, and relates to the technical field of agricultural intelligent decision making, and the method comprises the steps: inputting a farmland feature data set into a soil thermodynamic diagram generation model, carrying out the high-resolution reconstruction of the farmland feature data set through a GAN adversarial network, and generating a whole-field high-precision soil thermodynamic diagram; based on the whole-field high-precision soil thermodynamic diagram, a collision relation between the fertilization amount and historical farming data is detected according to an FCL causal algorithm, a preliminary causal diagram is generated, and a causal diagram structure of the fertilization amount and the historical farming data is constructed by adopting a multiple linear regression method; based on a causal diagram structure, the multi-order causal effect of the fertilization amount, the soil parameters and the historical yield is analyzed through a dynamic allocation algorithm, and a water and fertilizer regulation and control strategy is formulated in combination with a multi-objective optimization algorithm. According to the method, the soil thermodynamic diagram generation model is constructed, so that the fuzzy problem of the edge of the field and the salinization area is solved, a high-fidelity soil space state substrate is provided for water and fertilizer regulation and control, and invalid irrigation is reduced.

The invention discloses a non-point source pollution control and ecological agriculture decision-making method based on reinforcement learning, and relates to the technical field of new-generation information. The method comprises the steps that meteorological parameters, soil parameters and crop growth parameters are collected through a farmland Internet of Things system, and historical management data in the crop growth process and water quality monitoring information of surface runoff are collected to serve as original data; preprocessing the original data to construct a multi-modal data set; constructing an ecological agriculture multi-modal reinforcement learning model based on the multi-modal data set; integrating the ecological agriculture multi-modal reinforcement learning model to an agricultural intelligent decision-making platform, so that the ecological agriculture multi-modal reinforcement learning model is coupled with the agricultural intelligent decision-making platform; and an agricultural intelligent decision-making platform is utilized to issue operation instructions to irrigation, fertilization and pesticide application equipment so as to realize non-point source pollution management and control and ecological agriculture decision-making. Through a machine learning algorithm and real-time data analysis, many limitations of traditional ecological agriculture are overcome.

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 a water and fertilizer management control method based on image data processing, and the method comprises the steps: collecting a visible light image, a multispectral image and a thermal infrared image of a crop, carrying out the feature extraction, calculating the plant morphological parameters, vegetation indexes and vegetation canopy temperature distribution of the crop, creating a three-dimensional physiological feature matrix, creating an image segmentation network model, and carrying out the feature extraction. Inputting the visible light image, the multispectral image, the thermal infrared image and the three-dimensional physiological feature matrix into an image segmentation network model to segment plant organs, quantifying organ-level phenotypic parameters, and establishing a water and fertilizer decision model based on reinforcement learning; and taking real-time data of the three-dimensional physiological feature matrix, the organ-level phenotypic parameters and the soil specified parameters as state input, taking a water and fertilizer proportioning scheme as an action space, and implementing a water and fertilizer management scheme through a dynamic optimization control strategy. The technical problems that data integration of a single sensor is insufficient, phenotype analysis precision is limited, and adaptability of a decision model is poor in a traditional water and fertilizer regulation scheme are solved.

The invention provides a desert photovoltaic support pile foundation uplift bearing capacity detection and construction parameter optimization method, and relates to the technical field of construction optimization, and the method comprises the following steps: dividing construction subareas according to soil parameter information of a desert target area, carrying out test pile detection in each construction subarea, and establishing a distribution rule of uplift bearing capacity of a pile-soil interface; the pile foundation diameter, the pile foundation burial depth and the pile foundation surface treatment mode serve as variables, a multi-objective optimization function including an uplift bearing capacity stability objective, an uplift bearing capacity redundancy objective and a construction efficiency objective is established, and the weight coefficient of the multi-objective optimization function is dynamically adjusted along with the environment temperature and sand bed migration; solving the multi-objective optimization function by adopting a particle swarm algorithm to obtain an optimal construction parameter combination of each construction subarea; and the optimal construction parameter combination is adopted for pile foundation construction, a pile foundation is selected for uplift bearing capacity rechecking, a rechecking result is fed back to the multi-objective optimization function, and dynamic fine adjustment is conducted on the construction parameters.

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 multi-layer soft soil foundation settlement prediction method, and relates to the field of settlement prediction, and the method comprises the steps: obtaining soft soil parameters through arranging a multi-source monitoring device, carrying out inversion to generate multi-dimensional feature vectors containing particle size distribution, void ratio, compression modulus gradient and effective stress, and dividing structure piece elements to construct a feature set; the Euclidean distance between the fragments is calculated and clustered to generate a structural similar region, and a judgment threshold is dynamically adjusted and combined based on the change rate of the structural information entropy to construct a dynamic structural map; newly added or cracked similar areas are identified, a structure sudden change strength index is generated, and when a threshold value is exceeded, a jump prediction model is constructed according to entropy change, pore pressure gradient and deformation rate to output discontinuous settlement increment; and generating an error field by using the difference value of the actually measured settlement and the predicted value, and correcting the structural information entropy and merging the threshold value after weighted averaging according to the similar region, thereby realizing the self-adaptive correction of the structural map and iterative optimization prediction.

The invention relates to the technical field of agriculture, and discloses a soil fertility remote sensing inversion method based on machine learning. Acquiring and preprocessing a historical soil database, historical remote sensing image data and target year remote sensing image data, including radiometric calibration, atmospheric correction and geometric correction; calculating a vegetation index and a leaf area index based on the preprocessed historical remote sensing image data, and determining a bare soil window period; creating a fishing net grid covering the research area based on the spatial distribution of the bare soil window period; historical soil parameters and remote sensing image wave band reflectivity values of corresponding window periods are extracted from the fishing net grids, and training samples are constructed; the training samples are divided into a training set and a verification set, and feature selection is carried out through a correlation coefficient method; establishing a soil parameter inversion model based on the training set by adopting a multi-model cooperative training mode; and performing soil fertility inversion on the remote sensing image data of the target year by using the trained model. In conclusion, the prediction precision of the soil fertility can be improved.

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 foundation pit dewatering treatment method and system, electronic equipment and a storage medium, and relates to the field of foundation pit dewatering treatment. The method comprises the following steps: acquiring various data from sensors mounted at preset positions around a foundation pit and inside the foundation pit, integrating the various data with meteorological data and geological survey data, and establishing a space-time database; according to the data in the space-time database, using a preset LSTM neural network model to predict a first water level change in a future preset time, and according to the three-dimensional seepage field dynamic simulation engine, using a soil parameter inversion algorithm of machine learning to invert soil parameters; according to the first water level change, a variable frequency pump group cooperative control algorithm is used for cooperative control over the pump set, and pump set operation parameters are dynamically adjusted for drainage; and controlling the recharge pressure and the water quality according to the second water level change of the foundation pit after drainage and the soil parameters. According to the technical scheme, efficient and intelligent treatment of foundation pit dewatering is achieved.

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 a near-slope foundation bearing capacity estimation method and system considering slope body stability, and the method comprises the steps: collecting geological data and environmental data of a target region, constructing a target region simulation model, and carrying out the simulation obtaining of the slope body parameter changes of the target region under different environments and load effects; determining a potential sliding surface based on the simulated slope parameter change, a preset sliding surface determination mode and a preset slope stability coefficient calculation method, and calculating a slope stability coefficient of the target area; determining a geological risk level of the target area, and when the geological risk level is greater than a preset risk level, selectively collecting a slope surface image of the target area, and calculating a soil parameter adjustment value; and correcting the foundation bearing capacity formula by using the slope stability coefficient or the slope stability coefficient recalculated according to the soil parameter adjustment value, and outputting an estimated value of the foundation bearing capacity. According to the method, the slope stability can be considered under the complex terrain condition, and the bearing capacity estimated value of the near-slope foundation can be obtained more accurately.

The invention provides an earth surface net flux calculation method and system based on a frequency domain soil moisture equation. The method comprises the following steps: establishing a frequency domain soil moisture motion model of soil water changing along with depth; performing frequency domain decomposition on the time sequence observation data and the upper flux boundary of the site soil water; calibrating the soil parameters according to the obtained upper flux and the frequency component of the soil water; carrying out discrete Fourier decomposition on the soil moisture time sequence at the next time period and any depth, and extracting soil moisture fluctuation signals at different frequencies; according to the obtained parameters and models, carrying out back calculation on soil moisture at different frequencies to obtain surface net flux signals at different frequencies, and superposing the surface net flux signals in a time domain; and in combination with remote sensing data, obtaining the surface net flux of the regional range according to long-time soil moisture data. According to the method, the problem of the time domain is converted to the frequency domain, so that the long-time-sequence soil water sequence can be processed, and the increase of the calculation amount caused by step-by-step calculation by dividing the long-time sequence is reduced.

The invention discloses a method for randomly predicting and assigning parameters of an excavated soil body of a beach-overtopping foundation pit. The method comprises the following steps of: collecting geological survey data and construction information of an excavated area of the beach-overtopping foundation pit, screening redundant parts of characteristic parameters, retaining variables which have influences on mechanical parameters of the soil body, and constructing a complete original database; collected data is divided into a training set and a verification set, the training set is used for preliminary training of the model, and the verification set is used for optimization and verification of the model. According to the method, the nonlinear relation of the complex variables is captured through the random forest model, dynamic monitoring and real-time optimization are combined, the soil parameter prediction precision is remarkably improved, and the construction uncertainty risk is reduced; the dynamic monitoring function quickly responds to soil body changes in construction, the prediction result is optimized, and the construction safety is improved; the random field assignment method effectively reflects the spatial variability of soil parameters, supports multi-scene risk assessment, and provides scientific basis and reliable guarantee for engineering design and construction.

The invention relates to the technical field of soil component analysis, in particular to a five-pin soil multi-parameter sensor, which comprises a soil parameter measurement and calibration module, a measurement data integrity guarantee module, a measurement frequency dynamic adjustment module, a soil state change monitoring module and a regional state distribution identification module, and the measurement frequency is dynamically adjusted. According to the method, energy consumption of data is reduced when the environment is stable, meanwhile, quick response is achieved when conditions change, the real-time performance and accuracy of monitoring are improved, data integrity is enhanced through dual storage and a re-measurement mechanism when necessary, information completeness is guaranteed, and in addition, through accurate time point and regional anomaly analysis, the accuracy of data analysis is improved. According to the scheme, the risk area is effectively identified and positioned, decision support is optimized, and soil monitoring is more scientific and efficient.

The invention discloses an automatic deviation rectifying method for long-distance jacking of a pipe-jacking tunneling roadway, and relates to the technical field of automatic deviation rectifying of pipe tunneling, which comprises the following steps: collecting the compactness, water content and hardness data of a soil layer in a tunneling path in real time through a multi-point distributed sensing device, and calculating the deviation of the pipe-jacking tunneling roadway in combination with soil parameters and pipe-jacking equipment propulsion mechanical characteristics; and generating a geological condition profile map, constructing a resistance distribution model, predicting resistance change trends under different geological conditions, and establishing a correction early warning mechanism. According to the invention, through real-time stress monitoring, multi-point sensing and intelligent feedback control, the accuracy, efficiency and complex geology adaptability of pipe-jacking tunneling are improved. The equipment can quickly identify resistance abnormity, dynamically regulate and control propulsion in real time, reduce the influence of asymmetric force, and reduce personal error and construction loss. And in combination with a geological profile map, the equipment actively deals with soil layer changes through automatic deviation correction, the clamping stagnation problem is solved, safety and continuity are guaranteed, and risks and cost are reduced.

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 belongs to the technical field of Internet of Things prediction, and discloses a soil moisture content prediction method and system based on a BiLSTM-Transform dynamic weight hybrid architecture, and the method comprises the steps: collecting an observation data set of soil moisture content influence factors of an irrigation region, the observation data set comprising a meteorological element sequence and a soil parameter sequence; the method comprises the following steps: collecting time series data of soil moisture content influence factors, carrying out noise reduction processing on the time series data by adopting a wavelet noise reduction method to obtain noise reduction data, and filling missing values of the noise reduction data by adopting a linear interpolation method to obtain a continuous data set. According to the method, a feature fusion strategy is adaptively adjusted through a dynamic weight mechanism, the time sequence modeling capability of soil moisture content prediction is remarkably improved, parameter quantities are compressed while the prediction precision is kept, the feasibility of deployment on edge computing equipment is achieved, and the constructed framework has the advantages of improving the stability of a prediction result and improving the prediction efficiency. And the prediction result error of the soil moisture content can be effectively reduced.

The invention discloses an irrigation water demand calculation method considering fine distribution of crops, electronic equipment and a storage medium, and aims to accurately calculate the irrigation water demand and spatial distribution of the crops in a target area through a remote sensing technology, meteorological data and a soil moisture balance model. The method comprises the following steps: collecting remote sensing crop classification, weather and soil parameter data; reference crop evapotranspiration is calculated at the grid scale; configuring crop parameters; calculating root zone soil moisture change based on a water balance model; and calculating the water demand of crops and the water demand of irrigation by adopting an FAO-56 crop coefficient method. Through refined crop distribution and dynamic soil moisture simulation, high-resolution irrigation water demand distribution information is provided, and a scientific basis is provided for agricultural water resource management.

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 relates to a method for predicting soil displacement caused by double-line shield construction based on number-object fusion. Comprising the steps of establishing a finite element numerical model based on shield parameters and geological data; numerical model parameters are corrected through actual monitoring data, the double-line shield distance, the advancing speed difference and the soil displacement field evolution law are analyzed, and dynamic visual prediction of the displacement field at different construction stages is achieved. According to the method, a two-way feedback mechanism of numerical simulation and engineering actual monitoring data is constructed, a three-dimensional dynamic coupling analysis method is innovatively provided, and the problem of insufficient prediction precision caused by dynamic change of soil parameters and a double-line construction coupling effect in traditional construction is solved. Through iterative optimization of a digital model and measured data, the maximum displacement error of a soil body is further reduced, the stratum standard-exceeding deformation risk is predicted in advance, the quantitative evaluation problem of the stratum disturbance superposition effect in double-line shield construction is effectively solved, and intelligent decision support is provided for tunnel construction safety control in a complex urban environment.

The invention belongs to the field of geotechnical engineering, and provides a geotechnical engineering investigation data analysis method and system based on machine learning, and the method comprises the steps: obtaining geotechnical engineering investigation data; taking the sampling points as nodes of the rock-soil data graph; according to the spatial proximity relation and the geological similarity, determining edges of the rock-soil data graph, and endowing the edges with weights, so that nodes with similar spaces and similar geological characteristics are connected; carrying out sparse processing on the rock and soil data graph; performing weighted fusion according to the spatial distance weight and the geological similarity weight to form a final rock-soil data graph; a graph convolutional neural network is adopted, and the initial features and the adjacency relation of the nodes are used for information propagation; neighbor information of each node is aggregated through multi-layer graph convolution calculation, and node features are updated step by step; a semi-supervised learning or supervised learning method is adopted to train a GNN model, and prediction error minimization is taken as a target; and deducing rock and soil parameters of an unknown area through the trained GNN.