298 results about "Partial differential equation" patented technology

The invention belongs to the technical field of turbulent flow field prediction and deep learning, and discloses an incompressible turbulent flow field prediction method based on a potential diffusion model. The method comprises the following steps: acquiring original turbulence data; processing the turbulence data; constructing a turbulence prediction model; model training; and evaluating the model and the like. The model of the technical scheme of the invention specifically comprises the following steps: designing a multi-scale Fourier auto-encoder for extracting multi-scale space and frequency domain features in a turbulence field and obtaining a global structure and a local scale structure of turbulence; a novel accelerated sampling method is proposed and introduced in the diffusion process, namely a diffusion probability model solver greatly shortens the reasoning time in a potential space and keeps high fidelity in long-time-sequence prediction; a physical constraint loss item based on a partial differential equation is introduced, and a Navier-Stokes equation (N-S) is explicitly introduced into a training process, so that the physical consistency of results is effectively improved, and errors are remarkably reduced.

The invention discloses a soil water and salt dynamic monitoring and quality evaluation method based on multispectral remote sensing, and relates to the field of geological monitoring, and the method comprises the steps: carrying out the scattering deviation correction and roughness correction of an observation spectrum through a satellite remote sensing image, an unmanned aerial vehicle multispectral image, a ground sample and meteorological driving data; the soil intrinsic reflectivity, the vegetation coverage component and the salt crusting component are obtained; a water-salt inversion model is established by combining thermal infrared and red edge wave band information, and multi-temporal soil volumetric moisture content and surface salinity index are inverted; by introducing meteorological conditions and vegetation dynamic characteristics, a water-salt coupling partial differential equation and a graph space-time constraint model are constructed, and continuous space-time distribution of soil water content and salinity is obtained; extracting salinity, moisture, vegetation response and soil health indexes, establishing a soil quality comprehensive evaluation model, and outputting high-risk plaques and a treatment priority sequence. The method realizes dynamic monitoring and quality grading evaluation of soil water and salt, and is suitable for saline-alkali soil treatment and ecological restoration.

The invention discloses a physical field solving method based on a Bayesian physical information extreme learning machine, and the method comprises the steps: constructing a single-layer full-connection neural network, carrying out the random initialization, and fixing the weight of an input layer; based on a partial differential equation of a physical system and boundary conditions thereof, defining a training loss item containing physical information; a physical system solving problem is converted into a linear least square problem, and a linear equation set is constructed; supposing that an output layer weight parameter obeys Gaussian prior distribution with the mean value being zero, and controlling a covariance matrix by an adjustable hyper-parameter; constructing a Gaussian likelihood function based on the observation data, and calculating posterior distribution of the output weight in combination with the prior distribution; carrying out iterative optimization on the hyper-parameter by adopting an evidence maximization method to obtain a mean value and a covariance of posterior distribution; based on posterior distribution, adopting a Monte Carlo integral method to generate prediction output of the physical system; and performing uncertainty quantization based on the variance of prediction output, and outputting a prediction value and a confidence interval thereof.

The invention discloses a method and system for solving a partial differential equation based on a KAN and MLP parallel structure, and belongs to the technical field of deep learning. The method comprises the following steps: constructing a parallel neural network comprising a KAN branch and an MLP branch, and introducing a fusion factor to carry out weighted fusion on the output of the KAN branch and the output of the MLP branch; based on a physical information neural network principle, constructing a loss function comprising an equation residual term, an initial condition term and a boundary condition term; training the parallel neural network by using an adaptive optimization algorithm, and minimizing the loss function; and inputting variables in the partial differential equation into the trained parallel neural network, and outputting a solution of the partial differential equation. By effectively combining the nonlinear expression ability of MLP and the function modeling advantage of KAN, the expression and solution ability of a multi-scale, non-stationary and complex physical field is improved; the modeling precision and convergence stability of a complex physical system, especially a partial differential equation driving problem, can be effectively improved.

The invention relates to the technical field of image analysis, and discloses an end-to-end visual tactile perception method and system based on a morphology-force field analysis model, a terminal and a storage medium, and the method comprises the steps: employing a morphology reconstruction module to achieve the analysis of a micron-order contact surface, employing a force field analysis model to achieve the precise analysis of a force field in each direction, and taking the two results as a data set, inputting and mapping the image into contact morphology, normal and shear force distribution through an end-to-end network, integrating the high efficiency of data driving and the reliability of physical consistency, and finally introducing partial differential equation residual error sum into a loss function to carry out optimization. And the morphology and the force field meet the set physical consistency on the whole. According to the method, micron-sized morphology analysis and multi-axial force synchronous estimation are realized, the real-time robust performance under a high-frequency dynamic task is realized, the cost of tactile perception is reduced, and meanwhile, the accuracy of a tactile perception result is improved.

The invention discloses a rapid heat transfer simulation method and device based on a neural network, and relates to the technical field of physical simulation. The method comprises the steps that a hybrid neural network model is trained, the model learns operator mapping from an input function to a temperature or heat flow field, and meanwhile physical constraints such as a heat conduction partial differential equation are coded into a loss function; for a new simulation task, single forward inference is carried out by using the operator mapping, and an initial prediction result is rapidly generated; then, according to physical constraints of coding, calculating a physical residual error of initial prediction, and when the residual error exceeds a preset threshold value, executing a small amount of optimization iteration by taking the prediction as an initial value to carry out rapid local correction; the problems that a traditional numerical method is long in calculation time and an existing neural network method is insufficient in physical fidelity are solved, and high efficiency and high precision of heat transfer simulation are achieved.

The invention relates to the technical field of underground engineering safety monitoring and disaster simulation, in particular to a digital twinborn deduction platform for underground engineering disaster chain evolution simulation, which comprises a multi-disaster coupling numerical simulation module used for acquiring multi-source data including design drawing data, geological data and engineering monitoring data, and based on the multi-source data, establishing an initial stress field model by using a numerical calculation method combining a finite element and a finite difference, and carrying out leakage-settlement-structure failure multi-disaster coupling numerical simulation. According to the method, the partial differential equation for describing the evolution law of the underground engineering is used as a constraint term to be embedded into the deduction model, and online identification and dynamic extrapolation of parameters are executed by fusing real-time monitoring data, so that the accuracy of disaster evolution simulation under complex working conditions is remarkably improved; and the problem that the traditional numerical calculation method is difficult to meet the real-time requirement of digital twinning is solved.

The invention provides a flow field solving method based on a time sequence physical information neural network, and belongs to a partial differential equation calculation method. The method comprises the following steps: firstly, determining a flow field motion law and a flow field computational domain based on a fluid motion control equation, configuring data points for training and performing data fusion operation; secondly, constructing a physical constraint residual equation, taking the training data set as input data of a neural network, calculating a data residual term, weighting a calculation result of the physical constraint residual equation and a calculation result of the data residual term, and constructing a data-physical mixed loss function; and finally, constructing a time sequence-based physical information neural network, performing training optimization on parameters of the time sequence-based physical information neural network, completing training and obtaining simulated flow field features. By introducing the time sequence module, the accuracy of flow field dynamic simulation based on the time sequence physical information neural network is improved, the requirement for the model solving speed can be met, and the feasibility and accuracy of the model are guaranteed.

The invention discloses a multi-modal physical constraint energy storage battery health state cross-domain evaluation method and system, and the method comprises the steps: constructing an RC equivalent circuit model of an energy storage battery, and constructing an electrothermal coupling model of the energy storage battery based on the RC equivalent circuit model and a thermodynamic equation; constructing a neural network model by taking the electrothermal coupling model as a constraint condition, constructing a three-dimensional temperature field in the battery based on a loss function of a partial differential equation in the neural network model, and training the neural network model; constructing a battery segment capacity attenuation model according to the three-dimensional temperature field, and optimizing the battery segment capacity attenuation model through the trained neural network model; and further adjusting the optimized battery segment capacity attenuation model according to the chemical composition of the energy storage battery to obtain a battery segment capacity attenuation model adaptive to the chemical composition of the current energy storage battery, and predicting the health state of the energy storage battery. According to the invention, high-precision and low-cost cross-domain battery health assessment can be realized.

The invention discloses a three-dimensional physical field real-time prediction method and system based on geometric deep learning and physical constraint, and relates to the technical field of computer-aided engineering and artificial intelligence. The method comprises the following steps: directly extracting native boundary representation data (B-Rep) of a three-dimensional model from a computer aided design system; constructing a heterogeneous dual graph taking a parameterized curved surface as a graph node, skipping finite element grid division, and aggregating local and global topological features by using a graph neural network; in combination with a physical information driving mechanism, a partial differential equation (PDE) residual error is introduced as a loss function for constraint training, and generalization prediction of a novel geometric structure is realized; and finally, the physical field state quantity is predicted through direct regression and is rendered in real time. An incremental reasoning mechanism based on a local topology subgraph is adopted, millisecond-level physical field real-time feedback under design modification is achieved, and the method is suitable for scheme rapid screening and trend prediction in the initial stage of design.

The invention discloses a water quality antibiotic traceability method and system based on data analysis, and the method comprises the steps: firstly constructing a water system network into a topological graph, building a physical information-graph attention network as a pollutant propagation prediction model, and enabling the model to integrate a pollutant convective diffusion partial differential equation as a physical constraint into a loss function, the physical fidelity prediction of the spatial and temporal distribution of the antibiotic concentration in the water body is realized; secondly, constructing a pollution source tracing problem as a reverse reinforcement learning task, and reversely learning a pollution source reward function capable of optimally explaining an observation result from real downstream monitoring data by adopting an antagonism reverse reinforcement learning framework and taking the propagation prediction model as a dynamic environment through antagonism training of a generator and a discriminator; and finally, generating a probability distribution diagram of the upstream candidate pollution source position according to the reward function, and performing accurate positioning in combination with a hydrodynamic constraint condition, thereby remarkably improving the accuracy, robustness and calculation efficiency of traceability.

The invention discloses a rock and ore microscopic image splicing method and system based on deep learning, and relates to the field of image processing and the technical field of microscopes, and the method comprises the steps: obtaining a local rock and ore microscopic image of a rock and ore slice, carrying out the preprocessing of the local rock and ore microscopic image, and carrying out the overlapping region coarse registration of the preprocessed local rock and ore microscopic image through a phase correlation method; based on an improved image feature detection model, basic features and description features in the local rock and ore microscopic image after coarse registration are extracted, and local image features of the local rock and ore microscopic image are obtained; performing feature matching on the local image features of the two groups of local rock and mineral microscopic images by using an image feature matching model to obtain a matching corresponding relation of the local image features; and based on an image fusion algorithm of a homography matrix and a partial differential equation, splicing and optimizing the local rock and ore microscopic images in combination with a matching corresponding relation of local image features, and generating a large-view-field rock and ore microscopic image. According to the method, the complex transformation between the images can be better processed.

The invention belongs to the technical field of aero-engine performance testing, particularly relates to a physical constraint-based aero-engine state prediction model construction method and system, and aims to solve the problems of high calculation complexity and poor data quality of an existing physical information model. The method comprises the following steps: acquiring historical operation data of the aero-engine and a physical constraint rule set of engineering simplification; predicting performance parameters by adopting a deep learning model; constructing a total loss function formed by weighting a data loss item and a physical loss item to train the model; wherein the physical loss item is generated based on the deviation degree of the predicted performance parameter and the engineering simplified physical constraint rule set, and is used for replacing the complex partial differential equation constraint. According to the method, the engineering simplified physical rule is introduced, so that the calculation overhead of model training is remarkably reduced, the model is effectively guided to learn the characteristics conforming to the physical rule, and the accuracy and generalization ability of the prediction model are remarkably improved under the condition of limited data.

The invention discloses a GNN-PINN fusion dynamic error enhancement current transformer error prediction method, and the method comprises the following steps: firstly, building a sliding window steady-state recognition method based on a kurtosis-entropy collaborative criterion, and achieving the precise segmentation of current features under a complex working condition through multi-dimensional statistic fusion; secondly, a dynamic error simulation injection mechanism is designed, a current data enhancement strategy guided by a physical rule is constructed, and the working condition coverage of training data is effectively improved; on the basis, a GNN-PINN-based current transformer error prediction model is provided, and deep coupling of an error mechanism and data characteristics is realized through embedding a physical connection relation of equipment through a topological graph and combining partial differential equation constraints, so that a current transformer error is obtained. According to the current transformer error prediction method provided by the invention, the key problems of difficulty in steady-state feature extraction, scarcity of data samples, lack of physical constraints and the like in current transformer error prediction during online calibration of the transformer substation current transformer can be solved.

The invention provides a delay compensation control method for a double-vibrator wave energy conversion device, which comprises the following steps of: performing numerical modeling and hydrodynamic analysis on the double-vibrator wave energy conversion device, establishing a time domain motion equation of the double-vibrator wave energy conversion device, and introducing a time delay function into the time domain motion equation to simulate control signal transmission delay; simulating the execution delay of the brake by using a partial differential equation; establishing a state-space equation of the device, replacing a convolution term of the time-domain motion equation with the state-space equation, and calculating to obtain a motion state of the device; a Hamiltonian function is defined to convert a constrained optimization problem into an unconstrained optimization problem, and the Hamiltonian function is solved to obtain an optimal control criterion considering control delay so as to realize maximization of energy capture under the optimal control criterion. According to the method, the operation characteristics of a physical system are truly reflected by introducing control delay, so that the locking control method is effectively implemented in an actual physical device, and the energy capture efficiency of a wave energy conversion device and the reliability of system operation are effectively improved.

The invention provides an underground pipeline leakage detection method and system based on physical enhancement thermal inertia imaging, and belongs to the technical field of pipeline detection, and the method comprises the steps: obtaining an earth surface thermal field and environment meteorological parameters of a current target area; the surface thermal field and the environmental meteorological parameters are input into a double-flow physical information neural network model, a predicted background thermal field is obtained, the double-flow physical information neural network model is obtained through training based on leakage-free historical data and a total loss function, and the total loss function comprises physical loss items determined based on a one-dimensional heat conduction partial differential equation; calculating a space-time residual image of the earth surface thermal field and the predicted background thermal field, and obtaining an apparent thermal inertia distribution map of the earth surface based on the space-time residual image and environmental meteorological parameter inversion; and detecting whether the underground pipeline in the target area leaks based on the space-time residual map, the apparent thermal inertia distribution map and a preset condition. According to the invention, the accuracy of underground pipeline leakage detection can be improved.

The invention relates to the field of constructional engineering, and discloses a dynamic risk prediction system, which generates space-time alignment input through multi-source data fusion, adopts tensor field modeling to embed contract constraint to construct a risk dynamic model, and solves and outputs a continuous risk field through a partial differential equation; a propagation path is analyzed in combination with asymmetric causal analysis, model parameters are adjusted in real time through a dynamic optimization algorithm, and closed-loop optimization of a risk field is achieved; and finally, through four-dimensional thermodynamic diagram interaction early warning and resource intelligent scheduling, a whole-process closed-loop system of data modeling-causal analysis-dynamic optimization-visual management and control is formed. According to the method, dynamic optimization of risk field parameters is realized through adjoint equation back propagation, and the modeling precision of a complex scene is improved; a four-dimensional space-time thermodynamic diagram rendering technology is innovated to solve the problem of fragmentation of multi-modal information expression, and risk disposal response is accelerated; key task resource supply is guaranteed by combining video memory preemption and containerization scheduling strategies, and the system stability bottleneck in a high-load scene is overcome.

The invention discloses a cooperative control method for a multi-domain unmanned system. The method comprises the following steps: acquiring an original sensor sequence and a late message set; according to the original sensor sequence, calculating a dominant function and anti-fact regret to obtain a value signal; generating a time delay weight according to the late message set; constructing a space-time source item based on the value signal and the time delay weight; constructing a neural pheromone field by solving partial differential equation dynamics by using space-time source items and pheromone field parameters; under the guidance of the neural pheromone field, sampling to generate a candidate path set; and selecting a cooperative path from the candidate path set for the unmanned system to execute. According to the method, the problems of value characterization and timeliness under asynchronous communication are solved, deep coupling of a decision strategy and a physical model is realized, and the robustness and the adaptive ability of collaborative decision are improved.

The invention discloses a method for predicting the service behavior of an oil and gas pipeline under a stress corrosion cracking condition, and belongs to the field of material performance analysis and prediction. Firstly, based on a convection diffusion equation, a real stress intensity factor of an oil and gas pipeline crack tip is used as an unknown quantity to obtain a stress distribution condition; then, applying boundary conditions and a crack propagation criterion in fracture mechanics, and performing preliminary calculation to obtain crack propagation behavior characteristics of the one-dimensional semi-analytical model; and finally, calibrating and calculating the diffusion coefficient of the material parameter in the one-dimensional semi-analytical model to obtain real full-life service time information of the oil and gas pipeline material. According to the method, the full-life behavior information of stress corrosion cracking can be quickly obtained, and a partial differential equation does not need to be solved; on the basis of fracture mechanics, the mechanism of repeated fracture and reconstruction of the passivation layer in the fracture process can be reproduced; the full-life service time of the oil and gas pipeline can be effectively predicted, and the parameter calibration method can be applied to other conditions and is wide in application range.

The embodiment of the invention relates to a plant metabolite synthesis center and key gene analysis method and device. The method comprises the steps of constructing a partial differential equation, designing two neural network models, setting two ordinary differential equations, setting a data format of a sampling data sequence, designing a loss function LALL and designing a model training process. Intercepting experimental materials and storing the experimental materials; sampling and storing the frozen slices of the experimental material; after sampling is finished, setting sampling data sequences of various observation substances, and performing one-time targeted training on the two models; simulating the temporal and spatial change state of the current substance based on the model parameter set corresponding to each substance, analyzing the synthesis center based on simulation data, and analyzing the key gene by comparing the gene sequencing results of the synthesis center / comparison area of each substance. According to the invention, the positioning accuracy of the synthesis center and the identification accuracy of key gene information can be improved.

The invention belongs to the technical field of oil and gas field development engineering and multiphase flow numerical simulation, and relates to a rapid prediction method for gas-liquid two-phase flow key flow parameters of a horizontal gas well. Constructing the mixed momentum equation, the mass conservation equation and the drift velocity constitutive relation into a partial differential equation residual group; secondly, constructing a physical information Fourier operator network architecture, and learning integral operator mapping from working condition parameters to flow field distribution in a frequency domain by using a lifting layer, a Fourier layer and a projection layer; and finally, calculating a partial derivative of an output variable relative to a space-time coordinate by utilizing an automatic differential technology, and constructing a loss function fusing a data error and a physical PDE residual error to carry out model training. According to the method, millisecond-level accurate prediction of the pressure, the liquid holdup and the flow velocity field of the whole wellbore is achieved, and the problems that traditional numerical simulation calculation is long in time consumption, the real-time monitoring requirement is difficult to meet, and a pure data driving model lacks physical consistency are solved.

Provided are a grouting subgrade water-vapor-heat coupling simulation method and system, a device and a medium, including: constructing a subgrade water-vapor-heat coupling geometric model; acquiring a partial differential equation of a subgrade water-vapor-heat coupling process, and establishing a relationship between physical fields; setting a temperature and water boundary condition; performing mapping and free triangle mesh generation on the subgrade water-vapor-heat coupling geometric model to obtain a meshing model; selecting initial data, and performing a simulation solution on the meshing model to obtain a water-vapor-heat coupling simulation result; and analyzing the impact of a double-layer polyurethane grouting thermal insulation structure on a temperature distribution, a freeze-thaw cycle depth, and water migration of a subgrade.

The application discloses a large-scale PDE neural operator pre-training method based on a high-frequency enhancement module, a partial differential equation (PDE) data set is composed into a mixed data set, a large-scale PDE neural operator is constructed, preprocessed PDE data is mapped to a latent representation space through a space-time encoder, a frequency decomposition module is used for frequency space mapping, the frequency decomposition module includes parallel high-frequency branches and low-frequency branches, and high-frequency features and low-frequency features are obtained respectively; a multi-frequency fusion module (GFM) adaptively fuses the high-frequency features and the low-frequency features through a gating mechanism; finally, a prediction head is used for processing the fused features, and final output features, i.e., predicted physical features of a next time step, are obtained. The application firstly introduces an explicit frequency division and a high-frequency enhancement mechanism, the input field is divided into low-frequency and high-frequency parts, the low-frequency branches / high-frequency branches are used for processing respectively, and thus the model can simultaneously consider global trend modeling and local gradient detail reconstruction.

The invention discloses a high-precision self-adaptive control method and system for a floating sealing device of a lithium extraction rotary kiln, and relates to the technical field of intelligent control, and the method comprises the steps: collecting the multi-modal characteristic parameters of the operation state of the rotary kiln, inputting the multi-modal characteristic parameters into a physical information neural network, solving a thermoelastic mechanical partial differential equation, and obtaining the high-precision self-adaptive control of the floating sealing device of the lithium extraction rotary kiln. The method comprises the steps of obtaining a deformation field prediction matrix, outputting a deformation field prediction matrix, solving a multi-target optimization model with minimum leakage rate and wear energy consumption as a joint target based on the deformation field prediction matrix, outputting a sealing compensation instruction vector, and driving a magneto-rheological-piezoelectric composite execution mechanism to complete instruction control of a sealing ring based on the sealing compensation instruction vector. Collecting feedback result data controlled by the instruction, adjusting the weight of each weight coefficient in the multi-objective optimization function on line by adopting a reinforcement learning algorithm based on the feedback result data, and feeding back the adjusted weight to the multi-objective optimization model; and the control stability of the rotary kiln under complex working conditions is ensured.

The application belongs to the technical field of artificial intelligence, and particularly relates to a digital cultural creative content generation method based on artificial intelligence. The method comprises the following steps: step 1, calculating global text embedding; step 2, obtaining latent variables after manifold transformation; step 3, taking the transformed latent variables as initial states, constructing a potential energy function in a latent space, which comprises text semantic constraints and a global regularization term, and realizing diffusion update of the latent variables through a discrete random differential equation; adopting an inverse Fourier transform method to generate a local texture map; and step 4, taking the generated local texture map as a local driving factor, constructing a partial differential equation based on diffusion and reaction mechanism, and realizing spatiotemporal evolution of image brightness distribution under a preset initial condition, so as to realize adaptive synthesis of image structure layout and generate image content. The application realizes intelligent generation from text description to high-quality image content.

A system and method are provided for training a neural network for controlling operation of a system having non-linear dynamics represented by partial differential equations (PDEs). The method includes collecting a digital representation of time series data indicative of an instance of a function space of the system and a measurement of a state of operation of the system. A configuration point corresponding to the solution of the PDE is generated. A neural network is trained using training data including the collected time series data and the configuration points to train parameters of the non-linear operator. The neural network has an autoencoder architecture, the autoencoder architecture comprising: an encoder to encode each instance of training data into a potential space; a non-linear operator for propagating the encoded instance into a potential space using a transformation determined by a parameter of the non-linear operator; and a decoder to decode the transformed encoded instance of the training data to minimize the hybrid loss function.

The invention relates to the technical field of hydrology and water resource monitoring and emergency scheduling, in particular to a water source multi-modal monitoring fusion method and system, and the method comprises the steps: firstly obtaining multi-source water environment observation, and forming evidence graph initialization and assimilation initialization; secondly, carrying out cross-modal evidence fusion, generating topological boundary constraints, flow direction constraints and assimilation time windows, and compiling the topological boundary constraints, the flow direction constraints and the assimilation time windows into a constraint set; then executing data assimilation and source item joint inversion under the constraint of a partial differential equation to obtain a source item confidence set, the arrival time of a pollution front outer envelope and a key position, and a quantile interval of peak concentration and standard exceeding duration; and finally, constructing a risk functional and an opportunity constraint based on a quantile interval, and generating a scheduling instruction and partition early warning by adopting rolling predictive control, and performing closed-loop recharge. The method gives consideration to accuracy, robustness and performability.

The invention provides a lithium battery cargo abnormal temperature rise identification method, and belongs to the technical field of lithium battery customs detection.The lithium battery cargo abnormal temperature rise identification method comprises the steps that a temperature sensor array is arranged on the surface of a lithium battery cargo stacking body to collect multi-point temperature time sequence data, and trend characteristics are extracted through wavelet packet decomposition denoising and singular spectrum analysis; establishing a state space model based on a heat conduction partial differential equation, estimating an internal temperature field state by using Kalman filtering, solving a heat conduction inverse problem by using a conjugate gradient regularization iterative algorithm to invert an internal three-dimensional temperature distribution field, and inputting an inversion result and statistical characteristics into a thermal anomaly identification model fused with manifold learning. Dimensionality reduction is carried out through a local linear embedding algorithm, a mahalanobis distance is calculated in a low-dimensional manifold space, an abnormal temperature rise risk score is output, when the score exceeds a preset threshold value, early warning is triggered, and the technical problem that the abnormal temperature rise in the lithium battery cargo stacking body is difficult to accurately recognize through surface temperature measurement is solved.

The invention discloses a stay cable analysis digital twinborn model construction method, which comprises the following steps of: constructing a stay cable space three-dimensional vibration motion partial differential equation based on a material linear elasticity hypothesis and a double-coordinate system theory, and setting boundary conditions and initial conditions of the stay cable space three-dimensional vibration motion partial differential equation; a Galerkin multi-mode truncation method is adopted to obtain a displacement function of superposition of an end displacement excitation item and a vibration mode item; deducing a three-way strong coupling ordinary differential equation set through symbolic operation, and solving by using a step-variable Runge-Kutta algorithm to obtain a stay cable analysis digital twin model with stable parameters; structural response data are obtained through the sensor network and calculated, and when the obtained cable force, flexural rigidity and damping ratio exceed threshold values, a model updating mechanism is triggered for updating; and then the updated stay cable analysis digital twin model is obtained. According to the method, the real dynamic characteristics of the stay cable can be restored with high precision, and effective support is provided for structural state evaluation and early warning.

The invention provides an early warning method for sea surface temperature anomaly detection, which belongs to the technical field of ocean forecasting, and comprises the following steps of: constructing a multi-source sea temperature data fusion system and a multi-scale adaptive grid, generating a high-resolution temperature field by adopting ensemble Kalman filtering data assimilation, extracting an abnormal component by utilizing ensemble empirical mode decomposition, and carrying out early warning on the abnormal component. Multi-level anomaly discrimination is performed based on a sparse coding recognition model and fractal dimension mutation detection, anomaly types are distinguished in combination with an atmospheric compulsive event feature library and a random forest classifier, and partial differential equation inverse problem reverse deduction is performed on ocean endogenous anomaly to reconstruct a three-dimensional anomaly structure. And finally, the early warning level is determined through the early warning decision function and the multi-dimensional indexes, and the technical problem that the real-time performance and the accuracy of sea surface temperature anomaly detection are difficult to guarantee at the same time is solved.