The invention discloses a
big data intelligent
analysis method and
system, and relates to the technical field of
big data intelligent analysis, and the method comprises the steps: collecting multi-source
monitoring data, such as pavement temperature,
humidity, strain and
axle load, and calculating a daily average
humidity value, a daily
maximum temperature gradient value and a normalized strain
peak value; constructing a continuous weight based on the
strain response, and distinguishing a dry state sample from a wet state sample according to a daily average
humidity value; and respectively carrying out weighted
linear fitting on the two types of samples to obtain a
hysteresis humidity loss temperature amplification index for depicting a humidity effect amplification effect, generating a road section average humidity loss
risk index in combination with a continuous weight and a
temperature gradient, and realizing road section humidity loss risk sorting according to the road section average humidity loss
risk index. According to the invention, automatic quantification and comparative analysis of the
moisture damage risk can be realized in a large-scale
monitoring data scene, and the accuracy of a road maintenance decision is improved.