1417 results about "Reconstruction error" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent anomaly recognition and intervention processing method, device, equipment and medium. The method comprises the following steps: carrying out feature fusion by using a gating fusion network and generating a preliminary abnormal score, determining a reconstruction error through an auto-encoder and triggering abnormal early warning, calculating a causal effect value of key features in combination with a causal graph model and anti-factual reasoning, and calibrating the abnormal score to generate a final abnormal score and an intervention instruction. And executing an intervention action and recording a result. According to the method, the multi-dimensional feature information and the causal reasoning mechanism are fused, the self-encoder reconstruction error is combined to carry out anomaly judgment, the intervention instruction is generated and executed, closed-loop control of anomaly detection, reasoning analysis and intervention execution is achieved, and the recognition accuracy of complex events and the system response capacity are improved.

The invention belongs to the technical field of power batteries, and particularly relates to a power battery attenuation prediction method based on a depth auto-encoder, which comprises the following steps: firstly, collecting new energy automobile power battery sample data, pre-processing, and constructing a depth auto-encoder model; adjusting the parameters of the depth auto-encoder model according to the gradient information of the loss function by adopting an optimization algorithm until the training of the depth auto-encoder model is completed; then collecting real-time operation data of a current target new energy automobile as original data, inputting the original data into the depth auto-encoder model, outputting reconstruction data, calculating reconstruction errors, associating the reconstruction errors with the health state of the power battery, and representing the reconstruction errors as a health index of the power battery; and finally, based on the time sequence data of the power battery health index, predicting the decline trend and the residual life of the power battery by adopting a bidirectional LSTM + Attention mechanism. The problem of low accuracy of a power battery life prediction technology in the prior art can be solved.

The invention relates to a network flow anomaly detection strategy generation method based on machine learning, and belongs to the technical field of machine learning. The method comprises the following steps: firstly, collecting network traffic data in a preset time window, and extracting feature vectors containing traffic, a time sequence and a protocol type; and inputting the feature vector into a long short-term memory auto-encoder model, and calculating a reconstruction error to judge whether the network flow is abnormal or not. Aiming at the abnormal feature vector, adopting a multi-agent depth deterministic strategy gradient algorithm to construct a plurality of cooperative agents, and independently generating a candidate abnormal detection strategy by each agent; through a cross-agent strategy evaluation mechanism, the difference between a joint strategy and a single-agent strategy in the aspect of anomaly detection accuracy is compared, cooperation gain is calculated, strategy exploration parameters of all agents are adjusted according to the cooperation gain, and a global optimal anomaly detection strategy is optimized and determined in real time. According to the method, high-precision and low-missing-report network traffic anomaly detection can be realized, and the method has good self-adaptability and real-time performance.

The invention discloses an industrial equipment fault early warning analysis method and system based on a graph neural network, and relates to the technical field of intelligent fault diagnosis and equipment state monitoring, and the method comprises the steps: constructing a time slice data tensor, and forming a graph neural network input structure; on the basis of the graph neural network input structure, feature learning and correlation analysis of the graph neural network are executed, and node-level embedding representation is obtained; and based on the node-level embedding representation, calculating fault relevance response distribution of the nodes, evaluating propagation risks and positioning equipment fault sources. According to the method, the problem that a traditional method cannot effectively express a spatial structure or a cooperative relationship between measuring points in an early modeling stage is solved, the spatial perception capability of overall modeling is improved, so that a data foundation is laid for subsequent feature extraction and anomaly analysis, and the reconstruction accuracy is improved by comparing an embedded reconstruction value with an original observation value and calculating a node-level reconstruction error. Fault influence range evaluation and traceability positioning are realized, and quantitative support is provided for early warning response and maintenance decision.

The invention relates to the technical field of spatial-temporal trajectory data anomaly detection, in particular to a trajectory anomaly route detection method and system based on self-supervised trajectory representation learning. The method comprises the following steps: preprocessing acquired trajectory data; track double-view comparison representation learning is carried out based on a double-view-angle synchronous mask strategy; coding time dynamic based on a space-time fusion mechanism and fusing the time dynamic with spatial features; learning essential representation from the fused spatio-temporal features to perform trajectory reconstruction; and error checking is carried out based on the reconstructed trajectory, and an abnormal trajectory is judged through the reconstructed error. The invention provides a brand new trajectory anomaly detection model. According to the model, a GPS track and a grid-based track feature are fused, so that track representation is enriched; and meanwhile, a double-view synchronous mask mechanism is designed, so that the model can sense local disturbance of space and time dimensions at the same time in a training stage, and thus the sensitivity to local anomaly is improved.

The invention discloses an adaptive mask medical image segmentation method based on a self-supervised mask and deep reinforcement learning, and the method comprises the steps: employing a classic encoder-decoder architecture for a self-supervised mask reconstruction network, fusing a Swin Transform encoder, and carrying out the feature fusion of local image blocks through a self-attention mechanism; according to the self-adaptive mask model, a PPO deep reinforcement learning algorithm is adopted, a strategy network and a value network are constructed, mask actions are dynamically regulated and controlled, reconstruction errors are gradually reduced, a mask strategy is continuously optimized in multiple times of strategy updating for self-adaptive optimization, and high-quality reconstruction of a medical image influenced by missing information is achieved; according to the method, high-quality feature representation can be obtained in an unlabeled data environment, and relatively high precision and accuracy are presented on a public data set.

The invention discloses an industrial image anomaly detection method based on deep learning, and particularly relates to the technical field of industrial visual detection. The problems of high false alarm rate, fuzzy fine defect positioning, insufficient real-time response capability, difficulty in model increment updating and the like caused by data distribution drift in an industrial scene are solved. According to the method, robust features are extracted through a multi-scale feature fusion auto-encoder, and a dynamic memory bank is constructed to update a normal sample prototype online; a dual-path detection mechanism is adopted to cooperate with a pixel-level reconstruction error and attention weighted feature matching deviation; efficient edge reasoning is realized in combination with block parallel processing and model compiling optimization; and designing an elastic incremental learning framework to prevent disastrous forgetting. And finally, false alarms caused by environmental changes are reduced, accurate positioning of pixel-level defects is realized, millisecond-level detection requirements of high-resolution images are met, safe and efficient model online evolution is supported, and adaptability and reliability of an industrial quality inspection system are comprehensively improved.

The invention provides a gas turbine exhaust gas temperature prediction and fault early warning system and method, and relates to the technical field of gas turbine state monitoring and fault diagnosis. The system comprises a data acquisition and preprocessing module used for acquiring and preprocessing data; the dynamic graph construction module is used for constructing graph structure data; the graph neural network module is used for extracting spatio-temporal features through learning; the improved auto-encoder module is used for realizing feature reconstruction by learning normal working condition data and then outputting reconstruction errors to the fault early warning module; the temperature prediction module is used for predicting a future exhaust temperature value; the fault early warning module is used for performing fault judgment and decision; and the result display and output module is used for summarizing analysis results. According to the system, the prediction precision of the exhaust temperature is remarkably improved, the fault detection and early warning capabilities are enhanced, the system has better feature representation capability and adaptability to complex working conditions, and close integration and mutual enhancement of prediction and early warning are realized.

The invention relates to the technical field of structural damage identification, in particular to an unsupervised structural damage identification method based on CAE-LSTM. The method comprises the following steps: training a CAE-LSTM model by using a training set to obtain a trained model, and reconstructing unknown data including health data and damage data by using the trained model to obtain a reconstruction error of the health data and a reconstruction error of the damage data; determining a damage sensitivity factor of the acceleration response signal of each batch by combining a probability density function of health data, a probability density function of damage data and a reconstruction error in the acceleration response signal of each batch of the undamaged structure so as to determine a damage threshold and screen a damage position; acquiring a damage factor according to the health state data and the damage state data of the damage position sensor; and judging the damage degree based on the size of the damage factor. According to the invention, the accuracy and reliability of the structural damage identification result are improved.

A computer implemented method for defect detection comprises obtaining an imaging dataset of a wafer, and verifying a defect criterion in a subset of the imaging dataset of the wafer. The defect criterion comprises an observation representation of the subset of the imaging dataset with respect to a number of characteristic elements derived from reference images of semiconductor structures. The observation representation and the characteristic elements define a reconstruction of minimal reconstruction error, and a tolerance statistic on defect-free representations of subsets of defect-free observed imaging datasets. Each of the defect-free representations and the characteristic elements define a reconstruction of minimal reconstruction error of a subset of the defect-free imaging datasets. The computer implemented method further comprises generating defect information.

The invention relates to an industrial Internet of Things anomaly detection method based on time sequence and text joint modeling, and belongs to the technical field of industrial Internet of Things anomaly detection. The method comprises the following steps: constructing text prompt information based on collected industrial Internet of Things time sequence data, and respectively taking the text prompt information as inputs of a time sequence channel and a text prompt channel; a sensor association graph is constructed by using a multi-hop GCN, and on the basis of the association graph, time feature modeling from local to global is completed by using multi-scale expansion convolution and combining a differential attention mechanism; performing word segmentation processing on the text prompt information through a word segmentation device, and encoding the text prompt information into vector representation; and calculating attention weight between time sequence embedding and text prompt embedding, fusing to obtain joint embedding representation, enhancing the joint embedding representation, inputting the enhanced joint embedding representation into MLP for reconstruction, calculating an abnormal score through a reconstruction error, and carrying out industrial Internet of Things anomaly detection according to the abnormal score. The method is high in anomaly detection accuracy, and can improve the equipment anomaly perception and risk early warning capability.

The invention discloses a battery abnormal data prediction and state evaluation method, and relates to the technical field of battery state prediction, and the method mainly comprises the steps: carrying out the preprocessing of an experiment data set, and obtaining multi-dimensional time series data; a combined feature encoder, a pre-response encoder and a memory analysis module are constructed to realize a battery abnormal data fault prediction model; training the model by using the multi-dimensional time sequence data to obtain a trained model, and predicting the to-be-predicted data to obtain a prediction result; and calculating a reconstruction error between a prediction result and original data, constructing an AUROC evaluation model, and evaluating the battery abnormal data fault prediction model. By implementing the battery abnormal data prediction and state evaluation method provided by the invention, the feature extraction efficiency, the abnormal recognition precision, the detection stability and the generalization ability can be improved.

The invention discloses a power distribution cabinet bus temperature change analysis and early warning method and system, and relates to the technical field of power distribution cabinets, and the method comprises the steps: collecting the temperature data of a three-phase bus of a power distribution cabinet, dividing a temperature sequence into time-space units, and calculating the spatial characteristics and time characteristics of each time-space unit; performing frequency domain analysis on the temperature data of the space-time unit, decomposing the temperature data to obtain a low-frequency component, an intermediate-frequency component and a high-frequency component, extracting sub-components of different time scales, and calculating a time coupling feature, a space coupling feature and a physical coupling feature; fault-free data is collected to establish a normal mode library, historical fault data is collected to establish a fault mode library, the deviation degree of features and a baseline is calculated in real time, and marking and feedback updating are carried out on potential new faults; on the basis of distributed monitoring, temperature abnormal points are positioned, reconstruction errors of all scales are calculated, the abnormal origin scale is determined, and graded early warning is triggered, so that the problem that a traditional monitoring method is insufficient in fault recognition capability is solved.

The invention discloses an operation data management system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the following steps: 1, collecting multi-source data in real time through an intelligent sensor and industrial equipment, achieving time synchronization, and ensuring data alignment; step 2, cleaning, labeling and standardizing the collected data, extracting feature vectors of the data, and generating multi-modal feature vectors through dynamic weighted fusion of an attention mechanism; step 3, calculating a reconstruction error based on an auto-encoder to identify an abnormal state, quantifying the contribution degree of each feature to the abnormity in combination with an SHAP algorithm, and positioning key influence factors; and step 4, dynamically adjusting the detection threshold by adopting reinforcement learning to reduce the false alarm and missing alarm rate, and evaluating the prediction confidence by quantifying the model error and the data error to realize the dynamic adjustment of the early warning priority. According to the invention, the condition that abnormal data and false alarm of the abnormal data are difficult to effectively warn in the prior art can be improved.

The invention relates to a wind power equipment blade fault detection technology, discloses an unsupervised wind power equipment blade fault detection method based on a phase perception parallel attention mechanism, and solves the problems that an existing wind power equipment blade fault detection method is high in dependence on labeled data, insufficient in generalization ability under strong noise and variable working conditions and high in fault detection efficiency. And a weak transient fault signal and a dynamic change characteristic are difficult to capture robustly. According to the scheme of the invention, the method comprises the steps: collecting a blade operation audio signal, and extracting a dual-channel time-frequency feature containing an amplitude spectrum and a phase spectrum through improved short-time Fourier transform; a deep adversarial auto-encoder is constructed by using an encoder containing a phase perception parallel attention module, a decoder and an auxiliary encoder, and normal working condition feature distribution is learned by reconstructing an error loss, potential representation consistency loss, adversarial loss and phase consistency loss optimization model during off-line training; in the reasoning stage, the fault is judged based on the feature distance score and the reconstruction error score.

The invention discloses a substation remote fault early warning system and method based on deep learning. The method comprises the following steps: obtaining a standardized substation operation data set; constructing model data of the improved variational auto-encoder; an optimized improved variational auto-encoder model is obtained; performing multi-round training on the operation data set of the standardized transformer substation by using the optimized improved variational auto-encoder model to form a high-sensitivity and high-accuracy anomaly detection model; acquiring operation data of the transformer substation in real time, inputting the operation data into the anomaly detection model, analyzing the operation data of the transformer substation in real time, and detecting an abnormal mode having significant deviation from the normal operation state of the transformer substation; and when the abnormal detection model detects an abnormal mode, the system automatically triggers remote fault early warning. According to the method, typical modes of temperature rise abnormity, voltage fluctuation abnormity, equipment decline trend abnormity and sudden composite abnormity are further identified according to the deviation degree of the reconstruction error, and the interpretability and guidance of early warning are enhanced.

The invention discloses a time series anomaly detection method based on combination of hierarchical adaptive attention and Mamba. According to the method, a multi-granularity token routing strategy is provided, the strategy dynamically allocates computing resources in a time context, adaptively concentrates processing capacity on an information segment, and keeps wider perception at the same time, so that attention computing can be dynamically focused on different time scales and modes according to the complexity of input data; according to the method, the Mama is improved, so that parameters of the Mama can be dynamically adjusted according to characteristics of an input sequence, the long-distance dependency relationship is effectively simulated, and meanwhile, the modeling capability of a nonlinear time mode is enhanced. The abnormal score calculation process comprises three stages: reconstruction error calculation, error normalization and hierarchical score fusion. Different from a traditional method using a fixed threshold, the self-adaptive threshold selection strategy constructed by the method considers time context and data set features, the efficiency and precision of anomaly detection are improved, and effective support is provided for development of time sequence anomaly detection.

The invention discloses a power transmission line icing galloping risk early warning system and early warning method, and belongs to the technical field of icing galloping risk early warning, and the system comprises the following modules: a historical data processing module reconstructs historical monitoring data by using a long and short term memory auto-encoder, recognizes abnormal data through reconstruction errors, and sends the abnormal data to an early warning module; removing outliers based on a 3sigma-dynamic threshold algorithm, and then marking a space-time credibility weight; the terrain compensation module constructs a micro-terrain feature vector, calculates the similarity through a Siamese network, and compensates low-confidence data; the real-time data processing module obtains the icing thickness according to the monitoring data of the current time point and the previous time point; the data acquisition and analysis module fuses historical and real-time data and analyzes a galloping state; and the risk early warning module takes the historical data with the weight and the galloping state information as input, outputs a risk value through the prediction model, and triggers early warning. According to the system, through cooperative work of all the modules, the icing galloping risk is accurately warned in real time, and the safety and reliability of power grid operation are improved.

The invention provides a drilling risk determination method and device. Before specific implementation, a graph auto-encoder is introduced and used, and a preset detection model which is based on physical constraints and has a good application effect is obtained through unsupervised learning and training. In specific implementation, current target logging data of a target well and a logging data set of a current time period can be firstly obtained; determining a current working condition according to the target logging data; according to the current working condition, the logging data set of the current time period and a preset geological-engineering pre-drilling evaluation profile, a target dynamic threshold value which aims at a current target well and is based on working condition constraints and considers the change condition of data processed in the current time period before graph self-coding is determined; processing the target logging data by using a preset detection model to obtain a corresponding target reconstruction error; and detecting whether a drilling risk exists according to the target reconstruction error and the target dynamic threshold. Therefore, the drilling risk can be accurately detected and identified, and the false alarm rate is reduced.

The invention relates to the technical field of signal noise reduction and reconstruction, in particular to a microseismic signal noise reduction and reconstruction method and system based on multi-scale decomposition, and the method comprises the steps: S1, carrying out the multi-scale Shaplet decomposition of a noise-containing microseismic signal based on different sequence lengths, measuring the matching degree between each signal block and all candidate Shaplets through Euclidean distance, and obtaining the matching degree between each signal block and each candidate Shaplet; constructing a multi-scale feature matrix M; s2, based on the multi-scale feature matrix M, multi-scale weighted importance measurement is carried out through a time convolution network and an attention mechanism so as to evaluate the importance of each signal block in the noise reduction process; and S3, constructing a U-Net microseismic signal noise reduction model, designing a loss function in combination with the reconstruction error and dynamic time warping, and carrying out multiple iterative training on the U-Net microseismic signal noise reduction model until the error meets a preset requirement. According to the method, noise can be effectively identified and removed, and especially in data containing different noise, the method is beneficial to high-quality noise reduction of micro-seismic monitoring data.

The invention relates to the technical field of computer vision, discloses an unsupervised anomaly detection method based on multi-scale features, and proposes a multi-scale feature adaptive unsupervised detection framework MSFA, which comprises the following steps: after processing an input image through a pre-training network, extracting a multi-scale intermediate feature map; all the feature maps are flattened and uniformly mapped to a fixed dimension, and position codes are superposed at the same time; the extracted features are processed by introducing a global dynamic transformation unit (GDT) and a local region interaction module (LRI), the GDT and the LRI cooperatively execute in an encoder, a fusion result is combined with guide reference representation, and a final reconstructed feature map is generated; and comparing the reconstructed feature map with the original feature map layer by layer, calculating the similarity of a reconstruction error and a cosine, generating a multi-scale anomaly score, performing up-sampling on all score maps to the size of the original image, and finally outputting an anomaly positioning map. Compared with the prior art, the method has the advantages that dependence on local redundant information is reduced, and the recognition capability of the abnormal key area is remarkably enhanced.

The invention discloses a single-person abnormal behavior identification method and system based on multi-modal skeleton feature fusion, and the method comprises the steps: S1, collecting continuous RGB images and infrared thermal imaging images in a monitoring video, carrying out the human body detection and key point estimation of visible light and infrared images through employing a multi-modal fusion model of YOLOv12 in combination with Transform, and constructing a single-person posture time series data set; s2, key point speed vectors are calculated for the continuous skeleton frame sequence of each target person, skeleton key point information and speed information are fused, and an action feature sequence is formed; s3, inputting the motion feature sequence into an MPED-RNN model, decomposing skeleton motion into a global displacement component and a local attitude deformation component, and performing joint coding, decoding and prediction through a dual-channel GRU network; and S4, calculating a prediction error and a reconstruction error according to a reconstruction result and a future skeleton key point prediction result, evaluating whether the current behavior deviates from a normal trajectory, and judging whether the current behavior is in an abnormal state. According to the invention, real-time identification of abnormal behaviors of a single person in a complex scene is realized.

The invention provides a hydropower station AI supervision system and method based on a multi-modal large model, and relates to the technical field of intelligent hydropower. The system comprises a multi-modal data acquisition module, a cross-modal space-time alignment module, a multi-modal feature extraction module, a multi-modal large model processing module and an intelligent reasoning and decision module. A neural differential equation model is introduced to carry out space-time alignment on asynchronous sensing data, networks such as Vision Transformer, MelCNN, TCN and the like are utilized to extract multi-modal features, cross-modal fusion analysis is realized by combining a local and global attention mechanism and dynamic weight distribution, and equipment abnormality is further reasoned based on a reconstruction error, a mahalanobis distance and a knowledge graph and a maintenance strategy is generated. According to the method, high-precision anomaly detection, fault root cause positioning and dynamic maintenance optimization of key equipment of the hydropower station are realized, diagnosis errors caused by traditional manual inspection and data splitting are avoided, and the operation and maintenance intelligence level and the equipment operation reliability are improved.

The invention provides an offshore wind turbine generator fault diagnosis method and system based on multi-source sensor data fusion. The method comprises the steps that a vibration signal from at least one component of a wind turbine generator is acquired through a vibration sensor; performing time-frequency conversion on the vibration signal by applying synchronous compression wavelet transform to obtain time-frequency representation of the vibration signal; when the reconstruction error exceeds a preset threshold value, it is judged that an abnormal event exists in the vibration signal; obtaining the position of a part corresponding to the abnormal event; starting an image sensor and an acoustic sensor according to the position of the component, and acquiring an image signal and a sound signal of the component according to the image sensor and the acoustic sensor; according to the DS evidence theory, the vibration signal, the image signal and the sound signal, obtaining the confidence of the fault type; the fault type of the component is judged according to the maximum confidence allocation principle, high-resolution time-frequency analysis can be achieved through synchronous compression wavelet transform (SST), and the fault feature identification degree is improved in combination with the self-encoding neural network and the D-S evidence theory.

The invention discloses an industrial product defect detection method based on multi-granularity feature fusion, and the method comprises the steps: carrying out the preprocessing of an original image, inputting the preprocessed image into a multi-model encoder, and generating a multi-granularity feature representation; inputting the multi-granularity feature representation into a CBAM module to generate a weighted feature map; inputting the weighted feature map into a Transform module, and generating a fusion feature map; inputting the fused feature map into a multi-layer decoder to generate a reconstructed image; performing optimization training on the model according to the reconstructed image and the input image by minimizing a multi-scale loss function; and generating a new reconstructed image, and generating a defect thermodynamic diagram through a pixel-level reconstruction error. According to the method, a novel multi-granularity feature extraction and fusion framework is constructed, defect detection requirements of different industrial scenes can be well met, and a new technical thought is provided for recognition of complex anomalies.

The invention provides a power big data-based sparsity feature adaptive compression method, which comprises the steps of adaptively optimizing a sensing matrix through a dictionary learning method aiming at a screened multi-granularity feature subset, introducing a sparse regularization item to enhance the sparsity of data reconstruction, and according to a missing mode of a missing value and context information, carrying out adaptive compression on the sparse feature of the power big data. Establishing a mapping relationship among the compression ratio, the reconstruction error and the feature subset, and determining a self-adaptive compression strategy; based on a self-adaptive compression strategy, a compressed sensing method is adopted to carry out self-adaptive compression on the multi-time-granularity power data, the compression ratio is dynamically adjusted according to reconstruction error feedback, the data compression and reconstruction quality is balanced, and the reconstructed multi-granularity power data is synchronized according to timestamp precision.

The invention discloses an industrial image change anomaly detection method and system based on artificial intelligence, and relates to the technical field of image recognition, and the method comprises the steps: collecting a dual-light-source industrial image, employing frequency domain saliency to guide fusion, and carrying out visual enhancement processing through color mapping and edge enhancement; inputting the enhanced image into a CNN convolutional network to generate a multi-scale feature map, extracting a multi-scale high-pass residual image through two-dimensional fast Fourier transform and a high-pass filtering template, and splicing all scales and coding to generate a token sequence through local attention guide fusion; constructing a self-induction visual model, and performing feature reconstruction on the token sequence to generate a reconstructed feature map; and calculating and reconstructing an error scoring graph by adopting double error indexes, sampling to obtain an abnormal smooth graph, and segmenting an abnormal region based on the abnormal smooth graph. And finally, a dual anomaly detection system of image-level judgment and region-level identification is constructed.

The invention provides a terminal operation and maintenance management method and platform for a medical big data platform, and the method comprises the steps: firstly collecting multi-mode historical operation and maintenance data of a platform terminal in a normal working state, and constructing a time sequence feature vector sequence after preprocessing and fusion; a deep sequence learning model training process is then utilized to learn the potential representation to capture the context state of the normal workflow mode and establish a reconstructed baseline model. Acquiring real-time operation and maintenance data of the terminal, constructing a feature vector, extracting potential context representation through the trained model, and calculating a reconstruction error; a current workflow state is identified based on the potential representation, and a context-aware anomaly metric value is calculated in conjunction with state information and reconstruction errors. And analyzing the time evolution characteristic of the abnormal metric value and comparing the time evolution characteristic with a preset abnormal mode criterion to judge whether the terminal has workflow abnormality, and if so, generating an early warning signal containing abnormal evolution characteristic description. The method has the effect of improving the operation and maintenance detection accuracy of the terminal.

The invention discloses an intelligent automobile interpretable abnormity diagnosis method and system, and relates to the technical field of intelligent traffic. The method comprises the steps of collecting multi-dimensional sensor data based on an intelligent automobile test platform, and constructing a directed causal graph and a causal adjacency matrix which are used for describing a causal relationship between the sensor data; designing a causal constrained graph attention mechanism based on the causal adjacency matrix, and constructing a causal constraint enhanced graph attention anomaly diagnosis model; and based on the directed causal graph and the graph attention anomaly diagnosis model, constructing a hierarchical anomaly diagnosis strategy integrating a feature reconstruction error, a variable causal relationship and a graph attention network weight, positioning an anomaly root cause and identifying a propagation path of the anomaly in the sensor network. According to the invention, the problems of false correlation and lack of exception explanation ability of graph attention network learning in the prior art can be overcome, and reliable exception detection and root cause diagnosis of intelligent automobile multi-sensor data are realized.

The invention discloses an attribute network anomaly detection method based on reconstruction bias learning. The attribute network anomaly detection method based on reconstruction bias learning is composed of a graph reconstruction module, a reconstruction bias dynamic adjustment module, an anomaly enhancement classification module and an anomaly score calculation module. And under the condition that the calculation complexity is not obviously increased, the property network anomaly detection performance is obviously improved. The method comprises the following specific conditions: firstly, a graph reconstruction module adopts a graph auto-encoder, learns a potential mode of graph data by minimizing a reconstruction error, and measures an abnormal degree by using a difference degree between node reconstruction information and original information; secondly, a reconstruction deviation dynamic adjustment module continuously interacts with the graph reconstruction module in the iterative optimization process of the graph reconstruction module, and dynamically modifies a loss function penalty coefficient to force the graph reconstruction module to deviate to fit a normal mode; thirdly, an anomaly enhancement classification module takes a pseudo normal node set and a pseudo abnormal node set which are finally screened out by the former as training samples, and an anomaly score is calculated by utilizing a classification probability, so that the anomaly performance is enhanced; and finally, the abnormal score calculation module combines the abnormal scores of the graph reconstruction module and the abnormal enhancement classification module to calculate the final abnormal score of each node, thereby achieving the purpose of abnormal detection.