1307 results about "Data Matrix" patented technology

To significantly improve the prediction accuracy, response speed and management efficiency of large river bank slope disasters, an integrated bank slope monitoring method based on multi-parameter collaborative recognition is proposed. The solution includes step S1 of synchronously collecting data on bank slope displacement, pore water pressure, inclination angle, vibration frequency and environmental temperature and humidity to form an original monitoring dataset and construct a multi-parameter collaborative recognition network; step S2 of using a multi-modal data fusion algorithm to generate a fusion data matrix including spatiotemporal correlation features and perform spatiotemporal data alignment and outlier cleansing; step S3 of combining a geomechanical parameter library and a past disaster case library to output a risk level map and perform dynamic risk assessment model analysis; and step S4 of triggering a multi-level early warning mechanism and generating linked control commands including treatment suggestions to perform multi-level early warning and linked control.

The invention relates to a dynamic risk control method and device, equipment and a storage medium. The method comprises the following steps: collecting and preprocessing multi-source data to obtain a standardized multi-dimensional data matrix; constructing a dynamic risk control model according to the standardized multi-dimensional data matrix, and calculating a risk control model parameter set; performing risk factor sensitivity analysis by using the risk control model parameter set and the standardized multi-dimensional data matrix to obtain a dynamic weight vector; executing iterative risk assessment based on the dynamic weight vector to obtain a comprehensive risk score; constructing a multi-layer threshold structure based on the comprehensive risk score and executing dynamic adjustment to obtain a personalized risk threshold; and according to the comprehensive risk score and the personalized risk threshold, triggering a risk control measure and executing a feedback mechanism to obtain a risk control execution result. The importance of different risk factors can be adjusted in real time, the market environment change is quickly responded, and the exploration capability of the system for unknown risks is enhanced.

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention provides an intelligent temperature field analysis-based heat dissipation optimization method for an IGBT (Insulated Gate Bipolar Translator) power module of an offshore flexible direct current converter valve. According to the method, an IGBT module space temperature data matrix and multi-point temperature sensor data are collected, and filtering processing is carried out through a multi-scale sliding window and a self-adaptive threshold value; establishing a multi-layer thermal network parameter model, and analyzing temperature dynamic change characteristics; layering the feature data according to the thermal response speed, and performing adaptive mapping and weight calculation; establishing a reinforcement learning model based on the heat dissipation efficiency index set to optimize a heat dissipation strategy; and predicting the temperature field distribution by using the graph structure neural network model. Accurate sensing, dynamic characteristic analysis, self-adaptive control and predictive maintenance of the temperature field of the IGBT module are realized, the heat dissipation efficiency and the temperature uniformity are improved, and the service life of equipment is prolonged.

The invention relates to the technical field of strength detection, and discloses radiator strength detection equipment and a detection method. The method comprises the following steps: carrying out adaptive wavelet denoising and structural association mapping on collected multi-scale structural feature data and multi-environment coupling material feature data of a radiator to be detected, and carrying out multi-dimensional feature extraction, space-time alignment and interactive mapping on an obtained fusion data set to obtain a multi-physical field coupling data matrix; carrying out physical field control equation set separation iteration solution and heat dissipation cyclic load and environmental action calculation on the coupling data matrix to obtain a damage evolution state; and performing multi-dimensional damage sensitive area identification based on the damage evolution state, generating a risk distribution map, performing failure comparison and life calculation based on historical failure data of the same type of radiators, and generating an intensity comprehensive detection result. Through multi-physical field coupling analysis and multi-dimensional damage evaluation, accurate detection of the strength state of the radiator is realized, and the accuracy of structural reliability evaluation is improved.

The invention discloses an intelligent operation and maintenance management system and method based on a charging pile, and belongs to the technical field of fault early warning, and the method comprises the steps: building a unified time sequence operation data matrix through collecting multi-source state data generated in the operation process of the charging pile; key features are extracted to construct feature vectors, and a multi-classification neural network model is utilized to evaluate a health state; a micro-degradation evolution path model is constructed in combination with the health trend in the continuous observation period, and a fault prediction curve is generated; performing similarity matching on a prediction result and a fault prior curve library, calculating a risk weight coefficient, identifying potential fault nodes and outputting an early warning list; constructing a regional task scheduling graph based on the high-risk pile position, fusing geographic position, power level and residual life information, and optimizing to generate an operation and maintenance path and a resource configuration scheme; according to the method, the fault prediction accuracy and operation and maintenance efficiency of the charging pile can be remarkably improved, and intelligent operation and maintenance and response optimization are realized.

The invention discloses a time sequence data management method of an edge computing gateway, which relates to the technical field of edge computing and industrial Internet of Things, and comprises the following steps of: respectively recording a communication bandwidth occupancy rate, a buffer area residual rate and a scheduling thread occupancy rate of the edge computing gateway in a preset fixed time period; and constructing a resource use original data matrix covering all time points in the fixed time period. According to the method, by periodically monitoring the resource use state and fusing the high-priority task scheduling performance, the scheduling resource abnormal occupancy index is dynamically generated, and intelligent sensing and scheduling optimization of the edge computing gateway on the resource pressure are achieved. When the abnormal index is increased, the system automatically triggers buffer area redistribution and low-optimal task data compression, data writing and scheduling real-time performance of key tasks are guaranteed preferentially, the problems of task starvation and data loss are effectively avoided, and the stability and the response capability of the system in a high-pressure environment are improved.

The invention relates to an internet big data extraction method and device, equipment and a storage medium, and the method comprises the following steps: carrying out distributed crawler collection on an internet data source, obtaining original network data, and converting the original network data into a structured data matrix; and performing multi-level semantic analysis on the matrix, constructing a semantic feature map, and performing topic segmentation and classification to form a topic domain knowledge tree. Association rules in the knowledge tree are further mined, and an implicit knowledge network is constructed. Performing semantic decomposition and expansion on query conditions based on the network to generate expanded query data, and performing similarity matching with the knowledge network to obtain a candidate data set; and finally, performing multi-factor sorting and extraction on the candidate data, and outputting target data, thereby solving the technical problem that in the second-hand car market, due to wide data sources and fuzzy semantics, an existing system has relatively large deviation when performing price prediction and maintenance cost analysis.

The invention provides a real-time deep sea subsurface buoy monitoring system, which belongs to the technical field of deep sea measurement, and comprises a control chip, a multi-parameter sensor array, a data storage device and a power supply, high-precision monitoring is realized through the following steps: recording ocean data acquired by a multi-parameter sensor; correcting the sound wave propagation model and calculating the relative position of the subsurface buoy; constructing a three-dimensional data matrix and realizing data compression; identifying abnormal data by using a matrix disorder index; processing the time sequence data by adopting a sliding window weighted average method and eliminating jump; carrying out distributed preprocessing on the multi-source heterogeneous data and carrying out parallel processing by applying a matrix partitioning technology; accumulated errors are corrected by combining historical data and applying a Kalman filtering algorithm, and the problems of environment interference and sensor drift are solved through a deep sea environment disturbance compensation network model and a self-adaptive weighted error optimization function.

The invention discloses a fresh food quality monitoring method fusing fresh food after-ripening characteristics and logistics data, and relates to the field of food science and technology, and the method comprises the steps: collecting logistics environment data, logistics process data and fresh food ontology data in real time through a distributed sensor network; time stamp synchronization and space coordinate mapping technologies are adopted, space-time alignment is carried out on heterogeneous data with different sampling frequencies, and a unified data matrix is established; the method comprises the following steps: establishing a variable parameter dynamical model comprising variety difference factors, harvest maturity and environment interaction coefficients based on post-harvest physiological mechanisms: dynamically updating model parameters of various varieties in different logistics scenes through a federated learning framework crossing supply chain nodes; when the quality attenuation curve predicted in real time deviates from a preset threshold value, graded early warning is triggered, and logistics parameter adjustment suggestions are generated. According to the invention, full-link accurate management and control of fresh products from a supply chain to a consumption terminal is realized through multi-dimensional technology collaboration.

The invention discloses a reservoir dam safety monitoring method and system based on edge calculation, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the steps: obtaining monitoring data by each edge calculation node, carrying out the preprocessing, and generating a standardized data matrix; establishing a reference database, executing anomaly detection, and generating a labeling time sequence data matrix; performing multi-scale decomposition on the labeled time sequence data matrix, and constructing a node response feature matrix; the central processing unit receives data of each edge computing node, analyzes a multi-parameter spatial propagation mode by constructing a parameter-spatial correlation matrix, and obtains a spatial correlation feature matrix; establishing a Bayesian risk assessment model, predicting a dam risk level and outputting a risk evolution trend; and issuing a differentiated early warning instruction according to the risk assessment result. Through a distributed architecture combining edge calculation and central processing, real-time monitoring, intelligent analysis and accurate early warning of the dam safety state are realized, and the monitoring efficiency and the risk identification accuracy are improved.

The invention discloses a contract filing method, system and device based on automatic recognition and a medium, and the method specifically comprises the steps: taking a contract document uploaded by a user as input, and extracting contract key data; inputting the signature area coordinate, the signature date metadata and the clause keyword into a space-time correlation model to generate a context enhanced candidate contract number set; inputting the candidate contract number set into a preset knowledge graph, and outputting a target contract number; inputting the distribution characteristics of the clause keywords into a bidirectional long-short-term memory network classifier for processing, and constructing a multi-dimensional metadata matrix in combination with the target contract number; and inputting the desensitized contract text content and the multi-dimensional metadata matrix into a file DNA atlas generator, and generating a unique feature code by fusing a semantic embedding vector and a document structure fingerprint. The efficiency bottleneck and accuracy problems of a traditional archiving mode are solved, and the efficiency, accuracy and compliance of contract archiving are remarkably improved.

An application system fault prediction method based on an association rule and a deep learning integrated model, the method comprising: collecting historical fault data of an application system; performing data preprocessing on the collected historical fault data to obtain a data matrix, and then converting the data matrix into a data form suitable for analysis by an association rule algorithm and a deep learning algorithm; performing feature extraction on the data matrix to obtain effective fault-related features; establishing an association rule model and a deep learning LSTM model; fusing outputs of the association rule model and the deep learning LSTM model to form a multi-layer decision tree model; and predicting a fault by combining the association rule model, the deep learning LSTM model, the multi-layer decision tree model, and current state data of the application system, and obtaining a fault prediction result via weighted voting. The present invention achieves accurate fault prediction and early warning for an application system, and enhances the accuracy and robustness of fault prediction by introducing optimizations for a deep learning LSTM model.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine heterogeneous data visualization processing method combined with artificial intelligence, and the method comprises the steps: collecting multi-source heterogeneous data above and under a coal mine to construct a real-time heterogeneous data matrix; feature categories are divided based on data dynamic relevance, and stable data areas are screened through a dynamic analysis window to generate an optimization matrix; performing pattern recognition on the unstable region to extract an abnormal data cluster, and calculating an abnormal feature factor of a data point; analyzing the feature distribution difference to generate a stable feature index, and calculating an abnormal risk value in combination with the distance, the abnormal feature factor and the stable feature index; and mapping the center coordinate of the stable region to a three-dimensional space of the coal mine, and positioning a key information region in combination with the abnormal risk value. An apparatus includes a memory, a processor, and a computer program. The coal mine heterogeneous data processing efficiency and the safety monitoring accuracy are improved, intelligent positioning of the key information area is achieved, and powerful support is provided for coal mine safety production.

The invention discloses an LSTM-COF-based power equipment data anomaly detection method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the following steps: collecting historical data, and constructing a three-dimensional data matrix; predicting equipment parameters at the extreme temperature through an LSTM model; compressing the features, quantifying the covariance deviation degree between the features in combination with a correlation abnormal factor algorithm, and detecting abnormal points; the system integrates a data collection module, a data preprocessing module, a data prediction module, a detection model generation module and a visualization module. According to the method, the multi-dimensional historical operation data of the power equipment is collected, the equipment parameters in the extreme temperature environment are predicted by using the LSTM model, the principal component analysis dimensionality reduction and correlation abnormal factor algorithms are combined, insulation degradation type and electrical connection type faults can be dynamically identified, the data distribution change is adapted through the incremental learning mechanism, and the fault diagnosis accuracy is improved. The problems of low high-dimensional data processing efficiency and poor anomaly detection adaptability due to manual experience dependence in a traditional method are solved.

The invention discloses a concrete internal defect quantitative detection method and system based on reflected waves, and belongs to the technical field of nondestructive testing. A reflected wave data matrix is obtained through multi-angle excitation and synchronous receiving; calculating energy characteristics of each channel, and constructing an energy response residual field; extracting waveform offset, spectrum jitter and phase change caused by defects by adopting a disturbance comparison algorithm to form a disturbance feature vector set; a defect-response function curved surface is further constructed, a defect topological structure is inversed based on gradient and curvature analysis, and defect geometric parameters are output; and finally, inputting the multi-moment defect parameters into the recurrent neural network, and predicting a defect evolution path and a failure risk. The method has high resolution and trend prediction capability, and is suitable for detection and early warning of concrete structures in bridges, tunnels and nuclear power projects.

The invention discloses a method and system for predicting the service life of a fire valve, and relates to the field of prediction maintenance, and the method comprises the steps: obtaining a multi-dimensional monitoring data matrix which comprises the sealing performance data, operation torque data and corrosion degree data of the fire valve continuously monitored in a preset time period; determining a parameter incidence matrix according to the multi-dimensional monitoring data matrix; determining a plurality of principal component vectors and corresponding characteristic values according to the parameter incidence matrix through a principal component analysis algorithm, and determining a current health state score of the fire valve according to the characteristic values corresponding to the plurality of principal component vectors; determining a degradation law function of the fire-fighting valve according to the historical operation record of the fire-fighting valve; and predicting the service life of the fire valve according to the current health state score and the degradation law function. According to the scheme, the accuracy of life prediction of the fire valve is improved, and the problem that the accuracy of life prediction of the fire valve is low is solved.

The invention discloses an intelligent operation and maintenance scheduling and resource optimization method and system for a new energy power station, and belongs to the technical field of data processing and management.The method comprises the steps that real-time power generation data, equipment sensor data, weather forecast data, a power grid scheduling instruction and electricity market electricity price information of the new energy power station are obtained; a unified data matrix is generated through space-time alignment processing to execute bidirectional feedback prediction, and a corrected power generation plan is generated; a cooperative scheduling decision is generated through a multi-target dynamic balance algorithm, and a personnel dispatching scheme and a material allocation path are generated through real-time path planning; and generating a historical decision data set based on the execution result and the actual execution deviation, and adjusting prediction parameters and scheduling parameters through an incremental learning model. A closed-loop optimization method combining data fusion, collaborative prediction, multi-target scheduling and adaptive learning is adopted, global collaborative optimization of multiple factors such as power generation, operation and maintenance, energy storage and markets can be achieved, and the economic benefit and the intelligent level of overall operation of a power station are improved.

The invention relates to the field of high-voltage cable fault diagnosis, and discloses a high-voltage cable insulation fault intelligent diagnosis method and system based on multi-information fusion, and the method comprises the following steps: S1, obtaining the operation multi-source data of a high-voltage cable, and constructing a multi-source original data matrix; s2, preprocessing to obtain a time-space aligned standardized data matrix; s3, performing feature extraction to obtain a multi-dimensional feature vector, and learning internal association between features by using a multi-modal deep network to obtain a joint multi-modal feature; s4, constructing a fault type identification model based on Bayesian reasoning and Monte Carlo sampling, and obtaining a fault type classification result; s5, in combination with deep learning and a physical model, obtaining a fault occurrence interval, positioning information and a fault level; and S6, based on a fault type classification result, a fault occurrence interval and positioning information, obtaining a fault level, and carrying out early warning pushing on a generated diagnosis report. According to the invention, high-precision identification, positioning and risk assessment of high-voltage cable insulation faults are realized.

The invention relates to a water conservancy facility operation and maintenance method based on multi-source data, and the method comprises the steps: collecting multi-source heterogeneous data of a water conservancy facility, and obtaining a standardized multi-dimensional data matrix; multi-modal features of the standardized multi-dimensional data matrix are extracted through a graph neural network fusion algorithm, and a fusion feature vector is obtained; performing fault diagnosis on the fusion feature vector through a knowledge graph reasoning algorithm automatically constructed by a domain ontology to obtain fault diagnosis information; based on the fault diagnosis information, performing facility state evolution analysis through a space-time coupling degradation prediction model to obtain a space-time degradation prediction result; according to the space-time degradation prediction result, state evaluation of the water conservancy facilities is carried out through a dual-objective optimized dynamic weight distribution algorithm, and an operation and maintenance state evaluation result is obtained; and on the basis of the operation and maintenance state evaluation result, performing cross-basin cascade fault prevention and control on the water conservancy facilities to obtain a cascade fault prevention and control scheme. The method improves the prediction precision of the degradation of the water conservancy facilities, and enhances the anti-risk capability of the water conservancy infrastructure.

The invention relates to pipeline circumferential weld detection, and discloses a pipeline circumferential weld defect detection method and system based on deep learning, multiple sensors are arranged on the surface layer of a pipeline to collect multi-modal signals, an inertial measurement device is combined to carry out pose correction, and a multi-modal time sequence data matrix is constructed; performing preprocessing and segmentation, calculating to obtain a dynamic reference band, and outputting upper and lower bounds; performing broken line fitting on each segment to extract a phase change factor triple, and comparing with a reference band to screen event candidate segments; constructing a multi-segment time domain coupling relation graph based on the candidate segments, and generating a dominant response mode candidate set; inputting a multi-branch time sequence coding network to extract a three-mode feature and generate a time sequence fusion feature; and performing similarity matching with a defect prototype library to obtain a defect category probability sequence, performing single-mode ablation and time sequence playback verification to obtain a high-confidence index, mapping the high-confidence index to a spatial position, and outputting a defect category and severity in combination with a feature index. According to the method, high-precision defect detection and reliable classification under the trenchless condition can be realized.

The invention relates to the technical field of electronic detection, and discloses an electronic component dispensing detection method. The method comprises the following steps: acquiring dispensing image data of the surface of the electronic component, and generating a standardized dispensing data matrix containing glue position, thickness and uniformity characteristics through standardized preprocessing; constructing a dispensing correction matrix based on an adaptive window frame, and performing spatial reference dynamic correction on the standardized data matrix to obtain a spatial correction dispensing data matrix; inputting the data into a multi-layer sensor fusion network for feature fusion, and outputting a multi-source fusion dispensing data set; constructing a multi-dimensional abnormal feature incidence matrix based on the data set, and identifying abnormal dispensing data nodes by using a dynamic threshold detection algorithm; performing parameter optimization iteration on the multi-source fusion data set by using a gradient descent optimization algorithm to generate an optimized dispensing parameter set; and finally, constructing a three-dimensional visual dispensing quality model, and establishing a dynamic mapping relationship between model parameters and glue physical characteristics. The method can more comprehensively detect the glue quality.

The invention discloses a non-stationary industrial process early fault diagnosis method based on a common trend model, and belongs to the field of industrial process monitoring and fault diagnosis. The method comprises the following steps: collecting training data of a non-stationary industrial process under a normal working condition, performing denoising by using singular spectrum analysis, and obtaining a stationary projection matrix and a stationary data matrix by using a common trend model; orthogonal conversion is carried out on the stationary data matrix, a conversion projection matrix is calculated, and an orthogonal conversion data matrix and a statistic data matrix thereof are obtained; calculating a mean value and a covariance matrix of the statistic data matrix to obtain a control limit; the method comprises the following steps: acquiring test data under a real-time working condition of an industrial process, processing to obtain statistic data, calculating a mahalanobis distance index, and comparing with a control limit to judge whether a fault occurs; and if a fault is detected, realizing fault separation according to fault reconstruction and a mapping relation. According to the method, the interference of the non-stationary characteristic on the fault diagnosis model can be reduced, and fault detection and separation in the non-stationary process are realized.

The invention provides a coherent signal arrival direction estimation method and device based on a deep convolutional network, and belongs to the field of array signal processing. The method comprises the following steps: receiving a to-be-detected signal containing a coherent signal by using a uniform linear array antenna to obtain an array receiving data matrix and extract a covariance matrix; forming an input feature vector by right upper triangular elements divided from a diagonal line in the covariance matrix, inputting the input feature vector into a covariance estimation model formed by a deep convolutional network, obtaining an estimation value of the right upper triangular elements under an ideal incoherent condition, and reconstructing the estimation value to obtain a covariance matrix estimation value; and performing characteristic decomposition on the covariance matrix estimation value, and generating a spatial spectrum by using a MUSIC algorithm to obtain an estimation result of the signal arrival direction. According to the method, the noise-containing mixed signal covariance matrix is mapped into the ideal incoherent noise-free signal covariance matrix through a physical constraint supervised learning framework, so that the estimation precision and robustness of the MUSIC algorithm in a coherent scene are improved.

The invention provides a medical examination data analysis system and method based on artificial intelligence. The method comprises the steps of obtaining a time sequence data matrix of multiple examination indexes of a target patient at different time nodes through medical examination data of the target patient; labeling pathological labels of various examination indexes in the medical examination data, and determining medical semantic association features among different examination indexes of the target patient through the pathological labels; according to the medical semantic association features and the time sequence data matrix, determining a time sequence association relationship of different inspection indexes of the target patient on a pathological level, and generating a fusion feature vector of the health state of the target patient according to the time sequence association relationship; and an abnormal evolution feature of the current health state of the target patient is obtained by combining an abnormal detection model with the fusion feature vector, and then an auxiliary analysis result of the pathological risk of the target patient is output to medical personnel. By adopting the scheme of the invention, the dynamic change trend analysis of the disease course risk state of the patient can be realized based on the medical semantic perception ability.

The invention discloses an intelligent fire-fighting risk prediction and dynamic early warning method and system based on multi-source data fusion, and relates to the technical field of intelligent fire-fighting risk prediction, and the method comprises the steps: collecting multi-source fire-fighting data, and constructing a fire-fighting safety state multi-dimensional data matrix; constructing a risk prediction model based on the fire safety state multi-dimensional data matrix to carry out real-time risk scoring; and executing dynamic early warning through a risk prediction result. According to the method, through the multi-source data acquisition and fusion module, multi-type fire-fighting data are comprehensively acquired and standardized, a structured and multi-dimensional fire-fighting safety state matrix is constructed, and the problems of data dispersion and information splitting are solved. In combination with a risk collaborative prediction and scoring module, the risk level of each region is dynamically output based on multi-factor analysis, and the accuracy and foresight of risk prediction are improved. Through a dynamic early warning generation and linkage module, real-time grading early warning and linkage of a fire fighting system are realized, and rapid response and intelligent prevention and control of a high-risk area are realized.

The invention belongs to the technical field of mechanical equipment wear state monitoring. The method comprises the following steps of: extracting wear rate characteristics, wear degree characteristics, cutting index characteristics, fatigue index characteristics and oxidation index characteristics in an abrasive particle sample image, and respectively clustering to obtain five groups of discrete stage labels; carrying out normalization processing on the five groups of discrete stage labels to obtain a normalized data matrix; according to the data matrix, weights corresponding to all the features are calculated in multiple modes, and the average value of the weights calculated in the multiple modes serves as the final weight of all the features; according to the final weight of each feature and the normalized data matrix, calculating a comprehensive score sequence of all the abrasive particle sample images; performing clustering according to the comprehensive score sequence to obtain a plurality of optimal clustering centers, and determining a current wear stage according to the optimal clustering centers; and more comprehensive and more accurate division of the wear stages is realized.

The invention relates to the field of river bank slope monitoring, in particular to an integrated bank slope monitoring method based on multi-parameter collaborative awareness, which comprises the following steps: S1, multi-parameter collaborative awareness network construction: collecting bank slope displacement, pore water pressure, inclination angle, vibration frequency and environment temperature and humidity data to form an original monitoring data set; s2, data space-time alignment and abnormal value cleaning: generating a fusion data matrix including space-time correlation features by adopting a multi-modal data fusion algorithm; s3, dynamic risk assessment model analysis: outputting a risk level map in combination with the geomechanical parameter library and the historical catastrophe case library; s4, multi-stage early warning and linkage control: triggering a multi-stage early warning mechanism and generating a linkage control instruction including a disposal suggestion; powerful support can be provided for accurately carrying out large-scale river bank slope collapse monitoring, real-time risk assessment and efficient emergency response, and remarkably improving the prediction accuracy, response speed and management efficiency of large-scale river bank slope disasters.

The invention belongs to the technical field of nondestructive testing, and discloses a vector coherence factor-based thick-wall austenitic stainless steel welding seam full-focus imaging method, which comprises the following steps of: acquiring full-matrix data through a TRL area array probe, constructing an imaging area, calculating a spatial relationship, optimizing a propagation path in combination with a Fermat principle, and obtaining a full-focus imaging result; the method comprises the following steps of: firstly, extracting an instantaneous phase of an ultrasonic signal to construct a vector coherence factor, reconstructing an imaging data matrix according to the coherence factor, and obtaining a phase coherence weighted full-focusing imaging result, so as to ensure that each pixel point can obtain an optimal propagation path, thereby generating a full-focusing image data matrix by adopting a point-by-point delay superposition technology; the method can effectively improve the signal-to-noise ratio and positioning and quantification precision of the thick-wall austenitic stainless steel weld joint detection image, and has good engineering application value.

The invention provides a method for predicting the residual life of a power transformer based on digital-analog fusion, and belongs to the technical field of transformer detection.The method comprises the steps that multi-dimensional sensor data of the power transformer is collected, wavelet transform preprocessing is conducted, a normalized data matrix is established, a physical equation is established, and a deterministic physical model is formed; a data-driven model is established based on an improved adaptive multi-scale network to realize multi-scale feature adaptive extraction, a topological phase change algorithm is introduced to identify key transition points in an aging process, and a deterministic physical model and the data-driven model are fused to establish a digital-analog fusion prediction framework. A generative adversarial network is adopted to perform data enhancement to solve the problem of scarcity of fault samples, a Bayesian neural network and a Monte Carlo random inactivation technology are utilized to construct an uncertainty quantization framework to output a residual life prediction value and a confidence interval thereof, and the technical problem that the prediction precision of the residual life of the transformer is not high is solved.