Objectives Disaster prevention data is the fundamental resource for disaster prevention work. The rapid and accurate integration and management of disaster prevention data is an important research content in the field of disaster prevention. In view of the multi-source heterogeneity and spatiotemporal multi-dimensional characteristics of disaster prevention data, this paper studies and creates a full information management model for disaster prevention.

Methods A six-tuple tag data management strategy of space, time, theme, link, timeliness and department for disaster defense has been constructed to achieve feature extraction and rapid marking of massive disaster data. Focusing on the six major business nodes of emergency preparedness, risk identification, risk assessment, risk control, emergency rescue, and post-disaster evaluation, the adaptive business chain flow technology was studied. Relevant data was dynamically captured following the changes of business nodes to achieve the automatic flow and analysis of disaster data under the current business nodes.

Results Based on this model, this paper establishes the disaster prevention database of Zhejiang Province, integrates the data of 14 major categories and 138 minor categories from 16 departments, and forms the disaster reduction resource database, risk monitoring database, risk hazard database, model product database and case database. Based on the database and model, this paper builds the Zhejiang Province Natural Disaster Risk Prevention and Control and Emergency Rescue Platform, generating business data such as one map for disaster element analysis, disaster situation forecast map, and the five-color map of multi-disaster risk. It has provided strong support during the defense against multiple typhoon disasters, including Typhoon“Yanhua”in 2021, Typhoon“Minghua”in 2022 and Typhoons“Dusuri”and“Kanu”in 2023.

Conclusions The application results show that this model can efficiently organize the disaster prevention data of the entire province, achieve the management and application of more than 500 million pieces of data every year, and provide effective technical support for disaster prevention and control.