Abstract
Because of their portability, electric motorcycles are usually pushed into elevators by residents and charged in the home, which has serious safety risks. Traditional manual-based methods to manage this behavior have poor monitoring effects and high costs. As for automatic management systems using artificial intelligence (AI), the deployment method matters. Cloud-based deployment methods have the disadvantages of high latency, high risk of privacy leakage, and heavy network transmission loads. In this paper, we propose a highly secure edge-intelligent electric motorcycle management system for elevators. By using edge-based deployment method, the monitor pictures are processed locally without being uploaded to the cloud, which can effectively resist network attacks and prevent residents’ private data from being leaked. To improve the system security, we fully analyze the challenges faced in the application scenarios and introduce security threat identification (STI-1H8) model to identify the security threats. In addition, we propose several data enhancement methods to improve the system recognition accuracy. Experimental results show that our system can achieve a high recall rate of 0.82. By using data enhancement and data mixing strategies, it can reduce the misjudgment rate by 0.35. Moreover, compared to cloud computing, our edge-based method can reduce the latency by 19.6%, meeting real-time requirements.
Highlights
Due to their high performance and ease of use, electric motorcycles are a common means of transportation for people around the world
To improve the system recognition accuracy, we fully analyze the challenges faced in the application scenarios and propose several data enhancement methods
Simulated attack experiment shows that the STI-1H8 model can recognise 100% of the application layer attacks, 81% of the network layer attacks, and 84% of the perception layer attacks
Summary
Due to their high performance and ease of use, electric motorcycles are a common means of transportation for people around the world. System design In this paper, we propose an edge-based automatic electric motorcycle management system for elevators, which aims to ensure the timeliness of data processing and realtime feedback results. When the picture is transferred to the Dynamic Random Access Memory (DRAM) of the Cambricon 1H8 edge-intelligent device, the program will call the neural network model embedded in the 1H8 device to process the picture and recognize the electric motorcycle in the picture It passes the position of the electric motorcycle to the OSD Detection Target Overlay Module. If the voice alarming device has not been operated and the electric motorcycle has been recognized for three consecutive times, the system starts the voice alarming device and controls the relay to work, so that the elevator door is continuously opened. 1: initialize:Set Num_gpio ← 0, Num_Motorcycle ← 0, Num_Not_Motorcycle ← 0, ShakeNum ← 3
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