5results about How to "Achieve understanding" patented technology

This invention discloses a method for generating poses of articulated objects based on physical perception and graph diffusion, relating to the field of computer vision. The method first reconstructs the componentized geometric and physical properties of objects from images using a bi-branch neural implicit network and initializes component connectivity relationships. Subsequently, it refines the component relationship graph through kinematic fitting and temporal consistency checks, and uses a physically enhanced graph diffusion process to infer the prior pose distribution of components on an SE(3) manifold. Finally, using this prior and reconstructed information as conditions, a conditional diffusion model is constructed on the SE(3) manifold, and backsampling is performed through a physically guided two-step backsampling framework to generate a diverse and physically plausible set of pose assumptions for articulated objects. This invention achieves efficient generation of diverse and highly physically plausible poses of articulated objects by tightly coupling physical laws with data-driven generation.

The present application belongs to the technical field of generator equipment monitoring, and particularly relates to a method for preventing the sparking of a generator excitation brush. First, a monitoring module is arranged at the rotor excitation brush of the generator to obtain a digital switching signal and an analog signal of sparking frequency when the brush sparks. After the digital switching signal and the analog signal are preprocessed, it is determined whether the signal threshold is exceeded. If so, a de-excitation signal is generated. According to the de-excitation signal result, a solution is matched and pushed to the user. The present application can solve the problem of poor monitoring effect of the generator excitation system in the prior art.

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.

This invention belongs to the field of intelligent transportation and artificial intelligence technology, specifically disclosing a method for traffic flow clustering and prediction based on semantic similarity of road nodes. The steps include: road traffic data modeling, node semantic vector construction, semantic similarity matrix calculation, semantic mask matrix construction, a spatial attention mechanism based on the semantic mask, and clustering and prediction output. This method analyzes the semantic similarity between road nodes to cluster and model traffic flow data, thereby more accurately capturing local and global spatiotemporal dependencies and improving the accuracy and robustness of traffic flow prediction. This technology can be widely applied in urban traffic management, route planning, congestion early warning, and other fields, providing core decision support for intelligent transportation systems.

The visual position recognition method and device, computer device and storage medium provided in the application relate to the technical field of artificial intelligence. The method comprises the following steps: acquiring a target image, and performing multi-scale feature extraction on the target image to obtain a plurality of initial feature maps; performing channel dimension unification on each initial feature map to obtain a target feature map; taking each target feature map as a current feature map in turn, and performing attention fusion based on the current feature map and all target feature maps to obtain a current attention feature map; updating a historical position memory matrix according to the current attention feature map to obtain a current position memory matrix, and then performing position enhancement to obtain a position enhancement feature map; performing feature fusion on a plurality of position enhancement feature maps to obtain a position embedding vector, and then performing position detection to obtain a target position. The application is suitable for the financial field and the medical field, and can solve the technical problem of decreased visual position recognition accuracy in a dynamically changing environment.