The invention relates to the technical field of service signaling, in particular to a voice call
data analysis system based on
cloud computing, which comprises a signaling acquisition module, a stability evaluation module, a dynamic compression module, an abnormal track clustering module and a cloud feature
library module. According to the method, link multi-dimensional parameters are collected through distributed nodes and stored in a time slice mode, a continuous three-hop
rate difference is calculated through a sliding difference value, path stable segmentation is accurately recognized, time
delay in a mean value compression screening segment is generated to generate feature vectors to reduce redundancy, cloud K-means integrates state duration,
timeout rate and
retransmission times to detect trajectory offset, and abnormal clustering labels are constructed.
Time sequence association storage and elastic architecture
dynamic screening path segments reduce
invalid data processing amount, compression mean reduces calculation complexity, multi-dimensional clustering improves detection precision, a time window optimizes a
storage structure, a sliding difference enhances fluctuation capture capability, elastic storage improves
mass data access efficiency, and an end-to-end quality analysis
closed loop is formed.