Bayesian belief network (BBN) can be a powerful tool in decision making processes. Development of a BBN requires data or expert knowledge to assist in determining the structure and probabilistic parameters in the model. As data are seldom available in the engineering decision making domain, a major barrier in using domain experts is that they are often required to supply a huge and intractable number of probabilities. Techniques for using fractional data to develop complete conditional probability tables were examined. The results showed good predictability of the missing data in a linear domain by the piecewise representation method. By using piecewise representation, the number of probabilities to be elicited for a binary child node with k binary parent nodes is now 2k rather than 2 k .

Aiming at the shortcomings of the existing community emergency service platform, such as single function, poor scalability, and strong subjectivity, an intelligent community emergency service platform based on convolutional neural network was constructed. Firstly, the requirements analysis of the emergency service platform was carried out, and the functional demand of the emergency service platform was analyzed from the aspects of community environment, safety, infrastructure, health management, emergency response, and so on. Secondly, through logistics network, big data, cloud computing, artificial intelligence, and all kinds of applications, the intelligent community emergency service platform was designed. Finally, a semantic matching emergency question answering system based on convolutional neural network was developed to provide key technical support for the emergency preparation stage of intelligent community. The results show that the intelligent community emergency service platform plays an important role in preventing community emergency events and taking active and effective measures to ensure the health and safety of community residents.

No abstract

No abstract