7results about How to "Accurate Anomaly Detection" patented technology

The embodiment of the invention provides an air conditioning system and a control method thereof, relates to the technical field of air conditioners, and aims to improve the fault detection precision of the air conditioning system. The air conditioning system includes: at least one processor; and at least one computer memory operably connected to the at least one processor and storing instructions, and when the instructions are executed by the at least one processor, the at least one processor performs the following operations: obtaining real-time operation data of the air conditioning system; on the basis of the multiple pieces of normal operation data and the real-time operation data under the same operation category where the air conditioning system is currently located, a local outlier factor of the real-time operation data is determined, and the local outlier factor is used for representing the outlier degree of the real-time operation data in the multiple pieces of normal operation data; and based on the local outlier factor, fault detection of the refrigerant charging amount is conducted on the air conditioner system, a fault detection result is obtained, and the fault detection result is used for representing whether the air conditioner system is normally charged with the refrigerant or not.

The application discloses a kind of warehouse safety monitoring method and system based on visual detection, it is related to warehouse safety monitoring technical field.A kind of warehouse safety monitoring system based on visual detection, including have: warehouse goods monitoring module and warehouse safety judging module.The application can detect the state of warehouse stacking in real time by stereoscopic modeling analysis and continuous monitoring to warehouse stacking, and improve the sensitivity to small changes through feature enhancement processing, to realize the dynamic monitoring and timely warning to warehouse safety, effectively prevent the loss caused by stacking instability or other safety problems;Feature enhancement processing based on video stream identifies anomaly, can also classify the safety state, provide different risk levels and corresponding safety operation suggestions, ensure that necessary intervention measures are taken in time in warehouse environment.

The invention discloses an intelligent operation leakage analysis system and method applied to a cryptographic device, and relates to the technical field of encryption device.Dynamic thresholds of an abnormal signal and a characteristic signal are obtained through calculation based on a dynamic threshold factor model, and a fluctuation score is obtained through calculation according to the difference value of the abnormal signal and the characteristic signal; a fluctuation score curve graph is drawn, change parameters of each abnormal signal and each characteristic signal are calculated, and an influence factor model is generated in combination with fluctuation scores; the method comprises the following steps: acquiring a real-time side channel signal, calculating to obtain a real-time dynamic threshold based on a dynamic threshold factor model, calculating to obtain a key time point through an influence factor model according to the real-time dynamic threshold, a real-time fluctuation score and a real-time change parameter, and reminding a worker to carry out safety assessment at the key time point. And the timeliness and pertinence of a safety protection strategy are improved.

The application discloses a data anomaly detection method based on intelligent industry, a storage medium and a terminal, belongs to the technical field of anomaly detection, and various industrial data are processed by using an improved KF algorithm and then input into a first neural network model for anomaly prediction. The improved KF algorithm specifically learns the change trend of the covariance matrix by using a second neural network model, and then updates the covariance matrix. The improved KF algorithm can dynamically adjust the process excitation noise covariance matrix Q, thereby reducing the influence of noise on the KF algorithm, and then ensuring the anomaly prediction accuracy and reliability of the subsequent neural network model. Meanwhile, the improved KF algorithm can process the industrial data, eliminate redundant data, uniformly process each information source, and thus ensure the measurement accuracy. Meanwhile, the improved KF algorithm can also determine the time sequence characteristics of the industrial data stream, and thus the method can accurately detect the data stream without time sequence characteristics.

The present application belongs to the technical field of intelligent video processing, and particularly relates to a future frame anomaly detection method based on meta learning and space-time relationship. The present application method proposes a meta learning module, and a model-based meta learning method enables the model to learn general features from multiple tasks, so that the system can learn more discriminative and generalizable feature representations from data. In different monitoring scenes and abnormal behaviors, the model can also accurately perform anomaly detection without overfitting or underfitting. The present application method introduces the meta learning module into the autoencoder, uses the learning characteristics of the meta learning module to extract, save and update the features extracted by the autoencoder, automatically acquires the feature importance of the input video frame through learning, and assigns important weights to the features that are more worthy of attention, which helps to improve the utilization rate of key features in the input stage of the future frame prediction network.

The specification discloses a method and device for training an anomaly detection model, obtaining real face images as positive samples, obtaining synthetic face images as negative samples, and taking each positive sample and negative sample as a training sample. The sample features of each training sample are extracted through a feature extraction layer, the detection results of each training sample are obtained through a classification layer, and the representative features for representing the commonality of the positive samples are determined based on the sample features of each positive sample. Then, the anomaly detection model is trained according to the differences between the sample features of each positive sample and the representative features, the differences between the sample features of each negative sample and the representative features, and the differences between the detection results of each training sample and the labels thereof. This method can learn an accurate feature extraction method based on the representative features, so as to accurately extract the face features of the face images for accurate anomaly detection in the subsequent process, thereby ensuring the accuracy of the anomaly detection.