7results about How to "Alleviate scarcity" patented technology

The application discloses a battery external short circuit diagnosis method based on multi-scale fusion, and belongs to the technical field of lithium ion battery safety monitoring and artificial intelligence fault diagnosis. The method only collects battery terminal voltage time series data, and constructs event level labels after preprocessing; a virtual and real hybrid sample generation module based on real voltage samples is established to generate virtual external short circuit samples and difficult negative samples; further, multi-scale voltage features such as statistics, segmentation, context, transient and morphology are extracted, and through input projection, residual feature enhancement, multi-token reconstruction, multi-head attention fusion and hierarchical prediction head, the output is the external short circuit occurrence probability, fault scene, severity and alarm confidence. The application does not depend on the measured current and temperature signals, and can alleviate the problems of real external short circuit sample scarcity, normal disturbance false alarm and high resistance early fault identification difficulty.

The application relates to the technical field of computer vision and artificial intelligence, and provides a remote sensing image target detection method based on an improved YOLOv9s, which comprises the following steps: acquiring a remote sensing image and performing a pretreatment operation; introducing a kernel selection feature fusion (KSFF) module into a neck network of a YOLOv9s model, and replacing at least one Concat structure in the original model; adding a cross-space multi-scale attention (CSMA) module in the neck network; replacing at least one SPPELAN structure in the original model with a parallel pooling feature modulation (PPFM) module in the neck network; and training, testing and evaluating the improved YOLOv9s network. Through the comprehensive application of network structure improvement, data pretreatment optimization, a semi-supervised learning strategy and an advanced training method, the improved YOLOv9s model has higher detection precision in a remote sensing image target detection task.

This invention discloses a satellite fire detection method based on dynamic exponential features and deep learning. The method includes: acquiring multi-band spatiotemporal observation data from geostationary meteorological satellites; filtering candidate pixels from the full-disk satellite data using multiple threshold conditions; extracting spatiotemporal input features and channel input features of the candidate pixels based on the multi-band spatiotemporal observation data from the geostationary meteorological satellites; and inputting the spatiotemporal input features and channel input features of the candidate pixels into a pre-trained fire spatiotemporal network model to obtain the fire detection result. This invention effectively solves the problem of high false alarms and false negatives caused by cloud cover, high-temperature ground surfaces, and vegetation interference in traditional methods, overcomes the difficulties of weak fire signal extraction and spatial positioning in mixed pixel backgrounds, and significantly improves detection accuracy, stability, and computational efficiency. It can provide key technical support for forest fire monitoring and emergency decision-making.

The invention provides an intention recognition method and system for programming technical issues, and belongs to the technical field of software engineering.The method comprises the steps that the programming technical issues of a target platform are collected and screened; performing intention annotation on the programming technical problem by adopting an iterative statistical sampling mode, and constructing a multi-level intention classification system to obtain a basic data set; performing data enhancement on samples in the basic data set based on a large language model to obtain an enhanced data set; and training an intention recognizer through the enhanced data set, and carrying out intention recognition on the to-be-tested programming technical problem through the trained intention recognizer. According to the method, the multi-level intention classification system is constructed and the intention recognizer is trained, so that understanding of deep semantics of programming technical problems is realized, the problem of category imbalance in a data set can be relieved through a data enhancement mechanism, and the intention recognition accuracy is improved.

The present application relates to the field of display technology and human-computer interaction, and provides a flat panel display visual comfort prediction method and system based on a multi-modal fusion model, wherein the method comprises the following steps: step one, collecting an original image set, for each original image, using a generative adversarial network model to convert an input random sequence into enhanced materials, and then fusing the enhanced materials and the original image at a preset ratio to obtain a plurality of new images; step two, collecting physiological features and physical features to obtain training samples; step three, training a multi-modal fusion model based on a stacked ensemble framework; and step four, inputting test data to obtain a visual comfort prediction result. The present application aims to solve the problems of a lack of high-quality labeled samples, a lack of coupling relationship in single-modal analysis, and poor scene adaptability in the field of flat panel display visual comfort prediction.

The present application relates to the technical field of automatic driving end-to-end perception, in particular to an end-to-end automatic driving long-tail recognition method based on contrast learning pre-training, first, a synthetic image data with long-tail distribution characteristics is generated through a conditional diffusion model, then a fine-grained scene classifier is used to systematically organize and semantically label the generated samples, and a structured multi-modal graph-text alignment dataset is constructed; finally, the enhanced dataset and the original training set are fused, the visual-linguistic joint embedding space is optimized through a multi-task contrast loss function, and the parameter update of the pre-training model is realized. The method innovatively establishes a closed-loop optimization mechanism of generative data enhancement and contrast learning framework, effectively alleviates the data scarcity problem under the long-tail distribution scene, and significantly improves the cross-modal representation ability and downstream task generalization performance of the model on low-resource classes.

The invention discloses a hydroelectric generating set stability prediction method and system, and aims to solve the problem of insufficient generalization ability of stability prediction under extreme working conditions such as a flood period and a dry season in the prior art. The method comprises the following steps: preprocessing historical operation data containing extreme working conditions of a target hydroelectric generating set; acquiring extreme working condition data of the associated cross-domain unit; constructing a stability prediction model fusing physical mechanism embedding and cross-domain transfer learning; and inputting real-time data, and outputting prediction results of the vibration amplitude, the throw value and the winding temperature rise. The system correspondingly comprises a unit historical data processing module, a unit screening and data acquisition module, a physical mechanism-cross-domain migration prediction model construction module and a real-time data input and prediction output module. The method can supplement extreme working condition training data, improves the prediction precision under the extreme working condition, guarantees the stable operation and maintenance of the hydroelectric generating set under all working conditions, and is high in practicability.