As the State Grid Multi-cloud IoT platform grows and improves, an increasing number of IoT applications generate massive amounts of data every day. To meet the demands of intelligent management of State Grid equipment, we proposed a scheme for constructing the defect knowledge graph of power equipment based on multi-cloud. The scheme is based on the State Grid Multi-cloud IoT architecture and adheres to the design specifications of the State Grid SG-EA technical architecture. This scheme employs ontology design based on a fusion algorithm and proposes a knowledge graph reasoning method named GRULR based on logic rules to achieve a consistent and shareable model. The model can be deployed on multiple clouds independently, increasing the system’s flexibility, robustness, and security. The GRULR method is designed with two independent components, Reasoning Evaluator and Rule Miner, that can be deployed in different clouds to adapt to the State Grid Multi-cloud IoT architecture. By sharing high-quality rules across multiple clouds, this method can avoid vendor locking and perform iterative updates. Finally, the experiment demonstrates that the GRULR method performs well in large-scale knowledge graphs and can complete the reasoning task of the defect knowledge graph efficiently.

With the advancement of robotics, intelligent robots are widely used in substation inspections. In view of the problem that the parameters of the deep learning model are too large, and the performance of embedded devices is limited, this paper proposes a meter detection and recognition method based on a lightweight deep learning model, which provides support for deploying the model to the substation intelligent inspection robot. First perform target detection on the input image to detect the position frame of the dashboard; then extract the target area, perform semantic segmentation in the target area, segment the mask of the pointer and scale, and convert the mask into two-dimensional by scanning the image is converted into a one-dimensional array, and the position of the pointer and scale is predicted through peak detection, and finally the scale is calculated according to the scale and range. The invention applies the lightweight method of runing and knowledge distillation based on the YOLOv7-tiny model in the target detection stage, so that the model is greatly compressed while maintaining the prediction accuracy; in the semantic segmentation stage, a lightweight method based on depth-wise separable convolution is used. The lightweight U2NetP model replaces the U2Net model, which greatly reduces the amount of model parameters. The experimental results show that the lightweight method used in this paper can compress the original YOLOv7-tiny model by 95.7%, the average accuracy rate can reach 90.5%, the original U2NetP model can be compressed by 76.8%, the average IOU can reach 88.7%, and the average pixel accuracy rate can reach 99.4%.

Equipment defect domain ontology is the semantic basis for constructing equipment defect knowledge graph, which can be used to organize, share, and analyze equipment defect related knowledge. At present, there are many relevant data in the field of equipment defects, such as equipment information model, equipment management documents, and equipment defect reports. These equipment defect data often focus on a single aspect of the equipment defect field. It is difficult to integrate the database with various types of equipment defect information. This paper combines the characteristics of existing equipment defect data sources to build a general equipment defect domain ontology. This research can break the barrier of multi-source heterogeneous knowledge, build an efficient storage engine for multimodal data, and empower the safety of Industrial applications, data, and platforms in multi-clouds for Internet of Things (IoT).

No abstract

Equipment defect domain ontology is the semantic basis for constructing equipment defect knowledge graph, which can be used to organize, share, and analyze equipment defect related knowledge. At present, there are many relevant data in the field of equipment defects, such as equipment information model, equipment management documents, and equipment defect reports. These equipment defect data often focus on a single aspect of the equipment defect field. It is difficult to integrate the database with various types of equipment defect information. This paper combines the characteristics of existing equipment defect data sources to build a general equipment defect domain ontology. This research can break the barrier of multi-source heterogeneous knowledge, build an efficient storage engine for multimodal data, and empower the safety of industrial Internet.