221 results about "Kriging" patented technology

The invention discloses a distributed real-time monitoring and early warning system for a temperature field of a smelting furnace, and relates to the technical field of industrial process intelligent monitoring. The problems of accumulated measurement errors and non-stationary hotspot escape reconstruction hysteresis caused by static emissivity setting in an existing system are solved. Collecting multiband radiation intensity and voltage signals through time domain alignment of the multispectral sensor array and the thermocouple array; iterating emissivity parameters in real time by adopting a dynamic ash body spectrum ratio algorithm in combination with flue gas absorption characteristics; fusing non-contact and contact temperature measurement data based on weighted Kalman filtering and complementary filtering; constructing a space-time variable covariance function to carry out non-stationary Kriging interpolation; dynamically optimizing the local grid resolution by combining an adaptive grid module; the processing flow is accelerated through the parallel computing module; early warning is triggered based on abnormal probability judgment and is fed back to emissivity correction and grid optimization; according to the invention, the monitoring precision and real-time performance of the temperature field are obviously improved, and the risks of false alarm, missing alarm and equipment melting loss are effectively inhibited.

The invention discloses an urban water supply management data trend analysis method based on space-time analysis, and relates to the field of data processing, and the method comprises the steps: collecting data in real time through an urban water supply pipe network sensor network, building a space-time unified coordinate system, building a space-time Kriging interpolation model based on pipe network topology, and achieving the space-time alignment of multi-source data; dividing an adaptive space-time grid by using a Voronoi diagram and a sliding window mechanism, and calculating multi-dimensional features; constructing a dynamic space-time diagram by taking a grid as a node, performing multi-step prediction in combination with a space-time diagram convolution circulation network, fusing a Kriging interpolation result, and evaluating an abnormal probability and a confidence interval through a Bayesian neural network; a monitoring layer, a prediction layer and a risk layer are overlaid in a three-dimensional GIS, a dynamic thermodynamic diagram is generated, an early warning path is optimized based on a Dijkstra algorithm, and a minimum risk topology path is output. The method has the advantages that through space-time analysis and accurate prediction, the intelligence, stability and emergency response efficiency of urban water supply management are remarkably improved, and powerful support is provided for smart city construction.

The invention discloses an optimization design method for a control surface of a wide-speed-range large-attack-angle reusable carrier, and relates to optimization design of aerodynamic configuration of an aircraft. The method comprises the following steps: S1, setting a control surface aerodynamic configuration design variable range, and generating an initial sample library by adopting Latin hypercube sampling; s2, aerodynamic parameters are obtained through CFD simulation, and a Kriging proxy model is trained; s3, evaluating the precision of the proxy model by taking a U learning function as a criterion, and stopping adding points when the minimum U function value is smaller than a threshold value; s4, constructing an optimization model which takes maximization of the lift-drag ratio and the static stability margin under the hypersonic speed as a target and takes the condition that the aerodynamic parameters of the supersonic speed / subsonic speed are not lower than a reference value and the hinge moment as constraints; and S5, performing iterative optimization by adopting an improved multi-target particle swarm algorithm based on genetic algorithm crossover mutation operation, updating the proxy model after the optimal solution of each generation is subjected to CFD verification, and outputting an optimal solution set. The problem of wide-speed-range aerodynamic configuration contradictions is solved, and the comprehensive performance of the carrier is remarkably improved.

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 discloses a rotor reliability constrained rolling bearing assembly parameter robust design method. The method comprises the following steps: constructing a dynamic model for an actual rotor-bearing system; constructing an uncertainty parameter vector and a design variable vector; a target function based on robustness and a constraint function based on reliability are constructed, so that an uncertainty optimization model is obtained; constructing an augmented input variable, and establishing a candidate orthogonal polynomial basis function set; on the basis, constructing and evaluating polynomial chaos-Kriging models for the target function and the constraint function respectively, and screening out an optimal polynomial chaos-Kriging model; calculating the expectation and the standard deviation of the target function and the failure probability of the constraint function under each design variable vector; and converting the uncertainty optimization model into an unconstrained single-target optimization model, randomly generating population individuals of a heuristic optimization algorithm in a feasible region of design variables, and iteratively searching an optimal solution of the unconstrained single-target optimization model as a rolling bearing assembly scheme.

The invention discloses a water quality safety monitoring and early warning method based on big data, and relates to the technical field of monitoring and early warning. Setting sampling points according to areas, pasting scene labels, and arranging sensors to obtain water quality information; establishing a water quality evaluation model based on a support vector machine improved model, inputting the water quality information into the water quality evaluation model, outputting to obtain a water quality category, and dividing the sampling points into normal sampling points and key sampling points according to the water quality category; based on the normal point data, using LSTM to predict water quality safety and performing graded early warning; based on the key point data, positioning a pollution source by using space-time Kriging interpolation, and simulating a pollution diffusion path by using multivariable collaborative interpolation; and combining normal point early warning and key point diffusion simulation to obtain a water quality safety monitoring early warning result. According to the method, a global risk grading report is generated by fusing a normal sampling point early warning result and a key sampling point diffusion path, and emergency response and long-term treatment strategies are matched.

The invention provides a Yangtze river export deep and far sea environment data association analysis method based on multi-modal fusion, belongs to the technical field of far sea environment analysis, and achieves accurate data registration by establishing a multi-source data space-time standardization model and adopting an adaptive space-time Kriging interpolation algorithm. A sparse coding ocean signal separation algorithm is utilized to construct an over-complete dictionary separation mixed signal to extract pure features, a feature extraction network is constructed based on an attention mechanism to automatically learn deep feature representation of physicochemical biological parameters, and a matrix rank loss detection algorithm is adopted to automatically supplement feature compensation vectors to ensure feature integrity. A cross-scale attention mechanism is constructed based on wavelet transform to fuse multi-scale features, a Shapley value interpretability evaluation system is established to quantify correlation feature importance, and the technical problem that correlation feature extraction is inaccurate in the multi-source heterogeneous marine environment data fusion process is solved.

The invention relates to the technical field of geological engineering safety risk evaluation, in particular to a regional tectonic stress risk quantitative analysis method for tunnel engineering. The method comprises the following steps: collecting multi-source geomechanical parameters, simulating and inverting a tectonic stress field, and verifying the tectonic stress field; constructing a multi-scale geomechanical model, coupling simulation tectonic stress and gravity stress, and generating a stress space basic layer; utilizing Kriging interpolation and inversion decomposition to obtain a stress value and a directional diagram layer; calculating an included angle between the maximum principal stress direction and the tunnel axis, and performing vector decomposition to obtain a vertical stress component; and carrying out standardized normalization on the factors, determining a weight calculation risk value, and finally generating a tectonic stress risk quantitative grading graph and carrying out GIS visual rendering. The tunnel engineering-oriented risk quantitative analysis mechanism is constructed by retaining the directivity and spatial heterogeneity characteristics of the tectonic stress tensor, and the authenticity, resolution and engineering applicability of tectonic stress identification are remarkably improved.

The invention relates to the technical field of remote sensing land prediction, in particular to a farmland non-grain spatial distribution prediction method and system, and the method comprises the steps: abstracting a farmland plot into a graph node, defining an edge weight as a weighted combination of an inter-plot Euclidean distance, crop type similarity and irrigation system connectivity, constructing a three-layer dynamic graph structure comprising land parcels, neighborhoods and administrative units; taking the three-layer dynamic graph structure as input, and performing node feature extraction by adopting a graph attention long-short-term memory network to obtain a non-grain ratio preliminary predicted value; non-stationary state space-variational Kalman filtering is adopted to correct the preliminary prediction value of the non-grain ratio, and a plot-level prediction value is obtained; and performing spatial reconstruction on the plot-level predicted value by using policy-sensitive Kriging interpolation. According to the invention, spatial continuity and time serialization cultivated land non-grain trend prediction is realized.

The invention provides a structure reliability analysis method based on an adaptive variable fidelity model, and the method comprises the steps: generating an initial sample point set which is uniformly distributed and has representativeness through an improved random sampling method KMODMC, enabling the initial sample point set to comprise a low-fidelity sample set and a high-fidelity sample set, training a BP neural network through employing the low-fidelity sample set, and carrying out the training of the BP neural network through employing the high-fidelity sample set; a low-fidelity BP neural network model is obtained; meanwhile, based on an error training Kriging model of a high-fidelity sample set and a low-fidelity model predicted value, an error correction Kriging model is constructed, the low-fidelity BP neural network model and the error correction Kriging model are combined to form a multi-fidelity mixed agent model, and adaptive iterative optimization is performed through a double-model alternate point adding sampling strategy. And finally obtaining a high-precision multi-fidelity hybrid agent model for structural reliability evaluation. According to the method, the problems of high cost of high-fidelity simulation calculation and insufficient precision of a low-fidelity model are solved, and the adaptive capacity and prediction precision of the model in a complex nonlinear problem are effectively improved.

The invention discloses a turbine disc baffle structure optimization method and device, equipment, a medium and a product and relates to the field of turbine rotors, and the method comprises the steps that actual structure information of a turbine disc-baffle rotor system is obtained; performing finite element modeling on the actual structure information to obtain a finite element model; transient thermal analysis and transient stress analysis are sequentially carried out according to the finite element model, and the most dangerous time step where the maximum equivalent stress and the maximum contact stress are located is obtained; establishing a response surface model based on a Kriging model according to the most dangerous time step where the maximum equivalent stress and the maximum contact stress are located and actual structure information; according to the response surface model, a multi-target genetic algorithm is used for optimization, and optimized turbine disc baffle structure information is obtained. Stress concentration is effectively reduced, and the fatigue life is prolonged.

The invention relates to the technical field of ventilation control, in particular to a multi-parameter real-time monitoring system of an indoor large ventilation system. The system uses a monitoring data acquisition module to acquire basic monitoring data. Kriging interpolation is carried out by using a spherical semi-variation function according to the collected temperature data to obtain an optimal unbiased estimated value under each coordinate, and an upflow tendency diagram is formed. And segmenting the real-time upflow tendency diagram to determine a plurality of connected domains, evaluating a pollution plume mode index of the current indoor environment by comparing particle concentration data and heat energy data, and further determining a control strategy mode. In each control strategy mode, regulation and control parameters can be determined according to the area of a connected domain or a pixel value in a real-time upflow tendency map. According to the embodiment of the invention, the real-time upflow tendency diagram is constructed through data monitoring and feature analysis, and indoor dynamic, low-disturbance and low-delay ventilation control of a large commercial room is realized.

The invention discloses a geological settlement monitoring method and system fusing deep learning and multi-source data, and relates to the technical field of geological settlement, and the method comprises the steps: collecting multi-source settlement observation and driving data, generating a settlement risk area mask based on historical records, and carrying out the self-adaptive grid division; a multi-source settlement field is generated through Kriging interpolation, and a driving factor grid field is mapped; performing spatial pyramid and time multi-scale decomposition on the settlement field and the driving factor to obtain a time-space sub-band; performing Bayesian fusion based on the sub-band confidence weight to obtain a fusion settlement field; inputting the fusion field and the driving factor into a deep learning model to train a multi-scale prediction sub-model, and reconstructing a global continuous settlement prediction field through cross-scale consistency; and dynamically optimizing the risk mask and the grid according to a prediction result to realize iterative monitoring. The problems of difficulty in multi-source data fusion, spatial scale heterogeneity and difficulty in accurate prediction of local high-risk area settlement in geological settlement monitoring are solved.

The invention relates to the technical field of complex industrial equipment monitoring and digital twinning, and discloses a gas turbine data visualization and intelligent interaction system which comprises a sensor array, a computing unit, a force feedback device, an eye tracker and a display terminal. The system constructs a three-dimensional physical field and corrects a velocity vector by using Kriging interpolation and Euler equation momentum balance, and generates a variable radius flow tube reflecting the compressibility of fluid; calculating mass, momentum and energy conservation residual errors to construct a generalized potential energy field, and generating gradient guide force and self-adaptive damping force by driving force feedback equipment; and dynamically adjusting solving precision and rendering parameters through sight tracking. According to the method, the flow field is reconstructed through physical constraint, multi-modal feedback is introduced, the problem that discrete data feature distortion and single visual interaction are difficult to perceive physical consistency is solved, and high-fidelity reduction of the gas turbine flow field and visual touch synchronous accurate diagnosis of fault types are achieved.

The invention provides an aerosol concentration inversion method and system under a transient working condition, and the method comprises the steps: 1, carrying out the time processing and time sequence feature construction of a transient aerosol concentration data sequence obtained by a monitoring point, and obtaining a time covariance matrix; 2, based on the time covariance matrix and the space information of the monitoring points and the target inversion points, initial inversion is carried out through a space-time Kriging model, and an initial inversion concentration value is obtained; 3, constructing and solving a nonlinear correction coefficient based on the preliminary inversion concentration value and the corresponding actual observation concentration value, and performing secondary correction on the preliminary inversion concentration value to obtain a weighted space-time Kriging inversion concentration value; and 4, carrying out local peak value matching on the weighted space-time Kriging inversion concentration value and an actual observation concentration value, and amplifying and correcting the inversion concentration value in a successfully matched peak value region to obtain a final aerosol concentration inversion result. According to the method, the precision and stability of pollutant concentration field reconstruction under the transient working condition are improved.

The invention provides an urban storm surge forecasting method based on multi-source information coupling, and the method comprises the steps: taking multi-source typhoon driving element data as the input information of a preset typhoon structure reconstruction model, and outputting typhoon key physical parameters through the typhoon structure reconstruction model; based on a Kriging interpolation and radial basis function mixing method, carrying out fusion processing on the multi-source topographic data, and constructing topographic grid information; extracting tide harmonic constants of a specified number of partial tides from the multi-source tide factor data; based on the typhoon key physical parameters, the terrain grid information and the tide harmonic constants, determining input information of a predetermined urban storm surge forecasting model; and outputting a storm surge level abnormal value and a horizontal flow velocity component corresponding to the multi-source prediction associated data through the urban storm surge prediction model. According to the scheme, the precision of urban storm surge forecasting is improved.

The invention relates to the technical field of agricultural Internet of Things, and discloses an edible mushroom growth environment regulation and control method and system based on multi-source sensing data, and the method comprises the steps: deploying a multi-source sensor network, unifying time synchronization, and carrying out event-driven space-time alignment and three-dimensional mapping. Carrying out multi-dimensional reliability evaluation and environment correction, and obtaining an environment parameter fusion data set by adopting a fuzzy logic reasoning method; performing space-time Kriging interpolation, and obtaining a heterogeneous microenvironment feature database by adopting a multi-source data collaborative correction method; carrying out cross-modal attention fusion; performing multi-scale convolution and time sequence attention classification; carrying out deep reinforcement learning and multi-objective optimization; carrying out online incremental learning optimization to obtain a self-adaptive optimized partition collaborative regulation and control strategy; according to the method, the technical problems of insufficient multi-source data fusion precision, lack of environment spatial and temporal distribution modeling, inaccurate parameter coupling prediction, lack of self-adaptability of regulation and control strategies and the like are solved.

The invention provides a characterization method and system for soil fertility of a citrus plantation, and relates to the technical field of characterization of soil fertility, and the characterization method comprises the following steps: collecting deep soil samples according to uniform grids in the citrus plantation, arranging three-parameter sensors, and obtaining a multispectral remote sensing image at the same time; secondly, respectively constructing a space continuous function, a time continuous function and a spectrum continuous function by using common Kriging interpolation, Gaussian process regression and spectral index inversion; then selecting high-yield area samples with the first 10% of yield in continuous three years, generating high fertility reference distribution based on kernel density estimation, and fusing the three types of functions by using an entropy evaluation method to obtain a comprehensive fertility function; finally, principal components are extracted through function principal component analysis, the Tukey depth of the principal components relative to reference distribution is calculated, a soil fertility index is generated, and five-level fertility areas are divided through an improved natural fracture method.

The invention discloses a Beidou GNSS-R-based strong wind disaster monitoring and early warning method, and the method comprises the steps: 1, enabling an unmanned plane to carry GNSS-R receiving equipment to be located at the periphery of a strong precipitation region, carrying out the wind speed detection of an internal water surface, and carrying out the data calculation of an obtained satellite observation quantity; step 2, constructing an LSTM wind speed inversion model fused with two-factor decoupling, performing inversion prediction on the water surface wind speed of a reflection point area by inputting the received satellite observed quantity and a resolving result and performing error correction, and obtaining future time step wind speed prediction based on a past time step wind speed inversion result; step 3, establishing a boundary adaptive grid and a dynamically updated Kriging interpolation model, obtaining wind speed data of a specific signal reflection point missing area, and assisting wind speed data supplement of the reflection point missing area; and 4, constructing a wind speed time sequence extrapolation and inversion stability coordinated double-effect strong wind early warning strategy, judging whether strong wind is about to appear or not, and performing early warning.

The invention provides an efficient and high-precision prediction method for the minimum failure probability of an aviation structure system based on an exponential penalty learning mechanism, and relates to the technical field of structural reliability analysis. Respectively generating an initial sample and a candidate sample according to the probability density function of the random variable of the performance function to be analyzed; real performance function responses corresponding to the initial samples are calculated to form an initial training sample set, and an initial Kriging agent model is constructed; selecting an optimal sample point from the candidate sample set through the proposed EPAL function; merging the optimal sample and the real response thereof into the initial training sample set, and iteratively updating the Kriging model until an error-based stopping criterion is met; judging whether the failure probability variation coefficient meets the requirement or not; and finally, calculating the failure probability of the structure based on a trained Kriging model and a Monte Carlo method.

The invention belongs to the technical field of digital twinning, and particularly provides a cluster learning Kriging-driven bridge digital twinning modeling updating system and method, and the system comprises a data collection and processing module which is used for obtaining the performance feature information of an actual bridge structure; the digital twinborn model construction module is used for constructing a bridge digital twinborn model and processing to obtain performance characteristic information of a simulated bridge structure; the objective function construction module is used for constructing a residual objective function and obtaining an optimization variable and a parameter space of the optimization variable based on the residual objective function; the optimization module is used for guiding the Kriging agent model to search the optimal parameter in the parameter space by utilizing a cluster optimization mechanism driven by multiple learning functions in parallel; and the model updating module is used for updating the bridge digital twin model through the optimal parameters. According to the method, the bridge structure state recognition precision is effectively improved, the model updating cost is reduced, and the method is suitable for bridge performance prediction and operation and maintenance management under complex working conditions.

The invention provides a three-dimensional sedimentary facies modeling method and device based on planar graph constraint, and the method comprises the steps: loading a sedimentary facies graph and a thickness graph to a three-dimensional space, projecting a planar graph to an oil reservoir grid through space coordinate transformation, and coarsening the planar graph into an oil reservoir grid surface attribute; establishing a rapid sedimentary facies model by utilizing the plane sedimentary facies diagram, and establishing lithologic probability volume data by utilizing the plane thickness diagram and through coKriging interpolation; and the logging sedimentary facies data serve as hard data, the rapid sedimentary facies model serves as spatial constraint, the probability body serves as trend constraint, and a sequential indication simulation algorithm is utilized to generate the sedimentary facies model. According to the three-dimensional sedimentary facies modeling scheme based on the planar graph constraint provided by the invention, information such as the logging facies, the planar facies and the thickness graph is comprehensively utilized, the understanding of geologists can be introduced between wells, the problems of mathematics and datamation of a conventional method are solved, and the precision of a sedimentary facies model is improved.

The invention belongs to the technical field of CFD (computational fluid dynamics) parameter calibration, and discloses a CFD parameter adaptive calibration method and system based on measured data and a double-agent model, and the method comprises the steps: obtaining a CFD input parameter sample, inputting the CFD input parameter sample into a CFD solver, and obtaining an initial simulation result; determining an error evaluation index according to the initial simulation result based on a target actual measurement data result; constructing a double-agent model based on a Kriging model and a radial basis function neural network by taking a CFD input parameter sample as an independent variable and an error evaluation index as a dependent variable; the double-agent model is trained, the trained double-agent model takes the error evaluation index as fitness, and CFD input parameter values are obtained based on a genetic algorithm; the CFD input parameter values are input into the CFD solver for a simulation experiment, a calibrated simulation result is output, the reliability and generalization ability of prediction are improved through a double-agent model, a high-fidelity simulation result is output through the CFD solver, and the number of times of calling the CFD solver is reduced while the calibration precision is guaranteed.

The invention discloses a 2.5 D statistical optimal distributed modeling method for a geologic structure of a thin coal seam group, and belongs to the technical field of geologic modeling and mineral resource development. The method comprises the steps that drilling space information and sequence constraints of all stratums are obtained based on drilling data, elevation estimation values and corresponding estimation value variances of all points in estimation value grids of all the stratums are obtained through independent Kriging interpolation operation of all the stratums, and constraint conditions are constructed according to the sequence of all the stratums; and constructing a target function of'minimizing the sum of variance with the original value 'by utilizing the elevation estimation value and the corresponding variance on each stratum estimation value grid point, and solving the elevation value of each stratum on the grid point in the estimation value range. The method fuses adjacent horizon information and spatial statistical constraints, can more accurately describe and express complex three-dimensional spatial relationships such as stratum pinching, crossing and merging in the thin coal seam group, remarkably improves the spatial fidelity of the model, realizes automatic and quantitative correction of the stratum sequence in strip mine full-stratum modeling, eliminates manual intervention errors, and improves the modeling efficiency. And the modeling efficiency and the geometric accuracy are improved.

The invention discloses a marine environment simulation and search and rescue formation method and system, and a computer program product, and belongs to the technical field of virtual reality technology and marine emergency rescue, and the method comprises the steps: designing a mixed data cleaning model of a space-time Kriging difference method and a long-short term memory network, and building a multi-modal dynamic environment database; establishing a marine meteorological data prediction model; combining the multi-modal dynamic environment database with the predicted marine meteorological data model to generate a dynamic marine environment; establishing a multi-agent training framework, and constructing a search and rescue formation strategy; and designing a hybrid planning model of a fast random tree algorithm and a long-short term memory network, realizing real-time path planning and path correction, and adjusting a search and rescue formation strategy according to the real-time path planning and path correction result. According to the invention, the problems of scene staticization and decision experience in traditional search and rescue training are solved, and the environment simulation precision, dynamics and decision accuracy are improved.

The invention discloses a motor multi-objective optimization design method and system based on a Kriging agent model. Comprising the following steps: constructing an initial data set through an experimental design method and finite element simulation calculation, so as to train an initial Kriging agent model which takes a motor design variable combination as an input variable and takes motor performance as an output response; the proxy model serves as a target function, a multi-target optimization problem is solved, the proxy model is updated, and an updating strategy is as follows: after each round of optimization is finished, a high-error solution in a current Pareto solution set is screened based on a Kriging model prediction mean square error, a finite element response of the high-error solution is obtained and supplemented to a data set, and the proxy model is updated and trained; and repeating the process until the optimization result converges. According to the method, high-uncertainty region samples are selectively supplemented, the calculation cost is remarkably reduced while the local prediction precision of the proxy model at the Pareto leading edge is improved, and the method has good practicability and economical efficiency.

This application belongs to the field of aircraft structure simulation calculation, and specifically relates to a method for constructing a high-dimensional output aircraft structure global proxy model, including: step 1, according to the probability distribution f of the structural model input variable X X (x), extract N input samples {x (1) ,…,x (N)} T , build a sample pool for global modeling. Step 2: Select N0 input samples from the sample pool and substitute each input sample into the finite element model for calculation to obtain the corresponding output response sample. Step 3: Perform PCA dimensionality reduction decomposition on the output response sample and convert the output response sample into the mean-centered output principal component sample. Step 4: Construct the Kriging model g with the input sample output principal component sample. K (X); Step 5, use the variance learning function to learn the Kriging model g K (X) is updated to obtain the Kriging model g for calculation K (X); Step 6: Calculate the Kriging model g K (X), perform matrix reconstruction to obtain a high-dimensional output prediction model.

The invention relates to the technical field of data analysis, and discloses a parameterization analysis method and system based on a sea wave spectrum mode. The method comprises the following steps: carrying out abnormal value detection and missing data filling on multi-source ocean data by adopting a 3 sigma criterion and Kriging interpolation, and unifying to a 0.1-degree grid to obtain a standardized data set; decomposing a sea wave energy balance equation source item into three modules for numerical calculation; configuring ST2, ST4 and ST6 schemes for the numerical solutions, and carrying out statistical analysis to obtain performance evaluation indexes; based on the evaluation indexes, a genetic algorithm is combined with particle swarm optimization to carry out parameter optimization; dividing the offshore China into four sub-regions for differential adjustment according to the optimization parameters; and carrying out trend checking and parameter increment updating by adopting a sliding window technology based on region configuration. The technical problems that a sea wave spectrum mode parameterization scheme lacks a systematic comparison and optimization mechanism, parameter configuration cannot adapt to regional marine environment differences, and parameters lacks real-time monitoring and dynamic adjustment capabilities are solved.

The invention belongs to the field of industrial robot high-precision control and reliability maintenance, and particularly relates to a space-time Kriging model-based robot precision compensation method, which comprises the following steps of: 1, acquiring a joint angle vector, accumulated working time and tail end pose deviation of a robot, constructing a space-time universal Kriging model, and calculating a space-time Kriging model; pre-calculating and storing a Kriging weight matrix and a residual error; 2, acquiring a current state in real time, retrieving a nearest neighbor point set through a spatial index algorithm, and outputting an error prediction value in combination with a pre-stored weight; 3, solving the error predicted value through inverse kinematics, generating a compensation joint angle, and driving an execution mechanism to correct the pose; and 4, in the process of correcting the pose by the execution mechanism, periodically triggering model updating and returning to execute the step 3 for compensation circulation. According to the method, the dynamic adaptability of the time dimension is integrated into a precision compensation system, and the problem of collaborative optimization between the initial high-precision calibration state and the long-term operation precision stability maintaining capability in a traditional method is solved.

The invention provides an industrial site soil pollutant concentration prediction method using three-dimensional distribution interpolation, and belongs to the field of soil pollution prediction, and the method comprises the following steps: S1, extracting soil pollution distribution characteristics based on sample point positions and attribute information; s2, measuring the anisotropy of the pollution concentration value in the two directions by estimating the ratio R of the vertical direction gradient to the horizontal direction gradient of the soil pollutant concentration through the soil sample point pollution concentration and a difference method, and multiplying the z value in the original coordinate space by the R by taking the R as an expansion factor to obtain a new vertical coordinate value; s3, expanding the spatial position representation module of the DKNN from a two-dimensional space to a three-dimensional space; s4, selecting a space encoder; and S5, on the basis of the spatial encoder, performing spatial prediction by using a general Kriging equation. According to the method, a GeoAI framework based on deep learning and geoscience knowledge fusion is introduced and expanded, the problem of three-dimensional distribution simulation of industrial site soil pollutants can be solved, and soil pollution can be accurately predicted.