Power transmission line monitoring system and method and recognition system and method for cloud-side cooperation
A transmission line and monitoring system technology, applied in character and pattern recognition, circuit devices, neural learning methods, etc., can solve problems such as difficult on-site fault diagnosis, insufficient timeliness of information processing, and difficult information transmission, so as to avoid image and The transmission of large amount of video data, significant application value, and the effect of solving insufficient efficiency
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Embodiment 1
[0084] An intelligent computing framework for cloud-edge collaborative architecture, such as figure 1 Shown is the five-layer power Internet of Things architecture of "intelligent cloud, pipe, edge and end", based on the cloud-edge collaborative architecture, which includes the five-layer architecture of "intelligence, cloud, management, edge, and end";
[0085] Among them, intelligence refers to intelligent applications, and intelligent applications corresponding to "cloud, pipe, edge, and end" are described as cloud intelligence, network intelligence, edge intelligence, and terminal intelligence;
[0086] The cloud layer takes the cloud platform as the core, including the main functional entities such as the operation management platform, the IoT management platform, and the enterprise middle platform. For different scales of the Internet of Things, choose to deploy public cloud, private cloud or hybrid cloud. "Cloud" is the main station platform of cloudification, and there...
Embodiment 2
[0099] The specific implementation process includes: the transmission line perception layer image monitoring device and the video camera device generate picture data and video data, including two schemes:
[0100] Solution 1 is to form sample data that can be used for training after preprocessing and labeling directly on the sensing terminal, and then gather and upload to the cloud (or directly upload to the cloud) through the edge IoT agent, and access it through the IoT management platform. Collected into the sample bank;
[0101] The second option is to form sample data that can be used for training after preprocessing and labeling on the edge IoT agent, upload it to the cloud, access it through the IoT management platform, and collect it into the sample library.
[0102] The artificial intelligence platform (that is, the cloud platform is equipped with an artificial intelligence training module) can use the sample data in the sample library to organize model training, and ...
Embodiment 4
[0123] As a preferred embodiment, the intelligent recognition of the edge side of the transmission line is realized by using a deep learning network model. The specific algorithm includes using MobileNetv2 for the head network to extract features; using YOLOv3 for the main network to deepen target classification and target detection based on the features extracted by the head network. Based on this basic network structure, a high-performance identification algorithm for the transmission line edge side is established. The network structure of MobileNetv2+YOLOv3 adopted in the present invention, depth separable convolution is introduced in the calculation of input eigenvalues of YOLOv3, so that YOLOv3 can match with MobileNetv2, and the amount of parameters and calculations are greatly reduced. Because MobileNetv2 is a lightweight network structure, compared with other popular structures such as VGG and ResNet, MobileNetv2 effectively reduces storage and computing overhead at t...
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