The invention discloses a road investigation design method and
system based on a mobile network, and relates to the technical field of road investigation, and the method comprises the steps: dividing geological anomaly probability distribution data into dynamic grid units, constructing a
reinforcement learning state space, and generating an optimal sampling path instruction in combination with a dual-network deep
Q learning architecture; analyzing the optimal sampling path instruction into
executable parameters, executing the
executable parameters, collecting multi-source spatio-temporal data, establishing
data association through a spatio-temporal hash
algorithm, and generating a reconnaissance
data set; and on the basis of the survey
data set, through parameterized spline curve modeling, generating a candidate road design scheme, and in combination with a non-dominated sorting
genetic algorithm, performing optimization to generate a three-dimensional road design scheme. According to the method, the exploration path is optimized by using the dynamic grid coding and the dual-
network architecture, the high-
risk area coverage and the moving efficiency are balanced, meanwhile, the method adapts to real-time geological changes in combination with priority experience playback, and the exploration reliability and the
resource utilization rate are improved.