24 results about "Propagation matrix" patented technology

The invention discloses a robust robust sequence extraterrestrial celestial body terrain reference surface fitting method and system, and relates to the technical field of terrain reference surface fitting, and the method comprises the steps: collecting three-dimensional laser point cloud data to construct a reference plane geometric model, and building a joint error model of an observation value error and a design matrix error; adopting a sequence PEIV total least square framework to carry out grouping iteration adjustment on the point cloud data, and recursively updating parameter estimation by utilizing an error propagation matrix and a matrix inversion formula; introducing a two-factor robust mechanism, dynamically calculating a weight according to a residual error and estimating a robust scale; and circularly optimizing the parameters through an iterative strategy until a convergence condition is met, and outputting reference plane parameters. According to the method, different terrain complexity, data scales and error distribution scenes can be covered, the adaptability and engineering practicability of terrain datum plane fitting are remarkably improved, and reliable geometric reference is provided for subsequent obstacle detection and safe area decision making.

The invention discloses a water transfer project risk cascade propagation and risk assessment method and system based on a multi-level topological structure, and the method comprises the steps: obtaining a water transfer project topological network, and generating a node comprehensive risk vector of each node based on multi-source operation data; constructing a basic risk propagation matrix according to the physical connection relationship, and performing structured modulation on the basic risk propagation matrix based on the node comprehensive risk vector to generate a comprehensive propagation operator; performing cascade propagation iterative evolution on the node comprehensive risk vector based on the comprehensive propagation operator, and continuously calculating network dynamics stability characteristic parameters of the comprehensive propagation operator in the evolution process; when it is judged that the system is in a divergent phase state according to the parameters, a comprehensive propagation operator is reconstructed, iterative evolution continues till convergence, and steady-state risk distribution is obtained; and outputting a comprehensive risk assessment result of the system based on the steady-state risk distribution. The method can improve the operation safety management and control capability of the water regulation project under complex operation conditions and external disturbance conditions.

This invention belongs to the field of building energy consumption monitoring technology and provides an anomaly detection method based on ARIMA residuals. It utilizes an ARIMA model to extract the residual sequence after the linear trend of data, learns normal patterns using LSTM-AE, and identifies anomalies. Simultaneously, it combines correlation coefficient matrices and anomaly propagation matrices to analyze fault propagation paths, featuring high detection accuracy and strong interpretability. This invention improves anomaly detection accuracy and effectively reduces false alarm and false negative rates. It also effectively handles sparse data, making it suitable for scenarios such as building energy consumption monitoring. By constructing correlation coefficient matrices and anomaly propagation matrices, it analyzes the propagation paths and impact mechanisms of anomalies in multi-parameter systems, providing a reliable basis for anomaly tracing and system optimization. Through practical application, this invention helps enterprises promptly identify and resolve energy waste problems, save energy costs, optimize energy management systems, and improve energy utilization efficiency.

The invention discloses an electromagnetic pulse propagation calculation method based on a generalized propagation matrix and a transfer function method, and the method comprises the steps: determining the explosion point height and the receiving point height of an electromagnetic pulse, and constructing an ionosphere parameter on a propagation path; calculating a wave vector of the position of an electromagnetic pulse explosion point, and calculating a reflection coefficient matrix and a transmission coefficient matrix of the electromagnetic pulse which upwards penetrates through the ionized layer and downwards radiates to the ground through a generalized propagation matrix method; defining the type of the electromagnetic pulse, and calculating the amplitude of the superposed uplink and downlink electromagnetic waves at the explosion point position through the reflection coefficient matrix; in combination with a generalized propagation matrix method and a transfer function method, calculating the frequency spectrum and time domain waveform of the electromagnetic wave penetrating through the ionosphere at the explosion point position through inverse Fourier transform; time-frequency characteristic analysis is carried out according to the frequency spectrum and the time-domain waveform of the electromagnetic waves penetrating through the ionosphere, a time-frequency graph is obtained, the experiment cost is remarkably reduced, and efficient and low-cost theoretical support is provided for propagation of the electromagnetic pulses in the ionosphere.

The invention relates to a federal recommendation method based on a sharable space diagram and a hypergraph diffusion model. The federal recommendation method comprises the following steps: a client constructs a personalized hypergraph and a hypergraph propagation matrix by using local data; enhancing user characterization by means of a diffusion model and then performing multi-dimensional characterization fusion; then collaborating with global geographic knowledge provided by a server, and constructing a loss function to update a recommendation model; clustering the clients, and carrying out data self-adaptive aggregation and retraining on edge ends corresponding to each category after clustering; then the server performs weighted aggregation on the trained training parameter set on each edge end again; and repeatedly iterating for multiple rounds to obtain a final optimal client recommendation model. According to the method, the accuracy of federal POI recommendation can be remarkably improved on the premise of strictly protecting user data privacy.

The invention relates to a microservice system abnormal root cause positioning method based on a space-time diagram convolutional neural network and a thermal diffusion model, and the method mainly comprises the following steps: S1, collecting the performance index data of a service node and calling metadata through Prometheus, carrying out the anomaly detection through a sliding time window mechanism, and outputting an abnormal node and a time window; s2, constructing a node cascade propagation feature vector, and obtaining an abnormal time window service call directed weighted dependency graph in combination with a GAT mechanism; s3, constructing a space-time diagram convolutional neural network, extracting space-time features of node anomalies, and depicting an anomaly cascade propagation effect by adopting a thermal diffusion mechanism to obtain space-time diffusion features; and S4, calculating an abnormal score based on the node cascade propagation characteristics and the space-time diffusion characteristics, constructing a propagation matrix by adopting PageRank, and outputting an abnormal root cause node list. According to the method, the abnormal spatial-temporal characteristics and cascade propagation effects of the micro-service nodes are modeled, so that the root cause positioning accuracy in a complex service scene is effectively improved.

The invention relates to a contact network power supply high-voltage system insulation detection method, device and equipment and a medium. The method comprises the following steps: constructing a network topology model based on electrical connection data of a convergence area, and generating a topology matrix for describing structural characteristics; accurately simulating a test signal propagation path by using the matrix in combination with a time domain reflection method, and quantifying reflection and attenuation characteristics to form a signal propagation matrix; based on this, the insulation sensitivity of each component is calculated, and a high-priority monitoring area set is screened; iteratively generating a least observation point layout scheme meeting monitoring requirements through a coverage optimization algorithm; a virtual sensor is deployed, measurement data are dynamically processed by adopting a particle filtering algorithm, and the insulation degradation trend is tracked in real time; and finally, when an anomaly is detected, accurately determining a fault position through a triangulation positioning principle by using the time delay difference data of the multiple observation points. According to the technology, the fault positioning precision and the response speed in a dynamic environment are remarkably improved, so that the system resource utilization efficiency is optimized while the detection comprehensiveness is guaranteed.

This invention provides a method for optimizing and constructing a human-machine collaborative full-process quality management system, comprising: real-time collection of multi-source heterogeneous industrial data from each process node in the production process to construct a quality data resource pool for human-machine collaboration; constructing a multi-process quality deviation coupling and propagation model based on a deviation propagation matrix, and introducing an expert experience correction mechanism to form a human-machine collaborative deviation propagation model, so as to trace and quantitatively characterize the propagation of quality deviations in the entire process chain; constructing an industrial digital twin for full-process quality management, generating collaborative optimization decision schemes through quality risk quantitative assessment and human-machine two-way decision fusion, and implementing dynamic adaptive control of process parameters throughout the entire process; and constructing a full-process quality feedback mechanism and a continuous iterative optimization mechanism. This invention aims to solve the technical problems in the prior art where quality prediction models rely on training data, expert experience is difficult to quantify and integrate, and the full-process deviation propagation mechanism is missing.

The application discloses a method and device for determining a lithologic trap boundary, electronic equipment and a storage medium. The method comprises the following steps: simulating a pseudo-well vertical lithologic combination based on a continuous-time Markov chain model, determining a pseudo-well lithofacies curve of different lithologic combinations according to a simulation result; determining a target elastic parameter through the pseudo-well lithofacies curve of the different lithologic combinations based on elastic parameter Monte Carlo random simulation with lithofacies constraints; determining pseudo-well simulation seismic data of the different lithologic combinations through the target elastic parameter based on a wave equation forward modeling method of a propagation matrix; and determining a lithologic trap boundary according to the pseudo-well simulation seismic data of the different lithologic combinations. The technical scheme of the application avoids the problems of strong subjectivity, large deviation of boundaries delineated by different personnel, and insufficient objective basis in the determination of a traditional lithologic trap boundary, thereby avoiding the situation of deviation of the delineated boundary caused by human factors.

The application is suitable for the technical field of optical design parameter optimization, and provides a design parameter optimization method and system for a long optical path gas absorption cell, which comprises the following steps: based on a theoretical model of a light propagation matrix, theoretically verifying the feasibility of mirror parameters of a spherical mirror, determining initial mirror parameters and a key design parameter range; establishing an optical cavity model composed of two spherical mirrors, and performing automatic light ray tracing simulation according to the initial mirror parameters, the key design parameter range and a preset step value; verifying simulation data, determining the number of light spots, and selecting an optimal key design parameter combination for maximizing the optical path according to the number of light spots. The method is mainly used for long optical path gas absorption cell design in an infrared absorption spectrum trace gas sensor detection, has practical significance, realizes verification of optimal design parameters, reduces development difficulty and improves development efficiency.

The invention discloses a wafer graph defect knowledge graph evolution reasoning method based on structural causal intervention, and belongs to the technical field of semiconductor manufacturing defect analysis and knowledge reasoning. Constructing a three-layer directed weighted knowledge graph comprising preparation process nodes, crystal grain defect nodes and wafer graph defect nodes; calculating conditional probability edge weights according to historical statistical data to form a causal propagation matrix; a structural causal model is constructed, causal intervention is performed on the preparation process nodes, and a causal purification propagation matrix is generated; constructing a graph convolution evolution reasoning model based on the purification propagation matrix, and obtaining node embedding representation; introducing a time variable to carry out incremental updating on the propagation matrix to realize knowledge graph evolution; and finally outputting the prior probability distribution vector of the defect type of the wafer graph. According to the method, wafer graph defect prior distribution can be generated under the condition that new products are put into production or few samples exist, and knowledge constraints are provided for defect prediction and detection model generation.

The application provides a subway vibration Green function fast calculation method and system, relates to the subway vibration evaluation technical field, and includes the following steps: a near-field finite element model of a subway tunnel is constructed, and a plurality of infinite domain virtual vibration sources are arranged outside the near-field finite element model; a coupling equation between the near-field finite element model and the infinite domain virtual vibration source array is established, and the coupling equation is solved for each frequency point to obtain an infinite domain virtual vibration source amplitude value vector irrelevant to a layered site; based on physical parameters of each layer of a target layered site, a layered site propagation matrix from a virtual vibration source position to an arbitrary site position is constructed; the infinite domain virtual vibration source amplitude value vector is input into the layered site propagation matrix, vibration response of the target layered site at a corresponding frequency is calculated, and subway vibration Green function is obtained; and the application overcomes the dual imbalance of the two dimensions of "precision-efficiency" and "stability-practicality" by coupling an infinite domain basic solution with a layered site.

The application discloses a kind of suitable for deep brackish aquifer seismic reflection wave response high fidelity simulation calculation method, the method includes S10: according to brackish aquifer development characteristics, establish brackish aquifer pore fluid rock physics model, calculate the viscosity, density, longitudinal wave velocity and bulk modulus of brackish aquifer pore fluid;S20: according to brackish aquifer development characteristics, establish brackish aquifer multi-scale rock physics model, obtain brackish aquifer equivalent stiffness matrix;S30: establish geology-seismic forward modeling;S40: utilize propagation matrix method to calculate brackish aquifer reflection and transmission coefficient matrix;S50: calculate brackish aquifer reflection wave seismic response.The application combines seismic rock physics modeling and seismic forward simulation technology, considers the viscoelasticity characteristics and thickness of brackish aquifer, is more in line with actual geological conditions, low in cost, high in accuracy, can quickly and accurately calculate deep brackish aquifer seismic reflection wave response, provides theoretical support for the evaluation and optimization of carbon storage space.

The application discloses a horizontal array matching positioning method based on matrix filtering. A high-order parabolic equation model is adopted to calculate sound field propagation matrices at different steps, and a system propagation matrix in a sound source-receiving range is obtained through multiplication operation of the different step matrices; according to the depth of the sound source and the positions of the horizontal array elements, matrix coefficients are extracted from the system propagation matrices at different distances to form a new sparse system propagation matrix; subsequently, the sparse system propagation matrix is processed; a matrix filter set is formed; finally, the horizontal array receiving signal is processed by using the matrix filter set to form a distance-depth ambiguity diagram, and a target positioning result is obtained. The application can effectively improve the energy ratio of the main lobe and the side lobe in the depth dimension, reduce the number of high noise points in the whole two-dimensional ambiguity diagram, improve the depth and distance resolution of target positioning, and has important value for performance improvement of target positioning in an actual environment.

The invention relates to the technical field of digital twinning and disaster early warning, in particular to an engineering geological area disaster early warning method and system based on digital twinning. The method comprises the following steps: dividing structural units with homogeneous mechanical response by constructing a structural evolution type digital twinborn body, establishing a directional stress propagation matrix and a coupling dynamics evolution equation, and simulating the mechanical state and evolution process of a regional geologic body; a disturbance trigger chain propagation matrix is constructed, the trigger chain strength and evolution rate of each unit are calculated, a potential chain type instability path is identified, and grading early warning is realized in combination with a dynamic threshold model; constructing a structure safety redundancy dynamic attenuation model, identifying an evolution acceleration region, and establishing a structure instability evolution index to carry out multi-physical quantity fusion evaluation; virtual micro-disturbance is actively applied, an irreversible evolution stage is judged by analyzing nonlinear amplification and residual offset of redundant response, and a closed-loop dynamic early warning mechanism is formed. According to the invention, high-reliability disaster early warning from trend prediction to active verification is realized.

The application discloses a towed array passive positioning method based on wideband matrix filtering, calculates a sound field propagation matrix by using a two-dimensional parabolic equation model, obtains different distance sound source-receiver system propagation matrices through matrix multiplication operation, extracts matrix coefficients from different distance system propagation matrices according to sound source depth and horizontal array element positions, and forms a new sparse system propagation matrix. The sparse system propagation matrix is processed to form a wideband matrix filter set, finally, the horizontal array receiving signal is processed by using the wideband matrix filter set, energy normalization is performed on different distance processing results, a wideband distance-depth ambiguity diagram is formed, and target depth and distance estimation results are obtained. The application can effectively improve the target position resolution of the towed array wideband signal passive positioning, reduce the sidelobe intensity in the two-dimensional ambiguity diagram, and greatly reduce the energy outside the main lobe, and has important value for improving the target positioning performance in the actual environment.

The invention relates to the technical field of human deep neural network model quantification, and discloses a hierarchical variance propagation and gradient cooperative modulation-based neural network uncertainty quantification method, which comprises the following steps: S1, parameter posteriori distribution extraction: using a Bayesian neural network trained by Gaussian prior with a parameter of the mean value to obtain posteriori distribution with the same parameter, and extracting the posteriori distribution; wherein the parameter mean value is a variance; s2, posterior distribution truncation: truncating posterior distribution by using a dynamic distribution truncation mechanism; s3, constructing a continuous belief function, carrying out Mobius inversion, and obtaining confidence quality distribution for uncertainty expression from the belief function; and S4, fitting Dirichlet distribution, and constructing a hierarchical variance propagation matrix. And S5, carrying out further quantification on the trained model by adopting gradient weighting, layer selection gradient and gradient perturbation integral methods of a specific category. According to the method, the reliability, stability and risk controllability of model prediction are remarkably improved.

The invention discloses a label propagation algorithm based on triple integration similarity. The method comprises the following steps of: 1, respectively representing an original data set as similarity graphs under three different visual angles based on three measures of distance, correlation coefficient and density, and representing all known label information in the data set as a label matrix by using one-hot coding; 2, respectively solving three corresponding local similarity graphs and three local kernel matrixes; 3, solving a final triple integration similarity graph through an iteration mode; 4, performing corresponding transformation on off-diagonal elements and diagonal elements in the triple integration similarity graph to obtain a standard propagation matrix; and 5, spreading label information from a labeled sample to a non-labeled sample in an iteration mode, wherein the maximum index corresponding to each sample in the label matrix is the category to which the sample is divided. The method has the advantages that the adaptability to the distribution condition of a data set is better, the adaptability to data distribution is higher, and the robustness is higher.

The invention relates to the technical field of carbon emission anomaly detection, and provides a graph topology constraint and multi-modal Bayesian fusion anomaly detection method, which comprises the steps of collecting electric quantity and carbon factors, and constructing a hierarchical tree graph; based on a time delay propagation matrix, embedding three types of constraints including instantaneous conservation, sliding window accumulation and quota accumulation into layered Lagrangian; calculating a nonlinear weight according to the quota and the time residue and stabilizing the nonlinear weight; a graph neural state space model is trained through graph topology and robust weight, and cross-time coupling loss is constructed; in the prediction stage, multi-target projection of constraint conflict perception is used for digestion according to the priority of carbon emission reduction, and correction is carried out through Pareto and monotonicity projection; and carrying out change point extraction, distribution drift and tail dependency evidence on the residual error, and carrying out Bayesian fusion to obtain an abnormal probability and judgment. According to the invention, the accuracy and stability of carbon emission anomaly detection of the multi-level ammeter network are improved.

The application relates to the technical field of digital twinning and disaster early warning, in particular to an engineering geological regional disaster early warning method and system based on digital twinning; the method comprises the following steps: constructing a structure evolution type digital twinning body, dividing structure units with homogeneous mechanical response, establishing a directional stress propagation matrix and a coupled dynamic evolution equation, simulating the mechanical state and evolution process of regional geological bodies; constructing a disturbance trigger chain propagation matrix and calculating the trigger chain strength and evolution rate of each unit, identifying a potential chain instability path and realizing hierarchical early warning in combination with a dynamic threshold model; constructing a structure safety redundancy dynamic attenuation model, identifying an evolution acceleration area and establishing a structure instability evolution index for multi-physical quantity fusion evaluation; actively applying a virtual micro disturbance, determining an irreversible evolution stage by analyzing the nonlinear amplification and residual offset of the redundancy response, and forming a closed-loop dynamic early warning mechanism. The application realizes high-reliability disaster early warning from trend prediction to active verification.

This invention discloses a method and system for risk cascading propagation and risk assessment of water transfer projects based on a multi-level topology. The method includes: acquiring the topology network of the water transfer project; generating a node comprehensive risk vector for each node based on multi-source operational data; constructing a basic risk propagation matrix based on physical connections; structurally modulating the node comprehensive risk vectors to generate a comprehensive propagation operator; performing cascading propagation iterative evolution of the node comprehensive risk vectors based on the comprehensive propagation operator, and continuously calculating the network dynamics stability characteristic parameter of the comprehensive propagation operator during the evolution process; when the system is determined to be in a divergent phase based on this parameter, reconstructing the comprehensive propagation operator and continuing iterative evolution until convergence to obtain a steady-state risk distribution; and outputting the system comprehensive risk assessment result based on the steady-state risk distribution. This invention can improve the operational safety management capability of water transfer projects under complex operating conditions and external disturbances.

The invention provides a metro vibration Green function rapid calculation method and system, and relates to the technical field of metro vibration evaluation.The metro vibration Green function rapid calculation method comprises the steps that a near-field finite element model of a metro tunnel is constructed, and a plurality of infinite domain virtual vibration sources are arranged outside the near-field finite element model; establishing a coupling equation between the near-field finite element model and the infinite domain virtual vibration source array, and solving the coupling equation for each frequency point to obtain an infinite domain virtual vibration source amplitude vector irrelevant to a layered site; based on the physical parameters of each layer of the target layered site, constructing a layered site propagation matrix from the virtual vibration source position to any site point position; inputting the amplitude vector of the infinite domain virtual vibration source into the layered site propagation matrix, calculating the vibration response of a target layered site at a corresponding frequency, and obtaining a subway vibration Green function; according to the method, through coupling of an infinite domain basic solution and a layered site, double unbalance of two dimensions of precision-efficiency and stability-practicability is overcome.

The application provides a lithology category prediction method, device, equipment, medium and product, and relates to the oil and gas exploration technical field. The method comprises the following steps: obtaining a node feature matrix corresponding to each of a plurality of training wells from feature data corresponding to each sampling point of the plurality of training wells; obtaining a full-well node feature matrix from the node feature matrix corresponding to each of the plurality of training wells; obtaining a training adjacency matrix from a full-well propagation matrix; training an initial residual graph neural network model according to the full-well node feature matrix, the training adjacency matrix and a lithology category posterior distribution probability corresponding to each sampling point in the plurality of training wells, to obtain a pre-trained residual graph neural network model; and obtaining a lithology category posterior distribution probability corresponding to each sampling point in a to-be-predicted well according to a node feature matrix of the to-be-predicted well, the training adjacency matrix and the pre-trained residual graph neural network model. The method explicitly fuses spatial neighborhood stratigraphic sequence information, and improves the accuracy of lithology prediction.

The invention relates to the technical field of building engineering construction, and particularly discloses a deformation control method of a large-span beam string structure, which comprises the following steps of: constructing a real initial state three-dimensional data model of the large-span beam string structure, calculating and designing an initial error vector of a theoretical model, constructing a finite element model, and calculating a finite element model of the large-span beam string structure; performing nonlinear analogue simulation on a key construction stage according to a designed construction process by combining the initial error vector, generating an error propagation matrix and an error sensitivity coefficient by adopting Monte Carlo simulation, defining a comprehensive target optimization function to optimize the designed construction process, and performing solution by combining the error propagation matrix and the error sensitivity coefficient, so as to obtain an optimal solution. Generating a compensation control scheme to start a construction process; determining construction analysis steps and defining an analysis step mark value; collecting deviation analysis data for each construction analysis step to carry out deformation deviation judgment; circularly judging whether to trigger updating of the compensation control scheme or not, and judging whether construction is finished or not according to the analysis step mark value, so as to complete intelligent deformation control.