7results about How to "Remove redundant information" patented technology

The application discloses a variable working condition cross-component causal coupling modeling and fault diagnosis method and device, and relates to the technical field of rotating machinery fault diagnosis. The method comprises the following steps: collecting and preprocessing multi-component signals under variable rotating speed working conditions of a rotating machinery; based on multivariate Granger causal modeling, prior information and dynamic disturbance information are fused to construct a dynamic causal diagram; a double-view structure of a main view of the dynamic causal diagram and an auxiliary view of a correlation diagram is constructed, multi-scale coupling features are extracted through a multi-order spectrum filter and an attention mechanism, a symmetric InfoNCE contrast learning loss and a supervised classification loss are combined to optimize and train a model, and fault classification is realized. The device is correspondingly provided with signal acquisition, dynamic causal diagram construction, feature fusion, model training and fault prediction modules. The application improves the accuracy of fault feature representation and the robustness of diagnosis under variable working conditions, is suitable for complex industrial scenes with multi-component coupling and frequent rotating speed switching, and has strong engineering practicability.

The present application relates to the technical field of hydroelectric generating set bolt monitoring, and particularly relates to a hydroelectric generating set bolt state dynamic monitoring method and system. The method comprises the following steps: performing resonance frequency prediction on the bolt, and performing adaptive resonance excitation to obtain a bolt resonance characteristic spectrum; performing bolt acoustic signal collection according to the bolt resonance characteristic spectrum to obtain a multi-channel original acoustic waveform set; performing working condition noise preprocessing on the multi-channel original acoustic waveform set to obtain an acoustic propagation time-domain-frequency domain matrix; performing multi-dimensional stress state mapping analysis according to the acoustic propagation time-domain-frequency domain matrix, and performing bolt loosening state recognition to obtain a bolt structure stress distribution atlas; and performing multi-source heterogeneous data fusion and edge computing according to the bolt structure stress distribution atlas to obtain a connection reliability index. The present application realizes high-precision, real-time and online monitoring of the bolt state through acoustic characteristic change analysis under a complex stress state.

The application provides a video-language understanding method and system, relates to the technical field of video understanding, and obtains a video and text to be understood; based on a trained understanding model, the input video and text are processed to generate a final understanding result; wherein the understanding model comprises a video encoder and a text query component, the video encoder comprises a clustering component and a timing component; the image blocks of all video frames in the video are filtered by using the clustering component to obtain video frame embeddings after eliminating redundant information; the timing dependency between the video frame embeddings is reconstructed by using the timing component to obtain a video feature matrix; based on the video feature matrix, the embedding of the text is performed by using the text query component to obtain the final understanding result; the application comprehensively considers three challenges of information redundancy, time dependency and scene complexity, proposes an understanding model composed of three key components, each component is aimed at a specific challenge, and the video-language understanding ability of an intelligent agent is significantly improved.

The invention provides an RAG system answer generation method, device and equipment based on a large language model, and relates to the field of artificial intelligence, and the method comprises the steps: receiving a query request input by a user; obtaining a target document block according to the query request, and further generating a target prompt word; according to the target prompt word, adopting a preset large language model to generate an answer text corresponding to the query request; wherein the target document block comprises a first document block; the obtaining method of the first document block comprises the steps that keyword retrieval is conducted on a preset knowledge base according to keywords in a query request, and a target knowledge document with the keywords is obtained; and according to the query request and the target knowledge document, generating an abstract text corresponding to the target knowledge document as a first document block. The answer generation accuracy of the RAG system can be improved.

This invention provides a method and system for predicting multimodal drug interaction events based on compact representation learning. The method includes: acquiring multimodal features of each drug in a drug pair to be predicted, wherein the multimodal features include at least two of biological features, molecular structure features, and knowledge graph features; performing intramodal compact representation learning on each modal feature of each drug to eliminate redundant information within a single modality and generate compact sub-representations for each modality; using the compact sub-representations corresponding to the molecular structure features as anchors, performing cross-modal alignment of the compact sub-representations corresponding to the biological features and knowledge graph features through mutual information minimization constraints to eliminate intermodal redundant information; constructing a multimodal fusion representation for each drug based on the compact sub-representations of each modality; concatenating the multimodal fusion representations of the two drugs in the drug pair and inputting them into a classifier to output the prediction result of the interaction event type of the drug pair.

The invention belongs to the technical field of computer vision, and provides a lightweight aerial photography target detection method based on mixed cavity regulation and control perception and partial decoupling aggregation. The invention provides a partial channel decoupling aggregation network structure aiming at the problems that tiny target features in aerial images are easy to lose and complex background interferences exist. The method comprises the following steps: firstly, designing a mixed cavity regulation and control sensing unit (MS-DGPU), introducing deep convolutional layers with different expansion rates into a regulation and control branch, constructing a multi-scale receptive field to capture context information of a fine-grained space, and generating a dynamic weighting factor through nonlinear activation; and secondly, constructing a partial decoupling aggregation module (PDAM) based on the unit, decoupling a feature channel into a spatial representation and semantic retention part by utilizing partial convolution (PConv), only performing regulation and control weighting on the spatial representation part, and then realizing feature multiplexing through residual connection. The module is embedded into a neck network of a target detection model, so that redundancy calculation is effectively reduced, and the detection precision and reasoning speed of the model on a multi-scale aerial photography target are remarkably improved.

This invention discloses a method, apparatus, and storage medium for event content verification based on a large model, belonging to the field of data processing technology. The method includes: identifying events in target text using a large model, rewriting the events in the target text according to target rewriting rules to obtain reference events; the target text is standard data that has been reviewed; storing the reference events in an event knowledge base; retrieving target reference events associated with the event to be verified from the event knowledge base based on the event to be verified; and comparing the event to be verified and the target reference events using the large model to obtain a comparison result characterizing whether the event to be verified is correct. This application utilizes the powerful semantic understanding capability of the large model to accurately identify events in standard data and rewrite the events to form structurally unified and semantically clear reference events, thereby constructing a high-quality event knowledge base and improving the accuracy of event content verification.