Under the background of the development of industrial Internet and digital economy era, artificial intelligence is widely used in all businesses of power grid, among which the identity authentication of personnel in substation operation and maintenance area is a common requirement in the business scenario of power grid. This paper proposes a " cloud to edge integration" solution based on offline feature extraction + cloud recognition and authentication, which extracts face features from the end side, compares face information in the cloud, and realizes identity authentication through minimized data transmission. Compared with the traditional non biometric identification means such as key and ID card, fingerprint and iris identification and other biometric identification means, this scheme has the advantages of insensitive identification operation, fast speed, high accuracy and good economic benefits, and has high promotion value.
At present, the use of robots to carry out transformer internal inspection work has been related research, if the intelligent detection of small targets such as foreign bodies and small discharge traces inside the transformer can be realized, the efficiency of robot internal inspection will be greatly improved. For small target detection, the current popular method in the industry is to improve the detection accuracy by optimizing the structure of the network model, but the disadvantage is that it increases the difficulty of the algorithm design and the computational complexity. In this paper, based on the Faster-RCNN model, small target enhancement and contrast learning methods are proposed for small target detection in the industrial field under the premise of ensuring the detection accuracy of large-scale targets. The experimental results on the transformer internal inspection data set show that our proposed method is superior to the existing methods. It provides a new solution to the problem of improving the recognition effect of small targets.
Aiming at the problem of “1 hour to charge and 4 hours just to queue” during the National Day holiday in 2021, in order to reduce the problems of poor charging experience and reduced confidence in high-speed travel caused by too long charging queuing time, this paper proposes a charging path planning. Based on the state model of electric vehicles (EV), charging stations, traffic network and distribution network, this paper fully considers the charging resources around the expressway network when planning the charging path for users on the expressway network, and proposes a new method considering the “EV-pile-road-grid” state electric vehicle charging path planning. By calling the Baidu map API interface and the data of the charging piles of the Internet of Vehicles Platform, the optimized selection of charging stations is completed, and a feasible navigation path with the shortest travel (time) is finally formed.
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