The invention relates to the technical field of
big data analysis, and discloses a
prospecting target area positioning method and
system based on
big data, and the method comprises the steps: collecting multi-source exploration data in real time through distributed nodes, completing coordinate normalization,
semantic alignment and
time synchronization through a spatial heterogeneous data flow engine, and generating a standardized incremental data block; performing local feature
sensitivity analysis based on the
historical model library, identifying a newly added
feature dimension, and performing parameter increment updating by adopting a sliding window
gradient descent method; inputting the updated model into a target evolution model driven by a Bayesian space-time probability field, and dynamically calculating the metallogenic probability of each space grid in combination with a
stress field, an element migration path and historical
verification data; and generating high, medium and low three-level target area maps according to probability sorting, and pushing the high, medium and low three-level target area maps to a three-dimensional visual decision terminal. According to the method, minute-level dynamic response of the target region under triggering of newly-added data is realized, computing
resource consumption is reduced to be less than 5% of that of an original
system, and
prospecting efficiency and abnormal region identification timeliness are improved.