Developing a Digital System for Disaster Prevention Planning: An Analysis Based on Local Government Policy Texts

The prediction, prevention and control of disasters have occurred throughout the history of human development, so the development of China's new era has put forward new requirements for China's disaster prevention policies and plans, especially for the formulation and implementation of policies by local governments. Local governments are the main administrators of a region and the main responders to disasters, so they have an unshirkable responsibility for the prediction and prevention of local disasters. In recent times, with the rapid development of digitalisation and informationisation, productivity and technology have been further improved, and disaster models can be predicted with the help of computers, mobile platforms and other devices. Compared with the traditional prediction and management models, digital management and prediction make simulation and calculation faster and more accurate. Therefore, this paper is based on the digital system model of the local government in the past 10 years for disaster policy as the object of study, the use of quantitative and qualitative combination of risk models and disaster prediction models, through data analysis and integration of the overall analysis of the performance characteristics of the disaster management policy and the relevant preventive measures, analysis and prediction of the tendency of the local government to needle disaster prevention policy. Through the digital analysis of statistics related to the frequency of disasters and the frequency of local government policymaking, analysis of the inevitable connection between the two, and the policy text to disaster prevention level of the hierarchy, from which to analyse the logic of disaster prevention and relief ideas and government experience, in order to better improve the prevention and relief of disasters related to the laws and regulations, and to provide relevant recommendations. © 2025 Bin Jiang et al., published by Sciendo.