5results about How to "Insufficient relief" patented technology

This invention relates to a method and system for anomaly identification in power control terminals based on contrastive learning, belonging to the field of network data security technology. It solves the problem that existing technologies cannot effectively mine complex abnormal behaviors in power control data. The method includes: dividing network traffic at the data interaction interface into multiple time sliding windows, extracting multi-dimensional traffic features to obtain data interaction feature vectors; constructing a time-series feature graph using the data interaction feature vectors and constructing positive and negative contrast samples, and performing contrastive learning training on a graph attention network model; generating an embedding vector for each sliding window using the encoder of the pre-trained graph attention network model, scoring all sliding windows for anomalies based on the embedding vectors, and identifying sliding windows with abnormal data interaction behavior. This invention combines graph learning, contrastive learning, and time-series modeling techniques to provide a high-precision, low-dependency, and highly generalizable anomaly identification and control method.

The application discloses an ancient Chinese automatic sentence breaking and semantic analysis system based on deep learning, and relates to the technical field of ancient Chinese analysis.The system combines a gating recurrent unit (GRU) and a convolutional neural network (CNN) through a sentence breaking module, combines image character features, and can effectively process semantic ambiguity, long-distance dependence, and problems of homophones and variant characters.A knowledge graph containing historical and cultural common knowledge and dynamic relationships between words is constructed through a semantic analysis module, prior knowledge is injected through a graph neural network, and the depth of semantic understanding is improved.A sentence breaking result containing sentence breaking basis and a visual semantic analysis result are provided through an output module, so that users can clearly understand the processing logic and the trustworthiness and convenience are improved.The system can efficiently process a large number of unlabeled ancient Chinese texts, fully excavate the value of various cultural heritage such as classics and inscriptions, promote the research progress of grammar and vocabulary, and help the inheritance and spread of ancient Chinese culture.

The application discloses a robust micro-expression segment segmentation method and system which avoids head movement, adopts computer vision tools to recognize head posture of each frame image in a video sample, and extracts translation variable and rotation variable of the head posture relative to the same coordinate axis; adopts frame difference method to calculate head movement amplitude, divides the video sample into several continuous frame intervals with head movement amplitude less than a set displacement threshold; adjusts and aligns each frame of the video sample with continuous frame intervals to obtain a micro-expression sample; the system comprises a video sample library, a vision tool, a calculation module, a comparison module, a video segmentation module, a micro-expression processing module and a micro-expression sample library; the application divides small-amplitude head movement video segments which can be used as micro-expression samples, avoids the influence of head posture on micro-expression detection, eliminates the influence of head movement within a certain displacement threshold by alignment or removal of global motion, and improves the performance of micro-expression detection.

The application discloses a kind of data augmentation methods for multi-attribute word extraction, it is related to data processing field, including: first, for the original training sample sentence containing multiple attribute words, attribute word labeling is carried out based on text fragment;Then, according to the labeling result, construct multi-attribute word label data set;Finally, each subset in the multi-attribute word label data set is combined with the original sample sentence to generate new training samples;The application reduces the cost of artificial data labeling, alleviates the problem of insufficient labeled data, designs a data augmentation method based on text fragment label in the multi-attribute extraction application scenario, synthesizes new training samples, and expands the training data;The augmented training data set enables the model to learn more features from the data, enhances the robustness of the model, prevents overfitting, and improves the generalization ability.