Multivariate Information Collaborative Optimization of Emergency Management Based on Chaos Theory

Extreme changes in natural climate have led to an increase in waterlogging in urbanization year after year.The study aims to optimize the multivariate information synergy of emergency management and improve the efficiency and effectiveness of disaster response.First, the multi-objective problem model of emergency settlement site selection and material distribution is constructed by means of mathematical modeling.Second, a non-dominated sorting genetic algorithm is used as the basis of the optimization framework, and the Tent chaotic sequence in chaos theory is introduced for the optimization of population initialization and genetic steps.Finally, a multivariate information processing model for emergency management is proposed.The experimental results indicated that the new model has the highest placement point generation rate of 92% and the highest distribution route generation rate of 95% compared to the same type of chaotic model.Compared with the advanced technology models in this field, the new model generated a maximum of 112 settlement schemes and the optimal scheme was 10.The maximum number of distribution route solutions was 121, and the optimal solution was 11.It can be concluded that the introduction of chaotic mapping can significantly improve the global search capability of the model and the efficiency of the algorithm.The study provides a new idea for urban emergency management, which is especially valuable when dealing with complex and dynamic disaster response scenarios.