The present application relates to the technical field of generating
resource scheduling, and particularly relates to a
raw material resource scheduling method for plywood production based on
big data, comprising the following steps: S1, obtaining
raw material characteristics and order requirements, and generating dynamic
scheduling instructions; S2, constructing a probabilistic
transition network model; S3, generating abnormal transmission events caused by non-standard logistics paths; S4, dynamically reconstructing the topological structure of the probabilistic
transition network model, and recalculating the
raw material source tracing results of finished products; statistical tracing under dynamic scheduling is realized by constructing a probabilistic
transition network model, and non-standard logistics in actual production is intelligently detected and fused based on the principle of
material balance, so that the problem of broken tracing chain caused by scattered and reorganized scheduling in plywood production, and the problem of invalid
mathematical model caused by abnormal paths such as manual carrying are effectively solved without adding new hardware, and production efficiency, tracing accuracy and implementation cost are considered.