Garbage sorting plays a very important role in ensuring a life safety. Aiming at the problems of poor real-time detection and low recognition accuracy of current garbage classification, an improved multi-objective real-time garbage classification recognition algorithm based on YOLOv5s is proposed. By combining the Coordinate Attention (CA) self-attention module and the neck part of YOLOv5s, the defect of insufficient receptive field is reduced to improve the detection accuracy, and the DeepSort algorithm is introduced to optimize the multi-target garbage feature recognition and strengthen the real-time detection ability. The experimental results show that the improved YOLOv5s garbage classification detection model can effectively identify 44 different types of garbage. Compared with the original YOLOv5s algorithm, the detection mAP value is 76.56

The development of artificial intelligence technology provides a new model for substation inspection in the power industry, and effective defect diagnosis can avoid the impact of substation equipment defects on the power grid and improve the reliability and stability of power grid operation. Aiming to combat the problem of poor recognition of small targets due to large differences in equipment morphology in complex substation scenarios, a visual fault detection algorithm of substation equipment based on improved YOLOv5 is proposed. Firstly, a deformable convolution module is introduced into the backbone network to achieve adaptive learning of scale and receptive field size. Secondly, in the neck of the network, a simple and effective BiFPN structure is used instead of PANet. The multi-level feature combination of the network is adjusted by a floating adaptive weighted fusion strategy. Lastly, an additional small object detection layer is added to detect shallower feature maps. Experimental results demonstrate that the improved algorithm effectively enhances the performance of power equipment and defect recognition. The overall recall rate has increased by 7.7%, precision rate has increased by nearly 6.3%, and mAP@0.5 has improved by 4.6%. The improved model exhibits superior performance.

及时发现并诊断变电站运行中设备存在的问题,是保障电网安全运行的关键手段之一.基于深度网络与知识图谱技术,提出一种关联变电站设备多模态信息的故障智能诊断方法.利用深度学习技术和知识图谱方法对采集的多模态数据进行知识提取和融合,构建一个多模态信息融合的语义知识图谱;使用YOLOv4算法对故障样本聚类并提取先验框参数;将多模态知识图谱和YOLOv4视觉检测相结合,应用到变电站场景中,实现变电站设备的自主预警诊断.实验表明,该模型可以实现故障诊断决策智能化的目标,从而提高电网的日常运行、维护和管理效率.

In order to realize the remote control of the meeting documents in progress, the traditional method uses infrared remote control or 2.4 GHz wireless remote control. However, the shortcomings of carrying and storing the remote control, the infrared itself cannot pass through obstacles or the remote control of the device from a large angle, the 2.4 GHz cost is slightly higher, etc., this article introduces the use of PyTorch model and YOLO network gesture control to facilitate this practical problem. The plan proposes to use the PyTorch model to establish a neural network, train to achieve the purpose of classifying gestures, and use the YOLO network to cooperate with the corresponding control algorithm to achieve the purpose of controlling conference documents. The experimental results show that the proposed scheme is feasible and complete to achieve the required functions.

Garbage classification is a social issue related to people’s livelihood and sustainable development, so letting service robots autonomously perform intelligent garbage classification has important research significance. Aiming at the problems of complex systems with data source and cloud service center data transmission delay and untimely response, at the same time, in order to realize the perception, storage, and analysis of massive multisource heterogeneous data, a garbage detection and classification method based on visual scene understanding is proposed. This method uses knowledge graphs to store and model items in the scene in the form of images, videos, texts, and other multimodal forms. The ESA attention mechanism is added to the backbone network part of the YOLOv5 network, aiming to improve the feature extraction ability of the network, combining with the built multimodal knowledge graph to form the YOLOv5-Attention-KG model, and deploying it to the service robot to perform real-time perception on the items in the scene. Finally, collaborative training is carried out on the cloud server side and deployed to the edge device side to reason and analyze the data in real time. The test results show that, compared with the original YOLOv5 model, the detection and classification accuracy of the proposed model is higher, and the real-time performance can also meet the actual use requirements. The model proposed in this paper can realize the intelligent decision-making of garbage classification for big data in the scene in a complex system and has certain conditions for promotion and landing.

目的 针对目前包装产业存在的产业链长、数据大而散、包装领域知识信息检索不精准等问题,设计一个基于知识图谱的包装领域智能问答系统.方法 采用知识图谱、智能问答、自然语言处理、深度学习和个性化推荐等技术,对互联网上各类包装领域数据进行采集汇聚、抽取知识、融合计算,最终形成一个包装领域混合型智能问答系统.结果 系统主要功能包括知识图谱、文本相似匹配、图像识别和自动问答,实现了包装领域大数据的知识卡片、语义搜索和沉浸问答.结论 该系统使用方便,通过问答方式,一站式快速按需获取多视图、多维度包装领域数据,提升了行业的数字化、信息化和智能化水平.