870 results about "Differential equation" patented technology

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

The invention 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 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 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 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.

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

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

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

The invention provides a liquid crystal display lightening parameter self-adaptive calibration method, and relates to the technical field of image display. The method comprises the following steps: firstly, establishing a characteristic matrix and a parameter library of liquid crystal molecules, and dynamically correcting according to a real-time working state; and based on the characteristic matrix and the parameter library, executing pixel-level forward prediction on the input image, and generating a color error distribution diagram by solving a coupling solution of a liquid crystal molecule dynamic response differential equation and a backlight attenuation compensation equation. And taking the error distribution diagram as input, obtaining a gradient optimization direction of the calibration parameter mapping diagram through back propagation calculation, and iteratively approaching the optimal pixel value compensation amount in each frame period. And injecting a test pulse into the characteristic pixel point, capturing a voltage response curve through a sampling circuit, and converting the voltage response curve into a real-time correction value mapping graph. And finally, carrying out weighted fusion on the real-time correction value mapping graph and the pixel value compensation amount to generate a calibration parameter mapping graph. The self-adaptive calibration of the display parameters is improved, and the display consistency and the color accuracy are effectively improved.

The invention discloses an earth and rockfill dam three-dimensional seepage simulation and parameter inversion analysis method and system based on a finite element method, and relates to the technical field of seepage simulation and parameter inversion analysis of geotechnical engineering. Comprising the following steps: deriving an earth and rockfill dam unstable unsaturated seepage control differential equation based on a Darwest unsaturated seepage theory, solving the equation based on a finite element method, and developing a software platform HYDRO-GEO / S based on a Matlab programming language; establishing an earth and rockfill dam three-dimensional seepage finite element model; performing three-dimensional seepage calculation and seepage safety analysis on the earth and rockfill dam; and constructing a parameter inversion framework to obtain the hydraulic parameters of the material. According to the method, an unsaturated seepage theory and a finite element method are coupled, an ICSSA-RBF inversion framework is constructed, an HYDRO-GEO / S software platform is developed, and in combination with instance analysis, accurate simulation and safety evaluation of earth and rockfill dam seepage and efficient inversion of seepage parameters are achieved.

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

The invention relates to the technical field of electric energy quality management of an electric power system, and discloses an industrial power grid voltage fluctuation suppression method and system, and the method comprises the steps: firstly building a system-level voltage and current forward transmission model composed of a power unit, a filter circuit and power grid impedance which are cascaded; reversely constructing a feedforward controller based on a voltage and current forward transfer model, and generating a main compensation voltage instruction according to the power grid disturbance quantity detected in real time; state parameters of each power unit are obtained, a differential equation is constructed, and a comprehensive real-time health degree index is obtained through fuzzy reasoning; dynamically correcting an equivalent impedance parameter in the forward transfer model by using the index, and calculating a voltage distribution weight of each unit; and decomposing the total compensation voltage instruction into each unit modulation signal according to the weight. Through systematic modeling, intelligent state evaluation and dynamic fault-tolerant distribution, high-speed, accurate and high-reliability suppression of the voltage fluctuation of the industrial power grid is realized.

The invention relates to the technical field of charging pile demand prediction, in particular to a charging pile demand prediction method based on frequency domain multi-head space-time attention and fluctuation propagation, and the method comprises the steps: collecting charging pile demand space-time sequence data; frequency domain conversion is carried out on the actual charging pile demand space-time sequence data through fast Fourier transform; according to a multi-head space-time attention mechanism, performing dynamic fusion on output of multi-head attention; constructing a fluctuation propagation layer based on a layered high-order differential equation, a multi-head fusion output matrix and a random turbulence model; constructing a multi-task loss function; constructing a multi-level spatio-temporal representation learning framework; and training the multi-level space-time representation learning framework according to the to-be-tested charging pile demand space-time sequence data set and the multi-task loss function, and outputting a charging pile demand prediction model. According to the method, complex features in the space-time sequence data can be fully mined, the features are enhanced by using the frequency domain information, and the accuracy of a prediction result is remarkably improved.

The invention relates to a wireless power transmission system parameter identification method based on a time domain differential model, and belongs to the technical field of wireless power transmission system control. The method comprises the following steps: establishing a parameterized time domain dynamic physical model; collecting system dynamic response data; constructing an optimization target based on a time domain residual error; and performing optimal parameter estimation based on iterative optimization. According to the method, a time domain dynamic physical model of a wireless power transmission system is constructed, and unknown parameters are solved through an iterative optimization algorithm with a minimum waveform matching error as a target. According to the algorithm, in an iteration process, a physical model is called to carry out forward differential equation solution, a time domain residual error between a model solution current and a real measurement current is used to drive a parameter estimation value to converge, and finally high-precision and high-robustness wireless power transmission system parameter identification under any working frequency is realized. The problem that in the prior art, due to static stiffness and sensitivity to waveform distortion of a theoretical model, the identification precision is insufficient is effectively solved.

The invention belongs to the field of stability analysis of a grid-connected system of a grid-building converter, and particularly relates to a grid-building converter grid-connected resonance risk analysis method considering virtual impedance, which comprises the following steps: S1, establishing a three-phase time domain differential equation and simplifying the equation into a single-phase equivalent time domain model; converting into a harmonic state space model of the main circuit; s2, constructing a harmonic state space model of a control system comprising an active power control loop and a reactive power control loop; s3, introducing a virtual impedance control link, and correcting the voltage amplitude reference phasor output by the reactive power control loop; deriving an expression of a modulation signal disturbance phasor; s4, obtaining a complete frequency domain admittance model YGSC of the network construction converter considering the influence of the virtual impedance; an open loop gain TGFM of the grid converter grid-connected system is constructed; and S5, drawing a Nyquist curve based on TGFM, and carrying out resonance risk determination. According to the method, the interaction dynamic characteristics of the network building converter and the power grid under the influence of the virtual impedance can be accurately represented, and the resonance risk of the grid-connected system is evaluated.

The invention discloses a gold mine resource potential prediction method fusing a geological knowledge graph and a large model, and belongs to the technical field of mineral resource prediction, and the method comprises the steps: aggregating geological exploration data streams, constructing a space-time event knowledge graph, driving a pre-trained large model to carry out continuous tensor projection, and carrying out prediction. Encoding the metallogenic mechanism into a gauge tensor to construct a geological semantic Riemannian manifold; constructing a neural differential equation set representing metallogenic dynamics on the manifold; by taking observation data as a boundary, calculating an energy dissipation minimum value path through variational solution and geodesic line optimization; locking and representing a negentropy flow accumulation area of the ore body through phase-space reconstruction and persistence coherence test; and holographic mapping is carried out on the accumulation area to form a materialized prospecting target area, and a dynamic causal transcription report is generated. According to the method, a generative dynamic inversion normal form based on a physical mechanism is adopted, metallogenic prediction is reconstructed into solving of an evolution problem of a differential equation in a manifold space, the space-time inevitability of a metallogenic system can be replayed from static observation data, and a prospecting target region with causal interpretation is generated.

The invention provides a real-time prediction method and system for milling deformation of a thin-wall part and electronic equipment. The method comprises the steps that a partial differential equation set used for reflecting the deflection and stress coupling relation in the milling machining process of the thin-walled workpiece is constructed based on a von Karman control equation set; constructing a deformation prediction model based on a depth operator network; according to the partial differential equation set, constructing a loss function comprising a partial differential equation residual term and a boundary condition term; training the deformation prediction model according to the loss function and two-dimensional coordinate points and load function sampling points used for training the deformation prediction model; and deploying the trained model in a machine tool system, inputting real-time load data and real-time coordinate data of the thin-wall part in the milling process, predicting deformation response of the thin-wall part in the milling process, and obtaining a target prediction result. The method can quickly and accurately predict the deformation response of the thin-wall part under the complex load working condition, and improves the machining quality and efficiency of the thin-wall part.

The invention discloses an image return method and device of an endoscope, a storage medium and a terminal. The method comprises the following steps: acquiring an initial image and a triaxial angular velocity signal of the endoscope; according to the triaxial angular velocity signal, attitude calculation is carried out by using a quaternion differential equation to obtain an attitude quaternion, and the attitude quaternion is updated in real time; performing interpolation on the attitude quaternions at adjacent moments within one frame of exposure time to obtain a row-level quaternion and generate a row-level rotation matrix; and based on the row-level rotation matrix, performing return and compensation on the initial image by using reverse mapping and bilinear interpolation to obtain a return image. The problems of universal joint deadlock and function failure caused by the universal joint deadlock in Euler angle attitude calculation are avoided, line-by-line compensation is carried out through an interpolation algorithm, the jelly effect is eliminated, the problems of large delay, insufficient real-time performance and difficulty in integration in a limited space in post-processing of a traditional software algorithm or an external micro-control unit are avoided, and the method is suitable for being applied to the field of Euler angle attitude calculation. And the imaging requirement of clinical operation of the medical endoscope can be better met.

The invention discloses an engine state estimation and system modeling correction method based on double-layer variational inference, which relates to the field of engine state estimation and comprises a variational inference stage aiming at component performance states and kinetic model parameters; a system output prediction stage based on an observation equation; and a solving stage of performing objective function optimization through an evidence lower bound. The structure clearly presents information flow and key calculation links of the proposed algorithm in state estimation and model learning. According to the method, combined reasoning of state variables and model parameters is achieved by building a probability modeling structure, the modeling problem when system dynamics is partially or completely unknown is solved by combining a modeling method of a stochastic differential equation, and while the state variables and the model parameters are optimized, the modeling efficiency is improved. Precise inference of component states and reliable identification of fault features are achieved, the fault detection accuracy of sudden gas circuit abnormity reaches the standard, and meanwhile the performance is better in the aspect of tracking long-term performance degradation.

The invention discloses a concrete dam modal parameter automatic identification method, which belongs to the technical field of water conservancy project structure health monitoring, and comprises the following steps: obtaining monitoring data of a target concrete dam under environmental excitation, constructing and training a singular value decomposition neural network, decomposing a response matrix formed by the monitoring data, and calculating the modal parameter of the target concrete dam; the modal order of the system is automatically determined through singular values obtained through decomposition; constructing and training a blind source separation neural network according to the determined modal order, separating the multi-channel monitoring data into independent single-degree-of-freedom modal response signals, and extracting a modal shape matrix from network weights; and constructing a parameterized vibration equation neural network for the separated modal response of each order, and identifying the inherent frequency and the damping ratio of the modal of the order by taking a single-degree-of-freedom system vibration differential equation as a physical constraint training network. The technical problems that in the prior art, the modal recognition process is low in automation degree, depends on artificial experience and is not high in precision are solved.

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

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

The invention provides a mineral resource overburden risk assessment method and system, and the method comprises the steps: obtaining multi-source heterogeneous geological data, carrying out the deep fusion of the multi-source heterogeneous geological data through employing a game theory gradient control robust neural network training method, and obtaining a fusion data set; performing deep mining and reasoning on high-dimensional geological data by utilizing the pattern recognition and correlation analysis capability of a large model and combining a manifold perception regularization technology, and recognizing geological structure features; establishing a dynamic safety mining depth evaluation model by using a fractional peak differential equation neural network with efficient accompanying parameter training, and calculating safety mining depth intervals and risk probabilities under different confidence degrees by integrating geological structure risks, mining disturbance effects and engineering safety thresholds; and constructing an assessment report generation module including core risk point analysis, key technology demonstration and prospective risk early warning, and generating an intelligent overburden risk assessment report. According to the invention, the accuracy and reliability of the pressing and covering risk assessment can be improved.

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

The invention is suitable for the technical field of fault distance measurement, and provides a power distribution network fault distance measurement method and system based on fault point voltage transient waveform comparison, and the method comprises the steps: collecting three-phase voltage and current signals at two ends of a line when a power distribution network fault occurs, and extracting a transient component signal in a preset frequency range; performing phase-mode transformation decoupling processing on the transient component signal; constructing a transient voltage differential equation from the two ends of the line to a hypothetical fault point, dynamically adjusting the fault position hypothetical point in a preset distance measurement interval by adopting a dichotomy, and calculating a transient voltage calculation waveform from the two ends of the line to the fault position hypothetical point; performing time domain similarity analysis on the transient voltage calculation waveform, and calculating a waveform consistency index; judging whether a preset convergence condition is met or not; if yes, determining that the current fault position hypothesis point is a target fault point; otherwise, adjusting the dichotomy interval according to the waveform comparison difference direction, and carrying out the next round of iterative calculation until the convergence condition is met, thereby effectively improving the fault distance measurement accuracy.

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

The invention belongs to the cross technical field of artificial intelligence, satellite remote sensing and insurance science and technology, and particularly relates to a generative AI remote sensing image disaster dynamic monitoring system, equipment and application. Generating a synthetic data set with a physical label by integrating the hydrodynamic model and the building structure dynamic model; a three-source space-time alignment module is used for aligning satellite images, unmanned aerial vehicle video streams and meteorological station data, after three-source asynchronous input data are synchronized, a disaster recognition result of semantic segmentation is output through a wave band self-adaptive attention diffusion model, and then a disaster evolution model of a continuous time domain is constructed through a neural differential equation deduction engine. Outputting disaster diffusion paths and intensity thermodynamic diagrams in future T hours; and automatically generating a disaster damage interval valuation report in combination with OpenStreetMap building semantic information and a regional economic density map. According to the invention, a closed-loop system of physical synthesis, dynamic deduction, insurance pricing and temperature drift correction is constructed, and a monitoring and actuarial integrated solution is formed.