5results about How to "Improve anomaly detection performance" patented technology

Provided are an abnormality detection method and device for a variable flow system of a wind turbine generator. The abnormality detection method comprises: determining a temperature threshold of a target component in the variable flow system based on power of the wind turbine generator; determining whether the temperature threshold of the target component and an operating temperature of the target component satisfy a preset condition, wherein the preset condition comprises a difference between the operating temperature and the temperature threshold being greater than a preset difference; in response to the temperature threshold and the operating temperature satisfying the preset condition, determining whether an operating state of a water-cooling heat dissipation system of the wind turbine generator is abnormal; and in response to the operating state of the water-cooling heat dissipation system being normal, determining that the target component has an abnormal temperature. The abnormality detection method and device for the variable flow system of the wind turbine generator improve the effect of abnormality detection of the wind turbine generator.

The embodiment of the application provides a kind of detection model training method, comprising: obtaining the sample data corresponding to sample graph structure, wherein the initial node attribute of each node and the initial edge attribute of each edge are included in sample data.The initial node attribute is input into the encoding unit of detection model, to obtain the first feature vector output by encoding unit, and the initial edge attribute is input into the encoding unit of detection model, to obtain the second feature vector output by encoding unit.According to the first feature vector and the second feature vector, reconstruct the sample graph structure, and determine the reconstruction error corresponding to the reconstructed sample graph structure.According to the first feature vector and the second feature vector, determine the semantic information corresponding to the sample graph structure, and determine the semantic error corresponding to the sample graph structure according to the semantic information.According to reconstruction error and semantic error, update the model parameter of detection model.The technical scheme of the application can effectively improve the anomaly detection effect of detection model.

This invention relates to a method and related equipment for detecting abnormal object behavior. The method includes: acquiring a behavior log stream and mapping the logs to feature vectors including object identifiers; for any object, generating a behavior state sequence based on the feature vectors, constructing reachable links between state points based on the distance relationships between state points in the behavior state sequence, thereby constructing a topological structure of the object's behavior state space, calculating the topological closure of the object's historical state vector set under the topological structure, and using the topological closure as the normal behavior baseline region of the object; wherein, the behavior state space consists of multiple state points obtained by mapping state vectors from the behavior state sequence; acquiring the target state vector corresponding to the behavior of the object to be detected, determining whether the target state vector belongs to the normal behavior baseline region of the corresponding object, and if not, determining it as abnormal behavior. This method at least partially solves the problems of high storage overhead and poor anomaly detection effect in related technologies.

The application discloses an industrial image anomaly detection method and system based on feature-level abnormal synthesis and reconstruction, and the method is as follows: S1, acquiring an image, and extracting multi-scale local features through a pre-trained network; S2, adjusting the features of different scales to a unified size, connecting the features of the unified size, and obtaining a feature map; S3, transferring the feature map of S2 to a target domain through feature adaptation, and obtaining a new feature map; S4, adding noise to the feature map obtained in S3, and obtaining a feature map simulating an anomaly; S5, inputting the feature map obtained in S4 into a feature reconstruction network, and reconstructing the features into features without anomalies; S6, calculating a loss through the difference between the feature map before adding noise and the reconstructed feature map, training the feature reconstruction network according to the loss value; and S7, reconstructing the features by using the trained network, and obtaining a segmentation map according to the difference before and after the feature reconstruction. The application solves the problems of high calculation cost, unreal synthesized anomaly and reconstructed anomaly area of the existing anomaly detection method.

A method for anomaly detection in nuclear power DCS logs based on weighted reconstruction and state transition probabilities includes: acquiring DCS controller log data; preprocessing the log data, including log parsing, log template extraction, and log annotation, to obtain structured log data, log templates, and anomaly tags; constructing log sequences, including device grouping and time window segmentation, converting DCS logs into structured sequences, and dividing them into training, testing, and validation sets; constructing a sparse-based hybrid weighting mechanism, and using this mechanism to train a weighted LSTM autoencoder on the training set; constructing a Markov transition matrix to comprehensively extract deep temporal reconstruction features and local state transition patterns of the log sequences; using the validation set for dynamic threshold optimization to determine the optimal anomaly judgment boundary; and completing anomaly detection and performance evaluation on the testing set based on the optimized joint model. This method can effectively improve the accuracy and reliability of anomaly detection in nuclear power DCS systems.