1019 results about "Mutual information" patented technology

The invention discloses an industrial data analysis and mining system based on a knowledge graph, relates to the technical field of data mining, and aims to solve the problems of difficulty in cross-process parameter association and low abnormal traceability precision. The system calculates the correlation strength of the oven temperature in the coating process and the direct-current internal resistance of the battery cell in the formation and capacity grading process through a time-lag mutual information algorithm, generates an influence relation edge with a time-varying weight in combination with a temperature-internal resistance negative correlation process rule, and constructs an industrial knowledge graph; taking a direct-current internal resistance abnormal batch as a starting point, reversely traversing the knowledge graph along a process path, fusing a parameter Z-score deviation degree and a dynamic weight to generate a root cause parameter list, and realizing quality root cause positioning; instantiating abnormal parameters into event nodes, identifying star-type and chain-type propagation topologies through subgraph isomorphism detection, and extracting a standardized fault mode; based on an FCI causal discovery algorithm, a real causal structure is identified, the weight of the knowledge graph is dynamically adjusted, adaptive evolution of the graph is realized, and the accuracy and the intelligent level of complex process quality analysis are improved.

The invention discloses a multi-modal remote sensing semantic segmentation method and system for learning frequency domain fusion. The method comprises the following steps: respectively extracting multi-scale features of two modal input images by adopting a double-branch encoder; sequentially executing frequency domain decoupling and fusion, mutual information constraint-based feature optimization and low-frequency guided cross-modal fusion processing on each scale feature to generate a fused semantic feature; and performing up-sampling and feature refining on the fused features through a decoder, and outputting a full-resolution segmentation prediction map. According to the multi-modal remote sensing image semantic segmentation method, modal sharing information and specific details are effectively separated through frequency domain decoupling, feature representation is optimized through mutual information constraint, adaptive feature fusion is achieved in combination with an attention mechanism, and the accuracy and robustness of multi-modal remote sensing image semantic segmentation are remarkably improved.

The invention relates to a water quality prediction method and system based on gating residual enhancement and feature fusion, and belongs to the technical field of water environment intelligent analysis and deep learning. Taking each water quality index as a node of the graph, and constructing two complementary variable relation graph structures by utilizing a Pearson's correlation coefficient and mutual information; respectively inputting the two graph structures into a graph convolutional network, extracting deep dependency features among indexes, and splicing and fusing the deep dependency features. A multi-head attention mechanism is used as a trunk to extract global time dependence, a GRU network is introduced to extract local time sequence features, GRU output is used as an adjustable residual term to be injected into the attention trunk through a residual gating mechanism, self-adaptive enhancement of local dynamic features is achieved, and finally a self-adaptive fusion mechanism is introduced to generate comprehensive representation. According to the method, the complex dependency relationship between the water quality indexes and the time dynamic evolution process can be modeled in a collaborative manner, the response capability to key local change and sudden change events is remarkably enhanced, and the accuracy and robustness of water quality prediction are improved.

The invention relates to the technical field of safety monitoring, and particularly discloses an embankment danger rapid identification method and system, and the method comprises the steps: collecting multi-modal data in real time through arranging a multi-source sensor network; according to the phase space trajectory, extracting a Lyapunov exponent spectrum, correlating the dimension and the Kolmogorov entropy, and forming a structure response chaos degree index; calculating a hydrogeological coupling coefficient in combination with multi-scale decomposition and mutual information analysis; and fusing the two into a three-dimensional dangerous case feature tensor, inputting the three-dimensional dangerous case feature tensor into a pre-training model based on a deep convolutional neural network and a long-short-term memory network, realizing intelligent discrimination of high, medium and low risk levels, generating an adaptive monitoring instruction for a low-risk working condition, outputting a risk evolution trend map, and supporting closed-loop management and control.

The invention provides a circuit board electrical performance test and diagnosis method, which comprises the following steps of: constructing a structured process-electrical data set by collecting process parameters such as etching time, pressing temperature, drilling precision, copper foil thickness and the like and electrical test indexes such as impedance deviation, leakage current, signal attenuation and the like; screening key process variables by adopting mutual information analysis and a maximum correlation-minimum redundancy criterion; through modeling of a three-layer causal diagram and a structural equation, quantitative influence evaluation of process parameters on electrical performance and fault types is realized; when a fault is detected, the system can automatically perform reverse reasoning, identify a main manufacturing deviation item and generate attribution diagnosis and process optimization suggestions, so that the accuracy of fault diagnosis and the pertinence of process adjustment are improved, and the fault occurrence rate is reduced and the product quality is improved.

The invention discloses a multi-modal offshore wind power ultra-short-term prediction method in the field of offshore wind power plant cluster power prediction, and aims to solve the technical problems of spatial-temporal feature splitting and insufficient dynamic dependency relationship modeling. The method comprises the steps of performing anomaly detection and restoration on fan data, and generating a corrected wind power cluster data set; extracting a mean value, a standard deviation and a latest value of core operation data of each fan through a dynamic time window, and constructing a multi-dimensional node feature; a static geographic similarity matrix is generated based on geographic coordinates, a basic wake effect matrix is generated in combination with real-time wind direction data, correction is carried out through the maximum mutual information quantization time-delay effect, and then a dynamic adjacency matrix is obtained through self-adaptive fusion; and integrating the multi-dimensional node features and the dynamic adjacency matrix into a space-time diagram sequence data architecture, inputting the space-time diagram sequence data architecture into a multi-scale wake flow perception diagram space-time prediction model, and outputting a multi-fan power prediction value. According to the invention, high-precision multi-fan power prediction can be realized.

The invention discloses a cross-modal heterogeneous data processing method and system. The method comprises the following steps: acquiring multi-source heterogeneous data including a vibration signal, a temperature signal, an oil spectral signal and the like, and forming a cross-modal alignment index sequence through event detection and time alignment processing; constructing a heterogeneous graph structure based on the aligned index sequence, and determining an edge weight according to correlation between modal fragments to obtain a cross-modal heterogeneous graph; performing representation decoupling on the heterogeneous graph, generating a cross-modal shared semantic subspace and a modal specific subspace, and reducing redundancy and noise interference through mutual information minimization constraint; establishing a prototype library, dynamically generating positive and negative sample pairs by adopting cross-modal contrast learning to enhance the discrimination ability, and obtaining an optimized cross-modal fusion result; and finally, outputting a diagnosis result including the fault type and severity, generating a diagnosis evidence track, and binding the key cross-modal fragment with the semantic centroid of the prototype library to realize interpretable traceability and self-correction of the result.

The invention relates to the crossing field of hydraulic engineering hydrological monitoring technology and machine learning prediction technology, discloses a sediment concentration prediction method based on deep learning, and aims to solve the problems that in existing sediment concentration prediction, hyper-parameter manual tuning is low in efficiency, key feature attention is insufficient, local and time sequence information is difficult to consider by a single model and the like. Accurate prediction is realized through five core modules: a data preprocessing module performs missing value filling, abnormal value processing and derivative feature generation on hydrological data; the feature selection module screens key features based on mutual information; the time sequence construction module generates time sequence data through a sliding window; the hyper-parameter automatic optimization module adopts Bayesian optimization iteration to obtain an optimal hyper-parameter; the CNN-LSTM-attention prediction module fuses CNN local feature extraction, bidirectional LSTM time sequence dependence capture and multi-head self-attention mechanism key feature focusing capability, is suitable for scenes such as river channels and channels, and provides efficient decision support for hydrological regulation and control.

The invention discloses a farmland precise fertilization control method based on multi-source data fusion and deep learning, and the method comprises the steps: obtaining the multi-dimensional heterogeneous data of a target farmland in real time, including the crop spectrum time series data of a satellite image, the ion concentration data collected by a soil sensor network, and the microenvironment time series monitored by a meteorological station; and constructing a Delaune triangulation network spatial index, and re-sampling satellite image data to be matched with a soil sensor network space. Calculating a time-varying mutual information entropy, extracting related microenvironment parameters as coupling factors, combining the coupling factors with soil basic parameters to form a three-dimensional feature matrix, and converting the three-dimensional feature matrix into a growth period feature vector fused with time-space correlation; the collaborative decision model is input, the main branch predicts the basic fertilization amount, the auxiliary branch detects the ion concentration space mutation area and corrects the fertilization amount, a fertilization control instruction is output, and the fertilization device is controlled to conduct precise fertilization. The problem that fertilization control cannot be accurately and efficiently performed on farmland in the prior art is effectively solved.

The invention relates to the technical field of computer vision, in particular to a high-rise facility operation risk monitoring method based on deep learning and point cloud detection, and the method comprises the steps: collecting a three-dimensional point cloud in real time, and extracting a target point cloud through dynamic threshold denoising and template registration; performing joint coding on space geometry and time sequence motion by using a pre-trained space-time diagram network in combination with an attention mechanism; high-reflectivity beacon points are identified, and the change rate of displacement and angular velocity is calculated; constructing a gating fusion model, dynamically weighting and coupling semantic features and measurement data, and generating risk probability distribution through mutual information consistency check; a fuzzy logic classifier with membership degree optimization is used for mapping to four-level early warning, and grading response is triggered; after early warning, a key frame incremental learning fine tuning model is extracted, and preprocessing parameters are reversely optimized to form a closed loop. According to the method, through multi-source heterogeneous data fusion, dynamic adaptive weighting and a self-evolution mechanism, the real-time performance, accuracy and robustness of risk monitoring in a complex construction environment are remarkably improved.

The invention discloses a target multi-attribute identification method based on feature decoupling and cross-task collaboration, and belongs to the technical field of computers of specific calculation models, and the method comprises the following steps: firstly, extracting the initial features of each task through a lightweight backbone network, carrying out feature decoupling in a subspace, and according to the cross-task feature similarity, carrying out feature extraction; according to the target multi-attribute identification method based on feature decoupling and cross-task collaboration, an orthogonal constraint weight is dynamically adjusted, then mutual information confrontation minimization is adopted to further suppress statistical dependence between tasks, task residual errors are injected in a cross-task feature aggregation stage, differentiation enhancement is achieved, and finally unified joint feature representation is formed. According to the method, subspace statistical independence is realized, independence and necessary collaborative information are considered, a stable basis is provided for subsequent fusion, statistical dependence between tasks is further suppressed through mutual information confrontation minimization, complementation information is reserved while independence is ensured, and feature discrimination and robustness are improved.

The invention discloses a data security risk assessment method based on a big data model, and relates to the technical field of data security, and the method comprises the steps: collecting and preprocessing multi-source data, collecting security-related data from network equipment, a server and an application system, carrying out the preprocessing, carrying out the adaptive feature extraction, and carrying out the data security risk assessment. The feature importance is evaluated by calculating the mutual information amount of features and risk tags, a standardized feature vector set is constructed, multi-model collaborative analysis is performed, feature vectors are input into a cascade collaborative network composed of an anomaly detection model, a threat recognition model, a correlation analysis model and a prediction model, and a risk risk is obtained. Through cross-model feature transmission and a bidirectional information feedback mechanism, deep collaborative analysis and multi-model deep fusion decision making are carried out, a weight is calculated according to historical accuracy of each model, a comprehensive risk score is calculated by adopting dynamic gating deep fusion, and a dynamic threshold value is calculated based on a sliding time window. And the risk is divided into three levels of high risk, medium risk and low risk.

Provided is a day-ahead electricity price prediction method based on a parallel multi-dimensional attention mechanism, which method belongs to the technical field of day-ahead electricity price prediction in a power market. The maximum mutual information coefficient is used to select variables having a certain relevance with an electricity price, so as to assist in predicting future electricity price data; next, a decomposition algorithm is used to decompose an original electricity price signal; and then, electricity price sub-components obtained by means of decomposition and the electricity-price-related variables are inputted into an MBI-IPMDA-PBISA deep learning model, so as to predict a future electricity price. In this way, an MBI-IPMDA-PBISA deep learning model can fully learn a complex association relationship between electricity price sub-components and electricity-price-related variables and a future electricity price, the problem of error superimposition caused by separately predicting the electricity price sub-components and then superimposing same to obtain the future electricity price is also avoided, and the problem of the accuracy of existing attention mechanisms during prediction being low is solved, thereby improving the prediction accuracy.

The invention discloses a steel pipe joint defect identification method based on a cross-modal fusion technology, and the method comprises the steps: collecting image data and corresponding ultrasonic signals of the surface of a steel pipe joint, adding a time sequence label to construct original multi-modal data, and carrying out the rough alignment through a signal registration algorithm in combination with space-time correction; respectively extracting multilevel characteristic spectrums of the image and the ultrasonic mode by using an exclusive depth characteristic extraction network; a cross-modal mixed attention mechanism is introduced, the reliability weight of each modal signal is adaptively evaluated, the weight of an abnormal signal is automatically reduced or dynamic correction is triggered, and feature recombination is guided based on category-level prior; a mutual information constraint loss and category-specific unwrapping error correction structure is introduced to optimize recombination features; and performing defect identification based on the optimization features to obtain defect types and distribution information of the steel pipe joints. According to the method, through multi-modal data fusion and a cross-modal attention mechanism, the capability of distinguishing hidden defects is improved.

The invention relates to a circuit board on-line defect detection method and system, and the method comprises the steps: carrying out the synchronous collection and structural integration of multi-source technological parameters such as production line environment temperature and humidity, equipment operation states, material batches and the like, and defect detection images, and achieving the construction of large-sample original data in a production process; through standardization and de-noising preprocessing, multi-modal features are fused, and a distribution mapping model of process and defect features is established by using algorithms such as mutual information analysis and principal component analysis. Based on a feature distribution model and real-time data, a dynamic anomaly detection threshold is adaptively generated, Bayesian inference and evidence reasoning are combined, multi-level confidence levels and risk response suggestions are output, and self-learning evolution of the model and the threshold is realized through a closed-loop feedback mechanism. According to the scheme, the accuracy of anomaly detection, the response timeliness and the risk disposal intelligent level are improved, the method adapts to complex working conditions and batch changes, and the closed-loop optimization and safety control capability of the production process is remarkably enhanced.

The present invention relates to a method and system for predicting the remaining useful life of a rolling bearing. The method comprises: acquiring sample data to be predicted, and inputting same into a trained remaining useful life prediction neural network to obtain an output prediction result, wherein the remaining useful life prediction neural network comprises a feature encoder and a regression predictor, and the training process comprises using the feature encoder to preliminarily extract features from source domain sample data; using a temporal mixed contrastive domain adaptation training module to calculate contrastive loss, and using the contrastive loss to iteratively train the feature encoder, so as to further extract mutual information from target domain sample data features as a high-level feature; and using a fine-grained structural domain adaptation training module to calculate domain discrimination loss and a fine-grained matching degree between the source domain sample data and target domain sample data, and using the domain discrimination loss and the fine-grained matching degree to iteratively train the feature encoder, so as to further extract domain-invariant features between a source domain and a target domain. The system comprises an input interface, an output interface, a processor, a computer readable storage medium and stored program instructions, wherein the processor calls the program instruction to train the remaining useful life prediction neural network, calls the program instruction of the trained remaining useful life prediction neural network to instruct the feature extractor to perform feature extraction on rolling bearing vibration data to be detected, and inputs the extracted features into the regression predictor for prediction processing to obtain a prediction result. The present invention effectively improves the accuracy of the prediction result of the remaining useful life of rolling bearings.

The invention discloses an AI intelligent decision support method and system oriented to multiple scenes of a hospital and a doctor, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: collecting multi-modal medical data, extracting a standardized feature vector, evaluating the feature sensitivity based on mutual information, and only injecting differential privacy noise into a high-sensitivity dimension to generate noise adding feature representation. And constructing a dual-path embedding model in a local training process, and respectively outputting a task prediction result and a federal feedback embedding abstract. Each participant uploads a local model parameter and an embedded abstract to the federated coordination end, and the coordination end generates a competition feedback signal, guides a next round of embedded optimization, calculates an aggregation weight based on a semantic offset degree, executes weighted aggregation to obtain a global fusion model, and completes reasoning output. On the premise of not leaking original data, cross-mechanism intelligent decision-making collaboration is realized, and the task accuracy and the feedback alignment capability are improved.

The invention relates to the technical field of production process intelligent regulation and control, in particular to an intelligent regulation and control method and system for an instant gelatin production process, and the method specifically comprises the following steps: firstly, collecting process parameters such as reaction kettle temperature, acid-base concentration and the like in a production line sensor and control system to form production batch data; then, an optimization model and an intelligent regulation and control strategy are constructed, a dynamic incidence matrix is constructed through time-varying mutual information entropy, multiple types of features are fused to generate a process state descriptor, a constraint dominating relation and a population initialization strategy are optimized, and a self-adaptive genetic manipulation adjustment mechanism and a state-guided search and constraint processing method are designed; iteratively executing an improved NSGA-II algorithm, obtaining an optimized Pareto solution set, and evaluating diversity; and finally, selecting an optimal solution through a multi-criterion decision and a dynamic regulation and control strategy, and converting the optimal solution into an executable process parameter set value. According to the method, the accuracy and the high efficiency of the production process can be improved by realizing intelligent optimization regulation and control of the process parameters.

The invention discloses a sudden death risk real-time assessment system and method based on multi-modal physiological signal fusion, relates to the field of human physiological state monitoring and early warning, and solves the problems of low accuracy and poor real-time performance of sudden death risk assessment by single-modal signals. The system comprises a signal acquisition module, a preprocessing module, a high-dimensional feature extraction module, a multi-modal feature fusion module, a sudden death risk quantification module, a model updating engine module and an early warning feedback module, and each module integrates a self-adaptive filtering unit, a time sequence convolutional network unit, an improved multi-head self-attention mechanism unit and the like. According to the scheme, multi-mode signals such as electrocardio are synchronously collected through multiple channels, a dynamic risk index is calculated through self-adaptive noise reduction, parallel extraction of time-frequency domain nonlinear features and mutual information weighted fusion in combination with kernel density estimation, and online incremental updating and multi-stage early warning of a model are achieved; the sudden death risk can be accurately evaluated in real time, the anomaly detection sensitivity and the early warning timeliness are improved, and the method is suitable for daily health monitoring and high-risk group risk management and control.

The present invention relates to the technical field of intelligent out-of-distribution fault detection for construction machinery, and discloses an out-of-distribution fault detection method and system based on energy propagation and graph learning, and the method includes: acquiring vibration acceleration signals in typical fault states, carrying out similarity calculation to obtain an adjacency matrix composed of the maximum mutual information coefficients, and taking the adjacency matrix as input in a graph neural network; carrying out feature extraction on the adjacency matrix through adopting a GraphSage graph convolution method, and generating each node representation; calculating an energy score of each node, and distinguishing between in-distribution data and out-of-distribution data; and enhancing out-of-distribution data confidence estimation for each node, and carrying out out-of-distribution data identification and out-of-distribution data detection under different working conditions of a rolling bearing.

The invention relates to a water conservancy monitoring data abnormal state identification method based on multi-modal learning, and the method specifically comprises the following steps: deploying a heterogeneous sensor network at a water conservancy facility, collecting original monitoring data, combining the text modal data of a work log, and carrying out the abnormal state marking to form a training data set; constructing uniform time grid tensor alignment multi-modal data, and obtaining a complete alignment tensor by adopting a low-rank tensor completion algorithm of fusion modal mutual information constraint; constructing a machine learning model comprising a cross-modal feature collaborative enhancement module, a heterogeneous feature projection and gating fusion module and a spatio-temporal context sensing anomaly recognition module, inputting a complete alignment tensor into the model to obtain an anomaly probability value, and training the model through a loss function; new data is collected, preprocessed and input into the trained model, an abnormal probability value is compared with a set threshold value, and an abnormal type is judged. According to the method, the feature representation capability is enhanced through multi-module cooperation, and the accuracy and timeliness of water conservancy facility anomaly recognition can be improved.

The invention relates to the technical field of remote sensing data processing and earth surface parameter inversion, and discloses an earth surface matrix parameter hyperspectral data inversion method and system based on machine learning. Comprising the following steps: constructing a multi-source heterogeneous hyperspectral data set; performing feature screening on the preprocessed hyperspectral data set based on an adaptive band selection algorithm, constructing a dynamic weight matrix by calculating mutual information entropy and inter-class distance measurement between spectral bands to realize intelligent screening of key feature bands, and combining spectral derivative conversion and spectral index calculation to generate an enhanced feature vector; and a multi-task transfer learning neural network model is constructed, and an output layer realizes multi-parameter collaborative inversion based on a multi-task learning architecture. And performing preprocessing and feature enhancement operation which is the same as that of the training data on the hyperspectral image data of the to-be-inverted region, inputting the trained neural network model, and outputting a surface matrix parameter inversion result.

The invention provides a vehicle working condition virtual debugging simulation method based on multi-modal perception fusion, and relates to the technical field of digital twinning, and the method comprises the steps: achieving the precise fusion of multi-modal perception data through the synchronous collection of visual, radar and acoustic signals and the unification of a time reference and a coordinate system; constructing a vehicle dynamics state model based on mutual information weighting and recursive filtering, and obtaining dynamically consistent state vectors; combining space-time modeling and robust coding to generate driving characteristics capable of being physically explained, and inputting the driving characteristics into a simulation environment; in the digital twinborn model, parameter adaptive tracking is realized through online correction and joint recursive updating; and closed-loop feedback debugging is formed by using the health index and the fault probability, so that virtual and real consistent and dynamically adjustable vehicle working condition simulation and intelligent debugging are realized. According to the method, fusion of multi-mode sensing data can be realized, so that the simulation model dynamically tracks the working condition of the vehicle.

The invention discloses an automobile electronic wire harness insulation sheath crack detection system based on computer vision, and the system comprises a multi-channel input module which is used for obtaining and preprocessing multi-channel original data frames; the double-domain feature extraction module is used for extracting a double-domain feature pyramid; the double-domain fusion module is used for executing cross-domain attention and mutual information consistent weighting and inhibiting mirror surface highlight pseudo response; the topological structure output module is used for outputting a preliminary crack structuring result through the multi-head crack structure prediction head set; the communication repair module is used for carrying out breakpoint bridging and conflict rollback on the preliminary crack structuring result; and the geometric measurement module is used for measuring crack data and forming a structured detection report. According to the method, polarization reflection suppression and flattening geometric modeling are fused, a double-domain crack detection network is constructed, accurate identification and quantitative measurement of sheath cracks are achieved, and the method has the advantages of being high in reflection resistance, stable in topology and traceable in result.

The invention relates to the technical field of power system operation and control, and particularly discloses a time-sharing electric quantity prediction method based on a logarithmic load density growth curve, which comprises the following steps of: firstly, performing causal detection and dynamic time-delay optimization on historical load and multivariate external data through convergence cross mapping and mutual information technologies, and constructing a causal time-delay feature set; and the problems of multi-element coupling and time-delay effect quantization are solved. Secondly, fitting a load trend by using time-frequency decomposition in cooperation with a segmented logistic model, extracting dynamic parameters representing a growth rate and a saturation capacity, and endowing the model with a sensing ability for a load evolution stage; then, causal features, growth parameters and load components are deeply fused through cross-domain modulation and a gating mechanism, the nonlinear modulation effect of an external environment on a load mode is explicitly modeled, and finally, a probability interval is generated in combination with quantile regression and residual error correction. According to the scheme, accurate and probabilistic prediction of the time-sharing electric quantity in a complex scene is realized, and the scientificity of an agent electricity purchase decision is improved.

The invention discloses an acoustic emission signal noise reduction and feature extraction method and system suitable for a deep roadway, and relates to the technical field of safety monitoring of deep mineral resource mining, and the method comprises the specific steps: arranging an acoustic emission sensor array along the deep roadway, collecting multi-source data, converting the multi-source data into digital signals, and storing the digital signals; identifying an interference type through a wavelet packet decomposition and environment correction algorithm; self-adaptive noise reduction is carried out by using a complexity sensitive penalty algorithm; time domain and frequency domain features are extracted, coupling parameters are calculated, and time domain and frequency domain features are obtained through Hilbert-Huang transform; and finally, through principal component analysis dimensionality reduction and mutual information entropy screening, constructing a feature vector and transmitting the feature vector to a safety early warning system. According to the invention, the processing precision and reliability of the acoustic emission signal are improved through the multi-source signal acquisition module, the interference identification module and the adaptive noise reduction module; signal characteristics are comprehensively described through algorithm optimization, key characteristics are output through characteristic optimization, real-time monitoring and early warning are achieved through a dynamic damage vector algorithm, and deep roadway construction safety is guaranteed.

The invention discloses a pump station unit state comprehensive evaluation method, and belongs to the technical field of pump station unit detection. The method comprises the following steps: acquiring multi-source monitoring data and generating a standardized monitoring data set; time-varying mutual information between indexes is calculated, a dynamic threshold value is determined in combination with current working condition parameters and a water level difference correction term, and a dynamic association network is constructed; performing physical mechanism characteristic decoupling on the data, including stripping a vibration signal working condition drift component based on a reference curve to obtain a vibration residual error, and calculating an equivalent standard working condition temperature based on a heat balance principle; respectively calculating a first weight based on data statistics and a second weight based on network topology, and adaptively generating a comprehensive coupling weight according to a consistency coefficient of the first weight and the second weight; and finally, judging a health state level by using a cloud model. Through deep fusion of a physical mechanism and data driving, the problems that fault features are difficult to extract and the model robustness is poor under variable working conditions are solved, and accurate evaluation of the unit state is achieved.

The application provides a hydrogen pipeline defect detection method and system, equipment and a storage medium, and belongs to the technical field of defect detection. The method comprises the following steps: acquiring phased array ultrasonic detection data, digital radiographic imaging data and hydrogen concentration data of a hydrogen pipeline; extracting defect boundary features based on the phased array ultrasonic detection data, extracting defect microstructure features based on the digital radiographic imaging data, and extracting hydrogen concentration spatiotemporal distribution features based on the hydrogen concentration data; constructing a modal correlation graph by taking the defect boundary features, the defect microstructure features and the hydrogen concentration spatiotemporal distribution features as nodes, and determining the edge weight between every two nodes in the modal correlation graph according to the mutual information of the two nodes; generating a multi-modal correlation feature vector based on the modal correlation graph; and obtaining a defect detection result of the hydrogen pipeline based on the multi-modal correlation feature vector. The application can improve the accuracy of hydrogen pipeline defect detection.

The invention provides a health state prediction method for an 8K display device, and relates to the technical field of display health prediction.The health state prediction method comprises the steps that a multi-modal feature vector is extracted from each pixel node of a multi-modal frequency spectrum image, a mutual information feature vector of the multi-modal feature vector is calculated, and subtle changes of cooperative work among different physical attributes in the nodes are captured; on the basis, each pixel node is further regarded as a node in a pixel node grid, the connection weight is quantized by calculating the norm of a mutual information feature vector of the pixel node and obtaining the similarity between adjacent nodes, then the attenuation rate of the connection weight of a continuous time window is calculated, the distribution entropy value of the attenuation rate is analyzed, and a stability degradation entropy index is obtained; trivial connection change information is condensed into a quantitative index representing the overall orderliness decline degree of the system, then time sequence prediction is carried out on an entropy index historical sequence, the expected time of the entropy index historical sequence reaching the dynamic control upper limit is calculated, and the crossing from current state monitoring to future trend prediction is achieved.

The invention discloses a bridge structure crack real-time monitoring and progress analysis system based on deep learning, and belongs to the technical field of bridge structure health monitoring. The system comprises a time sequence image acquisition and multi-modal data fusion module, a self-adaptive crack feature extraction and identification module, a dynamic evolution tracking and trend prediction module and a closed-loop feedback optimization and early warning decision module, and innovatively introduces feature fusion of time sequence consistency constraint and mutual information maximization based on Riemannian manifold. Deep fusion of multi-modal data is realized; a multi-scale convolutional neural network and an attention mechanism are adopted to accurately extract crack features; predicting a crack development trend by using a long short-term memory neural network and Bayesian inference; a closed-loop feedback mechanism is designed to dynamically optimize system parameters, the four modules are deeply coupled to form a complete closed loop of data forward transmission, performance evaluation and parameter reverse feedback, real-time monitoring, dynamic tracking and intelligent early warning of cracks are realized, and a scientific decision basis is provided for bridge safety management.