792 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 fluid mechanics equation solving method based on a physical information neural network, and relates to the technical field of fluid mechanics. The problems that in the prior art, a large amount of training data is needed, physical information is not embedded, prediction does not conform to physical laws, generalization is difficult, and sample generation consumes resources are solved. The fluid mechanics equation is solved through the physical information neural network, physical consistency and prediction precision of model training are ensured, mathematical descriptions of fluid motion and heat transmission are determined, accurate input is provided for the neural network, definition of a loss function is combined with a basic physical law of fluid mechanics, and the prediction precision is improved. The reliability of model output is guaranteed, the training and optimization process effectively guides the learning of the neural network and improves the solving efficiency, the numerical verification and prediction step combines the prediction capability of high-precision numerical simulation and the neural network, the solving precision is guaranteed, the prediction efficiency in an unsampled area is improved, and the prediction efficiency of the neural network is improved. And an efficient and accurate tool is provided for fluid mechanics research.

The invention discloses a data-mechanism dual-drive-based rock burst early warning method and system, and the method comprises the following steps: deploying a multi-modal sensor network to collect coal and rock stratum data, building a rock burst disaster precursor information sample database, providing a rock burst disaster multi-modal data precursor feature recognition algorithm, and carrying out the recognition of rock burst disaster multi-modal data precursor features. Mining the relevance between the multi-modal data and disaster-causing key risk indexes, and establishing a rock burst disaster multi-modal data prediction model; establishing a three-dimensional geological geometric model, fusing a multi-field coupling dynamics constitutive model and a catastrophe criterion, constructing a PINN physical information neural network prediction model of the rock burst disaster, and obtaining a time-space evolution rule of an energy field of a target area; providing a loss function coupling calculation method of a multi-modal data driving sample error and a physical driving control equation residual error, dynamic data and mechanism prediction result weight, comprehensively calculating a risk score, and accurately judging a top disaster danger level.

The invention discloses a multi-sensing and data physical fusion frozen soil hot water force response test system and method, and the system integrates an ultra-weak fiber grating, an active heating fiber, a miniature dielectric constant sensor and an optical frequency domain reflection technology, and constructs a freezing process-oriented temperature, moisture, ice content and strain synchronous monitoring network; the method comprises the steps of constructing a multi-field fusion data set based on thermal disturbance lag correction, temperature-strain decoupling and a moisture-strain residual term compensation mechanism, and extracting key criterion characteristics of an ice lens growth rate, frost heaving force evolution and a shear deformation change rate; and in combination with a physical information neural network (PINN) embedded into a hot water force control equation, risk identification and grade judgment of the freezing abnormal behavior are realized. The method supports indoor heating and water pressure linkage adjustment, realizes a complete closed loop of multi-source sensing-risk identification-response verification under model driving, and breaks through the bottlenecks of low multi-physics field coupling identification precision, large parameter cross interference and insufficient risk identification real-time performance of the existing monitoring technology.

The invention relates to the technical field of slope protection, in particular to a slope protection intelligent detection system based on deep learning. According to the technical scheme, the system comprises a multi-source heterogeneous data sensing module, a data fusion and feature extraction module, a slope state intelligent diagnosis and early warning module, an edge-cloud collaborative computing architecture and a system optimization module. Registration and feature complementation of multi-source heterogeneous data are realized through a multi-modal detection network, an overfitting phenomenon is effectively inhibited through a physical information neural network architecture, risk quantitative evaluation is realized through construction of a dynamic risk evaluation model, early warning response time is shortened in cooperation with a four-level early warning strategy, the false alarm rate is reduced, and the early warning efficiency is improved. Besides, the detection precision of the system in an extreme scene is improved through a physical constraint adversarial training method, so that the environmental adaptability of the system is improved, continuous updating and evolution of the model are realized through an online incremental learning module, and the problem of performance degradation of a traditional system caused by change of geological conditions is solved.

The invention discloses a power grid dynamic modeling method based on a physical information neural network and a related device, and the method comprises the steps: obtaining power grid observation data, inputting the power grid observation data into a neural network for power grid dynamic behavior prediction, and obtaining a power grid state prediction value; calculating data item loss through a power grid state prediction value and a power grid state actual measurement value, and calculating physical residual item loss through power grid observation data; the physical residual item loss comprises current conservation constraint loss, voltage closed-loop constraint loss and generator dynamic response loss; and network parameters of the neural network are updated through the data item loss and the physical residual item loss until the neural network converges, and a power grid state model is obtained. According to the method, the mapping relation between the state variable and the system input is established by using the neural network, and the physical rule of the power grid is introduced into the loss function as a hard constraint term, so that physical consistency control during dynamic behavior modeling of the power system is realized, and the accuracy of dynamic behavior modeling of the power grid is improved.

The invention discloses an inertial microsystem thermal coupling analysis method based on a physical information neural network, and the method comprises the steps: S1, setting material parameters and boundary conditions of an inertial microsystem, and building a thermal coupling analysis model; s2, performing electrothermal coupling analysis to obtain temperature distribution of the inertial microsystem; s3, thermal stress distribution of the microsystem is obtained through thermal-mechanical coupling simulation analysis; s4, predicting a temperature field of the microsystem through the physical information neural network; s5, taking the temperature field as a boundary condition of mechanical simulation of the microsystem to obtain mechanical properties such as stress and strain of the microsystem; and S6, performing mechanical-electric coupling simulation analysis, and analyzing the influence of structural deformation on each parameter in electrical performance. According to the method, the neural network solving precision is improved through the improved adaptive weight strategy, the complete polynomial basis function is combined with the neural network, the state dimension is reduced by introducing the expansion basis function, the calculation cost and time are reduced, accurate prediction of the micro-system multi-moment temperature field is achieved, and the micro-system performance under electric heating power multi-physics field coupling is calculated.

The invention relates to the field of civil engineering and artificial intelligence, in particular to a transfer learning optimization system and method for predicting the early-age strength of concrete, and the system comprises a multi-scale data sensing module, a physical information constrained deep neural network module, a formula adaptive transfer learning module, a Bayesian optimization prediction module and a federated learning feedback module. The whole process from data collection to model optimization is achieved, through the system, the concrete strength prediction errors of the extremely early age and the standard age are remarkably reduced to + / -5% and + / -3% respectively, meanwhile, the number of concrete test pieces for testing is reduced by 85%, the material and labor cost is greatly saved, and the method not only improves the prediction precision, but also reduces the construction cost. And through continuous learning and feedback, the prediction model is continuously optimized, and an efficient and economical concrete strength prediction solution is provided for actual engineering.

The invention discloses an intelligent self-monitoring temperature management system for a box-type substation, and relates to the technical field of intelligent power grid equipment monitoring. The problems that an existing system is large in temperature measurement deviation, low in reliability, delayed in early warning, inaccurate in hot spot positioning and extensive in heat dissipation control are solved. According to the scheme, multi-source signals are acquired in parallel through a data acquisition module, and a temperature time sequence is extracted; a boundary calibration module is adopted to fuse data to generate a three-dimensional boundary condition; the multi-physical field solving module obtains an internal temperature / stress field; the physical information prediction module is fused with a heat transfer physical constraint training graph neural network to predict a hotspot migration trend; the hierarchical scheduling module is used for solving a fan and oil pump collaborative optimization instruction in real time based on model predictive control; according to the invention, the accuracy of internal temperature monitoring of the box transformer substation, the reliability of hot spot prediction and the accuracy of heat dissipation control are remarkably improved, the insulation life of equipment is effectively prolonged, and the operation safety and reliability of the system are improved.

The invention belongs to the technical field of ocean engineering, and discloses a column structure response forecasting method and system based on a residual connection neural network. According to the method, a physical information neural network (PINN) and a Kolmogorov-Arnold (KAN) theorem are combined, and the precision and the calculation efficiency of a prediction model are remarkably improved through a multi-layer nonlinear mapping framework and residual connection optimization. According to the method, physical equation constraint loss and environmental parameter input are introduced, so that the model can more accurately simulate dynamic response characteristics in an extreme marine environment, and the limitation of a traditional method in nonlinear factor processing and real-time calculation is effectively solved. Experiments show that the method has remarkable advantages in the aspects of accuracy, generalization ability and calculation efficiency, provides reliable and efficient technical support for ocean platform structure design and operation state monitoring, and has important theoretical value and wide application prospects.

The invention discloses a PINNs high Reynolds number flow field solving method based on regulatory factor optimization, and the scheme comprises the steps: firstly building a physical model, on one hand, building a geometric model according to the actual condition of a reactor core rod bundle channel, and on the other hand, building a fluid mechanics model containing continuity, momentum conservation and energy conservation equations; a data set is obtained, and experimental data is preprocessed and then integrated with simulated data through experimental measurement and numerical simulation; and then training a physical information neural network, designing a neural network model of specific input and output, introducing a regulatory factor to balance the difference between a physical equation and data, constructing a comprehensive loss function containing internal data points, boundary data points and equation loss, and training the network by using a training data set until convergence. And finally, solving a flow field by using the trained neural network, outputting predicted values of a velocity field, a pressure field and a temperature field, and solving the high Reynolds number flow field of the reactor core.

The invention discloses a quasi-brittle material damage field inversion method based on a physical information neural network. The method comprises the steps of data acquisition and processing; presetting initial damage field data, and taking the modulus of each unit in the initial damage field data as an independent to-be-inverted parameter; taking the position information of each node in the space as the input of a neural network, processing through a hidden layer of the neural network, and taking displacement data corresponding to the position information as network output data; constructing a loss function of a neural network according to the preprocessed displacement data, network output data and a mechanical law; and performing training optimization on the initial damage field data by using a loss function of a minimized neural network, optimizing hyper-parameters of the neural network by adjusting weights of control item loss, boundary item loss and data item loss until a preset convergence condition is met, and determining target damage field data. According to the method, the dependence of a neural network model on a data set is remarkably reduced, and the inversion result is ensured to have relatively high physical interpretability.

The invention provides a rock mass high-temperature deformation simulation method and system based on deep learning near-field dynamics, and relates to the field of rock mass thermal-mechanical coupling analysis and research, and the method comprises the steps: obtaining the parameter information of a to-be-simulated target rock mass, including the geometric shape of the rock mass and the physical attribute of the rock mass; dispersing the target rock mass into a plurality of material points according to the parameter information, and constructing a thermal-mechanical coupling model of the target rock mass; based on the initialized thermal-mechanical coupling model, time step iterative simulation of a solid mechanical field is conducted, and finally the crack propagation process and temperature distribution of the target rock mass are obtained; according to the method, a solid field near-field dynamics theory based on strain energy density is introduced, a fluid field near-field dynamics equation is combined, temperature distribution in a rock medium is predicted through a physical information neural network, a deep learning near-field dynamics framework of rock mass high-temperature deformation damage is formed, and therefore rock mass high-temperature deformation is efficiently and accurately simulated.

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 discloses a physics-based method for discovering a control equation from scarce and noise data. According to the method, a physical information neural network and a sparse regression method are combined, and a partial differential control equation of a dynamic system is found from scarce and noisy data. Firstly, the size of a candidate function library is effectively reduced through a dimension verification method. And then modeling a physical system and calculating a candidate function by using the strong nonlinear fitting capability and automatic differential characteristic of the deep neural network. And finally, the form of a control equation and the coefficient of the equation can be obtained through sparse regression, and the coefficient is finely adjusted through DNN. The method not only can discover the control equation from the data, but also can obtain the network model to realize the response prediction of the dynamic system. The method is easy to implement, efficient, high in precision and high in universality. The method can be widely applied to physical knowledge mining, modeling and reasoning of a complex dynamic system.

The invention discloses a multi-scale fatigue crack propagation prediction method and system based on a physical information neural network, and aims to improve the prediction precision of a crack propagation rate. The method comprises the following steps: S1, acquiring crack lengths and cycle cycles of a metal material under different scales by using a microscope; s2, calculating an initial small crack propagation rate based on the acquired data, performing fitting by adopting a Newman-Wu model, constructing an initial crack propagation physical model, further combining experimental data to identify physical characteristics influencing propagation behaviors, introducing a PSC-LC turning point to divide small crack and long crack stages, and constructing a crack propagation rate model fusing multiple characteristics; s3, preprocessing the expansion rate data; and S4, constructing a physical loss function in combination with a crack propagation rule in an experiment, fusing the physical loss function with data loss to form a total loss function, and training the neural network model to realize accurate prediction of the multi-scale fatigue crack propagation behavior of the metal material.

The invention discloses a physical information fused aero-engine residual life prediction method, and belongs to the field of neural network residual life prediction. The method comprises the following steps: acquiring sensor data of the whole process from engine simulation operation to fault, and performing standardization and windowing preprocessing; establishing a time-space dual-channel multi-layer perceptron model, extracting time dimension and feature dimension information of the signal, performing self-attention fusion with original data, completing mapping to a life prediction value, and calculating an error with a real life; establishing a physical information neural network model, learning a hidden partial differential equation among a predicted life value, time and a hidden state, and performing error calculation with an equation in an equilibrium state; and performing self-attention fusion with the life error to obtain comprehensive loss so as to continuously train the model until the error is minimum, and finishing final life prediction. Physical information is fused on the basis of the multi-layer perceptron, the complexity of the model is simplified, and the prediction precision of the residual life of the engine is improved.

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 discloses a neural network inertial attitude estimation method fusing physical information constraints. The method comprises the following steps: acquiring IMU data and attitude quaternion values at corresponding moments; performing windowing processing on the IMU data, and screening an attitude quaternion value as an attitude quaternion pseudo-true value based on physical consistency; training the constructed neural network prediction model; optimizing the neural network prediction model through a model loss function, wherein the model loss function comprises a physical constraint loss function and a supervision loss function; and during target carrier attitude estimation, inputting IMU data of a corresponding window into the trained neural network prediction model, and calculating to obtain a target carrier attitude quaternion estimated value of the corresponding window. According to the method, the generalization ability of the neural network is enhanced through physical information constraint, the precision and robustness of inertial attitude estimation are improved, and the method is particularly suitable for inertial navigation application in a high-dynamic environment.

An earth and rockfill dam termite nest detection method based on MSCCEAUNet and physical constraint combined driving comprises the following steps that a miscellaneous geologic model of an earth and rockfill dam structure with a termite nest is simulated, and corresponding b-scan data and underground dielectric constant data are generated through a finite difference method FDTD to serve as a simulation data set; 2, constructing an MSCCEAUNet network with multi-scale cascade convolution (MSC) and an efficient channel attention mechanism (ECA), and inputting the complex geologic model data generated by simulation in the step 1 into the MSCCEAUNet network for training; according to the method, the MSCCEAUNet is trained again by adding Gaussian noise and random medium disturbance data with different signal-to-noise ratios, the structure of the MSCCEAUNet is optimized according to the training condition of the model, and the anti-jamming capability and robustness of the model are improved; a physical residual equation meeting physical information constraints of Maxwell equations of the ground penetrating radar is used as a physical loss function to be fused into an MSCCEAUNet framework, and data and physics combined drive inversion is achieved.

The invention provides a reservoir physical property parameter pre-stack seismic inversion method based on resistivity logging constraint. The method comprises the following steps: cutting observation data and depth; normalizing the seismic data, the resistivity data and the travel time to form network input; constructing a neural network framework, substituting the obtained porosity and saturation into an elastic rock physical model, and calculating to obtain an elastic parameter; the elastic parameters are substituted into a zoeppritz equation, and an underground reflection coefficient is obtained through calculation; generating a Ricker wavelet, and performing convolution calculation on the Ricker wavelet and the reflection coefficient to obtain predicted pre-stack seismic data; the porosity and the saturation are substituted into an Archie formula, and the predicted resistivity is obtained through calculation; constructing a target function; automatic differentiation is carried out, network parameters are updated, and then network output porosity and saturation are optimized. Reservoir parameter inversion driven by physical information is realized, and an intelligent scheme is provided for reservoir parameter prediction and evaluation in oil-gas exploration and development.

The invention discloses a multi-fidelity data fusion and physical constraint-based cold heading process parameter staged optimization method, which comprises the following steps of: 1) performing calculation through Deform finite element simulation and an empirical formula, constructing a multi-fidelity initial data set, and improving data consistency through normalization and deviation calibration; 2) constructing a multi-fidelity physical information neural network (PINN) model, and establishing a mapping relation between process parameters and forming quality indexes by adopting a staged training strategy and an adaptive weight adjustment mechanism; and 3) verifying the generalization ability of the model by dividing a training set and a test set, ensuring that a prediction result accords with a volume conservation criterion and a material forming limit, and realizing optimization of cold heading process parameters. According to the method, through multi-fidelity data fusion and physical information embedding, on the basis of enhancing a physical mechanism and multi-data collaboration, the data acquisition cost is reduced, and the generalization of the model is improved; and through dynamic weight distribution and a staged training strategy, the prediction precision and reliability are improved.

The invention relates to a small sample fault prediction method based on physical information guidance and multi-source adaptive fusion. The method comprises the following steps: collecting real operation data of preprocessing target equipment; according to the physical model or domain knowledge of the target equipment, generating simulation sensor data conforming to a physical rule under various fault modes of different degrees; constructing diversified training samples in combination with real data and simulation data; for different types of sensor data, designing corresponding feature extraction branches, mining potential fault features in the data, and dynamically adjusting the weight of each data source fault feature for fusion based on an output result of a physical model and data-driven feature correlation analysis; inputting the obtained fusion features into a fault prediction model based on a small sample learning framework for training; comprehensively considering the fault prediction result, the real operation data and the analysis result of the physical model, and carrying out quantitative evaluation on the overall health state of the target equipment; and causal diagnosis and visual interpretation are carried out.

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 discloses an underground water exploitable quantity evaluation method and system based on coupling of a physical mechanism and a neural network, and relates to the technical field of water resource management, meteorological data, surface water hydrological monitoring data, geological survey data, underground water resource development and utilization data and hydrogeological parameter data are integrated and preprocessed, and a basic data set is obtained; a three-dimensional geological model of a heterogeneous structure is constructed by utilizing a digital twin physical engine and combining drilling geological data. By introducing a digital twin technology and a numerical model, the underground water flowing process can be simulated more accurately, a digital twin physical engine is utilized to construct a heterogeneous aquifer digital model, a dynamic interaction process of surface water and underground water is embedded, and the model is enhanced through a graph neural network and a physical information neural network, so that the underground water flowing process is simulated more accurately. Errors caused by hypothesis of aquifer homogeneity, neglecting of surface water-underground water coupling process and the like of a traditional model are effectively reduced, and the accuracy of underground water exploitable quantity evaluation is improved.

The invention discloses an in-structure sound field bounded uncertainty analysis method based on a physical information neural network. The method comprises the following steps: establishing a physical information loss function and establishing a physical information driven neural network; obtaining an input uncertain parameter value range of the internal sound field, obtaining a plurality of parameter levels, and training a sound pressure prediction model under the parameter levels; acquiring sound pressure of a sample point and establishing a data set; establishing a data driving residual error based on the data set, establishing an overall loss function by using the data driving residual error and a physical information loss function, and training a parameter-containing physical information driving neural network to obtain a parameter-containing internal sound field sound pressure prediction model; and establishing an interval model, and obtaining an internal sound field sound pressure uncertainty distribution range by using a global search algorithm and the parameter-containing internal sound field sound pressure prediction model. The method can solve the problem of prediction of the sound field in the structure containing uncertain parameters, and can effectively improve the calculation efficiency on the premise of ensuring that the calculation precision meets the engineering requirements.

The invention belongs to the technical field of reliability-based design optimization, and particularly relates to a reliability design optimization method combining transfer learning and a physical information neural network. Comprising the following steps: constructing a physical information neural network model by adopting training data of a source domain, and evaluating the reliability of the source domain. A physical information neural network model constructed by adopting training data of a source domain is updated by utilizing a layer freezing and fine tuning technology in transfer learning, so that the reliability of a target domain with different statistical attributes is evaluated, the calculation overhead is saved, and the calculation efficiency is improved. A first-order scoring function is utilized to carry out random sensitivity analysis on the failure probability so as to ensure normal operation of design optimization. And finally, providing estimation reliability, an objective function value and corresponding sensitivity information for an optimizer to obtain a corresponding updated design point. And repeating the iterative design process until the optimal design is realized. The training speed of the physical information neural network model is increased, and the precision of the physical information neural network model is improved.

The invention discloses a geothermal abnormal point identification system based on remote sensing detection. The system comprises a data layer which collects thermal infrared remote sensing data, multispectral / hyperspectral remote sensing data, radar data, geological and topographic data, meteorological data and geophysical prospecting data; the processing layer is used for extracting key features, capturing dynamic changes of geothermal anomalies, distinguishing continuous geothermal anomalies and transient events, combining hyperspectral data and magnetotelluric sounding data, and constructing'earth surface-deep part 'associated features; the analysis layer is used for learning a diffusion rule of geothermal anomalies along a fault zone through a graph propagation model, taking a geothermal field heat conduction equation as a loss function constraint by utilizing a physical information neural network, jointly training a temperature field prediction model, constructing a deep three-dimensional geological structure based on gravity and geophysical prospecting data, and constructing a geologic structure model; constructing a geothermal anomaly detection model through the features provided by the processing layer to recognize geothermal anomaly points; and the output layer is used for generating a geothermal anomaly probability graph.

The invention discloses an ice condition prediction method based on multi-feature fusion and physical constraint, and belongs to the technical field of hydrological forecasting. Firstly, various types of data of a target area are collected and preprocessed to serve as a data set, features of the various types of data are extracted, feature fusion is conducted on obtained image features, time sequence features and environment features through a multi-head attention mechanism, and a fusion feature vector is generated. Secondly, adopting a physical information neural network model, taking the fusion feature vector as input, taking ice thickness and ice stress as output layers, carrying out constraint by using a composite loss function, carrying out model optimization by using a verification set, carrying out processing through a full connection layer in the network, and carrying out end-to-end training and regularization of a prediction model; and finally, evaluating the final prediction model obtained by training through the test set, and verifying the effectiveness and generalization ability of the final prediction model. The method combines multi-source and multi-mode observation data, can effectively capture complex relations between ice surface changes and various factors, can improve prediction precision, stability and reliability, and can improve model training efficiency and generalization ability.