Aiming at the problems of low level of informationization, high risk of construction safety and waste of resources due to lack of information management in traditional construction industry, this paper proposes to design and implement a set of smart site management platform based on IoT and QR code. The platform realizes the monitoring of load-bearing tower cranes and other equipment on construction sites with the help of IOT devices, and receives real-time alarm signals of over-limit or interference collision of tower cranes, which reduces the safety risk; realizes the remote control of over-limit alarm and switch of electricity equipment in office and living areas, which avoids energy waste. At the same time, the system combines QR code technology to realize the information tracking of maintenance orders by supervisors, simplifying their process of declaring faults and dealing with safety hazards, and improving construction efficiency and project quality.
The increasing number of car ownership aggravates the traffic problem, so the intelligent transportation system is proposed. As a component of intelligent transportation system, vehicle identification system has been studied by researchers all over the world. In this paper, the theoretical knowledge of deep learning is introduced at first, and then the migration training is carried out by using VGG-16, RESNET-50 and the improved neural network based on these two kinds of networks on the self-made model dataset. In the experiment, a verification set was used to verify the training effect, and the training results of the four models were compared in terms of the number of parameters, training time and accuracy. The training results show that the improved RESNET-50 has the best performance, and its accuracy rate reaches 93%.
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