462 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 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 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 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 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 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 discloses a dynamic monitoring method, system and device for soil carbon loss and a storage medium, and relates to the technical field of environment monitoring, and the method comprises the steps: obtaining soil parameters through a multi-mode sensing array and a runoff monitoring device which are arranged in a target soil area, the soil parameters comprise a soil gaseous parameter, conductivity, a soil solid parameter and a liquid carbon parameter; calculating a carbon loss amount generated by soil respiration according to the soil gas state parameter, and taking the carbon loss amount as a respiration carbon flux; according to the conductivity, the soil solid parameters and a preset soil organic carbon content relation model, calculating the carbon loss amount of soil erosion migration as erosion carbon flux; calculating the carbon loss amount of soil leaching loss according to the liquid carbon parameters, and taking the carbon loss amount as leaching carbon flux; and performing weighted summation on the respiration carbon flux, the erosion carbon flux and the leaching carbon flux to obtain the total carbon loss of the soil in the target area. A more comprehensive carbon income and expenditure evaluation method is provided by establishing a soil total carbon loss calculation framework covering'gas-solid-liquid 'three phases.

The invention belongs to the technical field of ocean geotechnical engineering, and discloses full-strain range soil body strength characteristic testing equipment and a testing method for simulating a deep sea temperature and pressure environment. The system comprises an integrated probe, a stepping motor, a linear module, a hollow probe rod, a torsion module, a loading platform, a sample box, a water depth simulation system and a temperature control system. The hydraulic source and the temperature controller are used for simulating the deep sea low-temperature and high-pressure environment, the simulation effect is good, soil sample visualization is achieved, static sounding and vane shear tests are combined by designing the integrated probe, the soil permeability coefficient, the consolidation coefficient, the undrained shear strength and the sensitivity are measured, the precision and the efficiency of soil parameter analysis are improved, and the method is suitable for popularization and application. And data complementation, efficiency optimization and precision improvement are realized.

The invention discloses a permafrost hydrothermal state simulation method and system based on a physical model, and relates to the technical field of permafrost region engineering. The method comprises the following steps: acquiring meteorological driving data and land surface feature data; constructing a numerical model of a permafrost hydrothermal process by considering a permafrost freezing and thawing process and a soil ice effect by utilizing land surface feature data; preparing boundary constraint conditions of the numerical model according to the meteorological driving data and the existing temperature field data; and performing permafrost hydrothermal state simulation on the land surface characteristic data by using meteorological driving data, constraint condition data, key vegetation and soil parameters and a driving numerical model to obtain a sample set formed by key permafrost hydrothermal information. According to the method, the dependence of an existing model on data and structures is broken through, the multi-scale application requirement of the permafrost region is met, and finally a high-quality meteorological driving and boundary constraint condition data set suitable for permafrost simulation without an observation region can be generated.

The invention discloses a real-time monitoring device based on intelligent agriculture in the technical field of agricultural Internet of Things, comprising a stand column, a connecting block is fixedly mounted below one side surface of the stand column, a sliding groove is formed in one side surface of the connecting block, and a screw rod is vertically and rotatably connected to the inner side of the sliding groove; the top end of the lead screw extends to the position above the outer end of the connecting block, the surface of the lead screw is in threaded connection with a connecting rod in a sleeving mode, the connecting rod is arranged in the sliding groove in a sliding mode, a soil parameter sensor module is fixedly installed at the end of the connecting rod, and the bottom of the soil parameter sensor module is sequentially connected with a humidity probe, a temperature probe and an EC value probe. A main control box is fixedly installed on one side of the stand column, an air temperature and humidity sensor is fixedly installed on the outer side surface of the main control box, the monitoring device can obtain three-dimensional data of soil, air environment and crop growth at the same time, and therefore precise agricultural decision can be better supported.

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 relates to the technical field of smart farmland, in particular to a backwash water and fertilizer integrated machine irrigation method based on a soil state. According to the recent fluctuation similarity and the spatial distribution difference of the same soil index between the sampling points, obtaining the reference degree of anomaly analysis between the sampling points, and further obtaining the anomaly feature value of each monitoring parameter by combining the local fluctuation feature difference of the monitoring parameters of the same soil index at the same moment; according to the abnormal characteristic value of each monitoring parameter and the fluctuation characteristic of the corresponding soil index, obtaining the isolated abnormal degree of each monitoring parameter; an optimal interpolation curve of each soil parameter sequence is screened out according to the isolated anomaly of the monitoring parameters, and then all soil index parameters are fused to evaluate the soil state at the current moment so as to regulate and control irrigation parameters. According to the method, the interpolation error is evaluated in combination with the isolated anomaly of each monitoring parameter, and the influence of noise on interpolation fitting is reduced, so that the soil state at each moment is accurately evaluated.

The invention belongs to the technical field of smart agriculture, and relates to a smart agriculture environment data real-time analysis method based on Internet of Things, which comprises the following steps: acquiring soil parameters of a first detection layer and a second detection layer of a farmland soil layer, and calculating a soil porosity compensation coefficient by combining a pressure difference value between the two layers, a soil convection measurement value is generated by combining the temperature difference value between the two layers; matching a preset growth period characteristic database to generate a growth period fingerprint code; constructing a dynamic data matrix containing the temperature gradient and the humidity change rate, giving a weighting coefficient to the time in combination with the growth period fingerprint code, and generating a smooth trend fluctuation curve; generating a three-dimensional imbalance index with economic weight adjustment; and selecting an irrigation mode according to the three-dimensional imbalance index hierarchy, and dynamically adjusting the communication priority of the equipment. According to the method, a hard coding decision-making mechanism which easily ignores market price factors and economic weights is solved, so that a resource allocation strategy deviates from actual benefit requirements.

The invention discloses a highway bridge pile foundation nondestructive testing system based on an inspection robot. The highway bridge pile foundation nondestructive testing system comprises an inspection robot cluster module, a data sensing module, an analysis model establishing module, a difference analysis module and a feedback module. The inspection robot cluster module is used for collecting physical parameters and geometric morphology parameters of a pile foundation and surrounding rock soil under the action of a vehicle load in real time; the physical parameters comprise pile foundation stress, side area and mechanical parameters of rock soil. The invention belongs to the technical field of highway bridge detection, and aims at solving the problems that in the prior art, pile foundation deformation, rock and soil parameters and multi-dimensional data of vehicle dynamic loads cannot be obtained, and damage to the interior of a pile foundation caused by the dynamic transfer effect is ignored. The method has the technical effects that the problems that multi-dimensional data of pile foundation deformation, rock and soil parameters and vehicle dynamic loads cannot be obtained, and damage to the interior of the pile foundation caused by the dynamic transfer effect is ignored are solved.