The invention discloses an
electromagnetic environment test stationing optimization method based on
big data analysis, relates to the technical field of
electromagnetic environment test, and is used for solving the problems of low efficiency and
distortion of test stationing. According to the method, an electromagnetic
test data base is generated by collecting electromagnetic
monitoring data and interference
event data, credible hierarchical management is carried out on records through quality tags, space-time aggregation and partition clustering are executed on the basis to generate risk partitions and cover gaps, interpretable generation of candidate distribution points is driven, the candidate distribution points are worked into work orders, and the reliability of the work orders is improved. The work order comprises a measurement parameter set, a constraint parameter set, a replacement
point set and a priority rule, linkage scheduling solving is carried out in combination with spatial constraint and a resource state, an
execution plan comprising work order selection,
resource allocation, a
route sequence and time period scheduling is output, sampling is transmitted back in the execution process, consistency
verification is carried out, and the work order is obtained. And when the trigger rule is met, a supplementary work order or a retest work order is generated, closed-loop iteration of stationing optimization is realized, and invalid field work and
rework are reduced.