46427 results about "Soil science" patented technology

The invention provides a pre-control and monitoring method for a whole process of actual grouting engineering based on digital geological model, comprising: extracting discontinuous fracture surfaces in a geologic body structure, to build a underground geologic body structure model; build a multi-source geologic body attribute model, optimizing and solving grouting simulations of a disaster high-risk region by using multiphase flow calculation method, and controlling grouting equipment by using optimized grouting parameters to carry out an actual grouting engineering; obtaining a multi-factor diffusion range of variable coefficients by adjusting physical values of the parameters in the grouting simulation and attribute data structure model by using a control variable method, learning and capturing complex mapping relationship between different parameters by using a neural network, optimizing the grouting parameters in the actual engineering in real-time, so as to realize the pre-controlling, monitoring and optimization of the whole process of the actual grouting engineering.

The invention relates to the technical field of information processing, in particular to a water conservancy slope ecological management method and system based on multi-source sensing data. The method comprises the following steps: collecting meteorological monitoring data, soil hydrological monitoring data, vegetation coverage monitoring data, topographic surveying and mapping data and water and soil loss monitoring data as original slope ecological environment data; performing data pre-registration processing on the original slope ecological environment data to obtain standardized slope ecological environment data; performing erosion-runoff coupling process simulation by using the standardized slope ecological environment data to obtain water and soil erosion dynamic parameters; performing water and soil loss trend prediction based on the water and soil erosion kinetic parameters, and generating water and soil loss risk assessment data; and designing an ecological management scheme based on the water and soil loss risk assessment data. By dynamically adjusting and optimizing the ecological management scheme, the ecological state of the slope is monitored in real time, and continuous improvement and self-adaptive optimization of ecological management of the water conservancy slope are achieved.

The invention relates to the technical field of soil pollution detection, in particular to a soil heavy metal pollution intelligent detection method and system.The method comprises the steps that soil multi-source information and regional environment parameters of to-be-detected soil are collected, and soil pollution characteristics are recognized according to the soil multi-source information; establishing a pollution identification analysis model according to the soil pollution characteristics and the regional environment parameters, and analyzing whether the to-be-detected soil contains heavy metal pollutants or not; if the heavy metal pollutants are contained, pollution diffusion paths of the heavy metal pollutants in time and space are predicted according to a pollution identification analysis model; and if the soil does not contain the heavy metal pollutants, performing time sequence analysis on soil pollution characteristics, monitoring and analyzing future concentration distribution of the heavy metal pollutants, and establishing pollution risk early warning. According to the invention, the problems of long sampling analysis time consumption, incapability of dynamically monitoring pollutant concentration change in real time, insufficient diffusion prediction accuracy and difficulty in comprehensively evaluating high-concentration and potential risk areas in a traditional soil heavy metal detection method are effectively solved.

The invention relates to the field of road compactness monitoring, in particular to a road compactness real-time regulation and control method and system based on digital twinning, and the method comprises the steps: collecting vibration acceleration, temperature and position data, and generating a time-space aligned multi-source fusion feature data set after processing; inputting a pre-trained LSTM model to construct a dynamically updated digital twinborn body, and outputting a three-dimensional compaction energy spectrum; dispersing the atlas and calculating parameters, and generating a compaction degree deviation matrix; a regulation and control instruction is generated and issued based on matrix optimization; and according to the measured data and the predicted value residual error, triggering re-optimization and calibrating the sensor. According to the method, the problems of non-uniform compaction quality and over-high energy consumption caused by poor adaptability, decision lag and error coupling of a traditional static model are solved.

Disclosed is an intelligent monitoring method for soil moisture status based on remote sensing technology, including the following steps: dividing a monitoring region into a plurality of sub-regions by a processing end, acquiring image data of various sub-regions on the basis of remote sensing satellites, acquiring multi-source data related to soil moisture status of the monitoring region on the basis of sensor devices, and pre-processing the image data and the multi-source data; comprehensively analyzing the image data and the multi-source data, and assessing soil moisture status of the various sub-regions; and comprehensively analyzing overall soil moisture status of the monitoring region according to the soil moisture status of the various sub-regions. The soil moisture status of the monitoring region is assessed after the multi-source data is comprehensively analyzed by the monitoring method, bringing about a more comprehensive and accurate analysis.

A soil layer detection drilling device includes a drilling cylinder, and a soil-sampling assembly is provided inside the drilling cylinder. When the soil is drilled, equipment is in a vertical state, a controller is turned on to control a rotary motor to rotate, and the drilling cylinder is driven to drill the soil with the cooperation of drive wheels and a drive belt. Four slide blocks are evenly distributed around an outer circumference of a sliding disk and are matingly connected with slide grooves provided on an inner wall of the drilling cylinder, so when the drilling cylinder rotates, the soil-sampling assembly also rotates. A conical head first in contact with the ground starts to drill the ground, then the drilling cylinder gradually drills into a soil layer, and the drilling cylinder is idled as the drilling cylinder arrives at a certain depth, to clear soil on auger blades.

The invention discloses a soft soil foundation deformation prediction method and system based on big data, and relates to the technical field of rock and soil monitoring. InSAR satellite data, Beidou GNSS displacement data, optical fiber strain data and meteorological and geological parameters are integrated through a multi-source sensing network. ERA5 reanalysis data is adopted to establish an atmospheric delay compensation function, and dynamic sliding window filtering and strain gradient constraint are combined to realize data space-time alignment and anomaly cleaning. Based on a generalized Kelvin creep constitutive model, environment coupling functions of temperature, humidity and pore water pressure are fused to dynamically correct model parameters, and the creep response characterization capability in a complex environment is enhanced. And performing distributed joint training on the regionalized geological data through a federated learning framework, fusing differential privacy encryption and a node credibility verification mechanism, realizing safety aggregation and migration optimization of cross-regional data, and generating a geological partition adaptive deformation prediction result. The method effectively improves the reliability of soft soil foundation deformation prediction.

The present invention relates to an automatic speed regulation and grouting regulation system for construction of a deep mixing pile in a reclamation area, the main steps comprising: first, configuring and installing a multi-modal sensing system (S101) to monitor key parameters, torque, energy consumption, grouting pressure and stratum permeability of a construction site in real time; constructing a data acquisition and fusion module (S102), integrating multi-sensor data, and forming real-time mapping of stratum-construction parameters; establishing a relation model of formation input and formation response through an extended Kalman filter (EKF) and predictive control (MPC) algorithm (S103), and intelligently evaluating formation compactness and grouting quality; on the basis of a feedback closed-loop regulation and control system (S104), the stirring speed, the grouting amount and the construction pressure are adaptively adjusted in real time, and precise control is achieved; through construction data storage and self-learning optimization (S105), the system records all construction data, and through a deep reinforcement learning optimization control algorithm, the long-term construction precision is improved.

The invention discloses a composite interference-oriented cultivated land system toughness evaluation and optimization method, and relates to the technical field of cultivated land toughness optimization, and the method comprises the steps: collecting and preprocessing multi-source cultivated land data, constructing a toughness evaluation index system, and calculating the weight of each index; and calculating a comprehensive toughness score based on weight distribution, drawing a time sequence change curve based on the score, and performing region division to generate a toughness distribution diagram. According to the method, the single-factor pollution index, the Nemo comprehensive index and the interaction effect index of heavy metal pollution are combined, the soil pollution condition is accurately quantified through a weighted summation method, a scientific and reliable basis is provided for toughness evaluation, the dynamic toughness distribution diagram is generated in combination with the LSTM model and the GIS technology, the spatio-temporal dynamic characteristics of a cultivated land system under composite interference are revealed, and the method has the advantages of being high in reliability and high in reliability. According to the method, the defects of an existing model in space-time analysis are overcome, differentiated optimization strategies are formulated based on region division and weak region labeling, and the resource allocation efficiency and the pertinence of an optimization scheme are remarkably improved.

The invention provides a frozen soil roadbed thaw collapse prediction method based on multi-source data and deep learning driving, and relates to the field of cold region engineering. The method comprises the steps that mathematical description is conducted on the thermal-mechanical coupling process of the frozen soil roadbed, and a frozen soil roadbed thermal-mechanical coupling physical model is obtained; the method comprises the following steps: acquiring frozen soil roadbed field monitoring data, remote sensing data, indoor test data and engineering environment data, and performing cleaning and normalization processing on multi-source data to obtain a multi-source data set for constructing and training a physical information neural network model; constructing a physical information neural network based on the frozen soil roadbed physical model and the multi-source data set; constructing a physical residual error of the physical information neural network model through a differential equation, and constructing a loss function based on a residual error item; and training the physical information neural network model to obtain a deep learning model for predicting thaw collapse deformation of the frozen soil roadbed. According to the method, the accuracy and reliability of thaw collapse deformation prediction of the frozen soil roadbed can be improved.

The invention provides a water and soil loss dynamic monitoring method and system based on a remote sensing image, and the method comprises the steps: firstly obtaining a multi-temporal remote sensing image data set of a target region, which comprises a plurality of time period remote sensing image subsets and land surface coverage information, and then carrying out the land surface feature extraction of the multi-temporal remote sensing image data set, after vegetation coverage, terrain gradient and soil exposure features are obtained, a water and soil loss prediction model based on time-space correlation is constructed, the features are input for prediction, a water and soil loss grade distribution map is generated, a target loss risk area is identified according to the water and soil loss grade distribution map, and a treatment priority sequence and a vegetation recovery strategy are generated. And finally, the information is fed back to a monitoring platform to trigger regional governance task allocation operation, so that dynamic monitoring of water and soil loss is realized, and governance tasks are effectively planned.

The invention discloses a grouting diffusion prediction method and system for complex geology multi-attribute constraint, and the prediction method comprises the steps: obtaining the multi-source attribute information of a complex geologic body, and the multi-source attribute information comprises fracture characteristics, pore characteristics and water burst characteristics; the multi-source attribute information is input into the trained grouting diffusion dynamic prediction model, a grouting diffusion prediction result of the complex geologic body is output, and the grouting diffusion result comprises the permeation rate, pressure distribution and boundary conditions. In the training process of the grouting diffusion dynamic prediction model, the deep learning model and the real-time monitoring system are combined, prediction is continuously carried out according to monitoring data in the grouting process, the prediction result is adjusted through a feedback mechanism, the prediction model is optimized, and the precision and reliability of the prediction result are improved.

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a meteorological data fused water-saving irrigation control method, device and equipment and a medium. According to the method, a historical meteorological data set is constructed through multi-source meteorological data fusion, a dynamic water demand table is generated in combination with a crop water demand characteristic database, and a crop water demand model is established based on soil moisture content data. A grid irrigation unit division and growth period coupling soil moisture content response matrix construction technology is adopted, a cooperative constraint is established between a water demand threshold value and a water saving benefit through a multi-objective optimization learning algorithm, and a personalized irrigation scheme is generated. A soil moisture content dynamic evaluation matrix containing a dynamic time warping operator is designed, and dynamic matching of the soil layered soil moisture content and a standard template is achieved. The contradiction between meteorological response lag and low water resource utilization rate in traditional irrigation is effectively solved, accurate irrigation decision is realized through multi-dimensional data fusion and an intelligent optimization algorithm, and the water-saving benefit and the agricultural water resource utilization efficiency are improved.

A method and a system for constructing a soil water movement model based on the physical information neural networks are provided. The soil water movement model is constructed by fusing the physical information neural network and a control equation of the soil water movement model, and the model adopts automatic differentiation to replace difference operation of grid scale, so that the calculation error caused by equation discretization in the solving process of numerical differentiation is avoided, and the calculation accuracy is improved; and the automatic differentiation mode is mainly carried out aiming at the output of a neural network, so that the numerical value of gradient calculation is more accurate.

The invention discloses a slope rainfall infiltration stability dynamic evaluation and landslide prediction method. The method comprises the following steps: constructing a numerical model coupling rainfall infiltration and slope stress field response; integrating multi-source data, and updating model boundary conditions in real time; performing dynamic stability evaluation based on a local safety coefficient method; and training a landslide prediction model and realizing intelligent early warning identification. The seepage-stress-strength three-field coupling numerical model is provided, the physical response process of the unsaturated soil body is dynamically simulated, the precursor mechanism of rainfall-induced landslide can be truly restored, and prediction scientificity and accuracy are improved; remote sensing topographic data, soil property survey parameters and real-time meteorological monitoring information are integrated, model boundary conditions and initial states are dynamically updated through a system interface, real-time response to environmental changes is achieved, and simulation reliability is improved; a local safety coefficient evaluation mechanism is introduced, and a continuous weak region discrimination algorithm is combined, so that space identification and time sequence tracking of a potential slip region and a damage zone are realized, and the slope stability analysis refinement level is improved.

The invention discloses a water-saving control method for agricultural irrigation, a cloud platform and equipment, and relates to the technical field of irrigation water-saving control, and the method comprises the steps: determining the historical rainfall of the geographic position of a target farmland and the historical soil moisture content parameter of the target farmland; determining farmland water demand rule characteristics of the target farmland based on the historical rainfall and the historical soil moisture content parameters; constructing a farmland water-saving control model according to the farmland water demand rule characteristics; dividing a water demand area of the target farmland based on the crop information; based on the farmland water-saving control model and the soil moisture content parameter of each water demand area, obtaining a predicted irrigation value of each water demand area; acquiring a real-time irrigation value of each water demand area; and based on the predicted irrigation value and the real-time irrigation value, performing real-time regulation and control on a preset intelligent irrigation water valve in each water demand area. The problem that the utilization rate of water resources is low can be effectively solved.

The invention relates to the technical field of agricultural product quality detection, in particular to an agricultural product quality detection method and system. The method comprises the following steps: carrying out spectrum scanning on an apple through a near infrared spectrometer, and removing baseline drift to obtain an accurate reflection correction spectrum; spectral bands of sugar, acidity and cellulose are identified based on a reflection spectrum, peak shape variation structural strength analysis is carried out, furthermore, quantitative processing of background interference bands is carried out on peak shape variation structural strength data, and unnecessary interference signals are eliminated; and finally, according to the quantitative data of the background interference wave band, constructing a quality detection and identification model, and identifying the content distribution of sugar, acidity and cellulose of the apples through the model to obtain accurate component content distribution data. The process realizes efficient and accurate quality detection, and ensures the reliability and intelligence of agricultural product quality control. The agricultural product quality detection technology is optimized, so that the agricultural product quality detection technology is more perfect.

The present invention relates to the technical field of land engineering, and relates to a water and soil resource comprehensive improvement method for loess hilly and gully region channels. The method comprises: (1) soil layer general survey: performing soil layer thickness survey and soil nutrition content measurement; (2) soil body reconstruction: on the basis of soil layer general survey data, selecting a soil body construction mode, and performing soil body profile reconstruction, soil body nutrition reconstruction and field parcel arrangement; and (3) constructing a water resource regulation and control system for the loess hilly and gully region channels. According to the present invention, investigation and survey are performed firstly, then treatment is performed, water and soil are treated simultaneously, and steps are coordinated, so that ecological treatment of the loess hilly and gully region channels is finally completed, and comprehensive improvement of water and soil resources is achieved. The present invention ensures mechanical cultivation of the region after the improvement, effectively increases the area of the irrigable land and the area of the non-irrigated land, effectively improves the farmland quality and the grain yield, and effectively reduces the soil erosion amount of the channels.

The invention relates to the technical field of intelligent decision making, in particular to an accurate fertilization intelligent decision making method and system based on multi-source remote sensing, and the method comprises the steps: obtaining a multi-source remote sensing image which comprises an unmanned aerial vehicle image and a stationary satellite image; acquiring soil nutrient data and fertility indexes of the sample field parcels; acquiring meteorological data and soil data; carrying out image preprocessing and feature extraction; constructing a crop identification and classification model; carrying out crop type identification and block division on the multi-source remote sensing image; extracting crop growth features based on the multi-source remote sensing image and the result of the crop identification and classification model; based on the crop growth characteristics and the soil nutrient data, analyzing the relationship between the crop growth stage and the nutrient demand; constructing a nutrient utilization efficiency maximization model; based on a nutrient utilization efficiency maximization model, generating a precise fertilization decision scheme; and outputting a precise fertilization decision scheme, and carrying out precise analysis and intelligent decision making on the growth state and nutrient demand of farmland crops.

The invention provides a soil fertility quality analysis method and system based on corn planting cultivated land, and relates to the technical field of soil quality analysis and evaluation.The soil fertility quality analysis method comprises the steps that a visible-near infrared spectrum image of a soil sample is collected, reflectivity data of a key wave band is extracted from the spectrum image, and a soil health index and a nutrient balance index are calculated; calculating a soil fertility quality comprehensive score for comprehensively evaluating the soil fertility according to the soil health index, the nutrient balance index and the soil moisture content; comparing the comprehensive score of the soil fertility quality with a preset threshold value, and evaluating the soil fertility; a machine learning model is introduced, historical soil data is trained and predicted, a quality analysis model is constructed based on a random forest algorithm, the trained quality analysis model is put into use, and new data is continuously learned to dynamically adjust and update, so that the soil detection process is simplified, and the soil fertility change can be monitored in real time. And agricultural managers are helped to make scientific decisions.

Methods of creating and using digital twins of vegetation / crops in relation to their growing environments. Some embodiments include creating a digital twin of the growing environment using sensor data from a subsurface sensor suite, creating a digital twin of the vegetation crop using data from an overhead sensor suite, and using at least one mathematical model to relate the digital twin of the crop to the digital twin of the growing environment. In some embodiments, the mathematical model(s) implement a root cause analysis that uses a limiting factor approach. In some embodiments, the subsurface sensor suite is on a multi-sensor soil probe. In some embodiments, the multi-sensor soil probe comprises a video microscope, a tip stress sensor, a sleeve stress sensor, and a volumetric- water-content sensor. Other related systems, devices, methods, and software are also disclosed.

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

The present invention relates to the technical field of compression-resistance testing devices, and particularly relates to an apparatus for measuring the content of water in soil. The apparatus comprises a workbench and an iron sampling head, wherein an adjustable telescopic rod is fixedly mounted at the top of the workbench; a cabin is mounted at the top end of the adjustable telescopic rod; a driving assembly is accommodated inside the cabin and provided at the top end of the workbench; an anti-collision mechanism is further provided in the driving assembly; and an open slot corresponding to the iron sampling head is provided inside the workbench; and a sampling device is provided inside the iron sampling head. During the process of a driving assembly driving an iron sampling head to pass through an open slot and then be pushed into soil, when stones and hard objects appear, an anti-collision mechanism on the driving assembly comes into contact with the stones and the hard objects first, thereby preventing the iron sampling head from coming into direct contact with the stones and the hard objects and thus being damaged; and when the iron sampling head is inserted into the soil and needs to perform sampling for measurement, soil at different depths can be collected by means of a sampling device, thereby preventing soil at other depths from being mixed and thus affecting the measurement accuracy.

The invention provides a geological exploration drilling device and method for geotechnical engineering design, and relates to the technical field of geological sampling, the geological exploration drilling device comprises a vehicle body, a propelling mechanism, a drill rod and a drill bit, the drill rod comprises a plurality of sampling barrels and locking assemblies arranged between every two adjacent sampling barrels, the drill bit comprises a drill barrel arranged at the bottom of the drill rod, a spacer bush arranged in the drill barrel and a plurality of cutting plates arranged on the side wall of the spacer bush at equal intervals in a sliding mode, the knocking assembly is arranged on the support, and the knocking assembly can strike the exterior of the drill rod in the process that the propelling mechanism drives the drill rod to vertically move. A soil core pressed into the drill rod can be cut off from an external soil layer, the bottom of the drill rod is sealed, it is prevented that in the upward moving process of the drill rod, the soil core slides off to affect soil sample collection, the drill rod can be assembled according to needs so that the soil core can be conveniently divided into multiple small sections, the soil core in the drill rod can be stably taken out, and the working efficiency is improved. The soil core is prevented from being taken out from a relatively long drill rod and fractured and crushed to influence subsequent detection.

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 crop yield analysis, in particular to a crop yield prediction method based on a time sequence remote sensing image, and the method comprises the steps: obtaining an optical remote sensing image and a synthetic aperture radar image in a crop growth period, and constructing a time sequence image through complementary information fusion and under-cloud information recovery; the problems of cloud interference and data missing are solved, a multi-scale time regulation and control convolution module is used for processing a time sequence image, multi-scale time sequence features are extracted, feature fusion is carried out in combination with attention difference jump connection, and a rice distribution map is obtained; determining a rice planting area based on the rice distribution diagram, selecting an optimal assimilation time period in the rice planting area, and assimilating the leaf area index subjected to remote sensing inversion and a crop growth model simulation result to generate a simulated yield after assimilation; based on the multi-scale time sequence characteristics, a parameter dynamic response matrix related to crop growth model parameters is established, and residual error correction is performed on assimilated simulation yield, so that the rice yield prediction precision is improved.

The invention relates to the technical field of reservoir earth and rockfill dam leakage abnormity safety monitoring and early warning, in particular to an earth and rockfill dam leakage abnormity real-time monitoring and early warning system based on deep learning and a medium. The system comprises a data sensing transmission module, a data fusion processing and analysis module, an early warning evaluation module, a system management and maintenance module, a database management module and an emergency response command module. Through a well-ground collaborative full-dimensional electrical method and shallow earth surface and full-section distributed optical fiber sensing, the system collects and transmits multi-source data. And multi-mode fusion and a deep learning algorithm are adopted to realize multi-physical field feature extraction and three-dimensional modeling. The system generates graded early warning information based on dynamic threshold and multi-factor coupling, and realizes automatic real-time monitoring, intelligent early warning and efficient management of leakage abnormity of the earth and rockfill dam in combination with a database, management maintenance and emergency response functions. According to the invention, the accuracy of earth and rockfill dam leakage abnormity identification and the intelligent level of early warning are improved.

The invention discloses a soil fertility intelligent assessment method and system based on image recognition, and relates to the technical field of data processing, and the method comprises the steps: obtaining land image information of a to-be-assessed region, the land image information comprising a multispectral image, a thermal imaging image and an RGB image of the to-be-assessed region; carrying out image preprocessing on the land image information, and carrying out vegetation coverage and health assessment according to the multispectral image: speculating soil characteristics according to the RGB image: speculating the humidity change of a soil surface layer according to the temperature distribution of a thermal imaging image, and identifying a drought region and a region with sufficient moisture; integrating the high-resolution texture features of the RGB image, the spectral information of the multispectral image and the speculation result of the temperature distribution of the thermal imaging image to generate a comprehensive analysis result; and classifying soil types and evaluating fertility grades according to a comprehensive analysis result. The problem that in the prior art, in intelligent soil fertility evaluation, workers need to carry out on-site sampling examination is solved, and the technical effect of improving the soil fertility evaluation efficiency is achieved.

A method for inversing a depth of a subsurface chlorophyll-a maxima (SCM) of an oceanic water body based on remote sensing reflectance is provided. The method includes: band selection, R value calculation, data collection, model construction, and ZSCM calculation. This method can not only reduce the dependence of machine learning on sea surface chlorophyll-a concentrations, sea surface temperature data and sea surface height data, and reduce the calculation resource consumption of running machine learning, but also effectively improve the inversion accuracy, and effectively reduce the calculation resource consumption and inversion difficulty of inversing the SCM.

The invention discloses a precise irrigation decision analysis method and system based on crop water demand, and particularly relates to the technical field of crop irrigation. A monitoring area is divided, soil humidity sensors with different depths are installed, moisture state data of a root zone are monitored in real time, moisture gradient change characteristics and moisture retention time deviation characteristics are extracted and analyzed, a moisture fluctuation abnormal index is calculated, the moisture fluctuation abnormal index is compared with a threshold value, and the moisture fluctuation abnormal index is calculated. The method comprises the following steps: dividing underground water fluctuation into high, middle and low underground water fluctuation abnormal levels, and taking corresponding irrigation adjustment measures according to different levels of fluctuation: taking a drainage measure to reduce excessive soil moisture for high-level abnormal fluctuation; crop water demand model parameters are dynamically adjusted through medium-grade fluctuation; a normal irrigation strategy is maintained by low-grade fluctuation, so that negative effects of underground water fluctuation and excessive irrigation on crop root zones and soil structures can be effectively avoided, soil permeability and water storage capacity are maintained, long-term productivity of farmland is guaranteed, and irrigation efficiency and water resource utilization rate are improved.