435 results about "Physical information" patented technology

The present invention relates to the field of industrial intelligent predictive maintenance, and in particular to an interpretable deep feature fusion network-based industrial intelligent predictive maintenance method, comprising: acquiring gearbox vibration data comprising noise; performing preliminary extraction and noise suppression on features of the acquired data by establishing an interpretable feature extraction module having a physical information constraint; integrating multi-scale features comprising long-distance and local dependencies by means of a dual-branch feature fusion module having global and local feature fusion capabilities; performing dimensionality reduction on a high-dimensional feature and generating an output by means of a classifier to obtain a final fault identification result; and performing interpretability analysis on a diagnosis process of a model. In the present invention, by embedding the signal processing technology having a well-defined physical theory support into a deep neural network, the interpretability and reliability of model inference results are effectively improved while the fault identification accuracy of the model is improved.

The invention discloses a physical information neural network-driven ocean island typhoon scene generation method and system. The method comprises the steps of collecting multi-source heterogeneous meteorological data and performing space-time alignment preprocessing; constructing a coarse-scale space-time probability prediction model, capturing space correlation of meteorological elements by using a graph topology learning network, efficiently processing long-time-sequence dependence of typhoon evolution by integrating a state space model with linear complexity, and generating a probabilistic typhoon scene with coarse resolution through a multivariable joint distribution probability model; further constructing a physical downscaling model, taking a coarse-scale prediction result as condition input, and performing physical consistency downscaling on a coarse-scale scene by embedding an atmospheric fluid mechanics equation in a loss function as a physical hard constraint; and finally, outputting a high-resolution typhoon scene with probability reliability and physical authenticity.

The invention relates to a wellbore multiphase flow model numerical solution and gas-liquid distribution state inversion method and system, and belongs to the technical field of petroleum engineering, and the method comprises the steps: 1, constructing and training a physical information neural network for drilling wellbore multiphase flow dynamic simulation and overflow gas distribution state inversion; determining input and output of the physical information neural network; determining a loss function of the physical information neural network; training a physical information neural network; 2, designing an adaptive optimization algorithm, optimizing the final solution precision and convergence speed of the physical information neural network, and obtaining an adaptive physical information neural network; designing an adaptive activation function; designing a self-adaptive sampling mechanism based on residual errors; 3, based on the self-adaptive physical information neural network, numerical solution and gas-liquid distribution state inversion of the shaft multiphase flow model are achieved. According to the method, the problem that a traditional numerical method usually needs high-precision grid division and a large number of computing resources is effectively solved.

The invention is suitable for the field of atomic layer deposition, and discloses an atomic layer deposition process management method and system based on digital twinning, and the method comprises the steps: building a digital twinning model of an atomic layer deposition production system based on a multi-scale physical information neural network model; acquiring real-time reaction chamber process parameters and real-time state variables, and inputting the real-time reaction chamber process parameters and the real-time state variables into the digital twin model to obtain physical field distribution in the reaction chamber and a predicted growth state of a film on the surface of the substrate; based on the physical field distribution in the reaction cavity and the substrate surface film prediction growth state, decision analysis is carried out through a pre-trained reinforcement learning agent, and correction parameters used for optimizing the film growth quality are obtained; and generating a correction instruction based on the correction parameter, and updating the process parameter of the reaction cavity based on the correction instruction, so that the film on the surface of the substrate in the reaction cavity reaches the target film thickness and uniformity, and thus real-time intelligent closed-loop control of perception-prediction-decision-execution can be formed.

The invention relates to the technical field of geophysical exploration, in particular to a multichannel deep learning magnetotelluric inversion method based on physical information constraint. The method comprises the following steps: generating a synthetic data set containing a geoelectric model and forward modeling response thereof, and adding a noise simulation actual observation condition; constructing a hybrid network architecture combining Transform and U-Net, taking apparent resistivity and impedance phase as dual-channel input, extracting global features by using an encoder, gradually recovering spatial resolution through a decoder, and outputting an underground resistivity model; network training adopts a composite loss function fusing model loss and data loss, and an inversion process is constrained by introducing a magnetotelluric forward modeling physical rule, so that a result is ensured to fit observation data and conform to a physical mechanism; after training is completed, preprocessed actual measurement data are input into the model, and a resistivity image can be directly obtained. The method is used for geological structure identification and reservoir interpretation, and the inversion precision and reliability are effectively improved.

The invention provides a geological disaster intelligent early warning method based on fusion of a physical information neural network and space-air-ground monitoring, and the method comprises the following steps: S10, obtaining geological structure features of different depths under the ground of a city, and recognizing the spatial distribution and thickness of an underground abnormal body; acquiring a surface deformation time sequence, crack and landform information, a three-dimensional point cloud and a continuous vibration signal of a surface-shallow stratum; s20, constructing a three-dimensional twin substrate, performing space-time alignment and resampling on multi-source heterogeneous data, mapping the data to a three-dimensional grid of a unified coordinate system, and extracting and fusing geological disaster precursor features; s30, taking the generated fusion feature field as an input training neural network model, carrying out geological disaster forward prediction and parameter inversion, and outputting future stability probability distribution, a potential slip plane and key parameter evolution; and S40, adaptively adjusting a risk threshold and an early warning rule, constructing an incremental data set, and carrying out periodic incremental training and parameter optimization on the physical information neural network in the step S30.

The invention discloses an open-pit mine slope risk prediction method and system based on multi-source data, and relates to the technical field of intelligent geological engineering, and the method comprises the steps: collecting geological data, displacement monitoring data and environmental activity data of an open-pit mine slope, carrying out the preprocessing, and constructing a three-dimensional digital twinborn body; carrying out fusion calculation on the preprocessed displacement monitoring data and environmental activity data through a physical information neural network, predicting a displacement value and a stress value of the strip mine slope, and obtaining full-field physical quantity prediction data; according to the strip mine slope slip crack surface damage evolution path, the stability risk levels of the whole strip mine slope and different areas in the strip mine slope are judged, high-risk areas are positioned, and a strip mine slope risk prediction report is generated. According to the method, the damage evolution path of the slip crack surface of the strip mine slope is dynamically deduced, so that space-time continuous modeling of the damage evolution process, early recognition of the slope instability precursor and accurate insight of the risk evolution trend are realized.

The invention provides a three-dimensional wind field prediction method and system based on multi-modal complementary fusion learning. The method comprises the following steps: S1, acquiring remote sensing observation data and numerical simulation data; s2, obtaining standardized remote sensing features and simulation features; s3, obtaining a unified scene representation; s4, splicing the unified scene representation with the to-be-predicted space-time coordinates, inputting the spliced scene representation and the to-be-predicted space-time coordinates into a physical enhancement decoder, and outputting three-dimensional wind speed vectors at the corresponding space-time coordinates; and S5, iteratively optimizing parameters of the bimodal encoder, the cross-modal attention fusion module and the physical enhancement decoder to form a closed-loop prediction model. According to the method, multi-modal data complementation and physical information deep fusion are realized, through innovating a neural network architecture and a constraint mechanism, the prediction precision under a sparse data condition is remarkably improved, the physical credibility of a result is enhanced, and a technical support is provided for intelligent development of the wind power industry.

The invention discloses a rolling bearing generalization fault diagnosis method based on a quantum physical information neural network, and the method comprises the following steps: S1, obtaining vibration signals and working condition parameters in the operation process of a rolling bearing, and carrying out the preprocessing and domain division to obtain a meta-training set and a meta-test set; s2, constructing a quantum-physical information neural network model which comprises a quantum path and a physical path; s3, feature fusion and classification; and S4, based on physical perception element learning training, repeating inner ring-outer ring iteration until the model is converged. Compared with the prior art, the rolling bearing fault diagnosis field generalization method based on the quantum physical information neural network has the advantage that the field offset problem existing in a traditional fault diagnosis method is solved.

The invention discloses an underground medium multi-scale forward and reverse modeling method and system based on a decoupling neural network, and belongs to the technical field of underground multi-physics field coupling. The underground medium multi-scale forward and reverse modeling method based on the decoupling neural network comprises the following steps: generating a spatio-temporal evolution data set of multiple physical field variables; a physical field decoupling physical information neural network architecture is constructed, the architecture comprises three special sub-networks, each sub-network takes time-space coordinates as basic input and dynamically receives output of other sub-networks as auxiliary input features, and strong coupling modeling between physical fields is realized through feature sharing and a joint loss function; executing a two-stage training strategy; and executing forward intelligent prediction or key physical property parameter inversion based on the trained neural network architecture. According to the method, a unified neural network framework composed of a plurality of special sub-networks is constructed, physical consistency and data-efficient cross-scale modeling from a rock core scale to a site scale are realized, and forward simulation and parameter inversion are synchronously supported.

The invention relates to a transient electromagnetic and seismic wave multi-mode joint inversion imaging method based on physical information Transform, and belongs to the crossing field of geophysical exploration and artificial intelligence. Comprising the following steps: carrying out anomaly detection, interpolation, filtering and normalization preprocessing on transient electromagnetic and seismic wave original data; extracting features representing electrical property, elasticity and cross physical significance; serializing the spatial data through a gridding and alternating fusion strategy, and constructing an enhanced code fusing absolute and relative positions and physical attributes; a physically constrained encoder-decoder architecture is designed to carry out multi-scale forward modeling-inversion; and quantizing the uncertainty of an inversion result by adopting a Bayesian Monte Carlo method, and generating a confidence map. According to the method, through physical rule driving and multi-modal depth complementary fusion, the fine recognition capability and interpretation reliability of hidden disaster-causing geologic bodies such as underground goaf and collapse columns are effectively improved while the physical consistency of data is kept.

The invention belongs to the technical field of nuclear reactor physical numerical calculation, and particularly relates to a method for improving precision and convergence of solving a diffusion equation based on a PINN, and the method comprises the following steps: S1, building a neural network algorithm model combining a physical information neural network PINN and source iteration; s2, aiming at a multi-region smooth transition neutron diffusion problem with small cross section difference between materials, optimizing the neural network algorithm model by adopting an optimization strategy of a training level; and S3, aiming at the multi-region neutron diffusion problem of large neutron flux gradient at the boundary due to large cross section difference between materials, improving the neural network algorithm model by adopting a domain-divided neural network architecture. According to the method, the problem that the calculation cost is exponentially increased in a high-dimensional or complex geometric scene due to the fact that a traditional finite difference method and a finite element method depend on grid division is avoided.

The invention provides an electric quantity prediction method and system fusing physical constraint factors, and relates to the technical field of electric quantity prediction. Historical load, weather, electricity price and calendar data are collected, and a key feature set is constructed through preprocessing and feature selection; a prediction model with the physical information neural network as the core is constructed, the prediction model comprises a recursion sub-module used for short-term prediction and a trend sub-module used for long-term prediction, and a physical constraint loss item based on a physical rule is introduced into model training so as to enhance the generalization ability; a multi-time granularity modeling framework is adopted, uncertainty quantization is achieved through a Monte Carlo Dropout or Bayesian neural network, and a confidence interval of a predicted value is output; and finally, causal reasoning is carried out through a Shapley value algorithm and anti-fact simulation, and key influence factors are identified. According to the method, the precision, stability and interpretability of electric quantity prediction are effectively improved, and reliable support is provided for power grid dispatching and decision making.

The invention discloses a method for realizing ocean sound field prediction based on a pre-training optimized physical information neural network, which comprises the following steps of: pre-training: constructing a hypothetical ocean environment, generating simulation data in a sound pressure envelope form by using an acoustic numerical calculation tool, and pre-training the physical information neural network to learn a physical rule of sound field propagation; fine tuning: adjusting an energy scale factor according to the proportion of the sound pressure amplitude of the actual measurement point to the sound pressure amplitude of the pre-trained environment, and performing fine tuning by using the envelope amplitude data of the actual measurement sound field for the pre-trained physical information neural network to obtain an ocean sound field prediction model through a pre-training-fine tuning dual-stage strategy; and ocean sound field prediction: according to the ocean sound field prediction model, obtaining a target position envelope from the to-be-predicted range, and then converting the target position envelope into a sound pressure result to realize ocean sound field prediction. The method can improve the high-frequency sound field modeling precision and training efficiency, and can be widely applied to the fields of ocean sound field modeling, sound source localization and environment inversion.

The invention relates to the technical field of ocean current data analysis, in particular to a multi-scale modeling and verification method and system based on ocean current spring layer structure analysis. And obtaining the thermocline and halocline parameters of the target sea area and carrying out abnormal value elimination and spatial interpolation to form a thermocline data set. A convolutional neural network is used to extract spring layer space structure features, and based on Gaussian curvature quantification boundary complexity, a thermocline and halocline equivalent geometric model is constructed. On the basis, taking the equivalent geometric model as a boundary condition, and performing multi-scale decomposition on the Navier-Stokes equation set to generate a dynamic approximate equation; and then inputting the approximate equation and the equivalent geometric model into a physical information neural network for constraint training to obtain a multi-scale flow field prediction model, and comparing the multi-scale flow field prediction model with actually measured flow field data to complete verification. According to the method, automation, refinement and multi-scale coupling of spring layer structure modeling are achieved, and scientificity and engineering applicability of ocean current modeling are improved.

The invention belongs to the technical field of computational mechanics and artificial intelligence crossing in civil engineering, and discloses a foundation pit retaining wall deformation prediction method based on a physical information neural network, and the method comprises the following steps: 1, building a retaining wall physical information neural network agent model based on multi-source data fusion; 2, building a displacement-force inversion model of the retaining wall; and 3, carrying out iterative coupling solution on the support system. According to the method, mixed training is carried out by fusing physical rules and field measured data, so that the physical information neural network agent model follows a basic mechanics principle and can also fit specific engineering practice, the prediction result is more reliable, and compared with traditional finite element analysis, the efficiency is improved by several orders of magnitude; therefore, parameter optimization and real-time safety evaluation based on a large amount of calculation become possible.

The invention provides a temperature controller power adjusting and detecting method and system based on AI intelligence, and relates to the technical field of intelligent manufacturing. Online multi-mode sensing data of a to-be-processed material is obtained, microdefect distribution is recognized through a physical information neural network and a graph neural network, and the temperature controller power adjusting and detecting method and system based on AI intelligence are obtained; and generating personalized time-varying evolution field data representing the thermodynamic evolution law of the material in the heating process, and constructing a digital twinborn model of the material. In a layered reinforcement learning framework, an intelligent agent interacts with a digital twinborn model, a high-level strategy selects a macroscopic process curve, and a low-level strategy generates a partitioned candidate power regulation scheme capable of actively avoiding a thermally induced failure risk according to the macroscopic process curve. Through Monte Carlo simulation, an expected time integral performance value of the scheme under various material disturbance scenes is evaluated, and a composite reward signal is formed to iteratively optimize an intelligent agent strategy, so that the product yield and the performance consistency are improved.

The invention relates to the technical field of power system equipment state monitoring and fault diagnosis, in particular to a GIS basin-type insulator operation state evaluation method and system, and the system comprises an asset information and baseline modeling module which is used for building a high-fidelity multi-physics field finite element reference model and generating a defect state-external representation mapping data set; the physical information driven agent model generation module is used for constructing a neural network agent model fusing physical law constraints; the real-time data acquisition and feature extraction module is used for acquiring and processing online monitoring data such as UHF, gas, temperature and vibration; the state inversion and digital twinning calibration module is used for inverting internal defect parameters by adopting a Bayesian inference and MCMC method so as to realize real-time calibration of the model; and the evaluation diagnosis and life prediction module carries out fault mode identification and residual life prediction based on the calibration model.

The invention relates to the technical field of power equipment fault diagnosis, and discloses a partial discharge type identification method based on deep learning, and the method comprises the steps: enabling a discharge physical equation to be embedded into a neural network structure through a physical constraint element learning network, and building a basic model with physical consistency; correcting physical model parameters by using a Bayesian fusion algorithm, and fusing priori knowledge of simulation data and observation information of an actual measurement sample; enabling the generative adversarial network to generate an enhanced training sample meeting the law of conservation of physics through the correction parameters and the conditions; a physical information guide network model is adopted to rapidly adapt to sample feature information and finely adjust model parameters, and rapid and accurate identification of discharge types under extreme working conditions is achieved. According to the method, the technical problem that the identification model is difficult to establish when the samples are extremely scarce under the extreme working condition is solved, and the technical effect of establishing the high-accuracy identification model within the hour-level time is achieved.

The invention discloses a sand permeability coefficient prediction method based on Bayes and a physical information neural network, which comprises the following steps: constructing a physical-data hybrid driven enhanced data set which comprises actually measured anchor point data and physical enhanced data generated based on a seepage physical mechanism; constructing a dual-channel feature decoupling fusion neural network, extracting gradation morphological features and soil compaction features through a particle size channel and a structure channel, and performing fusion in a deep layer; introducing a multi-physical constraint embedding mechanism and a physical constraint activation layer, constructing a total loss function, and performing posterior inference on network parameters through an HMC algorithm to obtain a parameter sample set; and performing permeability coefficient prediction based on the parameter sample set. According to the method, the problem of overfitting caused by small samples in geotechnical engineering is effectively solved through a two-channel architecture and a data enhancement strategy, generation of non-physical prediction values is avoided through two aspects of network architecture and physical loss embedding, cooperation of high-precision prediction and uncertainty quantification is realized, and engineering adaptability is greatly improved.

The invention discloses an integrated core particle thermal performance prediction method driven by a physical information neural network, and the method comprises the steps: constructing a parameterized geometric model of a to-be-predicted integrated core particle system, and the parameterized geometric model comprises a solid domain geometric model and a fluid domain geometric model; based on the parameterized geometric model, design parameters of the to-be-predicted integrated core particle system are obtained, and the design parameters comprise geometric parameters of a solid domain and geometric parameters of a fluid domain; acquiring a plurality of space coordinates from the parameterized geometric model to obtain a target space coordinate; and inputting the design parameters and the target space coordinates into a trained thermal field physical information neural network model, so that the model outputs temperature field distribution of the to-be-predicted integrated core particle system under the design parameters through forward reasoning, the model is obtained by training based on a trained flow field physical information neural network model, design parameters and a preset thermal field loss function. According to the method, the prediction time can be shortened while the thermal performance prediction accuracy of the integrated core particles is improved.

The invention belongs to the technical field of geotechnical engineering precision monitoring and calculation intelligent crossing, and discloses a deep foundation pit support pile micro-deformation capturing and predicting method and system fusing physical mechanism constraints, and the method comprises the following steps: obtaining and fusing a static physical field and a dynamic displacement field of a deep foundation pit support pile; constructing an enhanced space-time field atlas containing physical boundary conditions; constructing a micro-deformation prediction model, constructing a physical information loss function based on a support pile beam unit fourth-order differential equation, and training the micro-deformation prediction model based on the physical information loss function to obtain a trained model; and inputting the enhanced time-space field atlas into the trained model, extracting spatial morphological characteristics of pile body micro-strain in the enhanced time-space field atlas, and obtaining a support pile deformation prediction result. According to the method, the technical bottleneck of'micro-feature annihilation 'of a general model is broken through, and high-fidelity capture of micron-sized deformation is realized.

The invention discloses a method for predicting shock waves through a multi-region conservation enhanced physical information neural network, and belongs to the field of flow field prediction of shock waves and strong nonlinear physical phenomena in high-speed flow, and the method comprises the steps: S1, carrying out numerical simulation through fluid dynamics software to obtain a reference flow field solution of a to-be-solved N-S control equation, the verification module is used for verifying a model solving result; s2, constructing a neural network comprising an integral layer, a micro-layer and a loss function error layer; s3, constructing a physical information neural network; s4, training is carried out after physical information neural network training parameters are set, and whether a convergence standard is met or not is determined by constructing a joint loss function corresponding to the physical information neural network; and S5, applying the trained neural network model to shock wave prediction of high-speed flow field simulation under different working conditions. Through the multi-region local conservation constraint, the width of the shock wave transition region is effectively compressed, so that the relative error of the physical quantity is obviously reduced compared with the existing method.

The invention discloses a formation collapse pressure prediction method based on a feature marking physical information neural network, and relates to the technical field of oil exploration and development. The method comprises the following steps: collecting multi-scale data of logging, earthquakes, cores and the like, and constructing a physical constraint framework in combination with a well track stress field model; a data enhancement module combining expert knowledge and a Beta distribution mixed strategy is utilized to carry out interpolation on missing values and generate an enhanced data set conforming to geological laws; a multi-head self-attention encoder comprising a feature word segmentation device and a Transform is constructed, original parameters are converted into embedded vectors, and deep interaction features are mined; a physical consistency measurement function based on a collapse pressure mechanism is introduced, and a total loss function containing data loss and physical loss is constructed for joint optimization. According to the method, the problem of geological sample scarcity is effectively solved, it is ensured that the prediction result meets the rock mechanics equilibrium condition, and the prediction precision and robustness of the complex formation collapse pressure are remarkably improved.

The invention belongs to the field of aerodynamic analysis of unmanned aerial vehicles, and particularly relates to a method and system for predicting aerodynamic force of a rotor wing based on a physical information neural network, and the method comprises the following steps: determining key parameter variables and variation ranges which affect the aerodynamic performance of the rotor wing; then, a Latin hypercube sampling method is adopted, and a sampling space is generated in the parametric variable interval; obtaining flow field data through CFD simulation; constructing and training a physical information neural network reduced-order model, and constructing a physical information neural network model by taking the sample point coordinate information of the sampling space as input data and taking the corresponding flow field aerodynamic force data as a training target; in the training process of the model, network parameters are optimized through a composite loss function; by minimizing the composite loss function, training to obtain a reduced-order model of the predicted flow field; and solving the aerodynamic force based on the reduced-order model. According to the method, a physical information network is adopted, the precision of a CFD calculation method is reserved, and the efficiency of aerodynamic force estimation under parameter change is improved.

The invention provides a gamma-ray energy spectrum analysis method and system based on a deep learning physical information network, and relates to the technical field of gamma-ray energy spectrum analysis. The method comprises the following steps: acquiring sample data containing an energy spectrum and corresponding analysis data, and preprocessing the sample data; constructing an energy spectrum analysis model based on a multi-scale convolutional neural network, an attention mechanism module and a physical information neural model; training an energy spectrum analysis model by using the preprocessed sample data to generate a target energy spectrum analysis model; and inputting the to-be-analyzed energy spectrum into the target energy spectrum analysis model, and outputting an analysis result. By constructing a multi-module collaborative energy spectrum analysis model, accurate decomposition of energy spectrum multi-scale features, efficient interaction of cross-feature information and collaborative prediction of multiple parameters are achieved, the problems that spectrum peak overlapping is difficult to separate, weak peaks are difficult to recognize, and the anti-jamming capability is weak in a traditional method are solved, and the precision, efficiency and automation degree of energy spectrum analysis are improved.

The invention discloses a rock stratum drilling in-situ identification method based on physical information machine learning and an application thereof, which are used for solving the technical problem of in-situ sensing of unknown rock mass properties in a drilling process. Multi-source signal parameters including drilling pressure, drilling rod torque, drilling rod rotating speed, drilling speed and drilling vibration and acoustic emission signals are collected in the drilling process, CEEMDAN-VMD combined denoising and feature extraction are carried out, and fused feature vectors are input into a physical information machine learning model; and outputting the strength prediction value of the drilled rock stratum and the prediction value of the rock mass category on line in real time. The method is applied to innovative combination of freeze-thaw environment simulation, rock drilling process test and lithology intelligent identification, an advanced means is provided for drillability evaluation of freeze-thaw rock mass, data support can be provided for blasting parameter intelligent design and slope stability early warning, and the method has good engineering applicability and popularization value.

The invention discloses an intraocular light field simulation method and system based on physiological constraint and pathology traceability, the system takes a Physics Informed Kolmogorov-Arnold Net (PI-KAN) network as a core, namely physical information KAN, the method comprises the following steps: obtaining personalized parameters of eyeballs; 5-dimensional light field parameters including space, wavelength and time are input into a pre-trained PI-KAN model for light field solving, a three-layer network architecture including an input layer, a hidden layer and an output layer is established, a physical information edge function is constructed, and training is performed through fusion of a Helmholtz equation and a loss function of boundary conditions; generating an OCT image based on the light field solved by simulation; by analyzing side function mapping, visualization and pathological traceability of a light field propagation physical mechanism are realized. According to the method, the sparsity and interpretability of PI-KAN are utilized, the problems that a traditional method is low in calculation efficiency, difficult in high-dimensional modeling, weak in physical constraint and poor in interpretability are solved, millisecond-level, high-precision and interpretable simulation of the eye light field is achieved, and the method is suitable for ophthalmic clinical auxiliary diagnosis, surgical planning and equipment optimization.

According to the fine correction method for the icing microtopography and microclimate of the long-span power transmission line provided by the invention, the icing mass growth model is established, and the liquid water content and the water drop median diameter in meteorological data are determined according to the actually measured icing mass; and establishing a corresponding computational domain and boundary conditions on the basis of the strain tower of the power transmission line so as to train the physical information neural network, then predicting the meteorological distribution state of the power transmission lines on the two sides of the canyon only by using meteorological parameters near the strain tower, and then calculating the icing state according to the distribution state. Therefore, accurate correction of icing prediction is completed, the hypothesis of a uniform meteorological field in the traditional technology is effectively avoided, the accuracy of an icing prediction result is effectively ensured, and the actual working condition is better met.

The invention discloses an environment gas leakage intelligent detection method based on big data analysis. The method comprises the following steps: collecting monitoring data and constructing a sensor network space-time diagram fused with meteorological conditions; predicting the background concentration of each node by using a sequence decomposition attention model; calculating a concentration residual error and constructing a space-time residual error map; extracting an abnormal feature vector through a physical information enhanced graph attention network; calculating an abnormal score in combination with the normal mode memory bank and generating a leakage source probability distribution diagram; when the conditions are met, calling a computational fluid dynamics model to simulate a theoretical concentration field; and determining a leakage event through spatial correlation analysis. According to the invention, the false alarm rate of gas leakage detection in an open environment is obviously reduced, and the leakage source positioning precision is improved.