5results about How to "Semantic rich" patented technology

The invention provides a human-computer interaction activity-oriented text description model construction method. The method comprises the following steps of obtaining encrypted network traffic generated when a user interacts with intelligent equipment; acquiring auxiliary data synchronously acquired when the user interacts with the intelligent equipment, and inputting the auxiliary data into a pre-trained multi-modal large language model to obtain a corresponding language behavior text; performing timestamp alignment on the encrypted network traffic and a corresponding language behavior text to form a traffic-description pair data set; performing feature extraction on the encrypted network traffic in the traffic-description pair data set to generate a statistical feature sequence; inputting the statistical feature sequence into an end-to-end model until the model converges; in the trained model, the behavior description can be directly generated only by inputting the network traffic, and any auxiliary data acquisition is not needed.

The application provides a construction worker unsafe behavior intelligent identification method and early warning system, the method comprises the following steps: obtaining a to-be-detected image of a construction site; inputting the to-be-detected image into a construction worker unsafe behavior identification model to obtain an unsafe behavior identification result of a construction worker; wherein the unsafe behavior at least includes an unworn state, a wrong wearing state or a blocked state of personal protective equipment; and the construction worker unsafe behavior identification model is a neural network model for target detection. The detection accuracy is significantly improved, and the fine-grained identification capability is enhanced. Since a learnable position coding component is introduced into the backbone network, the model can explicitly model the spatial position relationship of the feature map, enhance the spatial position perception capability of the personnel (especially small targets and blocked targets) in the construction scene under complex background interference, solve the problem that the traditional convolutional network is not sensitive to absolute position information, and improve the feature extraction accuracy in a complex background.

The present application relates to the field of robot environment perception, and is used for solving the problem that the existing robot environment perception system lacks a unified and efficient hierarchical fusion architecture to systematically process multi-source heterogeneous data, in particular to a robot real environment perception early warning system for multi-sensor data fusion; the present application significantly improves the comprehensiveness and accuracy of environment perception through the cooperative work of laser radar, visual camera, millimeter wave radar and ultrasonic sensor; through the progressive processing flow of space-time registration, cross-checking, feature definition to deep fusion, the multi-source information is sorted by confidence and features are extracted, and the heterogeneous feature fusion module generates an environment dynamic semantic map rich in texture, structure, motion and semantic information by means of convolutional neural network, which provides accurate and comprehensive information basis for early warning decision, and at the same time, the dynamic hierarchical early warning mechanism provides reliable guarantee for robot safety and autonomous operation.

This invention discloses a language model-driven zero-shot object detection method and system. The method includes: training a supervised detection model using visible class data from a dataset; extracting semantic vectors from a large language model based on data class names to generate external knowledge; extracting visual features of visible class images using the supervised detection model, and training a generative adversarial network (GAN) based on pseudo-visual features synthesized from external knowledge; using the GAN to synthesize pseudo-visual features for invisible class data, training an invisible visual feature classifier, and obtaining an updated supervised detection model through parameter fusion, thereby achieving zero-shot object detection of image data. Through the technical solution of this invention, visual features with rich semantics and discriminative power can be generated, improving the understanding and expression of visual content, better aligning the visual space with the semantic space, and solving the problem of semantic confusion in zero-shot object detection.

The invention discloses a solid waste monitoring and data management system based on artificial intelligence, and relates to the technical field of water environment monitoring, the system is composed of an image acquisition module, a data preprocessing module, an encoder feature extraction module, an up-sampling fusion module, a detection head module and a data management module, and the system is based on an RW-YOLOv11 architecture. A C3K2Sc feature extraction unit is introduced into an encoder, and floating garbage edge and texture expression is enhanced through space attention and a dynamic channel reconstruction mechanism; a SurfCAU water surface content awareness enhanced up-sampling module is adopted in the neck network, detail compensation of low-resolution features is achieved, and the multi-scale feature fusion quality is improved; a SurfMSDFHead multi-branch structure is adopted in the detection head, multi-scale feature fusion is realized through dynamic weight, and the positioning precision of overlapped garbage is improved by using a Focaler-IOU interval weighting strategy. The system can realize real-time detection, classification and positioning of the floating garbage in the river channel, and has the advantages of high detection precision, good lightweight degree, strong adaptability to complex water surface scenes and the like.