The invention relates to the technical field of
mobile communication network optimization, in particular to a
base station position intelligent prediction method and device based on an improved
DBSCAN algorithm, and the method comprises the steps: carrying out the rasterization of collected
original data and stock
base station data, obtaining an effective
Enodeb list, carrying out the classification according to an operator and a network type, and obtaining a plurality of effective
Enodeb sub-lists; positioning a
raster data index of each
Enodeb by using a
binary search algorithm, extracting
latitude and
longitude coordinates of all grids under the same Enodeb, and constructing a clustering data
list; performing clustering analysis on the
raster data in each Enodeb to generate a predicted
base station position data table; wherein during clustering analysis, clustering parameters are dynamically adjusted based on the number of
cell identifiers under each Enodeb; and quantitatively predicting the matching degree of the base
station and the actual base
station, screening out the optimal parameter of the improved
DBSCAN algorithm, and predicting the position of the base
station based on the optimal parameter. Therefore, different base station density scenes are adapted, and the base station position prediction precision and efficiency are improved.