4765 results about "Dimensionality reduction" patented technology

The invention provides an intelligent traffic decision-making method based on multi-source sensor data, and the method comprises the steps: obtaining vehicle sensor data and road information, and obtaining a standardized traffic flow feature vector through data synchronization, fusion, feature extraction and dimension reduction processing; inputting the standardized traffic flow feature vector into a traffic flow prediction model, and predicting the traffic flow through a vehicle dynamics model constraint state transition matrix; according to the predicted traffic flow and the real-time road condition information, the road congestion state is judged, and a real-time congestion map is generated; in combination with the real-time congestion map, the road network speed limit rule and the starting and ending point position information, an optimal path is planned, and path smoothing processing is performed through a driving behavior mode; and constructing a vehicle intelligent decision-making system, integrating traffic flow prediction, congestion detection and a path planning model, and outputting a vehicle driving strategy according to real-time vehicle sensor data.

The present invention relates to the field of industrial intelligent predictive maintenance, and in particular to an interpretable deep feature fusion network-based industrial intelligent predictive maintenance method, comprising: acquiring gearbox vibration data comprising noise; performing preliminary extraction and noise suppression on features of the acquired data by establishing an interpretable feature extraction module having a physical information constraint; integrating multi-scale features comprising long-distance and local dependencies by means of a dual-branch feature fusion module having global and local feature fusion capabilities; performing dimensionality reduction on a high-dimensional feature and generating an output by means of a classifier to obtain a final fault identification result; and performing interpretability analysis on a diagnosis process of a model. In the present invention, by embedding the signal processing technology having a well-defined physical theory support into a deep neural network, the interpretability and reliability of model inference results are effectively improved while the fault identification accuracy of the model is improved.

The invention relates to the technical field of network security, in particular to a network security big data state evaluation method based on pattern recognition, which comprises the following steps of: extracting multi-modal features from a network flow log, a system event log, a host behavior log and threat intelligence data, generating a feature matrix, performing feature dimensionality reduction by adopting an auto-encoding network, and obtaining a network security big data state evaluation result; carrying out attack behavior classification and abnormal mode identification in combination with unsupervised clustering and a graph neural network; constructing an attack transition probability matrix based on a Markov model; forming a time sequence attack chain; predicting an attack development trend; and a dynamic protection instruction is issued to the safety equipment. According to the method, the unknown attack detection capability can be improved, the time sequence attack traceability is enhanced, the security situation assessment is optimized, and the method is suitable for security situation awareness in cloud computing, industrial internet and large-scale network environments.

The invention discloses a multi-modal-based power equipment health state monitoring method, and relates to the technical field of power equipment detection.The power equipment monitoring method is based on multi-modal data and knowledge graph fusion, cross check, expert rule cleaning and label correction are implemented by collecting sensing data such as chromatography, temperature, current and vibration in oil, and the detection result is obtained. Outputting high-credibility data; a graph model is constructed based on equipment topology by adopting self-encoder dimension reduction fusion, early anomaly detection is realized by utilizing a graph neural network, and an anomaly alarm is generated; mechanism matching and consistency evaluation are carried out based on the fault mechanism knowledge graph, and interpretable diagnosis is output; and when the diagnosis result is significantly deviated from the actual operation and maintenance conclusion, triggering an online increment and transfer learning updating model and expanding the knowledge graph to form a closed-loop self-learning mechanism. According to the method, the fault detection accuracy is remarkably improved, false alarms and missing alarms are reduced, the operation and maintenance decision-making efficiency is improved, and meanwhile operation and maintenance intelligence and real-time alarm are enhanced.

The invention relates to the technical field of image detection, in particular to a PCBA board defect detection method and system based on image processing, and the method comprises the following steps: carrying out the meshing calculation of a gray scale deviation after a gray scale image is subjected to Gaussian filtering denoising, generating change rate data, carrying out the statistics of a frequency number, constructing a histogram, combining with an Otsu algorithm, and generating a candidate mask; extracting pixels based on a mask, calculating a gradient modulus, screening edge candidate points, carrying out gradient direction connection and morphological processing to generate a complete edge structure, expanding a connected domain through a region growing algorithm, aligning the connected domain with a template contour, and outputting defect coordinates. According to the method, the defect identification sensitivity is improved through combination of gray level image gridding processing and dynamic threshold calculation, a candidate mask is generated through grid gray level change rate statistics and an Otsu algorithm to avoid over-segmentation missing detection, and the contour precision is improved through combination of gradient modulus difference screening and morphological closed operation optimization. The region growing algorithm and template dynamic alignment reduce deformation misjudgment, and staged dimension reduction and feature enhancement reduce calculation complexity and solve resource waste.

The invention discloses a point cloud data rapid processing system based on 3D vision, and relates to the technical field of point cloud data processing. The binocular acquisition module generates an anti-interference high-precision point cloud; the preprocessing module is used for reducing noise and dimensionality and retaining key geometric features; the hierarchical feature extraction module fuses curvature weight and a non-maximum suppression strategy, and improves the robustness of an ORB algorithm in rotation, scale and noise scenes; the industrial scene segmentation module accurately separates stacked objects through normal vector clustering and a dynamic region growing algorithm, and secondarily verifies boundaries in combination with a lightweight semantic model; the two-stage registration module dynamically adjusts a threshold value based on error feedback to realize pose optimization from coarse registration to fine registration; the coordinate mapping module establishes a space mapping model through multi-attitude calibration and robot multi-axis linkage trajectory optimization. The problem that in the prior art, a 3D machine vision algorithm is high in time delay is solved, the real-time performance and rapidity of 3D model recognition and feature extraction are improved, and the efficiency requirement of industrial production is met.

A layout optimization method of water quality monitoring points based on a RF-C-SOM clustering algorithm includes: preprocessing collected water quality data to obtain preprocessed water quality data that are used as data, and using water quality categories as labels to train a random forest model to determine feature importance of water quality indicators; selecting important features based on the feature importance and model training accuracy, performing dimensionality reduction on the preprocessed water quality data to obtain dimension-reduced data; performing a fuzzy clustering on the dimension-reduced data to obtain a water quality section classification result, and based on it, determining initial weight values of a self-organizing mapping algorithm; initializing neurons and training a self-organizing mapping network model with the initial weight values; obtaining a point clustering result through the self-organizing mapping network model; and conducting a water quality index evaluation for the point clustering result before and after screening.

The invention discloses an intelligent evaluation method for real-time learning and teaching effect feedback, and the method comprises the steps: carrying out the preprocessing of collected multi-modal teaching scene data of a target classroom, and obtaining a multi-modal data stream which comprises the aligned target video data, target audio data and a timestamp; pre-training the visual basic model based on the ViT architecture by adopting a plurality of education scene data sets to obtain a first model, carrying out alignment fusion on the first model and a preset variational automatic encoder structure to obtain a preset target model, and carrying out dimension reduction processing on the extracted spatial-temporal features and feature vectors by utilizing the model to obtain target features; and determining a classroom atmosphere index, a student participation index and a teaching interaction effect index based on teaching behavior characteristics, student behavior characteristics and teaching interaction behavior characteristics identified from the target characteristics, and synchronously generating a real-time teaching quality evaluation report. According to the method, the classroom information can be accurately and comprehensively analyzed, and a real-time and objective evaluation result is synchronously generated.

The invention discloses a compressed air energy storage system operation state evaluation method based on principal component clustering analysis. The method comprises the steps that system operation data are collected and preprocessed; performing multi-time scale decomposition and feature extraction on the preprocessed data set; performing nonlinear time-varying adaptive principal component analysis on the multi-time scale feature set, and extracting main features of a system operation state; carrying out clustering analysis on the dimension reduction feature space by adopting a PCA-clustering iterative optimization mechanism; establishing an evaluation model of each functional area of the system based on thermodynamic constraints, and calculating performance index values of the functional areas in combination with the principal component transformation matrix and an operation mode clustering result; dynamically adjusting the weight of each evaluation index; and generating a CAES comprehensive operation state evaluation result. According to the method, the problems of poor PCA adaptability, difficulty in multi-time scale data fusion, low abnormal mode detection sensitivity and the like under the nonlinear time-varying characteristic of the CAES system are solved.

The invention relates to the field of road traffic, in particular to a multi-feature fusion fatigue driving detection method and a fatigue driving detection system. The method comprises the following steps: extracting facial features from a face image of a driver; extracting vehicle features from the vehicle driving parameters of the vehicle driven by the driver; and fusing the facial features and the vehicle features to judge whether the driver is in fatigue driving. Extracting facial features by designing a CNN model; extracting basic convolution features; extracting local convolution features; extracting global convolution features; performing pooling operation; aggregating global features; and carrying out dimensionality reduction mapping. A self-encoder is designed to extract vehicle characteristics; a symmetric deep neural network structure is adopted, and high-dimensional time sequence data is compressed to a low-dimensional potential space through nonlinear mapping; through combination and matching of the CNN model and the auto-encoder, the technical defects of feature redundancy, noise interference, information loss and suboptimal decision existing in an existing multi-feature fusion fatigue driving detection system are thoroughly solved.

The invention relates to the field of power equipment, and discloses a power equipment fault early warning system, which comprises a data acquisition module, a data fusion module, a state evaluation module, a trend prediction module, an early warning judgment module and an information interaction module. Key operation parameters are cooperatively acquired through multiple types of sensors, time series data are uniformly calibrated by adopting a timestamp mechanism, the problems of fragmentation of operation state information of power equipment and superposition of acquisition errors are effectively solved, and then feature fusion and dimension reduction compression are performed on high-dimensional heterogeneous data by introducing a principal component analysis and auto-encoder neural network, so that the operation state information of the power equipment is acquired. According to the method, redundant information is eliminated, meanwhile, key discrimination features are reserved, the sensing dimension of the system for the equipment operation state is more comprehensive, the representation capacity is higher, the Bayesian network and the support vector machine are adopted to jointly evaluate the equipment state health level, higher state recognition accuracy is achieved in a dynamic scene, and the method is suitable for popularization and application. And the model generalization ability is enhanced through historical samples, so that the equipment state can be judged more stably.

The invention provides a methane leakage anomaly detection system and method based on dual-light fusion, and belongs to the field of industrial safety detection. According to the system, a double-light-path holder type laser detector is arranged in a monitoring area, and methane concentration and image data are synchronously acquired through a 1653nm near-infrared laser module and a visible light image module. Time features (mean values, trends and the like) are extracted from the concentration data through time sequence analysis, and spatial features (edges, textures and the like) are extracted from the image data through deep learning; the system fuses time and space features to generate a comprehensive feature vector, reduces the calculation complexity through a dimension reduction technology, and retains core features at the same time. And constructing an anomaly detection model based on an unsupervised learning algorithm, identifying anomaly features in real time, and triggering an alarm. The system realizes real-time and accurate detection of methane leakage, and enhances the reliability and efficiency of industrial safety.

The invention provides a power supply energy analysis method and system for a medium-voltage power distribution network. The method comprises the following steps: collecting multi-modal data of the medium-voltage power distribution network; constructing a health state evaluation model, and predicting a health score and a fault risk of the transformer of the medium-voltage power distribution network according to the fusion features after dimension reduction; constructing an optimization target of the power electronic equipment of the medium-voltage distribution network according to the health score and the fault probability of the transformer; constructing a multi-modal load prediction model to predict the load change of the power electronic equipment in the future; constructing a multi-objective optimization function, and generating an optimal scheduling strategy; and executing the optimal scheduling strategy to actually control the medium-voltage power distribution network equipment. According to the method, the defects of a traditional method in the aspects of equipment monitoring, load prediction and scheduling optimization are overcome, and the overall management level and coping capacity of a power grid are improved by introducing a leading-edge artificial intelligence algorithm and a data fusion technology.

The invention relates to the technical field of communication, in particular to a low-delay data transmission system and method for marine observation data, and the system comprises a data collection module which collects the multi-modal data of a marine environment, and carries out the priority classification of the data according to the real-time performance and importance; the route optimization module dynamically adjusts a multi-path fragmentation strategy and single-path task distribution in combination with a reinforcement learning algorithm and path optimization data; the communication module supports multi-mode communication modes such as laser communication and low-orbit satellite communication, and monitors the link state in real time; and the data fusion and analysis module performs dimension reduction, fusion and real-time analysis on the data by using manifold learning and a graph neural network, and meanwhile, predicts a long-term trend through time sequence analysis to generate observation report data. The method effectively reduces the transmission delay of high-priority data, improves the response efficiency of the system to abnormal events, and is suitable for the scenes of marine disaster early warning, environment monitoring and the like.

The invention relates to the technical field of data processing, and discloses a fuel gas monitoring fault analysis method and system and a storage medium. The method comprises the following steps: acquiring multi-dimensional data through a sensor group, and transmitting the multi-dimensional data to a processing unit for pre-screening through an Internet of Things protocol; carrying out filtering, feature calculation and dimension reduction processing on the data to obtain a feature vector; establishing a fault database and generating a mapping relation; time sequence and space data are fused to calculate a fault judgment value; an early warning result is obtained by combining sensor state data evaluation; historical data is retrieved, similarity is calculated, and a disposal instruction is generated. According to the method, the accuracy of gas pipe network fault diagnosis is improved through fusion analysis of multi-source data, and early recognition and early warning of faults are achieved.

Computer-implemented techniques for multimodal content relevance prediction using neural networks involves processing multimodal content comprising a digital image and text. Initially, dense embeddings are obtained: an image embedding from a pretrained convolutional neural network, and a text embedding from a pretrained transformer network. These embeddings encapsulate the features of the image and text respectively. Two pretrained dense neural sub-networks then reduce the dimensionality of these embeddings. A third dense neural sub-network determines a numerical score for the multimodal content using the reduced embeddings and an additional feature embedding. This score reflects various aspects of the multimodal content, leading to an action taken based on this numerical evaluation, providing a comprehensive and nuanced understanding and management of multimodal digital content.

The invention provides an intelligent material damage diagnosis method and system based on magnetic characteristic data fusion, and relates to the technical field of nondestructive testing, and the method comprises the following steps: collecting magnetic characteristic data by using a magnetic stress detection robot; performing multi-scale decomposition on the data to construct a magnetic domain evolution characteristic spectrum; inputting the characteristic spectrum into a composite neural network to extract a material damage characteristic matrix; constructing a fusion feature vector by using tensor dimensionality reduction and a multilayer belief transfer network; and realizing damage calculation through temperature field modulation and self-adaptive weight distribution to obtain the damage grade and the residual life. According to the invention, accurate diagnosis of the material damage in the welding seam area of the pressure vessel is realized.

The present invention proposes a vector database retrieval method and system, which relates to the technical field of big data processing and intelligent information retrieval. The method includes preprocessing high-dimensional multimodal data to obtain a dimension reduction feature vector; constructing a cluster center based on the dimension reduction feature vector; establishing an inverted file and an intra-cluster plane index based on the cluster center, compressing the inverted file and the intra-cluster plane index to obtain a compressed inverted file and an intra-cluster plane index; obtaining a user's query vector, using the inverted file to screen candidate clusters for the query vector, introducing a hierarchical navigable small-world graph in the intra-cluster plane index, performing an intra-cluster search on the cluster cluster, and obtaining a vector database retrieval result. The present invention effectively takes into account the efficiency and accuracy of high-dimensional data retrieval, and improves the retrieval efficiency and accuracy.

The invention relates to a charging pile intelligent operation management method and system based on the Internet of Things. The method comprises the following steps: performing Internet of Things topology construction on charging pile nodes to obtain a node communication distance matrix and a network connectivity matrix; performing deep learning dimension reduction and node screening based on the network connectivity matrix to obtain a fault feature vector; the fault feature vector and the charging pile operation state data are input into a hybrid neural network for risk calculation, a fault risk assessment value is obtained, and the hybrid neural network comprises a space perception sub-network and a time sequence analysis sub-network; performing normalization and dimension reduction mapping on the power response data of the charging pile to obtain a time domain mapping matrix; and performing particle swarm optimization calculation based on the time domain mapping matrix and the fault risk assessment value to obtain a charging power distribution scheme, and generating a control instruction set according to the charging power distribution scheme. According to the invention, the dynamic optimization of the charging power distribution scheme is realized, and the charging efficiency and the load balance are effectively balanced.

The invention relates to a hydrological flow prediction method and system based on multi-site time-space association. The prediction method comprises the following steps: carrying out dimension reduction and feature reconstruction on original multi-site hydrological data through an auto-encoder; space-time correlation modeling and adjacency matrix dynamic construction are carried out, a multi-dimensional Euclidean distance matrix between stations is calculated based on a multivariable dynamic time warping (MDTW) algorithm, a similarity matrix is generated in combination with dynamic programming, and a dynamic adjacency matrix is constructed by fusing a geographic space adjacency relation; extracting spatial features of a GCN (Graphics Convolutional Network); carrying out adaptive time sequence decomposition and trend-period modeling; and carrying out multi-stage fusion prediction and result output, and generating a final prediction result through a decoder in combination with the decomposed trend item and periodic item. According to the method, accurate extraction and dynamic correlation modeling of spatial-temporal characteristics of multi-site hydrological data are realized, the accuracy and robustness of single-site flow prediction are improved, and the problems that multi-site spatial-temporal correlation modeling is insufficient, non-linear time sequence alignment is difficult, and single-site prediction precision is limited are solved.

The invention relates to the technical field of equipment fault prediction, in particular to a cup labeling equipment fault prediction method and system based on machine learning. According to the method, equipment operation state parameters are converted into a multi-mode pulse sequence with a timestamp synchronization characteristic; loading the purified pulse flow to a quantum bit array for entanglement state evolution, and extracting a three-mode entanglement association tensor; carrying out dimensionality reduction projection on the three-mode correlation tensor to an equipment degradation manifold space, and determining quantum tunneling probability density distribution; constructing a time-varying Hamiltonian of an equipment degradation state based on quantum tunneling probability density distribution, and generating a degradation track cluster according to the time-varying Hamiltonian; and performing time sequence convolution processing on the degradation track cluster, performing probability amplitude amplification on a fault critical point in the track cluster by using an energy level splitting characteristic of a time-varying Hamiltonian, and generating a space-time probability cloud picture. The fault evolution law can be visually presented, the accuracy and timeliness of early fault early warning are improved, and a reliable basis is provided for predictive maintenance.

The invention provides an intelligent scheduling and control method and device for an integrated energy system. According to the method, power, gas and heat resource operation data are acquired, multi-scale layered modeling is performed according to a time scale and a space scale, and a power resource state space model, a gas resource flow continuity model, a heat resource heat balance model and a multi-energy coupling characteristic constraint model are established; carrying out feature extraction and dimension reduction representation by adopting a deep auto-encoder network; cooperative training of multiple groups of cognitive models is carried out through a split hierarchical federal learning framework, and a global intelligent model is obtained; constructing a neural architecture search network with a hybrid bionic learning rule, setting a hierarchical scheduling target, and generating a hierarchical intelligent scheduling strategy; and a fault-tolerant control mechanism is constructed, and error detection and correction of operation deviation are realized. According to the invention, multi-time scale collaboration, collaborative learning under multi-device group privacy protection and high-reliability fault-tolerant control are realized, and the operation efficiency and reliability of the integrated energy system are remarkably improved.

The invention discloses an intelligent partial discharge online monitoring and fault diagnosis system based on multi-sensor fusion, and the system comprises a multi-sensor collection module which is used for synchronously collecting data in a partial discharge process; the data preprocessing module is used for preprocessing the partial discharge signal data; the feature fusion module is used for constructing a fusion weighted feature matrix; the feature dimension reduction module is used for constructing a fusion feature matrix after dimension reduction; the partial discharge classification model training module is used for constructing a partial discharge type classification model by adopting a lightweight capsule network; the hyper-parameter search optimization module is used for optimizing the partial discharge type classification model; the classification model deployment module is used for deploying the optimized partial discharge type classification model; and the online reasoning and fault diagnosis module is used for receiving data in real time, generating a fault alarm signal and recording and returning fault event information. According to the invention, a real-time partial discharge on-line monitoring and intelligent fault diagnosis scheme is provided for equipment.

The invention discloses an equipment state sensing system based on peak shifting and danger situation sensing, which relates to the technical field of electric digital data processing and comprises a data acquisition unit, a model training and optimizing unit, a situation sensing unit, a peak shifting regulation and control unit and a system interface and user interaction unit. According to the method, the device state data feature similarity is dynamically calculated by using the Gaussian kernel function, the similarity matrix is adjusted in combination with the gradient update item, dynamic weighting is performed in feature extraction, and redundant features are reduced by adopting the adaptive discarding probability, so that the feature extraction efficiency of the model in a high-dimensional feature space is improved; the problem that the dimension reduction effect is affected by too many redundant features of the equipment state data is solved. Equipment state features are extracted layer by layer through a deep forest algorithm of a multi-layer random forest structure, a decision tree is optimized in combination with a dynamic adjustment weight mechanism and a reinforcement learning strategy, and the classification capability of the model for equipment state changes and abnormal states is enhanced.

The invention relates to the technical field of program exception positioning, in particular to an AI-based data interface program exception fault pre-positioning algorithm, which comprises the following steps: S1, data stream pre-analysis; s2, capturing an abnormal signal; s3, performing abnormal clustering and feature extraction; s4, interface behavior modeling is carried out; s5, abnormal traceability positioning; s6, generating a self-adaptive repair suggestion; s7, performing feedback circulation and continuous learning; and S8, performing visualization and report generation. Through the steps of data stream classification, key field extraction, behavior modeling and the like, the system can accurately capture abnormal signals in mass data, meanwhile, an abnormal mode is visually displayed through clustering analysis and a dimension reduction technology, a reliable foundation is laid for subsequent positioning and repairing, and in addition, the method has the advantages of being high in practicability and easy to popularize. The abnormal source is quickly positioned by adopting methods such as topological sorting and causal network reasoning, the false alarm rate and the missing report rate in the positioning process are effectively reduced by combining a sliding window and time sequence analysis, and the problem solving efficiency is greatly improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as agent autonomous decision making, financial science and technology and medical health, and discloses a multi-modal hierarchical feature fusion and decision making method, device, equipment and medium, and the method comprises the steps: obtaining vision, language and motion data, and carrying out the hierarchical feature extraction to generate a multi-modal initial feature set; feature importance is analyzed, screening and dimension reduction are carried out, and screened multi-modal features are obtained; performing semantic enhancement on the screened multi-modal features to generate multi-modal semantic enhancement features; executing cross-modal attention fusion on the multi-modal semantic enhancement features to obtain cross-modal fusion features; and inputting the cross-modal fusion features into a semantic reasoning network to generate a decision result. According to the method, through multi-level screening dimension reduction, semantic enhancement and cross-modal attention fusion, the model can accurately utilize key feature relationships among multi-modal data, redundant interference is reduced, and semantic reasoning accuracy and decision-making efficiency are improved.

The invention provides a deep learning-driven coastline change dynamic monitoring method, and belongs to the technical field of image understanding and recognizing.The deep learning-driven coastline change dynamic monitoring method comprises the steps that a time sequence attention matrix is formed by constructing a multi-source satellite data fusion network, and coastline form change features are extracted by applying a fourth-order time sequence convolutional neural network to construct a first fluctuation matrix; and performing dimension reduction processing by using a space-time attention mechanism to generate a second fluctuation matrix, and converting the second fluctuation matrix into a quantitative index through a fluctuation conversion matrix. The method comprises the steps of inputting data into a pre-trained CoastDynNet model to analyze a physical mechanism, performing fine adjustment by applying a coastline evolution optimization function, performing multi-scale decomposition by using a WaveletTempNet network to separate long and short term changes, and finally constructing a multi-model integration framework by using a Bayesian model averaging method to generate a comprehensive coastline dynamic evaluation report.

The invention discloses a distribution network tree barrier real-time analysis method and system based on dynamic vision and SLAM. The method comprises the following steps: generating a dynamic visual baseline by cooperatively controlling the translation and flight displacement of an unmanned aerial vehicle holder, and constructing a bionic binocular model to simulate a time sequence image into a binocular image pair; generating a depth point cloud through epipolar correction and stereo matching; key targets are recognized and extracted through a semantic segmentation network, and semantic point clouds are generated; establishing a dimensionality reduction motion model by utilizing pan-tilt stability augmentation, and fusing a visual inertial odometer and RTK data by adopting a filtering or optimization algorithm to realize centimeter-level pose estimation; and finally, performing optimization processing on the semantic point cloud, completing three-dimensional reconstruction based on multi-modal fusion, and outputting a risk assessment result through tree line spacing calculation and safety margin analysis. According to the invention, accurate, efficient and automatic routing inspection and risk early warning of the distribution network tree obstacles are realized.

The invention relates to the technical field of industrial Internet of Things, in particular to a machine equipment online state monitoring and fault diagnosis system, which comprises the following steps of: acquiring multi-source heterogeneous sensing data through an edge computing node deployed on an equipment body, performing adaptive noise filtering and feature dimension reduction processing on original data, and acquiring multi-source heterogeneous sensing data; outputting a standardized equipment state vector set; inputting the equipment state vector set into a dynamic knowledge graph engine, constructing a fault evolution network comprising space-time correlation characteristics based on an equipment operation entropy change quantification model, and generating a graph node connection relationship with a weight coefficient; and inputting the fault evolution network into a migration reinforcement learning module, and outputting a diagnosis decision set comprising a fault type, a severity degree and an evolution path through knowledge migration of a cross-device fault mode. According to the method, the problems of edge redundancy and single feature expression in traditional rule-based atlas construction are effectively avoided, and the structuring ability and physical traceability of fault recognition are improved.

The invention provides a consistency test method and device for single-to-wire communication, and the method comprises the steps: receiving a communication signal transmitted by a tested device, extracting a time domain feature, a frequency domain feature and a modulation feature of the communication signal, generating a protocol feature value, carrying out the dimension reduction processing, and generating a protocol feature vector; performing matching degree analysis on the protocol feature vector and a predefined mapping dictionary, and determining a target communication protocol type with the highest matching degree; based on the determined communication protocol, dynamically generating a consistency test flow through a pre-trained neural network model; configuring a signal generator to inject an excitation signal according to the test flow, and synchronously acquiring various parameters of the tested equipment; based on the real-time feedback data, dynamically adjusting the signal amplitude, frequency and judgment threshold through a pre-trained neural network model; and summarizing feedback data of multiple rounds of tests, and generating a test report of the consistency index. According to the method, the protocol of the tested equipment can be automatically identified, the test flow is generated for the protocol, and the test efficiency is effectively improved.