Abstract:

Aiming at the problems of large amount of data, diverse data types and high threshold of knowledge mining and visual analysis technology in the field of forestry and grass, the forestry and grass research big data platform is built based on computer application technology to realize the aggregation of forestry and grass research data resources and knowledge discovery, taking into account the current situation of scientific research data in the forestry and grass industry. The platform adopts the design pattern of four layers including infrastructure layer, data resource layer, data analysis layer, data service layer, and uses Java language, based on Spring MVC framework and ECharts visualization tool. The system realizes the functions of deep search, big data analysis, knowledge mapping and visualization display of forest and grass knowledge, and relies on Spring framework to integrate security authentication shiro, log management log4j and caching redis components. This paper first summarizes the connotation and research status of big data for forest and grass research; expounds the data resource construction process of forest and grass research big data platform from the perspectives of data types, data processing and integration, data analysis and interpretation; introduces in detail the technical implementation framework and four typical feature applications of forest and grass research big data platform; Finally, summarizes the achievements of a big data platform for forestry and grass research and put forward prospects. Our research shows that the construction of the big data platform for forestry and grass research, the aggregation and sharing of forestry and grass data resources, and the provision of accurate knowledge-based services effectively promote the efficient management and utilization of forestry and grass research data and the deep mining of research knowledge, which are of great significance to accelerate forestry and grass science and technology innovation.

Key words: forest and grass, scientific research big data, knowledge service, application