1471 results about "Differential equation" patented technology

The invention relates to the technical field of battery monitoring, in particular to an electric vehicle battery state real-time monitoring system based on deep learning. Comprising a battery data acquisition module, a feature extraction module, a battery state prediction module, a fault monitoring module and a safety regulation and control module. The battery data acquisition module acquires battery parameters in real time, the feature extraction module extracts key features through deep learning, and the battery state prediction module predicts battery capacity, health state and performance degradation based on a neural network and a differential equation. The fault monitoring module adopts integrated learning to detect abnormity and output an alarm signal, and the safety regulation and control module optimizes a charging and discharging strategy through reinforcement learning and fuzzy control to guarantee safe and stable operation of the battery. According to the invention, accurate prediction and intelligent monitoring of the battery state are realized through the deep learning technology, comprehensive state evaluation and fault early warning are provided, and the accuracy and stability of battery state monitoring are effectively improved, so that the operation safety of the electric vehicle is improved, and the service life of the battery is prolonged.

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

The invention discloses a cable insulation life prediction method and system based on LSTM accelerated aging mapping, and relates to the technical field of submarine cable insulation life prediction. The existing method has the defects of insufficient multi-stress nonlinear modeling, laboratory and field data separation, difficulty in small sample modeling and the like, and the insulation life of the submarine cable is difficult to accurately predict. The method comprises the following steps: data acquisition: acquiring parameters of insulation electrical performance, physical and chemical performance and mechanical performance under an accelerated aging condition; carrying out data preprocessing: carrying out homodromous processing on the inverse indexes, improving box plot denoising, filling missing data with a space-time K nearest neighbor algorithm, and carrying out normalization; constructing an LSTM model: embedding a dielectric constant differential equation as a physical constraint, and introducing an index weight; and model training and evaluation: adopting five-fold cross validation, and quantizing prediction precision through mean square errors and decision coefficients. According to the technical scheme, the prediction precision is improved, the fault risk caused by insulation aging is reduced, the maintenance cost is reduced, and the reliability of the ocean energy transmission system is improved.

The invention discloses a supply chain financial risk management system based on credit assessment, and relates to the field of financial science and technology. A five-dimensional data model is constructed through a dynamic data modeling unit, and transaction subject levels and legal potency dimensions are integrated; the causal analysis engine adopts a dynamic updating algorithm of time sequence feature coding and compliance score constraint, and introduces a legal potency score constraint mechanism to strengthen compliance verification. The cross-domain risk conduction model combines physical space voxel grid monitoring with a digital space fluid dynamic equation, and the risk conduction velocity is quantified through a risk diffusion coefficient and a partial differential equation; and the self-evolution defense module integrates a confrontation sample generator and an incorruption degree feedback mechanism, simulates logistics track abnormity through Levy distribution, and improves the defense capability of the system in combination with a dynamic weight adjustment strategy of approval skipping times. According to the system, the deep supply chain risk conduction path identification precision is improved, and the problems of a multi-level network risk monitoring blind area and cross-domain risk tracking lag are effectively solved.

The invention relates to a proton exchange membrane fuel cell (PEMFC) high current density performance prediction method, system, equipment and medium. The method comprises the following steps: establishing a multi-physical field coupling model which comprehensively considers complex processes such as electrochemical reaction, proton conduction, gas diffusion and heat transfer, and describing the change of each physical quantity by adopting a partial differential equation based on a basic physical law; performing grid division and numerical discretization on the proton exchange membrane fuel cell model; selecting model parameters, and verifying the model through experimental data of different working conditions; inputting actual working condition parameters into a model to predict performance, and analyzing a simulation result; using a convolutional neural network, a recurrent neural network and an auto-encoder to extract features from different types of data and fuse the features to form a comprehensive feature vector; a deep neural network prediction model is constructed, and a cross entropy loss function and an Adam optimizer are adopted for training; dropout, L1 and L2 regularization, k-fold cross validation and transfer learning are utilized to optimize the model, and the generalization ability is improved; the system, the equipment and the medium realize high current density performance prediction of the proton exchange membrane fuel cell (PEMFC) based on the method; the prediction precision is improved, the experiment cost is reduced, the internal mechanism can be deeply understood, and powerful support is provided for design optimization, operation management and fault diagnosis of the fuel cell.

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 rural power grid user power load prediction method and system based on a liquid neural network. The method comprises the following steps: firstly, collecting rural power grid user power load historical data including multi-dimensional features such as weather and agricultural modes, and carrying out data preprocessing; then constructing liquid neurons based on biological neuron dynamics, and modeling the state of the liquid neurons through a differential equation; thirdly, constructing a liquid neural network based on liquid neurons, improving the characterization capability of multi-scale time sequence data through multi-level time constant setting and time gating residual connection, and supplementing network initial information in combination with a multi-layer perceptron architecture; completing model training by using the time sequence data set; the actual application performance of the model is tested based on the test data and the actual application scene; and finally, deploying the model to practical application, and carrying out power load prediction on rural power grid users. According to the method, the expression capability of the model for the multi-scale time sequence data is improved, and high-precision rural power grid user power load prediction is realized.

The invention provides an automatic driving-oriented kinematics priori guided vehicle trajectory generation method, and belongs to the technical field of trajectory generation and motion control in automatic driving. Comprising the following steps: constructing a vehicle kinematics differential equation, performing nonlinear compensation and control correction based on a double-flow mechanism to obtain a vehicle explicit physical state, and converting the vehicle explicit physical state into a vehicle implicit physical state; feature extraction, target detection and multi-mode fusion are carried out on the collected point cloud data and the vehicle surrounding image data, and environment information of a scene where the vehicle is located is provided; anchoring Gaussian distribution to simulate a feasible noise track of a vehicle in a current scene, generating a noise track candidate sample through sampling and noise adding, performing reverse denoising reasoning on the noise track candidate sample, generating a track anchor point, and generating a reasoning noise track around the track anchor point; environment information of a scene where the vehicle is located, a reasoning noise track and an implicit physical state are input into a diffusion decoder for iterative training, and kinematic prior guides generation of a future track of the vehicle in the denoising process.

The invention provides an ocean wind field prediction method based on a neural network, and belongs to the technical field of ocean wind field prediction.The method comprises the steps that sparse ocean observation data are collected, a spatial covariance matrix is established, the spatial covariance matrix is converted into a graph structure, and then multi-hop neighborhood feature aggregation is conducted through a graph convolutional network; a tensor decomposition algorithm is combined for modeling high-order feature interaction to generate a gridding wind field, a bidirectional long-short-term memory network encoder is used for extracting space-time invariant features, a multi-layer perceptron predictor is used for directly mapping a future multi-step wind field, and a course learning strategy and a Shenchang differential equation boundary layer are matched for correction. The technical problem that sparse ocean observation data are difficult to accurately reconstruct into a high-resolution gridding wind field is solved.

The invention relates to the technical field of public safety and intelligent fire protection, in particular to a real-time processing method and system based on fire alarm data, and the method comprises the steps: environment steady state reconstruction: accessing non-fire environment dynamic parameters; presetting thermotechnical static parameters of the building space; constructing a space thermal inertia differential equation; generating an ideal reference baseline; knowledge-driven simulation: receiving the ideal reference baseline; calling a synthesis operator in a preset disaster interference knowledge base; executing dynamic superposition injection; generating a virtual sensor state flow which has physical characteristics and contains environmental background characteristics; homomorphic judgment: executing double difference calculation; obtaining a real residual feature vector; geometric homomorphism verification is executed; outputting fire alarm triggering, interference filtering or fault prompting instructions; according to the method, the problem of false alarm caused by non-stable fluctuation of the environment background in the background technology is solved, and dynamic fusion and scene adaptation of the standard signal model and the real environment background are realized.

The invention provides a learning data accurate mining method based on cognitive calculation driving. The learning data accurate mining method comprises the following steps of S1, performing multi-modal learning behavior data acquisition and heterogeneous integration; s2, a dynamic feature weight optimization step based on calculus; s3, performing cognitive state differential equation modeling; s4, a cognitive diagnosis hybrid model based on statistics; s5, incremental construction of the dynamic knowledge graph is carried out; s6, constructing a federated learning framework for privacy protection; s7, a cognitive intervention strategy is generated; s8, constructing a multi-granularity effect evaluation system; s9, a step of constructing an interpretability enhancement module; s10, a step of carrying out adaptive iterative optimization; the learning data accurate mining method based on cognitive calculation driving has the following advantages that the data utilization rate breaks through the limitation of a traditional method through federated learning and heterogeneous graph fusion; the attention prediction error is reduced through differential equation modeling, and the method is superior to all existing ARIMA / LSTM baseline models.

The invention discloses an electric power information operation violation risk supervision system based on a knowledge graph, which relates to the field of violation risk supervision and comprises a dynamic graph construction module, a causal analysis module, a strategy analysis module, a strategy modeling module and a supervision decision module. The method comprises the following steps: obtaining multi-type electric power operation field sensing data and information system data, and carrying out data processing and knowledge graph dynamic construction to obtain a dynamic knowledge graph; performing risk situation quantification and risk decision point inference based on the dynamic knowledge graph to obtain a key causal decision point set; based on the key causal decision point set, through strategy logic analysis, obtaining a logic rule set which can be directly deployed and executed; according to the method, a logic rule set which can be directly deployed and executed is subjected to dynamic strategy evolution of a stochastic differential equation to obtain a dynamic strategy model, and the dynamic strategy model is subjected to decision optimal screening of measurement transformation to obtain an optimal supervision decision set, so that a risk value can be accurately calculated, and the supervision response speed can be increased.

The invention relates to the technical field of ocean data interpolation filling, in particular to a space-time deficiency filling method and system based on context association and physical guidance. The method comprises the following steps: acquiring seawater dissolved oxygen data and context data; multivariable space-time dependence extraction is carried out based on the obtained seawater dissolved oxygen data and context data; gaussian noise diffusion is carried out based on the obtained seawater dissolved oxygen data; noise prediction is carried out based on double-view space-time correlation; and the prediction error is constrained based on the joint loss function. According to the method, a physical consistency constraint mechanism based on a partial differential equation is introduced in a model training process, so that model output better conforms to a physical coupling rule among variables in a marine environment. The constraint effectively inhibits non-physical fluctuation possibly occurring in the interpolation result, enhances the physical credibility and interpretability of the result, and provides a more reliable data basis for subsequent scientific analysis and process modeling.

The invention relates to a railway bridge post-earthquake traffic safety probability evaluation method and device, which are applied to the technical field of traffic safety, and the method comprises the steps: obtaining an earthquake-induced damage value set of each component through a probability distribution function of different material parameters of a railway track-bridge system; the method comprises the following steps: acquiring a mapping relation between the earthquake-induced damage of a key component and track irregularity through a balance differential equation of a bridge and railway track structural mechanical model, and acquiring earthquake-induced track random irregularity samples of different components based on an earthquake-induced damage value set of each component and the mapping relation between the earthquake-induced damage of the key component and track irregularity; constructing a power spectrum of the track irregularity caused by vibration of different components; establishing a rapid prediction model of the driving performance indexes on the axle after the earthquake through the earthquake-induced track irregularity sample and the coupling dynamic response result; through a Monte Carlo method, based on the earthquake-induced track irregularity power spectrum and the rapid prediction model, the overrun probability and the confidence interval of the driving safety on the axle after the earthquake are rapidly and accurately obtained.

The invention discloses an Alzheimer's disease long-term prediction and dynamic intervention method based on deep learning. Accurate dynamic intervention from group statistics to individual dynamics is realized through time sequence alignment, multi-scale time sequence feature extraction, dynamic risk assessment and personalized intervention. Through time sequence alignment and multi-scale time sequence feature extraction, a time-sensitive personalized intervention scheme can be generated. And carrying out continuous time modeling by adopting a cubic Hermite interpolation method and a neural control differential equation, and predicting the long-term risk. By establishing a double-track interaction model, a pathological track and a functional track of a user are analyzed, so that time-varying association among multi-modal data can be dynamically captured. A personalized intervention strategy is provided through reinforcement learning, the intervention strategy is dynamically adjusted in combination with risk reduction amplitude, intervention measure compliance and physiological index change, the effect of short-term behavior change and long-term prediction is balanced, and the Alzheimer's disease is further promoted to be converted from passive treatment to active intervention.

The invention discloses a three-dimensional unstructured grid adaptive refinement method and system based on machine learning, and belongs to the field of machine learning, partial differential equation solving and computational fluid mechanics simulation. Residual errors of a fluid control equation are used as a novel error indicator and a refinement criterion, imprecise flow field data and the fluid control equation are obtained by fusing coarse grids of simulation flow through a physical information neural network, and the total residual errors of the equations are conveniently calculated by using automatic differentiation after training is completed. Therefore, the coarse grid units with relatively high residual errors can be adaptively marked and refined. By matching an h-refinement scheme, Delaunay tetrahedron subdivision is executed after vertexes are strategically inserted to maintain the quality of the refined grid. According to the method, any numerical solver can be flexibly matched to carry out grid adaptive refinement, so that various typical flow problems can be solved with high precision. The method achieves better balance between calculation precision and the number of grids, and has the advantages of being simple, convenient, high in compatibility and universality and the like.

The invention discloses a magnetic field measurement method and system based on a multi-sensor fusion technology, and relates to the technical field of sensor fusion and magnetic field measurement, and the method comprises the steps: deploying a multi-sensor data array, carrying out the adaptive initialization of a bistable SR parameter range, defining an SR system differential equation, and carrying out the iterative optimization through employing an MPA population. Carrying out Hilbert transform edge detection on enhanced signal component data, calculating an array inclination angle, carrying out abbe error and bidirectional projection error compensation, and carrying out metasurface grid coordinate quantization mapping; the collected and cross-scale magnetic field data set is preprocessed and packaged into data cells, quality evaluation and weight distribution are carried out on the data cells, and extended Kalman filtering data fusion is carried out; by introducing a bistable stochastic resonance system and an MPA population optimization algorithm, a weak magnetic field signal is obviously enhanced, and by calculating an array inclination angle and compensating an Abbe error and a bidirectional projection error, the space consistency of a measurement result is improved.

The invention discloses a dough humidity and temperature dynamic regulation and control system based on multi-modal sensor fusion, and relates to the technical field of operation parameter regulation and control, and the method comprises the steps: carrying out the non-blind area monitoring of a three-dimensional temperature and humidity field in a fermentation box, and eliminating the local failure risk of a point-type sensor; capturing a space-time coupling relationship of temperature and humidity-carbon dioxide concentration-dough volume through a self-attention mechanism; constructing a differential equation set comprising at least one state variable; establishing a fermentation equipment thermodynamic model, optimizing refrigeration and heating time sequences through reinforcement learning, and designing an energy consumption optimization strategy based on digital twinning; performing cross-device cooperative control; constructing a hierarchical diagnosis tree containing fault features; performing microbial activity enhanced humidity regulation and control, and forming a protective layer on the surface of the dough based on a humidity gradient control algorithm of a water molecule diffusion coefficient. And by arranging the environment regulation and control module, the time compression module and the time sequence optimization module, the product quality, the production efficiency and the energy utilization rate are remarkably improved.

The invention relates to the technical field of coal mine geological safety monitoring and disaster early warning, in particular to a coal mine disaster early warning method and system based on a geological model and a storage medium, and the method comprises the steps: firstly obtaining high fractal fracture information and monitoring data such as gas, hydrology and stress through chaotic wave excitation and fractal analysis; and a three-dimensional fractal grid with multi-level fracture representation is constructed. And discretizing a mechanical field and a fluid field by using a fractal partial differential equation operator, and globally solving a large-scale nonlinear equation through a quantum annealing mode or a quantum and classical combined annealing mode to form a quantum fractal coupling simulation result. And finally, integrating the grid units and monitoring data in the fuzzy hypergraph structure, carrying out fuzzy membership analysis on a multi-disaster element coupling sign, and outputting early warning information once the risk is judged to reach a threshold value. According to the invention, the precision and timeliness of coal mine geological disaster monitoring can be obviously improved.

The invention discloses a pipeline flow field remodeling method based on LAAF-PINN, and the method comprises the steps: collecting the flow field data of a pipeline measurement point; dividing the measuring points into monitoring points and testing points, and preprocessing the data sequence; constructing an LAAF-PINN, randomly selecting a matching point along a pipeline, and inputting a time-space sequence of the monitoring point and the matching point to obtain model output; calculating a data loss item according to the model output of the monitoring point, calculating a residual error according to the model output of the collocation point to obtain a partial differential equation loss item, and combining the two items to obtain total loss; after multiple rounds of iteration updating, a trained PINN model is obtained, a time-space sequence of a test set is input, and corresponding flow field information can be quickly and accurately reconstructed. According to the method, the LAAF-PINN is applied to pipeline hydraulic transient research, an existing flow field remodeling method is expanded, good robustness is achieved for data containing uncertain factors, and meanwhile the problem that the calculation precision is not high possibly existing in forward simulation is solved.

The invention relates to a steep slope stability prediction method and system based on a manifold graph convolutional network, and belongs to the technical field of geological disaster monitoring and prediction. The method comprises the following steps: data acquisition and preprocessing; constructing a graph structure fused with multivariate features; constructing a manifold graph convolutional neural network model; model training; predicting the surface displacement of the steep slope; and according to the predicted earth surface displacement result, steep slope stability evaluation is carried out. The real-time state of the slope is reflected by integrating multi-source data such as geology and terrain; through multi-dimensional data fusion, key factors are captured, and prediction comprehensiveness and accuracy are improved. The method comprises the following steps: constructing a graph structure fusing multivariate features, and deeply mining spatial correlation and time dynamic change by using a manifold graph convolutional network and a Shenchang differential equation to realize deep fusion of spatio-temporal features. The model has good adaptability to slight deformation and complex geological conditions, has high robustness, and can be suitable for monitoring different types of slopes.

The invention discloses a dangerous driving critical state identification method, and relates to the technical field of intelligent driving safety. According to the method, multi-mode signals of eye movement, electrocardio, skin electricity, vehicle operation and the like are collected and converted into a unified phase field, and the synchronous coherence of the unified phase field is analyzed; the individual phase dynamics manifold of the driver is learned on line by using a Shenchang differential equation, and a system instability precursor is identified by detecting the behavior that a state point escapes from a steady state attractor; further, multi-dimensional indexes such as synchronous collapse and topological fracture are fused, collapse time is estimated in combination with a Lyapunov index, and an advanced early warning instruction is generated; and finally, based on the model predictive control and the personalized phase response curve, generating and executing targeted multi-mode phase reset intervention, and forming a sensing-early warning-intervention active safety closed loop. According to the invention, normal form transformation from post-event alarm to beforehand regulation and control is realized, and early warning advancement and intervention accuracy are improved.

The invention relates to the technical field of multi-modal sensing, and discloses a multi-modal large model detection and recognition robot recognition system for a complex scene, the system constructs a dynamic manifold modeling module, realizes cross-modal joint denoising through a stochastic differential equation and depth score matching, constructs a drift term and an anisotropic diffusion term by using an optical flow field, and realizes multi-modal detection and recognition of a multi-modal large model. Dynamic noise interference such as rain fog and motion blur is eliminated; on the basis, designing an information geometric alignment module, and based on Riemannian manifold optimization and orthogonal projection matrix calculation, realizing geometric equidistant mapping of vision-Li DAR features through multi-scale measurement tensor fusion; a dynamic external parameter calibration module is further provided, SE (3) manifold Kalman filtering is combined with a noise self-adaptive scaling technology, and external parameter offset is tracked and compensated in real time. Compared with a traditional method, the method has the advantages that the core problems of cross-modal data geometric mismatch, external parameter drift accumulation, low semantic fusion efficiency and the like are solved, and the sensing precision and robustness of the automatic driving system in a complex dynamic scene are remarkably improved.

The invention belongs to the technical field of speech enhancement, and discloses a Schrodinger bridge-based diffusion model speech enhancement method and system, and the method comprises the steps: converting a diffusion process of a diffusion model into a solving process of a stochastic differential equation, and determining the stochastic differential equation according to the theoretical principle of a Schrodinger bridge, and the complex spectrum is directly used as the input of the diffusion model, so that the tedious work of extracting a phase spectrum and an amplitude spectrum from the complex spectrum and the memory overhead caused by inverse transformation are omitted, and meanwhile, the alignment problem between the phase spectrum and the amplitude spectrum is also avoided. According to the speech enhancement method, unique features of time sequence signals are captured through a Transformer module, multi-scale information is fused through a U-Net module, a loss function covering a time domain, a frequency domain and a time-frequency domain is matched, the difference between a prediction sample and a clean sample is gradually reduced, the nonlinear diffusion process from a noisy sample to the clean sample can be directly learned, and the speech enhancement accuracy is improved. Therefore, the structure information of more initial samples is reserved.

The invention relates to the technical field of belt conveyor dynamic characteristic research, in particular to a trailer type belt conveyor dynamic characteristic simulation method, which comprises the following steps: respectively carrying out stress analysis on a trailer frame, each wheel and a conveying belt unit in a trailer unit, and sorting the degree of freedom; establishing an overall coordinate system, a vehicle body coordinate system, a contact coordinate system and a conversion relation; respectively establishing kinetic equations of the wheel pair and the vehicle supporting frame; kinetic parameters of the trailer unit are determined; according to the Hamiltonian principle, a vehicle supporting frame vertical motion equation and a vehicle supporting frame nodding motion equation are established, and then a linear second-order differential equation of a vehicle supporting unit is obtained; and compiling a calculation program by using numerical calculation software, and simulating the wheel-rail vibration characteristics of the trailer unit in the straight-line-segment advancing process. On the basis of the multi-degree-of-freedom multi-body dynamics theory, a trailer type belt conveyor system nonlinear dynamics equation is constructed, and a wheel pair, a trailer frame and a six-degree-of-freedom motion equation for coupling the wheel pair and the trailer frame are analyzed.

A method for training a shape optimization neural network to produce an optimized point cloud defining desired shapes of materials with given properties is provided. The method comprises collecting a subject point cloud including points identified by their initial coordinates and material properties and jointly training a first neural network to iteratively modify a shape boundary by changing coordinates of a set of points in the subject point cloud to maximize an objective function and a second neural network to solve for physical fields by satisfying partial differential equations imposed by physics of the different materials of the subject point cloud having a shape produced by the changed coordinates output by the first neural network. The method also comprises outputting optimized coordinates of the set of points in the subject point cloud, produced by the trained first neural network.

The invention discloses a tunnel engineering structure deformation monitoring method based on deep learning. The method comprises the following steps: S1, constructing a data set; s2, data preprocessing; s3, extracting spatio-temporal features by adopting a graph attention spatio-temporal converter; s4, constructing a prediction model by adopting a Poincare sphere embedding and control differential equation; s5, adopting a hyperbolic element learning and variational Bayesian optimization model; s6, monitoring in real time and adopting a self-adaptive early warning mechanism; and S7, establishing an intelligent feedback mechanism, and optimizing monitoring parameters. According to the method, accurate prediction and efficient monitoring of tunnel engineering structure deformation are realized, the prediction precision, the calculation efficiency and the early warning capability are remarkably improved, the generalization capability and the anomaly detection performance of the model are enhanced, the method effectively adapts to deformation characteristics of different tunnel structures, and the method is suitable for large-scale popularization and application. The method can be widely applied to the field of tunnel engineering structure deformation monitoring in a complex geological environment.

The invention discloses a power distribution equipment health assessment method and system based on multi-source data, and the method comprises the steps: mapping each modal feature into a comparable measure, constructing a learnable cost containing power flow and heat consistency, outputting a modal weight through scene gating, and forming a fusion representation of physical consistency; driving a neural differential equation by fusing the stress force obtained through representation decoding, adopting monotone weight parameterization and introducing equipment-level damage budget, and obtaining damage and health indexes which are irreversible along with time; under the constraint of physical baseline life, combining a working condition input time-scene gating danger rate model, performing causal consistency correction through virtual intervention, and outputting an interval failure probability and residual life; and calibrating a dynamic threshold value in the working condition cluster, and triggering routing inspection, sampling and load shedding based on the risk sensitivity and the topological linkage risk priority. According to the method, multi-source physical consistent fusion, individualized health modeling and dynamic closed-loop optimization can be realized, and the accuracy and interpretability of health assessment of the power distribution equipment are improved.

The invention discloses a rainstorm torrential flood physical constraint prediction method and system based on a graph neural differential equation, and belongs to the technical field of rainstorm torrential flood prediction. Carrying out space-time attention fusion based on a graph; carrying out modeling and dynamic deduction based on a graph neural differential equation of physical constraints; predicting and outputting a multi-task flood hydrograph; and carrying out joint loss function design and end-to-end training. The system comprises a multi-modal hydrological feature obtaining and coding module used for multi-source heterogeneous data feature extraction, a graph-based space-time attention fusion module used for deep fusion of multi-source heterogeneous features, and a physical constraint-based graph neural differential equation dynamic core module used for continuous dynamic process modeling. And the prediction output module is used for outputting the spatial distributed flood hydrograph. According to the method, the problems of low reliability, poor timeliness and poor extrapolation capability during rainstorm torrential flood prediction in the prior art are solved.

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.