2results about How to "Self-learning" patented technology

The embodiment of the present application relates to the technical field of computer information security and artificial intelligence, and discloses a sensitive information automatic identification method combining a rule engine and a natural language processing technology and related devices, which comprises the following steps: receiving original data to be identified; performing rule matching on the original data to be identified to obtain a matching result; calculating a confidence score corresponding to the matching result; when the confidence score is lower than a first confidence threshold and higher than or equal to a second confidence threshold, inputting the matching result into a lightweight model for verification to obtain a first verification result; when the first verification result is consistent with the matching result, taking the matching result as a text information identification result; when the confidence score is lower than the second confidence threshold or the verification result of the lightweight model on the matching result is inconsistent, inputting the matching result into a large model for analysis and identification to obtain a large model identification result, and taking the large model identification result as the text information identification result. The embodiment of the present application realizes the balance between the text information identification efficiency and accuracy.

The application provides an environment-adaptive wind farm intelligent early warning and operation and maintenance decision system and method, the system of the application comprises: a first data acquisition module for acquiring multi-source environment data in a wind farm area; a second data acquisition module for collecting operation state data of each device in the wind farm; a data processing module for spatiotemporal alignment and feature fusion of the multi-source environment data and the operation state data to generate multi-modal fusion features; an environment-device coupling early warning module for risk prediction of wind farm devices based on the multi-modal fusion features through multiple environment-device coupling early warning models to obtain risk early warning information; a device health assessment module for assessing the health status of each device in the wind farm according to the operation state data to generate a device health score; and an operation and maintenance decision support module for generating corresponding operation and maintenance decision suggestions according to the risk early warning information and the device health score in combination with an operation and maintenance strategy knowledge base.