Research and application on lightweight key technologies for massive forecasting data in the"three lines of defense"
ZOU Xiaotao
SUN Shiyou
YANG Pu
LIU Yanmin
GUO Wei
DI Suchuang
Abstract：In recent years,with the frequent and intense occurrence of extreme weather,flood disasters have shown new characteristics and patterns such as strong suddenness and high unpredictability.The traditional monitoring system has obvious shortcomings in meeting the requirements of the forecast,early warning,rehearsal and plan for flood and drought disaster prevention in the new era.Therefore,building a"three lines of defense"system for rainfall monitoring has become an important measure for disaster prevention,mitigation,and relief.However,with the construction of the"three lines of defense",massive forecast data has brought huge challenges in storage,transmission,and processing.This article aims to study the key technologies for lightweight massive forecast data in the"three lines of defense".Using the link matrix method,a lightweight technology system for massive forecast data has been designed from the aspects of lightweight processing of forecast result data,rapid call of forecast result data,and storage of forecast data.By using technical means such as forecast data parsing and conversion,extraction and grading processing,vectorization and factored processing,collaborative adaptation of rainfall data input,and distributed object storage,the lightweight processing of massive forecast data can be achieved.Taking the Beijing region as an example,the practice of key technologies for lightweight massive forecast data has been carried out.Practice has proved that the application of lightweight technology for massive forecast data greatly reduces the storage and transmission burden of forecast data,reduces costs,improves the application efficiency and effectiveness of forecast results,and enhances the efficiency of flood forecasting while maintaining the effectiveness and accuracy of data.
Keywords："three lines of defense"massive forecast datalightweightdistributed object storagemoving average algorithm
Publication Date：2025-04-12
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 30-36 )