| Abstract |
With the development of urbanization, urban public safety is becoming more and more important. Urban public safety is not only the foundation of urban development, but also the basic guarantee for the stability of citizens' lives. In the context of today's artificial intelligence (AI), the concept of smart cities is constantly being practiced. Urban public safety has also ushered in some new problems and challenges. To this end, this article aimed to use AI technology to build an efficient public safety data resource management system in a smart city environment. A major goal of AI research was to enable machines to perform complex tasks that normally require human intelligence. In this article, a data resource management system was constructed according to the city security system and risk data sources, and the data processing method of neural network (NN) was adopted. Factors affecting urban public safety were processed as indicator data. In this article, the feedforward back-propagation neural network (BPNN) was used to predict the index data in real time, which has realized the management functions of risk monitoring and early warning of public safety data indicators. The BPNN model was used to test the urban risk early warning capability of the constructed system. BPNN is a multi-layer feed-forward NN trained according to the error back-propagation algorithm, which is one of the most widely used NN models. The results showed that the average prediction accuracy of the BPNN model for indicator prediction was about 89%, which was 16.1% higher than that of the traditional NN model. The average risk warning accuracy rate of the BPNN model was 90.3%, which was 16.5% higher than that of the traditional NN model. This shows that the BPNN model using AI technology in this article can more efficiently and accurately carry out early warning of risk and management of urban public safety. |
