Abstract: Disclosed is a method for implementing antenna azimuth correction based on user data. Five prediction algorithms are combined to avoid some defects of an individual algorithm. By means of a method for applying a plurality of prediction model weights, a plurality of algorithms is organically combined, a high weight is applied to an algorithm having a good prediction effect, and a low weight is applied to an relatively bad algorithm, such that not only the accuracy of data prediction but also the stability of data prediction for different regions are ensured. By drawing a grid-level user data RSRP heat map for analyzed data to obtain sampling point information around a cell and a predicted azimuth, the present invention can find out, by means of deeper analysis, problems in engineering parameters, such as a longitude/latitude error, an azimuth error, and a reversely connected cell.

Abstract: Disclosed is a method for implementing antenna azimuth correction based on user data. Five prediction algorithms are combined to avoid some defects of an individual algorithm. By means of a method for applying a plurality of prediction model weights, a plurality of algorithms is organically combined, a high weight is applied to an algorithm having a good prediction effect, and a low weight is applied to an relatively bad algorithm, such that not only the accuracy of data prediction but also the stability of data prediction for different regions are ensured. By drawing a grid-level user data RSRP heat map for analyzed data to obtain sampling point information around a cell and a predicted azimuth, the present invention can find out, by means of deeper analysis, problems in engineering parameters, such as a longitude/latitude error, an azimuth error, and a reversely connected cell.