Reliability Analysis of Multisensor Fusion Technology in Disaster Nursing Application

Abstract

The harm and impact of various natural disasters are becoming more and more obvious. Due to the high suddenness of natural disasters and the low efficiency of information sharing, our stress system will still not be able to respond to disasters in time. Therefore, facing the current complex natural environment and the diversity of disasters, we urgently need a practical and comprehensive online collaborative natural disaster protection system. Therefore, this paper proposes a disaster protection application system based on a multisensor information fusion algorithm. Firstly, the software platform of the natural disaster protection system based on multisensor is constructed; the hardware platform of multisensor disaster acquisition is designed. An information fusion algorithm based on Kalman joint filter is proposed. The main problems of joint filter are (1) improving the filtering accuracy, (2) improving the fault-tolerant performance of the filter, and (3) optimizing the fusion algorithm and reducing the amount of calculation and data communication, so as to facilitate real-time implementation of the algorithm. Combined with DS evidence theory, BP neural network, and support vector mechanism, a comprehensive diagnosis model is established. The model uses the preliminary diagnosis results of the BP neural network and support vector machine to carry out the BPA of DS evidence theory, which avoids the blindness caused by human operation. The simulation results show that the comprehensive diagnosis model can significantly improve the prediction accuracy of disasters, solve the uncertainty and fuzziness of diagnosis results of a single diagnosis model, and provide a new method for disaster protection. The results of the BP neural network in the disaster nursing system are fused at the decision level, the results show that the distribution probability corresponding to the fusion disaster category is greater than the threshold value of 0.8, and the diagnosis result can be given immediately. The diagnosis result is completely consistent with the actual disaster category.