2results about How to "Improve anomaly detection accuracy" patented technology

The application discloses an agricultural process big data statistical analysis and alarm method, collects meteorological, soil, insect, irrigation state and equipment working condition data in agricultural production, carries out cleaning, time alignment and unified modeling on multi-source data, forms a monitoring sequence, carries out standardization and rank statistical processing on the monitoring sequence, identifies candidate abnormal points, further combines local significance test and interval distribution change test, confirms abnormal intervals, and then comprehensively scores mutation strength, significance strength and distribution change strength, generates early warning grades, early warning time and related index information, and outputs alarm results. The application realizes statistical analysis and graded early warning of abnormal changes in agricultural processes.

The application provides an anomaly detection method, device and storage medium for image data, and relates to the technical field of image processing. The method comprises the following steps: performing feature extraction on image data, fusing spectral and spatial features to obtain a joint feature matrix, and inputting the joint feature matrix into an anomaly detection model; the model uses an alternating direction multiplier algorithm to solve a low-rank sparse decomposition problem, and a target function comprises a data fidelity term, a regularization term and a band weight term; the regularization term comprises a low-rank constraint and a sparse constraint, and the band weight term acts on the low-rank constraint in a weighted form; in the solving process, an iteration method is used to update a background low-rank tensor, an anomaly sparse tensor and a Lagrange multiplier, as well as a sparse constraint weight, a band weight term and a penalty parameter of the algorithm; the iteration is repeated until a preset termination condition is reached, an anomaly score map is calculated based on the anomaly sparse tensor, and an anomaly target is determined by comparison. The application can solve the problem that it is difficult to accurately identify an anomaly target in a complex scene, and improve detection accuracy and efficiency.