423 results about "Causal graph" patented technology

The invention relates to the technical field of fault detection, in particular to an industrial fault diagnosis method and system based on intelligent causal correction. The method comprises the following steps: acquiring monitoring data and user task requirements; constructing task structure information based on the obtained user task demand; processing the monitoring data through a three-layer cascaded framework to generate metadata; constructing an initial causal graph structure by utilizing the task structure information and the metadata; performing deep optimization on the initial causal graph structure through a graph neural network to obtain a causal composite relation graph; fault diagnosis and information retrieval are carried out by utilizing the causal composite relation graph and combining a knowledge base; and generating a structured diagnostic report. Through an innovative structure integrating the LLM and the graph neural network, the system can dynamically optimize a causal relationship model, automatically learn and correct fault association, enhance the interpretability of a causal graph by using semantic reasoning of the LLM and a graph attention mechanism, and significantly improve the robustness and accuracy of diagnosis.

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 discloses an engineering safety progress intelligent monitoring method based on multi-source data collaboration, which relates to the technical field of intelligent engineering monitoring, and comprises the following steps of: mapping multi-source engineering monitoring data into nodes and edges of a graph in real time by utilizing an incremental graph updating algorithm, generating a dynamic knowledge graph, and generating a dynamic mapping result; performing graph traversal on the dynamic knowledge graph through an association subgraph extraction algorithm, extracting a security event matrix and a project progress matrix, calculating an SPI index value by using a dynamic weighted fusion algorithm, synchronously performing multi-threshold interval grading on the SPI index value, generating an SPI early warning level, performing state coding on the SPI early warning level, and generating a comprehensive feature vector; according to the method, data of different types can be effectively integrated and a basis is provided for formulating a targeted engineering safety progress solution through an incremental graph updating algorithm and a Bayesian causal graph model, and node probability distribution characteristics are extracted by using a forward propagation layer. The method is advantaged in that the incremental graph updating algorithm and the Bayesian causal graph model are utilized to effectively integrate data of different types and provide a basis for formulating a targeted engineering safety progress solution.

The invention relates to the technical field of engineering safety monitoring, in particular to a foundation pit deformation intelligent early warning system based on multi-modal fusion and a method thereof.According to the system, quality evaluation and weighting processing are conducted on multi-modal sensor data through a self-adaptive weight dynamic distribution module, and the data credibility is ensured; the multi-modal feature cross extraction module extracts and interacts features by using a specific sub-network and a multi-head attention mechanism, integrates information through a space-time diagram convolutional network, and generates accurate fusion feature representation; the multi-granularity abnormal mode identification module is combined with a mixed density network and time sequence analysis to accurately identify deformation anomalies; the causal reasoning and weight feedback module analyzes deformation reasons through a causal graph model and provides feedback for sensor weight adjustment; according to the system, the precision and reliability of deformation detection are remarkably improved, the detection precision is improved to the millimeter level, the accuracy is improved by 40%, and powerful technical support is provided for engineering safety monitoring.

The invention discloses a task scheduling optimization and feedback control method and system based on a causal graph structure, and relates to the technical field of task scheduling, and the method comprises the steps: integrating multi-source information, identifying an entity, constructing a directed edge connection entity, calculating an edge weight, and forming a task causal graph; performing deep analysis on the causal graph, and identifying risk nodes and propagation paths thereof in the current project; combining the edge weight and the node attribute, quantifying the risk propagation possibility and influence range, and generating a risk thermodynamic diagram; on the basis of the identified risk nodes and propagation paths thereof, mining controllable variables in a task scheduling process, constructing a scheduling optimization objective function, searching a scheduling solution space under constraint conditions by using a search method, and evaluating the influence of different strategies on a task network through multiple rounds of simulation, so as to improve the task scheduling efficiency. A current optimal task adjustment strategy is screened out and practically applied; and collecting data generated in a project execution process, and updating the causal diagram. The method is suitable for intelligent scheduling and fine management scenes of projects.

The invention provides a hydropower station generator set excitation system fault prediction method and device, and relates to the technical field of intelligent power grids, and the method comprises the steps: carrying out the continuous sampling of a generalized state observation vector through a sliding time window, so as to form an operation parameter observation tensor; fusing the operation parameter observation tensor and the boundary constraint type operation parameters of the generator, and constructing a fault observation tensor fused by multi-source operation parameters; introducing a structural causal atlas in a modeling layer, establishing a causal path network embedded based on causal reasoning and topological time sequence for a fault observation tensor, and dividing system interaction faults encountered by excitation system prediction into a plurality of typical modes; and performing graph attention modeling on the evolution trend and the instability boundary of each key parameter in the fault causal chains of different typical modes contained in the causal path network, and dynamically identifying the path characteristics of the excitation system entering the fault critical state. According to the method, the interaction type fault of the excitation system of the hydropower station generator set can be predicted.

The invention relates to a fabric flaw detection and traceability system based on edge calculation and computing power scheduling, which is suitable for intelligent quality control in a textile production process. The system comprises an acquisition unit, a modeling unit and the like. The acquisition unit acquires fabric images and environmental data through a multispectral imaging device and a process parameter sensor, and constructs time-aligned multi-modal feature tensors. The modeling unit extracts texture features by using unsupervised comparative learning in combination with fabric material characteristics, and generates potential texture fingerprint vectors. And the detection unit adopts a target detection network of a channel attention mechanism to identify fabric flaws and output positions, types and severity. The traceability unit analyzes correlation between defects and process parameters through time sequence causal reasoning, and constructs a causal atlas. And the optimization unit generates a process optimization vector according to the causal atlas and the risk score, and realizes visual display and edge control feedback, thereby constructing a real-time defect control and explainable traceability-oriented closed-loop quality management system.

The invention relates to an electrical load prediction and optimization regulation and control method and system for high-energy-consumption equipment, and solves the problems of inaccurate load prediction, single regulation and control means and difficulty in dynamic adaptation of the high-energy-consumption equipment, and the method comprises the steps: collecting multi-source data of the high-energy-consumption equipment in real time, constructing a dynamic equipment collaborative causal graph after preprocessing, and extracting key constraints; inputting the data and the constraints into the dynamic digital sample model to obtain a system state simulation result; based on the result, a multi-objective optimization regulation and control strategy is generated and executed by using a meta-learning + reinforcement learning decision framework; and collecting actual data comparison deviation, starting hierarchical federated learning when a threshold value is exceeded, grouping and aggregating similar experiences according to a causal graph topology, and dynamically calibrating model parameters and a decision framework. The method has the following effects that accurate load prediction and multi-target cooperative regulation and control of the high-energy-consumption equipment are achieved, working condition changes are dynamically adapted, the cost is reduced, and continuous production and the service life of the equipment are guaranteed.

The invention is suitable for the technical field of data analysis, and provides a transformer substation fault handling method combining causal reasoning knowledge graph modeling, comprising: acquiring multi-source heterogeneous data and performing data cleaning processing to obtain a space-time alignment data set, the space-time alignment data set comprising one or more quaternary data sets, the quaternary data set comprises a device identifier, a timestamp, a feature vector and an event tag; causal modeling processing is carried out on the time-space alignment data set to obtain a causal graph, and the causal graph comprises node information of nodes and relation information between the nodes; constructing a space-time diagram neural network model according to the causal diagram and the equipment connection relation diagram, wherein the space-time diagram neural network model realizes dynamic evolution of the graph based on an incremental updating strategy; and outputting fault root cause positioning information according to the time-space diagram neural network model.

The invention provides an intelligent power grid state monitoring method and system based on digital twinning, and the method comprises the steps: obtaining multi-source data in a power grid operation environment, and forming a scene feature vector and a corresponding operation scene label; selecting a basic twinborn model template according to the operation scene label, mapping the scene feature vector into a node parameter in the basic twinborn model template, and obtaining a corresponding parameter set and a digital twinborn model instance; according to the digital twinborn model instance, constructing a causal graph structure of the operation state between the devices so as to reflect a causal dependence path between current power grid state variables; and performing trend risk judgment according to the abnormal causal path set, and outputting structured early warning response information. According to the method, the problem of insufficient generalization ability caused by the static state of the traditional twin structure is effectively solved.

The invention discloses a power distribution network fault accurate positioning method and system based on a graph convolutional neural network, and relates to the technical field of power systems, and the method comprises the steps: deploying monitoring equipment at a power distribution network node; in response to the distributed power supply switching event, generating a dynamic graph structure based on a pre-stored simulation model; taking the dynamic graph structure as a reference to initialize graph convolution kernel parameters, and generating two types of operation parameters based on a communication delay condition; fusing the new energy output prediction data, the electrical quantity monitoring data and the meteorological data to construct a dynamic causal graph; when a fault feature signal is detected, extracting electrical quantity monitoring data, a topological connection relationship and causal reasoning knowledge of the associated node; and constructing a graph convolutional network taking a dynamic graph structure as a network topology, selecting an operation parameter of a corresponding communication delay region as a convolution kernel weight, processing electrical quantity monitoring data, a topological connection relationship and causal reasoning knowledge of associated nodes, and outputting a fault coordinate.

The invention discloses a cross-platform power transaction data interaction optimization method, and particularly relates to the technical field of power transaction data digital twinning. Simulation transaction data of market participants is generated based on an antagonistic neural network model; identifying a causal relationship among market variables from the simulation transaction data of the digital twin through a causal discovery algorithm, and constructing a causal graph; modeling spatial dependence and causal conduction paths among market participants by using a graph neural network model, and performing anti-factual reasoning in a single external impact scene in a digital twinborn body by intervening key variables in a causal graph; simulating dynamic game behaviors of market participants in an extreme weather parameter distribution scene; the power market failure assessment is carried out by combining the causal conduction path to obtain the power market failure assessment index, and early warning is carried out based on the power market failure assessment index, so that the adaptability and stability of the power market in extreme weather scenes are improved.

The invention provides a communication risk identification method and system based on multi-modal behavior fusion, and relates to the field of communication risk identification, and the method comprises the steps: fusing a network environment vector, an authority feature vector, a multimedia vector and a social behavior vector of a target terminal in a communication process within a preset time period, and obtaining a multi-dimensional feature vector; inputting the multi-dimensional feature vector to a preset causal graph model to obtain a fusion vector; inputting the fusion vector to a TCN-Transform hybrid model to obtain a target risk probability value; and generating a target defense instruction according to the target risk probability value and a preset dynamic defense threshold. The method can dynamically adapt to archive security level changes and semantic association scenes, and the real-time performance and accuracy of prediction are improved. According to the method, the behavior characteristics of the target terminal in the communication process can be reflected more comprehensively, so that the potential communication risk can be identified more accurately, real-time monitoring and early warning of the communication risk are realized, the false alarm rate and the missing report rate are effectively reduced, and the reliability of risk identification is improved.

The invention provides an interpretable node classification prediction method based on adversarial causal graph learning. The method comprises the steps that a constructed prediction model comprises a redundancy filtering module and an adversarial causal graph learning module; a redundancy filtering module and an adversarial causal graph learning module realize a graph information bottleneck mechanism; the redundancy filtering module adopts a two-layer graph attention network GAT structure to carry out information aggregation, and node embedding is obtained; the confrontation causal graph learning module adopts a learnable sub-graph sampler based on an attention mechanism to generate a causal interpretation sub-graph for node embedding, performs gradient disturbance optimization on interpretation sub-graph embedding based on a PGD confrontation training strategy of a causal enhancement mechanism, generates confrontation embedding, and obtains final disturbance interpretation sub-graph embedding through multiple rounds of disturbance iteration; performing end-to-end prediction model training through multi-target loss joint optimization; and after training is completed, embedding of the nodes is input into a classifier, and a prediction result is output. According to the method, the structural transparency and interpretability of the model are remarkably improved.

The invention relates to the technical field of fault detection, and particularly discloses a prediction reconstruction framework causal perception space-time network for explaining anomaly monitoring in a complex industrial process, comprising the following steps: S01, constructing graph data E (V) and a causal graph; automatically adjusting the fusion proportion of the time-frequency characteristics according to the data characteristics so as to ensure that the model can comprehensively capture the information of the data in the time domain and the frequency domain; secondly, introducing a residual image attention network (RGAT), and converting the image data E (V) into image structure data G (S (V), E (V)); and S03, reconstructing a prediction error by adopting a variational automatic encoder (VAE), learning an error mode of normal data, providing an anomaly judgment AD (V) for anomaly detection, analyzing a causal relationship between data in combination with a causal graph, and positioning an anomaly reason according to an anomaly score, so as to form a prediction result. The network solves the problem that a traditional monitoring network is high in false alarm rate.

The invention relates to a multivariate time series data abnormal root cause identification method based on a space-time cause and effect diagram, and belongs to the technical field of anomaly detection. According to the method, multi-window expansion causal convolution is adopted for multivariate time series data, mutual information screening is combined, and time embedding covering short-term mutation and long-time dependence at the same time is extracted; non-local space correlation is learned through multi-head self-attention, the directional causal intensity is measured through conditional entropy, and a sparse and interpretable space-time causal graph is generated through normalization-pruning; introducing a causal enhancement graph attention network on the space-time causal graph, and performing multiple rounds of causal propagation updating on node embedding; and calculating a root cause score by integrating the abnormal degree and the causal influence, identifying a key source node in an abnormal propagation path, and realizing accurate root cause positioning of the system abnormality. According to the method, the adaptability to the dynamic behavior mode and the capturing capability to the abnormal driving factor are enhanced, and the modeling precision and the root cause identification capability of the abnormal propagation process are improved.

The invention belongs to the field of advertisement putting, and provides an advertisement putting method and system based on multi-source data analysis, and the method comprises the steps: collecting multi-source original data related to a user; identifying a plurality of cognitive state nodes based on the click behavior data and the transaction path data; constructing a cognitive behavior causal atlas based on the plurality of cognitive state nodes, wherein nodes of the causal atlas represent user cognitive states; predicting an advertisement response probability and a conversion probability of a target user by adopting a Bayesian inference model in combination with a historical behavior sample and a path structure in the causal atlas, and estimating a state transition probability of the user from a current state node to a target state node based on different advertisement intervention contents; and based on the state transition probability and the causal atlas structure, determining an optimal advertisement intervention path of the user from the current cognitive state to the expected conversion state, and constructing a corresponding advertisement putting sequence based on the path.

The invention discloses a multi-stage task processing method and system based on an intelligent Agent model, and relates to the technical field of task processing, and the method comprises the steps: collecting heterogeneous data streams through a distributed sensor network, and generating a dynamic feature vector through a feature encoder inspired by quantum annealing; calculating a path expected utility value by using a Bayesian optimization algorithm, and projecting a high-order task space to a Kupman space; starting multi-thread asynchronous calculation, collecting execution state data in real time and constructing a causal graph model; the short-term execution logs are integrated through a neural Turing machine, edge computing nodes are called for distributed knowledge extraction, a multi-mode interpretable report is generated, and a long-term memory library is updated. According to the method, by starting multi-thread asynchronous calculation, the execution state data are collected in real time, the causal graph model is constructed, the execution result matrix with the confidence score is output, and the stability and reliability of task execution are improved.

The invention provides a multivariate time sequence anomaly detection method based on an adaptive causal diagram and spatio-temporal evolution, and belongs to the technical field of time sequence anomaly detection. According to the technical scheme, firstly, unification, missing value filling and Min-Max normalization processing are carried out on multivariate time series data, on this basis, a graph attention network is utilized to construct an adaptive correlation graph, a causal relationship between variables is quantized through Granger causal test, then the correlation graph and a causal graph are fused to generate a causal correlation mixed graph, and then, the causal correlation mixed graph is subjected to data processing. And inputting the mixed graph into a space-time converter to carry out future numerical value and structure prediction, finally calculating a prediction residual error and generating a comprehensive anomaly score, and further judging an abnormal node. According to the method, dynamic detection and interpretable analysis of abnormal events can be realized, and the problems that in the prior art, static state, causality and correlation of a graph structure are not fused, structural evolution modeling is lacked, and the judgment dimension is single are solved. According to the method, the anomaly detection coverage and sensitivity are remarkably improved.

The invention relates to the technical field of power system measurement, and discloses a current transformer error dynamic monitoring method and system.The current transformer error dynamic monitoring method comprises the steps that current transformer time sequence data and a system event log are obtained; constructing a time sequence causal graph to represent the time correlation between the event and the error change; identifying potential causal links by applying a counter causal model; designing a multi-world simulation engine to generate an anti-fact scene; quantifying a causal effect by comparing actual observation with an anti-fact simulation result; establishing a monitoring mechanism to track key trigger events in real time; generating a dynamic causal interpretation report and adjusting a compensation strategy; according to the method, the limitation of traditional correlation analysis is broken through, the causal relationship and the correlation can be accurately distinguished, the real triggering factor of the error change of the current transformer can be accurately identified, the false alarm rate and the missing report rate are reduced, and the accurate dynamic monitoring of the error of the current transformer is realized.

The invention discloses a dial plate processing optimization system based on big data, and relates to the technical field of dial plate processing, data is collected, anti-fact sample data is generated, a causal atlas structure including processing process nodes and an edge weight function model is constructed, processing path performance is evaluated based on an abstract objective function, and the processing process is optimized. According to the method, the causal atlas is constructed based on multi-source data such as tool wear degree data and spindle vibration amplitude data, and path deduction and parameter optimization of a target state are realized in combination with an anti-fact sample; self-adaptive control over the machining state is achieved through a structured deviation matrix, a causal relationship and a weight function model are dynamically corrected through a feedback learning mechanism, and data-driven optimization and defect prevention in the machining process are achieved.

The invention discloses a morphological gradient region replacement method based on SAM semantic segmentation and user guidance, and relates to the technical field of computer vision and image processing, and the method comprises the steps: carrying out the semantic segmentation of a to-be-processed image through an SAM model, extracting a multi-level semantic feature, carrying out the standardization and dimension reduction, extracting a causal factor based on independent component analysis, and carrying out the user guidance. A directed causal factor association graph is generated through Granger causal relationship test, and a causal attribution probability graph is generated through reverse mapping; constructing a structured causal graph, and generating a causal mask through a graph convolutional network; encoding the original interaction signal into a guide thermodynamic diagram; constructing a diffusion equation, forming a gradual change control equation by dynamically fusing and guiding the intensity distribution of the thermodynamic diagram and an image semantic diffusion item, and iteratively solving the gradual change control equation; generating an anisotropic morphological operation kernel according to the geometric curvature characteristics of each region in the replacement mask; and fusing the optimized replacement mask with the target content based on a gradient domain optimization algorithm to generate a gradient replacement image.

The invention discloses an electromechanical equipment abnormal behavior detection and fault prediction method based on a causal time-space Transform, and belongs to the technical field of electromechanical equipment abnormal detection, and the method comprises the steps: S1, constructing an initial causal graph according to the causal relationship between the physical structure and the functional part of electromechanical equipment; s2, correcting the initial causal graph based on historical operation data of the electromechanical equipment to generate a causal graph; s3, introducing the causal graph as prior information into a Transform model, predicting sensor time sequence data of the electromechanical equipment to be detected through the trained Transform model, and outputting corresponding high-dimensional feature representation; and S4, according to the high-dimensional feature representation, calculating the abnormal weight of each component, and carrying out component-level abnormal identification and fault prediction. The method breaks through the limitation that only the data correlation is fitted and the causal relationship is ignored in the electromechanical equipment anomaly detection of a traditional time sequence model, and explicit modeling of an equipment fault chain propagation mechanism is realized by fusing a causal reasoning mechanism and feature modeling.

The invention relates to a grid-connected scheduling management method and device constructed in combination with a knowledge graph, equipment and a medium. According to the method, a comprehensive data set is constructed by integrating multi-source data such as new energy output, power grid topology, load, weather and historical fault records, and then a dynamic knowledge graph is formed by using entity recognition and relation extraction technologies; a probability causal graph model is constructed by extracting a causal path and adding probability parameters, and uncertainty propagation intensity is quantified in combination with a sequence diagram neural network; on the basis of a propagation model, risk index conditional probability is calculated by adopting probability causal reasoning, and a fault propagation sequence is simulated through a cascade failure theory to realize multi-level risk assessment; based on a multi-objective optimization model and deep reinforcement learning, an adaptive scheduling strategy is generated, a complete technical closed loop from data fusion and causal reasoning to intelligent decision is realized, and the technical effects of describing a new energy uncertainty propagation path, prospectively evaluating a power grid risk situation and dynamically generating an optimal grid-connected scheduling scheme are achieved.

The invention discloses a machine room monitoring method and system based on a multi-source data fusion intelligent inspection robot, and belongs to the technical field of machine room automatic monitoring, and the method comprises the steps: applying adversarial transfer learning on a four-dimensional fault semantic feature field, and generating a cross-modal causal atlas representing a fault evolution path through a graph neural network; according to the method, a loss function is combined to align feature distribution of a standard machine room and a current machine room, a gradient inversion layer is utilized to force feature distribution alignment of a source domain and a target domain, meanwhile, an attention mechanism and a causal strength weight are combined to generate a cross-modal causal atlas, and a graph neural network further models physical connection, functional dependence and time sequence association between nodes, so that the cross-modal causal atlas is obtained. A causal relationship is coded into an edge weight, noise correlation is filtered through a causal mask, the stability of the causal atlas is improved, and the cross-modal causal atlas can accurately capture a fault propagation path.

The invention discloses an electric power system safety early warning method and system based on multi-modal cooperation, and relates to the technical field of electric power system safety early warning, and the method comprises the steps: collecting multi-source operation data, carrying out the preprocessing, carrying out the multi-modal feature extraction and fusion based on the preprocessed data, and carrying out the multi-modal feature extraction and fusion. Inputting an edge detection model and outputting an abnormal confidence score in combination with an attention mechanism; and performing alarm grading according to the abnormal confidence score, constructing a causal diagram for alarms with high risk levels in combination with associated security events, and performing future attack path prediction by adopting a time sequence diagram neural network. According to the method, multi-scale convolution and a channel attention mechanism are fused, the extraction capability of the multi-source data time sequence features of the power system is enhanced, the anomaly detection precision is improved, dynamic attack path prediction is realized in combination with RMTPP and causal atlas topological constraints, sequence modeling is enhanced through self-attention and position coding, and the detection accuracy is improved. And the perspectiveness and the reliability of the safety early warning of the power system are obviously enhanced.

The invention discloses a millisecond regulation and control method for hydrogen fluoride production based on reinforcement learning and model prediction control, and relates to the field of hydrogen fluoride production regulation and control. In the multi-scale neural symbol dynamics modeling step, GNN, a cellular automaton and a state space model are fused, and parameters are updated in real time; in the causal reinforcement learning decision optimization step, an effect is calculated through a causal graph, and a reward function is optimized; in the time-space fractional order sliding mode control step, a fractional order sliding mode surface and a controller are designed, and rapid and stable control is achieved; in the memory enhancement element learning adaptation step, a DNC storage strategy is utilized, new working conditions are quickly adapted through element gradient, and millisecond-level precise regulation and control are achieved. According to the method, prediction errors are greatly reduced, the response speed is increased, and overshoot is reduced; the product yield is improved, the energy consumption is reduced, and new working conditions are quickly adapted; fault detection and risk early warning are more accurate, equipment operation is more stable, production efficiency is effectively improved, cost is reduced, and safety is enhanced.

The invention discloses a multi-omics causal structure relation learning method based on comparative learning, which comprises the following steps: firstly, respectively constructing corresponding encoders for preprocessed gene mutation and gene expression data, and respectively carrying out feature extraction on two kinds of omics data; then, constructing a projection head with shared parameters to realize cross-modal feature alignment; then, using the aligned features as nodes, and constructing causal graph data through a learnable causal graph structure; constructing a graph neural network to learn causal graph representation, and constructing a contrast loss function; and finally, a model prediction result is obtained through a multi-layer perceptron, a survival prediction loss function is constructed, and a total loss function is obtained for multi-omics causal structure model training. Based on gene mutation and gene expression data, a cross-omics causal structure relationship is constructed and learned through comparative learning, more accurate prognosis prediction is provided for diseases such as acute myelogenous leukemia and the like, and potential biomarkers and key regulatory factors are helped to be found.

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 electric power safety monitoring method, system and device based on big data analysis and a storage medium, and the method comprises the steps: collecting target data of an electric power system, and constructing a knowledge graph model based on the target data; acquiring power system risk propagation nodes based on the knowledge graph model, generating a causal analysis basic graph, and constructing a causal graph model; performing feature learning on the causal relationship of the causal graph model, and predicting a risk propagation path; and calculating a risk score, and performing multi-level early warning according to the risk score. According to the method, the node attributes and the edge weights are dynamically updated by fusing the multi-modal data, so that the causal relationship between the equipment is accurately quantified. Meanwhile, by analyzing a risk propagation path, calculating a risk score and performing multi-level early warning, the intelligence and adaptability of risk monitoring are greatly improved, and particularly, higher reliability and accuracy are shown in a complex dynamic environment, so that the safe operation level of a power system is remarkably improved.