196 results about "Numerical stability" patented technology

The invention discloses a landslide image instance segmentation method based on a dual adaptation mechanism, relates to the technical field of image processing, and ensures that a model can have a better effect and numerical stability on data from different sources through a complete process from multi-source data collection to data normalization processing. The designed model is based on a multi-scale state alignment mechanism, in the forward process of the model, exponential moving average fusion is carried out on feature information in the same level, transmission fusion is carried out on feature information of different levels, feature robustness is enhanced, and error accumulation caused by local deviation is reduced. A meta-context incremental learning mechanism is designed, and input data are dynamically converted into a series of key value vector sequences. In the reasoning process of the model, data distribution different from a training domain is dynamically recognized, a gradient descent process is implicitly executed according to the characteristics of data, and model parameters are finely adjusted, so that the characterization capability is greatly improved, and efficient and robust instance segmentation is realized.

On the basis of tight coupling of the SINS and the USBL, a dual-hydrophone differential model is provided, constant errors in USBL measurement are eliminated, and the influence of slant range noise on navigation precision is relieved; a measurement equation of a dual-hydrophone differential model is established, so that the precision and reliability of the integrated navigation system are improved; a robust iterative Kalman filtering algorithm based on approximate least-square estimation is introduced, measurement dimensions are comprehensively evaluated, measurement noise covariance is dynamically adjusted, and the numerical stability and robustness of the algorithm are improved. According to the method, whether the measured data at the current moment is an abnormal value or not is judged according to previous data, the robust weight is reasonably distributed to the measured data, the adverse effects of subsequent steps such as state estimation are reduced through a method of reducing the weight of the abnormal value, and normalization operation is carried out on the measured residual error according to the adverse effects, so that the residual error distribution is more regular, and the accuracy is improved. And the updating result of the measurement noise covariance is further optimized, so that the precision of the whole Kalman filtering algorithm is improved.

The invention relates to an overlapped chromatographic peak positioning and splitting method and system based on a numerical stable function. The method comprises the following steps: de-noising chromatographic data and correcting a baseline; calculating a second derivative by using SG filtering, grouping continuous points according to a negative threshold value, and determining an initial position of each peak by using a second-order minimum value; establishing a peak function of bidirectional exponential correction by taking a peak position as an initial value, setting parameters and boundaries such as area, center, width, trailing and the like, constructing a residual error minimization target, and performing robust fitting by adopting a trust region reflection algorithm; and indexes such as suitability, output peak area, center, broadening, separation degree, theoretical plate number and the like are verified by residual errors. According to the method, valley point or prior component information is not needed, the method is insensitive to noise and baseline drift, serious overlapping and trailing / leading edge peaks of multiple components can be effectively processed, convergence is high, the method is insensitive to initial values, parameters can be expanded to different peak shapes, and the method is suitable for qualitative and quantitative analysis of automatic and high-flux chromatographic data.

The invention discloses a multi-view image Gaussian reconstruction method and system guided by LiDAR point cloud. The method comprises the following steps: step S1, realizing accurate registration of LiDAR point cloud and a multi-view image in the same earth-centered earth-fixed coordinate system through GNSS constrained aerial triangulation, and uniformly carrying out center-of-gravity normalization to ensure space consistency and numerical stability; s2, estimating surface normal and scale parameters based on the local geometric features of the LiDAR point cloud, constructing a Gaussian ellipsoid consistent with the local geometry, and realizing Gaussian ellipsoid initialization of geometric perception; and S3, generating a sub Gaussian ellipsoid according to the Gaussian gradient and the tangent plane direction, and improving the modeling density and geometric continuity of the detail region through local encryption and parameter inheritance. According to the method, the fusion of LiDAR point cloud high-precision geometry and multi-view image texture information is realized, the geometric precision, the structural integrity and the detail expression capability of three-dimensional reconstruction are effectively improved, and the method is particularly suitable for high-precision live-action three-dimensional modeling of unmanned aerial vehicle multi-view images.

The invention relates to the field of aero-engine complete machine full-three-dimensional numerical simulation, and discloses an aero-engine complete machine full-three-dimensional simulation initial field presetting method and system, and the method comprises the steps: obtaining initial flow field boundaries of different parts from the zero-dimensional level of a complete machine by solving inlet section parameters and outlet section parameters of each part; then obtaining an initial field of each blade row in a turbine part, which has the most critical influence on the convergence of an engine, through a quasi-three-dimensional layer S2, and realizing initial field decomposition from the part to the part; and finally, by adopting a radial basis function interpolation method, mapping the zero-dimensional data and the S2 quasi-three-dimensional parameters to each grid unit of the complete machine full-three-dimensional model of the aero-engine, and then smoothly splicing the initialized flow fields of all the components at the interface to form a continuous complete machine three-dimensional simulation initial field under the examination working condition. The convergence of a whole machine simulation example, the simulation convergence speed and the numerical stability are greatly improved, the simulation trial and error iteration is reduced, the pretreatment time consumption is reduced, and the simulation speed and efficiency are improved.

The invention discloses a hydropower station reservoir runoff intelligent forecasting method based on a large language model, and belongs to the technical field of hydrological forecasting, and the method comprises the following steps: S1, data collection and preprocessing; s2, constructing a BERT-Hydrology deep learning model for the runoff forecast of the reservoir of the hydropower station; s3, designing a weighted mixed loss function, and introducing an optimizer, learning rate scheduling and an early stop mechanism to improve the convergence speed and numerical stability of model training; and S4, establishing an evaluation system, performing rolling forecasting, and outputting an evaluation index and a significance test result. According to the intelligent hydropower station reservoir runoff forecasting method based on the large language model, the accuracy and the stability of runoff forecasting are remarkably improved by constructing the runoff forecasting model, excavating the hidden rule in the hydrological sequence and establishing the multi-step forecasting mapping, and the forecasting accuracy and the forecasting stability are improved. And technical support is provided for intelligent scheduling of a hydropower station reservoir and efficient management of water resources.

The invention discloses an industrial robot motion pose error calibration method based on constraint solution. The industrial robot motion pose error calibration method comprises the steps that S1, joint angles, tail end coordinates and temperature data of multiple measurement points are collected; s2, correcting the kinematic model by using the thermal elongation and constructing a parameter identification model; s3, constructing a mixed integer nonlinear constraint pool according to mechanical limitation; s4, introducing a semi-definite programming slack variable to convert the non-convex constraint into a linear matrix inequality; s5, combining a primal-dual interior point method and an alternating direction multiplier method to iteratively solve the optimal kinematics parameter correction; s6, introducing a square root information filtering improvement mechanism to correct a state vector square root factor, and calculating an updated nominal kinematics parameter; and S7, based on the condition number of the error Jacobian matrix, judging observability and repeatedly calibrating. According to the method, the problem of precision reduction caused by thermal errors is solved, and the identification precision, the numerical stability and the self-adaptive calibration capability of the kinematics parameters of the robot are improved.

The invention relates to the technical field of solid oxide fuel cells, and discloses a construction method of a solid oxide fuel cell multi-physical field bidirectional strong coupling simulation model and a computer program product. The method comprises the following steps: respectively constructing an electrochemical model, a positive / negative electrode fluid flow model and a heat transfer model of the solid oxide fuel cell based on an established geometric structure model; solving equations of the physical fields in all the models are combined to form a full coupling matrix, and iterative solving is carried out according to a set initial value; and judging whether the constructed model is converged or not according to an iterative solution result. According to the model constructed by the method, the mutual influence among electrochemistry, flow and heat transfer processes is fully considered, and synchronous solving is performed through a unified matrix system, so that real-time interaction and collaborative updating among multiple physical fields are realized, and the convergence efficiency and numerical stability of a nonlinear coupling problem are remarkably improved; through inspection, the deviation between a simulation result and actually measured data is less than 8%, and the method has high engineering practical value.

The invention relates to the technical field of electromagnetic transient simulation, in particular to an electromagnetic transient simulation decoupling method, system and device based on a single-impedance branch and a medium, and the method comprises the steps: dividing a system to be simulated into two subsystems which are connected through the single-impedance branch, and carrying out the equivalent reconstruction to form a reconstructed branch model; establishing a discrete state space equation based on the reconstructed branch model; according to the main step length, the number of sub-steps meeting the stability condition is determined based on a small-step synthesis algorithm, and the main step length is equally divided into differential step lengths; in each main step length interval, performing iterative calculation on the state-space equation by using a differential step length to obtain a branch current and a capacitor voltage at the next moment; and injecting the calculated branch current serving as a known current source into an original network equation, solving in parallel to obtain the port voltage of the subsystem, and completing decoupling calculation and promoting simulation. According to the method, effective decoupling of the universal single-impedance branch can be realized, and the applicability, the numerical stability and the calculation efficiency of parallel simulation are improved.

The invention belongs to the technical field of electric vehicle charging load prediction, and discloses a method for predicting the load of an electric vehicle charging station on a highway, and the method can accurately sense the sudden change trend of charging demands during holidays and festivals through introducing holiday category codes and a model, and improves the prediction precision during the peak period of holidays and festivals. A multi-granularity frequency-time sequence fusion module is adopted, long-term periodicity and short-time disturbance characteristics are effectively integrated, and the modeling capacity of the model for a non-stationary load sequence is enhanced; the designed dynamic Tanh normalization module has an adaptive learning capability, and can improve the numerical stability and nonlinear expression capability of the model in a severe load fluctuation scene; the parallel TCN-MLP structure realizes collaborative modeling of nonlinear interaction and time dependency relationship between variables, and enhances the multivariable prediction performance. The method is accurate in prediction and good in stability, has expandability and engineering landing potential, and can provide efficient and reliable technical support for intelligent scheduling, power resource configuration and holiday and festival energy management.

The invention relates to the technical field of marine environment numerical simulation, in particular to a near-shore typhoon wave refined simulation method based on terrain adaptive grids and parameter dynamic correction, and the method comprises the steps: obtaining basic data and simulation parameters of a target sea area, constructing a computational domain and an initial grid, and constructing a target sea area; the initial grid is an unstructured grid fused with multiple physical constraint factors; performing optimization processing on the initial grid based on a numerical stability criterion to obtain a final calculation grid meeting a calculation convergence requirement; establishing a bidirectional coupling model, and applying the final computational grid to the bidirectional coupling model; carrying out localization iteration calibration on key physical parameters of the bidirectional coupling model to obtain an optimal parameter set; and carrying out typhoon wave process simulation calculation and constructing a typhoon wave feature database by utilizing the final calculation grid and the optimal parameter set, and realizing high-precision simulation and prediction of the typhoon wave process by constructing an adaptive optimization grid, a bidirectional coupling model and a parameter localization calibration mechanism.

The application discloses a lightweight target detection method for unmanned aerial vehicle (UAV) inspection of insulators, first proposes a rotating rectangular frame labeling method for aerial insulator images, and labels the insulator images; then proposes an improved GhostConv module based on a YOLOv5 model, greatly reduces network parameters of the model through ghost convolution, and enhances numerical stability of the model through a Hardswish function; proposes an improved C3Ghost module, reduces parameter quantity and floating point calculation quantity of the model through lightweight network design; introduces an SE attention mechanism between a feature extraction module and a feature fusion module, and suppresses attention degree of a feature channel that is unimportant to a current task through weight distribution; introduces a CSL to replace regression by classification to avoid loss sudden increase problems caused by boundary problems during rotation angle loss calculation; and proposes a high-precision rotating frame regression loss function R-EIoU Loss to accelerate the training process of the model and improve detection precision of the model.

The application discloses a power distribution network prediction auxiliary state estimation method and system based on an improved iteration process, and the method comprises the following steps: establishing a nonlinear discrete time state space model; generating a group of Sigma points through an unscented transformation, and substituting the Sigma points into a state transition equation to calculate a state prediction value and a state prediction error covariance matrix at a current time; substituting the Sigma points into a measurement equation to obtain a measurement prediction value, and calculating a Kalman gain; calculating a process noise covariance matrix at a next time; and correcting the state prediction value and the state prediction error covariance matrix at the current time to obtain an optimal state estimation value and an error covariance matrix thereof. The application avoids the risk of losing semi-positive definiteness of the covariance matrix in the iteration process from the algorithm mechanism, significantly enhances the numerical stability and robustness of the UKF algorithm, and ensures reliable application in high-dimensional complex power distribution network state estimation.

This invention relates to the field of fluid machinery and valve engineering, and discloses a computational fluid dynamics (CFD) steady-state simulation method based on the adjustable opening of a V-type ball valve. This method establishes a three-dimensional geometric model including the valve body, valve sleeve, and V-type ball valve core, defining the cone angle of the V-type ball valve core as a function of the valve opening to achieve a continuously adjustable description of the valve opening. By meshing the internal fluid computational domain of the valve and locally refining the mesh at the valve core leading edge, the valve core-seat gap, and the boundary layer region, and combining the set initial and boundary conditions, the continuity equation and momentum conservation equation are solved using computational fluid dynamics methods to obtain the steady-state flow characteristics of the V-type ball valve at different openings. This invention describes the valve opening change in a functional form, improving the numerical stability of steady-state simulation under small opening conditions, while also improving the accuracy of flow characteristic calculations under large opening conditions, making it suitable for multi-condition parametric analysis of V-type ball valves.

The application discloses an online semantic momentum stabilization system and method using a Welford algorithm and an optimistic concurrent control. In view of the semantic centroid calculation deviation and timing conflict problems caused by data noise, distribution drift and distributed out-of-order writing in the process of continuous incremental input of a large-scale vector database, the application combines a Welford online iterative algorithm to realize high-precision incremental updating of the semantic centroid mean and variance under constant space complexity. Meanwhile, a distributed optimistic concurrent control mechanism is used to implement version revision number checking on semantic segments and block obsolete data coverage caused by cross-node out-of-order arrival. The application guarantees the numerical stability and timing consistency of semantic feature expression in a dynamic growing data environment.

The invention relates to a stable Q compensation reverse time migration method for seismic exploration data, a medium and equipment, and belongs to the technical field of geophysical exploration data processing. According to the method, based on a fractional order Laplace viscous acoustic wave equation, a physically guided time-varying regularization compensation operator is constructed in a wavenumber domain, the energy amplitude of a previous time step length is introduced as a reference, an energy growth monitoring mechanism is established, and the energy growth is monitored. And designing a three-level adaptive constraint system: local energy growth constraint, high-frequency adaptive attenuation and boundary region stabilization processing, and realizing adjustment of excessive compensation and adaptive suppression of noise in the wave field continuation process. According to the method, amplitude attenuation and phase frequency dispersion caused by stratum absorption are effectively recovered, meanwhile, the stability of numerical calculation can be guaranteed, the precision and resolution of deep structure imaging are remarkably improved, and good numerical stability and anti-noise capacity are achieved.

The application discloses a kind of full-rotation ship decomposed type alternate optimization thrust distribution method, belongs to ship propeller control technical field.This method will full-rotation ship thrust distribution problem be divided into thrust amplitude and azimuth alternate optimization sub-problems, by constructing the augmented Lagrangian function with box constraint, in combination with gradient descent and backtracking line search strategy, give the explicit solution of optimal thrust and azimuth, realize propeller thrust and azimuth collaborative optimization.This method can unify the processing singularity of geometric matrix, thrust and azimuth size, change rate, energy consumption, accuracy and other constraint requirements, with low computational complexity, high efficiency, strong real-time and numerical stability, avoid the mutation phenomenon of traditional algorithm under boundary constraint, suitable for the high-frequency control demand of intelligent tugboat and other full-rotation propelling ships, with good engineering adaptability and popularization value.

The invention relates to a stochastic dynamics system energy prediction method and device, electronic equipment and a medium. The method comprises the following steps: setting a displacement variable of the stochastic dynamics system about time, and establishing an energy function of the stochastic dynamics system according to the displacement variable; constructing a multi-scale data set according to the energy function; the initial model is trained through the multi-scale data set, and a feature extraction model is obtained after training is completed; the feature extraction model is used for extracting dynamic features of the stochastic dynamic system; the dynamic characteristics at least comprise an energy predicted value at the current moment; constructing a drift term according to the drift network and the dynamic characteristics, and constructing a diffusion term according to the diffusion network and the dynamic characteristics; and calculating an energy predicted value of the next moment based on the energy predicted value of the current moment, the drift term and the diffusion term. According to the method, based on the improved discretization incremental energy prediction algorithm, the discretization scheme conforming to the physical law is adopted to improve the energy prediction numerical stability and prediction precision.

The invention belongs to the technical field of surface wave frequency dispersion inversion, and particularly provides a multi-mode high-precision surface wave frequency dispersion inversion method based on shallow optimization, and the method specifically comprises the steps: 1, building a geophysical model; establishing a geophysical model containing three layers of media; step 2, forward modeling calculation; an improved global matrix method is adopted to solve a Rayleigh wave frequency dispersion equation; step 3, self-adaptive phase velocity searching; the search range is dynamically adjusted to be Vmin-Vmax according to the frequency V; 4, a shallow optimization objective function is constructed; and 5, performing simulated annealing global optimization. According to the method, the shallow layer parameter precision can be remarkably improved, the thickness error of the first layer is reduced from 5-8% to 1-2%, and the speed error of the first layer is reduced from 6-10% to 1-3%; the calculation efficiency is greatly improved, the numerical stability is enhanced, the multi-mode data is fully utilized, and the result display is complete and clear.

The invention discloses an electric power simulation adaptability test processing method for a new energy unit, and relates to the technical field of simulation testing, the method comprises the following steps: obtaining PLL bandwidth and controller time constant upper and lower limits, and generating a plurality of parameter combinations; applying each combination to a model, simulating under the disturbance of a preset power system, and collecting dynamic response and numerical stability information for preliminary screening to obtain candidate parameter combinations; the method comprises the following steps: selecting a candidate combination, collecting simulation state information of the candidate combination, calculating a priority selection index, and selecting a group with the maximum index as a final optimization parameter. The optimal PLL bandwidth and controller time constant can be found to balance the dynamic response speed and stability of the electric power simulation model in the electric power system environment, and reasonable global balance is formed between the PLL bandwidth and the controller time constant, so that the applicability of the final electric power simulation model does not deviate to a certain side; and the reliability of behavior prediction of the new energy unit in the power system is improved.

The invention provides a multi-model state estimation method and system based on a semantic constraint graph and hovering spectrum perception, belongs to the technical field of unmanned aerial vehicle navigation and target tracking, and aims to solve the problems that hovering jitter is difficult to model and the numerical stability is insufficient in existing unmanned aerial vehicle state estimation.The estimation method comprises the steps that S1, state estimation parameters are obtained; s2, establishing a semantic constraint graph; s3, calculating a transition probability; s4, operating the spectrum sensing hovering model, the path cruising model and the fast maneuvering model in parallel; s5, updating the mode probability; and S6, performing square root fusion on the state estimation of each model, and then outputting the state estimation. The system comprises a semantic constraint and transition probability calculation module, a square root filtering module and a probability updating and fusing module. According to the method, transfer is driven by a semantic constraint graph in an IMM framework, jitter is described by a spectrum sensing hovering model, and numerical stability is guaranteed by a square root unscented Kalman filter.

The invention discloses a human resource information processing system and method, and relates to the technical field of human resource management information processing.A prototype measurement mechanism is introduced into an embedding space, a limited number of labeled samples are aggregated into stable category center representation, dependence on large-scale historical data is reduced, and the accuracy of human resource information processing is improved. Representative high-loss risk and low-loss risk category prototypes can also be formed in sample scarcity scenes such as medium and small enterprises, new business departments or new posts; characteristic embedding is carried out through cooperation of a pre-trained neural network model and transfer learning, the method effectively inherits the general characterization capability in large-scale human resource data, and then a prototype calculation module and two-norm-based normalization processing are superposed, so that feature vectors of different employees or candidates are more uniform in the numerical scale, and the accuracy of the human resource data is improved. The interference of the abnormal sample modulus length difference on the subsequent distance measurement is obviously weakened, so that the numerical stability during the similarity calculation in the high-dimensional embedding space is improved.

The invention discloses a sliding mode observation method and a sliding mode observation system for permanent magnet synchronous motor non-inductive control, which are used for improving numerical stability and observation precision of a discrete system and realizing permanent magnet synchronous motor non-inductive closed-loop control with good robustness and stability. The method comprises the following steps: discretizing a current state equation according to a sampling period to obtain an initial discrete sliding mode observation equation; determining a proportional adjustment coefficient k based on an inductance parameter and a sampling period of the motor, performing weighted adjustment on a voltage related item and a sliding-mode control quantity item in the initial discrete sliding-mode observation equation by using the proportional adjustment coefficient k, and constructing an improved sliding-mode observation equation; extended counter electromotive force is extracted based on the output of the sliding mode control law, wherein the contribution proportion of the high-frequency buffeting component output by the sliding mode control law in the extended counter electromotive force is weakened through a proportion adjustment coefficient; and inputting the extended back electromotive force into a phase-locked loop module to calculate the rotor position and the rotating speed of the motor so as to realize closed-loop control of the motor.

The invention provides a black triangle scattering noise training sample generation method based on a random medium, and belongs to the technical field of geological exploration. Depicting transverse heterogeneity and longitudinal heterogeneity of the theoretical velocity model; according to a spectral element method numerical stability correlation theory, calculating a time step length and a space step length suitable for the constructed velocity model; calculating an inverse quality factor corresponding to the velocity model, and quantifying the attenuation effect of the complex near-surface on seismic waves; observing whether black triangular noise appears in the shot record under different parameter conditions, and calculating an inverse quality factor of each forward modeling result; the corresponding relation between the inverse quality factor and whether the inverse quality factor is a black triangle or not is arranged, and the generated samples are subjected to label division through the inverse quality factor in combination with the attenuation theory of seismic waves in a random medium. The method is more suitable for actual description and higher-precision numerical calculation, so that a'black triangle 'generation mechanism is researched, and a synthetic training set is not provided by deep learning denoising.

The invention relates to the technical field of carbon dioxide capture, in particular to a carbon capture structured filler design and optimization method based on fluid topological optimization, which comprises the following steps: constructing a carbon capture reaction design domain geometric unit, taking a filler basic unit as a core, and setting a fluid inlet and outlet extension section, distinguishing a fluid domain, a solid domain and a transition state between the fluid domain and the solid domain through a continuous material density field, and defining boundary conditions such as a gravitational field matched with an actual absorption tower and adiabatic slippage-free boundary conditions; establishing a polynomial modified topological optimization multi-physics field coupling equation set, introducing a polynomial improved Darcy interpolation function to optimize fluid-solid interface characteristics, and meanwhile, improving numerical stability by combining a filtering method and a projection method; on the basis of the modeling solving platform, a global convergence moving asymptote method is adopted for iterative solving, and a carbon capture filler structure with a fine and complex flow channel is generated; the problems that traditional filler design depends on experience, the design freedom degree is low, and the optimization efficiency is limited can be solved.

The invention discloses a multi-industrial equipment-oriented ELM fault identification method fusing feature attention and manual LU decomposition, and belongs to the technical field of industrial equipment fault diagnosis. The core of the method is that a feature attention mechanism and a manual LU decomposition module are fused: firstly, the feature contribution degree is quantified through a channel attention mechanism, key fault features are automatically focused, weak correlation feature interference is inhibited, and the quality of a hidden layer output matrix is improved; secondly, the manual LU decomposition module realizes autonomous controllability and numerical stability of matrix inverse operation through row principal component selection and regularization optimization, and is separated from the dependence of a third-party math library. According to the method, the accuracy of equipment fault identification is remarkably improved, the omission ratio and the false alarm rate are effectively reduced, memory limitation of edge equipment is overcome, and an efficient and reliable real-time identification scheme is provided for bearing abrasion and gearbox abnormity of a wind power variable pitch system and faults of other industrial equipment such as a machine tool and an industrial motor.

The invention relates to the technical field of computers, and discloses an endocrine metabolism index real-time processing method based on edge calculation. The method comprises the following steps: acquiring time sequence data of blood glucose, insulin, cortisol and thyroid stimulating hormone collected by wearable equipment; constructing a lightweight linear state space model of the diagonal dominant sparse state transition matrix; initializing a noise covariance matrix; iterative Kalman filtering based on a sparse matrix inversion algorithm is executed; the observation noise covariance is dynamically adjusted according to the residual error; and when the filtering result exceeds a clinical critical value, triggering local early warning and uploading to the cloud. According to the scheme, the single filtering delay is controlled within 80 milliseconds while the numerical stability is guaranteed, and the early warning timeliness and the system energy efficiency are improved.

The invention discloses an axial strain rate field simulation method of a distributed optical fiber DAS-VSP based on strain tensor projection under a non-vertical well layout condition, and belongs to the field of exploration. Aiming at the problem that a traditional vertical well simulation method is difficult to accurately simulate wave field propagation characteristics in a curved well trajectory, high-precision wave field simulation under a non-vertical well type condition is realized by establishing a parameterized well trajectory model based on a curvilinear function and combining a first-order speed-stress elastic wave equation and a finite difference algorithm. A tangential direction projection algorithm is introduced to convert a velocity field into optical fiber axial strain rate response, and a dynamic scale distance segmentation method is adopted to truly simulate the spatial sampling characteristics of an actual DAS system. By constructing PML absorption boundary conditions and a stability control mechanism, the numerical stability of highly-deviated well section wave field simulation is ensured. According to the method, a reliable numerical experiment method is provided for DAS-VSP data interpretation and forward modeling under the non-straight well type condition, and important technical support is provided for non-straight well optical fiber earthquake monitoring in oil-gas exploration.

The application discloses a ground TT&C station fault diagnosis system and relates to the technical field of space TT&C.The application aims at the problems that the existing fault diagnosis of the ground TT&C station under the environment of low signal-to-noise ratio, multi-node concurrency and strong interference depends on fixed threshold, is difficult to depict the space-time correlation of faults and is highly sensitive to the quality of spectral data, etc., and the application is characterized in that: a plurality of monitoring branches are arranged before and after a plurality of key devices of a link, and multi-modal data such as spectrum, temperature, voltage and environmental parameters are collected, so that the diagnosis system no longer depends on single spectral information, and other observation quantities can be used to maintain the discrimination of the device state when the signal is seriously attenuated or interrupted for a short time; in cooperation with a data preprocessing module, the missing value compensation and targeted normalization or interval scaling are performed on the data from different sources, so that the characteristics of the same node at each time point are kept continuous and dimensionally unified, and the numerical stability of the training and reasoning process of the graph attention network is significantly improved.

The invention relates to the technical field of carbon emission calculation, and discloses an electric power carbon emission factor calculation method, system, equipment, medium and product based on QR matrix factorization. According to the method, a node power balance equation and a carbon emission conservation equation are determined by obtaining carbon emission information and tidal current power information of all generators in a power grid; the method comprises the following steps: constructing a carbon emission factor linear equation, solving the carbon emission factor linear equation based on QR matrix decomposition to obtain a carbon emission factor value of each node, so that efficient solving is realized by utilizing sparse matrix characteristics and QR decomposition, rapid response of multi-node carbon emission factor solving is realized, and the method does not depend on initial solution setting; the slow convergence or failure risk of an iteration method is avoided, the numerical stability is excellent, and the accuracy and reliability of carbon emission factor calculation are improved.