Research On Sensitive Data Declassification Of Geographic Information System

Since the beginning of the 21st century,information technology has been widely used in China's economic and military fields.Geographic information technology,in particular,has reached unprecedented heights in the past two decades.Geographic information is not only used in national defense construction,but also serves people's daily life more and more.However,the sharing degree of geographic information data in China is relatively low,and the phenomenon of repeated construction of geographic information data generally exists,which not only restricts the progress of information technology in China,but also seriously restricts the development of social economy in China.Geographic information data is related to national security.Therefore,under the premise of ensuring that geographic information data conforms to national security application standards,how to use new technical in geographic information data declassification and improve the sharing capacity of geographic information data is an urgent problem to be solved.There are two main parts of the information declassification of geographic information:one is the data attribute declassification of geographic information,and the other is the spatial coordinate declassification of geographic information.This paper selects geographic information vector data as the research object,analyzes the geometric characteristics of geographic information vector data and the national security requirements,and studies the key technologies of reducing the accuracy of geographic information vector data and the research status at domestic and abroad.Geographical information about the improved particle swarm optimization random vector data coordinates transformation method has been proposed,chaos thought has been introduced in this method to chaos particle swarm processing.At the same time,the particle swarm adaptive mechanism has been introduced to activated particles global search ability,and multi-core parallel computing technology is introduced to avoid particle swarm into local optimal solution.On the whole,the global search ability and convergence efficiency of particle swarm optimization are improved,and the efficiency of vector data information declassification is effectively improved.Experimental results show that the improved particle swarm optimization algorithm based on gis vector data has high efficiency.At last,a visual declassification system of geographic information vector data is designed and implemented,which not only guarantees the security and accuracy of geographic information,but also has good commercial value.